bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microscopic geometrical structure analysis of 2D and 2.5D fabrics based on a simple predictive model
Authors: Wang Yang , Wang Qiuhui , Yang Jian 
Published: October 2021

Abstract
The braided structure has a great influence on the properties of composites, and it is of great significance to predict and design the microscopic geometrical structure of fabric. In this paper, a simple model for predicting the yarn morphologies in 2D plain weave fabric and 2.5D shallow-cross bending fabric is established. Compared with the test results, this predictive model has relatively high prediction accuracy and the influences of warp/weft density and yarn fineness on the maximum pore volume in the fabric are analyzed in detail. Based on this model, assume the yarn fineness and warp density is 3 K and 3/cm, respectively, when the weft density increases from 2/cm to 9/cm, and the volume fraction increases from 15% to 35%, the maximum pore volume in the 2D fabric decreases from 2.69 mm3 to 0.195 mm3, compared with that in the 2.5D fabric decreases from 2.67 mm3 to 0.125 mm3. At the same volume fraction, the lower the yarn fineness, the smaller the maximum pore volume in 2D and 2.5D fabrics. In addition, when the sum of the warp and weft yarn densities is a certain value, the maximum pore volumes in 2D and 2.5D fabrics decrease as the weft yarn densities increases. Conversely, as the warp density increases, the maximum pore volumes increase.
Corrosion behavior of Q345R steel influenced by Thiobacillus ferrooxidans
Authors: Huang Xin Jie , Fu Lei , Lin Li , Lai Sheng , Fan Qi , Zhang Yingqian , Li Xiulan , Liu Cheng 
Published: October 2021

Abstract
Here, the corrosion weight-loss method, surface analysis technology, and electrochemical test methods were used to study the corrosion behavior and electrochemical characteristics of experimental samples of Q345R steel in a sterile solution (pH 2.0) and a solution containing T. ferrooxidans. The growth cycle of T. ferrooxidans was determined to be approximately 8 days. The corrosion weight-loss method showed that the corrosion rate of Q345R carbon steel coupons decreased with time in the T. ferrooxidans system and the sterile system; the corrosion rate was approximately two times higher in the T. ferrooxidans system than in the sterile system. The corrosion morphology results showed that the presence of T. ferrooxidans promotes the corrosion of Q345R steel and increases the local corrosion of the matrix material. The electrochemical test results showed that after 5 days of corrosion, the polarization resistance of the T. ferrooxidans system was approximately 50% of that of the sterile system, and the corrosion current density of the T. ferrooxidans system was approximately twice as high as that of the sterile system. Therefore, T. ferrooxidans can accelerate the corrosion of Q345R steel two-fold.
Detecting quantum entanglement with unsupervised learning
Authors: Chen Yiwei , Pan Yu , Zhang Guofeng , Cheng Shuming 
Published: October 2021

Abstract
Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.
Low Social Support at Work and Ambulatory Blood Pressure in a Repeated Cross-sectional Study of White-Collar Workers
Authors: Trudel Xavier , Tiwa Diffo Edwige , Gilbert-Ouimet Mahée , Mésidor Miceline , Talbot Denis , Milot Alain , Brisson Chantal 
Published: October 2021

Abstract

Objectives
Previous studies on the effect of low social support at work on blood pressure showed mixed results. Few previous studies have used ambulatory blood pressure and examined whether the effect of low social support at work vary among men and women. The aim of this study was to examine the association between low social support at work, ambulatory blood pressure means and hypertension prevalence, in a sample of white-collar workers men and women.

Methods
A repeated cross-sectional design was used. Data were collected three times during a 5-year period, among 3919 white-collar women and men. At each time, coworker and supervisor social support at work were measured using validated scales. Ambulatory blood pressure was measured every 15 min during a working day. General estimating equations were used.

Results
In adjusted models, women exposed to low coworker (+0.6 mmHg) and low supervisor social support at work (+0.7 mmHg) had slightly higher diastolic blood pressure means when compared to unexposed women. In men, those with low coworker social support at work had higher diastolic (+0.7 mmHg) blood pressure while those with low supervisor social support had a higher prevalence of hypertension (prevalence ratio = 1.14, 95% CI: 1.04–1.24).

Conclusions
Men with low supervisor social support at work had a higher prevalence of hypertension. Low social support at work was associated with modest increases in diastolic blood pressure among men and women. Workplace prevention strategies aiming to increase social support at work could lead to beneficial effects on worker’s cardiovascular health.
Dynamics of the prokaryotic and eukaryotic microbial community during a cyanobacterial bloom
Authors: Qian Yilin , Okano Kunihiro , Kodato Miwa , Arai Michiko , Yanagiya Takeru , Li Qintong , Amano Chie , Ruike Kakeru , Itayama Tomoaki , Iwami Norio , Utsumi Motoo , Lei Zhongfang , Zhang Zhenya , Sugiura Norio , Shimizu Kazuya 
Published: October 2021

ABSTRACT
Toxic cyanobacterial blooms frequently develop in eutrophic freshwater bodies worldwide. Microcystis species produce microcystins (MCs) as a cyanotoxin. Certain bacteria that harbor the mlr gene cluster, especially mlrA, are capable of degrading MCs. However, MC-degrading bacteria may possess or lack mlr genes (mlr+ and mlr− genotypes, respectively). In this study, we investigated the genotype that predominantly contributes to biodegradation and cyanobacterial predator community structure with change in total MC concentration in an aquatic environment. The 2 genotypes coexisted but mlr+ predominated, as indicated by the negative correlation between mlrA gene copy abundance and total MC concentration. At the highest MC concentrations, predation pressure by Phyllopoda, Copepoda, and Monogononta (rotifers) was reduced; thus, MCs may be toxic to cyanobacterial predators. The results suggest that cooperation between MC-degrading bacteria and predators may reduce Microcystis abundance and MC concentration.
MHC-II distance between parents predicts sex allocation decisions in a genetically monogamous bird
Authors: Pineaux Maxime , Merkling Thomas , Danchin Etienne , Hatch Scott A , Leclaire Sarah , Blanchard Pierrick 
Published: October 2021

Abstract
Theory predicts that parental heritable characteristics should shape sex allocation decisions when their effects on reproduction or survival are offspring sex-dependent. Numerous studies have questioned to what extent characteristics displayed by one of the parents matched theoretical expectations. This contrasts with the handful of studies that investigated whether compatibility between parents could also trigger selective pressures for sex allocation adjustments. We studied the genetically monogamous black-legged kittiwake (Rissa tridactyla), where previous data revealed that female chicks suffered higher fitness costs from low diversity at genes of the major histocompatibility complex (MHC) than male chicks. We predicted, and found in our dataset, that MHC-similar parents, producing low MHC-diverse offspring, should avoid the production of females. The relation between MHC-distance between parents (i.e. the functional distinctness of their MHC alleles) and offspring sex was not linear, such that MHC-dissimilar parents also overproduced sons. Overall, our results suggest that the genetically monogamous black-legged kittiwake parents flexibly adapt their reproduction and circumvent the costs of suboptimal pairing by manipulating offspring sex.
DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features
Authors: Xue Min-Qi , Zhu Xi-Liang , Wang Ge , Xu Ying-Ying 
Published: October 2021

AbstractMotivationKnowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with complex cellular processes. Currently, the subcellular location annotations of proteins in most studies and databases are descriptive terms, which fail to capture the protein amount or fractions across different locations. This highly limits the understanding of complex spatial distribution and functional mechanism of multi-label proteins. Thus, quantitatively analyzing the multiplex location patterns of proteins is an urgent and challenging task.ResultsIn this study, we developed a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. This model used a deep convolutional neural network to construct feature representations, and combined multiple nonlinear decomposing algorithms as the pattern unmixing method. Our experimental results showed that the DULoc can achieve over 0.93 correlation between estimated and true fractions on both real and synthetic datasets. In addition, we applied the DULoc method on the images in the human protein atlas database on a large scale, and showed that 70.52% of proteins can achieve consistent location orders with the database annotations.Availability and implementationThe datasets and code are available at: https://github.com/PRBioimages/DULoc.Supplementary informationSupplementary data are available at Bioinformatics online.
Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke
Authors: Adhikari Mohit H , Griffis Joseph , Siegel Joshua S , Thiebaut de Schotten Michel , Deco Gustavo , Instabato Andrea , Gilson Matthieu , Corbetta Maurizio 
Published: October 2021

AbstractRecent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-state networks. Whole-brain computational modelling studies, at the individual subject level, using undirected effective connectivity derived from empirically measured functional connectivity, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional effective connectivity from zero-lagged and lagged covariance matrices, then, to compare it to empirically measured functional connectivity for predicting stroke versus healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of functional connectivity versus model effective connectivity in predicting the long-term outcome from acute measures. Both functional and effective connectivity predicted healthy from stroke individuals significantly better than the chance-level; however, accuracy for the effective connectivity was significantly higher than for functional connectivity at 1- to 2-week, 3-month and 1-year post-stroke. Predictive functional connections mainly included those reported in previous studies (within-network inter-hemispheric and between task-positive and -negative networks intra-hemispherically). Predictive effective connections included additional between-network links. Effective connectivity was a better predictor than functional connectivity of the number of behavioural domains in which patients suffered deficits, both at 2-week and 1-year post-onset of stroke. Interestingly, patient deficits at 1-year time-point were better predicted by effective connectivity values at 2 weeks rather than at 1-year time-point. Our results thus demonstrate that the second-order statistics of functional MRI resting-state activity at an early stage of stroke, derived from a whole-brain effective connectivity, estimated in a model fitted to reproduce the propagation of neuronal activity, has pertinent information for clinical prognosis.
Nigral diffusivity, but not free water, correlates with iron content in Parkinson’s disease
Authors: Langley Jason , Huddleston Daniel E , Hu Xiaoping 
Published: October 2021

AbstractThe loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinson’s disease. Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and reductions in fractional anisotropy as well as increases in the free water compartment in metrics derived from bi-compartment models. However, studies examining nigral diffusivity changes from Parkinson’s disease with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. We aimed to examine Parkinson’s disease-related changes in nigral iron and diffusion measures from single- and bi-compartment models as well as assess the effect of iron on these diffusion measures in two separate Parkinson’s cohorts. Iron-sensitive data and diffusion data were analysed in two cohorts: First, a discovery cohort consisting of 71 participants (32 control participants and 39 Parkinson’s disease participants) was examined. Second, an external validation cohort, obtained from the Parkinson’s Progression Marker’s Initiative, consisting of 110 participants (58 control participants and 52 Parkinson’s disease participants) was examined. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment (discovery cohort: P = 0.006; external cohort: P = 0.01) and iron content (discovery cohort: P &lt; 0.001; validation cohort: P = 0.02) were found to increase in substantia nigra of the Parkinson’s disease group in both cohorts. However, diffusion markers derived from the single-compartment model (i.e. mean diffusivity and fractional anisotropy) were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort (iron-mean diffusivity: r = −0.400, P = 0.006) and validation cohort (iron-mean diffusivity: r = −0.387, P = 0.003) but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. In conclusion, the variability of nigral diffusion metrics derived from the single-compartment model in Parkinson’s disease may be attributed to competing influences of increased iron content, which tends to drive diffusivity down, and increases in the free water compartment, which tends to drive diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.
AAV1.NT-3 gene therapy in a CMT2D model: phenotypic improvements in GarsP278KY/+ mice
Authors: Ozes Burcak , Moss Kyle , Myers Morgan , Ridgley Alicia , Chen Lei , Murrey Darren , Sahenk Zarife 
Published: October 2021

Abstract
Glycyl–tRNA synthetase mutations are associated to the Charcot–Marie–Tooth disease type-2D. The GarsP278KY/+ model for Charcot–Marie–Tooth disease type-2D is known best for its early onset severe neuropathic phenotype with findings including reduced axon size, slow conduction velocities and abnormal neuromuscular junction. Muscle involvement remains largely unexamined. We tested the efficacy of neurotrophin 3 gene transfer therapy in two Gars mutants with severe (GarsP278KY/+) and milder (GarsΔETAQ/+) phenotypes via intramuscular injection of adeno-associated virus setoype-1, triple tandem muscle creatine kinase promoter, neurotrophin 3 (AAV1.tMCK.NT-3) at 1 × 1011 vg dose. In the GarsP278KY/+ mice, the treatment efficacy was assessed at 12 weeks post-injection using rotarod test, electrophysiology and detailed quantitative histopathological studies of the peripheral nervous system including neuromuscular junction and muscle. Neurotrophin 3 gene transfer therapy in GarsP278KY/+ mice resulted in significant functional and electrophysiological improvements, supported with increases in myelin thickness and improvements in the denervated status of neuromuscular junctions as well as increases in muscle fibre size along with attenuation of myopathic changes. Improvements in the milder phenotype GarsΔETAQ/+ was less pronounced. Furthermore, oxidative enzyme histochemistry in muscles from Gars mutants revealed alterations in the content and distribution of oxidative enzymes with increased expression levels of Pgc1a. Cox1, Cox3 and Atp5d transcripts were significantly decreased suggesting that the muscle phenotype might be related to mitochondrial dysfunction. Neurotrophin 3 gene therapy attenuated these abnormalities in the muscle. This study shows that neurotrophin 3 gene transfer therapy has disease modifying effect in a mouse model for Charcot–Marie–Tooth disease type-2D, leading to meaningful improvements in peripheral nerve myelination and neuromuscular junction integrity as well as in a unique myopathic process, associated with mitochondria dysfunction, all in combination contributing to functional outcome. Based on the multiple biological effects of this versatile molecule, we predict neurotrophin 3 has the potential to be beneficial in other aminoacyl-tRNA synthetase-linked Charcot–Marie–Tooth disease subtypes.
Tackling clinical heterogeneity across the amyotrophic lateral sclerosis–frontotemporal dementia spectrum using a transdiagnostic approach
Authors: Ahmed Rebekah M , Bocchetta Martina , Todd Emily G , Tse Nga Yan , Devenney Emma M , Tu Sicong , Caga Jashelle , Hodges John R , Halliday Glenda M , Irish Muireann , Kiernan Matthew C , Piguet Olivier , Rohrer Jonathan D 
Published: October 2021

Abstract
The disease syndromes of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) display considerable clinical, genetic and pathological overlap, yet mounting evidence indicates substantial differences in progression and survival. To date, there has been limited examination of how profiles of brain atrophy might differ between clinical phenotypes. Here, we address this longstanding gap in the literature by assessing cortical and subcortical grey and white matter volumes on structural MRI in a large cohort of 209 participants. Cognitive and behavioural changes were assessed using the Addenbrooke’s Cognitive Examination and the Cambridge Behavioural Inventory. Relative to 58 controls, behavioural variant FTD (n = 58) and ALS–FTD (n = 41) patients displayed extensive atrophy of frontoinsular, cingulate, temporal and motor cortices, with marked subcortical atrophy targeting the hippocampus, amygdala, thalamus and striatum, with atrophy further extended to the brainstem, pons and cerebellum in the latter group. At the other end of the spectrum, pure-ALS patients (n = 52) displayed considerable frontoparietal atrophy, including right insular and motor cortices and pons and brainstem regions. Subcortical regions included the bilateral pallidum and putamen, but to a lesser degree than in the ALS–FTD and behavioural variant FTD groups. Across the spectrum the most affected region in all three groups was the insula, and specifically the anterior part (76–90% lower than controls). Direct comparison of the patient groups revealed disproportionate temporal atrophy and widespread subcortical involvement in ALS–FTD relative to pure-ALS. In contrast, pure-ALS displayed significantly greater parietal atrophy. Both behavioural variant FTD and ALS–FTD were characterized by volume decrease in the frontal lobes relative to pure-ALS. The motor cortex and insula emerged as differentiating structures between clinical syndromes, with bilateral motor cortex atrophy more pronounced in ALS–FTD compared with pure-ALS, and greater left motor cortex and insula atrophy relative to behavioural variant FTD. Taking a transdiagnostic approach, we found significant associations between abnormal behaviour and volume loss in a predominantly frontoinsular network involving the amygdala, striatum and thalamus. Our findings demonstrate the presence of distinct atrophy profiles across the ALS–FTD spectrum, with key structures including the motor cortex and insula. Notably, our results point to subcortical involvement in the origin of behavioural disturbances, potentially accounting for the marked phenotypic variability typically observed across the spectrum.
Hippocampus- and thalamus-related fiber-specific white matter reductions in mild cognitive impairment
Authors: Zhou Yu , Si Xiaopeng , Chen Yuanyuan , Chao Yiping , Lin Ching-Po , Li Sicheng , Zhang Xingjian , Ming Dong , Li Qiang 
Published: October 2021

AbstractEarly diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer’s disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber’s DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson’s correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.