Amino acid substitutions in the thyroglobulin gene are associated with susceptibility to human and murine autoimmune thyroid disease

Yoshiyuki Ban, David A. Greenberg, Erlinda Concepcion, Lucy Skrabanek, Ronald Villanueva, Yaron Tomer

Division of Endocrinology, Diabetes, and Bone Diseases, Department of Medicine, and Institute for Computational Biomedicine, Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029; and Division of Statistical Genetics, Columbia University, New York, NY 10032

Abstract: The 8q24 locus, which contains the thyroglobulin (Tg) gene, was previously shown to be strongly linked with autoimmune thyroid disease (AITD). We sequenced all 48 exons of the Tg gene and identified 14 single-nucleotide polymorphisms (SNPs). Case control association studies demonstrated that an exon 10-12 SNP cluster and an exon 33 SNP were significantly associated with AITD ( P < 0.01). Haplotype analysis demonstrated that the combination of these two SNP groups was more significantly associated with AITD ( P < 0.001). Gene-gene interaction studies provided evidence for an interaction between HLA-DR3 and the exon 33 SNP, giving an odds ratio of 6.1 for Graves' disease. We then sequenced exons 10,12, and 33 of the mouse Tg gene in 19 strains of mice. Fifty percent of the strains susceptible to thyroiditis had a unique SNP haplotype at exons 10 and 12, whereas none of the mouse strains that were resistant to thyroiditis had this SNP haplotype ( P = 0.01). We concluded that Tg is a susceptibility gene for AITD, both in humans in and in mice. A combination of at least two Tg SNPs conferred susceptibility to human AITD. Moreover, the exon 33 SNP showed evidence for interaction with HLA-DR3 in conferring susceptibility to Graves' disease.


Dual function for Tango1 in secretion of bulky cargo and in ER-Golgi morphology

Proceedings of the National Academy of Sciences

L. D. Ríos-Barrera, S. Sigurbjörnsdóttir, M. Baer, M. Leptin

Directors' Research Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany;

Deutsche Forschungsgemeinschaft, European Molecular Biology Organization, EMBL Interdisciplinary Postdoctoral Programme, NRW Graduate School for Genetics and Functional Genomics

Abstract: Significance Exporting bulky molecules poses challenges for cells, since the membrane vesicles that transport normal-sized molecules may not be sufficiently large. The protein Tango1 allows transport vesicles to grow much larger to accommodate bulky cargo. It has been puzzling why many smaller cargos also fail to be transported when Tango1 is absent. We show that this is because bulky cargos “clog up” the transport system, resulting in a general traffic jam. Once the blocking, large cargo is removed, the jam resulting from missing Tango1 is resolved, and other cellular stress signals also subside. However, structural defects in the transport system remain, showing that these are due to a direct requirement for Tango1, independent of its function in transport as such.


Pan-mitogenomics reveals the genetic basis of cytonuclear conflicts in citrus hybridization, domestication, and diversification

Nan Wang, Chaochao Li, Lihua Kuang, Xiaomeng Wu, Kaidong Xie, Andan Zhu, Qiang Xu, Robert M. Larkin, Yongfeng Zhou, Xiuxin Deng, Wenwu Guo

Key Laboratory of Horticultural Plant Biology (Ministry of Education), Huazhong Agricultural University, Wuhan 430070, China

Abstract: Although interactions between the cytoplasmic and nuclear genomes occurred during diversification of many plants, the evolutionary conflicts due to cytonuclear interactions are poorly understood in crop breeding. Here, we constructed a pan-mitogenome and identified chimeric open reading frames (ORFs) generated by extensive structural variations (SVs). Meanwhile, short reads from 184 accessions of citrus species were combined to construct three variation maps for the nuclear, mitochondrial, and chloroplast genomes. The population genomic data showed discordant topologies between the cytoplasmic and nuclear genomes because of differences in mutation rates and levels of heteroplasmy from paternal leakage. An analysis of species-specific SVs indicated that mitochondrial heteroplasmy was common and that chloroplast heteroplasmy was undetectable. Interestingly, we found a prominent divergence in the mitogenomes and the highest genetic load in the, which may provide the basis for cytoplasmic male sterility (CMS) and thus influence the reshuffling of the cytoplasmic and nuclear genomes during hybridization. Using cytoplasmic replacement experiments, we identified a type of species-specific CMS in mandarin related to two chimeric mitochondrial genes. Our analyses indicate that cytoplasmic genomes from mandarin have rarely been maintained in hybrids and that paternal leakage produced very low levels of mitochondrial heteroplasmy in mandarin. A genome-wide association study (GWAS) provided evidence for three nuclear genes that encode pentatricopeptide repeat (PPR) proteins contributing to the cytonuclear interactions in the Citrus genus. Our study demonstrates the occurrence of evolutionary conflicts between cytoplasmic and nuclear genomes in citrus and has important implications for genetics and breeding.


Climate change, adaptive cycles, and the persistence of foraging economies during the late Pleistocene/Holocene transition in the Levant

Institute of Archaeology, University College London, London WC1H 0PY, United Kingdom; and

Abstract: Climatic forcing during the Younger Dryas (∼12.9–11.5 ky B.P.) event has become the theoretical basis to explain the origins of agricultural lifestyles in the Levant by suggesting a failure of foraging societies to adjust. This explanation however, does not fit the scarcity of data for predomestication cultivation in the Natufian Period. The resilience of Younger Dryas foragers is better illustrated by a concept of adaptive cycles within a theory of adaptive change (resilience theory). Such cycles consist of four phases: release/collapse (Ω); reorganization (α), when the system restructures itself after a catastrophic stimulus through innovation and social memory—a period of greater resilience and less vulnerability; exploitation (r); and conservation (K), representing an increasingly rigid system that loses flexibility to change. The Kebarans and Late Natufians had similar responses to cold and dry conditions vs. Early Natufians and the Pre-Pottery Neolithic A responses to warm and wet climates. Kebarans and Late Natufians (α-phase) shifted to a broader-based diet and increased their mobility. Early Natufian and Pre-Pottery Neolithic A populations (r- and K-phases) had a growing investment in more narrowly focused, high-yield plant resources, but they maintained the broad range of hunted animals because of increased sedentism. These human adaptive cycles interlocked with plant and animal cycles. Forest and grassland vegetation responded to late Pleistocene and early Holocene climatic fluctuations, but prey animal cycles reflected the impact of human hunting pressure. The combination of these three adaptive cycles results in a model of human adaptation, showing potential for great sustainability of Levantine foraging systems even under adverse climatic conditions.


Universal signals control slime mold stalk formation.

S van Es, B W Nieuwenhuijsen, F Lenouvel, E M van Deursen, P Schaap

Cell Biology Unit, University of Leiden, The Netherlands.

Abstract: The primitive slime mold Dictyostelium minutum does not display oscillations during aggregation, cannot form migrating slugs, and does not form a prestalk/prespore pattern, all of which are characteristic for development of its advanced relative Dictyostelium discoideum. We used D. minutum to investigate whether slime molds share common mechanisms controlling development. In D. discoideum, the morphogen differentiation inducing factor (DIF) can induce stalk-cell differentiation in vitro. However, stalk formation in vivo is supposedly triggered by local depletion of DIF antagonists such as ammonia or cAMP. A homologue of the D. discoideum stalk gene ecmB was cloned in D. minutum that encodes a 3.4-kb mRNA, and its deduced amino acid sequence shows repeats of 24 amino acids that are characteristic for the D. discoideum ecmB gene. Remarkably, DIF effectively induces expression of the D. minutum ecmB gene and ammonia inhibits its expression. D. discoideum cells were transformed with a construct of the D. minutum ecmB promoter fused to the lacZ reporter gene and showed expression in the stalk, but not in the upper and lower cup of the fruiting body, which also express the D. discoideum ecmB gene. In D. discoideum, the D. minutum ecmB and the ecmB promoter are similarly activated by DIF and repressed by both cAMP and ammonia, suggesting that additional signaling is required for ecmB expression in upper and lower cup cells. Our data indicate that the extracellular signals controlling stalk formation and their intracellular signaling cascades including gene regulatory proteins remained highly conserved during slime mold evolution.


The impact of threshold decision mechanisms of collective behavior on disease spread

Bryce Morsky, Felicia Magpantay, Troy Day, Erol Akçay

Department of Mathematics, Florida State University, Tallahassee, FL 32306

Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada, United States - Israel Binational Science Foundation

Abstract: Humans are a hyper-social species, which greatly impacts the spread of infectious diseases. How do social dynamics impact epidemiology and what are the implications for public health policy? Here, we develop a model of disease transmission that incorporates social dynamics and a behavior that reduces the spread of disease, a voluntary nonpharmaceutical intervention (NPI). We use a “tipping-point” dynamic, previously used in the sociological literature, where individuals adopt a behavior given a sufficient prevalence of the behavior in the population. The thresholds at which individuals adopt the NPI behavior are modulated by the perceived risk of infection, i.e., the disease prevalence and transmission rate, costs to adopt the NPI behavior, and the behavior of others. Social conformity creates a type of “stickiness” whereby individuals are resistant to changing their behavior due to the population’s inertia. In this model, we observe a nonmonotonicity in the attack rate as a function of various biological and social parameters such as the transmission rate, efficacy of the NPI, costs of the NPI, weight of social consequences of shirking the social norm, and the degree of heterogeneity in the population. We also observe that the attack rate can be highly sensitive to these parameters due to abrupt shifts in the collective behavior of the population. These results highlight the complex interplay between the dynamics of epidemics and norm-driven collective behaviors.


Triplet structure of von Willebrand factor reflects proteolytic degradation of high molecular weight multimers.

M Furlan, R Robles, D Affolter, D Meyer, P Baillod, B Lämmle

Central Hematology Laboratory, Inselspital, University of Bern, Switzerland.

Abstract: High molecular weight (HMW) and low molecular weight (LMW) forms of von Willebrand factor (vWF) were isolated from normal human plasma in the presence of protease inhibitors. HMW and LMW vWF preparations were subjected to reduction of interdimeric disulfide bridges under mild reducing conditions. Following sodium dodecyl sulfate electrophoresis in 3% agarose, the vWF bands were detected by immunoblotting with a polyclonal rabbit anti-vWF antiserum as well as with two monoclonal antibodies directed against epitopes located in the NH2-terminal (MAb 418) or in the COOH-terminal (MAb 9) region of the vWF subunit. Our results suggest that the slowest migrating band of the dimeric triplet set of LMW vWF represents an asymmetric structure composed of an intact subunit to which one NH2-terminal and one COOH-terminal fragment are linked by disulfide bridges. The intermediate band of the first triplet of LMW vWF strongly reacted with MAb 9 but not with MAb 418, indicating that it represents a dimer of COOH-terminal fragments. The fastest migrating band of the same triplet is apparently a dimer of the NH2-terminal fragments because it reacted with MAb 418 but not with MAb 9. Each next higher family of triplets seems to contain one more asymmetric fragment of dimeric size. These results are compatible with a model according to which LMW forms of vWF are derived from HMW vWF by proteolytic cleavage in the circulating blood.


The splicing-factor oncoprotein SF2/ASF activates mTORC1

Rotem Karni, Yoshitaka Hippo, Scott W. Lowe, Adrian R. Krainer

Abstract: The splicing factor SF2/ASF is an oncoprotein that is up-regulated in many cancers and can transform immortal rodent fibroblasts when slightly overexpressed. The mTOR signaling pathway is activated in many cancers, and pharmacological blockers of this pathway are in clinical trials as anticancer drugs. We examined the activity of the mTOR pathway in cells transformed by SF2/ASF and found that this splicing factor activates the mTORC1 branch of the pathway, as measured by S6K and eIF4EBP1 phosphorylation. This activation is specific to mTORC1 because no activation of Akt, an mTORC2 substrate, was detected. mTORC1 activation by SF2/ASF bypasses upstream PI3K/Akt signaling and is essential for SF2/ASF-mediated transformation, as inhibition of mTOR by rapamycin blocked transformation by SF2/ASF in vitro and in vivo . Moreover, shRNA-mediated knockdown of mTOR, or of the specific mTORC1 and mTORC2 components Raptor and Rictor, abolished the tumorigenic potential of cells overexpressing SF2/ASF. These results suggest that clinical tumors with SF2/ASF up-regulation could be especially sensitive to mTOR inhibitors.


Selective engagement of G protein coupled receptor kinases (GRKs) encodes distinct functions of biased ligands

David A. Zidar, Jonathan D. Violin, Erin J. Whalen, Robert J. Lefkowitz

Abstract: CCL19 and CCL21 are endogenous agonists for the seven-transmembrane receptor CCR7. They are equally active in promoting G protein stimulation and chemotaxis. Yet, we find that they result in striking differences in activation of the G protein-coupled receptor kinase (GRK)/ß-arrestin system. CCL19 leads to robust CCR7 phosphorylation and β-arrestin2 recruitment catalyzed by both GRK3 and GRK6 whereas CCL21 activates GRK6 alone. This differential GRK activation leads to distinct functional consequences. Although each ligand leads to β-arrestin2 recruitment, only CCL19 leads to redistribution of β-arrestin2-GFP into endocytic vesicles and classical receptor desensitization. In contrast, these agonists are both capable of signaling through GRK6 and β-arrestin2 to ERK kinases. Thus, this mechanism for “ligand bias” whereby endogenous agonists activate different GRK isoforms leads to functionally distinct pools of β-arrestin.


A phylogenetic perspective on the distribution of plant diversity

Department of Ecology and Evolutionary Biology and Peabody Museum of Natural History, Yale University, P. O. Box 208106, New Haven, CT 06520-8106

Abstract: Phylogenetic studies are revealing that major ecological niches are more conserved through evolutionary history than expected, implying that adaptations to major climate changes have not readily been accomplished in all lineages. Phylogenetic niche conservatism has important consequences for the assembly of both local communities and the regional species pools from which these are drawn. If corridors for movement are available, newly emerging environments will tend to be filled by species that filter in from areas in which the relevant adaptations have already evolved, as opposed to being filled by in situ evolution of these adaptations. Examples include intercontinental disjunctions of tropical plants, the spread of plant lineages around the Northern Hemisphere after the evolution of cold tolerance, and the radiation of northern alpine plants into the Andes. These observations highlight the role of phylogenetic knowledge and historical biogeography in explanations of global biodiversity patterns. They also have implications for the future of biodiversity.


Adaptive evolution of voltage-gated sodium channels: The first 800 million years

Section of Neurobiology, University of Texas, Austin, TX 78712; and Marine Biological Laboratory, The Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Woods Hole, MA 02543

Abstract: Voltage-gated Na + -permeable (Nav) channels form the basis for electrical excitability in animals. Nav channels evolved from Ca 2+ channels and were present in the common ancestor of choanoflagellates and animals, although this channel was likely permeable to both Na + and Ca 2+ . Thus, like many other neuronal channels and receptors, Nav channels predated neurons. Invertebrates possess two Nav channels (Nav1 and Nav2), whereas vertebrate Nav channels are of the Nav1 family. Approximately 500 Mya in early chordates Nav channels evolved a motif that allowed them to cluster at axon initial segments, 50 million years later with the evolution of myelin, Nav channels “capitalized” on this property and clustered at nodes of Ranvier. The enhancement of conduction velocity along with the evolution of jaws likely made early gnathostomes fierce predators and the dominant vertebrates in the ocean. Later in vertebrate evolution, the Nav channel gene family expanded in parallel in tetrapods and teleosts (∼9 to 10 genes in amniotes, 8 in teleosts). This expansion occurred during or after the late Devonian extinction, when teleosts and tetrapods each diversified in their respective habitats, and coincided with an increase in the number of telencephalic nuclei in both groups. The expansion of Nav channels may have allowed for more sophisticated neural computation and tailoring of Nav channel kinetics with potassium channel kinetics to enhance energy savings. Nav channels show adaptive sequence evolution for increasing diversity in communication signals (electric fish), in protection against lethal Nav channel toxins (snakes, newts, pufferfish, insects), and in specialized habitats (naked mole rats).


EGCG remodels mature α-synuclein and amyloid-β fibrils and reduces cellular toxicity

Jan Bieschke, Jenny Russ, Ralf P. Friedrich, Dagmar E. Ehrnhoefer, Heike Wobst, Katja Neugebauer, Erich E. Wanker

Max Delbrueck Center for Molecular Medicine, Robert-Roessle-Strasse 10, 13125 Berlin-Buch, Germany

Abstract: Protein misfolding and formation of β-sheet-rich amyloid fibrils or aggregates is related to cellular toxicity and decay in various human disorders including Alzheimer’s and Parkinson’s disease. Recently, we demonstrated that the polyphenol (-)-epi-gallocatechine gallate (EGCG) inhibits α-synuclein and amyloid-β fibrillogenesis. It associates with natively unfolded polypeptides and promotes the self-assembly of unstructured oligomers of a new type. Whether EGCG disassembles preformed amyloid fibrils, however, remained unclear. Here, we show that EGCG has the ability to convert large, mature α-synuclein and amyloid-β fibrils into smaller, amorphous protein aggregates that are nontoxic to mammalian cells. Mechanistic studies revealed that the compound directly binds to β-sheet-rich aggregates and mediates the conformational change without their disassembly into monomers or small diffusible oligomers. These findings suggest that EGCG is a potent remodeling agent of mature amyloid fibrils.


Opportunities in Reprogramming: Fred Gage, Kristin Baldwin, and Hongkui Deng


Deteriorating Infrastructure in the Aged Muscle Stem Cell Niche


Fetal to Adult Hematopoiesis with the “Flk” of a Switch


In This Issue


Mentoring the Next Generation: John Dick


Connie Eaves (May 22, 1944–March 7, 2024)

Ali Turhan, Franck Nicolini, François Lemoine, Laure Coulombel, Saghi Ghaffari


A Roundabout Way to the Niche


Voices: Crossing the Valley of Death


Differing Standards for the NIH Stem Cell Registry and FDA Approval Render Most Federally Funded hESC Lines Unsuitable for Clinical Use


A Great Match


Untapping the Potential of Human Retinal Pigmented Epithelial Cells


A Quiescent Bcl11b High Stem Cell Population Is Required for Maintenance of the Mammary Gland

Shang Cai, Tomer Kalisky, Debashis Sahoo, Piero Dalerba, Weiguo Feng, Yuan Lin, Dalong Qian, Angela Kong, Jeffrey Yu, Flora Wang, Elizabeth Y. Chen, Ferenc A. Scheeren, Angera H. Kuo, Shaheen S. Sikandar, Shigeo Hisamori, Linda J. van Weele, Diane Heiser, Sopheak Sim, Jessica Lam, Stephen Quake, Michael F. Clarke

National Cancer Institute, National Cancer Institute, National Cancer Institute, Breast Cancer Research Foundation, California Institute of Regenerative Medicine, U.S. Department of Defense, National Institutes of Health


Notch and Basal Cells Take Center Stage during Airway Epithelial Regeneration

Jeffrey A. Whitsett, Vladimir V. Kalinichenko


EZH1 repression generates mature iPSC-derived CAR T cells with enhanced antitumor activity

Ran Jing, Irene Scarfo, Mohamad Ali Najia, Edroaldo Lummertz da Rocha, Areum Han, Michael Sanborn, Trevor Bingham, Caroline Kubaczka, Deepak K. Jha, Marcelo Falchetti, Thorsten M. Schlaeger, Trista E. North, Marcela V. Maus, George Q. Daley


Marmoset and human trophoblast stem cells differ in signaling requirements and recapitulate divergent modes of trophoblast invasion

Dylan Siriwardena, Clara Munger, Christopher Penfold, Timo N. Kohler, Antonia Weberling, Madeleine Linneberg-Agerholm, Erin Slatery, Anna L. Ellermann, Sophie Bergmann, Stephen J. Clark, Thomas M. Rawlings, Joshua M. Brickman, Wolf Reik, Jan J. Brosens, Magdalena Zernicka-Goetz, Erika Sasaki, Rüdiger Behr, Florian Hollfelder, Thorsten E. Boroviak

University of Cambridge Centre for Trophoblast Research, Wellcome Trust


The Young and the Restless: Regulation of Adult Neurogenesis by Wnt Signaling


How Tets and Cytoskeleton Dynamics MET in Reprogramming


Institutional Report Cards for Gender Equality: Lessons Learned from Benchmarking Efforts for Women in STEM

Whitney H. Beeler, Kristin A. Smith-Doody, Richard Ha, Raeka S. Aiyar, Elizabeth Schwarzbach, Susan L. Solomon, Reshma Jagsi


Mapping Gene Circuits Essential for Germ Layer Differentiation via Loss-of-Function Screens in Haploid Human Embryonic Stem Cells

Atilgan Yilmaz, Carmel Braverman-Gross, Anna Bialer-Tsypin, Mordecai Peretz, Nissim Benvenisty

Israel Science Foundation, Rosetrees Trust, United States-Israel Binational Science Foundation, Azrieli Foundation


Organoids Develop Motor Skills: 3D Human Neuromuscular Junctions

New York Stem Cell Foundation, Spinal Muscular Atrophy Foundation, National Institutes of Health


20 Years of Human Pluripotent Stem Cell Research: It All Started with Five Lines

Tenneille E. Ludwig, Angela Kujak, Antonio Rauti, Steven Andrzejewski, Susan Langbehn, James Mayfield, Jacqueline Fuller, Yoshimi Yashiro, Yasushi Hara, Anita Bhattacharyya


Studying Tumor-ReacTive T Cells: A Personalized Organoid Model


The Roadmap to Approval under Japan’s Two-Track Regulatory System: Comparing Six Regenerative Medical Products

Shunsuke Matsushita, Keisuke Tachibana, Tetsuya Kusakabe, Ryuichi Hirayama, Yasuo Tsutsumi, Masuo Kondoh


Rewind to Recover: Dedifferentiation after Cardiac Injury


Quality Control: H2A.X Links to Better iPSCs


Generation of Induced Pluripotent Stem Cells from Human Cord Blood

Alexandra Haase, Ruth Olmer, Kristin Schwanke, Stephanie Wunderlich, Sylvia Merkert, Christian Hess, Robert Zweigerdt, Ina Gruh, Johann Meyer, Stefan Wagner, Lars S. Maier, Dong Wook Han, Silke Glage, Konstantin Miller, Philipp Fischer, Hans R. Schöler, Ulrich Martin


Overcoming Autocrine FGF Signaling-Induced Heterogeneity in Naive Human ESCs Enables Modeling of Random X Chromosome Inactivation

Chenrui An, Guihai Feng, Jixiang Zhang, Shiwei Cao, Ying Wang, Nannan Wang, Falong Lu, Qi Zhou, Haoyi Wang

National Key Research and Development Program of China Stem Cell and Translational Research


Functional Crosstalk between Bmi1 and MLL/Hoxa9 Axis in Establishment of Normal Hematopoietic and Leukemic Stem Cells

Lan-Lan Smith, Jenny Yeung, Bernd B. Zeisig, Nikolay Popov, Ivo Huijbers, Josephine Barnes, Amanda J. Wilson, Erdogan Taskesen, Ruud Delwel, Jesús Gil, Maarten Van Lohuizen, Chi Wai Eric So


Pathogen-Induced TLR4-TRIF Innate Immune Signaling in Hematopoietic Stem Cells Promotes Proliferation but Reduces Competitive Fitness

Hitoshi Takizawa, Kristin Fritsch, Larisa V. Kovtonyuk, Yasuyuki Saito, Chakradhar Yakkala, Kurt Jacobs, Akshay K. Ahuja, Massimo Lopes, Annika Hausmann, Wolf-Dietrich Hardt, Álvaro Gomariz, César Nombela-Arrieta, Markus G. Manz


Bone Marrow-Derived Cell Therapy Stimulates Endogenous Cardiomyocyte Progenitors and Promotes Cardiac Repair

Francesco S. Loffredo, Matthew L. Steinhauser, Joseph Gannon, Richard T. Lee


Ballroom Dancing with Stem Cells: Placement and Displacement in the Intestinal Crypt


Tailor Made Protein Synthesis for HSCs


Generation of Multipotent Induced Neural Crest by Direct Reprogramming of Human Postnatal Fibroblasts with a Single Transcription Factor

Yong Jun Kim, Hotae Lim, Zhe Li, Yohan Oh, Irina Kovlyagina, In Young Choi, Xinzhong Dong, Gabsang Lee


What Endothelial Cells from Patient iPSCs Can Tell Us about Aortic Valve Disease


Human Parthenogenetic Embryo Stem Cells: Appreciating What You Have When You Have It


Puma: Mauling the Intestinal Crypt


Editors' Notes


Defining the First Part of the Oncogenic KRAS Journey

Cancer Prevention and Research Institute of Texas, National Cancer Institute


The Educated Person’s Video Guide to Stem Cells: The History, the Science, the Promise


Breaking the Canon: Indirect Regulation of Wnt Signaling in Mammary Stem Cells by MMP3


Islet-Resident Endocrine Progenitors: A New Hope for Beta Cell PROCReation?


APA Makes a Short Cut for Ramping up HSC Metabolism


Introductions to the Community: Early-Career Researchers in the Time of COVID-19

Marta Shahbazi, Samira Musah, Ankur Sharma, Jeevisha Bajaj, Giacomo Donati, Weiqi Zhang


Pluripotency on Lockdown after Deletion of Three Transcription Regulators


Browsing (Epi)genomes: A Guide to Data Resources and Epigenome Browsers for Stem Cell Researchers


Cancer Stem Cells and Tumor Metastasis: First Steps into Uncharted Territory


Limited Acquisition of Chromosomal Aberrations in Human Adult Mesenchymal Stromal Cells

Luc Sensebé, Karin Tarte, Jacques Galipeau, Mauro Krampera, Ivan Martin, Donald G. Phinney, Yufang Shi


Metal-lifespan analysis shows scale of waste


Versatile gel assembly on a chip


A mitochondrial brake on vascular repair


Devices for Increasing Accuracy in Weighing


Our Book Shelf


Beyond academia: Planning the perfect exit strategy for a scientific career move


Assassins of eyesight


Trans-science


Permeability Factors of Rabbit Skin associated with Delayed Vascular Response to Thermal Injury


Regulatory Mechanisms of Glycogen Deposition in Liver of Normal and Tumour-bearing Rat, and in Novikoff Ascites Hepatoma


Fission track dating of zeolites

Sohan L. Koul, Lewis T. Chadderton, C. Kent Brooks


A geographical error


A Positive Feedback Mechanism of Adenosine Triphosphate Synthesis in Erythrocytes

MAKOTO NAKAO, TAKASHI MOTEGI, TOSHIKO NAKAO, SABURO YAMAZOE, HARUHISA YOSHIKAWA


Enhanced Light Absorption by Compounds in Chromatograms


An anterior pituitary factor stimulates thymidine incorporation in isolated thymocytes


The latest in Saint Louis


COVID vaccine enthusiasm, astronomy bullies and an enzyme chip


Daily briefing: How a meteorite became King Tutankhamen’s dagger


‘Undruggable’ cancer-causing protein meets its match


Support for LGBTQ+ people in later life


How three refugee scientists kept their research hopes alive


Mysterious form of high-energy radiation spotted in thunderstorms


US realigns misconduct offices at NIH


A Darwinian Universe?


How head-hunters track down the winners for science's top jobs


Ready ⃛ aim ⃛ fire!


Clerk Maxwell's Papers


A new class of nonthermal radio sources


Iron distribution in the intracluster gas of the Virgo cluster of galaxies

Katsuji Koyama, Shiro Takano, Yuzuru Tawara


Detection of the underlying galaxy in the QSO 3C48


Pugwash Joins the Establishment


Monkeys in the West Indies


Carbon nanotubes: the new asbestos?


The 155-day solar period in the sixteenth century and later


Suspension Culture of Mammalian Cells and Macromolecular Growth-Promoting Fractions of Calf Serum


Radiocæsium in Human Beings


Internal structure and polarization of the optical jet of the quasar 3C273

R. C. Thomson, C. D. Mackay, A. E. Wright


Lime and Bones


What a personal saga reveals about scientists’ lives — and about science itself


Earliest systematically crafted bone tools date back 1.5 million years


Quantum effects brought to light


Grad students’ long overtime , and more — this week’s best science graphics


A Query


A mighty antibody is the bane of multiple coronaviruses


An Introduction to the Design of Beams, Girders, and Columns in Machines and Structures, with Examples in Graphic Statics Mechanics for Engineers, a Text-book of Intermediate Standard


Diary of Societies


Surgical wonders and dodgy medical ethics: the Hunterian Museum reopens


Mind in the Lower Animals


Side-Chain Reactions of Benzene Derivatives


RELATIONAL PLANT MORPHOLOGY


COVID-19 COVID-19 COVID-19 COVID-19 COVID-19 COVID-19

Medizinischen Klinik, Hospital zum heiligen Geist GmbH, Frankfurt/Main


Anergy in COVID-19 Infected and COVID-19 vaccinee

Journal of International Perspectives on COVID-19


PSYCHOLOGICAL IMPACT OF COVID-19


MANAGEMENT STRATEGIES OF COVID - 19


THE WORLD AFTER COVID -19


ENFRENTAR COM SOLIDARIEDADE À COVID-19

Abstract: The COVID-19 pandemic imposed on humanity rapid and profound changes in the way it lived and related. In this unstable and complex scenario, in which public health and ethical issues were revisited, this essay sought to visualize some solidarity actions to offer elements of reflection in an attempt to deepen the theme on solidarity as a gift of care and responsibility, a human resource effective to face the pandemic reality. In the health crisis with SARS-CoV-2, solidarity rekindled, in people and societies, ethical humanitarian and community attributes, of care and responsibility, which generated solidarity actions in the confrontation and overcoming of COVID-19, Solidarity can be a possible promising and safe path for the development of human and social potential, an ethical paradigm that redirects individual rights towards collective rights and the common good. An ethical duty of care and responsibility for the good of people.


Is COVID-19 a Housing Disease? Housing, COVID-19 Risk, and COVID-19 Harms in the UK

Abstract: This chapter examines evidence from the UK to assess whether COVID-19 and lockdown harms are housing diseases. It focuses on poor housing conditions, such as overcrowding and its relationship with poor health, in order to analyze the ongoing harms caused by the pandemic and factors that inhibit abilities to contain it. It also looks at issues concerning the shortage of space, lack of bedrooms, and poor-quality housing to mental health, noting that much of the population are staying at home during lockdowns. The chapter explains that COVID-19 is a disease that exposes vulnerabilities in the UK housing system, in both the public sector and the private market. It points out that the UK has had among the highest COVID-19 death rates in the world.


IMMUNE RESPONSE TO COVID-19 INFECTION


ADMINISTRATION OF CURRENT MEDICINE AGAINST COVID-19


ROLE OF INFORMATION SCIENCE DURING COVID -19


EMERGENCE OF ONLINE LEARNING ON COVID 19


DEVELOPMENT OF VACCINATION DURING COVID 19


Humans, COVID-19 and Platform Societies

Abstract: Abstract and Keywords to be supplied.


ROLE OF HEALTHCARE PROFESSIONALS IN COVID-19 SITUATION


INFLUENCE OF COVID-19 AT GLOBAL LEVEL


THE GLOBAL COUNTRY WISE STATISTICS ON COVID-19


Psychological risks during the COVID-19 pandemic

издательство Московского Университета

PSYCHOLOGICAL GUIDANCE DURING THE COVID-19 PANDEMIC


ရွေးကောက်ပွဲများနှင့် COVID-19

International Institute for Democracy and Electoral Assistance


Covid-19 Aşıları

Medical Journal of Suleyman Demirel University

Abstract: Covid-19 pandemisi 1 yılı aşkın bir süredir devam etmektedir. Alınan çeşitli önlemlere karşın pandemi sonlandırılamamıştır. Bu nedenle aşı çalışmaları önem kazanmıştır. Bazı aşıların etkinliklerinin yüksek olmasına rağmen çalışmalar hala devam etmektedir.


NEUROLOGICAL IMPACT OF COVID-19 PANDEMIC: LESSONS & CAUTIONS


Covid-19: Reflections on the Pathology Caused by Coronavirus COVID-19


IMPACT OF COVID 19 ON THE GLOBAL ECONOMY


IMPACTS OF THE COVID-19 PANDEMIC ON GLOBAL EDUCATION


IMPACT OF COVID-19 ON ACADEMICS AND RESEARCH


Inclusive Education in the Post-COVID-19 World

Abstract: Abstract and Keywords to be supplied.


INFLUENCE OF COVID-19 ON BUSINESS, MARKETING AND SUPPLY


COVID-19 and after

Abstract: This chapter assesses the impact of COVID-19, and policy responses to it, after a year of special measures. The challenges to health and well-being, mortality, work, welfare, the public finances, and civil liberties have all been severe. This chapter uses a range of data sources to make an assessment of the damage after a year of pandemic. It discusses dynamics of deficit, debt, unemployment, future growth and taxation. Finally, it considers the prospects, in both the short- and medium-term, for welfare after COVID-19.


From COVID-19 or because COVID-19?

Azienda Ospedaliera Ordine Mauriziano di Torino

Abstract: Infections and pandemics will condition us in an increasingly predominant way regarding diagnostic, medical and surgical activities in all specialist areas; and this particularly in cardiovascular one. Nevertheless in the future the need to cohabit with pandemic events and to be able to continue an elective and not only emergency cardiac surgery program represents an imperative.


Covid-19 and Parkinson's disease: Acute clinical implications, long-COVID and post-COVID-19 parkinsonism


Sağlık İletişimi Perspektifinden Instagramda COVID-19 Paylaşımlarının Analizi: #COVID-19

Abstract: Sosyal medya, COVID-19 gibi salgınlar hakkındaki bilgilerin değerlendirilmesi için önemli bir dijital alanı temsil etmektedir. Ayrıca gerekli sosyal yardım ve müdahalelerin planlanmasında önemli bir araç olarak hizmet edebilmektedir. Pandemi süreçlerinde acil kriz yönetiminde, Twitter, İnstagram, Facebook üzerinden yapılan sosyal medya analizleri durumsal farkındalığı desteklemek için kullanılmaktadır ve bulaşıcı hastalıkların izlenmesinden bölgesel planlama ve çok amaçlı kampanyalara kadar birçok farklı durum için gerekli görülmektedir. Bu çalışmada COVID-19 pandemisinde İnstagram’ın nasıl kullanıldığını belirlemek amaçlanmıştır. İnstagram’da “coronavirus” ve “COVID-19” anahtar kelimesi kullanılarak döküman analizi ile 10 Ocak – 20 Şubat 2020 tarihleri arasında görsel taraması yapılmıştır. Nitel türdeki bu araştırmada verilerin analizinde içerik analizi kullanılmıştır. 334 görselin çoğunda (%35.0) İngilizce altyazı olduğu belirlenmiştir. Görsellerde kullanılan mesaj yaklaşımının çoğunlukla korkutma (%38.9) olduğu saptanmıştır. Görsellerin çoğunluğunun (%71.0) fotoğraf olduğu belirlenmiştir. Fotoğrafları sırasıyla infografik (%17.7) ve karikatür (%11.4) izlemektedir. Paylaşılan görsellerin bilgi vermeye odaklı olduğu görülmüştür. Organizasyonlara ait resmi İnstagram hesapları tarafından paylaşılan kamu yararı taşıyan görsellerin çoğunluğunun (%53.1) haber niteliğinde olduğu saptanmıştır. Haberleri sağlık politikaları (%24.7) ve sosyal pazarlama (%22.2) içerikli görsellerin takip ettiği görülmüştür. Sonuç olarak, pandemi sürecinde İnstagram’ın, gözetim, bilgilendirme ve müdahaleye yardımcı olabilecek, kamuoyunu değerlendirmede katkı sunabilecek bir sosyal medya aracı olabileceği düşünülmektedir.


COVİD-19: РАДИОДИАГНОСТИЧЕСКИЕ МОМЕНТЫ

Abstract: Yeni tip koronavirusla assosiasiya olunan və bu gün bütün dünyanı sarsıdan kəskin respirator xəstəlik bəşəri problemə çevrilmişdir. Bu xəstəlik 2019-cu ilin dekabr ayında Çinin Uhan şəhərində qeydə alınıb və xəstəlik əksər hallarda ikitərəfli pnevmoniya ilə ağırlaşması ilə səciyyələnir. Bu səbəbdən COVİD-19 pnevmoniyasının diaqnostikasında RQ və KT müayinələrinin nəticələrini göstərən və diaqnostikada rəhbər rolu oynayan elmi araşdırmaların praktik nəticələrinin icmalı həm radioloqlar, həm də klinisitlər üçün böyük əhəmiyyətə malikdir. İcmalı tərtib etməkdə əsas məqsədimiz COVİD-19 pnevmoniyası olan xəstələrin şüa-diaqnostik əlamətləri haqqında məlumat vermək və xəstəliyin radioloji portretini yaratmaqdır. Вспышка коронавирусной болезни 2019 года (COVID-19), впервые обнаруженная в Ухане (Китай), быстро охватила весь мир всего за месяц, вызвав глобальную чрезвычайную ситуацию в области общественного здравоохранения. В представленной статье изложен обзор научных исследований, которые демонстрируют результаты рентгенографии и КТ в диагностике пневмонии COVID-19, необходим как для радиологов, так и для клиницистов. Основная цель – анализ радиологических признаков у пациентов с COVID-19 пневмонией. Coronavirus disease 2019 (COVID-19) outbreak, first found in Wuhan, China, has quickly swept around the world just within a month, causing global public health emergency. The article presents the review of scientific researches, which demonstrate the results of Radiography and CT in the diagnosis of COVID-19 pneumonia, is necessary for both radiologists and clinicians. The main aim is analyzing the radiographic signs in patients with COVID-19 pneumonia and creating the radiologic features of the disease.


COVID-19


Russia: Legal Response to Covid-19

The Oxford Compendium of National Legal Responses to Covid-19


Perspective of COVID-19 Patients on The Relationship Between Oral Health and COVID-19: Responses During COVID-19 Home-Quarantine

Sühan GÜRBÜZ, Beste İLASLAN HALLAÇ, Zeki Levent HALLAÇ, Ayşen BODUR

GAZİ ÜNİVERSİTESİ, DİŞ HEKİMLİĞİ FAKÜLTESİ, DİŞ HEKİMLİĞİ PR.

Abstract: Objective: Patients with coronavirus disease-2019 (COVID-19) have been reported to have a variety of oral symptoms, including acute periodontal lesions. However, there are a limited number of studies in the literature reporting the views of COVID-19 patients on oral health. The present study aims to examine the views of COVID-19 patients in-home quarantine regarding the possible relationship between oral health and COVID-19. Material and Methods: A face-to-face interview was conducted by the filiation team with 500 patients with a diagnosis of COVID-19 in-home quarantine, visited between January and April 2021. The patients were asked the question, “Do you think there is a relationship between COVID-19 and oral health?” The answers were analyzed by age, gender, and education level. Results: Of the 500 patients (290 female, age±SD, 38.2±12.7), 216 (43.2%) replied "yes". 50.7% of the females and 32.9% of the males responded stating that there is a relationship (p&lt;0.05). Among the patients, 54.9% of those responded as "there is a relationship" were Bachelor’s/Master's graduates, while 66% of the patients who said, "there is no relationship" were primary school graduates (p&lt;0.05). Conclusions: Among COVID-19 patients in-home quarantine, especially women and patients with higher education levels reported that there may be a relationship between oral health and COVID-19. It is crucial to inform all segments of society about the necessity of improving and maintaining oral hygiene during COVID-19 to reduce both the oral bacterial load and the risk of potential bacterial super-infections.


Is COVID-19 a Housing Disease? Housing, COVID-19 Risk, and COVID-19 Harms in the UK


The impact of COVID-19 and COVID-19 policy on the housing market

Abstract: UK policy sought to minimise the impact of the pandemic on the housing market, which was viewed as essential to sustaining economic growth. Home construction and marketing paused only for seven weeks, construction workers received £15.9 billion of pandemic support and at least £4.7 billion was spent on a cut in stamp duty (transactions tax). While completions, transactions and lending reduced in 2020, they soon recovered. In contrast to expectations, house price growth continued at 11% a year throughout 2020 and 2021. This contributed to inequalities in access to home ownership and to wealth.


Chile: Legal Response to Covid-19


Special Coverage on COVID-19

Ovid Technologies (Wolters Kluwer Health)


Mexico: Legal Response to Covid-19


IMPACT OF COVID-19 ON AERONAUTICAL ENGINEERING

Nishchha a, Margarita N Favorskaya, Angelina N. Zhukovskaya


Post-COVID-19 rehabilitation


Taiwan: Legal Response to Covid-19


THE GLOBAL RACE FOR MOST AWAITED VACCINE AGAINST COVID-19


Is COVID-19 a Housing Disease?


Serbia: Legal Response to Covid-19


Singapore: Legal Response to Covid-19


Austria: Legal Response to Covid-19


The TRIPS Agreement and COVID-19


France: Legal Response to Covid-19


U-Net: Convolutional Networks for Biomedical Image Segmentation

Olaf Ronneberger, Philipp Fischer, Thomas Brox


SciPy 1.0: fundamental algorithms for scientific computing in Python

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, SciPy 1.0 Contributors, Aditya Vijaykumar, Alessandro Pietro Bardelli, Alex Rothberg, Andreas Hilboll, Andreas Kloeckner, Anthony Scopatz, Antony Lee, Ariel Rokem, C. Nathan Woods, Chad Fulton, Charles Masson, Christian Häggström, Clark Fitzgerald, David A. Nicholson, David R. Hagen, Dmitrii V. Pasechnik, Emanuele Olivetti, Eric Martin, Eric Wieser, Fabrice Silva, Felix Lenders, Florian Wilhelm, G. Young, Gavin A. Price, Gert-Ludwig Ingold, Gregory E. Allen, Gregory R. Lee, Hervé Audren, Irvin Probst, Jörg P. Dietrich, Jacob Silterra, James T Webber, Janko Slavič, Joel Nothman, Johannes Buchner, Johannes Kulick, Johannes L. Schönberger, José Vinícius de Miranda Cardoso, Joscha Reimer, Joseph Harrington, Juan Luis Cano Rodríguez, Juan Nunez-Iglesias, Justin Kuczynski, Kevin Tritz, Martin Thoma, Matthew Newville, Matthias Kümmerer, Maximilian Bolingbroke, Michael Tartre, Mikhail Pak, Nathaniel J. Smith, Nikolai Nowaczyk, Nikolay Shebanov, Oleksandr Pavlyk, Per A. Brodtkorb, Perry Lee, Robert T. McGibbon, Roman Feldbauer, Sam Lewis, Sam Tygier, Scott Sievert, Sebastiano Vigna, Stefan Peterson, Surhud More, Tadeusz Pudlik, Takuya Oshima, Thomas J. Pingel, Thomas P. Robitaille, Thomas Spura, Thouis R. Jones, Tim Cera, Tim Leslie, Tiziano Zito, Tom Krauss, Utkarsh Upadhyay, Yaroslav O. Halchenko, Yoshiki Vázquez-Baeza

Abstract: Abstract SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.


Matplotlib: A 2D Graphics Environment

Institute of Electrical and Electronics Engineers (IEEE)


MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms

Sudhir Kumar, Glen Stecher, Michael Li, Christina Knyaz, Koichiro Tamura

Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA

National Institutes of Health, National Science Foundation, Japan Society for the Promotion of Science (JSPS)


Equation of State Calculations by Fast Computing Machines

Nicholas Metropolis, Arianna W. Rosenbluth, Marshall N. Rosenbluth, Augusta H. Teller, Edward Teller

Los Alamos Scientific Laboratory, Los Alamos, New Mexico

Abstract: A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration over configuration space. Results for the two-dimensional rigid-sphere system have been obtained on the Los Alamos MANIAC and are presented here. These results are compared to the free volume equation of state and to a four-term virial coefficient expansion.


QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

Paolo Giannozzi, Stefano Baroni, Nicola Bonini, Matteo Calandra, Roberto Car, Carlo Cavazzoni, Davide Ceresoli, Guido L Chiarotti, Matteo Cococcioni, Ismaila Dabo, Andrea Dal Corso, Stefano de Gironcoli, Stefano Fabris, Guido Fratesi, Ralph Gebauer, Uwe Gerstmann, Christos Gougoussis, Anton Kokalj, Michele Lazzeri, Layla Martin-Samos, Nicola Marzari, Francesco Mauri, Riccardo Mazzarello, Stefano Paolini, Alfredo Pasquarello, Lorenzo Paulatto, Carlo Sbraccia, Sandro Scandolo, Gabriele Sclauzero, Ari P Seitsonen, Alexander Smogunov, Paolo Umari, Renata M Wentzcovitch


Modern Applied Statistics with S


Quantum Mechanical Continuum Solvation Models

Jacopo Tomasi, Benedetta Mennucci, Roberto Cammi

Dipartimento di Chimica e Chimica Industriale, Università di Pisa, Via Risorgimento 35, 56126 Pisa, Italy, and Dipartimento di Chimica, Università di Parma, Viale delle Scienze 17/A, 43100 Parma, Italy


jModelTest 2: more models, new heuristics and parallel computing

Diego Darriba, Guillermo L Taboada, Ramón Doallo, David Posada


Experimental observation of the quantum Hall effect and Berry's phase in graphene

Yuanbo Zhang, Yan-Wen Tan, Horst L. Stormer, Philip Kim


Atoms in Molecules

Department of Chemistry, McMaster University, Ontario, Canada

Abstract: Abstract The molecular structure hypothesis - that a molecule is a collection of atoms linked by a network of bonds - was forged in the crucible of nineteenth-century experimental chemistry and has continued to serve as the principal means of ordering and classifying the observations of chemistry. There is a difficulty with the hypothesis, however, in that it is not related directly to the physics which governs the motions of the nuclei and electrons that make up the atoms and the bonds. It is the purpose of this important new book to show that a theory can be developed to underpin the molecular structure hypothesis - that the atoms in a molecule are real, with properties predicted and defined by the laws of quantum mechanics, and that the structure their presence imparts to a molecule is indeed a consequence of the underlying physics. As a result the classification based upon the concept of atoms in molecules is freed from its empirical constraints and the full predictive power of quantum mechanics. As a result the full predictive power of quantum mechanics can be incorporated into the resulting theory - a theory of atoms in molecules. The book is aimed at those scientists responsible for performing the experiments and collecting the observations on the properties of matter at the atomic level, in the belief that the transformation of qualitative concepts into a quantitative theory will serve to deepen our understanding of chemistry.


Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?

Institute for Advanced Study, Princeton, New Jersey


Meeting the Universe Halfway


Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels

Charles H. Bennett, Gilles Brassard, Claude Crépeau, Richard Jozsa, Asher Peres, William K. Wootters


Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model

Journal of the American Chemical Society

Michael J. S. Dewar, Eve G. Zoebisch, Eamonn F. Healy, James J. P. Stewart


A Method of Computing the Effectiveness of an Insecticide


Semiconductor Clusters, Nanocrystals, and Quantum Dots

American Association for the Advancement of Science (AAAS)

The author is in the Department of Chemistry, University of California, and Materials Sciences Division, Lawrence Berkeley Laboratory, Berkeley, CA 94720, USA.

Abstract: Current research into semiconductor clusters is focused on the properties of quantum dots—fragments of semiconductor consisting of hundreds to many thousands of atoms—with the bulk bonding geometry and with surface states eliminated by enclosure in a material that has a larger band gap. Quantum dots exhibit strongly size-dependent optical and electrical properties. The ability to join the dots into complex assemblies creates many opportunities for scientific discovery.


Gold Nanoparticles: Assembly, Supramolecular Chemistry, Quantum-Size-Related Properties, and Applications toward Biology, Catalysis, and Nanotechnology

Molecular Nanosciences and Catalysis Group, LCOO, UMR CNRS No. 5802, Université Bordeaux I, 33405 Talence Cedex, France


A tutorial on support vector regression


Data clustering

Association for Computing Machinery (ACM)

Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.


Quantum Calculation of Molecular Energies and Energy Gradients in Solution by a Conductor Solvent Model

Dipartimento di Chimica, Università Federico II, via Mezzocannone 4, I-80134 Napoli, Italy


Quantum cryptography based on Bell’s theorem


Anomaly detection

Varun Chandola, Arindam Banerjee, Vipin Kumar

University of Minnesota, Minneapolis, MN

Advanced Cyberinfrastructure, National Science Foundation, National Aeronautics and Space Administration, Division of Information and Intelligent Systems, Division of Computer and Network Systems

Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.


Quantum entanglement

Ryszard Horodecki, Paweł Horodecki, Michał Horodecki, Karol Horodecki


The NumPy Array: A Structure for Efficient Numerical Computation

Stefan van der Walt, S. Chris Colbert, Gael Varoquaux


GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems

Society for Industrial & Applied Mathematics (SIAM)

SIAM Journal on Scientific and Statistical Computing


I.—COMPUTING MACHINERY AND INTELLIGENCE


On the Quantum Correction For Thermodynamic Equilibrium


Sensitive measurement of optical nonlinearities using a single beam

M. Sheik-Bahae, A.A. Said, T.-H. Wei, D.J. Hagan, E.W. Van Stryland


Data Structures for Statistical Computing in Python

Proceedings of the Python in Science Conference


3D Slicer as an image computing platform for the Quantitative Imaging Network

Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy-Cramer, Julien Finet, Jean-Christophe Fillion-Robin, Sonia Pujol, Christian Bauer, Dominique Jennings, Fiona Fennessy, Milan Sonka, John Buatti, Stephen Aylward, James V. Miller, Steve Pieper, Ron Kikinis


Mixed-Effects Models in Sand S-PLUS


A tutorial on spectral clustering


Creating the CIPRES Science Gateway for inference of large phylogenetic trees

2010 Gateway Computing Environments Workshop (GCE)

Mark A. Miller, Wayne Pfeiffer, Terri Schwartz


Quantum Computing in the NISQ era and beyond

Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften

Institute for Quantum Information and Matter and Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena CA 91125, USA

Abstract: Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.


Advanced capabilities for materials modelling with Q <scp>uantum</scp> ESPRESSO

P Giannozzi, O Andreussi, T Brumme, O Bunau, M Buongiorno Nardelli, M Calandra, R Car, C Cavazzoni, D Ceresoli, M Cococcioni, N Colonna, I Carnimeo, A Dal Corso, S de Gironcoli, P Delugas, R A DiStasio, A Ferretti, A Floris, G Fratesi, G Fugallo, R Gebauer, U Gerstmann, F Giustino, T Gorni, J Jia, M Kawamura, H-Y Ko, A Kokalj, E Küçükbenli, M Lazzeri, M Marsili, N Marzari, F Mauri, N L Nguyen, H-V Nguyen, A Otero-de-la-Roza, L Paulatto, S Poncé, D Rocca, R Sabatini, B Santra, M Schlipf, A P Seitsonen, A Smogunov, I Timrov, T Thonhauser, P Umari, N Vast, X Wu, S Baroni

Leverhulme Trust, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Division of Materials Research, H2020 Research Infrastructures, U.S. Department of Defense, PASC Platform for Advanced Scientific Computing, Office of Science

Abstract: Abstract Q uantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the-art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudopotential and projector-augmented-wave approaches. Q uantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement their ideas. In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software.


Edge Computing: Vision and Challenges

Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, Lanyu Xu


Silicon quantum wire array fabrication by electrochemical and chemical dissolution of wafers

Royal Signals and Radar Establishment, St. Andrews Road, Great Malvern, Worcestershire WR14 3PS, United Kingdom

Abstract: Indirect evidence is presented that free-standing Si quantum wires can be fabricated without the use of epitaxial deposition or lithography. The novel approach uses electrochemical and chemical dissolution steps to define networks of isolated wires out of bulk wafers. Mesoporous Si layers of high porosity exhibit visible (red) photoluminescence at room temperature, observable with the naked eye under &lt;1 mW unfocused (&lt;0.1 W cm−2) green or blue laser line excitation. This is attributed to dramatic two-dimensional quantum size effects which can produce emission far above the band gap of bulk crystalline Si.


Quantum cryptography

Nicolas Gisin, Grégoire Ribordy, Wolfgang Tittel, Hugo Zbinden


Quantum Spin Hall Effect in Graphene


The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence

Biochimica et Biophysica Acta (BBA) - General Subjects

Bernard Genty, Jean-Marie Briantais, Neil R. Baker


A quantum theory of molecular structure and its applications


A view of cloud computing

Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, Matei Zaharia

UC Berkeley Reliable Adaptive Distributed Systems Laboratory (RAD Lab)

Division of Computer and Network Systems, University of California

Abstract: Clearing the clouds away from the true potential and obstacles posed by this computing capability.


Density matrix formulation for quantum renormalization groups


Quantum supremacy using a programmable superconducting processor

Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando G. S. L. Brandao, David A. Buell, Brian Burkett, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Andrew Dunsworth, Edward Farhi, Brooks Foxen, Austin Fowler, Craig Gidney, Marissa Giustina, Rob Graff, Keith Guerin, Steve Habegger, Matthew P. Harrigan, Michael J. Hartmann, Alan Ho, Markus Hoffmann, Trent Huang, Travis S. Humble, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Paul V. Klimov, Sergey Knysh, Alexander Korotkov, Fedor Kostritsa, David Landhuis, Mike Lindmark, Erik Lucero, Dmitry Lyakh, Salvatore Mandrà, Jarrod R. McClean, Matthew McEwen, Anthony Megrant, Xiao Mi, Kristel Michielsen, Masoud Mohseni, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Murphy Yuezhen Niu, Eric Ostby, Andre Petukhov, John C. Platt, Chris Quintana, Eleanor G. Rieffel, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Kevin J. Satzinger, Vadim Smelyanskiy, Kevin J. Sung, Matthew D. Trevithick, Amit Vainsencher, Benjamin Villalonga, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Hartmut Neven, John M. Martinis


Quantum Spin Hall Effect and Topological Phase Transition in HgTe Quantum Wells

B. Andrei Bernevig, Taylor L. Hughes, Shou-Cheng Zhang

Department of Physics, Stanford University, Stanford, CA 94305, USA.

Abstract: We show that the quantum spin Hall (QSH) effect, a state of matter with topological properties distinct from those of conventional insulators, can be realized in mercury telluride–cadmium telluride semiconductor quantum wells. When the thickness of the quantum well is varied, the electronic state changes from a normal to an “inverted” type at a critical thickness d c . We show that this transition is a topological quantum phase transition between a conventional insulating phase and a phase exhibiting the QSH effect with a single pair of helical edge states. We also discuss methods for experimental detection of the QSH effect.


On the generators of quantum dynamical semigroups


Quantum Dot Bioconjugates for Ultrasensitive Nonisotopic Detection

Department of Chemistry, Indiana University, Bloomington, IN 47405, USA.

Abstract: Highly luminescent semiconductor quantum dots (zinc sulfide–capped cadmium selenide) have been covalently coupled to biomolecules for use in ultrasensitive biological detection. In comparison with organic dyes such as rhodamine, this class of luminescent labels is 20 times as bright, 100 times as stable against photobleaching, and one-third as wide in spectral linewidth. These nanometer-sized conjugates are water-soluble and biocompatible. Quantum dots that were labeled with the protein transferrin underwent receptor-mediated endocytosis in cultured HeLa cells, and those dots that were labeled with immunomolecules recognized specific antibodies or antigens.


Deep Learning - Based Diabetic Retinopathy Detection: A Survey on Deep Learning Architectures

International Journal of Science and Research (IJSR)


Deep Chinese Teaching and Learning Model Based on Deep Learning

International Journal of Languages, Literature and Linguistics

Abstract: Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical application and teaching effect test. The results show that the deep Chinese teaching and learning model is conducive to improving students’ discourse presentation ability and comprehensive skills, cultivating the learners’ autonomous learning ability and intercultural communication competence, and strengthening the integration of language teaching and cultural teaching. At the same time, this model also has some limitations, need to be further adjusted and optimized.


AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning


Performance Analysis of Extensional Deep Learning And Boosted Intensional Deep Learning Method


Deep learning? What deep learning?

South African Journal of Higher Education


Deep Reinforcement Learning: An Innovative Approach that Combines Deep Learning and Reinforcement Learning

Advances in Engineering Technology Research

Abstract: Deep reinforcement learning (DRL) has emerged as a transformative paradigm that integrates the powerful representation-learning capabilities of deep neural networks with the sequential decision-making framework of reinforcement learning. Building upon the foundations of value functions, policy optimization, and Markov decision processes, DRL enables agents to operate effectively in high-dimensional, nonlinear, and dynamic environments where traditional methods fall short. This article provides a comprehensive overview of the theoretical principles, core algorithms, and key enabling techniques—such as neural network architectures, experience replay, Deep Q-Networks, and policy-gradient approaches—that underpin modern DRL systems. In addition, the paper examines several prominent application domains, including gaming, robotics, autonomous driving, finance, and healthcare, highlighting how DRL facilitates adaptive control, long-term optimization, and human-centered interaction. Challenges such as sample inefficiency, limited interpretability, and safety concerns are discussed, alongside emerging directions including model-based RL, multimodal learning, transfer learning, and ethical considerations. Overall, this study underscores the substantial potential of deep reinforcement learning to advance artificial intelligence and provides insights into future research necessary to ensure reliable, efficient, and responsible deployment of DRL technologies.


Learning Innovation in Global Citizenship Education by Integrating Deep Learning Approach

Sutrisno Sutrisno, Sunarto Sunarto, Dyah Mustikasari

Abstract: Conventional learning methods in dealing with the complexity of dynamic and multidimensional global issues in learning global citizenship education are still ineffective, this can be seen from the low understanding and awareness of students in solving global issues that occur. This research aims to develop and test learning innovations by integrating deep learning approaches to improve students' understanding and participation in the context of global citizenship. The research method used was quantitative research with an experimental approach. The research subjects consisted of senior high school students in a sample school. The research instruments included questionnaires, comprehension tests, and observations used to measure the effectiveness of learning before and after the application of the deep learning approach. The collected data were analyzed using descriptive and inferential statistics to assess the improvement of learning outcomes. The results showed that the integration of deep learning approach in global citizenship education significantly improved students' concept understanding, critical thinking skills, and learning motivation. Students become more active and able to connect civic education materials with contemporary global issues more deeply. The implication of this research finding is that the application of deep learning technology can be an effective innovative strategy in developing global citizenship education that is adaptive and responsive to the challenges of the times. This encourages educators to utilize artificial intelligence technology in the learning process to create a more interactive and meaningful learning experience.


Improving Student Learning Outcomes in Fiqh Learning through the Student Problem Based Learning Model at MI Nurul Jihad

DEEP LEARNING: Journal of Educational Research

Abstract: This study aims to improve the learning motivation of fifth grade students of MI Nurul Jihad in the subject of Fiqh through the application of the Problem Based Learning learning model. The background of the study identified that the learning process tends to focus on memorization, does not involve students actively, and results in low learning motivation. This condition is influenced by limited facilities and infrastructure, lack of mastery of science and technology by teachers, and low student interest in learning. Based on these problems, this classroom action research was formulated to answer the question: "Can the use of the Problem Based Learning learning model improve students' learning motivation in the field of Fiqh studies of fifth grade MI Nurul Jihad?" This study uses the Classroom Action Research method that focuses on the implementation of the Problem Based Learning model. Data were collected through tests, observations, and documentation, then analyzed descriptively qualitatively. The results of initial observations showed low student participation and learning outcomes, where only 66.7% of students (10 out of 15) achieved completion with an average score of 65.33. After implementing cycle I of the PBL model, there was a significant increase in student learning outcomes, with 80.0% of students (12 out of 15) achieving completion and the class average score increasing to 75.0.


Implementation of Problem Based Learning Model to Improve Learning Outcomes at SD Negeri Lawe Bulan

Abstract: This classroom action research aims to improve the learning outcomes of fourth-grade students at SD Negeri Lawe Bulan in the topic of "Approaching Puberty" through the implementation of the Problem Based Learning (PBL) model. The study is motivated by the low academic performance observed in students, which fell below the Minimum Competency Standard. The conventional and monotonous learning methods were identified as contributing factors to students’ lack of engagement and understanding, particularly in Islamic religious education, where concepts are often delivered as rote memorization. The research was conducted in two cycles, each consisting of planning, action, observation, and reflection. Data collection techniques included tests, observations, and documentation. The findings indicate that PBL significantly enhanced students' critical thinking, engagement, and learning outcomes. In Cycle I, the average student score was 77.92, while in Cycle II, it increased to 86.28. Student and teacher activities during learning also improved from "good" to "very good" qualifications. The PBL model, by incorporating real-life problems and collaborative learning, created a more meaningful learning experience and promoted students’ active participation. This approach helped students better understand the physical and emotional changes associated with puberty and encouraged them to take responsibility for their learning. The results of this study demonstrate that PBL is an effective instructional model to foster deeper comprehension, critical thinking skills, and improved academic performance in religious education. It also supports the development of independent, creative, and responsible learners.


Distraction Detection for Vehicle Drivers Using Deep Learning Techniques: A Review of Advanced Deep Learning Techniques

International Journal of Research Publication and Reviews


Deep Learning-Based Learning Strategies in Realizing Meaningful, Critical, and Enjoyable Learning

Rizwatul Hasanah, Harun Joko Prayitno, Suni Tuti, Sarilan Sarilan, Velandani Prakoso

Abstract: This study aims to examine the form and scope of the application of Deep Learning in the Vocational High School (SMK), especially at SMK Mantingan, through a literature study approach. Deep Learning, as part of artificial intelligence, has great potential in the world of vocational education, especially in supporting technology-based learning and improving student competencies in the digital field. This study relies on various literature sources such as scientific journals, books, articles, and relevant documents to examine the concepts, applications, and challenges faced in the integration of Deep Learning in vocational schools. The results of the study show that although the application of Deep Learning in vocational schools, including at SMK Mantingan, is still relatively new and limited, there are great opportunities for development in project-based learning and improving students' digital literacy. The scope of implementation includes the introduction of basic concepts, the use of supporting software, and the use of simple models for educational purposes. This study recommends the need for teacher training, the provision of adequate infrastructure, and collaboration with higher education institutions or industry to expand the application of this technology in vocational schools.


Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

Abstract: Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.


Conscious Learning Model in Improving Students' Learning Outcomes: A Classroom Action Research at MIN 5 Bone

Abstract: In the learning process, there are still many teachers who use the direct learning model with the lecture method, this makes students bored and bored during the learning process, so teachers need to vary the fun learning model according to learning so that students do not feel bored and bored. This study aims to examine the implementation of the Conscious Learning Model in improving students' learning outcomes through Classroom Action Research (CAR). The Conscious Learning Model emphasizes active student engagement, awareness in learning processes, and metacognitive strategies that enable learners to understand not only what they learn but how they learn. The research was conducted in two cycles, each consisting of planning, action, observation, and reflection stages. The subjects of this study were students from a fifth-grade class at a public elementary school in Indonesia. Data were collected through observation, tests, and documentation to measure changes in student learning outcomes and engagement. The results indicated a significant improvement in student learning outcomes after the implementation of the Conscious Learning Model. In the first cycle, students showed moderate improvement, with several still struggling to articulate learning goals and monitor their understanding. In the second cycle, however, there was a marked increase in students' ability to plan, evaluate, and regulate their learning processes. This was evidenced by higher test scores, increased participation in classroom discussions, and more reflective learning behaviors. The study concludes that the Conscious Learning Model is an effective approach to enhance student learning outcomes, particularly when applied consistently with supportive instructional strategies and reflective teaching practices. The implications of this research suggest that integrating conscious and reflective learning activities into daily instruction can foster more meaningful and self-directed learning experiences for students.


Questionable Practices in Methodological Deep Learning Research

Proceedings of the Northern Lights Deep Learning Workshop

Abstract: Evaluation of new methodology in deep learning (DL) research is typically done by reporting point estimates of a few performance metrics, calculated from a single training run. This paper argues that this frequently used evaluation protocol in DL is fundamentally flawed -- presenting 8 questionable practices that are widely adopted in the evaluation of new DL methods. The questionable practices are derived from violations of statistical principles of the scientific method, and from Hansson's definition of pseudoscience. A survey of recent publications from a top-tier DL conference indicates the widespread adoption of these practices in state-of-the-art DL research. Lastly, arguments in favor of the questionable practices, possible reasons for their adoption, and measures that have been taken to remove them, are discussed.


Seeing Deep into the Lungs with Deep Learning


Deep Learning vs Differentiated Learning: Learning Innovation in Islamic Boarding School-Based Middle Schools

Murwanto Setyo Nugroho, Harun Joko Prayitno, Koesoemo Ratih, Muhammad Samsudin

Abstract: This study aims to examine the implementation of deep learning and differentiated learning approaches in Islamic boarding school-based junior high schools (SMP) in order to improve the quality of meaningful, contextual, and humanistic learning. Using a descriptive qualitative approach, data were collected through in-depth interviews, classroom observations, and documentation studies in three Islamic boarding school junior high schools that integrate national and religious curricula. The results of the study indicate that learning strategies such as project-based learning, reflective discussions, and collaborative activities are effective in building conceptual understanding and spiritual values of students. Differentiated learning enables the creation of an inclusive learning environment by adjusting content, processes, and products according to students' learning profiles. Implementation challenges arise in terms of time constraints, dual curriculum burdens, and teacher professional readiness. Authentic project-based and portfolio-based evaluations are more relevant in measuring cognitive, affective, and spiritual competencies holistically. This study recommends the need for educational policies that are more adaptive to the characteristics of Islamic boarding schools, including contextual teacher training, curriculum flexibility, and an inclusive and sustainable evaluation system. These findings confirm that deep learning and differentiated learning have great potential in transforming Islamic boarding school education towards 21st-century learning that is more humanistic, reflective, and transformative.


OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS


From machine learning to deep learning: experimental comparison of machine learning and deep learning for skin cancer image segmentation

Auckland University of Technology (AUT) Library

Abstract: Skin lesion analysis is a tedious and challenging task, thus, in this research the suitability of employing machine learning or deep learning approaches for automatic lesion segmentation on dermoscopic skin cancer images is determined. The segmented region can assist clinical experts in understanding the complex lesion structure and internal pattern to find the correct skin cancer type for its early diagnosis and prevention. In this study, I present two methodologies for performing lesion segmentation: machine learning-based optimized K-means with Firefly Algorithm (FA) and Convolutional Neural Network (CNN). In the first model, the FA is hybridized with K-means clustering based on the novel average intensity fitness function to optimize the segmentation map. It is observed in the experimental results that the K-means algorithm may lead to poor results due to the wrong selection of initial centroid value, thus FA is hybridized into it to improve the performance. The second model is an enhanced encoder-decoder-based CNN framework implemented in an end-to-end fashion. These two models are compared to understand whether machine learning or deep learning is suitable to perform medical image segmentation based on a few performance metrics such as accuracy, Intersection over Union (IoU), and DICE index. These methods are evaluated and compared on two benchmark datasets provided by the International Skin Imaging Collaboration (ISIC) named ISIC 2016 [1] and ISIC 2017 [2]. Experimental results showed that the CNN model outperformed the machine learning model with an accuracy difference of 7.98% on ISIC 2016, and 7.32% on ISIC 2017. I concluded from the experimental findings that the deep learning model is more accurate and efficient in segmenting the lesion area as compared to the machine learning model. Thus, findings from this experimental work will be considered for the design of an automatic classification system by incorporating a deep learning-based segmentation approach as a pre-processing step.


Small Group Discussion Strategies Reviewed to Improve Student Learning Outcomes in Civics Learning at MI Sultan Agung

Abstract: This study aims to examine the effectiveness of small group learning strategies in improving student learning outcomes in Civics Education at MI Sultan Agung. Civics, as a subject, plays a crucial role in shaping students' character, sense of responsibility, and awareness of democratic values. However, traditional teaching methods often result in passive student participation and low academic performance. To address this issue, this research implemented a classroom action research (CAR) approach consisting of two cycles, each comprising planning, action, observation, and reflection. The subjects of the study were fifth-grade students at MI Sultan Agung, totaling 28 participants. Data collection techniques included observation, tests, and documentation. The findings indicate a significant improvement in student learning outcomes after applying small group learning strategies. In the first cycle, the average student learning outcome reached 68.21, with 60.71% of students achieving the minimum mastery criterion (MMC). In the second cycle, the average score increased to 82.14, with 89.29% of students achieving the MMC. Furthermore, student engagement and participation in the learning process showed marked improvement, indicating that small group strategies promote collaboration, active learning, and critical thinking. These results support the conclusion that small group learning is an effective pedagogical approach for enhancing both cognitive and affective learning outcomes in Civics Education. This study contributes to the growing body of research advocating for student-centered learning environments and highlights the importance of interactive teaching methods in primary education. The implementation of small group learning is recommended as a sustainable strategy to improve educational quality, particularly in the context of civic education, where active student involvement is essential. Future research may explore the long-term impact of this strategy and its application across different subjects and educational settings.


The Use of Domino Card Media to Improve Student Learning Outcomes in Natural Science Learning at MIS Mawu

Abstract: The results of students' science learning are still very low when viewed from the results of daily tests that have been held four times, namely the average is 6.45, this means that it has not met the KKM for science subjects, which is 6.7. The factors causing low student learning outcomes are the teaching methods applied by teachers tend not to activate students, teachers never use media during learning so that students are not interested and not motivated to participate in learning. To solve the above problems, researchers use domino card media in learning. This study aims to determine the impact of using domino card media on improving the science learning outcomes of class III students of MIS Mawu, Ambalawi District, Bima Regency in the 2022/2023 academic year. The method used in this study is classroom action research. PTK is action research carried out with the aim of improving the quality of learning practices in the classroom. The subjects in this study were 39 class III students of MIS Mawu. The type of data used in this study is quantitative data. Where quantitative data is data obtained from student learning outcome data for each evaluation. Data was collected using a learning outcome test. The results of the study showed that the average student learning outcomes in cycle I were 65.00 and had not met the KKM for science subjects and in cycle II the average student learning outcomes increased to 75.12 and had met the KKM for science subjects. Thus, it can be concluded that the use of domino card media can improve the science learning outcomes of class III students of MIS Mawu, Ambalawi District, Bima Regency in the 2022/2023 academic year.


Improving Student Learning Motivation in Islamic Education Learning Using an Activity-Based Approach at SD Negeri Inpres Baralau

Abstract: This study investigates the effectiveness of an activity-based approach in enhancing students' learning motivation in Islamic Education at SD Negeri Inpres Baralau. Learning motivation is a critical factor that significantly influences students’ academic success, particularly in religious education where engagement can be challenging due to traditional and passive instructional methods. The research employed a classroom action research design conducted in two cycles, each consisting of planning, implementation, observation, and reflection stages. Data were collected through observation sheets, student questionnaires, and teacher interviews to triangulate findings. The participants involved were 32 students in Grade V during the 2024/2025 academic year. The findings revealed a marked improvement in students’ motivation following the implementation of activity-based learning strategies, including group discussions, role-playing, games, and creative project assignments related to Islamic values. Quantitative data showed a significant increase in students’ motivation scores, rising from an average of 62.3 in the pre-cycle to 76.8 in the first cycle and 85.4 in the second cycle. Qualitative observations supported these results, indicating higher levels of enthusiasm, participation, and sustained attention during lessons. This research highlights that integrating interactive and student-centered learning activities into Islamic Education can foster a more engaging and motivating learning environment. The study recommends adopting activity-based approaches in primary religious education to cultivate not only students’ interest but also a deeper understanding and internalization of Islamic values. These findings are relevant for educators, policymakers, and curriculum developers aiming to improve educational practices in faith-based learning contexts. Future research may explore the long-term impact of such approaches across different educational levels and cultural settings.


Cooperative Learning Strategy to Improve Student Learning Outcomes on the Theme of Always Saving Energy at MI Nurul Ihsan

Abstract: This study investigates the effectiveness of domino card media in improving science learning outcomes for students at MIS Mawu. Recognizing the limitations of traditional, passive learning methods in fostering deep understanding and engagement in science, this research explores the potential of an interactive and collaborative learning tool. Domino cards, designed to connect various scientific concepts, terms, or processes, were implemented as a pedagogical intervention. The study examines the impact of this medium on student engagement, collaborative learning skills, critical thinking abilities, and ultimately, their academic achievement in Ilmu Pengetahuan Alam (Natural Science). Data was collected through [mention data collection methods if specified in a full paper, e.g., pre- and post-tests, observations, student feedback]. The findings of this research aim to provide empirical evidence on the efficacy of domino card media as a tool for enhancing science education in the specific context of MIS Mawu. The results of this study [mention key findings if specified in a full paper, e.g., demonstrated a statistically significant improvement in student test scores, increased student engagement during lessons, enhanced collaborative problem-solving skills]. These findings suggest that the application of domino card media offers a valuable and engaging approach to teaching science, promoting active learning, collaboration, and critical thinking among students at MIS Mawu. The interactive and visual nature of the domino cards facilitated a deeper understanding of interconnected scientific concepts and catered to diverse learning styles. This research contributes to the growing body of literature on innovative teaching methodologies in science education and provides practical insights for educators seeking to enhance student learning outcomes through the use of engaging and hands-on media.


RIDDLE: Rule Induction with Deep Learning

Abstract: Numerous applications rely on the efficiency of Deep Learning models to address complex classification tasks for critical decisions-making. However, we may not know how each feature in these models contributes towards the prediction. In contrast, Rule Induction algorithms provide an interpretable way to extract patterns from data, but traditional approaches suffer in terms of scalability. In this work, we bridge Deep Learning and Rule Induction and define the RIDDLE (Rule Induction with Deep Learning) architecture. We show that RIDDLE has state-of-the-art performance in Rule Induction via an empirical evaluation.


Deep Learning in diverse Computing and Network Applications Student Admission Predictor using Deep Learning


Designing Mathematics Teaching through Deep Learning Pedagogy: Toward Meaningful, Mindful, and Joyful Learning

Torang Siregar, Ahmad Fauzan, Yerizon Yerizon, Syafriandi Syafriandi

Abstract: Mathematics education in the 21st century requires an approach that focuses not only on procedural mastery but also on deep conceptual understanding, critical thinking skills, and the ability to transfer knowledge across various life contexts. This article discusses the design of mathematics learning based on a deep learning approach not in the context of artificial intelligence, but as a pedagogical approach integrating three core principles: meaningful, mindful, and joyful. Meaningful learning is built through contextual activities and open-ended problems that connect mathematical concepts to real-world situations. Reflective and process-aware learning is facilitated through discussion, metacognitive reflection, and evaluation. Meanwhile, a joyful learning environment is created through active participation, open exploration, and recognition of diverse student thinking styles. The theoretical framework includes constructivism, transfer theory, metacognition, and process-based assessment. The implementation of this approach requires a transformation of the teacher's role into a facilitator, a flexible and meaning-oriented curriculum, and a supportive, collaborative learning environment. It is concluded that the deep learning approach, through the principles of meaningful, mindful, and joyful learning, can enhance student engagement, deepen understanding, and foster a positive attitude toward mathematics.


Machine Learning and Deep Learning Approaches for Cybersecurity: A Review


"Deep Non-Parametic Learning Architecture: Performance Analysis Of Deep Learning Architecture"

International Journal of Mechanical  Engineering


Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) Algorithm

Advances in Machine Learning & Artificial Intelligence

Abstract: Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) algorithm is a new architecturebased algorithm for artificial neural networks. Rather than simply adjusting the weights in a fixed topology network, SFC-DLNN starts with a minimal topology (perceptron), then builds up their network by gradually trains and adds new nodes one by one, creating multiple layers’ network. Once a unit has been added to the network, the weights of the new architecture are generated. This unit then stands as a permanent detector of features in the network, and a more complex feature space is then created where the data is likely to be linearly separable. The SFC-DLNN algorithm has many advantages over existing ones: it has good learning speed, the network determines its topology size, and the structures it has built is retained after the training stage. We obtain from our built model (SFC-DLNN) an accuracy and specificity of 83:5% from a simulated data set using the uniform distribution. This is not the best but is enough to approve the model prediction capacity


Improving Understanding and Attitude of Gratitude Using Project Based Learning Model in Islamic Education Learning at SMP Negeri 1 Barumun Baru

Abstract: This study aims to improve student learning outcomes in Islamic religious education learning using Project Based Learning. This study is a classroom action research that uses four steps, namely planning, action, observation and reflection. The subjects of this study were junior high school students. The data for this study were obtained using test and observation techniques. Tests are used to measure learning outcomes and observations are used to analyze teacher and student learning activities. The data analysis technique used in this study is descriptive statistics by comparing the results obtained with indicators of research success. The results of the study indicate that Project Based Learning can improve student learning outcomes in Islamic religious education learning. This can be seen from the increase in the percentage of student learning completion in each cycle with details of the pre-cycle 48.71%, the first cycle 65.39% and in the second cycle it increased to 89.76%. Thus, the use of Project Based Learning can be used as an alternative to improve student learning outcomes in Islamic religious education learning.


FEATURE SELECTION AND DEEP LEARNING MODEL FOR AIR QUALITY PREDICTION

INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE AND DEEP LEARNING


Deep Learning –An Overview

Journal of Current Engineering and Technology


Komparasi Deep Learning Convolutional Neural Network (CNN) dan Deep Learning CNN- Support Vector Machine (SVM) untuk Identifikasi Tumor Otak dan Payudara

Siti Nur Khalisha, Pandji Triadyaksa, Ngurah Ayu Ketut Umiati

Abstract: Identifikasi citra MRI tumor otak dan payudara merupakan aspek penting dalam pengembangan system computer aided diagnosis (CAD) yang akurat dan andal. Penelitian ini membandingkan kinerja model CNN tunggal berbasis Res-Net 50 dan model hibrida CNN-SVM dalam mengklasifikasikan berbagai jenis tumor melalui citra MRI. Metode penelitian meliputi akuisisi dataset citra MRI tumor otak dan payudara dari repositori daring Kaggle dan diproses melalui tahap pra-pemrosesan citra berupa resizing, konversi citra grayscale ke RGB, serta augmentasi data pada data training. Pada pendekatan CNN tunggal, Res-Net-50 digunakan sebagai pengklasifikasi end-to-end, sedangkan pada model hibrida CNN-SVM, fitur diekstraksi dari lapisan layer global pooling dan diklasifikasikan menggunakan SVM. Evaluasi kinerja dilakukan menggunkan confusion matrix dan chart perbandingan metrik kerja. Hasil penelitian menunjukkan bahwa kedua model mencapai akurasi 99,16 % pada klasifikasi tumor otak multi-kelas serta akurasi pada klasifikasi tumor payudara biner yaitu 98,02 %. Model CNN-SVM menunjukkan performa yang lebih stabil pada seluruh metrik kerja.


Uncertainty Estimation in Deep Learning: Methods and Applications

Journal of Machine Learning and Deep Learning

Hina Batool, Tahir Abbas, Jamshaid Iqbal Janjua, Sadaqat Ali Ramay

Abstract: Uncertainty estimation in deep learning has emerged as a crucial area of research due to its significance in enhancing model reliability and decision-making in critical applications. This article explores various methods and applications of uncertainty estimation in deep learning, aiming to provide insights into its importance, methods, and potential impact. Through a comprehensive literature review and analysis, the study identifies key findings regarding the effectiveness, limitations, and ethical considerations associated with uncertainty estimation techniques. The results reveal the diverse range of methodologies employed, including Bayesian approaches, ensemble methods, and Monte Carlo sampling, each with its strengths and drawbacks. Furthermore, the article discusses the implications of uncertainty estimation in deep learning for fields such as healthcare, autonomous systems, and safety-critical domains. Overall, this study underscores the significance of uncertainty estimation in deep learning and provides valuable insights for researchers and practitioners in the field.


L.L. Thurstone, The law of effect and the dynamics of deep learning “a deep history of deep learning”

Abstract: Abstract Deep learning (DL), a variant of the neural network algorithms originally proposed in the early twentieth century, has resulted in a renaissance of artificial intelligence. Despite the growing dominance of DL networks, little is understood about the learning mechanism that makes these networks so effective across such a wide range of applications. Drawing on a century of psychological learning theory (e.g., LL Thurstone), an account is offered of the learning mechanism that may enable DL networks to perform so successfully across so many different tasks. Specifically, evidence is provided that learning in DL networks is fit best by a hyperbolic function. This function is independent of hyper/meta parameters–not a scaling function but a learning curve to a specific equilibrium, a function that also entails an autocatalytic mechanism through which complex structures, abstracted from sensory features, can in principle create and support cognitive function in both biological and artificial systems. Keywords: Learning theory, Deep Learning, hyperbolic, autocatalytic, AI, Thurstone


Forecasting Thai Agricultural Price: A Deep Learning Approach for Key Commodities

International Journal of Machine Learning