bluuebunny/crossref_metadata_2025 huggingface dataset:
Competition and co-association, but not phosphorous availability, shape the benefits of phosphate-solubilizing root bacteria for maize (Zea mays)
Authors: Williamson Joseph , Matthews Andrew Charles , Raymond Ben
Published: December 2023

Predicting the conditions under which rhizobacteria benefit plant growth remains challenging. Here we tested the hypothesis that benefits from inoculation with phosphate-solubilizing rhizobacteria will depend upon two environmental conditions: phosphate availability and competition between bacteria. We used maize-associated rhizobacteria with varying phosphate solubilization ability in experiments in soil, sterilized soil and gnotobiotic microcosms under conditions of varying orthophosphate availability, while we manipulated the intensity of competition by varying the number of isolates in plant inocula. Growth promotion by microbes did not depend on phosphate availability but was affected by interactions between inoculants: the beneficial effects of oneSerratiaisolate were only detectable when plants were inoculated with a single strain and the beneficial effects of a competition-sensitiveRhizobiumwas only detectable in sterilized soil or in microcosms inoculated with single strains. Moreover, microcosm experiments suggested that facilitation of a parasitic isolate, not competitive interactions between bacteria, prevented plants from gaining benefits from a potential mutualist. Competition and facilitation affected colonization of plants in microcosms but growth promotion bySerratiawas more affected by inoculation treatment than culturable densities on roots. Experimental manipulation of seed inocula can reveal whether plant growth stimulation is robust with respect to competition, as well as the ecological strategies of different rhizobacteria. From an applied perspective, phosphate solubilization may not provide the mechanism for bacterial growth promotion but may indicate mutualistic potential due to phylogenetic associations. Importantly, benefits to plants are vulnerable to interactions between rhizobacteria and may not persist in mixed inoculations.
Neural correlates of category learning in monkey inferior temporal cortex
Authors: Pearl Jonah E , Matsumoto Narihisa , Hayashi Kazuko , Matsuda Keiji , Miura Kenichiro , Nagai Yuji , Miyakawa Naohisa , Minanimoto Takafumi , Saunders Richard C , Sugase-Miyamoto Yasuko , Richmond Barry J , Eldridge Mark A G
Published: December 2023

We trained two monkeys implanted with multi-electrode arrays to categorize natural images of cats and dogs, in order to observe changes in neural activity related to category learning. We recorded neural activity from area TE, which is required for normal learning of visual categories based on perceptual similarity. Neural activity during a passive viewing task was compared pre- and post-training. After the category training, the accuracy of abstract category decoding improved. Specifically, the proportion of single units with category selectivity increased, and units sustained their category-specific responses for longer. Visual category learning thus appears to enhance category separability in area TE by driving changes in the stimulus selectivity of individual neurons and by recruiting more units to the active network.
Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data
Authors: Chen Pansheng , An Lijun , Wulan Naren , Zhang Chen , Zhang Shaoshi , Ooi Leon Qi Rong , Kong Ru , Chen Jianzhong , Wu Jianxiao , Chopra Sidhant , Bzdok Danilo , Eickhoff Simon B , Holmes Avram J , Yeo B.T. Thomas
Published: December 2023

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a “meta-matching” approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants (“meta-matching with dataset stacking” and “multilayer meta-matching”) to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original “meta-matching with stacking” approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.
Adaptation with naturalistic textures in macaque V1 and V2
Authors: Davila Aida , Kohn Adam
Published: December 2023

Adaptation affects neuronal responsivity and selectivity throughout the visual hierarchy. However, because most prior studies have tailored stimuli to a single brain area of interest, we have a poor understanding of how exposure to a particular image alters responsivity and tuning at different stages of visual processing. Here we assess how adaptation with naturalistic textures alters neuronal responsivity and selectivity in primary visual cortex (V1) and area V2 of macaque monkeys. Neurons in both areas respond to textures, but V2 neurons are sensitive to high-order statistics which do not strongly modulate V1 responsivity. We tested the specificity of adaptation in each area with textures and spectrally-matched ‘noise’ stimuli. Adaptation reduced responsivity in both V1 and V2, but only in V2 was the reduction dependent on the presence of higher-order texture statistics. Despite this specificity, the texture information provided by single neurons and populations was reduced after adaptation, in both V1 and V2. Our results suggest that adaptation effects for a given feature are induced at the stage of processing that tuning for those features first arises and that stimulus-specific adaptation effects need not result in improved sensory coding.

Significance statement

Nearly all sensory neurons adapt to recent input. However, how the adjustment triggered by a particular input is distributed across brain areas and how these changes contribute to sensory processing are poorly understood. Here we explore adaptation with naturalistic textures, for which neurons in primary visual cortex and area V2 have differing selectivity. Adaptation reduced responsivity in both areas, but the effects in V2 alone depended on the presence of the higher-level texture statistics to which V2 neurons are sensitive. Though prior work with simpler stimuli has argued that stimulus-specific adaptation improves visual encoding, we found adaptation reduced texture information. Thus, adaptation need not improve visual encoding, suggesting its effects may serve some other purpose.
Sord deficient rats develop a motor-predominant peripheral neuropathy unveiling novel pathophysiological insights
Authors: Rebelo Adriana P. , Abad Clemer , Dohrn Maike F. , Li Jian J. , Tieu Ethan , Medina Jessica , Yanick Christopher , Huang Jingyu , Zotter Brendan , Young Juan I. , Saporta Mario , Scherer Steven S. , Walz Katherina , Zuchner Stephan
Published: December 2023

BiallelicSORDmutations cause one of the most frequent forms of recessive hereditary neuropathy, estimated to affect approximately 10,000 patients in North America and Europe alone. PathogenicSORDloss-of-function changes in the encoded enzyme sorbitol dehydrogenase result in abnormally high sorbitol levels in cells and serum. How sorbitol accumulation leads to peripheral neuropathy remains to be elucidated. A reproducible animal model for SORD neuropathy is essential to illuminate the pathogenesis of SORD deficiency and for preclinical studies of potential therapies. Therefore, we have generated aSordknockout (KO),Sord−/−, Sprague Dawley rat, to model the human disease and to investigate the pathophysiology underlying SORD deficiency. We have characterized the phenotype in these rats with a battery of behavioral tests as well as biochemical, physiological, and comprehensive histological examinations.Sord−/−rats had remarkably increased levels of sorbitol in serum, cerebral spinal fluid (CSF), and peripheral nerve. Moreover, serum fromSord−/−rats contained significantly increased levels of neurofilament light chain, NfL, an established biomarker for axonal degeneration. Motor performance significantly declined inSord−/−animals starting at ∼7 months of age. Gait analysis evaluated with video motion tracking confirmed abnormal gait patterns in the hindlimbs. Motor nerve conduction velocities of the tibial nerves were slowed. Light and electron microscopy of the peripheral nervous system revealed degenerating myelinated axons, de– and remyelinated axons, and a likely pathognomonic finding – enlarged “ballooned” myelin sheaths. These findings mainly affected myelinated motor axons; myelinated sensory axons were largely spared. In summary,Sord−/−rats develop a motor-predominant neuropathy that closely resembles the human phenotype. Our studies revealed novel significant aspects of SORD deficiency, and this model will lead to an improved understanding of the pathophysiology and the therapeutic options for SORD neuropathy.
Proteins Need Extra Attention: Improving the Predictive Power of Protein Language Models on Mutational Datasets with Hint Tokens
Authors: Li Xinning , Perez Ryann , Giannakoulias Sam , Petersson E. James
Published: December 2023

In this computational study, we introduce “hint token learning,” a novel machine learning approach designed to enhance protein language modeling. This method effectively addresses the unique challenges of protein mutational datasets, characterized by highly similar inputs that may differ by only a single token. Our research highlights the superiority of hint token learning over traditional fine-tuning methods through three distinct case studies. We first developed a highly accurate free energy of folding model using the largest protein stability dataset to date. Then, we applied hint token learning to predict a biophysical attribute, the brightness of green fluorescent protein mutants. In our third case, hint token learning was utilized to assess the impact of mutations on RecA bioactivity. These diverse applications collectively demonstrate the potential of hint token learning for improving protein language modeling across general and specific mutational datasets. To facilitate broader use, we have integrated our protein language models into the HuggingFace ecosystem for downstream, mutational fine-tuning tasks.
Aversive memories can be weakened during human sleep via the reactivation of positive interfering memories
Authors: Xia Tao , Chen Danni , Zeng Shengzi , Yao Ziqing , Liu Jing , Qin Shaozheng , Paller Ken A. , Torres Platas S. Gabriela , Antony James W. , Hu Xiaoqing
Published: December 2023

Recollecting painful or traumatic experiences can be deeply troubling. Sleep may offer an opportunity to reduce such suffering. We developed a procedure to weaken older aversive memories by reactivating newer positive memories during sleep. Participants viewed 48 nonsense words each paired with a unique aversive image, followed by an overnight sleep. In the next evening, participants learned associations between half of the words and additional positive images, creating interference. During the following non-rapid-eye-movement sleep, auditory memory cues were unobtrusively delivered. Upon waking, presenting cues associated with both aversive and positive images during sleep, as opposed to not presenting cues, weakened aversive memory recall while increasing positive memory intrusions. Substantiating these memory benefits, computational modeling revealed that cueing facilitated evidence accumulation toward positive affect judgments. Moreover, cue-elicited theta brain rhythms during sleep predominantly predicted the recall of positive memories. A noninvasive sleep intervention can thus modify aversive recollection and affective responses.

Significance Statement

Not all memories are welcome: recollection and involuntary intrusions of aversive memories can be debilitating. Prior research shows that reactivating positive memories benefits psychological health, and memory reactivation can be externally triggered during human sleep. Here, we introduced a novel sleep-based memory updating procedure to weaken older aversive memories by reactivating newer positive memories. We found that such reactivation not only weakened the recall of aversive memories, but also increased involuntary intrusion of positive memories. Moreover, reactivation enhanced positive affect judgments. Our findings open new avenues for weakening aversive or traumatic memories during human sleep.
Numerical reproduction of the Sherrington-Adrian observations through a community of McCulloch-Pitts neurons with plastic remodelling
Authors: Irastorza-Valera Luis , Benítez José María , Montans Francisco J. , Saucedo-Mora Luis
Published: December 2023

Neurons form a highly complex network that produces cognition from simple associative rules. From previous results, this work shows the natural capability of the numerical network produced to modulate the output signal with independence of the intensity of the stimuli. Moreover, the plastic remodelling implemented in the model is capable to change the latency of a wide range of stimuli to synchronize them and adjust to a required delay of the signal.
Privacy-preserving impact evaluation using Difference-in-Differences
Authors: Huth Manuel , Garavito Carolina Alvarez , Seep Lea , Cirera Laia , Saúte Francisco , Sicuri Elisa , Hasenauer Jan
Published: December 2023

Difference-in-Differences (DID) is a widely used tool for causal impact evaluation but is constrained by data privacy regulations when applied to sensitive personal information, such as individual-level performance records or healthcare data, that must not be shared with data analysts. Obtaining consent can reduce sample sizes or exclude treated/untreated groups, diminishing statistical power or making estimation impossible. Federated Learning, which shares aggregated statistics to ensure privacy, can address these concerns, but advanced federated DID software packages remain scarce. We derived and developed a federated version of the Callaway and Sant’Anna DID, implemented within the DataSHIELD platform. Our package adheres to DataSHIELD’s security measures and adds extra protections, enhancing data privacy and confidentiality. It reproduces point estimates, asymptotic standard errors, and bootstrapped standard errors equivalent to the non-federated implementation. We demonstrate this functionality on simulated data and real-world data from a malaria intervention in Mozambique. By leveraging federated estimates, we increase effective sample sizes leading to reduced estimation uncertainty, and enable estimation when single data owners cannot share the data but only have access to the treated or untreated group.
Ergosterol mediates aggregation of natamycin in the yeast plasma membrane
Authors: Szomek Maria , Akkerman Vibeke , Lauritsen Line , Walther Hanna-Loisa , Juhl Alice Dupont , Thaysen Katja , Egebjerg Jacob Marcus , Covey Douglas F. , Lehmann Max , Wessig Pablo , Foster Alexander J. , Poolman Bert , Werner Stephan , Schneider Gerd , Müller Peter , Wüstner Daniel
Published: December 2023

Polyene macrolides are antifungal substances, which interact with cells in a sterol-dependent manner. While being widely used, their mode of action is poorly understood. Here, we employ ultraviolet-sensitive (UV) microscopy to show that the antifungal polyene natamycin binds to the yeast plasma membrane (PM) and causes permeation of propidium iodide into cells. Right before membrane permeability becomes compromised, we observed clustering of natamycin in the PM that was independent of PM protein domains. Aggregation of natamycin was paralleled by cell deformation and membrane blebbing as revealed by soft X-ray microscopy. Substituting ergosterol for cholesterol decreased natamycin binding and resulted in reduced clustering of natamycin in the PM. Blocking of ergosterol synthesis necessitates sterol import via the ABC transporters Aus1/Pdr11 to ensure natamycin binding. Quantitative imaging of dehydroergosterol (DHE) and cholestatrienol (CTL), two analogs of ergosterol and cholesterol, respectively, revealed a largely homogeneous lateral sterol distribution in the PM, ruling out that natamycin binds to pre-assembled sterol domains. Depletion of sphingolipids using myriocin increased natamycin binding to yeast cells, likely by increasing the ergosterol fraction in the outer PM leaflet. We conclude that ergosterol-specific aggregation of natamycin in the yeast PM underlies its antifungal activity, which can be synergistically enhanced by inhibitors of sphingolipid synthesis.

Significance

Ergosterol is the major sterol in the membranes of fungi and a major target for antifungal treatments. Polyene macrolides, such as natamycin, are known to target ergosterol but the underlying mechanisms for their preference for this yeast sterol compared to mammalian cholesterol is not understood. This study shows that natamycin forms aggregates when associated with yeast S. cerevisiae in an ergosterol-dependent manner. Cholesterol can only partially substitute for ergosterol with respect to natamycin binding and aggregation. Membrane-associated aggregation of natamycin is not the result of pre-formed sterol domains in the cell membrane, as we show by direct visualization of minimally modified ergosterol and cholesterol analogs. Inhibiting sphingolipid synthesis increased membrane association and antifungal activity of natamycin, suggesting that targeting sphingolipids in combination with polyene macrolides could lead to novel drug treatment approaches against fungal infections.
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Authors: Steach Holly , Viswanath Siddharth , He Yixuan , Zhang Xitong , Ivanova Natalia , Hirn Matthew , Perlmutter Michael , Krishnaswamy Smita
Published: December 2023

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.
Controlling confined collective organisation with taxis
Authors: Théry Albane , Chamolly Alexander , Lauga Eric
Published: December 2023

Biased locomotion is a common feature of microorganisms, but little is known about its impact on self-organisation. Inspired by recent experiments showing a transition to large-scale flows, we study theoretically the dynamics of magnetotactic bacteria confined to a drop. We reveal two symmetry-breaking mechanisms (one local chiral and one global achiral) leading to self-organisation into global vortices and a net torque exerted on the drop. The collective behaviour is ultimately controlled by the swimmers’ microscopic chirality and, strikingly, the system can exhibit oscillations and memory-like features.
Local tree cover predicts mosquito species richness and disease vector presence in a tropical countryside landscape
Authors: Farner Johannah E , Howard Meghan E , Smith Jeffrey R , Anderson Christopher B , Mordecai Erin A
Published: December 2023

Context: Land use change and deforestation drive both biodiversity loss and zoonotic disease transmission in tropical countrysides. For mosquito communities that can include disease vectors, forest loss has been linked to reduced biodiversity and increased vector presence. The spatial scales at which land use and tree cover shape mosquito communities present a knowledge gap relevant to both biodiversity and public health. Objectives: We investigated the responses of mosquito species richness and Aedes albopictus disease vector presence to land use and to tree cover surrounding survey sites at different spatial scales. We also investigated species compositional turnover across land uses and along environmental gradients. Methods: We paired a field survey of mosquito communities in agricultural, residential, and forested lands in rural southern Costa Rica with remotely sensed tree cover data. We compared mosquito richness and vector presence responses to tree cover measured across scales from 30m to 1000m, and across land uses. We analyzed compositional turnover between land uses and along environmental gradients of tree cover, temperature, elevation, and geographic distance. Results: Tree cover was both positively correlated with mosquito species richness and negatively correlated with the presence of the common invasive dengue vector Ae. albopictus at small spatial scales of 90 - 250m. Land use predicted community composition and Ae. albopictus presence. Conclusions: The results suggest that local tree cover preservation and expansion can support mosquito species richness and reduce disease vector presence. The identified spatial range at which tree cover shapes mosquito communities can inform the development of land management practices to protect both ecosystem and public health.
Joint Learning of Node Semantics and Graph Topology using a Transformer in the sparse network regime
Authors: Ubingazhibov Aidyn , Gomez-Cabrero David , Kiani Narsis A. , Tegner Jesper
Published: December 2023

The human interactome is a valuable tool for unraveling disease mechanisms, advancing precision medicine, facilitating drug discovery, and identifying biomarkers. Yet, current interactomes are incomplete, in part due to limited experimental coverage. Therefore, augmenting the human interactome by predicting missing links in the Protein-Protein interaction network (PPI), is a core challenge for precision medicine. This study proposes an end-to-end trainable transformer-based neural network for enhanced aggregation of Gene Ontology (GO) terms features. We augment the model’s predictive capabilities by incorporating semantic anc2vec features, complementing the structural node2vec embeddings specifically designed for sparse PPIs. As a result, by integrating semantic and graph features, we demonstrate superior performance in link prediction.
FITM2 deficiency results in ER lipid accumulation, ER stress, reduced apolipoprotein B lipidation, and VLDL triglyceride secretionin vitroand in mouse liver
Authors: Wang Haizhen , Nikain Cyrus , Amengual Jaime , La Forest Maxwell , Yu Yong , Wang Meng C. , Watts Russell , Lehner Richard , Qiu Yunping , Cai Min , Kurland Irwin J. , Goldberg Ira J. , Rajan Sujith , Hussain M. Mahmood , Brodsky Jeffrey L. , Fisher Edward A.
Published: December 2023

Structured Abstract

Objectives

Triglyceride (TG) association with apolipoprotein B100 (apoB100) serves to form very low density lipoproteins (VLDL) in the liver. The repertoire of factors that facilitate this association is incompletely defined. FITM2, an integral endoplasmic reticulum (ER) protein, was originally discovered as a factor participating in cytoplasmic lipid droplets (LDs) in tissues that do not form VLDL. We hypothesized that in the liver, in addition to promoting cytosolic LD formation, FITM2 would also transfer TG from its site of synthesis in the ER membrane to nascent VLDL particles within the ER lumen.

Methods

Experiments were conducted using a rat hepatic cell line (McArdle-RH7777, or McA cells), an established model of mammalian lipoprotein metabolism, and mice. FITM2 expression was reduced using siRNA in cells and by liver specific cre-recombinase mediated deletion of theFitm2gene in mice. Effects of FITM2 deficiency on VLDL assembly and secretionin vitroandin vivowere measured by multiple methods, including density gradient ultracentrifugation, chromatography, mass spectrometry, simulated Raman spectroscopy (SRS) microscopy, sub-cellular fractionation, immunoprecipitation, immunofluorescence, and electron microscopy.

Main findings

1) FITM2-deficient hepatic cellsin vitroandin vivosecrete TG-depleted VLDL particles, but the number of particles is unchanged compared to controls; 2) FITM2 deficiency in mice on a high fat diet (HFD) results in decreased plasma TG levels. The number of apoB100-containing lipoproteins remains similar, but shift from VLDL to LDL density; 3) Bothin vitroandin vivo, when TG synthesis is stimulated and FITM2 is deficient, TG accumulates in the ER, and despite its availability this pool is unable to fully lipidate apoB100 particles; 4) FITM2 deficiency disrupts ER morphology and results in ER stress.

Principal conclusions

The results suggest that FITM2 contributes to VLDL lipidation, especially when newly synthesized hepatic TG is in abundance. In addition to its fundamental importance in VLDL assembly, the results also suggest that under dysmetabolic conditions, FITM2 may be a limiting factor that ultimately contributes to non-alcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH).
Phenotypic consequences of logarithmic signaling in MAPK stress response
Authors: Jashnsaz Hossein , Neuert Gregor
Published: December 2023

How cells respond to dynamic environmental changes is crucial for understanding fundamental biological processes and cell physiology. In this study, we developed an experimental and quantitative analytical framework to explore how dynamic stress gradients that change over time regulate cellular volume, signaling activation, and growth phenotypes. Our findings reveal that gradual stress conditions substantially enhance cell growth compared to conventional acute stress. This growth advantage correlates with a minimal reduction in cell volume dependent on the dynamic of stress. We explain the growth phenotype with our finding of a logarithmic signal transduction mechanism in the yeast Mitogen-Activated Protein Kinase (MAPK) osmotic stress response pathway. These insights into the interplay between gradual environments, cell volume change, dynamic cell signaling, and growth, advance our understanding of fundamental cellular processes in gradual stress environments.
Beyond Active Learning: Using 3-Dimensional Learning to Create Scientifically Authentic, Student-Centered Classrooms
Authors: Cooper Melanie M , Caballero Marcos D. , Carmel Justin H. , Duffy Erin M. , Ebert-May Diane , Fata-Hartley Cori L. , Herrington Deborah G. , Laverty James T. , Nelson Paul C. , Posey Lynmarie A. , Stoltzfus Jon R. , Stowe Ryan L. , Sweeder Ryan D. , Tessmer Stuart , Underwood Sonia M.
Published: December 2023

In recent years, much of the emphasis for transformation of introductory STEM courses has focused on “active learning”, and while this approach has been shown to produce more equitable outcomes for students, the construct of “active learning” is somewhat ill-defined, and can encompass a wide range of pedagogical techniques. Here we present an alternative approach for how to think about the transformation of STEM courses that focuses instead on what students should know and what they can do with that knowledge. This approach, known as three-dimensional learning (3DL), emerged from the National Academy’s “A Framework for K-12 Science Education”, which describes a vision for science education that centers the role of constructing productive causal accounts for phenomena. Over the past 10 years, we have collected data from introductory biology, chemistry, and physics courses to assess the impact of such a transformation on higher education courses. Here we report on an analysis of video data of class sessions that allows us to characterize these sessions asactive, 3D, neither, orboth 3D and active. We find that 3D classes are likely to also involve student engagement (i.e. be active), but the reverse is not necessarily true. That is, focusing on transformations involving 3DL also tends to increase student engagement, whereas focusing solely on student engagement might result in courses where students are engaged in activities that do not involve meaningful engagement with core ideas of the discipline.
Identification roles of NFE2L3 in Digestive System Cancers
Authors: Li Fan , Wen Zhili
Published: December 2023

(1) Background

The morbidity and mortality rate of Digestive System Cancers (DSC) continue to threaten human lives and health. Nuclear factor erythroid 2-like protein 3 (NFE2L3) is associated with the development, growth, and progression of multiple cancers. Nevertheless, the clinical value and underlying mechanisms of NFE2L3 in DSC remains unknown. This study aimed to clarify the possibilities of NFE2L3 as a novel biomarker in DSC.

(2) Methods

We obtained the data of NFE2L3 in cancers from database to evaluate the expression level and clinical value of NFE2L3 in DSC, and to explore underlying mechanism and biological functions of NFE2L3 in DSC.

(3) Results

NFE2L3 expression is up-regulated in DSC and have both prognostic and diagnostic value. NFE2L3 contributes to oncogenesis through a variety of mechanisms. In vitro experiment showed that NFE2L3 stimulated the proliferation and migration ability of gastric cancer cells.

(4) Conclusion

Our study confirms the clinical applicability of NFE2L3 as a promising biomarker for DSC.
Predictive Power of Machine Learning Models for Relapse Outcomes in Acute Myeloid Leukemia: Unveiling Key Genes and Pathways for Improved Patient Management
Authors: Radak Mehran , Fallahi Hossein , Karami Keyvan
Published: December 2023

Acute Myeloid Leukemia (AML) is a challenging form of blood cancer requiring accurate relapse prediction for effective therapy and patient management. In this study, we applied multiple machine learning techniques to a dataset of AML patients in order to develop a reliable model for predicting relapse and guiding treatment decisions. We utilized various feature selection methods to identify the most relevant features associated with relapse. Additionally, we investigated gene ontology using the Gene Ontology (GO) database to gain insights into the biological processes and KEGG pathways related to the selected features. Our findings revealed key genes and pathways implicated in AML relapse. Among the machine learning models, Decision Tree (DT) showed the highest accuracy in predicting relapse outcomes. Furthermore, we compared the performance of DT models across different feature selections, highlighting the significance of specific factors such as MCL1, WBC, HGB, and BAD.p112 in relapse prediction. The results of our study have important implications for tailoring treatment plans and improving patient outcomes in AML. By accurately identifying patients at high risk of relapse, our model can aid in early interventions and personalized therapies. Ultimately, our research contributes to advancing the field of machine learning in AML and lays the foundation for developing effective strategies to combat relapse in this disease.
Enzymatic cleaving of entangled DNA rings drives scale-dependent rheological trajectories
Authors: Neill Philip , Crist Natalie , McGorty Ryan , Robertson-Anderson Rae
Published: December 2023

DNA, which naturally occurs in linear, ring, and supercoiled topologies, frequently undergoes enzyme-driven topological conversion and fragmentation in vivo, enabling it to perform a variety of functions within the cell. In vitro, highly concentrated DNA polymers form entanglements that yield viscoelastic properties dependent on the topologies and lengths of the DNA. Enzyme-driven alterations of DNA size and shape therefore offer a means of designing active materials with programmable viscoelastic properties. Here, we incorporate multi-site restriction endonucleases into dense DNA solutions to linearize and fragment circular DNA molecules. We pair optical tweezers microrheology with differential dynamic microscopy and single-molecule tracking to measure the linear and nonlinear viscoelastic response and transport properties of entangled DNA solutions over a wide range of spatiotemporal scales throughout the course of enzymatic digestion. We show that, at short timescales, relative to the relaxation timescales of the polymers, digestion of these ‘topologically-active’ fluids initially causes an increase in elasticity and relaxation times followed by a gradual decrease. Conversely, for long timescales, linear viscoelastic moduli exhibit signatures of increasing elasticity, and DNA diffusion, likewise, becomes increasingly slowed —in direct opposition to the short-time behavior. We argue that this scale-dependent rheology arises from the population of small DNA fragments, which increases as digestion proceeds, driving self-association of larger fragments via depletion interactions, giving rise to slow relaxation modes of clusters of entangled chains, interspersed among shorter unentangled fragments. While these slow modes dominate at long times, they are frozen out in the short-time limit, which instead probes the faster relaxation modes of the unentangled population.
Mathematical model of RNA-directed DNA methylation predicts tuning of negative feedbacks required for stable maintenance
Authors: Dale Renee , Mosher Rebecca A.
Published: December 2023

RNA-directed DNA Methylation (RdDM) is a plant-specificde novomethylation pathway that is responsible for maintenance of asymmetric methylation (CHH, where H=A, T, or G) in euchromatin. Loci with CHH methylation are transcriptionally silent and produce 24-nucleotide (nt) short interfering (si) RNAs. These siRNAs direct additional CHH methylation to the locus, thereby maintaining methylation states through DNA replication. To understand the necessary conditions to produce stable CHH methylation, we developed a stochastic mathematical model of RdDM. The model describes DNA target search of DNA or RNA by siRNAs derived from CHH-methylated loci. When the siRNA (bound by an Argonaute protein) finds the matching locus, the model uses the dwell time of the matched complex to determine the degree of CHH reinforcing methylation. Reinforcing methylation occurs either throughout the cell cycle (steady reinforcement), or immediately following replication (bursty reinforcement). Each simulation occurs over 10 cell cycles, and for 7 simulation replicates. We use nonparametric statistics to compare initial and final CHH methylation distributions to determine whether the simulation conditions produce stable maintenance. We apply this method to the low CHH methylation case, wherein the median is only 8%, and many loci have less than 8% methylation. The resulting model predicts that siRNA production must be linearly proportional to CHH methylation levels at each locus, that bursty reinforcement produces more stable systems, and that slightly higher levels of siRNA production are required for DNA target search, compared to RNA target search. Unlike CG methylation, which typically exhibits bi-modality, with loci having either 100% or 0% methylation, CHH methylation putatively exists at a range of methylation fractions. Our model predicts that careful tuning of the negative feedbacks in the system are required to balance the positive feedback loop of increasing CHH methylation and increasing siRNA production, enabling stable maintenance of a range of CHH methylation across replication events.
Effect of Insomnia on Repetitive Transcranial Magnetic Stimulation Treatment Outcomes for Depression
Authors: Kweon Jamie , Fukuda Andrew , Gobin Polly , Haq Lamaan , Brown Joshua C. , Carpenter Linda L.
Published: December 2023

While repetitive transcranial magnetic stimulation (rTMS) is safe and effective for 50-60% of those treatment-resistant depression, it is critical to identify factors to optimize therapy to help those who do not respond. Baseline sleep characteristics have been investigated as a potential predictor of TMS efficacy but results from various studies have been conflicting. We aimed to explore whether baseline sleep quality, specifically insomnia related symptoms, is associated with TMS outcomes in a naturalistic sample of 975 patients receiving a standard course of rTMS from two sites. One site recorded information on concurrent medication use. Among these 353 patients, we also examined whether pharmacological treatment of insomnia affected TMS treatment response. Depression was measured using the 30-item Inventory of Depressive Symptomology Self Report (IDS-SR) in site one and an abbreviated 16-item Quick Inventory of Depressive Symptomology (QIDS) in site two. Sleep disturbances were measured using three sleep-related questions overlapping between the two questionnaires. We found that sleep quality improves after TMS and correlates with improvement in depression. Upon dichotomous categorization of the sample by insomnia and hypnotics use, we found that among those who had significant insomnia at baseline, those not using sleep medications had significantly worse post-treatment IDS-SR scores compared to those receiving pharmacological treatments for sleep (p=.021). Together, our results suggest that while baseline insomnia is not associated with response to TMS treatment, treating insomnia may affect the trajectory of TMS therapy. Future prospective studies are necessary to examine the effect of insomnia treatment alongside TMS for depression.
Assessing the validity of a self-reported clinical diagnosis of schizophrenia
Authors: Woolway Grace E , Legge Sophie E , Lynham Amy , Smart Sophie E , Hubbard Leon , Daniel Ellie R , Pardiñas Antonio F , Escott-Price Valentina , O’Donovan Michael C , Owen Michael J , Jones Ian R , Walters James TR
Published: December 2023

Background

Diagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia.

Methods

The study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC).

Results

In the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts.

Conclusions

Self-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended.
Investigating the Determinants of COVID-19 Vaccine Uptake and Attitudes in Iraq: A Study Unveiling the Negative Impact of Misinformation and Vaccine Conspiracy
Authors: Sallam Malik , Kareem Nariman , Alkurtas Mohammed
Published: December 2023

Vaccine hesitancy is a major barrier challenging the control of infectious diseases. Previous studies demonstrated high rates of COVID-19 vaccine hesitancy in the Middle East. The current study aimed to investigate the attitudes towards COVID-19 vaccination and COVID-19 vaccine uptake among the adult population in Iraq. This cross-sectional self-administered survey-based study was conducted in August–September 2022. Recruitment of possible participants was done using chain-referral sampling. The survey instrument assessed participants’ demographics, attitudes toward COVID-19 vaccination, beliefs in COVID-19 misinformation, vaccine conspiracy beliefs, and sources of information regarding the vaccine. The study sample comprised a total of 2544 individuals, with the majority reporting the uptake of at least one dose of COVID-19 vaccination (n=2226, 87.5%). Positive attitudes towards COVID-19 vaccination were expressed by the majority of participants (n=1966, 77.3%), while neutral attitudes were expressed by 345 participants (13.6%), and negative attitudes were expressed by 233 participants (9.2%). Strong, moderate, slight, and absence of COVID-19 misinformation were expressed by 12.4%, 22.6%, 36.2%, and 28.7% participants, respectively. The majority of participants showed a neutral attitude towards COVID-19 vaccine conspiracies (n=1464, 57.5%), while 607 participants embraced these conspiracies (23.9%), and 473 disagreed with such beliefs (18.6%). In the multivariate analysis, factors associated with positive attitudes towards COVID-19 vaccination included disbelief in COVID-19 misinformation and disagreement with vaccine conspiracies. Higher COVID-19 vaccine uptake was significantly associated with history of COVID-19 infection, higher income, residence outside the capital, disbelief in COVID-19 misinformation, disagreement with vaccine conspiracies, and reliance on reputable information sources. COVID-19 vaccine coverage prevailed among the participants, with a majority having positive attitudes towards COVID-19 vaccination. Disbelief in COVID- 19 misinformation and disagreement with vaccine conspiracies were correlated with positive vaccine attitudes and higher vaccine uptake. These insights can inform targeted interventions to enhance vaccination campaigns.
Endogenous opioid receptor system mediates costly altruism in the human brain
Authors: Chen Jinglu , Putkinen Vesa , Seppälä Kerttu , Hirvonen Jussi , Ioumpa Kalliopi , Gazzola Valeria , Keysers Christian , Nummenmaa Lauri
Published: December 2023

Functional neuroimaging studies suggest that a large-scale brain network transforms others’ pain into its vicarious representation in the observer, potentially modulating helping behaviour. The neuromolecular basis of individual differences in vicarious pain and helping, however, have remain poorly understood. Here we investigated the role of the endogenous μ-opioid receptor (MOR) system – known for its role in analgesia and sociability – in altruistic costly helping. MOR density was measured with high-affinity agonist radioligand [11C]carfentanil. In a separate fMRI experiment, participants could choose to donate money to reduce the pain of the confederate who was subjected to electric shocks of varying intensity. We found that subjects were in general willing to engage in costly helping, and haemodynamic activity in amydala, anterior insula, anterior cingulate cortex, striatum, primary motor cortex, primary somatosensory cortex, and thalamus increased when participants witnessed the pain of others. These haemodynamic responses were negatively associated with MORs availability in the striatolimbic and cortical emotion circuits. In turn, haemodynamic responses during helping were positively associated with MOR availability in the anterior cingulate cortex and hippocampus. Altogether these data suggest that the endogenous MOR system modulates the processing of altruistic behaviour and costly helping in the human brain.
Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Transformation as a Critical Component of Therapeutic T Cell Efficacy
Authors: Hickey John W. , Agmon Eran , Horowitz Nina , Lamore Matthew , Sunwoo John , Covert Markus , Nolan Garry P.
Published: December 2023

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here we integrated CODEX multiplexed tissue imaging with multiscale modeling software, to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface.