bluuebunny/crossref_metadata_2025_split huggingface dataset:
Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction
Authors: He Lingfei , Chen Cheng , Wang Yaping , Fan Qingcheng , Chu Congying , Xu Junhai , Fan Lingzhong 
Published: November 2022

AbstractDeep learning frameworks utilizing convolutional neural networks (CNNs) have frequently been used for brain age prediction and have achieved outstanding performance. Nevertheless, deep learning remains a black box as it is hard to interpret which brain parts contribute significantly to the predictions. To tackle this challenge, we first trained a lightweight, fully CNN model for brain age estimation on a large sample data set (N= 3054, age range = [8,80 years]) and tested it on an independent data set (N= 555, mean absolute error (MAE) = 4.45 years,r= 0.96). We then developed an interpretable scheme combining network occlusion sensitivity analysis (NOSA) with a fine-grained human brain atlas to uncover the learned invariance of the model. Our findings show that the dorsolateral, dorsomedial frontal cortex, anterior cingulate cortex, and thalamus had the highest contributions to age prediction across the lifespan. More interestingly, we observed that different regions showed divergent patterns in their predictions for specific age groups and that the bilateral hemispheres contributed differently to the predictions. Regions in the frontal lobe were essential predictors in both the developmental and aging stages with the thalamus remaining relatively stable and saliently correlated with other regional changes throughout the lifespan. The lateral and medial temporal brain regions gradually became involved during the aging phase. At the network level, the frontoparietal and the default mode networks show an inverted U-shape contribution from the developmental to the aging stages. The framework could identify regional contributions to the brain age prediction model, which could help increase the model interpretability when serving as an aging biomarker.
COVID-19 and non-Hodgkin’s lymphoma: a common susceptibility pattern?
Authors: Sara De Matteis , Minelli Cosetta , Broccia Giorgio , Vineis Paolo , Cocco Pierluigi 
Published: November 2022

AbstractObjectivTo explore the link between COVID-19 incidence, socio-economic covariates, and NHL incidence.DesignEcological study design.SettingSardinia, Italy.ParticipantsWe used official reports on the total cases of COVID-19 in 2020, published data on NHL incidence, and socio-economic indicators by administrative unit, covering the whole regional population.Main outcomes and measuresWe used multivariable regression analysis to explore the association between the natural logarithm (ln) of the 2020 cumulative incidence of COVID-19 and the ln-transformed NHL incidence in 1974-2003, weighing by population size and adjusting by socioeconomic deprivation and other covariates.ResultsThe cumulative incidence of COVID-19 increased in relation to past incidence of NHL (p< 0.001), socioeconomic deprivation (p= 0.006), and proportion of elderly residents (p< 0.001) and decreased with urban residency (p= 0.001). Several sensitivity analyses confirmed the finding of an association between COVID-19 and NHL.ConclusionThis ecological study found an ecological association between NHL and COVID-19. If further investigation would confirm our findings, shared susceptibility factors should be investigated among the plausible underlying mechanisms.Strengths and limitations of this studyThis study exploited the availability of incidence data for non-Hodgkin’s lymphoma over a 30-year time frame and the 2020 cumulative incidence data for COVID-19 available for all administrative units in the Sardinia region, Italy.Using an ecological study design, we observed that COVID-19 occurrence increased with past incidence of non-Hodgkin’s lymphoma.Limitations of the ecological study design require further investigation for confirmation and for identification of susceptibility factors possibly shared between the two diseases.
Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation
Authors: Singer Neomi , Poker Gilad , Dunsky Netta , Nemni Shlomi , Doron Maayan , Baker Travis , Dagher Alain , Zatorre Robert J , Hendler Talma 
Published: November 2022

AbstractReward processing is essential for our mental-health and well-being. Here, we present the development and validation of a scalable fMRI-informed EEG model related to reward processing in the ventral-striatum (VS); a central reward circuit node. Simultaneous EEG/fMRI data were acquired from 17 healthy individuals listening to pleasurable music, and used to construct a one-class regression model for predicting the reward-related VS-BOLD signal using spectro-temporal features from the EEG. Validation analyses, applied on EEG/fMRI data from a different group (N=14), revealed that the EEG model predicted VS-BOLD activation from the simultaneous EEG to a greater extent than a model derived from another anatomical region. The VS-EEG-model was also modulated by musical pleasure and predictive of the VS-BOLD during a monetary reward task, further indicating it functional relevance. These findings provide compelling evidence for the use of a scalable yet precise EEG-only probe of VS-originated reward processing, which could serve for process specific neruo-monitoring and -modulation.
Post-Stimulus Encoding of Decision Confidence in EEG: Toward a Brain-Computer Interface for Decision Making
Authors: Sadras Nitin , Sani Omid G. , Ahmadipour Parima , Shanechi Maryam M. 
Published: November 2022

AbstractWhen making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. We investigate the neural correlates of confidence by collecting high-density EEG during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. We perform event-related potential (ERP) and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-related activity to confound stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.
Estimating the gendered impact of COVID-19
Authors: Singh Samer , Singh Rakesh Kumar 
Published: November 2022

AbstractThe extent of gendered COVID-19 impact remains undetermined for the lack of sex-disaggregated data. The prevailing view puts males nearly twice as impacted as females. Globally, access to resources and their usage are gendered- mostly favoring males. Gender gaps widen during natural/man-made calamities and pandemics. Modeling estimates of impact for top 70 countries reporting >300 sex-disaggregated COVID-19 deaths (>80% of total), indicates average mortality sex (male:female) ratio (COVID-MSR) of 1.37±0.30 (95% confidence interval:1.30-1.44; range:0.85-2.47) against prevalent pre-pandemic MSR of 1.79±0.41 (1.70-1.89; range:0.93-2.99). Contrary to the prevailing view, widened gender gaps globally increased female mortality by 19.57±21.16% (14.62%-24.88%; range: -22.46 to +68.50%) causing an estimated 22.03% excess deaths (360 thousand by 30 December 2021). Identification of factors favoring gendered impacts is needed for equitable pandemic management.One-Sentence Summary‘Missing Females’ in COVID-19?
The effects of maternal mental health in pregnancy on neonatal adversity: role of physical health and familial factors
Authors: Lin Emma , Wilson Elah , Kodesh Arad , Levine Stephen Z. , Fox Nathan , Reichenberg Abraham , Khachadourian Vahe , Janecka Magdalena 
Published: November 2022

AbstractBackgroundThere exists substantial evidence of the effects of maternal mental health on birth outcomes. However, the roles of (1) comorbidity between mental and physical health, and (2) familial confounding underlying this association, remain unclear.MethodsThis cohort study included a random sample of 19.5% children born between January 1, 1997, through December 31, 2008, within a health maintenance organization (HMO) in Israel, as well as their parents and siblings born within the cohort years. Outcomes were ICD-9 diagnoses of neonatal adversity (birth complications and congenital anomalies), and exposure was parental mental health diagnosis – all ascertained through the HMO records. Odds ratios (ORs) and their 95% CIs for the associations between the maternal mental health diagnosis and neonatal adversity were calculated using logistic regression, iteratively adjusting for covariates (maternal age, child’s year of birth, socioeconomic status, number of physical health diagnoses). We also examined potential familial confounding using a negative control approach based on paternal exposure.ResultsIn our sample of 74,533 children, 6,674 (9.1%) were born after birth complications and 14,569 (19.9%) with a congenital anomaly. Maternal mental health diagnosis around pregnancy was significantly associated with these measures of neonatal adversity, adjusting for demographic and socioeconomic factors (birth complications: OR=1.3 (1.2-1.4); p<0.001;congenital anomalies:OR=1.2 (1.1-1.3); p<0.001). These associations became attenuated and non-significant after further adjustment for the number of physical health diagnoses. In a joint model, maternal and paternal diagnosis of mental health disorder were independently associated with measures of neonatal adversity (birth complications: ORmat=1.3 (1.1-1.4); p < 0.001; ORpat=1.2 (1.1-1.3); p=0.004;congenital anomalies: ORmat=1.2 (1.1-1.3); p<0.001; ORpat=1.1 (1.0-1.2); p=0.01).ConclusionsPhysical health comorbidities and familial factors play a role in the associations between maternal mental health and neonatal adversity.
Epicardial reservoir-enabled multidose delivery of exogenous FSTL1 leads to improved cardiac function, healing, and angiogenesis
Authors: Varela Claudia E. , Monahan David S. , Islam Shahrin , Whyte William , Bonnemain Jean , Ngoy Souen , Fisch Sudeshna , Duffy Garry P. , Roche Ellen T. 
Published: November 2022

AbstractEpicardial delivery of human follistatin-like 1 protein (FSTL1) induces significant cardiac benefit following a myocardial infarction (MI). However, the optimal dosing regimen for maximal therapeutic benefit has not yet been elucidated. To investigate the impact of multiple FSTL1 doses, without the confounding effects of multiple surgical procedures for multidose delivery, alternative delivery strategies are needed. Here, we use an epicardial reservoir that allows non-invasive delivery of additional doses after implantation to investigate the impact of single, double, and triple FSTL1 dose regimens in a rat model of MI. Multidose delivery of FSTL1 improves ejection fraction (3 doses), fractional shortening (1, 2 and 3 doses), and chamber stiffness (2 doses) 28 days after MI. Histologically, multiple FSTL1 doses increase ventricular wall thickness (2 and 3 doses) and reduce infarct size (1, 2, and 3 doses). We also demonstrate a dose-dependent increase in blood vessel number and density in the infarct zone, with three FSTL1 doses leading to the highest improvements. This study shows that multidose delivery of FSTL1 improves cardiac function, healing, and angiogenesis following MI. The epicardial delivery platform used here may be essential in optimizing dosing regimens of various bioagent combinations for a range of clinical indications.
Equipment-Free Personal Protective Equipment (PPE) Fabrication from Bacterial Cellulose-Derived Biomaterials via Waste-to-Wealth Conversion
Authors: Veerubhotla Ramya , Bandopadhyay Aditya , Chakraborty Suman 
Published: November 2022

AbstractThe recent COVID-19 crisis necessitated the universal use of Personal Protection Equipment (PPE) kits, generating tons of plastic wastes that inevitably lead to environmental damage. Circumventing the challenges stemming from such undesirable non-degradability on disposal, here we present an eco-friendly, robust, yet inexpensive and equipment-free method of growing biodegradable PPE fabrics by the fermentation of locally-sourced organic feed stocks in a rural livelihood. Using a pre-acclimatized symbiotic culture, we report the production of a high yield (up to 3.2 g fabric/g substrate) of bacterial cellulose, a biopolymer matrix, obtained by bacterial weaving. This membrane has an intricate, self-assembled, nano-porous 3D architecture formed by randomly oriented cellulose fibres. Scanning electron microscopy reveals that the pore size of the membrane turns out to be in the tune of 140 nanometers on the average, indicating that it can filter out viruses effectively. In-vitro results demonstrate assured breathability through the membrane for a filter thickness of approximately 5 microns. When subjected to soil degradation, the fabrics are seen to disintegrate rapidly and fully decompose within 15 days. With a favourable cost proposition of less than 1 US$ per meter square of the developed fabric unit, our approach stands out in providing a unique sustainable, and production-ready alternative to synthetic PPE fabrics, solving community healthcare and environmental crisis, and opening up new avenues sustainable under-served livelihood at the same time.Graphical abstract
Controls of spatial grain size and environmental variables on observed beta diversity of molluscan assemblage at a regional scale
Authors: Bhattacherjee Madhura , Chattopadhyay Devapriya 
Published: November 2022

AbstractBeta diversity, which quantifies the compositional variation among communities, is one of the fundamental partitions of biodiversity and is associated with abiotic and biotic drivers. Unveiling these drivers is essential for understanding various ecological processes in past and recent faunal communities. Although the quantification of measures of beta diversity has improved over the years, the potential dependence of beta diversity on methodological choices are relatively understudied. Here, we investigate the effect of the variable scale of sampling on different measures of beta diversity at a regional scale. The west coast of India bordering the eastern margin of the Arabian sea, presents a coastal stretch of approximately 6100km from 8–21°N. We used marine bivalve distribution data, consisting of live occurrence data from literature reports and abundance data from death assemblages collected from localities representing latitude bins. We tested if the observed variation in beta diversity is explained by variable sampling scales due to differences in bin sizes and unequal coastline length. We developed a null model to generate a beta diversity pattern with an increase in spatial scale of sampling by increasing the spatial grain size along the 14 latitude bins progressively. Our null model demonstrates that for the both live and dead dataset, the total beta diversity measured by Bray-Curtis, Whittaker and Sorenson indices decreases with increasing sampling scale. The species replacement (turnover) evaluated by Simpson index decreases and the species loss (nestedness) measured by Sorenson index increases with increasing sampling scale. A comparison between the simulated and observed beta diversity distribution using K-S test demonstrated that the observed pattern of beta diversity is significantly different from the pattern generated from the null model in both live and death assemblages. This implies that sampling alone does not generate the spatial variation in beta diversity in this region. The results show that environmental parameters such as salinity, productivity, and cyclones play a significant role in shaping the regional beta diversity along the west coast. Our study provides an approach for evaluating the effect of variable sampling scale on comparing regional beta diversity. It also highlights the importance of spatial standardization while inferring about processes driving spatial diversity changes.
Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from fMRI data
Authors: Meng Xing , Iraji Armin , Fu Zening , Kochunov Peter , Belger Aysenil , Ford Judy M. , McEwen Sara , Mathalon Daniel H. , Mueller Bryon A. , Pearlson Godfrey , Potkin Steven G. , Preda Adrian , Turner Jessica , van Erp Theo G.M. , Sui Jing , Calhoun Vince D. 
Published: November 2022

AbstractBrain functional networks identified from resting fMRI data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale. In this work, we present an approach using scICA to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales (ICA model orders), which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N>1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into spatially constrained ICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. We also used the msFNC features as input to a classification model in cross-validated hold-out data and also in an independent test data. Visualization of predictive features was performed by evaluating their feature weights. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.
HB-EGF and zinc activate EGFR to induce reactive neural stem cells in the mouse hippocampus after seizures
Authors: Pastor-Alonso Oier , Durá Irene , Bernardo-Castro Sara , Varea Emilio , Muro-García Teresa , Martín-Suárez Soraya , Encinas-Pérez Juan Manuel , Pineda Jose Ramon 
Published: November 2022

ABSTRACTHippocampal seizures mimicking mesial temporal lobe epilepsy (MTLE) cause a profound disruption of the adult neurogenic niche in mice. Seizures provoke neural stem cells to switch to a reactive phenotype (reactive-neural stem cells, React-NSCs)) characterized by multibranched hypertrophic morphology, massive activation to enter mitosis, symmetric division and final differentiation into reactive astrocytes. As a result, neurogenesis is chronically impaired. Here we, using a mouse model of MTLE, show that the epidermal growth factor receptor (EGFR) signalization pathway is key for the induction of React-NSCs and that its inhibition exerts a beneficial effect on the neurogenic niche. We show that during the initial days after the induction of seizures by a single intrahippocampal injection of kainic acid, a strong release of zinc and heparin-binding epidermal growth factor, both activators of the EGFR signalization pathway in neural stem cells, is produced. Administration of the EGFR inhibitor gefitinib, a chemotherapeutic in clinical phase IV, prevents the induction of React-NSCs and preserves neurogenesis.SignificanceIn mouse models of MTLE-HS, seizures cause a profound disruption of the hippocampal neurogenic niche and neurogenesis results chronically impaired, in agreement with what occurs in the human MTLE-HS hippocampus. Thus, the normal cognitive functions associated with neurogenesis are altered, but also the endogenous regenerative capacity that could compensate the high rate of neurons in the granule cell layer of the dentate gyrus. We provide here for the first time a molecular mechanism (the EGFR transduction pathway) regulating the induction of React-NSCs.