Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping of Tilapia Lake Virus entry pathways with inhibitors reveals dependence on dynamin activity and cholesterol but not endosomal acidification

Tilapia Lake Virus (TiLV) is an emerging virus lethal to tilapia, which threatens the global tilapia aquaculture with severe implications for food security. TiLV possesses similar features to orthomyxoviruses but is classified in the sole and the monotypic genus Tilapinevirus of the family Amnoonviridae. TiLV enveloped virions encapsidate a genome comprising ten segments of single-stranded, negative RNA. Remarkably, nine of TiLV’s ten major proteins lack sequence homology to any known viral or cellular proteins. The mode of TiLV entry into tilapia cells is not known. Following the measurement of the entry window of TiLV (∼3 h), we applied a panel of inhibitors of known regulators of endocytic functions to map the molecular requirements for TiLV entry. We identified productive entry by quantification of TiLV nucleoprotein expression and the generation of infectious particles. Inhibition of dynamin activity with dynasore or dynole, or depletion of cholesterol with methyl-β-cyclodextrin, strongly inhibited TiLV protein synthesis and infectious virion production. Moreover, inhibition of actin cytoskeleton polymerization with latrunculin A or microtubule polymerization with nocodazole within the entry window resulted in partial inhibition of TiLV infection. In contrast, inhibitors of endosomal acidification (NH4Cl, bafilomycin A1, or chloroquine), an inhibitor of clathrin-coated pit assembly (pitstop 2), and erlotinib—an inhibitor of the endocytic Cyclin G-associated kinase (GAK), did not affect TiLV entry. Altogether, these results suggest that TiLV enters via dynamin-mediated endocytosis in a cholesterol-, cytoskeleton-dependent manner, and clathrin-, pH-independent manner. Thus, despite being an orthomyx-like virus, when compared to the prototypical orthomyxovirus (influenza A virus), TiLV shows a distinct set of requirements for entry into cells.
Band gap energy in zn-rich zn1xcdxte thin films grown by r.f. sputtering

Ternary Zn 1-X Cd x Te semiconducting thin films were grown on 7059 Corning glass substrates at room temperature by co-sputtering from a ZnTe-Cd target. The visible Cd-area ontO ZnTe target was varied to cover 0% - 4% of the total area. The optical and structural properties of the films were analysed as a function of the Cd concentration (x) on the layers. The sharp diffraction lines indicate mainly the ZnTe in cubic phase. when the cadmium was incorporated into the ZnTe lattice, the band gap energy (E g ) decreased from 2.2748 eV (x=0) to 2.2226 eV (X= 0.081). We found a linear relationship between E g and (x) in the interval of (x) studied, that predicts a value of the bowing parameter b in the E g (x) = Eg 0 + ax + bx 2 relationship, which coincides with the value of b calculated by making the same study for Cd-rich CdZnTe. This result becomes interesting given the large number of values reported for b and a for E g (x) of this ternary material.
IL-17 and TNF-β: Predictive biomarkers for transition to psychosis in ultra-high risk individuals

Background Dysregulation of immunity, such as levels of inflammatory factors, has been regarded as a sign of schizophrenia. Changes in cytokine levels are not only described in the early onset of disease, but also observed in ultra-high risk (UHR) individuals. This study aimed to investigate the potential of cytokines as biomarkers for psychotic disorders and in individuals at UHR of developing a psychotic disorder in the future. Methods The Luminex liquid chip technology was used to detect the concentrations of Interferon-gamma (INF-γ), Interleukin (IL)-2, Interleukin (IL)-4, Interleukin (IL)-6, Interleukin (IL)-17, Interleukin-1beta (IL-1β), and Tumor Necrosis Factor-beta (TNF-β) in the plasma of all subjects. Meanwhile, the plasma level of Tumor Necrosis Factor-Alpha (TNF-α) was measured with the enzyme-linked immunosorbent assay (ELISA) kits. Then, the levels of these cytokines were compared among patients with Drug-naïve first-episode schizophrenia (FES; n = 40), UHR population (UHR; n = 49), and healthy controls (HCs; n = 30). Baseline cytokine levels were compared among UHR individuals who later transitioned (UHR-T; n = 14), those who did not transition (UHR-NT; n = 35), and HCs (n = 30). Results Our analysis results showed that IL-1β levels were significantly higher in UHR group than HC group (p = 0.015). Meanwhile, TNF-α concentration was significantly increased in FES group compared with HC group (p = 0.027). IL-17 (p = 0.04) and TNF-β (p = 0.008) levels were significantly higher in UHR-T group compared with UHR-NT group. Conclusion In conclusion, our findings suggest that the immuno-inflammatory activation level is increased in the early stage of psychosis before psychotic conversion and the Drug-naïve FES. IL-1β and TNF-α are the representatives of the specific biomarkers for UHR and FES, respectively. IL-17 and TNF-β may be the potential selective predictive biomarkers for future transition in UHR individuals.
Quantifying uncertainty of machine learning methods for loss given default

Machine learning has increasingly found its way into the credit risk literature. When applied to forecasting credit risk parameters, the approaches have been found to outperform standard statistical models. The quantification of prediction uncertainty is typically not analyzed in the machine learning credit risk setting. However, this is vital to the interests of risk managers and regulators alike as its quantification increases the transparency and stability in risk management and reporting tasks. We fill this gap by applying the novel approach of deep evidential regression to loss given defaults (LGDs). We evaluate aleatoric and epistemic uncertainty for LGD estimation techniques and apply explainable artificial intelligence (XAI) methods to analyze the main drivers. We find that aleatoric uncertainty is considerably larger than epistemic uncertainty. Hence, the majority of uncertainty in LGD estimates appears to be irreducible as it stems from the data itself.
Density dependent regulation of inflammatory responses in macrophages

Macrophage distribution density is tightly regulated within the body, yet the importance of macrophage crowding during in vitro culture is largely unstudied. Using a human induced pluripotent stem cell (iPSC)-derived macrophage model of tissue resident macrophages, we characterize how increasing macrophage culture density changes their morphology and phenotype before and after inflammatory stimulation. In particular, density drives changes in macrophage inflammatory cytokine and chemokine secretion in both resting and activated states. This density regulated inflammatory state is also evident in blood monocyte derived-macrophages, the human monocytic THP-1 immortalized cell line, and iPSC-derived microglia. Density-dependent changes appear to be driven by a transferable soluble factor, yet the precise mechanism remains unknown. Our findings highlight cell plating density as an important but frequently overlooked consideration of in vitro macrophage research relevant to a variety of fields ranging from basic macrophage cell biology to disease studies.
An improved bundle adjustment model and algorithm with novel block matrix partition method

Sparse bundle adjustment is widely applied in computer vision and photogrammetry. However, existing implementation is based on the model of n 3D points projecting onto m different camera imaging planes at m positions, which can't be applied to commonly monocular, binocular or trinocular imaging systems. A novel design and implementation of bundle adjustment algorithm is proposed in this paper, which is based on n 3D points projecting onto the same camera imaging plane at m positions . To improve the performance of the algorithm, a novel sparse block matrix partition method is proposed. Experiments show that the improved bundle adjustment is effective, robust and has a better tolerance to pixel coordinates error.
Performance-preserving frequency weighted controller approximation: a coprime factorisation approach

Given a stabilising controller which satisfies a prespecified H/sub /spl infin// performance bound on the closed loop, we derive sufficient conditions for reduced order controllers to be stabilising and satisfy the same performance bound. The conditions are expressed as norm bounds on the frequency weighted error between coprime factor representations of the given controller and the reduced order controller. Reduced order controllers are then designed by interpreting the sufficient conditions as frequency weighted model reduction problems. <<ETX>>
Editorial: Innovative applications with artificial intelligence methods in neuroimaging data analysis

COPYRIGHT © 2022 Liu, Zhao, Lu and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Innovative applications with artificial intelligence methods in neuroimaging data analysis
Coronavirus disease 2019 (COVID-19) excess mortality outcomes associated with pandemic effects study (COPES): A systematic review and meta-analysis

Background and aim With the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times. Methods We searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Results Of 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06–0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38–1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07–1.30, p < 0.00001). There was “very low” certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain. Interpretation The COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain. Systematic review registration [https://www.crd. york.ac. uk/prospero/#recordDetails], identifier [CRD42020201256].
Chronic stressors and burnout in Dutch police officers: Two studies into the complex role of coping self-efficacy

Introduction Burnout complaints are high for Dutch police officers. According to Hobfoll’s Conservation of Resources theory, resources such as coping self-efficacy can play an important role in the burnout process. The aim of this study was to investigate the buffering effect of coping self-efficacy on burnout, as well as a possible depletion effect of burnout on coping self-efficacy. Methods As such, this research consists of two studies namely, a two-wave study (N = 166) and three-wave study (N = 95) on Dutch police officers. They expand on previous research regarding coping resources and police burnout. Results Both studies show that the chronic stressor, work scheduling, was positively associated with burnout. Also, coping self-efficacy weakened the effect of work scheduling (Study 1) and workload (Study 2) on burnout. Moreover, there was a direct negative relationship between burnout and coping self-efficacy. Discussion The results indicate that burnout can lead to lower coping resources, initiating a potential cycle of resource loss and burnout. Further investigation into this depletion effect is required to provide police officers and organisations with tools to prevent burnout.
Grapevine response to a Dittrichia viscosa extract and a Bacillus velezensis strain

The present study aims to evaluate the response of the three Mediterranean local grapevines ‘Garnacha Blanca’, ‘Garnacha Tinta’, and ‘Macabeo’ to treatments with biocontrol products, namely a botanical extract (Akivi, Dittrichia viscosa extract) and a beneficial microorganism (Bacillus UdG, Bacillus velezensis). A combination of transcriptomics and metabolomics approaches were chosen in order to study grapevine gene expression and to identify gene marker candidates, as well as, to determine differentially concentrated grapevine metabolites in response to biocontrol product treatments. Grapevine plants were cultivated in greenhouse under controlled conditions and submitted to the treatments. Thereafter, leaves were sampled 24h after treatment to carry out the gene expression study by RT-qPCR for the three cultivars and by RNA-sequencing for ‘Garnacha Blanca’. Differentially expressed genes (DEGs) were investigated for both treatments and highly influenced DEGs were selected to be tested in the three cultivars as treatment gene markers. In addition, the extraction of leaf components was performed to quantify metabolites, such as phytohormones, organic acids, and phenols. Considering the upregulated and downregulated genes and the enhanced metabolites concentrations, the treatments had an effect on jasmonic acid, ethylene, and phenylpropanoids defense pathways. In addition, several DEG markers were identified presenting a stable overexpression after the treatments in the three grapevine cultivars. These gene markers could be used to monitor the activity of the products in field treatments. Further research will be necessary to confirm these primary results under field conditions.
Isotope constraints on nitrogen dynamics in the upper water column of the South China Sea

The supply of nitrogen (N) from various external and internal sources into the euphotic zone, e.g., atmospheric N deposition (AND), upwelling, lateral intrusion, and remineralization, modulates the biogeochemical and climatic roles of oligotrophic oceans and complicates N dynamics in the upper water column (≤200 m). However, our ability to resolve the mechanisms controlling upper-ocean N cycling is limited by the lack of high-resolution vertical observations. Here, we analyzed concentrations and dual isotopes of nitrate (NO3 –) in the upper 200 m of the oligotrophic South China Sea. By examining dual isotopic signatures of NO3 – (δ15NNO3 and δ18ONO3) and multiple associated parameters vertically throughout the upper water column, we resolved the dominant N sources and processes, including AND/N2-fixation, assimilative fractionation, and nitrification, and quantitatively evaluated their contributions in the vertical distribution of NO3 –, which can be separated into the Δδ18ONO3-positive (δ18ONO3−obs−δ18ONO3−200m>0) and Δδ18ONO3-negative layers (δ18ONO3−obs−δ18ONO3−200m<0) according to the deviation in δ18ONO3 at a given depth (δ18ONO3-obs) from that at 200 m (δ18ONO3-200m). In the Δδ18ONO3-positive layer, the NO3 – assimilated by phytoplankton was largely sourced from nitrification (39 ± 11%) and AND/N2 fixation (17-28%), whereas these two processes accounted for 17 ± 10% and 7 ± 6% of the total NO3 – pool in the Δδ18ONO3-negative layer. Considering a substantial contribution of the regenerated (nitrification-sourced) NO3 – to the total NO3 – pool especially in the Δδ18ONO3-positive layer, caution should be taken that the new production assessed by the rates of NO3 – uptake may be significantly overestimated in the SCS. These findings not only highlight the importance of these biogeochemical processes to NO3 – dynamics in the upper water column of marginal seas, but also with important implications for the estimation of biological carbon pump and/or the f-ratio.
Seven-day sea surface temperature prediction using a 3DConv-LSTM model

Due to the application demand, users have higher expectations for the accuracy and resolution of sea surface temperature (SST) products. Recent advances in deep learning show great advantages in exploiting massive ocean datasets, and provides opportunities for investigating regional SST predictions in an efficiency approach. However, for deep learning-based SST prediction to be adopted by users, the output must be accurate. This paper investigates the 7-day SST prediction over the China seas and their adjacent waters at a 0.05° spatial resolution. To improve the prediction’s accuracy, we designed a deep learning model combining the three-dimensional convolution and long short-term memory under multi-input multi-output strategy. The Operational SST and Sea Ice Analysis (OSTIA) SST anomaly was used as training data. To test the model prediction ability, we verified the predicted results with the Sub-seasonal to Seasonal (S2S) prediction data from 2015 to 2019. Validation of the predicted SSTs using the OSTIA test datasets show that the root-mean-square error increases from 0.27°C to 0.53°C during the 1- to 7-day lead time, with predictability decreases from southeast to northwest in the study area. Furthermore, the comparison of predicted SST and S2S data with Argo shows that our model is slightly more accurate, which can achieve -0.08°C bias, with a standard deviation of 0.35°C for a 1-day lead time and -0.07°C bias, with a standard deviation of 0.59°C for a 7-day lead time. The results indicate that the proposed deep learning model is accurate and can be applied in regional daily SST prediction.
Utilization of marine diatom Thalassiosira weissflogii as a feed additive in seawater-tolerant Nile tilapia (Oreochromis niloticus, Linnaeus 1758) strain

The low omega-3 content of tilapia flesh, when compared to marine fish, affects its marketability. In marine animals, the highly unsaturated fatty acids (HUFAs) can be linked to the oil produced by marine diatoms. Among the marine diatoms, the genus Thalassiosira is known to exhibit high content of HUFAs such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Thus, in this study, the use of marine diatom Thalassiosira weissflogii as a dietary additive in the seawater-tolerant Nile Tilapia strain was evaluated. One hundred ninety-two, 1.40 ± 0.05g seawater tilapia were randomly allocated into 4 treatment groups in 4 replicates. The first treatment group was fed with a control diet (D0), without the diatoms while treatments 1, 2, and 3 were each fed with diets supplemented with T. weissflogii paste at 2.55% (D1), 6% (D2), and 12% (D3), respectively for 60 days. The diets were isonitrogenous, isolipodic and the omega-3 and omega-6 requirements were satisfied. Results demonstrated that D1 had the highest percent weight gain among treatments. Although not significantly different, other parameters such as percent survival, specific growth rate (SGR), protein efficiency ratio (PER), feed conversion ratio (FCR), and feed intake had desirable results in D1. The proximate composition of seawater tilapia showed that % crude protein was highest in D0 but % crude lipid was highest in D1. The fatty acid composition of tilapia in D1 had the highest omega-3 content at 9.29 mg/g tissue and also had the highest n3:n6 at 2.19. Muscle growth-related genes (MyoD and MYG) were up-regulated while liver genes involved in long-chain polyunsaturated fatty acid synthesis (oni-fads2 and elvol5) were down-regulated in D1 as compared to D0. Feeding the diatom-supplemented diet to tilapia had no significant effects on hepatic cells and intestinal morphology. The results suggested that a 2.55% supplementation dose of T. weissflogii could promote growth and enhance the tissue content of omega-3 fatty acids of the seawater strain Oreochromis niloticus.
Accuracy of serological tests for COVID-19: A systematic review and meta-analysis

Objective To determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (COVID-19). Methods PubMed, Embase and the Cochrane Library were searched from January 1 2020 to September 2 2022. We included studies that measured the sensitivity, specificity or both qualities of a COVID-19 serological test and a reference standard of a viral culture or reverse transcriptase polymerase chain reaction (RT–PCR). The risk of bias was assessed by using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). The primary outcomes included overall sensitivity and specificity, as stratified by the methods of serological testing [enzyme-linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs) or chemiluminescent immunoassays (CLIAs)] and immunoglobulin classes (IgG, IgM, or both). Secondary outcomes were stratum-specific sensitivity and specificity within the subgroups, as defined by study or participant characteristics, which included the time from the onset of symptoms, testing via commercial kits or an in-house assay, antigen target, clinical setting, serological kit as the index test and the type of specimen for the RT–PCR reference test. Results Eight thousand seven hundred and eighty-five references were identified and 169 studies included. Overall, we judged the risk of bias to be high in 47.9 % (81/169) of the studies, and a low risk of applicability concerns was found in 100% (169/169) of the studies. For each method of testing, the pooled sensitivity of the ELISAs ranged from 81 to 82%, with sensitivities ranging from 69 to 70% for the LFIAs and 77% to 79% for the CLIAs. Among the evaluated tests, IgG (80–81%)-based tests exhibited better sensitivities than IgM-based tests (66–68%). IgG/IgM-based CLIA had the highest sensitivity [87% (86–88%)]. All of the tests displayed high specificity (97–98%). Heterogeneity was observed in all of the analyses. The detection of nucleocapsid protein (77–80%) as the antigen target was found to offer higher sensitivity results than surface protein detection (66–68%). Sensitivity was higher in the in-house assays (78–79%) than in the commercial kits (47–48%). Conclusion Among the evaluated tests, ELISA and CLIA tests performed better in terms of sensitivity than did the LFIA. IgG-based tests had higher sensitivity than IgM-based tests, and combined IgG/IgM test-based CLIA tests had the best overall diagnostic test accuracy. The type of sample, serological kit and timing of use of the specific tests were associated with the diagnostic accuracy. Due to the limitations of the serological tests, other techniques should be quickly approved to provide guidance for the correct diagnosis of COVID-19.