Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepViral: infectious disease phenotypes improve prediction of novel virus–host interactions

Motivation Infectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. Results We developed DeepViral, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses. Availability https://github.com/bio-ontology-research-group/DeepViral Contact robert.hoehndorf@kaust.edu. sa
The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks

Any given visual object input is characterized by multiple visual features, such as identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pathway, they have so far been studied relatively independently. Here we document the relative coding strength of object identity and nonidentity features in a brain region and how this may change across the human ventral visual pathway. We examined a total of four nonidentity features, including two Euclidean features (position and size) and two non-Euclidean features (image statistics and spatial frequency content of an image). Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with identity outweighed the non-Euclidean features, but not the Euclidean ones, in higher levels of visual processing. A similar analysis was performed in 14 convolutional neural networks (CNNs) pretrained to perform object categorization with varying architecture, depth, and with/without recurrent processing. While the relative coding strength of object identity and nonidentity features in lower CNN layers matched well with that in early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Similar results were obtained regardless of whether a CNN was trained with real-world or stylized object images that emphasized shape representation. Together, by measuring the relative coding strength of object identity and nonidentity features, our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs. SIGNIFICANCE STATEMENT This study documented the relative coding strength of object identity compared to four types of nonidentity features along the human ventral visual processing pathway and compared brain responses with those of 14 CNNs pretrained to perform object categorization. Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with the coding strength of the different nonidentity features differed at higher levels of visual processing. While feature coding in lower CNN layers matched well with that of early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.
Cryo-EM Structures of the SARS-CoV-2 Endoribonuclease Nsp15

New therapeutics are urgently needed to inhibit SARS-CoV-2, the virus responsible for the on-going Covid-19 pandemic. Nsp15, a uridine-specific endoribonuclease found in all coronaviruses, processes viral RNA to evade detection by RNA-activated host defense systems, making it a promising drug target. Previous work with SARS-CoV-1 established that Nsp15 is active as a hexamer, yet how Nsp15 recognizes and processes viral RNA remains unknown. Here we report a series of cryo-EM reconstructions of SARS-CoV-2 Nsp15. The UTP-bound cryo-EM reconstruction at 3.36 Å resolution provides molecular details into how critical residues within the Nsp15 active site recognize uridine and facilitate catalysis of the phosphodiester bond, whereas the apo-states reveal active site conformational heterogeneity. We further demonstrate the specificity and mechanism of nuclease activity by analyzing Nsp15 products using mass spectrometry. Collectively, these findings advance understanding of how Nsp15 processes viral RNA and provide a structural framework for the development of new therapeutics.
The PNUTS-PP1 axis regulates endothelial aging and barrier function via SEMA3B suppression

Age-related diseases pose great challenges to health care systems worldwide. During aging, endothelial senescence increases the risk for cardiovascular disease. Recently, it was described that Phosphatase 1 Nuclear Targeting Subunit (PNUTS) has a central role in cardiomyocyte aging and homeostasis. Here, we determined the role of PNUTS in endothelial cell aging. We confirmed that PNUTS is repressed in senescent endothelial cells (ECs). Moreover, PNUTS silencing elicits several of the hallmarks of endothelial aging: senescence, reduced angiogenesis and loss of barrier function. To validate our findings in vivo, we generated an endothelial-specific inducible PNUTS-deficient mouse line (Cdh5-CreERT2;PNUTSfl/fl), termed PNUTSEC-KO. Two weeks after PNUTS deletion, PNUTSEC-KO mice presented severe multiorgan failure and vascular leakage. We showed that the PNUTS binding motif for protein phosphatase 1 (PP1) is essential to maintain endothelial barrier function. Transcriptomic analysis of PNUTS-silenced HUVECs and lungs of PNUTSEC-KO mice revealed that the PNUTS-PP1 axis tightly regulates the expression of semaphorin 3B (SEMA3B). Indeed, silencing of SEMA3B completely restored barrier function after PNUTS loss-of-function. These results reveal a pivotal role for PNUTS in endothelial homeostasis through a PP1-SEMA3B downstream pathway that provides a potential target against the effects of aging in ECs.
CAP1 and cofilin1 cooperate in neuronal actin dynamics, growth cone function and neuron connectivity

Neuron connectivity depends on growth cones that navigate axons through the developing brain. Growth cones protrude and retract actin-rich structures to sense guidance cues. These cues control local actin dynamics and steer growth cones towards attractants and away from repellents, thereby directing axon outgrowth. Hence, actin binding proteins (ABPs) moved into the focus as critical regulators of neuron connectivity. We found cyclase-associated protein 1 (CAP1), an ABP with unknown brain function, abundant in growth cones. Super-resolution microscopy and live cell imaging combined with pharmacological approaches on hippocampal neurons from gene-targeted mice revealed a crucial role for CAP1 in actin dynamics that is critical for growth cone morphology and function. Growth cone defects in mutant neurons compromised neuron differentiation and was associated with impaired neuron connectivity in CAP1 mutant brains. Mechanistically, we found that CAP1 and cofilin1 synergistically control growth cone actin dynamic and morphology. Together, we identified CAP1 as a novel actin regulator in growth cone that is relevant for neuron connectivity.
DeepKhib: A Deep-Learning Framework for Lysine 2-Hydroxyisobutyrylation Sites Prediction

As a novel type of post-translational modification, lysine 2-Hydroxyisobutyrylation (Khib) plays an important role in gene transcription and signal transduction. In order to understand its regulatory mechanism, the essential step is the recognition of Khib sites. Thousands of Khib sites have been experimentally verified across five different species. However, there are only a couple traditional machine-learning algorithms developed to predict Khib sites for limited species, lacking a general prediction algorithm. We constructed a deep-learning algorithm based on convolutional neural network with the one-hot encoding approach, dubbed CNNOH. It performs favorably to the traditional machine-learning models and other deep-learning models across different species, in terms of cross-validation and independent test. The area under the ROC curve (AUC) values for CNNOH ranged from 0.82 to 0.87 for different organisms, which is superior to the currently-available Khib predictors. Moreover, we developed the general model based on the integrated data from multiple species and it showed great universality and effectiveness with the AUC values in the range of 0.79 to 0.87. Accordingly, we constructed the on-line prediction tool dubbed DeepKhib for easily identifying Khib sites, which includes both species-specific and general models. DeepKhib is available at http://www.bioinfogo. org/DeepKhib.
Pleiotropic effect of Lactoferrin in the prevention and treatment of COVID-19 infection: randomized clinical trial, in vitro and in silico preliminary evidences

The current treatments against SARS-CoV-2 have proved so far inadequate. A potent antiviral drug is yet to be discovered. Lactoferrin, a multifunctional glycoprotein, secreted by exocrine glands and neutrophils, possesses an antiviral activity extendable to SARS-Cov-2. We performed a randomized, prospective, interventional study assessing the role of oral and intra-nasal lactoferrin to treat mild-to-moderate and asymptomatic COVID-19 patients to prevent disease evolution. Lactoferrin induced an early viral clearance and a fast clinical symptoms recovery in addition to a statistically significant reduction of D-Dimer, Interleukin-6 and ferritin blood levels. The antiviral activity of lactoferrin related to its binding to SARS-CoV-2 and cells and protein-protein docking methods, provided the direct recognition between lactoferrin and spike S, thus hindering the spike S attachment to the human ACE2 receptor and consequently virus entering into the cells. Lactoferrin can be used as a safe and efficacious natural agent to prevent and treat COVID-19 infection.
Alzheimer's Dementia Detection from Audio and Text Modalities

Automatic detection of Alzheimer's dementia by speech processing is enhanced when features of both the acoustic waveform and the content are extracted. Audio and text transcription have been widely used in health-related tasks, as spectral and prosodic speech features, as well as semantic and linguistic content, convey information about various diseases. Hence, this paper describes the joint work of the GTM-UVIGO research group and acceXible startup to the ADDReSS challenge at INTERSPEECH 2020. The submitted systems aim to detect patterns of Alzheimer's disease from both the patient's voice and message transcription. Six different systems have been built and compared: four of them are speech-based and the other two systems are text-based. The x-vector, i-vector, and statistical speech-based functionals features are evaluated. As a lower speaking fluency is a common pattern in patients with Alzheimer's disease, rhythmic features are also proposed. For transcription analysis, two systems are proposed: one uses GloVe word embedding features and the other uses several features extracted by language modelling. Several intra-modality and inter-modality score fusion strategies are investigated. The performance of single modality and multimodal systems are presented. The achieved results are promising, outperforming the results achieved by the ADDReSS's baseline systems.
A spatial joint analysis of metal constituents of ambient particulate matter and mortality in England

Supplemental Digital Content is available in the text. Few studies have investigated associations between metal components of particulate matter on mortality due to well-known issues of multicollinearity. Here, we analyze these exposures jointly to evaluate their associations with mortality on small area data. We fit a Bayesian profile regression (BPR) to account for the multicollinearity in the elemental components (iron, copper, and zinc) of PM10 and PM2.5. The models are developed in relation to mortality from cardiovascular and respiratory disease and lung cancer incidence in 2008–2011 at a small area level, for a population of 13.6 million in the London-Oxford area of England. From the BPR, we identified higher risks in the PM10 fraction cluster likely to represent the study area, excluding London, for cardiovascular mortality relative risk (RR) 1.07 (95% credible interval [CI] 1.02, 1.12) and for respiratory mortality RR 1.06 (95%CI 0.99, 1.31), compared with the study mean. For PM2.5 fraction, higher risks were seen for cardiovascular mortality RR 1.55 (CI 95% 1.38, 1.71) and respiratory mortality RR 1.51 (CI 95% 1.33, 1.72), likely to represent the “highways” cluster. We did not find relevant associations for lung cancer incidence. Our analysis showed small but not fully consistent adverse associations between health outcomes and particulate metal exposures. The BPR approach identified subpopulations with unique exposure profiles and provided information about the geographical location of these to help interpret findings.
Seismic Edge Detection by Application of Cepstral Decomposition to Data Driven Modeled Geologic Channel Feature in Niger Delta.

Seismic edge detection algorithm unmasks blurred discontinuity in an image and its efficiency is dependent on the precession of the processing scheme adopted. Data-driven modeling is a ast machine learning scheme and a formal usually automatic version of the empirical approach in existence for long time and which can be used in many different contexts. Here, a desired algorithm that can identify masked connection and correlation from a set of observations is built and used.Geologic models of hydrocarbon reservoirs facilitate enhanced visualization, volumetric calculation, well planning and prediction of migration path for fluid. In order to obtain new insights and test the mappability of a geologic feature, spectral decomposition techniques i.e. Discrete Fourier Transform (DFT), etc and Cepstral decomposition techniques, i.e Complex Cepstral Transform (CCT), etc can be employed. Cepstral decomposition is a new approach that extends the widely used process of spectral decomposition which is rigorous when analyzing very subtle stratigraphic plays and fractured reservoirs. This paper presents the results of the application of DFT and CCT to a two dimensional, 50Hz low impedance Channel sand model, representing typical geologic environment around a prospective hydrocarbon zone largely trapped in various types of channel structures. While the DFT represents the frequency and phase spectra of a signal, assumes stationarity and highlights the average properties of its dominant portion, assuming analytical, the CCT represents the quefrency and saphe cepstra of a signal in quefrency domain. The transform filters the field data recorded in time domain, and recovers lost sub-seismic geologic information in quefrency domain by separating source and transmission path effects. Our algorithm is based on fast Fourier transform (FFT) techniques and the programming code was written within Matlab software. It was developed from first principles and outside oil industry’s interpretational platform using standard processing routines. The results of the algorithm, when implemented on both commercial and general platforms, were comparable. The cepstral properties of the channel model indicate that cepstral attributes can be utilized as powerful tool in exploration problems to enhance visualization of small scale anomalies and obtain reliable estimates of wavelet and stratigraphic parameters. The practical relevance of this investigation is illustrated by means of sample results of spectral and cepstral attribute plots and pseudo-sections of phase and saphe constructed from the model data. The cepstral attributes reveal more details in terms of quefrency required for clearer imaging and better interpretation of subtle edges/discontinuities, sand–shale interbedding, differences in lithology. These positively impact on production as they serve as basis for the interpretation of similar geologic situations in field data.
Hybrid contraflow lane reversal approach for connected vehicles

Although the advent of connected vehicles (CV) has reshaped the urban traffic management strategies, little attention has been paid to the traffic throughput maximization problem by adjusting road configuration based on dynamic traffic condition at multiple intersections. In this paper, we model the vehicular traffic flow maximization problem as a maximum integer multi-commodity flow problem and propose a local intersection agent which maximizes traffic throughput by reversing the direction of a contraflow lane on an underutilized road segment. The agent detects unbalanced utilization of road segments and assesses the feasibility of lane reversal in collaboration with neighboring agents via communication between CVs or between CV and agents.
How linguistic cues affect the motivation of capital-giving in crowdfunding: a self-determination theory perspective

ABSTRACT The crowdfunding success depends on the collective capital giving by investors, and such behaviours are triggered by certain motivations. Thus we expect to identify investors’ motivation by exploring linguistic cues from project descriptions and examine what kinds of linguistic cues affect capital-giving decisions. Our research is based on self-determination theory, which falls human needs into one of two types, intrinsic motives or extrinsic motives. We create a word corpus and use it to classify the project descriptions into different motivations through text analytics. Finally, an econometric model is formulated to estimate the impact of investors’ motives on the fundraising success. Our empirical analyses demonstrate that the investors’ motivations for capital-giving vary in project categories. Furthermore, building relation and receiving gratitude are amongst the key determinants of investors’ involvement in crowdfunding, especially for community projects and creative works.
Ageing trajectories of health—longitudinal opportunities and synergies (ATHLOS) Healthy Ageing Scale in adults from 16 international cohorts representing 38 countries worldwide

Background Uniform international measurement tools for assessing healthy ageing are currently lacking. Objectives The study assessed the novel comprehensive global Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) Healthy Ageing Scale, using an Item Response Theory approach, for evaluating healthy ageing across populations. Design Pooled analysis of 16 international longitudinal studies. Setting 38 countries in five continents. Subjects International cohort (n=355 314), including 44.4% (n=153 597) males, aged (mean±SD) 61.7±11.5 years old. Methods The ATHLOS Healthy Ageing Scale (including 41 items related to intrinsic capacity and functional ability) was evaluated in a pooled international cohort (n=355 314 from 16 studies) according to gender, country of residence and age group. It was also assessed in a subset of eight cohorts with ≥3 waves of follow-up assessment. The independent samples t-test and Mann–Whitney test were applied for comparing normally and skewed continuous variables between groups, respectively. Results The ATHLOS Scale (range: 12.49–68.84) had a mean (±SD) value of 50.2±10.0, with males and individuals >65 years old exhibiting higher and lower mean scores, respectively. Highest mean scores were detected in Switzerland, Japan and Denmark, while lowest in Ghana, India and Russia. When the ATHLOS Scale was evaluated in a subset of cohorts with ≥3 study waves, mean scores were significantly higher than those of the baseline cohort (mean scores in ≥3 study waves vs baseline: 51.6±9.4 vs 50.2±10.0; p<0.01). Conclusions The ATHLOS Healthy Ageing Scale may be adequately applied for assessing healthy ageing across populations.
Functional morphology of the sperm‐containing chambers of the sea slug Okenia polycerelloides in the context of sexual selection

Sea slugs are interesting models to study post‐copulatory sexual selection in simultaneous hermaphrodites due to the enormous variation of their reproductive systems. However, the knowledge of the functional morphology of their reproductive system is limited to few species, and it is rarely discussed in the context of sexual selection theory. In this study, we investigated the functional morphology of the sperm‐containing chambers (i.e., ampulla, seminal receptacle, and bursa copulatrix) of the reproductive system of Okenia polycerelloides (Ortea & Bouchet, 1983), based on light, confocal, and electron microscopy. Although the morphology of the ampulla is similar to other species, indicating that it is a site for autosperm storage, we found some sperm facing the ampullar epithelium, a feature commonly regarded as characteristic of the seminal receptacle of sea slugs. The seminal receptacle of O. polycerelloides showed secretory activity and contained sperm with distribution and orientation suggestive of stratification of allosperm from distinct mating events, a feature that would affect sperm competition. The bursa copulatrix had epithelial cells with secretory and absorptive characteristics, and contained degraded sperm and yolk granules within its lumen. Comparative analyses of the contents of each organ demonstrated that sperm digestion occurs in the bursa copulatrix and affects sperm heads first, changing their morphology from slender and curved to shorter and ellipsoid before complete lysis. Although digestion and absorption of surplus sperm are currently the main hypothesized functions for the bursa copulatrix, its role in cryptic female choice should not be ruled out. The close structural connection between the seminal receptacle and bursa copulatrix, as well as their muscular walls, would enable control over the fate of the sperm received in each mating event, that is, storage or digestion.
General Budget Support and Tax Revenue Instability in Developing Countries

The main thrust of this article is to investigate the effect of general budget support (GBS) resources on tax revenue instability in recipient-countries, including when the latter experience higher economic growth volatility and higher terms of trade instability. The empirical findings suggest that GBS flows exert a negative effect on tax revenue instability, with the magnitude (in absolute value) of this negative effect being lower in Sub-Saharan African (SSA) countries compared to NonSSA countries in the sample. Additionally, the magnitude of the negative effect of GBS resources on tax revenue instability increases as countries experience higher economic growth volatility or greater terms of trade instability. It is worth noting that the magnitude of the GBS effect on tax revenue instability is small, including compared to other development aid inflows. Thus, from an economic perspective, GBS flows exert a lower effect on tax revenue instability than do other development aid flows.