Miracle-dz/UniFAR-FTUs huggingface dataset:
Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph.

BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD. METHODS We innovatively used a visibility graph (VG) to construct the time-dependent brain networks as well as functional connectivity network to investigate the underlying dynamics of AD brain based on functional magnetic resonance imaging. There were 32 AD patients and 29 normal controls (NCs) from the Alzheimer's disease neuroimaging initiative (ADNI) database. First, the VG method mapped the time series of single brain region into networks. By extracting topological properties of the networks, the most significant features were selected as discriminant features into a supporting vector machine for classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD brain, functional connectivity among different brain regions was calculated based on the correlation of regional degree sequences. RESULTS According to the topology abnormalities exploration of local complex networks, we found several abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions. The accuracy of characteristics of the brain regions extracted from local complex networks was 88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in AD. CONCLUSION These results would be helpful in revealing the underlying pathological mechanism of the disease.
Phosphorylation of a disordered peptide - structural effects and force field inconsistencies.

Phosphorylation is one of the most abundant types of post-translational modifications of intrinsically disordered proteins (IDPs). This study examines the conformational changes in the 15 residue long N-terminal fragment of the IDP Statherin upon phosphorylation, using computer simulations with two different force fields: AMBER ff99SB-ILDN and CHARMM36m. The results from the simulations are compared with experimental small-angle X-ray scattering and circular dichroism data. In the unphosphorylated state, the two force fields are in excellent agreement regarding global structural properties such as size and shape, however, they exhibit some differences in the extent and type of secondary structure. In the phosphorylated state, neither of the force fields performs well compared to the experimental data. Both force fields show a compaction of the peptide upon phosphorylation, greater than what is seen in SAXS experiments, although they differ in local structure. While the CHARMM force field increases the fraction of bends in the peptide as a response to strong interactions between the phosphorylated residues and arginines, the AMBER force field shows an increase of helical content in the N-terminal part of the peptide, where the phosphorylated residues reside, in better agreement with circular dichroism results.
Fluorine Doped Cage-Like Carbon Electrocatalyst: An Insight into the Structure-Enhanced CO Selectivity for CO2 Reduction at High Overpotential.

The critical bottleneck of electrocatalytic CO2 reduction reaction (CO2RR) lies in its low efficiency at high overpotential caused by competitive hydrogen evolution. It is challenging to develop efficient catalyst achieving both high current density and high Faradaic efficiency (FE) for CO2RR. Herein, we synthesized fluorine-doped cage-like porous carbon (F-CPC) by purposely tailoring its structural properties. The optimized F-CPC possesses large surface area with moderate mesopore and abundant micropores, as well as high electrical conductivity. When using as catalyst for CO2RR, F-CPC exhibits FE of 88.3 % for CO at -1.0 V vs. RHE with a current density of 37.5 mA·cm-2. Experimental results and finite element simulations demonstrate that the excellent CO2RR performance of F-CPC at high overpotential should be attributed to its structure-enhanced electrocatalytic process stemmed from its cage-like morphology.
A time-resolved multi-omic atlas of the developing mouse liver

Liver organogenesis and development are composed of a series of complex, well-orchestrated events. Identifying key factors and pathways governing liver development will help elucidate the physiological and pathological processes including those of cancer. We conducted multidimensional omics measurements including protein, mRNA, and transcription factor (TF) DNA-binding activity for mouse liver tissues collected from embryonic day 12.5 (E12.5) to postnatal week 8 (W8), encompassing major developmental stages. These data sets reveal dynamic changes of core liver functions and canonical signaling pathways governing development at both mRNA and protein levels. The TF DNA-binding activity data set highlights the importance of TF activity in early embryonic development. A comparison between mouse liver development and human hepatocellular carcinoma (HCC) proteomic profiles reveal that more aggressive tumors are characterized with the activation of early embryonic development pathways, whereas less aggressive ones maintain liver function–related pathways that are elevated in the mature liver. This work offers a panoramic view of mouse liver development and provides a rich resource to explore in-depth functional characterization.
Functional changes of the lateral pterygoid muscle in patients with temporomandibular disorders: a pilot magnetic resonance images texture study

Abstract Background: Texture features were the intrinsic properties of the human tissues and could efficiently detect the subtle functional changes of involved tissue. The pathologic changes of the lateral pterygoid muscle (LPM) were significantly correlated with the temporomandibular disc displacement. However, the occult functional changes of LPM could not be detected by the naked eye on the medical images. The current study was aimed to evaluate the functional changes of the LPM in the patients with temporomandibular disorders (TMDs) using texture analysis. Methods: Twenty-nine patients with TMD were performed with magnetic resonance (MR) imaging on a 3.0T MR scanner, who were consecutively recruited from the TMD clinic of Hainan Hospital of Chinese People's Liberation Army General Hospital from February 2019 to September 2019. The patients were classified into three groups according to the disc displacement: disc without displacement (DWoD), disc displacement with reduction (DDWR) and disc displacement without reduction (DDWoR). The gray-level co-occurrence matrix method was applied with the texture analysis of LPM on the axial T2-weighted imaging. The texture features included angular second moment, contrast, correlation, inverse different moment, and entropy. One-way analysis of variance was used for grouped comparisons and receiver operating characteristics (ROC) curve analysis was applied to evaluate the diagnostic efficacy of the texture parameters. Results: Texture contrast of LPM presented significantly lower in DDWoR (46.30 [35.03, 94.48]) than that in DWoD (123.85 [105.06, 143.23]; test statistic = 23.05; P < 0.001). Texture entropy of LPM showed significant differences among DWoD (7.62 ± 0.33), DDWR (6.76 ± 0.35), and DDWoR (6.46 ± 0.39) (PDWoD-DDWR < 0.001, PDWoD-DDWoR < 0.001, and PDDWR-DDWoR = 0.014). Area under the ROC curve (AUC) demonstrated that texture entropy had an excellent diagnostic accuracy for DWoD-DDWR (AUC = 0.96) and DWoD-DDWoR (AUC = 0.98). Conclusion: The texture contrast and entropy could identify the altered functional status of LPM in patients with TMD and could be considered as the effective imaging biomarker to evaluate the functional changes of LPM in TMD.
High Throughput Sediment DNA Sequencing Reveals Azo Dye Degrading Bacteria Inhabit Nearshore Sediments

Estuaries and coastal environments are often regarded as a critical resource for the bioremediation of organic pollutants such as azo dyes due to their high abundance and diversity of extremophiles. Bioremediation through the activities of azoreductase, laccase, and other associated enzymes plays a critical role in the removal of azo dyes in built and natural environments. However, little is known about the biodegradation genes and azo dye degradation genes residing in sediments from coastal and estuarine environments. In this study, high-throughput sequencing (16S rRNA) of sediment DNA was used to explore the distribution of azo-dye degrading bacteria and their functional genes in estuaries and coastal environments. Unlike laccase genes, azoreductase (azoR), and naphthalene degrading genes were ubiquitous in the coastal and estuarine environments. The relative abundances of most functional genes were higher in the summer compared to winter at locations proximal to the mouths of the Hanjiang River and its distributaries. These results suggested inland river discharges influenced the occurrence and abundance of azo dye degrading genes in the nearshore environments. Furthermore, the azoR genes had a significant negative relationship with total organic carbon, Hg, and Cr (p < 0.05). This study provides critical insights into the biodegradation potential of indigenous microbial communities in nearshore environments and the influence of environmental factors on microbial structure, composition, and function which is essential for the development of technologies for bioremediation in azo dye contaminated sites.
Brassica Rapa SR45a Regulates Drought Tolerance via the Alternative Splicing of Target Genes

The emerging evidence has shown that plant serine/arginine-rich (SR) proteins play a crucial role in abiotic stress responses by regulating the alternative splicing (AS) of key genes. Recently, we have shown that drought stress enhances the expression of SR45a (also known as SR-like 3) in Brassica rapa. Herein, we unraveled the hitherto unknown functions of BrSR45a in drought stress response by comparing the phenotypes, chlorophyll a fluorescence and splicing patterns of the drought-responsive genes of Arabidopsis BrSR45a overexpressors (OEs), homozygous mutants (SALK_052345), and controls (Col-0). Overexpression and loss of function did not result in aberrant phenotypes; however, the overexpression of BrSR45a was positively correlated with drought tolerance and the stress recovery rate in an expression-dependent manner. Moreover, OEs showed a higher drought tolerance index during seed germination (38.16%) than the control lines. Additionally, the overexpression of BrSR45a induced the expression of the drought stress-inducible genes RD29A, NCED3, and DREB2A under normal conditions. To further illustrate the molecular linkages between BrSR45a and drought tolerance, we investigated the AS patterns of key drought-tolerance and BrSR45a interacting genes in OEs, mutants, and controls under both normal and drought conditions. The splicing patterns of DCP5, RD29A, GOLS1, AKR, U2AF, and SDR were different between overexpressors and mutants under normal conditions. Furthermore, drought stress altered the splicing patterns of NCED2, SQE, UPF1, U4/U6-U5 tri-snRNP-associated protein, and UPF1 between OEs and mutants, indicating that both overexpression and loss of function differently influenced the splicing patterns of target genes. This study revealed that BrSR45a regulates the drought stress response via the alternative splicing of target genes in a concentration-dependent manner.
First clinical experience with the new Surpass Evolve flow diverter: technical and clinical considerations

Objective To describe the results in patients treated with the Surpass Evolve (SE) device, the new generation of Surpass flow diverters. Methods Twenty-five consecutive patients (20 women, average age 58 years), with anterior or posterior circulation aneurysms treated with SEs in two early-user centers, were included. Device properties and related technical properties, imaging and clinical follow-up data, and intraprocedural, early (<30 days) and delayed (>30 days) neurological complications, further divided into minor (silent/non-permanent) and major (permanent) complications, were recorded and analyzed. Results Twenty-nine SEs were successfully implanted in all subjects to treat 26 aneurysms using an 0.027" microcatheter with an average of 1.2 stents per patient. No intraprocedural thromboembolic or hemorrhagic complications were seen. At clinical follow-up, 24/25 (96%) patients had a modified Rankin Score of 0–2. Mortality was 0%. Imaging follow-up, available in 22/25 (88%) patients (median follow-up time 4 months), showed a complete aneurysm occlusion in 13/23 (57%) imaged lesions. Minor, transitory neurological deficits were recorded in 5/25 (20%) patients. One (4%) major complication was seen in one patient (4%) with a left-sided hemispheric stroke on postprocedural day 4 due to an acute stent thrombosis. Conclusions Preliminary experience in patients demonstrates a good performance of the SE. This newly designed implant maintains the engineering characteristics of Surpass flow diverters, including precise placement due to its lower foreshortening and a high mesh density, yet can be deployed through a significantly lower-profile delivery system.
Seven Weeks of Jump Training with Superimposed Whole-Body Electromyostimulation Does Not Affect the Physiological and Cellular Parameters of Endurance Performance in Amateur Soccer Players

Intramuscular density of monocarboxylate-transporter (MCT) could affect the ability to perform high amounts of fast and explosive actions during a soccer game. MCTs have been proven to be essential for lactate shuttling and pH regulation during exercise and can undergo notable adaptational changes depending on training. The aim of this study was to evaluate the occurrence and direction of potential effects of a 7-weeks training period of jumps with superimposed whole-body electromyostimulation on soccer relevant performance surrogates and MCT density in soccer players. For this purpose, 30 amateur soccer players were randomly assigned to three groups. One group performed dynamic whole-body strength training including 3 x 10 squat jumps with WB-EMS (EG, n = 10) twice a week in addition to their daily soccer training routine. A jump training group (TG, n = 10) performed the same training routine without EMS, whereas a control group (CG, n = 8) merely performed their daily soccer routine. 2 (Time: pre vs. post) x 3 (group: EG, TG, CG) repeated measures analyses of variance (rANOVA) revealed neither a significant time, group nor interaction effect for VO2peak, Total Time to Exhaustion and Lamax as well as MCT-1 density. Due to a lack of task-specificity of the underlying training stimuli, we conclude that seven weeks of WB-EMS superimposed to jump exercise twice a week does not relevantly influence aerobic performance or MCT density.
Progressive Alignment of Inhibitory and Excitatory Delay May Drive a Rapid Developmental Switch in Cortical Network Dynamics

Nervous system maturation occurs on multiple levels, synaptic, circuit, and network, at divergent time scales. For example, many synaptic properties mature gradually, while emergent network dynamics, as data show, change abruptly. Here, we combine experimental and theoretical approaches to investigate a sudden transition in spontaneous thalamocortical activity necessary for the development of vision. Inspired by in vivo measurements of time-scales and amplitudes of synaptic currents, we extend the Wilson and Cowan model to take into account the relative onset timing and amplitudes of inhibitory and excitatory neural population responses. We study the dynamics of this system and identify the bifurcations as the onset timescales of excitation and inhibition are varied. We focus on the specific typical developmental changes in synaptic timescales consistent with the experimental observations. These findings argue that the inhibitory timing is a critical determinant of thalamocortical activity maturation; a gradual decay of the ratio of inhibitory to excitatory onset time below one drives the system through a bifurcation that leads to a sudden switch of the network spontaneous activity from high-amplitude oscillations to a non-oscillatory active state. This switch also drives a marked change to a linear network response to transient stimuli, agreeing to the in vivo observations. The switch observed in the model is representative of the sudden transition in the sensory cortical activity seen early in development.
Oncological outcomes of addition of anti-PD1/PD-L1 to chemotherapy in the therapy of patients with advanced gastric or gastro-oesophageal junction cancer

Abstract Background: Patients with advanced gastric or gastro-oesophageal junction cancer (GC/GEJC) that fail to respond to prior chemotherapy have poor clinical prognosis. Lately, many trials have paid much attention on the oncological outcomes of immune checkpoint inhibitors (ICI). A new therapy based on programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors has recognized as promising prospects for advanced GC/GEJC. We assessed efficacy and safety of PD-L1 antibody versus chemotherapy alone in previously treated non-small cell lung cancer. Methods: Computerized literature search was done on the published trials in: Pubmed, Embase, Cochrane library updated on June 2019. Randomized controlled trials were selected investigating chemotherapy plus PD-1/PD-L1 versus chemotherapy alone. Results: Three randomized controlled trails were included. The pooled analysis of overall survival (OS) was longer with anti-PD1/PD-L1 than with chemotherapy alone in the OS (OR = 0.66, 95%CI = 0.47–0.92, P = .02) and sub-group OS of GEJC (OR = 0.73, 95%CI = 0.58–0.93, P = .01). Whereas, there is no significant difference in progression-free survival (OR = 0.93, 95%CI = 0.62–1.39, P = .72). The pooling adverse events (AE) data did not achieve advantage in the PD-1/PD-L1 targeted agents (OR = 0.53, 95%CI = 0.13–2.10, P = .36), the same as the treatment-related AE of grade 3 to 5 (OR = 0.53, 95%CI = 0.16–1.74, P = .30). Conclusions: Treatment of patients with advanced GC/GEJC with PD-1/PD-L1 targeted did result in an improvement in some but not all survival endpoints. Moreover, it had a comparable toxicity profile as compared with chemotherapy alone. More well designed studies are needed to develop a database of all anti-PD1/PD-L1 sub-groups and their individual impact on the differing anti-PD1/PD-L1 treatments.
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Motivation: Fast and accurate classiﬁcation of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various ﬁelds, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide-and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results: We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classiﬁcation. BionoiNet ﬁrst transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classiﬁer. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide-and 91.3% for heme-binding pock-ets. BionoiNet also computes signiﬁcance scores of pocket
FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems

A method for channel estimation in wideband massive Multiple-Input Multiple-Output systems using hybrid digital analog architectures is developed. The proposed method is useful for Frequency-Division Duplex at either sub-6 GHz or millimeter wave frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. To circumvent the estimation of large channel vectors, the posed algorithm relies on the slow time variation of the channel spatial covariance matrix, thus allowing for the utilization of very short training sequences. This is possibledue to the exploitation of the channel structure. After identifying the channel covariance matrix, the channel is estimated on the basis of the recovered information. To that end, we propose a novel method that relies on estimating the tap delays and the gains as sociated with each path. As a consequence, the proposed channel estimator achieves low computational complexity and significantly reduces the training overhead. Moreover, our numerical simulations show better performance results compared to the minimum mean-squared error solution.
Can the Implicit Association Test Measure Automatic Judgment? The Validation Continues

In this commentary, we welcome Schimmack’s reanalysis of Bar-Anan and Vianello’s multitrait multimethod (MTMM) data set, and we highlight some limitations of both the original and the secondary analyses. We note that when testing the fit of a confirmatory model to a data set, theoretical justifications for the choices of the measures to include in the model and how to construct the model improve the informational value of the results. We show that making different, theory-driven specification choices leads to different results and conclusions than those reported by Schimmack (this issue, p. 396). Therefore, Schimmack’s reanalyses of our data are insufficient to cast doubt on the Implicit Association Test (IAT) as a measure of automatic judgment. We note other reasons why the validation of the IAT is still incomplete but conclude that, currently, the IAT is the best available candidate for measuring automatic judgment at the person level.
Machine learning in nephrology: scratching the surface

Abstract Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.