bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria
Authors: Leifer Ian , Sánchez-Pérez Mishael , Ishida Cecilia , Makse Hernán A. 
Published: July 2021

Abstract
Background
Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the question whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression can be predicted from symmetries in the gene regulatory networks described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their ’input trees’, the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models—with gene input functions differencing between genes—predict symmetry breaking and desynchronization.

Results
To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes.

Conclusions
Thus, gene fibrations provide a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.
Single-cell classification using graph convolutional networks
Authors: Wang Tianyu , Bai Jun , Nabavi Sheida 
Published: July 2021

AbstractBackgroundAnalyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures.ResultsIn this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods.ConclusionsResults indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.
SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions
Authors: Lee Justin Y. , Nguyen Britney , Orosco Carlos , Styczynski Mark P. 
Published: July 2021

Abstract
Background
The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

Results
We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.

Conclusions
SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.
Dysbiosis of intestinal microbiota in early life aggravates high-fat diet induced dysmetabolism in adult mice
Authors: Miao Z. H. , Zhou W. X. , Cheng R. Y. , Liang H. J. , Jiang F. L. , Shen X. , Lu J. H. , Li M. , He F. 
Published: July 2021

Abstract
Background
Accumulating evidence have shown that the intestinal microbiota plays an important role in prevention of host obesity and metabolism disorders. Recent studies also demonstrate that early life is the key time for the colonization of intestinal microbes in host. However, there are few studies focusing on possible association between intestinal microbiota in the early life and metabolism in adulthood. Therefore the present study was conducted to examine whether the short term antibiotic and/or probiotic exposure in early life could affect intestinal microbes and their possible long term effects on host metabolism.

Results
A high-fat diet resulted in glucose and lipid metabolism disorders with higher levels of visceral fat rate, insulin-resistance indices, and leptin. Exposure to ceftriaxone in early life aggravated the negative influences of a high-fat diet on mouse physiology. Orally fed TMC3115 protected mice, especially those who had received treatment throughout the whole study, from damage due to a high-fat diet, such as increases in levels of fasting blood glucose and serum levels of insulin, leptin, and IR indices. Exposure to ceftriaxone during the first 2 weeks of life was linked to dysbiosis of the fecal microbiota with a significant decrease in the species richness and diversity. However, the influence of orally fed ceftriaxone on the fecal microbiota was limited to 12 weeks after the termination of treatment. Of note, at week 12 there were still some differences in the composition of intestinal microbiota between mice provided with high fat diet and antibiotic exposure and those only fed a high fat diet.

Conclusions
These results indicated that exposure to antibiotics, such as ceftriaxone, in early life may aggravate the negative influences of a high-fat diet on the physiology of the host animal. These results also suggest that the crosstalk between the host and their intestinal microbiota in early life may be more important than that in adulthood, even though the same intestinal microbes are present in adulthood.
Population genetic structure and connectivity of a riparian selfing herb Caulokaempferia coenobialis at a fine-scale geographic level in subtropical monsoon forest
Authors: Fu Qiong , Deng Jie , Chen Min , Zhong Yan , Lu Guo-Hui , Wang Ying-Qiang 
Published: July 2021

AbstractBackgroundRivers and streams facilitate movement of individuals and their genes across the landscape and are generally recognized as dispersal corridors for riparian plants. Nevertheless, some authors have reported directly contrasting results, which may be attributed to a complex mixture of factors, such as the mating system and dispersal mechanisms of propagules (seed and pollen), that make it difficult to predict the genetic diversity and population structure of riparian species. Here, we investigated a riparian self-fertilizing herbCaulokaempferia coenobialis, which does not use anemochory or zoochory for seed dispersal; such studies could contribute to an improved understanding of the effect of rivers or streams on population genetic diversity and structure in riparian plants. Using polymorphic ISSR and cpDNA loci, we studied the effect at a microgeographic scale of different stream systems (a linear stream, a dendritic stream, and complex transverse hydrological system) in subtropical monsoon forest on the genetic structure and connectivity ofC. coenobialispopulations across Dinghu Mountain (DH) and Nankun Mountain (NK).ResultsThe results indicate that the most recent haplotypes (DH: H7, H8; NK: h6, h7, h11, h12) are not shared among local populations ofC. coenobialiswithin each stream system. Furthermore, downstream local populations do not accumulate genetic diversity, whether in the linear streamside local populations across DH (H: 0.091 vs 0.136) or the dendritic streamside local populations across NK (H: 0.079 vs 0.112, 0.110). Our results show that the connectivity of localC. coenobialispopulations across DH and NK can be attributed to historical gene flows, resulting in a lack of spatial genetic structure, despite self-fertilization. SelfingC. coenobialiscan maintain high genetic diversity (H = 0.251;I = 0.382) through genetic differentiation (GST = 0.5915;FST = 0.663), which is intensified by local adaptation and neutral mutation and/or genetic drift in local populations at a microgeographic scale.ConclusionWe suggest that streams are not acting as corridors for dispersal ofC. coenobialis, and conservation strategies for maintaining genetic diversity of selfing species should be focused on the protection of all habitat types, especially isolated fragments in ecosystem processes.
RITS: a toolbox for assessing complex interventions via interrupted time series models
Authors: Cruz Maricela , Pinto-Orellana Marco A. , Gillen Daniel L. , Ombao Hernando C. 
Published: July 2021

Abstract
Background
Various interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure.

Results
This article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures.

Conclusions
The RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.
Intra-day variation in daily outdoor walking speed among community-dwelling older adults
Authors: Kawai Hisashi , Obuchi Shuichi , Hirayama Ryo , Watanabe Yutaka , Hirano Hirohiko , Fujiwara Yoshinori , Ihara Kazushige , Kim Hunkyung , Kobayashi Yoshiyuki , Mochimaru Masaaki , Tsushima Eiki , Nakamura Kozo 
Published: July 2021

Abstract
Background
Walking speed is an important measure associated with health outcomes in older individuals, such as dependency and death. This study aimed to examine whether the walking speed of community-dwelling older adults varies between time periods within a day, as measured outdoors in daily life. We aimed to determine the types of walking speed variations and examine the factors associated with them.

Methods
Daily life outdoor walking speed was measured in 92 participants (average age 71.9 years±5.64) using a GPS smartphone app for 1 month. Average walking speeds for five time periods were analyzed with a linear mixed model. Intra-day walking speed variation patterns were classified by latent class analysis. Factors associated with the class were identified by logistic regression analysis.

Results
A statistically significant difference in average walking speed was found between early morning (1.33 m/s), and afternoon (1.27 m/s) and evening (1.26 m/s) (p < 0.01). The intra-day variation in walking speed was attributed to variation in cadence. Two classes were identified: (1) fast walking speed with large variation and (2) slow walking speed with little variation; hypertension and frailty level were associated with the class.

Conclusion
The results suggest that there is intra-day variation in walking speed in daily life, wherein the speed is the fastest early in the morning and slower in the afternoon and evening. A larger variation in the walking speed was related to the health status without hypertension or frailty. These results suggest that if a person shows less intra-day variation in walking speed, this could be a sign that they are susceptible to hypertension and an increased frailty level.
Nasopharyngeal carriage of Streptococcus pneumoniae among healthy children in Kassena-Nankana districts of Northern Ghana
Authors: Narwortey Deborah K. , Owusu-Ofori Alex , Slotved Hans-Christian , Donkor Eric S. , Ansah Patrick O. , Welaga Paul , Agongo Godfred , Oduro Abraham R. 
Published: July 2021

Abstract
Background
Pneumococcal vaccine immunizations may be responsible for alterations in serotype epidemiology within a region. This study investigated the pneumococcal carriage prevalence and the impact of the 13-valent pneumococcal conjugate vaccine (PCV-13) on circulating serotypes among healthy children in Northern Ghana.

Methods
This was a cross sectional study conducted in the Kassena-Nankana districts of Northern Ghana from November to December during the dry season of 2018. Nasopharyngeal swabs collected from 193 participants were cultured per standard microbiological protocols and pneumococcal isolates were serotyped using the latex agglutination technique and the capsular Quellung reaction test. We examined for any association between the demographic characteristics of study participants and pneumococcal carriage using chi-square test and logistic regression.

Results
Of the 193 participants that were enrolled the mean age was 8.6 years and 54.4% were females. The carriage rate among the participants was 32.6% (63/193), and twenty different serotypes were identified. These included both vaccine serotypes (VT), 35% (7/20) and non-vaccine serotypes (NVT), 65% (13/20). The predominant serotypes (34 and 11A), both of which were NVT, accounted for a prevalence of 12.8%. PCV-13 covered only 35% of serotypes identified whiles 40% of serotypes are covered by PPV 23.

Conclusion
Post-vaccination carriage of S. pneumoniae is high and is dominated by non-vaccine serotypes. There is therefore a need for the conduct of invasive pneumococcal disease surveillance (IPD) to find out if the high non-vaccine serotype carriage translates to disease. And in addition, a review of the currently used PCV-13 vaccine in the country would be considered relevant.
Pooled sputum to optimise the efficiency and utility of rapid, point-of-care molecular SARS-CoV-2 testing
Authors: Burdett Alison , Toumazou Christofer , Sahoo Rashmita , Mujan Adam , Hon Tsz-Kin , Bedzo-Nutakor Judith , Casali Nicola , Karvela Maria , Sohbati Mohammadreza , Cooke Graham S. , Davies Gary W. , Moore Luke S. P. 
Published: July 2021

Abstract
Background
As SARS-CoV-2 testing expands, particularly to widespread asymptomatic testing, high sensitivity point-of-care PCR platforms may optimise potential benefits from pooling multiple patients’ samples.

Method
We tested patients and asymptomatic citizens for SARS-CoV-2, exploring the efficiency and utility of CovidNudge (i) for detection in individuals’ sputum (compared to nasopharyngeal swabs), (ii) for detection in pooled sputum samples, and (iii) by modelling roll out scenarios for pooled sputum testing.

Results
Across 295 paired samples, we find no difference (p = 0.1236) in signal strength for sputum (mean amplified replicates (MAR) 25.2, standard deviation (SD) 14.2, range 0–60) compared to nasopharyngeal swabs (MAR 27.8, SD 12.4, range 6–56). At 10-sample pool size we find some drop in absolute strength of signal (individual sputum MAR 42.1, SD 11.8, range 13–60 vs. pooled sputum MAR 25.3, SD 14.6, range 1–54; p < 0.0001), but only marginal drop in sensitivity (51/53,96%). We determine a limit of detection of 250 copies/ml for an individual test, rising only four-fold to 1000copies/ml for a 10-sample pool. We find optimal pooled testing efficiency to be a 12–3-1-sample model, yet as prevalence increases, pool size should decrease; at 5% prevalence to maintain a 75% probability of negative first test, 5-sample pools are optimal.

Conclusion
We describe for the first time the use of sequentially dipped sputum samples for rapid pooled point of care SARS-CoV-2 PCR testing. The potential to screen asymptomatic cohorts rapidly, at the point-of-care, with PCR, offers the potential to quickly identify and isolate positive individuals within a population “bubble”.
Clinical features and predictors for patients with severe SARS-CoV-2 pneumonia at the start of the pandemic: a retrospective multicenter cohort study
Authors: Cao Chao , He Li , Ma Jingping , Chen Meiping , Li Yiting , Jiang Qingwen , Wu Shiyu , Yu Lili , Huang Weina , Qian Guoqing , Zhu Chuanbing , Chu Jinguo , Chen Xiaomin 
Published: July 2021

Abstract
Background
This study was performed to investigate clinical features of patients with severe SARS-CoV-2 pneumonia and identify risk factors for converting to severe cases in those who had mild to moderate diseases at the start of the pandemic in China.

Methods
In this retrospective, multicenter cohort study, patients with mild to moderate SARS-CoV-2 pneumonia were included. Demographic data, symptoms, laboratory values, and clinical outcomes were collected. Data were compared between non-severe and severe patients.

Results
58 patients were included in the final analysis. Compared with non-severe cases, severe patients with SARS-CoV-2 pneumonia had a longer: time to clinical recovery (12·9 ± 4·4 vs 8·3 ± 4·7; P = 0·0011), duration of viral shedding (15·7 ± 6·7 vs 11·8 ± 5·0; P = 0·0183), and hospital stay (20·7 ± 1·2 vs 14·4 ± 4·3; P = 0·0211). Multivariate logistic regression indicated that lymphocyte count was significantly associated with the rate of converting to severe cases (odds ratio 1·28, 95%CI 1·06–1·54, per 0·1 ×  109/L reduced; P = 0·007), while using of low-to-moderate doses of systematic corticosteroids was associated with reduced likelihood of converting to a severe case (odds ratio 0·14, 95%CI 0·02–0·80; P = 0·0275).

Conclusions
The low peripheral blood lymphocyte count was an independent risk factor for SARS-CoV-2 pneumonia patients converting to severe cases. However, this study was carried out right after the start of the pandemic with small sample size. Further prospective studies are warranted to confirm these findings.

Trial registration
Chinese Clinical Trial Registry, ChiCTR2000029839. Registered 15 February 2020 - Retrospectively registered.
Lesion probability mapping in MS patients using a regression network on MR fingerprinting
Authors: Hermann Ingo , Golla Alena K. , Martínez-Heras Eloy , Schmidt Ralf , Solana Elisabeth , Llufriu Sara , Gass Achim , Schad Lothar R. , Zöllner Frank G. 
Published: July 2021

Abstract
Background
To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to $$T_1$$

T
1

, $${T_2}^*$$

T
2

∗

, NAWM, and GM- probability maps.

Methods
We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected $$T_1$$

T
1

and $${T_2}^*$$

T
2

∗

maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps.

Results
WM lesions were predicted with a dice coefficient of $$0.61\pm 0.09$$

0.61
±
0.09

and a lesion detection rate of $$0.85\pm 0.25$$

0.85
±
0.25

for a threshold of 33%. The network jointly enabled accurate $$T_1$$

T
1

and $${T_2}^*$$

T
2

∗

times with relative deviations of 5.2% and 5.1% and average dice coefficients of $$0.92\pm 0.04$$

0.92
±
0.04

and $$0.91\pm 0.03$$

0.91
±
0.03

for NAWM and GM after binarizing with a threshold of 80%.

Conclusion
DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.