bluuebunny/crossref_metadata_2025 huggingface dataset:
A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics
Authors: Ding Xuehao , Lee Dongsoo , Grant Satchel , Stein Heike , McIntosh Lane , Maheswaranathan Niru , Baccus Stephen
Published: December 2021

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.
Time Between Viral Loads for People with HIV during the COVID-19 Pandemic
Authors: El-Nahal Walid G. , Shen Nicola M. , Keruly Jeanne C. , Jones Joyce L. , Fojo Anthony T. , Manabe Yukari C. , Moore Richard D. , Gebo Kelly A. , Chander Geetanjali , Lesko Catherine R.
Published: December 2021

Background

During the COVID-19 pandemic, patients experienced significant care disruptions, including lab monitoring. We investigated changes in the time between viral load (VL) checks for people with HIV associated with the pandemic.

Methods

This was an observational analysis of VLs of people with HIV in routine care at a large subspecialty clinic. At pandemic onset, the clinic temporarily closed its onsite laboratory. The exposure was time period (time-varying): pre-pandemic (January 1st 2019-March 15th, 2020); pandemic lab-closed (March 16th-July 12th, 2020); and pandemic lab-open (July 13th-December 31st, 2020). We estimated time from an index VL to a subsequent VL, stratified by whether the index VL was suppressed (≤200 copies/mL). We also calculated cumulative incidence of a non-suppressed VL following a suppressed index VL, and of re-suppression following a loss of viral suppression.

Results

Compared to pre-pandemic, hazard ratios for next VL check were: 0.34 (95% CI: 0.30, 0.37, lab-closed) and 0.73 (CI: 0.68, 0.78, lab-open) for suppressed patients; 0.56 (CI: 0.42, 0.79, lab-closed) and 0.92 (95% CI: 0.76, 1.10, lab-open) for non-suppressed patients. The 12-month cumulative incidence of loss of suppression was the same in the pandemic lab-open (4%) and pre-pandemic period (4%). The hazard of re-suppression following loss of suppression was lower during the pandemic lab-open versus the pre-pandemic period (hazard ratio: 0.68, 95% CI: 0.50, 0.92).

Conclusions

Early pandemic restrictions and lab closure significantly delayed VL monitoring. Once the lab re-opened, non-suppressed patients resumed normal monitoring. Suppressed patients still had a delay, but no significant loss of suppression.

During the early COVID-19 pandemic, people with HIV experienced disruptions in viral load monitoring due to lab closure and pandemic restrictions. Loosening restrictions resolved delays for non-suppressed, but not suppressed patients. Delays did not significantly increase proportion of non-suppressed patients.
Effectiveness of different booster regimens for preventing infection and adverse outcomes in Puerto Rico
Authors: Robles-Fontán M.M. , Irizarry R.A.
Published: December 2021

Recent laboratory and observational studies have demonstrated that the COVID-19 vaccine effectiveness wanes over time. In response, several jurisdictions have authorized the administration of booster doses. Since August 13, 2021, Puerto Rico has administered 540,140 booster shots. We used data collected and made public by the Puerto Rico Department of Health (PRDH) to evaluate the effectiveness of four different booster regimens at preventing SARS-CoV-2 laboratory confirmed infections and adverse COVID-19 outcomes. Specifically, we analyzed data from all 115,995 SARS-CoV-2 infections occurring since the vaccination process commenced on December 15, 2020. We combined vaccination status, SARS-CoV-2 test results, and COVID-19 hospitalizations and deaths data, and fit a statistical model that adjusted for time-varying incidence rates and age group, to estimate time-varying vaccine effectiveness against infection and adverse outcomes. We find that, after 6 months, the mRNA-1273 and BNT162b2 effectiveness against infection wanes substantially to 61% (58%-63%) and 36% (34%-39%), respectively, while the Ad26.COV2.S wanes to 35% (31%-39%) after two months. However, after a booster shot of the corresponding initial vaccine manufacturer, effectiveness increased to 87% (83%-91%) and 82% (79%-85%) for mRNA-1273 and BNT162b2, respectively. The effectiveness for Ad26.COV2.S followed by either a mRNA-1273 or BNT162b2 booster increased to 88% (71%-100%), substantially higher than 65% (59%-70%), the peak effectiveness reached with just one shot. We also found that heterologous booster regimens restored effectiveness. Furthermore, we did not observe waning after two months of the booster shot. Finally, we found that all booster regimens provided increased protection against COVID-19 hospitalizations and deaths. Code and data to reproduce the analyses are provided here: https://github.com/rafalab/booster-eff-pr.
Parental provisioning drives brain size in birds
Authors: Griesser Michael , Drobniak Szymon M. , Graber Sereina M. , van Schaik Carel P.
Published: December 2021

Large brains support numerous cognitive adaptations and therefore may appear to be highly beneficial. Nonetheless, the high energetic costs of brain tissue may have prevented the evolution of large brains in many species. This problem may also have a developmental dimension: juveniles, with their immature and therefore poorly performing brains, would face a major energetic hurdle if they were to pay for the construction of their own brain, especially in larger-brained species. Here we explore the possible role of parental provisioning for the development and evolution of adult brain size in birds. A comparative analysis of 1,176 bird species shows that various measures of parental provisioning (precocial vs altricial state at hatching, relative egg mass, time spent provisioning the young) strongly predict relative brain size across species. The parental provisioning hypothesis also provides an explanation for the well-documented but so far unexplained pattern that altricial birds have larger brains than precocial ones. We therefore conclude that the evolution of parental provisioning allowed species to overcome the seemingly insurmountable energetic constraint on growing large brains, which in turn enabled bird species to increase survival and population stability. Because including adult eco-and socio-cognitive predictors only marginally improved the explanatory value of our models, these findings also suggest that the traditionally assessed cognitive abilities largely support successful parental provisioning. Our results therefore indicate that the cognitive adaptations underlying successful parental provisioning also provide the behavioral flexibility facilitating reproductive success and survival.

Significance Statement

The young of large brained species, if left to grow their own brain, would face a seemingly insurmountable energetic constraint, because brain tissue is energetically costly but adequate cognitive benefits arise only after a delay. We therefore hypothesize that protracted parental provisioning was a precondition for the evolution of large brains. Comparative analyses of 1,176 bird species confirmed that parental provisioning strongly predicts variation in relative brain size, suggesting that these two traits coevolved. These results provide the first explanation for the well-known difference in relative brain size between altricial and precocial birds. They also cast doubt on the explanatory value of previously considered social or technological cognitive abilities, suggesting we rethink our approach to cognitive evolution.
Testing for implicit emotion regulation in childhood
Authors: Uh Stepheni , Siugzdaite Roma , Anwyl-Irvine Alexander , Dalmaijer Edwin S. , Bignardi Giacomo , Smith Tess A. , Astle Duncan E.
Published: December 2021

Although implicit emotion regulation is thought to be critical for psychosocial development and mental wellbeing, few studies have investigated the neural underpinnings of this form of emotion regulation in children. We used a modified emotional Go/NoGo block design fMRI task to explore the neural correlates of implicit emotion regulation and individual differences in a sample of 40 children (50% female, mean age = 8.65 ± 0.77). Conditions included happy, sad, neutral, and scrambled faces as implicit distractors within the actual Go/NoGo targets. We used a relatively standard preprocessing pipeline via fMRIprep, with T-contrasts for response inhibition and emotional effects, and a nonparametric multiple comparisons procedure, with SnPM, for our group-level analysis. There were multiple significant response inhibition effects, including larger NoGo vs Go activation in the IFG, insula, and MCC/ACC. Valence effects showed significantly greater right putamen activity for the Sad NoGo vs Go contrast and greater bilateral putamen and right pallidum activity for the Happy Go vs Sad Go contrast. These results provide preliminary findings of neural substrates, particularly the putamen, that may be associated with implicit emotion regulation in children.
Relatedness modulates reproductive competition among queens in ant societies with multiple queens
Authors: Helanterä Heikki , Ozan Martina , Sundström Liselotte
Published: December 2021

Reproductive sharing in animal groups with multiple breeders, insects and vertebrates alike, contains elements of both conflict and cooperation, and depends on both relatedness between co-breeders, as well as their internal and external conditions. We studied how queens of the ant Formica fusca adjust their reproductive efforts in response to experimental manipulations of the kin competition regime in their nest, as well as their own reproductive status. Queens respond to the presence of competitors by increasing their egg laying efforts, but only if the competitors are highly fecund and distantly related. Furthermore, queens only engage in cannibalism of eggs when the risk of erroneously destroying own offspring is absent. We demonstrate that queens of Formica fusca fine-tune their behaviours in response to kinship and fecundity of others as well as their own physiological state in an unusually precise manner.
Genome organization by SATB1 binding to base-unpairing regions (BURs) provides a scaffold for SATB1-regulated gene expression
Authors: Kohwi Yoshinori , Wong Xianrong , Grange Mari , Sexton Thomas , Richards Hunter W. , Kitagawa Yohko , Sakaguchi Shimon , Liang Ya-Chen , Chuong Cheng-Ming , Botchkarev Vladimir A. , Taniuchi Ichiro , Reddy Karen L. , Kohwi-Shigematsu Terumi
Published: December 2021

Mammalian genomes are organized by multi-level folding, yet how this organization contributes to cell type-specific transcription remain unclear. We uncovered that the nuclear protein SATB1 establishes two-tiered chromatin organization, one through indirect binding and another by direct binding of base-unpairing regions (BURs), which are genomic elements with high unwinding propensities. Published ChIP-seq datasets show SATB1 binding to highly accessible chromatin at enhancers and CTCF sites, but not to BURs. By employing urea ChIP-seq, which retains only directly bound protein:DNA complexes, we found that BURs, but not CTCF sites, are direct SATB1 binding targets. SATB1-bound BUR interactions with accessible chromatin can cross multiple topologically associated domains (TADs) and SATB1 is required for these megabase-scale interactions linked to cell type-specific gene expression. BURs are mainly found within lamina associated domains (LADs) sequestered at the nuclear lamina, but also in inter-LADs, and SATB1 binds a subset of BURs depending on cell type. Notably, despite the mutually exclusive SATB1-binding profiles uncovered by the two ChIP-seq methods, we found most peaks in both profiles are real and require SATB1. Together, we propose that SATB1 has functionally distinct modes of chromatin interaction by directly binding BURs to form a chromatin scaffold to which it indirectly tethers open chromatin. Such chromatin organization may provide a gene-regulatory network underlying cell type-specific gene expression.
Intracranial Electric Field Recording During Multichannel Transcranial Electrical Stimulation
Authors: Wang Minmin , Han Jiawei , Jiang Hongjie , Zhu Junming , Feng Wuwei , Chhatbar Pratik Y. , Zhang Jianmin , Zhang Shaomin
Published: December 2021

Background

Multichannel transcranial electrical stimulation (tES) modeling and optimization have been widely studied in recent years. Its theoretical bases include quasi-static assumption and linear superposition. However, there is still a lack of direct in vivo evidence to validate the simulation model and theoretical assumptions.

Methods

We directly measured the multichannel tES-induced voltage changes with implanted stereotactic-electroencephalographic (sEEG) electrodes in 12 epilepsy subjects. By combining these measured data, we investigate the linear superposition and prediction accuracy of simulation models for multi-electrode stimulation and further compare the induced EF differences between transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS).

Results

Our in vivo measurements demonstrated that the multi-electrode tES-induced voltages were almost equal to the sum of the voltages generated independently by bipolar stimulation. Both measured voltages and electric fields obtained in vivo were highly correlated with the predicted values in our cohort (Voltages: r = 0.92, p < 0.001; electric fields: r = 0.74, p < 0.001). Under the same stimulation intensity, the tDCS-induced peak-zero voltages were highly correlated with the values of tACS (r = 0.99, p < 0.001; s = 0.99).

Conclusions

The in vivo measurements provides confirmatory results for linear superposition and quasi-static assumption within the human brain. Furthermore, we found that the individualized simulation model reliably predicted the multi-electrode tES-induced electric fields.
MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies
Authors: Syed Hamzah , Otto Georg W , Kelberman Daniel , Bacchelli Chiara , Beales Philip L
Published: December 2021

Background

Multi-omics studies are increasingly used to help understand the underlying mechanisms of clinical phenotypes, integrating information from the genome, transcriptome, epigenome, metabolome, proteome and microbiome. This integration of data is of particular use in rare disease studies where the sample sizes are often relatively small. Methods development for multi-omics studies is in its early stages due to the complexity of the different individual data types. There is a need for software to perform data simulation and power calculation for multi-omics studies to test these different methodologies and help calculate sample size before the initiation of a study. This software, in turn, will optimise the success of a study.

Results

The interactive R shiny application MOPower described below simulates data based on three different omics using statistical distributions. It calculates the power to detect an association with the phenotype through analysis of n number of replicates using a variety of the latest multi-omics analysis models and packages. The simulation study confirms the efficiency of the software when handling thousands of simulations over ten different sample sizes. The average time elapsed for a power calculation run between integration models was approximately 500 seconds. Additionally, for the given study design model, power varied with the increase in the number of features affecting each method differently. For example, using MOFA had an increase in power to detect an association when the study sample size equally matched the number of features.

Conclusions

MOPower addresses the need for flexible and user-friendly software that undertakes power calculations for multi-omics studies. MOPower offers users a wide variety of integration methods to test and full customisation of omics features to cover a range of study designs.
Electrical activity between skin cells regulates melanoma initiation
Authors: Tagore Mohita , Hergenreder Emiliano , Suresh Shruthy , Baron Maayan , Perlee Sarah C. , Melendez Stephanie , Hollmann Travis J. , Ideker Trey , Studer Lorenz , White Richard M.
Published: December 2021

Oncogenes can only initiate tumors in certain cellular contexts, which is referred to as oncogenic competence. In melanoma, whether cells in the microenvironment can endow such competence remains unclear. Using a combination of zebrafish transgenesis coupled with human tissues, we demonstrate that GABAergic signaling between keratinocytes and melanocytes promotes melanoma initiation by BRAFV600E. GABA is synthesized in melanoma cells, which then acts on GABA-A receptors on keratinocytes. Electron microscopy demonstrates synapse-like structures between keratinocytes and melanoma cells, and multi-electrode array analysis shows that GABA acts to inhibit electrical activity in melanoma/keratinocyte co-cultures. Genetic and pharmacologic perturbation of GABA synthesis abrogates melanoma initiation in vivo. These data suggest that electrical activity across the skin microenvironment determines the ability of oncogenes to initiate melanoma.
Time-resolved assessment of single-cell protein secretion by sequencing
Authors: Wu Tongjin , Womersley Howard John , Wang Jiehao , Scolnick Jonathan Adam , Cheow Lih Feng
Published: December 2021

Secreted proteins play critical roles in cellular communication and functional orchestration. Methods enabling concurrent measurement of cellular protein secretion, phenotypes and transcriptomes are still unavailable. Here, we describe time-resolved assessment of protein secretion from single cells by sequencing (TRAPS-seq). Released proteins are trapped onto cell surface via affinity matrices, and the captured analytes together with phenotypic markers can be probed by oligonucleotide-barcoded antibodies and simultaneously sequenced with transcriptomes. We used TRAPS-seq to interrogate secretion dynamics of pleiotropic cytokines (IFN-γ, IL-2 and TNF-α) of early activated human T lymphocytes, unraveling limited correlation between cytokine secretion and its transcript abundance with regard to timing and strength. We found that early central memory T cells with CD45RA expression (TCMRA) are the most effective responders in multiple cytokine secretion, and polyfunctionality involves unique yet dynamic combinations of gene signatures over time. TRAPS-seq presents a useful tool for cellular indexing of secretions, phenotypes, and transcriptomes at single-cell resolution.
Self-confocal NIR-II fluorescence microscopy for in vivo imaging
Authors: Zhou Jing , Wu Tianxiang , Zhu Liang , Li Yifei , Chen Liying , Qian Jun
Published: December 2021

Benefiting from low scatter of NIR-II light in biological tissues and high spatial resolution of confocal microscopy, NIR-II fluorescence confocal microscopy has been developed recently and achieve deep imaging in vivo. However, independence of excitation point and detection point makes this system difficult to be adjusted. New, improved, self-confocal NIR-II fluorescence confocal systems are created in this work. Based on a shared pinhole for excitation light and fluorescence, the system is easy and controlled to be adjusted. The fiber-pinhole confocal system is constructed for cerebrovascular and hepatocellular NIR-II fluorescence intensity imaging. The air-pinhole confocal system is constructed for cerebrovascular NIR-II fluorescence intensity imaging, hepatic NIR-II fluorescence lifetime imaging, and hepatic multiphoton imaging.
Towards mechanistic models of mutational effects: Deep Learning on Alzheimer’s Aβ peptide
Authors: Wang Bo , Gamazon Eric R.
Published: December 2021

Alzheimer’s Disease (AD) is a debilitating form of dementia with a high prevalence in the global population and a large burden on the community and health care systems. AD’s complex pathobiology consists of extracellular β-amyloid deposition and intracellular hyperphosphorylated tau. Comprehensive mutational analyses can generate a wealth of knowledge about protein properties and enable crucial insights into molecular mechanisms of disease. Deep Mutational Scanning (DMS) has enabled multiplexed measurement of mutational effects on protein properties, including kinematics and self-organization, with unprecedented resolution. However, potential bottlenecks of DMS characterization include experimental design, data quality, and the depth of mutational coverage. Here, we apply Deep Learning to comprehensively model the mutational effect of the AD-associated peptide Aβ42on aggregation-related biochemical traits from DMS measurements. Among tested neural network architectures, Convolutional Neural Networks (ConvNets) and Recurrent Neural Networks (RNN) are found to be the most cost-effective models with robust high performance even under insufficiently-sampled DMS studies. While sequence features are essential for satisfactory prediction from neural networks, geometric-structural features further enhance the prediction performance. Notably, we demonstrate how mechanistic insights into phenotype may be extracted from the neural networks themselves suitably designed. This methodological benefit is particularly relevant for biochemical systems displaying a strong coupling between structure and phenotype such as the conformation of Aβ42aggregate and nucleation, as shown here using a Graph Convolutional Neural Network (GCN) developed from the protein atomic structure input. In addition to accurate imputation of missing values (which here ranged up to 55% of all phenotype values at key residues), the mutationally-defined nucleation phenotype generated from a GCN shows improved resolution for identifying known disease-causing mutations relative to the original DMS phenotype. Our study suggests that neural network derived sequence-phenotype mapping can be exploited not only to provide direct support for protein engineering or genome editing but also to facilitate therapeutic design with the gained perspectives from biological modeling.
Myasthenia gravis-specific aberrant neuromuscular gene expression by medullary thymic epithelial cells in thymoma
Authors: Yasumizu Yoshiaki , Ohkura Naganari , Murata Hisashi , Kinoshita Makoto , Funaki Soichiro , Nojima Satoshi , Kido Kansuke , Kohara Masaharu , Motooka Daisuke , Okuzaki Daisuke , Suganami Shuji , Takeuchi Eriko , Nakamura Yamami , Takeshima Yusuke , Arai Masaya , Tada Satoru , Okumura Meinoshin , Morii Eiichi , Shintani Yasushi , Sakaguchi Shimon , Okuno Tatsusada , Mochizuki Hideki
Published: December 2021

Myasthenia gravis (MG) is a neurological disease caused by autoantibodies against neuromuscular-associated proteins. While MG is frequently developed in thymoma patients, the etiologic factors for MG are not well understood. Here, by constructing a comprehensive atlas of thymoma using bulk and single-cell RNA-seq, we identified ectopic expression of neuromuscular molecules in MG-associated thymoma (MG-thymoma). These molecules were originated from a distinct subpopulation of medullary thymic epithelial cells (mTECs), which we named neuromuscular mTECs (nmTECs). MG-thymoma also exhibited microenvironments dedicated to autoantibody production, including ectopic germinal center formation, T follicular helper cell accumulation, and type 2 conventional dendritic cell migration. Cell-cell interaction analysis also predicted the interaction between nmTECs and T/B cells viaCXCL12-CXCR4. The enrichment of nmTECs presenting neuromuscular molecules within MG-thymoma was further confirmed by immunohistochemically and by cellular composition estimation from MG-thymoma transcriptome. Altogether, this study suggests that nmTECs play a significant role in MG pathogenesis via ectopic expression of neuromuscular molecules.
PREVALENCE AND PREDICTORS OF STRESS AMONG COVID-19 HEALTH WORKERS IN KABWE DISTRICT- A CROSS SECTIONAL STUDY
Authors: Mukwasa L. L , Mutemwa R. , Zambwe M. , Nkhama E. , Chipimo P.J
Published: December 2021

OBJECTIVE

This study aimed at determining the magnitude of stress among COVID-19 health workers in Kabwe district.

METHODS

The study was a cross-sectional study which recruited 138 health care workers managing COVID-19 cases in Kabwe. Data were collected through structured questionnaires and in-depth interviews. Quantitative data was analyzed using SPSS version 16 while qualitative data was analyzed using Nvivo8.

RESULTS

The study obtained 100% responses from the respondents and the prevalence of stress among the respondents was 73%. The nurses were more perceived to experience stress compared to the pharmacy personnel (28% vs. 3%). Similarly, women displayed a higher likelihood of experiencing stress compared to men. Lack of support, increased workload and fear were among the factors leading to stress.

CONCLUSION

The study went out to determine stress among healthcare workers in Kabwe district. It was established that nurses were more vulnerable than groups. And women were found to be more stressed than men. It is therefore recommended that effective and meaningful interventions be put in place to mitigate the impact of long-term psychological distress and physical well-being in healthcare workers during the COVID-19 pandemic and future outbreaks.

ARTICLE SUMMARY

Strengths and limitations of the study

A cross-sectional study method was employed; it does not assist in determining the cause and effect, in addition, the timing of the snapshot may not guarantee representation of the situation overtime. Therefore, there may be need to evaluate the effects of the COVID-19 pandemic on health workers mental requirements using a longitudinal study design.

There might have been bias from respondents as the outcomes were self-reported. However, irrespective of the aforementioned limitations, this is a novel study in Kabwe describing stress among health workers in the district during the COVID-19 pandemic.

The study grants access to initial evidence on stress among health workers managing the COVID-19 pandemic in Kabwe district, with the expectation of drawing the attention to legislators, health facility supervisors and those involved in the response to COVID-19 or impending epidemics.
Comparing the Performance of HIV Rapid Diagnostic Tests used in Zambia - A Systematic Clinical Data Review
Authors: Mukuka L. , Theo A. , Zambwe M. , Chipimo P. J.
Published: December 2021

Objective

To investigate the performance of the HIV RDTs used in Zambia.

Method

2,564 participants aged between 15 and 95 years from two sites in Lusaka province years were tested on OraQuick ADVANCE, Abbot Determine™, and then confirmed on Uni-Gold™ Recombigen®. The data from the participants were analyzed using SPSS version 25.0.

Results

The 3 RDTs when compared to the 4th generation Abbot Architect results had the following results: OraQuick ADVANCE®, Alere Determine and Uni-Gold Ultra, at 95% CI had Sensitivities of: 91.8%, 93.3% and 92.5% respectively. The specificities of OraQuick ADVANCE® and Uni-Gold were the same (100.0%; 95% CI: 98.8 -100.0) but slightly different from Alere Determine (99.8%). Positive predictive values at 95% CI were 100% for OraQuick ADVANCE® and Uni-Gold and 98.4% for Alere Determine. Negative predictive values (at 95% CIs) were 99.1, 99.2 and 99.1 for OraQuick ADVANCE®, Alere Determine, and Uni-Gold Ultra respectively. The results showed that these RDTs could only detect 12 out of every 13 HIV positive results.

Conclusion

Third generation RDTs are not effective in detecting acute positive cases. Fourth generation Rapid Tests are required to capture the positive cases being missed out.
VaxiMap: optimal delivery of vaccinations for housebound patients
Authors: Kirk Thomas F. , Barker Adam J. , Bodossian Armen , Staruch Robert
Published: December 2021

Background

Throughout the UK’s Covid-19 vaccination campaign, responsibility for vaccinating housebound patients has rested with individual GP surgeries, posing them a difficult logistical challenge (the travelling salesman problem). In response to demand from GPs, and a lack of existing solutions tailored specifically to vaccination, VaxiMap was created. This tool provides optimal routes for vaccine delivery and has been free to all users since its inception in January 2021.

Methods

VaxiMap generates optimal routes subject to the constraint that the number of patients per route should be fixed. This ensures that a known quantity of vaccine can be set aside for each route and minimises wastage. The user need only upload an Excel spreadsheet of patient postcodes to be visited. A divide-and-conquer approach of iterative k-means clustering followed by within-cluster route optimisation is used to generate the routes.

Findings

We find substantial savings in the time taken to plan vaccinations, as well as savings in the time taken to visit housebound patients. We estimate total savings to date of 4,700 hours of practitioner time, equivalent to 2.5 work-years, or approximately £91k at typical practitioner salaries.

Interpretation

The adoption of VaxiMap yielded both time and cost savings for GP surgeries and accelerated the UK’s Covid-19 vaccination campaign at a critical moment.

Funding

Financial support for VaxiMap was provided by Magdalen College, Oxford, Oxford University Innovation, and JHubMed, part of UK Strategic Command. These parties were not involved in the preparation of this manuscript.
Worldwide clustering and infection cycles as universal features of multiscale stochastic processes in the SARS-CoV-2 pandemic
Authors: Dankulov Marija Mitrović , Tadić Bosiljka , Melnik Roderick
Published: December 2021

Predicting the evolution of the current epidemic depends significantly on understanding the nature of the underlying stochastic processes. To unravel the global features of these processes, we analyse the world data of SARS-CoV-2 infection events, scrutinising two eight-month periods associated with the epidemic’s outbreak and initial immunisation phase. Based on the correlation-network mapping, K-means clustering, and multifractal time series analysis, our results reveal universal patterns, suggesting potential predominant drivers of the pandemic. More precisely, the Laplacian eigenvectors localisation has revealed robust communities of different countries and regions that then cluster according to similar shapes of infection fluctuations. Apart from quantitative measures, the immunisation phase differs significantly from the epidemic outbreak by the countries and regions constituting each cluster. While the similarity grouping possesses some regional components, the appearance of large clusters spanning different geographic locations is persevering. Furthermore, cyclic trends are characteristic of the identified clusters, dominating large temporal fluctuations of infection evolution, which are prominent in the immunisation phase. Meanwhile, persistent fluctuations around the local trend occur in intervals smaller than 14 days. These results provide a basis for further research into the interplay between biological and social factors as the primary cause of infection cycles and a better understanding of the impact of socio-economical and environmental factors at different phases of the pandemic.
Analysis of cutoff point estimation for determining seropositivity in the context of SARS-CoV-2 infections
Authors: Domingues Tiago Dias , Mouriño Helena , Sepúlveda Nuno
Published: December 2021

In this work will apply mixture models based on distributions from the SMSN family to antibody data against four SARS-CoV-2 virus antigens. Furthermore, since the true infection status of individuals is knowna priori, performance measures will be calculated for the methods proposed for cutoff point estimation such as sensitivity, specificity and accuracy. The results of a simulation study will also be presented.
Accumulation of meningeal lymphocytes, but not myeloid cells, correlates with subpial cortical demyelination and white matter lesion activity in progressive MS patients
Authors: Ahmed Shanzeh M. , Fransen Nina , Touil Hanane , Michailidou Iliana , Huitinga Inge , Gommerman Jennifer L. , Bar-Or Amit , Ramaglia Valeria
Published: December 2021

Subpial cortical demyelination is an important component of multiple sclerosis (MS) pathology contributing to disease progression, yet mechanism(s) underlying its development remain unclear. Compartmentalized inflammation involving the meninges may drive this type of injury. Given recent findings identifying substantial white matter (WM) lesion activity in patients with progressive MS, elucidating whether and how WM lesional activity relates to meningeal inflammation and subpial cortical injury is of interest. Using post-mortem formalin-fixed paraffin-embedded tissue blocks (range, 5-72 blocks; median, 30 blocks) for each of 27 progressive MS patients, we assessed the relationship between meningeal inflammation, the extent of subpial cortical demyelination, and the state of subcortical WM lesional activity. Meningeal accumulations of T cells and B cells, but not myeloid cells, were spatially adjacent to subpial cortical lesions and greater immune-cell accumulation was associated with higher subpial lesion numbers. Patients with a higher extent of meningeal inflammation harboured a greater proportion of active and mixed (active-inactive) WM lesions, and an overall lower proportion of inactive and remyelinated WM lesions. Our findings support the involvement of meningeal lymphocytes in subpial cortical injury, and also point to a potential link between inflammatory subpial cortical demyelination and pathological mechanisms occurring in the subcortical white matter.
A tractometry investigation of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis
Authors: Jandric Danka , Parker Geoff JM , Haroon Hamied , Tomassini Valentina , Muhlert Nils , Lipp Ilona
Published: December 2021

Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts share a similar network structure it would be expected that tracts within a network are similarly affected by MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in diffusion metrics across white matter (WM) tracts. We investigated whether separate networks, based on normal variation or pathology, appear, and how this relates to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract components to predict test performance across cognitive domains. We found that a single component which captured pathology across all tracts explained the most variance and that there was little evidence for separate, smaller network patterns of pathology. WM tract components were weak, but significant, predictors of cognitive function in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.
Spatiotemporal patterns and progression of the Delta variant of COVID-19 and their health intervention linkages in Southeast Asia
Authors: Wei Luo , Zhaoyin Liu , Yuxuan Zhou , Yumin Zhao , Elita Li Yunyue , Masrur Arif , Yu Manzhu
Published: December 2021

The global pandemic of COVID-19 presented an unprecedented challenge to all countries in the world, among which Southeast Asia (SEA) countries managed to maintain and mitigate the first wave of COVID-19 in 2020. However, these countries were caught in the crisis after the Delta variant was introduced to SEA, though many countries had immediately implemented non-pharmaceutical intervention (NPI) measures along with vaccination in order to contain the disease spread. To investigate the potential linkages between epidemic dynamics and public health interventions, we adopted a prospective space-time scan method to conduct spatiotemporal analysis at the district level in the seven selected countries in SEA from June 2021 to October 2021. Results reveal the spatial and temporal propagation and progression of COVID-19 risks relative to public health measures implemented by different countries. Our research benefits continuous improvements of public health strategies in preventing and containing this pandemic.
Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy
Authors: Wu Zhenke , Li Zehang R. , Chen Irena , Li Mengbing
Published: December 2021

Summary

Determining causes of deaths (COD) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or “domains”) necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this paper, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a pre-specified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. Posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation data set. The paper concludes with a discussion on limitations and future directions.
Statistical Forecasting : Third Wave of COVID-19-With an Application to India
Authors: Rajeshbhai Sabara Parshad , Dhar Subhra Sankar , Shalabh
Published: December 2021

The pandemic due to the SARS-CoV-2 virus impacted the entire world in different waves. An important question that arise after witnessing the first and second waves of COVID-19 is - Will the third wave also arrive and if yes, then when. Various types of methodologies are being used to explore the arrival of third wave. A statistical methodology based on the fitting of mixture of Gaussian distributions is explored in this paper and the aim is to forecast the third wave using the data on the first two waves of pandemic. Utilizing the data of different countries that are already facing the third wave, modelling of their daily cases data and predicting the impact and timeline for the third wave in India is attempted in this paper. The Gaussian mixture model based on algorithm for clustering is used to estimate the parameters.
Oxytocin Signaling Regulates the Homeostatic Response to Cold Stress in Poikilothermic Vertebrates
Authors: Segev-Hadar Adi , Krispin Shani , Olthof Anouk M. , Hyatt Katery C. , Haller Liran , Barki Assaf , Nitzan Tali , Levkowitz Gil , Kanadia Rahul N. , Cnaani Avner , Biran Jakob
Published: December 2021

When exposed to low temperature, homeothermic vertebrates maintain internal body temperature by activating thermogenesis and by altered metabolism, synchronized by neuroendocrine responses. Although such physiological responses also occur in poikilothermic vertebrates, the prevailing notion is that their reactions are passive. Here, we explored molecular hypothalamic and physiological responses to cold stress in the tropical poikilotherm Nile tilapia (Oreochromis niloticus). We show that cold exposed tilapia exhibit complex homeostatic responses, including increased hypothalamic oxytocin, plasma glucose and cortisol concomitant with reduced plasma lactate and metabolic rate. Pharmacological or genetic blockage of oxytocin signaling further affected metabolic rate in two cold-exposed poikilothermic models. This indicates that oxytocin, a known thermoregulator in homeotherms, actively regulates temperature-related homeostasis in poikilotherms. Overall, our findings show that the brain of poikilotherms actively responds to cold temperature by regulating metabolic physiology. Moreover, we identify oxytocin signaling as an adaptive and evolutionarily conserved metabolic regulator of temperature-related homeostasis.
Membrane voltage fluctuations in human breast cancer cells
Authors: Quicke Peter , Sun Yilin , Arias-Garcia Mar , Acker Corey D. , Djamgoz Mustafa B. A. , Bakal Chris , Foust Amanda J.
Published: December 2021

Cancer cells feature a resting membrane potential (Vm) that is depolarized compared to normal cells, and express active ionic conductances, which factor directly in their pathophysiological behavior. Despite similarities to ‘excitable’ tissues, relatively little is known about cancer cell Vm dynamics. With high-throughput, cellular-resolution Vm imaging, we characterized Vm fluctuations of hundreds of human triple-negative breast cancer MDA-MB-231 cells and compared to non-cancerous breast epithelial MCF-10A cells. By quantifying their Dynamic Electrical Signatures (DESs) through an unsupervised machine-learning protocol, we identified four classes ranging from “noisy” to “blinking/waving”. The Vm of MDA-MB-231 cells exhibited spontaneous, transient hyperpolarizations that were inhibited by the voltage-gated sodium channel blocker tetrodotoxin. The Vm of MCF-10A cells was comparatively static, but fluctuations increased following treatment with transforming growth factor-β1, a canonical inducer of the epithelial-to-mesenchymal transition. These data suggest that the ability to generate Vm fluctuations is acquired during transformation and may participate in oncogenesis.
Gene regulation network inference using k-nearest neighbor-based mutual information estimation-Revisiting an old DREAM
Authors: Shachaf Lior I. , Roberts Elijah , Cahan Patrick , Xiao Jie
Published: December 2021

Background

A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past twenty years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization.

Results

In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoögbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods.

Conclusions

Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction - which combines CMIA, and the KSG-MI estimator - achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or choose gene candidates for experimental validations.
A Cartography of Differential Gene Methylation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Different Network Roles in the Protein-Protein Interactions Network Play Different, Biologically Relevant, Roles
Authors: Wilberforce Aurelia , Riva Giulio Valentino Dalla
Published: December 2021

Myalgic Encephalomyelitis, or Chronic Fatigue Syndrome (ME/CFS), is characterised by severe fatigue and associated with immune dysfunction. Previous studies of DNA methylation have found evidence of changes in immune cells for ME/CFS. However these studies have been limited by their small sample size. Here, we aggregate three comparable datasets to achieve a larger sample size and detect small changes to DNA methylation. We find 10,824 differentially methylated genes, with a small average change. Next, from the currently known interactions of relevant proteins, we build a Protein-Protein interaction network and,localising the network cartography analysis, we identify 184 hub genes. We find that different hub types play different, and meaningful, biological roles. Finally, we perform Gene ontology enrichment analysis, and we find that these hubs are involved in immune system processes, including response to TGF-β and LPS, as well as mitochondrial functioning, supporting previous theories about ME/CFS. We also show that dopaminergic signalling may potentially contribute to immune pathology in ME/CFS, suggesting a possible interplay with Long Covid. Our results demonstrate the potentiality of network analysis in shedding light on the epigenetic contribution to the immune dysregulation of ME/CFS.