Miracle-dz/UniFAR-FTUs huggingface dataset:
The fibrinolytic system enables the onset of Plasmodium infection in the mosquito vector and the mammalian host

Plasmodium uses the fibrinolytic system to overcome physical barriers in the mosquito and vertebrate host. Plasmodium parasites must migrate across proteinaceous matrices to infect the mosquito and vertebrate hosts. Plasmin, a mammalian serine protease, degrades extracellular matrix proteins allowing cell migration through tissues. We report that Plasmodium gametes recruit human plasminogen to their surface where it is processed into plasmin by corecruited plasminogen activators. Inhibition of plasminogen activation arrests parasite development early during sexual reproduction, before ookinete formation. We show that increased fibrinogen and fibrin in the blood bolus, which are natural substrates of plasmin, inversely correlate with parasite infectivity of the mosquito. Furthermore, we show that sporozoites, the parasite form transmitted by the mosquito to humans, also bind plasminogen and plasminogen activators on their surface, where plasminogen is activated into plasmin. Surface-bound plasmin promotes sporozoite transmission by facilitating parasite migration across the extracellular matrices of the dermis and of the liver. The fibrinolytic system is a potential target to hamper Plasmodium transmission.
When to explain: Identifying explanation triggers in human-agent interaction

With more agents deployed than ever, users need to be able to interact and cooperate with them in an effective and comfortable manner. Explanations have been shown to increase the understanding and trust of a user in human-agent interaction. There have been numerous studies investigating this effect, but they rely on the user explicitly requesting an explanation. We propose a first overview of when an explanation should be triggered and show that there are many instances that would be missed if the agent solely relies on direct questions. For this, we differentiate between direct triggers such as commands or questions and introduce indirect triggers like confusion or uncertainty detection.
The Natural Language Pipeline, Neural Text Generation and Explainability

End-to-end encoder-decoder approaches to data-to-text generation are often black boxes whose predictions are difficult to explain. Breaking up the end-to-end model into sub-modules is a natural way to address this problem. The traditional pre-neural Natural Language Generation (NLG) pipeline provides a framework for breaking up the end-to-end encoder-decoder. We survey recent papers that integrate traditional NLG submodules in neural approaches and analyse their explainability. Our survey is a first step towards building explainable neural NLG models.
The Presence of Preoperative Depression Symptoms Does Not Hinder Recovery After Anterior Cruciate Ligament Reconstruction

Background: The current literature suggests a link between psychosocial factors and poor surgical outcomes in patients with musculoskeletal complaints. However, there remains a limited body of literature examining the effect of depression on outcomes after anterior cruciate ligament reconstruction (ACLR). Purpose: The primary purpose of this study was to compare postoperative function patient-reported outcome scores between patients with and patients without preoperative depression symptoms undergoing ACLR. Secondary goals included comparing postoperative pain interference and depression scores between the 2 groups. Study Design: Cohort study; Level of evidence, 2. Methods: In this single-center retrospective cohort study, pediatric and adult patients who underwent ACLR were included. The Physical Function (PF), Pain Interference (PI), and Depression (D) domain scores of the Patient-Reported Outcomes Measurement Information System (PROMIS) were collected preoperatively and at 6 and 12 months postoperatively. Patients were separated into clinical depression (CD) and no clinical depression (NCD) groups based on their preoperative PROMIS-D score. Results: A total of 82 patients undergoing ACLR were included in this study. Of these, 19 (23%) patients met criteria for the CD group. Preoperatively, the CD group reported lower mean PROMIS-PF (33.3 vs 39.7, respectively; P = .001), higher PROMIS-PI (65.7 vs 59.2, respectively; P <.01), and higher PROMIS-D (62.4 vs 45.1, respectively; P < .001) scores than the NCD group. At 12 months postoperatively, the mean PROMIS-PF scores for the CD and NCD groups were 52.1 and 56.7, respectively (P = .12). The mean 12-month postoperative PROMIS-PI scores for the CD and NCD groups were 52.3 and 47.4, respectively (P = .04). At 12 months after ACLR, there was a substantial improvement in PROMIS-PF and PROMIS-PI scores for both the CD (delta = +18.8 and –13.4, respectively) and NCD (delta = +17.0 and –11.8) groups. Conclusion: There was a significant improvement, which exceeded currently accepted minimal clinically important difference values, in PROMIS-PF scores at 12 months after ACLR, regardless of the presence of preoperative depression symptoms. These data suggest that having depression symptoms preoperatively does not significantly hinder a patient’s recovery after ACLR.
Preseason Upper Extremity Range of Motion and Strength in Relation to In-Season Injuries in NCAA Division I Gymnasts

Background: Gymnastics is a demanding sport that places unique forces on the upper extremity. The repetitive nature of the sport and the high-impact forces involved may predispose the gymnast to overuse injuries. Risk factors for injuries in gymnastics are not well understood. Purpose/Hypothesis: The purpose of this study was to ascertain whether preseason upper extremity range of motion (ROM) and strength differ between National Collegiate Athletic Association (NCAA) Division I collegiate gymnasts who sustain an in-season upper extremity injury and those who do not. We hypothesized that gymnasts who sustain an upper extremity injury would demonstrate reduced ROM and strength compared with noninjured gymnasts. Study Design: Cohort study; Level of evidence, 3. Methods: Over 4 seasons, from 2014 to 2018, a total of 15 female NCAA Division I collegiate gymnasts underwent preseason upper extremity ROM (shoulder: flexion, internal and external rotation; elbow: extension; wrist: extension) and strength (shoulder: internal and external rotation, and middle and lower trapezius) testing. Overuse upper extremity injuries were tracked in each subsequent season. Gymnasts were dichotomized into injured and noninjured groups, and a 2 × 2 analysis of variance was used to measure differences in preseason measures between the groups as well as within arms (injured vs noninjured arm for the injured group; dominant vs nondominant arm for the noninjured group). Results: A total of 12 overuse upper extremity injuries (10 shoulders; 2 wrist/forearm) occurred during 31 gymnast-seasons. There were no significant interactions for preseason ROM and strength measurements between groups (injured vs noninjured) or within arms (injured and noninjured arm for the injured group; dominant and nondominant arm for the noninjured group; P = .07). Conclusion: Preseason upper extremity ROM and strength were not different between gymnasts who sustained an in-season upper extremity overuse injury and those who did not. It is possible that ROM and strength measures used to screen other overhead athletes may not capture the unique features and requirements of gymnastics. Further, it may be challenging to discern differences in clinical measures of ROM and strength in gymnastics populations owing to the bilateral nature of the sport.
A review of artificial intelligence for EEG‐based brain−computer interfaces and applications

The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain–computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)‐based BCIs for AI‐related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG‐based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI‐inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG‐based BCI are highlighted in line with the AI technologies and applications.
Demystifying signal processing techniques to extract task-related EEG responses for psychologists

To investigate neural mechanisms of human psychology with electroencephalography (EEG), we typically instruct participants to perform certain tasks with simultaneous recording of their brain activities. The identification of task‐related EEG responses requires data analysis techniques that are normally different from methods for analyzing resting‐state EEG. This review aims to demystify commonly used signal processing methods for identifying task‐related EEG activities for psychologists. To achieve this goal, we first highlight the different preprocessing pipelines between task‐related EEG and resting‐state EEG. We then discuss the methods to extract and visualize event‐related potentials in the time domain and event‐related oscillatory responses in the time‐frequency domain. Potential applications of advanced techniques such as source analysis and single‐trial analysis are briefly discussed. We conclude this review with a short summary of task‐related EEG data analysis, recommendations for further study, and caveats we should take heed of.
An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation

Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence‐based detections/classifications. Around 10 million transistors are integrated into a 1 mm2 silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog‐digital converters on the silicon surface makes it possible to place the analog front‐end circuits within a tiny packaged chip; therefore, enabling high‐count EEG acquisition channels. This article introduces and reviews the state‐of‐the‐art dedicated chip designs for EEG processing, particularly for wearable systems. Furthermore, the analog circuits and digital platforms are included, and the technical details of circuit topology and logic architecture are presented in detail.
Multiscale analysis of effect of solvents on the P3HT-PCBM active layer

Solvents are often used in the active layer formation process in many photonic applications such as organic solar cell, organic led, organic wavelength conversion and photorefractive materials. In this contribution, multiscale modeling and simulation was used to reveal phase separation in P3HT-PCBM at mesoscale level due to attractionrepulsion between different organic functional groups of active ingredient molecules and solvent molecules. Force field parameters for mesoscale calculation were obtained from dynamic mapping of results from molecular dynamic simulations. DFT calculation was used to describe energy changes of active ingredient molecules due to surrounded residual solvent molecules. The simulation results from no solvent, chloroform, dichlorobenzene and chlorobenzene cases indicated that chlorobenzene exhibits strong attraction with fullerene of PCBM and strong repulsion with all the other functional groups, therefore leads to least phase segregation. DFT calculation showed that residual solvent molecules can slightly lower the energy but do not alter the value of band gap.
Demystifying signal processing techniques to extract resting-state EEG features for psychologists

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.
Single-gap pseudospark discharge experiments

An experimental investigation of a single-gap pseudospark was conducted using a flexible discharge chamber. The voltage breakdown characteristics were studied against a wide range of parameters such as gas pressure, gap separation, cathode cavity depth, cathode aperture size, external capacitance and applied voltage. An empirical formula, VB=(0.20±0.05) p−4.02±0.18 d−1.77±0.01, was obtained for the breakdown voltage VB in kV, given the gas pressure p in Torr and the gap separation d in mm. The electron beam extracted from this single gap was also studied and a current of up to 100 A was measured at 10 kV.
Integrated Analysis Reveals ENDOU as a Biomarker in Head and Neck Squamous Cell Carcinoma Progression

Background Head and neck squamous cell carcinoma (HNSCC) is a leading cancer with high morbidity and mortality worldwide. The aim is to identify genes with clinical significance by integrated bioinformatics analysis and investigate their function in HNSCC. Methods We downloaded and analyzed two gene expression datasets of GSE6631 and GSE107591 to screen differentially expressed genes (DEGs) in HNSCC. Common DEGs were functionally analyzed by Gene ontology and KEGG pathway enrichment analysis. Protein-protein interaction (PPI) network was constructed with STRING database and Cytoscape. ENDOU was overexpressed in FaDu and Cal-27 cell lines, and cell proliferation and migration capability were evaluated with MTT, scratch and transwell assay. The prognostic performance of ENDOU and expression correlation with tumor infiltrates in HNSCC were validated with TCGA HNSCC datasets. Results Ninety-eight genes shared common differential expression in both datasets, with core functions like extracellular matrix organization significantly enriched. 15 genes showed prognostic significance, and COBL and ENDOU serve as independent survival markers in HNSCC. In-vitro ENDOU overexpression inhibited FaDu and Cal-27 cells proliferation and migration, indicating its tumor-suppressing role in HNSCC progression. GSEA analysis indicated ENDOU down-stream pathways like DNA replication, mismatch repair, cell cycle and IL-17 signaling pathway. ENDOU showed relative lower expression in HNSCC, especially HPV-positive HNSCC samples. At last, ENDOU showed negative correlation with tumor purity and tumor infiltrating macrophages, especially M2 macrophages. Conclusion This study identified ENDOU as a biomarker with prognostic significance in HNSCC progression.
Diagnosis of Alzheimer’s Disease Using Brain Network

Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer’s disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach using graph theory based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using different classification techniques. These techniques include linear support vector machine (LSVM), and regularized extreme learning machine (RELM). We used pairwise Pearson’s correlation-based functional connectivity to construct the brain network. We compare the classification performance of brain network using Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experimental results show that the SVM with LASSO feature selection method generates better classification accuracy compared to other classification technique.
Brain Metabolite, N-Acetylaspartate Is a Potent Protein Aggregation Inhibitor

Deposition of toxic protein inclusions is a common hallmark of many neurodegenerative disorders including Alzheimer's disease, Parkinson disease etc. N-acetylaspartate (NAA) is an important brain metabolite whose levels got altered under various neurodegenerative conditions. Indeed, NAA has been a widely accepted biological marker for various neurological disorders. We have also reported that NAA is a protein stabilizer. In the present communication, we investigated the role of NAA in modulating the aggregation propensity on two model proteins (carbonic anhydrase and catalase). We discovered that NAA suppresses protein aggregation and could solubilize preformed aggregates.
A New Marine Biomaterial: The Shell of Mangrove Horseshoe Crabs, Carcinoscorpius rotundicauda (Latreille, 1802) Emphasizing Its Physico-Chemical Characteristics

The paper aims to elucidate the physico-chemical characteristics of the shell of mangrove horseshoe crabs (Carcinoscorpius rotundicauda) and determine the compilation matrix for the first time. The shell composition matrix of C. rotundicauda has never been studied in detail before, especially the shape of the foam, the chemical composition, the functional groups and the mechanical-physical and thermal properties of the shell. Based on this study, the shell structure of the mangrove horseshoe crab has the potential to be used as the base structure for developing bio-foam insulator material in the future. Therefore, the shell of mangrove horseshoe crabs has a unique natural structure in the form of foam. Its robust and elastic structure has the potential for further development for new marine biomaterials. The formation and composition of horseshoe crab shells foam are also believed to be multifunctional in mobility, used for defense mechanisms and thermal stability. The horseshoe crab samples were collected from Pacitan coastal waters, East Java, Indonesia. The research was conducted using physico-chemical and mechanical-physical analysis. The scanning electron microscopy was used in order to clarify the physico-chemical characteristics. The measurements of the mechanical-physical characteristics included density, unit cell size, and water absorption. The tensile strength and compressive strength were analyzed based on the American Society for Testing Material. Thermal resistance was measured by thermal gravimetric analysis. The results showed that the horseshoe crab shells have a unique structure, where chitin, protein and some minerals are the main chemical elements. The combination and major constituents of the horseshoe crab shell material provide strong and plastic mechanical properties with a maximum tensile strength of 60.46 kPa and maximum compressive strength of 110.55 kPa, water absorption of 0.01195 ± 0.001% and a density value of 0.1545 ± 0.011 g/cm3 as well as the capability to withstand thermal loads with peak decomposition values of 267.4–823.2°C and thermal stability of 60.59%. Using natural marine biomaterials in the future will be beneficial because it leaves no harmful residues and therefore has environmental advantages and at the same time, it is also more cost-effective.
Diversification dynamics and (non-)parallel evolution along an ecological gradient in African cichlid fishes

Understanding the drivers and dynamics of diversification is a central topic in evolutionary biology. Here, we investigated the dynamics of diversification in the cichlid fish Astatotilapia burtoni that diverged along a lake-stream environmental gradient. Whole-genome and morphometric analyses revealed that divergent selection was essential at the early stages of diversification, but that periods in allopatry were likely involved towards the completion of speciation. While morphological differentiation was continuous, genomic differentiation was not, as shown by two clearly separated categories of genomic differentiation. Reproductive isolation increased along a continuum of genomic divergence, with a “grey zone” of speciation at ∼0.1% net nucleotide divergence. The quantification of the extent of (non-)parallelism in nine lake-stream population pairs from four cichlid species by means of multivariate analyses revealed one parallel axis of genomic and morphological differentiation among seven lake-stream systems. Finally, we found that parallelism was higher when ancestral lake populations were more similar.
The systemic inflammatory response and clinicopathological characteristics in patients admitted to hospital with COVID-19 infection: Comparison of 2 consecutive cohorts

Background: In order to manage the COVID-19 systemic inflammatory response, it is important to identify clinicopathological characteristics across multiple cohorts. Methods: Electronic patient records for 2 consecutive cohorts of patients admitted to two urban teaching hospitals with COVID-19 during two 7-week periods of the COVID-19 pandemic in Glasgow, U.K. (cohort 1: 17th March 2020 - 1st May 2020) and (cohort 2: 18th May 2020 - 6th July 2020) were examined for routine clinical, laboratory and clinical outcome data. Results: Compared with cohort 1, cohort 2 were older (p<0.001), more likely to be female (p<0.05) and have less independent living circumstances (p<0.001). More patients in cohort 2 were PCR positive, CXR negative (both p<0.001) and had low serum albumin concentrations (p<0.001). 30-day mortality was similar between both cohorts (23% and 22%). Over the 2 cohorts, age >70 (p<0.001), male gender (p<0.05), hypertension (p<0.01), heart failure (p<0.05), cognitive impairment (p<0.001), frailty (p<0.001), COPD (p<0.05), delirium (p<0.001), elevated peri-operative Glasgow Prognostic Score (p<0.001), elevated neutrophil-lymphocyte ratio (p<0.001), low haematocrit (p<0.01), elevated urea (p<0.001), creatinine (p<0.001), glucose (p<0.05) and lactate (p<0.01); and the 4C score were associated with 30-day mortality. When compared with the 4C score, greater frailty (OR 10.2, 95% C.I. 3.4 to 30.6, p<0.01) and low albumin (OR 5.6, 95% C.I. 2.0 to 15.6, p<0.01) were strongly independently associated with 30-day mortality. Conclusion: In addition to the 4C mortality score, frailty score and a low albumin were strongly independently associated with 30-day mortality in two consecutive cohorts of patients admitted to hospital with COVID-19. Article summary: In two consecutive cohorts of patients with COVID-19 infection admitted to two urban teaching hospitals in Glasgow, UK, there were variations in a number of clinicopathological characteristics despite similar mortality (23 and 22%). In these two cohorts, in a multivariate analysis that included the 4C mortality score, clinical frailty score >3, low serum albumin concentration (<35 g/L), high neutrophil-lymphocyte ratio (>5), and abnormal serum sodium concentration (<133/>145 mmol/L) remained independently associated with 30-day mortality.