bluuebunny/crossref_metadata_2025 huggingface dataset:
Physiology and Mathematical Modeling of ImmobilizedSaccharomycesspp. in Beer Fermentation
Authors: de Araujo Thiago M. , da Cunha Marcel M. L. , Barga Marcelo C. , Della-Bianca Bianca E. , Basso Thiago O.
Published: December 2022

There is an ever-increasing demand for reduction of unit operations and a growing interest in the physiology of yeasts used in beer fermentation. In this context, cell immobilization is an interesting alternative, since it reduces steps to separate biomass from fermented broth. Yet, physiological alterations in yeast metabolism caused by immobilization are still to be fully described. Thus, the main objective of this work was to evaluate the physiology of three brewer’sS. cerevisiaeyeast strains (SY025, SY067 and SY001) immobilized on a porous cellulose-based support. Batch fermentations in malt extract 12 °P were carried out for all strains both in free and immobilized forms in order to compare kinetic parameters obtained from distinct process conditions. Mathematical modeling was performed following two viewpoints: modeling of fermentation kinetics by parameter estimation from experimental data and application of a reaction-diffusion model for estimation of substrate concentration gradient inside the immobilization support. Moreover, fermentations with different initial substrate and biomass concentrations were carried out using strain SY025, aiming to evaluate their influence over flavor compounds, using statistical models. Compared to free cells, immobilized yeasts showed both higher glycerol yield (SY025, 40%; SY067, 53%; SY001, 19%) and biomass yield in the system (SY025, 67%; SY067, 78%; SY001, 56%). On the other hand, free cells presented higher ethanol yields when compared to immobilized ones (SY025, 9%; SY067, 9%; and SY001, 13%). According to the model developed, a substrate gradient inside the support was predicted, but with low mass transfer limitations.

KEY POINTS

Yeast immobilization not always hinder biomass growth, here it was stimulated.

A classic kinetic model describes accurately immobilized yeast fermentations.

Physiology changes occur in immobilization even with low mass transfer limitations.
Ganglioglioma deep transcriptomics reveals primitive neuroectoderm neural precursor-like population
Authors: Regal Joshua A , Guerra García María E , Jain Vaibhav , Chandramohan Vidyalakshmi , Ashley David M , Gregory Simon G , Thompson Eric M , López Giselle Y , Reitman Zachary J
Published: December 2022

Gangliogliomas are brain tumors composed of neuron-like and macroglia-like components that occur in children and young adults. Gangliogliomas are often characterized by a rare population of immature astrocyte-appearing cells expressing CD34, a marker expressed in the neuroectoderm (neural precursor cells) during embryogenesis. New insights are needed to refine tumor classification and to identify therapeutic approaches. We evaluated five gangliogliomas with single nucleus RNA-seq, cellular indexing of transcriptomes and epitopes by sequencing, and/or spatially-resolved RNA-seq. We uncovered a population of CD34+ neoplastic cells with mixed neuroectodermal, immature astrocyte, and neuronal markers. Gene regulatory network interrogation in these neuroectoderm-like cells revealed control of transcriptional programming by TCF7L2/MEIS1-PAX6 and SOX2, similar to that found during neuroectodermal/neural development. Developmental trajectory analyses place neuroectoderm-like tumor cells as precursor cells that give rise to neuron-like and macroglia-like neoplastic cells. Spatially-resolved transcriptomics revealed a neuroectoderm-like tumor cell niche with relative lack of vascular and immune cells. We used these high resolution results to deconvolute clinically-annotated transcriptomic data, confirming that CD34+ cell-associated gene programs associate with gangliogliomas compared to other glial brain tumors. Together, these deep transcriptomic approaches characterized a ganglioglioma cellular hierarchy - confirming CD34+ neuroectoderm-like tumor precursor cells, controlling transcription programs, cell signaling, and associated immune cell states. These findings may guide tumor classification, diagnosis, prognostication, and therapeutic investigations.
Clonally-Expanded, Thyrotoxic Autoimmune Mediator CD8+T cells Driven by IL21 Contribute to Checkpoint Inhibitor Thyroiditis
Authors: Lechner Melissa G. , Zhou Zikang , Hoang Aline T. , Huang Nicole , Ortega Jessica , Scott Lauren N. , Chen Ho-Chung , Patel Anushi Y. , Yakhshi-Tafti Rana , Kim Kristy , Hugo Willy , Famini Pouyan , Drakaki Alexandra , Ribas Antoni , Angell Trevor E. , Su Maureen A.
Published: December 2022

Autoimmune toxicity occurs in up to 60% of patients treated with immune checkpoint inhibitor (ICI) cancer therapy and is an increasing clinical challenge with the expanding use of these treatments. To date, human immunopathogenic studies of immune related adverse events (IRAEs) have relied upon sampling of circulating peripheral blood cells rather than affected tissues. Here, we directly obtained thyroid specimens from subjects with ICI-thyroiditis, one of the most common IRAEs, and compared immune infiltrates to those from subjects with spontaneous autoimmune Hashimoto’s thyroiditis (HT) or no thyroid disease. Single cell RNA sequencing revealed a dominant, clonally expanded population of thyroid-infiltrating cytotoxic CXCR6+CD8+T cells (“CD8+autoimmune mediators) present in ICI-thyroiditis, but not HT or healthy controls. Furthermore, we identified a crucial role for interleukin 21, a cytokine secreted by intrathyroidal T follicular (Tfh) and T peripheral helper (Tph) cells, as a driver of these thyrotoxic CD8+autoimmune mediators. In the presence of IL21, human CD8+T cells acquired the autoimmune mediator phenotype with upregulation of cytotoxic molecules (IFNγ, granzyme); the chemokine receptor CXCR6; and thyrotoxic capacity. We validated these findingsin vivousing a novel mouse model of IRAEs, and further demonstrated that genetic blockade of IL21 signaling protected ICI-treated mice from thyroid immune infiltration. Taken together these studies reveal novel mechanisms and therapeutic targets by which IL21+Tfh/Tph cells drive thyrotoxic CD8+autoimmune mediators for the development of IRAEs in humans.

One Sentence Summary

ScRNAseq reveals a novel role for CD8+autoimmune mediators and IL21+T helper cells in the pathogenesis of human checkpoint inhibitor thyroiditis.
Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins are Detected by Single-cell Proteomics
Authors: Hu Mo , Zhang Yutong , Yuan Yuan , Ma Wenping , Zheng Yinghui , Gu Qingqing , Xie X. Sunney
Published: December 2022

Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlations between the translational levels of any pair of proteins in a single mammalian cell. In measuring pairwise correlations among ∼1,000 proteins in a population of homogeneous K562 cells in a steady-state condition, we observed multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functionally interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations to bulk samples. However, some correlations of gene or protein expression in steady-state condition would be masked by perturbation. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.

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Handling ill-conditioned omics data with deep probabilistic models
Authors: Martínez-García María , Olmos Pablo M.
Published: December 2022

The advent of high-throughput technologies has produced an increase in the dimensionality of omics datasets, which limits the application of machine learning methods due to the great unbalance between the number of observations and features. In this scenario, dimensionality reduction is essential to extract the relevant information within these datasets and project it in a low-dimensional space, and probabilistic latent space models are becoming popular given their capability to capture the underlying structure of the data as well as the uncertainty in the information. This article aims to provide a general classification and dimensionality reduction method based on deep latent space models that tackles two of the main problems that arise in omics datasets: the presence of missing data and the limited number of observations against the number of features. We propose a semi-supervised Bayesian latent space model that infers a low-dimensional embedding driven by the target label: the Deep Bayesian Logistic Regression (DBLR) model. During inference, the model also learns a global vector of weights that allows to make predictions given the low-dimensional embedding of the observations. Since this kind of datasets is prone to overfitting, we introduce an additional probabilistic regularization method based on the semi-supervised nature of the model. We compared the performance of the DBLR against several state-of-the-art methods for dimensionality reduction, both in synthetic and real datasets with different data types. The proposed model provides more informative low-dimensional representations, outperforms the baseline methods in classification and can naturally handle missing entries.

Highlights

Inference of the latent space driven by the label value. The DBLR infers different low-dimensional latent distributions depending on the label value, forcing clustering in the latent space in an informative manner, thus capturing the underlying structure of the data.

Classification. During inference, the model additionally learns a global vector of weights that allows to make predictions given the low-dimensional representation of the data.

Handling missing data. As the DBLR is a probabilistic generative model, it can naturally handle partially missing observations during the training process, including not annotated observations as censored samples. In this article we cover the Missing at Random (MAR) case.

Regularization method to handle small datasets. In order to handle small high-dimensional datasets, which usually entail overfitting problems, we introduced an additional regularization mechanism following a drop-outlike strategy that relies in the generative semi-supervised nature of the model.

Handling different data types. We have defined and implemented different observation likelihood models that can be used to describe different data types. In particular, we show how to use the DBLR with binary and real-valued features.
Nucleic acid sensing by STING induces an interferon-like antiviral response in a marine invertebrate
Authors: Li Haoyang , Wang Sheng , Li Qinyao , Weng Shaoping , He Jianguo , Li Chaozheng
Published: December 2022

The cytosolic detection of pathogen derived nucleic acids has evolved as an essential strategy for host innate immune defense in mammals. The stimulator of interferon genes (STING) functions as a crucial signaling adaptor, linking the cytosolic detection of DNA by cyclic GMP-AMP (cGAMP) synthase (cGAS) to the downstream Type I interferon (IFN) signaling axis. However, this process remains elusive in invertebrates. Herein, we demonstrated that a STING ortholog from a marine invertebrate (shrimp)Litopenaeus vannameican directly sense DNA to activate an interferon-like antiviral response. Unlike STING homologs exclusively functioning as a sensor for cyclic dinucleotides (CDNs) in other eukaryotic organisms, shrimp STING can bind to double-stranded DNA (dsDNA) in addition to CDNs, including 2′3′-cGAMP.In vivo, shrimp STING can directly sense DNA nucleic acids from an infected virus, accelerate IRF dimerization, nuclear translocation and induce the expression of an interferon functional analog protein (Vago4), and finally establish an antiviral state. Surprisingly, the shrimp cGAS-like homolog is not involved in dsDNA-intrigued and STING-dependent IRF–Vago axis activation. Taken together, our results uncovered a novel dsDNA–STING–IKKε–IRF–Vago antiviral axis in an arthropod, and provided some novel insights into the functional origin of a DNA-sensing pathway in evolution.
Control of stereocilia length during development of hair bundles
Authors: Krey Jocelyn F. , Chatterjee Paroma , Halford Julia , Cunningham Christopher L. , Perrin Benjamin J. , Barr-Gillespie Peter G.
Published: December 2022

Assembly of the hair bundle, the sensory organelle of the inner ear, depends on differential growth of actin-based stereocilia. Separate rows of stereocilia, labeled 1 through 3 from tallest to shortest, lengthen or shorten during discrete time intervals during development. We used lattice structured illumination microscopy and surface rendering of mouse apical inner hair cells to measure stereocilia dimensions during early postnatal development; these measurements revealed a sharp transition at postnatal day 8 between stage III (row 1 and 2 widening; row 2 shortening) and stage IV (final row 1 lengthening and widening). Tip proteins that determine row 1 lengthening did not accumulate simultaneously during stages III and IV; while the actin-bundling protein EPS8 peaked at the end of stage III, GNAI3 peaked several days later—in early stage IV—and GPSM2 peaked near the end of stage IV. To establish the contributions of key macromolecular assemblies to bundle structure, we examined mouse mutants that eliminated tip links (Cdh23v2JorPcdh15av3J), transduction channels (TmieKO), or the row 1 tip complex (Myo15ash2).Cdh23v2J/v2JandPcdh15av3J/av3Jbundles had adjacent stereocilia in the same row that were not matched in length, revealing that a major role of these cadherins is to synchronize lengths of side-by-side stereocilia. Use of the tip-link mutants also allowed us to distinguish the role of transduction from effects of transduction proteins themselves. While levels of GNAI3 and GPSM2, which stimulate stereocilia elongation, were greatly attenuated at the tips ofTmieKO/KOrow 1 stereocilia, they accumulated normally inCdh23v2J/v2JandPcdh15av3J/av3Jstereocilia. These results reinforced the suggestion that the transduction proteins themselves facilitate localization of proteins in the row 1 complex. By contrast, EPS8 concentrates at tips of allTmieKO/KO, Cdh23v2J/v2JandPcdh15av3J/av3Jstereocilia, correlating with the less polarized distribution of stereocilia lengths in these bundles. These latter results indicated that in wild-type hair cells, the transduction complex prevents accumulation of EPS8 at the tips of shorter stereocilia, causing them to shrink (row 2 and 3) or disappear (row 4 and microvilli). Reduced rhodamine-actin labeling at row 2 stereocilia tips of tip-link and transduction mutants suggests that transduction’s role is to destabilize actin filaments there. These results suggest that regulation of stereocilia length occurs through EPS8, and that CDH23 and PCDH15 regulate stereocilia lengthening beyond their role in gating mechanotransduction channels.
Isolation and characterization ofOvine gammaherpesvirus type 2from an outbreak of Malignant Catarrhal Fever inArtiodactylaand horses in Mexico
Authors: Madrigal-Valencia Tania Lucia , Saavedra-Montañez Manuel , Pérez-Torres Armando , Hernández Jesús , Segalés Joaquim , Hernández Yesmín Domínguez , Candanosa-Aranda Irma Eugenia , Pérez-Guiot Alfredo , Mendoza Humberto Ramírez
Published: December 2022

Ovine gammaherpesvirus 2 (OvHV-2), a member of the Macavirus genus, causes sheep-associated malignant catarrhal fever (SA-MCF), a fatal lymphoproliferative disease that affects a wide variety of ungulates in addition to horses.This study described an outbreak of SA-MCF that occurred in Mexico and the identification of the OvHV-2 virus through viral isolation and different laboratory techniques such as immunofluorescence (IF), immunoperoxidase (IP), immunohistochemistry (IHC), end point PCR and partial sequencing of the ORF75 gene. The animals involved in this outbreak showed head and eye clinical signs and lesions. Based on the clinical-pathological outcome, buffy coats were taken, and virus isolation was attempted on primary cell cultures of the rabbit testicle. Small clusters of refractile cytomegalic cells characterized the cytopathic effect between 48 and 72 hours postinfection. In addition, inclusion bodies were identified, and cytoplasmic immunoreactivity was observed in the infected cells. The sequences obtained were aligned with OvHV-2 sequences reported in GenBank and revealed a nucleotide identity higher than 98%. The results indicate that the outbreak was caused by OvHV-2 and the horses are susceptible to SA-MCF.
Multisite evaluation and validation of Optical Genome Mapping for prenatal genetic testing
Authors: Stevenson R.E. , Liu J. , Iqbal A. , DuPont B. , Sahajpal N. , Ho M. , Yu J.W. , Brody S.J. , Ganapathi M. , Rajkovic A. , Smolarek T. , Boyar F. , Bui P. , Dubuc A.M. , Kolhe R. , Levy B.
Published: December 2022

Cytogenetic studies represent a critical component of prenatal genetic testing. Prenatal diagnostic testing of amniotic fluid, chorionic villus sampling, or more rarely, fetal cord blood, is recommended following a positive or unreportable NIPT, maternal serum screen, abnormal ultrasound or increased genetic risk based on family history. While chromosomal microarray is the recommended first-tier prenatal diagnostic test for the detection of sub-microscopic copy number variants, in practice, multiple assays are often assessed, in concert, to achieve a final diagnostic result. The use of multiple methodologies is costly, time consuming, and labor intensive.

Optical genome mapping is an emerging technique with application for prenatal diagnosis because of its ability to detect and resolve, in a single assay, all classes of pathogenic cytogenetic aberrations detectable by karyotyping, FISH, and microarray. In an effort to characterize the potential of optical genome mapping as a novel alternative to conventional testing, a multi-site, multi-operator, multi-instrument clinical research study was conducted to demonstrate its analytic validity and clinical utility. In the first phase a total of 200 specimens representing 123 unique cases demonstrated 100% concordance with standard of care methods and 100% reproducibility between sites, operators, and instruments. Analysis and interpretation of cases with incidental findings of potential clinical significance also were performed.
CT radiomics to predict checkpoint inhibitors treatment outcomes in patients with advanced cutaneous melanoma
Authors: ter Maat L.S. , van Duin I.A.J. , Elias S.G. , Leiner T. , Verhoeff J.J.C. , Arntz E.R.A.N. , Troenokarso M.F. , Blokx W.A.M. , Isgum I. , de Wit G.A. , van den Berkmortel F.W.P.J. , Boers-Sonderen M.J. , Boomsma M.F. , van den Eertwegh A.J.M. , de Groot J.W.B. , Piersma D. , Vreugdenhil G. , Westgeest H.M , Kapiteijn E. , van Diest P.J. , Pluim J.P.W. , de Jong P.A. , Suijkerbuijk K.P.M. , Veta M.
Published: December 2022

Introduction

Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumor characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting durable clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort.

Methods

Patients who received first-line anti-PD1 ± anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict durable clinical benefit, defined as stable disease for more than six months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-center-out cross validation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model.

Results

A total of 620 patients were included, of which 59.2% experienced durable clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95%CI 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95%CI 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95%CI 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001).

Discussion

The radiomics model achieved a moderate predictive value of durable clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT derived radiomics and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.
SARS-CoV-2 mRNA vaccination exposes progressive adaptive immune dysfunction in patients with chronic lymphocytic leukemia
Authors: Qin Kai , Honjo Kazuhito , Sherrill-Mix Scott , Liu Weimin , Stoltz Regina , Oman Allisa K. , Hall Lucinda A. , Li Ran , Sterrett Sarah , Frederick Ellen R. , Lancaster Jeffrey R. , Narkhede Mayur , Mehta Amitkumar , Ogunsile Foluso J. , Patel Rima B. , Ketas Thomas J. , Portillo Victor M Cruz , Cupo Albert , Larimer Benjamin M. , Bansal Anju , Goepfert Paul A. , Hahn Beatrice H. , Davis Randall S.
Published: December 2022

Chronic lymphocytic leukemia (CLL) patients have lower seroconversion rates and antibody titers following SARS-CoV-2 vaccination, but the reasons for this diminished response are poorly understood. Here, we studied humoral and cellular responses in 95 CLL patients and 30 healthy controls after two BNT162b2 or mRNA-2173 mRNA immunizations. We found that 42% of CLL vaccinees developed SARS-CoV-2-specific binding and neutralizing antibodies (NAbs), while 32% had no response. Interestingly, 26% were seropositive, but had no detectable NAbs, suggesting the maintenance of pre-existing endemic human coronavirus-specific antibodies that cross-react with the S2 domain of the SARS-CoV-2 spike. These individuals had more advanced disease. In treatment-naïve CLL patients, mRNA-2173 induced 12-fold higher NAb titers and 1.7-fold higher response rates than BNT162b2. These data reveal a graded loss of immune function, with pre-existing memory being preserved longer than the capacity to respond to new antigens, and identify mRNA-2173 as a superior vaccine for CLL patients.
Willingness to Enroll in Social Health Insurance and associated factors among Household Heads in Obio/Akpor Local Government Area of Rivers State
Authors: Chinelo Kenechukwu Tabansi , Harry Tamunotonye , Apugo Uchechukwu
Published: December 2022

Background

Social health insurance (SHI) schemes cover the healthcare needs of the informal sector workers, provide them with financial protection, minimize the equity gap, reduce Out-Of-Pocket (OOP) spending, and enhance utilization of the healthcare system.

Objective

To determine the willingness to enrol in SHI and its associated factors among household heads in Obio-Akpor Local Government Area (LGA) of Rivers state.

Method

This community-based cross-sectional study was carried out using the clusters and the EPI random walk sampling technique to obtain data from 205 selected household heads who were administered with an interviewer-administered questionnaire. The collected data was analysed using SPSS version 21.

Result

According to the findings, majority of the respondents were males (58.5%), aged between 31 – 45 years (52.7%), married (57.1%), completed the tertiary level of education (65.4%), employed or self-employed (95.1%), from a family size of 3 – 4 (56.1%) and earned 50,000 to 100,000 Naira monthly (27.8%). Also, 63.0% are not enrolled in any health insurance scheme out of which 60.5% are not willing to enrol, with lack of trust in the management of the scheme, no believe in paying for sickness or have other means of meeting their healthcare needs identified as the major reasons. However, younger age, higher educational level, employment status, and earning >50,000 – 100,000 monthly had statistically (p<0.05) significant effect on the willingness of the respondents to enrol on SHI

Conclusion

This result showed a low level of enrolment in health insurance as well as willingness to enrol in Shi, hence, both insurance companies, government and even Non-Governmental Organisation should carry out strategic campaigns to dispel false rumours about SHI while outlining the merits.
The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities
Authors: Venner Eric , Patterson Karynne , Kalra Divya , Wheeler Marsha M. , Chen Yi-Ju , Kalla Sara E. , Yuan Bo , Karnes Jason H. , Lee Breanna , Walker Kimberly , Smith Josh , Mcgee Sean , Radhakrishnan Aparna , Haddad Andrew , Wang Qiaoyan , Jarvik Gail , Toledo Diana , Musick Anjene , Gibbs Richard A.
Published: December 2022

Disparities in the data that underlies clinical genomic interpretation is an acknowledged problem but there is a paucity of data demonstrating it. The National Institutes of Health’sAll of UsResearch Program aims to collect whole genome sequences, electronic health record (EHR) data, surveys and physical measurements for over a million participants of diverse ancestry and varied access to healthcare resources. We grouped participants by computed genetic ancestry and summarized the frequency of pathogenic variation within these groups. The European subgroup showed the highest rate of pathogenic variation (2.1%), with other ancestry groups ranging from 1.04% (East Asian) to 1.87% (‘Other’). Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer, Hypercholesterolemia or Hemochromatosis. Variant frequencies were consistent with gnomAD and some notable exceptions were resolved using gnomAD subsets. We additionally use this data to enrich sets of participants for specific genetic findings and to calculate penetrance. Differences in the frequency of pathogenic variants observed between ancestral groups generally indicate biases of ascertainment, but some may indicate differences in disease prevalence. These analyses are available on theAll of UsResearcher Workbench.
Cervical Cancer Screening Using Residual Learning
Authors: Abdulsamad Mohamed , Alshareef Esam Alsadiq , Ebrahim Fawzi
Published: December 2022

Cervical cancer is a leading cancer in the female population. This disease is considered dangerous as its slow and unpredicted growth. The prevention of such cancer can be mostly achieved by screening its transformation zones. The cervical pre-cancerous zones can be considered as three types: type 1, type 2, and type 3. Screening and analyzing these three stages can be crucial for preventing their transformation into cancer. Hence, it is essentially important to have an automated and intelligent system that can grade the cervical pre-cancerous colposcopy images into one of the three types. This can help in providing the right treatment and prevent cancer transformation. In this paper, we develop a residual learning-based model (ResNet-50) to be trained for classifying the type of a colposcopy cervical image into type 1, type 2, and type 3. Experimentally, the model was fine-tuned and evaluated on a public dataset of colposcopy cervical images and achieved promising results in cervical cancer screening of accuracy of 77% and F1-score of 79%.
Research waste from poor reporting of core methods and results and redundancy in studies of reporting guideline adherence: a meta-research review
Authors: Dal Santo Tiffany , Rice Danielle B. , Amiri Lara S.N. , Tasleem Amina , Li Kexin , Boruff Jill T. , Geoffroy Marie-Claude , Benedetti Andrea , Thombs Brett D.
Published: December 2022

Objectives

We investigated meta-research studies that evaluated adherence to prominent reporting guidelines (CONSORT, PRISMA, STARD, STROBE) in health research studies to determine the proportion that (1) provided an explanation for how complex guideline items were rated for adherence and (2) provided results from individual studies reviewed in addition to aggregate results. We also examined the conclusions of each meta-research study to assess redundancy of findings across studies.

Design

Cross-sectional meta-research review.

Data sources

MEDLINE (Ovid) searched on July 5, 2022.

Eligibility criteria for selecting studies

Studies in any language were eligible if they used any version of the CONSORT, PRISMA, STARD, or STROBE reporting guidelines or their extensions to evaluate reporting in at least 10 human health research studies. We excluded studies that modified a reporting guideline or its items or evaluated fewer than half of reporting guideline items. Main outcomes were (1) the proportion of meta-research studies that provided a coding explanation that could be used to replicate the study or verify its results and (2) the proportion that provided individual-level study results in the main text, supplemental materials, or via an internet link.

Results

Of 148 included meta-research studies, 14 (10%, 95% confidence interval [CI] 6% to 15%) provided a fully replicable coding explanation, and 49 (33%, 95% CI 26% to 41%) completely reported individual study results. Of 90 studies that classified reporting as adequate or inadequate in the study abstract, 6 (7%, 95% CI 3% to 14%) concluded that reporting was adequate but none of those 6 studies provided information on how items were coded or provided item-level results for included studies.

Conclusions

Much of published meta-research on reporting in health research is likely wasteful. Few studies report enough information for verification or replication, and almost all find that reporting in health research studies is suboptimal. These findings highlight the importance of shifting the focus from assessing reporting adequacy to developing, testing, and implementing strategies to improve reporting.

Funding

There was no specific funding for this study.

Protocol

Posted on the Open Science Framework June 29, 2022 (https://osf.io/gtm4z/).
Knowledge on tuberculosis, services of TB control programme and associated socio-demographic inequity among rural participants of Jaipur, Rajasthan, India: A cross sectional study
Authors: Anand P.K. , Dhikav V. , Rathore M. , Toteja G.S. , Singh Mradula , Singh Bhanwar Manohar , Meena C.R. , Jangid Anil , Devanda M.R. , Mathur M.L.
Published: December 2022

Introduction

Tuberculosis (TB) is one of the top 10 causes of deaths worldwide. India is still a highest TB burden country. There is scarcity of data on TB knowledge from Rajasthan state of India.

Materials & Methods

Cross-sectional community based study was carried out at Model Rural Health Research Unit, Jaipur, an unit of Department of Health Research, Ministry of Health & Family Welfare, Government of India.

Results

Study reports the result from 1993 adult participants from 10 villages of 2 sub-districts of district Jaipur. About 88.9% of studied participants knew that TB is an infectious disease and it spreads from TB patient to healthy person in close contact. Only 22.3% of participants knew ‘DOTS is the name of treatment for TB’. While, only 58.9% knew ‘sputum is used for diagnosis of TB’ at health centres.Scheduled castes, scheduled tribesandbackward classessocial groups knew less than the mainstream ‘General’ social group. The observed difference was statistically significant (p<0.05). Logistic regression analysis estimated the relative contribution in knowledge status.

Discussio

The knowledge of study participants on transmission of tuberculosis was similar to the knowledge of population in country wide study. They poorly knew that sputum is used for diagnosing the tuberculosis disease; socio-demographic inequity exists in this knowledge too. People from older age groups, underprivileged social groups and minority need extra educational activities.
A spatio-temporal mapping and bayesian modelling of risk factors of pneumonia symptoms in under-five children in Nigeria
Authors: Atoloye K. A. , Lawal T. V. , Adebowale A. S. , Fagbamigbe A.F.
Published: December 2022

Background

Pneumonia remains a public health challenge in most parts of the world, with Nigeria having the highest number of pneumonia-related deaths. Understanding the geographical distribution, trends and risk factors associated with pneumonia symptoms will aid an appropriate intervention of pneumonia and subsequently reduce its burden in Nigeria.

Method

This cross-sectional study used data from the 2008, 2013 and 2018 Nigeria Demographic Health Survey. In each of the survey round, a multi-stage cluster sampling technique was used to select the eligible respondents who are women of reproductive age. The outcome variables are the presence of key symptoms of pneumonia: fever, cough, and short rapid breaths. Optimized hotspot analysis was used to identify states with a significantly high prevalence of pneumonia symptoms, MCMC mixed-effect models were fitted to each symptom.

Results

The prevalence of cough was 12.1%, 10.1% and 16.9% in 2008, 2013 and 2018 respectively, 16.2%, 13.3%, and 25.7% for fever; and 41.7%, 42.5% and 6.5% for short rapid breaths respectively with variations across the states. The adjusted odds of having a cough among the children aged 6-11, 12-23, 24-35 and 36-47 months were 95% higher (adjusted odds ratio (aOR) =1.95, 95% Credible Interval (CrI): 1.77, 2.18), 92% higher (aOR=1.92, 95% CrI: 1.73, 2.12), 45% higher (aOR=1.45, 95% CrI: 1.31, 1.62) and 15% higher (aOR=1.15, 95% CrI: 1.03, 1.27) respectively, relative to ages 0-5 months. Similar patterns were noticed for fever and short rapid breaths. Mothers’ education was significant for cough and fever but not for short rapid breaths. Mothers’ age was significant only for short rapid breaths at higher odds. Other significantly associated factors with symptoms include residence type, housing quality, wealth index and region.

Conclusion

Fever, cough, and short rapid Breaths are prevalent among under-five children in Nigeria. These symptoms are associated with different characteristics and varied across states in Nigeria. Therefore, it is pertinent that mothers improve on the available preventive and management strategies with the view to mitigating the consequences of pneumonia symptoms among under-five children in Nigeria.
Tumour-infiltrated cortex participates in large-scale cognitive circuits
Authors: Mandal Ayan S. , Assem Moataz , Romero-Garcia Rafael , Coelho Pedro , McDonald Alexa , Woodberry Emma , Morris Robert C. , Price Stephen J. , Duncan John , Santarius Thomas , Suckling John , Hart Michael G. , Erez Yaara
Published: December 2022

The extent to which tumour-infiltrated brain tissue contributes to cognitive function remains unclear. While prior studies have suggested involvement of tumour-infiltrated tissue in local circuits associated with language and motor function, it is unknown whether such tissue participates in distributed networks important for higher-order cognitive abilities like executive function. In this study, we tested the hypothesis that cortical tissue infiltrated by diffuse low-grade gliomas participates in large-scale cognitive circuits using a unique combination of intracranial electrocorticography (ECoG) and resting-state functional magnetic resonance (fMRI) imaging in four patients. We observed significant task-related high gamma (70-250 Hz) power modulations in tumour-infiltrated cortex in response to increased cognitive effort, implying preserved functionality of neoplastic tissue for complex tasks. Strikingly, we found that tumour locations corresponding to task-responsive electrodes exhibited functional connectivity patterns that significantly co-localised with canonical brain networks implicated in executive function. Finally, we discovered that tumour regions with larger task-related high gamma power elevations tended to be more functionally connected to the dorsal attention network, further demonstrating the participation of tumour-infiltrated cortex in large-scale brain networks that support executive function in health. Overall, this study contributes convergent fMRI-ECoG evidence that tumour-infiltrated cortex participates in large-scale neurocognitive circuits, reflecting preserved functionality of neoplastic brain tissue relevant to clinical management.

Significance statement

Gliomas interact with healthy neural circuits as they grow in the brain. Understanding these interactions is crucial for planning surgeries to remove gliomas without inducing long-term cognitive deficits. While prior studies have shown that glioma-infiltrated tissue can integrate within local functional circuits, it is unknown whether such tissue participates in large-scale whole-brain networks. Using electrocorticography, we show that glioma-infiltrated tissue responds significantly to tasks of increasing cognitive demand, reflecting its involvement in executive function processes. Using pre-operative functional neuroimaging, we found that tumour regions responsive to complex tasks were also functionally connected to large-scale networks implicated in executive function. These findings imply that gliomas participate within large-scale cognitive circuits, possibly reflecting preserved functionality relevant to clinical management.
Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity
Authors: Tanner Jacob , Faskowitz Joshua , Teixeira Andreia Sofia , Seguin Caio , Coletta Ludovico , Gozzi Alessandro , Mišić Bratislav , Betzel Richard F.
Published: December 2022

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features–e.g. diffusion parameters–or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal (combining diffusion and functional MRI data) regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participates in at least one shortest path. We also find evidence of robust asymmetries in edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
Omada: Robust clustering of transcriptomes through multiple testing
Authors: Kariotis Sokratis , Fang Tan Pei , Lu Haiping , Rhodes Chris , Wilkins Martin , Lawrie Allan , Wang Dennis
Published: December 2022

Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, however, selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with five datasets characterised by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.
A Multifaceted Framework To Assess tradeoffs in Interpretability, Explanatory and Predictive Performances Of Alternative Joint Species Distribution Models
Authors: Violet Clément , Boyé Aurélien , Chevalier Mathieu , Gauthier Olivier , Grall Jacques , Marzloff Martin P.
Published: December 2022

Joint Species Distribution Models(jSDM) are increasingly used to explain and predict biodiversity patterns. By accounting for species co-occurrence patterns and potentially including species-specific information,jSDMs capture the processes that shape ecological communities. Yet, factors like missing covariates or omitting ecologically-important species may alter the interpretability and effectiveness ofjSDMs. Additionally, while the specific formulation of ajSDMdirectly affects its performances, the effects of choices related to model structure, such as inclusion, or not of phylogeny or trait information, are not well-explored.

Here, we developed a multifaceted framework to comprehensively assess performances of alternativejSDMformulations at both species and community levels. We applied this framework to four alternative models fitted on presence/absence and abundance data of a polychaete assemblage sampled in two coastal habitats over 500 km and 8 years. Relative to a benchmarkjSDMonly capturing the effects of abiotic predictors and residual co-occurrence patterns, we explored the performance of alternative formulations that also included species phylogeny, traits, or some additional 179 non-target species, which were sampled alongside the species of interest. For both presence/absence and abundance data, explanatory power was good for all models but their interpretability and predictive power varied. Relative to the benchmark model, predictive errors on species abundances decreased by 95% or 53%, when including non-target species, or phylogeny, respectively. These differences across models relate to changes in both species-environment relationships and residual co-occurrence patterns. While considering trait data did not improve explanatory or predictive power, it facilitated interpretation of trait-mediated species response to environmental gradients.

This study demonstrates trade-offs injSDMformulation for explaining or predicting species data, highlighting the importance of using a comprehensive framework to compare models.

Furthermore, our study provides some guidance for model selection tailored to specific objectives and available data.
Deep learning-based predictive identification of functional subpopulations of hematopoietic stem cells and multipotent progenitors
Authors: Wang Shen , Han Jianzhong , Huang Jingru , Shi Yuheng , Zhou Yuyuan , Kim Dongwook , Islam Md Khayrul , Zhou Jane , Ostrovsky Olga , Li Chenchen , Lian Zhaorui , Liu Yaling , Huang Jian
Published: December 2022

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) are crucial for maintaining lifelong hematopoiesis. Developing methods to distinguish stem cells from other progenitors and evaluate stem cell functions has been a central task in stem cell research. Deep learning has been demonstrated as a powerful tool in cell image analysis and classification. In this study, we explored the possibility of using deep learning to differentiate HSCs and MPPs based on their light microscopy (DIC) images. After extensive training and validation with large image data sets, we successfully develop a three-class classifier (we named it the LSM model) that reliably differentiate long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. Importantly, we demonstrated that our LSM model achieved its differentiating capability by learning the intrinsic morphological features from cell images. Furthermore, we showed that the performance of our LSM model was not affected by how these cells were identified and isolated, i.e., sorted by surface markers or intracellular GFP markers. Prospective identification of HSCs and MPPs in Evi1GFPtransgenic mice by LSM model suggested that the cells with the highest GFP expression were LT-HSCs, and this prediction was substantiated later by a long-term competitive reconstitution assay. Moreover, based on DIC image data sets, we also successfully built another two-class classifier that can effectively distinguish aged HSCs from young HSCs, which both express the same surface markers but are functionally different. This finding is of particular interest since it may provide a novel quick and efficient approach, obviating the need for a time-consuming transplantation experiment, to evaluate the functional states of HSCs. Together, our study provides evidence for the first time that HSCs and MPPs can be differentiated by deep learning based on cell morphology. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into molecular mechanisms underlying the self-renewal feature of stem cells.
Greengenes2 enables a shared data universe for microbiome studies
Authors: McDonald Daniel , Jiang Yueyu , Balaban Metin , Cantrell Kalen , Zhu Qiyun , Gonzalez Antonio , Morton James T. , Nicolaou Giorgia , Parks Donovan H. , Karst Søren , Albertsen Mads , Hugenholtz Philip , DeSantis Todd , Song Se Jin , Bartko Andrew , Havulinna Aki S. , Jousilahti Pekka , Cheng Susan , Inouye Mike , Niiranen Teemu , Jain Mohit , Salomaa Veikko , Lahti Leo , Mirarab Siavash , Knight Rob
Published: December 2022

16S rRNA and shotgun metagenomics studies typically yield different results, usually attributed to biases in PCR amplification of 16S rRNA genes. Here, we introduce Greengenes2 and show that differences in reference phylogeny are more important. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy, and in phenotype effect size when analyzed with the same tree.