Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 vaccines effectiveness against symptomatic SARS-CoV-2 Delta variant infection: a population-based case-control study in St. Petersburg, Russia

Background: Studies of mRNA and vector-based vaccines used in different countries report acceptable levels of effectiveness against SARS-CoV-2 infection caused by the Delta variants of SARS-CoV-2. No studies estimated vaccine effectiveness (VE) of Gam-COVID-Vac and other vaccines used in Russia against symptomatic infection with Delta variant. In this population-based case-control study, we aimed to estimate the effectiveness of the Russian COVID-19 vaccines against symptomatic SARS-CoV-2 during the recent outbreak caused by the Delta VOC in October 2021 in St. Petersburg, Russia. Methods: In a population-based case-control study with density sampling of controls, we acquired information on cases and controls from two independent studies conducted in St. Petersburg. Cases were symptomatic patients with confirmed SARS-CoV-2 (using polymerase chain reaction (PCR) test) referred to low-dose computed tomography (LDCT) triage in two outpatient centres between October 6 and 14, 2021 during the Delta variant outbreak. We recruited the controls during the representative survey of the seroprevalence study conducted during the same period in St. Petersburg using random digit dialling. In the primary analysis, we used logistic regression models to estimate the adjusted (age, gender, and history of confirmed COVID-19) VE against symptomatic SARS-CoV-2 resulted in a referral to triage centre for three vaccines used in Russia: Gam-COVID-Vac, EpiVacCorona, and CoviVac. Findings: We included 1,198 cases and 2,747 controls recruited between the 6th and 14th of October in the final analysis. VE was 58% (95% CI: 50; 64) for Gam-COVID-Vac (Sputnik V), 50% (95% CI: 30; 64) for 1-dose Gam-COVID-Vac (Sputnik V) or Sputnik Light, -40% (95% CI: -191; 33) for EpiVacCorona and 38% (95% CI: 0; 62) for CoviVac. Without adjustment for the history of confirmed COVID-19 VE for all vaccines was lower, except for one-dose Gam-COVID-Vac (Sputnik Light). The adjusted VE was slightly lower in women - 52% (95% CI: 41; 62) than men - 66% (95% CI: 55; 74). It was also higher in younger age. However, in the analysis restricted to participants without a history of confirmed COVID-19, the differences in VE by age group were smaller. Interpretation: In contrast to other Russian vaccines, Gam-COVID-Vac is effective against symptomatic SARS-CoV-2 infection caused by Delta VOC. Effectiveness is likely higher than the estimated 58% due to bias arising from high prevalence of the past COVID-19 in St. Petersburg. Funding: Population-based survey in St. Petersburg was funded by Polymetal International, plc.
Effectiveness of the Pfizer-BioNTech Vaccine against COVID-19-Associated Hospitalizations among Lebanese Adults ≥75 Years Old—Lebanon, April–May 2021

Introduction In Lebanon, the nationwide vaccination against COVID-19 was launched in February-2021 using Pfizer-BioNTech vaccine and prioritizing elderly, persons with comorbidities and healthcare workers. Our study aims to estimate the post-introduction vaccine effectiveness (VE) of Pfizer-BioNTech in preventing COVID-19 hospitalization among [≥]75 years in Lebanon. Methods A case-control study design was used. Cases were Lebanese, [≥]75 years and hospitalized with positive PCR result during April-May 2021. Cases were randomly selected from the COVID-19 database of the Epidemiological Surveillance Unit(ESU) at the Ministry of Public Health(MOPH). Each case was matched by age and locality to 2 controls. Controls were hospitalized, non-COVID-19 patients with negative PCR result, randomly selected from the MOPH hospital admission database. VE was calculated for fully and partially vaccinated, using multivariate conditional logistic regression analyses. Results 345 cases and 814 controls were recruited. Half were females, with a mean age of 83 years. 14 cases(5%) and 143 controls(22%) were fully vaccinated. Univariate analysis showed significant association with: gender, month of confirmation/admission, general health, chronic medical conditions, main income source and living arrangement. After adjusting for month of admission and gender, multivariate analysis yielded a VE of 82% (95%CI = 69%-90%) against COVID-19 associated hospitalization for those fully vaccinated and 53% (95%CI = 23%-71%) for those partially vaccinated (<14 days of first or within 14 days of second dose). Conclusions Our study showed that Pfizer-BioNTech vaccine is effective in reducing risk for COVID-19-associated hospitalization in Lebanese elderly([≥]75 years). Additional studies are warranted to explore vaccine effectiveness in reducing hospitalization in younger age groups, as well as in reducing covid-19 infections.
Systemic Adverse Effects Induced by the BNT162b2 Vaccine Are Associated with Higher Antibody Titers from 3 to 6 Months after Vaccination

Objective: We aimed to determine the relationship between vaccine-related adverse effects and antibody (Ab) titers from 3 to 6 months after the second dose of the BNT162b2 coronavirus disease 2019 (COVID-19) mRNA vaccine (Pfizer/BioNTech) in Japan. Methods: We enrolled 378 healthcare workers (255 women, 123 men) whose Ab titers 3 and 6 months after the second dose were analyzed in our previous study and whose characteristics and adverse effects were collected previously using a structured self-report questionnaire. Results: Median age was 44 years. While injection-site symptoms occurred with almost equal frequency between the first and second doses, systemic adverse effects, such as general fatigue and fever, were significantly more frequent after the second dose than after the first. Multivariate analysis showed that fever was significantly correlated with female sex for the second dose (odds ratio [OR], 2.139; 95% confidence interval [95%CI], 1.185-3.859), older age for the first dose (OR, 0.962; 95%CI, 0.931-0.994) and second dose (OR, 0.957; 95%CI, 0.936-0.979), and dyslipidemia for the first dose (OR, 8.750; 95%CI, 1.814-42.20). Age-adjusted Ab titers at 3 months after vaccination were 23.7% and 23.4% higher in patients with fever than in those without fever after first and second dose, respectively. In addition, age-adjusted Ab titers at 3 and 6 months after the second dose were respectively 21.7% and 19.3% higher in the group with anti-inflammatory agent use than in the group without anti-inflammatory agent use. Conclusion: Participants with systemic adverse effects tend to have higher Ab titers from 3 to 6 months after the second dose of the BNT162b2 vaccine. Our results may encourage vaccination, even among people with vaccine hesitancy related to relatively common systemic adverse effects.
Estimation of Bistatic Radar Detection Performance Under Discrete Clutter Conditions Using Stochastic Geometry

We propose a metric called the bistatic radar detection coverage probability to evaluate the detection performance of a bistatic radar under discrete clutter conditions. Such conditions are commonly encountered in indoor and outdoor environments where passive radars receivers are deployed with opportunistic illuminators. Backscatter and multipath from the radar environment give rise to ghost targets and point clutter responses in the radar signatures resulting in deterioration in the detection performance. In our work, we model the clutter points as a Poisson point process to account for the diversity in their number and spatial distribution. Using stochastic geometry formulations we provide an analytical framework to estimate the probability that the signal to clutter and noise ratio from a target at any particular position in the bistatic radar plane is above a predefined threshold. Using the metric, we derive key radar system perspectives regarding the radar performance under noise and clutter limited conditions; the range at which the bistatic radar framework can be approximated to a monostatic framework; and the optimal radar transmitted power and bandwidth. Our theoretical results are experimentally validated with Monte Carlo simulations.
Early Alveolar Epithelial Cell Necrosis is a Potential Driver of ARDS with COVID-19

Rationale: Acute respiratory distress syndrome (ARDS) with COVID-19 is aggravated by hyperinflammatory responses even after passing the peak of viral load. However, the underlying mechanisms remain unclear. Objectives : Here, we assess whether alveolar epithelial cell necrosis and subsequent releases of damage associated molecular patterns (DAMPs) at an early disease stage aggravate ARDS with COVID-19 Methods: In patients with COVID-19 with and without ARDS and healthy adults, serum levels of the following were quantified: an epithelial total cell death marker, cytokeratin18-M65; an epithelial apoptosis marker, CK18-M30; HMGB-1; and alveolar epithelial and endothelial injury markers, sRAGE, angiopoietin-2, and surfactant protein-D. Molecular mechanisms of alveolar epithelial cell death and effects of HMGB-1 neutralization on alveolar tissue injury were assessed using a mouse model of COVID-19-induced ARDS. Measurements and main results: The levels of CK18-M65, CK18-M30, and alveolar tissue injury markers were elevated in early stages of ARDS. The median M30/M65 ratio, an epithelial apoptosis indicator, was 31.50% in patients with ARDS, a value significantly lower than that of non-ARDS patients or healthy subjects. Serum levels of HMGB-1, one of DAMPs released from necrotic cells, were also significantly elevated in ARDS versus non-ARDS patients. In a COVID-19-induced ARDS mouse model, alveolar epithelial cell necrosis involved two forms of programmed necrosis, necroptosis and pyroptosis. Finally, neutralization of HMGB-1 attenuated alveolar tissue injury in the mouse model. Conclusions: Necrosis, including necroptosis and pyroptosis, seems to be the primary form of alveolar epithelial cell death, and subsequent release of DAMPs is a potential driver of COVID-19-induced ARDS.
Analytical Methods for High-Rate Global Quantum Networks

The development of a future, global quantum communication network (or quantum internet) will enable high rate private communication and entanglement distribution over very long distances. However, the large-scale performance of ground-based quantum networks (which employ photons as information carriers through optical-fibres) is fundamentally limited by fibre quality and link length, with the latter being a primary design factor for practical network architectures. While these fundamental limits are well established for arbitrary network topologies, the question of how to best design global architectures remains open. In this work, we introduce a large-scale quantum network model called weakly-regular architectures. Such networks are capable of idealising network connectivity, provide freedom to capture a broad class of spatial topologies and remain analytically treatable. This allows us to investigate the effectiveness of large-scale networks with consistent connective properties, and unveil critical conditions under which end-to-end rates remain optimal. Furthermore, through a strict performance comparison of ideal, ground-based quantum networks with that of realistic satellite quantum communication protocols, we establish conditions for which satellites can be used to outperform fibre-based quantum infrastructure; {rigorously proving the efficacy of satellite-based technologies for global quantum communications.
Learning to Complete Code with Sketches

Code completion is usually cast as a language modelling problem, i.e., continuing an input in a left-to-right fashion. However, in practice, some parts of the completion (e.g., string literals) may be very hard to predict, whereas subsequent parts directly follow from the context. To handle this, we instead consider the scenario of generating code completions with"holes"inserted in places where a model is uncertain. We develop Grammformer, a Transformer-based model that guides code generation by the programming language grammar, and compare it to a variety of more standard sequence models. We train the models on code completion for C# and Python given partial code context. To evaluate models, we consider both ROUGE as well as a new metric RegexAcc that measures success of generating completions matching long outputs with as few holes as possible. In our experiments, Grammformer generates 10-50% more accurate completions compared to traditional generative models and 37-50% longer sketches compared to sketch-generating baselines trained with similar techniques.
Simplest Streaming Trees

Decision forests, including random forests and gradient boosting trees, remain the leading machine learning methods for many real-world data problems, especially on tabular data. However, most of the current implementations only operate in batch mode, and therefore cannot incrementally update when more data arrive. Several previous works developed streaming trees and ensembles to overcome this limitation. Nonetheless, we found that those state-of-the-art algorithms suffer from a number of drawbacks, including low accuracy on some problems and high memory usage on others. We therefore developed the simplest possible extension of decision trees: given new data, simply update existing trees by continuing to grow them, and replace some old trees with new ones to control the total number of trees. In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations. On those datasets, we also demonstrate that our approach often performs as well, and sometimes even better, than conventional batch decision forest algorithm. Thus, SDFs establish a simple standard for streaming trees and forests that could readily be applied to many real-world problems.
A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the slip ratio reference value can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, machine learning (ML) algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which create new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation.
On the Rank, Kernel, and Core of Sparse Random Graphs

We study the rank of the adjacency matrix A of a random Erdős-Rényi graph G ∼ G(n, p). It is well known that when p = (log(n) − ω(1))/n, with high probability, A is singular. We prove that when p = ω(1/n), with high probability, the corank of A is equal to the number of isolated vertices remaining in G after the Karp-Sipser leaf-removal process, which removes vertices of degree one and their unique neighbor. We prove a similar result for the random matrix B, where all entries are independent Bernoulli random variables with parameter p. Namely, we show that if H is the bipartite graph with bi-adjacency matrix B, then the corank of B is with high probability equal to the max of the number of left isolated vertices and the number of right isolated vertices remaining after the Karp-Sipser leaf-removal process on H. Additionally, we show that with high probability, the k-core of G(n, p) is full rank for any k ≥ 3 and p = ω(1/n). This partially resolves a conjecture of Van Vu [30] for p = ω(1/n). Finally, we give an application of the techniques in this paper to gradient coding, a problem in distributed computing.
Model-free reinforcement learning for robust locomotion using demonstrations from trajectory optimization

We present a general, two-stage reinforcement learning approach to create robust policies that can be deployed on real robots without any additional training using a single demonstration generated by trajectory optimization. The demonstration is used in the first stage as a starting point to facilitate initial exploration. In the second stage, the relevant task reward is optimized directly and a policy robust to environment uncertainties is computed. We demonstrate and examine in detail the performance and robustness of our approach on highly dynamic hopping and bounding tasks on a quadruped robot.
hybrid-Falcon: Hybrid Pattern Malware Detection and Categorization with Network Traffic and Program Code

Nowadays, Android is the most dominant operating system in the mobile ecosystem, with billions of people using its apps daily. As expected, this trend did not go unnoticed by miscreants, and Android became the favorite platform for discovering new victims through malicious apps. Moreover, these apps have become so sophisticated that they can bypass anti-malware measures to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to develop an efficient way to detect Android malware. This paper presents hybrid-Flacon, a hybrid pattern Android malware detection and categorization framework. It combines dynamic and static features of Android malware, which are from network traffic and code graph structure. In hybrid-Flacon, we treat network traffic as a dynamic feature and process it as a 2D image sequence. Meanwhile, hybrid-Flacon handles each network flow in the packet as a 2D image and uses a bidirectional LSTM network to process those 2D-image sequences to obtain vectors representing network packets. We use the program code graph for a static feature and introduce natural language processing (NLP) inspired techniques on function call graph (FCG). We design a graph neural network-based approach to convert the whole graph structure of Android apps to vectors. Finally, We utilize those converted vectors, both network and program code features, and concatenate them to detect and categorize the malware. Our results reveal that hybrid-Flacon yields better results as we get 97.16% accuracy on average for malware detection and 88.32% accuracy for malware categorization. Additionally, we release a dataset AndroNetMnist, which converts the network traffic to a 2D-image sequence and helps to accomplish malware detection on a 2D-image sequence.
GNP2 Encodes a High-Specificity Proline Permease in Candida albicans

The utilization of various nutrients is of paramount importance for the ability of Candida albicans to successfully colonize and infect diverse host niches. In this context, amino acids are of special interest due to their ubiquitous availability, relevance for fungal growth, and direct influence on virulence traits like filamentation. ABSTRACT The tight association of Candida albicans with the human host has driven the evolution of mechanisms that permit metabolic flexibility. Amino acids, present in a free or peptide-bound form, are abundant carbon and nitrogen sources in many host niches. In C. albicans, the capacity to utilize certain amino acids, like proline, is directly connected to fungal morphogenesis and virulence. Yet the precise nature of proline sensing and uptake in this pathogenic fungus has not been investigated. Since C. albicans encodes 10 putative orthologs of the four Saccharomyces cerevisiae proline transporters, we tested deletion strains of the respective genes and identified Gnp2 (CR_09920W) as the main C. albicans proline permease. In addition, we found that this specialization of Gnp2 was reflected in its transcriptional regulation and further assigned distinct substrate specificities for the other orthologs, indicating functional differences of the C. albicans amino acid permeases compared to the model yeast. The physiological relevance of proline uptake is exemplified by the findings that strains lacking GNP2 were unable to filament in response to extracellular proline and had a reduced capacity to damage macrophages and impaired survival following phagocytosis. Furthermore, GNP2 deletion rendered the cells more sensitive to oxidative stress, illustrating new connections between amino acid uptake and stress adaptation in C. albicans. IMPORTANCE The utilization of various nutrients is of paramount importance for the ability of Candida albicans to successfully colonize and infect diverse host niches. In this context, amino acids are of special interest due to their ubiquitous availability, relevance for fungal growth, and direct influence on virulence traits like filamentation. In this study, we identify a specialized proline transporter in C. albicans encoded by GNP2. The corresponding amino acid permease is essential for proline-induced filamentation, oxidative stress resistance, and fungal survival following interaction with macrophages. Altogether, this work highlights the importance of amino acid uptake for metabolic and stress adaptation in this fungus.
Identification of Key Determinants of Cerebral Malaria Development and Inhibition Pathways

Liver infection by Plasmodium sporozoites is a required step for infection of the organism. We found that alternate pathways of sporozoite liver infection differentially influence cerebral malaria (CM) development. ABSTRACT Cerebral malaria (CM), coma caused by Plasmodium falciparum-infected red blood cells (iRBCs), is the deadliest complication of malaria. The mechanisms that lead to CM development are incompletely understood. Here we report on the identification of activation and inhibition pathways leading to mouse CM with supporting evidence from the analysis of human specimens. We find that CM suppression can be induced by vascular injury when sporozoites exit the circulation to infect the liver and that CM suppression is mediated by the release of soluble factors into the circulation. Among these factors is insulin like growth factor 1 (IGF1), administration of which inhibits CM development in mice. IMPORTANCE Liver infection by Plasmodium sporozoites is a required step for infection of the organism. We found that alternate pathways of sporozoite liver infection differentially influence cerebral malaria (CM) development. CM is one of the primary causes of death following malaria infection. To date, CM research has focused on how CM phenotypes develop but no successful therapeutic treatment or prognostic biomarkers are available. Here we show for the first time that sporozoite liver invasion can trigger CM-inhibitory immune responses. Importantly, we identified a number of early-stage prognostic CM inhibitory biomarkers, many of which had never been associated with CM development. Serological markers identified using a mouse model are directly relevant to human CM.