bluuebunny/crossref_metadata_2025_split huggingface dataset:
Do training with blurred images make convolutional neural networks closer to humans concerning object recognition performance and internal representations?
Authors: Yoshihara Sou , Fukiage Taiki , Nishida Shin’ya 
Published: June 2022

AbstractIt is suggested that experiences of perceiving blurry images in addition to sharp images contribute to the development of robust human visual processing. To computationally investigate the effect of exposure to blurry images, we trained Convolutional Neural Networks (CNNs) on ImageNet object recognition with a variety of combinations of sharp and blurry images. In agreement with related studies, mixed training on sharp and blurred images (B+S) makes the CNNs close to humans with respect to robust object recognition against a change in image blur. B+S training also reduces the texture bias of CNN in recognition of shape-texture-cue-conflict images, but the effect is not strong enough to achieve a strong shape bias comparable to what humans show. Other tests also suggest that B+S training is not sufficient to produce robust human-like object recognition based on global con-figurational features. We also show using representational similarity analysis and zero-shot transfer learning that B+S-Net does not acquire blur-robust object recognition through separate specialized sub-networks, each for sharp and blurry images, but through a single network analyzing common image features. However, blur training alone does not automatically create a mechanism like the human brain where subband information is integrated into a common representation. Our analyses suggest that experience with blurred images helps the human brain develop neural networks that robustly recognize the surrounding world, but it is not powerful enough to fill a large gap between humans and CNNs.
Inequalities in children’s mental health care: analysis of routinely collected data on prescribing and referrals to secondary care
Authors: Ball William P , Black Corri , Gordon Sharon , Ostrovska Bārbala , Paranjothy Shantini , Rasalam Adelene , Ritchie David , Rowlands Helen , Rzewuska Magdalena , Thompson Elaine , Wilde Katie , Butler Jessica E 
Published: June 2022

AbstractBackgroundOne in eight children in the United Kingdom are estimated to have a mental health condition, and many do not receive support or treatment. The COVID-19 pandemic has negatively impacted mental health and disrupted the delivery of care. Prevalence of poor mental health is not evenly distributed across age groups, by sex or socioeconomic groups. Equity in access to mental health care is a policy priority but detailed socio-deomgraphic trends are relatively under-researched.MethodsWe analysed records for all mental health prescriptions and referrals to specialist mental health outpatient care between the years of 2015 and 2021 for children aged 2 to 17 years in a single NHS Scotland health board region. We analysed trends in prescribing, referrals, and acceptance to out-patient treatment over time, and measured differences in treatment and service use rates by age, sex, and area deprivation.ResultsWe identified 18,732 children with 178,657 mental health prescriptions and 21,874 referrals to specialist outpatient care. Prescriptions increased by 59% over the study period. Boys received double the prescriptions of girls and the rate of prescribing in the most deprived areas was double that in the least deprived. Mean age at first mental health prescription was almost 1 year younger in the most deprived areas than in the least. Referrals increased 9% overall. Initially, boys and girls both had an annual referral rate of 2.7 per 1,000, but this fell 6% for boys and rose 25% for girls. Referral rate for the youngest decreased 67% but increased 21% for the oldest. The proportion of rejected referrals increased steeply since 2020 from 17% to 30%. The proportion of referrals accepted for girls rose to 62% and the mean age increased 1.5 years.ConclusionsThe large increase in mental health prescribing and changes in referrals to specialist outpatient care aligns with emerging evidence of increasing poor mental health, particularly since the start of the COVID-19 pandemic. The static size of the population accepted for specialist treatment amid greater demand, and the changing demographics of those accepted, indicate clinical prioritisation and unmet need. Persistent inequities in mental health prescribing and referrals require urgent action.
Pharmaco-Pheno-Multiomic Integration Reveals Biomarker Profiles and Therapeutic Response Prediction Models in Leukemia and Ovarian Cancer
Authors: Silberberg Gilad , Walling Brandon , Wesa Amy , Audia Alessandra , Sloma Ido , Zeng Yi , Han Guanghui , Tang Jia , Pammer Paige , Bakayoko A’ishah , Ren Xuan , Ciznadija Daniel , Vishwakarma Bandana , Mosesson Yaron , Zipeto Marianna , Ritchie Michael 
Published: June 2022

AbstractDespite considerable progress made in improving therapeutic strategies, the overall survival for patients diagnosed with various cancer types remains low. Further, patients often cycle through multiple therapeutic options before finding an effective regimen for the specific malignancy being treated. A focus on building enhanced computational models, which prioritize therapeutic regimens based on a tumor’s complete molecular profile, will improve the patient experience and augment initial outcomes. In this study, we present an integrative analysis of multiple omic datasets coupled with phenotypic and therapeutic response profiles of Cytarabine from a cohort of primary AML tumors, and Olaparib from a cohort of Patient-Derived Xenograft (PDX) models of ovarian cancer. These analyses, termed Pharmaco-Pheno-Multiomic (PPMO) Integration, established novel complex biomarker profiles that were used to accurately predict prospective therapeutic response profiles in cohorts of newly profiled AML and ovarian tumors. Results from the computational analyses also provide new insights into disease etiology and the mechanisms of therapeutic resistance. Collectively, this study provides proof-of-concept in the use of PPMO to establish highly accurate predictive models of therapeutic response, and the power of leveraging this method to unveil cancer disease mechanisms.
In Silico insight to identify Potential Inhibitors of BUB1B from Mushroom bioactive compounds to Prevent Breast cancer metastasis
Authors: Mishra Divya , Mishra Ashish , Singh M.P. 
Published: June 2022

AbstractBUB1B (BUB1 Mitotic Checkpoint Serine/Threonine Kinase B) gene belongs to the spindle assembly checkpoint protein family, proven to be associated with many kinds of cancers. Protein kinases are crucial in metaphase to anaphase cellular transition. It is a primary element of spindle assembly checkpoint (SAC). This SAC is responsible for guarding the proper chromosomal segregation while any chromosomal instability hinders its normal functioning. The chromosomal instability may lead to cancer progression. And it occurs mainly due to overexpression of BUB1B in many cancer types especially in breast cancer metastasis. This could be controlled by designing proper inhibitor of this gene. For this purpose, there are proved existence of various mushroom compounds possessing some important medicinal properties with proven anti- cancerous effects. Proper screening and identification of molecules having these anti-cancerous effects and showing affinity against BUB1B with inhibition properties could be obtained from these compounds. So this study has incorporated 70 bioactive compounds (handpicked through literature mining) of distinct mushrooms that were considered and explored for identifying a suitable drug candidate. Their ADME/T properties were obtained to predict the drug-likeness of these 70 mushroom compounds based on Lipinski’s rule of 5 (RO5). The screening of these bioactive compounds and subsequent molecular docking against BUB1B provided compounds with the best conformation-based binding affinity. The best 2 complexes, i.e., BUB1B- Lepiotaprocerin D and BUB1B-Peptidoglycan, were subjected to the molecular dynamics simulation. Both the complexes were observed for their affinity, stability, and flexibility in protein-ligand complex systems. The MD simulation study revealed that Lepiotaprocerin D has an energetically favorable binding affinity with BUB1B. Results showed that the formation of a hydrogen bond between ASN123, SER157 residue and Lepiotaprocerin D has strengthened the affinity of Lepiotaprocerin D with BUB1B. Hence, this study identified Lepiotaprocerin D as a potential and novel inhibitor for BUB1B that could be a plausible drug candidate for checking and controlling the spread of breast cancer metastasis.
The application of neural networks to classify dolphin echolocation clicks
Authors: Seydi Vahid , Chapuis Lucille , Veneruso Gemma , Balaguru Sudha , Bristow Noel , Mills David , Le Vay Lewis 
Published: June 2022

AbstractPassive acoustic monitoring (PAM) is a common approach to monitor marine mammal populations, for species of dolphins, porpoises and whales that use sound for navigation, feeding and communication. PAM produces large datasets which benefit from the application of machine learning algorithms to automatically detect and classify the vocalisations of these animals. We present a deep learning approach for the classification of dolphins’ echolocation clicks into two species groups in an environment with high background noise. We compare the use of Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), in which we feed the models the raw waveform data and spectrograms. We show that both models perform well, with the highest performance achieved by a CNN fed with spectrograms (F1 score 97 %) and an RNN fed with raw data (F1 score 96%) fitted with Gated Recurrent Units (GRU). We recommend the use of such models to classify echolocation clicks in marine environments where background noise levels exhibit high spatial and temporal variance. In particular, the RNN showed excellent performance, while being fed with raw data, in terms of reduced processing time and storage. Deep learning automatically extracts effective features from the raw waveform in the training process through multiple layers of the model, without the need to rely on feature extraction in a separate pre-processing step.
In situ visualization of opioid and cannabinoid drug effects using phosphosite-specific GPCR antibodies
Authors: Fritzwanker Sebastian , Nagel Falko , Kliewer Andrea , Stammer Viviane , Schulz Stefan 
Published: June 2022

AbstractG protein-coupled receptors (GPCRs) are important signal transducers that are phosphorylated upon activation at intracellular serine and threonine residues. Although antibodies that specifically recognize the phosphorylation state of GPCRs have been available for many years, efficient immunolocalization of phosphorylated receptors in their tissues of origin has not been possible. Here, we show that phosphorylation of receptors is highly unstable during routine immunohistochemical procedures, requiring the use of appropriate phosphatase inhibitors particular during tissue perfusion, post-fixation, and cryoprotection but not during immunostaining of tissue sections. We provide proof of concept using μ-opioid receptor (MOP) and cannabinoid receptor 1 (CB1) antibodies. Indeed, three of four well-characterized phosphosite-specific MOP antibodies, including pS375-MOP, pT376-MOP, and pT379-MOP, showed robust neuronal immunostaining in brain and spinal cord sections of opioid-treated mice only after inclusion of phosphatase inhibitors. We then extended this approach to the CB1 receptor and demonstrated that one of three newly-generated phosphosite-specific CB1 antibodies, namely pS425-CB1, showed striking staining of fibers and varicosities in brain slices from cannabinoid-treated mice. Although subsequent experiments showed that phospho-CB1 immunostaining was less sensitive to phosphatases, we conclude that the use of phosphatase inhibitors should always be considered in the development of immunohistochemical procedures for new phosphosite-specific GPCR antibodies. In summary, we anticipate that this improved protocol will facilitate the widespread use of phosphorylation state-specific antibodies to monitor the activation of endogenous GPCRs under physiological and pharmacological conditions. Our approach may also prove useful to confirm target engagement of GPCR drug candidates in native tissues.
The G-protein-coupled estrogen receptor, a gene co-expressed with ERα in breast tumors, is regulated by estrogen-ERα signalling in ERα positive breast cancer cells
Authors: Pal Uttariya , Manjegowda Mohan , Singh Neha , Saikia Snigdha , Philip Betty S. , Kalita Deep Jyoti , Rai Avdhesh Kumar , Sarma Anupam , Raphael Vandana , Modi Deepak , Kataki Amal Chandra , Limaye Anil Mukund 
Published: June 2022

AbstractPurposeThe purpose of this study was to assess the relationship between GPER, a seven transmembrane G-protein coupled estrogen receptor, and ERα in breast tumors, and to make inroads into the mechanistic basis and clinical significance.MethodsTCGA-BRCA data was mined to examine the relationship between GPER and ERα expression. GPER mRNA, and protein expression were analyzed in ERα-positive or -negative breast tumors from two cohorts using immunohistochemistry, western blotting, or RT-qPCR. The Kaplan-Meier Plotter was employed for survival analysis. The influence of estrogen in vivo was studied by examining GPER expression levels in estrus or diestrus mouse mammary tissues, and the impact of 17β-estradiol (E2) administration in juvenile or adult mice. The effect of E2, or propylpyrazoletriol (PPT, an ERα agonist) stimulation on GPER expression was studied in MCF-7 and T47D cells, with or without tamoxifen or ERα knockdown. ERα-binding to the GPER locus was explored by analysing ChIP-seq data (ERP000380), in silico prediction of estrogen response elements, and chromatin immunoprecipitation assay.ResultsClinical data revealed significant positive association between GPER and ERα expression in breast tumors. The median GPER expression in ERα-positive tumors was significantly higher than ERα-negative tumors. High GPER expression was significantly associated with longer overall survival of patients with ERα-positive tumors. In vivo experiments showed a positive effect of E2 on GPER expression. E2 induced GPER expression in MCF-7 and T47D cells; an effect mimicked by PPT. Tamoxifen or ERα-knockdown blocked the induction of GPER. Estrogen-mediated induction was associated with increased ERα occupancy in the upstream region of GPER.ConclusionGPER expression is positively associated with ERα in breast tumors, and a transcriptional target of the estrogen-ERα signalling axis. More in-depth studies are required to establish the significance of GPER-ERα co-expression, and their interplay in breast tumor development, progression, and treatment.
GADMA2: more efficient and flexible demographic inference from genetic data
Authors: Noskova Ekaterina , Abramov Nikita , Iliutkin Stanislav , Sidorin Anton , Dobrynin Pavel , Ulyantsev Vladimir 
Published: June 2022

AbstractBackgroundInference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm.ResultsHere, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data and demonstrate an example of GADMA2 usage on two empirical datasets.ConclusionsWe demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2’s likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for two empirical datasets of inbred species.
Characterizing phytoplankton communities in the absence of resource-based competition
Authors: Behrenfeld Michael J. , Bisson Kelsey M. , Boss Emmanuel , Gaube Peter , Karp-Boss Lee 
Published: June 2022

AbstractUnder most natural marine conditions, phytoplankton cells suspended in the water column are too distantly spaced for direct competition for resources to be a routine occurrence. Accordingly, resource-based competitive exclusion should be rare. In contrast, contemporary ecosystem models typically predict an exclusion of larger phytoplankton size classes under low-nutrient conditions, an outcome interpreted as reflecting the competitive advantage of small cells having much higher nutrient ‘affinities’ than larger cells. Here, we develop mechanistically-focused expressions for steady-state, nutrient-limited phytoplankton growth that are consistent with the discrete, distantly-spaced cells of natural populations. These expressions are then encompassed in an ecosystem model that sustains diversity across all size classes over the full range in nutrient concentrations observed in the ocean. In other words, our model does not exhibit resource-based competitive exclusion between size classes. We show that the basis for species exclusions in earlier models is not a reflection of size-dependent nutrient ‘affinities’, but rather a consequence of inappropriate descriptions of non-grazing phytoplankton mortality.
Alterations in protein kinase networks in astrocytes and neurons derived from patients with familial Alzheimer’s Disease
Authors: Henkel Nicholas D. , Joyce Alex , Shedroff Elizabeth , Imami Ali Sajid , Alganem Khaled , Hamoud Abdul-rizaq , Xu Chongchong , Siciliano Benjamin , Ma Tao , Wen Zhexing , McCullumsmith Robert E. 
Published: June 2022

AbstractNeurons and astrocytes derived from Alzheimer’s Disease (AD) patient induced pluripotent stem cells are an evolving technology used to study the pathogenesis and etiology of AD. As the utility of mouse models of AD are increasingly coming into questions, using iPSC technology may offer an opportunity to study this disease with human substrates. Herein, we using a hypothesis generating platform, the PamGene12 Kinome Array, to identify core protein kinases in neurons and astrocytes derived from familial AD patient iPSCs. We identified five core protein kinases in these cells and examined the pathways in which they are enriched. Importantly, we complement our findings using an in-silico approach with postmortem AD brain datasets. While these protein kinases have been conceptualized in the context of traditional AD pathology, they have not been explored in the context of aberrant signaling in the pathophysiology of the disease.
Improved two-step testing of genome-wide gene-environment interactions
Authors: Kawaguchi Eric S. , Kim Andre E. , Lewinger Juan Pablo , Gauderman W. James 
Published: June 2022

AbstractTwo-step tests for gene-environment (G×E) interactions exploit marginal SNP effects to improve the power of a genome-wide interaction scan (GWIS). They combine a screening step based on marginal effects used to ‘bin’ SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by ‘displacing’ true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for step 2G×Etesting that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multi-study collaboration for understanding colorectal cancer (CRC) reveals aG×Sex interaction located within the SMAD7 gene.
A PTP1B-Cdk3 signaling axis promotes cell cycle progression of human glioblastoma cells through an Rb-E2F dependent pathway
Authors: Villamar-Cruz Olga , Loza-Mejía Marco Antonio , Vivar-Sierra Alonso , Saldivar-Cerón Héctor Iván , Patiño-López Genaro , Efraín Olguín Jonadab , Terrazas Luis Ignacio , Armas-López Leonel , Ávila-Moreno Federico , Saha Sayanti , Chernoff Jonathan , Camacho-Arroyo Ignacio , Arias-Romero Luis Enrique 
Published: June 2022

AbstractProtein tyrosine phosphatase 1B (PTP1B) plays a key role in developing different types of cancer. However, the molecular mechanism underlying this effect is unclear. To identify possible molecular targets of PTP1B that mediate its positive role in tumorigenesis, we undertook a SILAC-based phosphoproteomic approach, which allowed us to identify the Cyclin-dependent kinase 3 (Cdk3) as a novel PTP1B substrate. Molecular docking studies revealed stable interactions between the PTP1B catalytic domain and Cdk3. In addition, we observed that PTP1B dephosphorylates a Cdk3 derived peptide at Tyrosine residue 15in vitroand interacts with endogenous Cdk3 in the nucleus and cytoplasm of human glioblastoma (GB) cells. Finally, we found that the pharmacological inhibition of PTP1B or its depletion with siRNA leads to cell cycle arrest with the diminished activity of Cdk3, the consequent hypophosphorylation of Rb, and the down-regulation of E2F and its target genes Cdk1, Cyclin A, and Cyclin E1. These data delineate a novel signaling pathway from PTP1B to Cdk3 required for efficient cell cycle progression in an Rb-E2F dependent manner in human GB cells and suggest new therapeutic strategies for treating these tumors.