Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unique Trinucleotide-Bloc Mutation-Based Two SARS-CoV-2 Genotypes with Potential Pathogenic Impacts

SARS-CoV-2, the novel coronavirus behind the COVID-19 pandemic, is acquiring new mutations in its genome. Although some mutations provide benefits to the virus against human immune response, others may result in their reduced pathogenicity and virulence. By analyzing more than 3000 high-coverage, complete sequences deposited in the GISAID database up to April 2020, here I report the uniqueness of the 28881–28883: GGG > AAC trinucleotide-bloc mutation in the SARS-CoV-2 genome that results in two substrains, described here as SARS-CoV-2g (28881–28883: GGG genotype) and SARS-CoV-2a (28881–28883: AAC genotype). Computational analysis and literature review suggest that this bloc mutation would bring 203–204: RG (arginine-glycine)>KR (lysine-arginine) amino acid changes in the nucleocapsid (N) protein affecting the SR (serine-arginine)-rich motif of the protein, a critical region for the transcription of viral RNA and replication of the virus. Thus, 28881–28883: GGG > AAC bloc mutation is expected to modulate the pathogenicity of SARS-CoV-2. These analyses suggest that SARS-CoV-2 has evolved into SARS-CoV-2a affecting COVID-19 infectivity and severity. To confirm these assumptions, retrospective and prospective epidemiological studies should be conducted in different countries to understand the course of pathogenicity of SARS-CoV-2a and SARS-CoV-2g. Laboratory research should focus on the bloc mutation to understand its true impacts on the course of the pandemic. Potential drug and vaccine development should also keep the 28881–28883 region of the N protein under consideration.
A Two-Stage Hybrid Beamforming Design for Full-Duplex mmWave Communications

In this paper, we investigate the hybrid beamforming design for the full-duplex (FD) millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, aiming to maximize the overall spectral efficiency (SE) and cancel the self-interference (SI) simultaneously. The coupling transmit power constraints, unit-modulus constraints, and strong SI make the problem under study highly nonconvex. To solve the complicated problem, we first transform it into an equivalent yet more tractable weighted minimum mean square error minimization problem and then propose an effective two-stage hybrid beam-forming design. In the first stage, we propose to extend the known matrix approximation method to the FD scenario to obtain the initial hybrid beamforming design. In the second stage, we propose to use the block coordinate descent method to update the digital beamformers and combiners while retaining the obtained analog solutions, which sufficiently exploits the easily adjustable property of digital solutions to cancel the residual SI and compensate for the SE loss. We further analyze the computational complexity of the proposed design. Numerical results demonstrate that our proposed two-stage hybrid beamforming design is superior to conventional related methods.
Real-to-Synthetic: Generating Simulator Friendly Traffic Scenes from Graph Representation

Reproducing real-world traffic scenes in the simulator is fundamental to training self-driving systems. Creating a simulation scenario is a complex task, generally done manually: the ego-vehicle and other entities are placed and their trajectories defined, trying to recreate some situation found in real traffic. To reduce the manual burden, here we propose the Real-to-Synthetic toolset. This toolset provides synthetic traffic scene in openDrive format, which can be directly simulated in many simulators such as SUMO or CARLA. Also, we provide a scene generator which generates near-realistic scene from minimum user effort. To maintain the similarity between real-world scene and generated one, here we introduce the concept “Road Scene Graph”(RSG). In this graph, nodes represent entities while edges stand for pairwise relationships. These relationships could be maintained in the scene generation process while the actor is generated according to the distribution sampled from real-world data. Experiments proved that by using “Road Scene Graph”, our scene generator proposes a much more convenient way to conFigure traffic scenes rather than manually defining every actor’s initial status and trajectories.
University Student Perceptions of the Impact of the Social Unrest in Hong Kong

ABSTRACT Social unrest has a significant impact on all citizens but for university students, little is known about how they thrived through this period with certain disconnection to social support while living in residential halls. Therefore, this study focused on the lived experiences of a group of university students and explored how the social unrest had affected them. Focus group interviews were conducted with 20 university students in Hong Kong. Using thematic analysis, the transcriptions were categorized into four themes: disruptions to daily routines, impact on psychological wellbeing, relationships, and coping strategies. It was revealed that reduced social connections during the unrest impacted their coping strategies and relationships, and emotional distress. This sheds implications on policymaking in residential halls and for university counselling services
VEGA ZZ : a versatile toolkit for drug design and protein modelling

In the last years, the computer hardware and softwa re were significantly improved in terms of performance and friendliness of use. This trend is especially evident in the personal computer market, where the users require complex application s (e.g. productivity software, Web navigation, games, etc.) needing high computational power and e xceptional graphic performances. Using a common PC, it’s now possible to do what once needed an expensive graphic workstation. However, the computational chemistry was born in th e Unix workstation age and thus some important molecular modelling packages are still co mmand-line based and, in some cases, require an Unix-like operating system. Starting from this ground, some years ago, the VEGA ZZ 2 project started with a view to create an easy-to-use molecular modelling software with a com plete 3D graphic interface. It was specially developed to teach the students spending few time t o explain the software functionalities and the operating system use. Indeed, VEGA ZZ was created t o run on student-friendly Windows-based PCs and to do the most common molecular modelling o perations in fast and easy way. As time goes by, VEGA ZZ became a more complete sof tware, changing from a teaching tool to a complete molecular modelling suite for researcher, even preserving the starting philosophy. Today, VEGA ZZ can be successfully used to solve many comp utational chemistry problems about drug design, ligand optimization, homology modelling of proteins, and calculation of QSAR molecular descriptors. As an informative example of the fertile potentiali es of VEGA ZZ program, some successful applications on the metabolism prediction will be p r sented. Because metabolic problems lead to too many failures during clinical trials, much effo rt is devoted to in silico models to predict metabolic stability and metabolites. In these studi es, VEGA ZZ can afford a significant contribution as in the prediction of the hydrolytic activity of the carboxylesterase 1 and 2. The obtained results emphasize some crucial properties of the catalytic cavity of these enzyme, confirming that hCES1 prefers substrates with small alcohol groups and la rge hydrophobic acyl moieties, whereas hCES2 better accommodates substrates with with large alco ho groups and small acyl moieties. Finally, the docking results confirm the relevance of hydrophobi c interactions, which seem to govern the hCES1 recognition and allows a robust prediction of hCES1 catalytic activity.
Sugar and Proline Accumulation in Grapefruit Flavedo and Leaves during Cold Hardening of Young Trees1

Four-year-old ‘Marsh’ grapefruit (Citrus paradisi Macf.) trees on trifoliate orange [Poncirus trifoliata (L.) Raf. ] rootstock were temperature acclimated to 5°C in controlled environment facilities with approximately 400 μeinsteins m−2s−1 PAR. Total soluble carbohydrates and proline increased in both leaves and fruit flavedo as temperatures were progressively decreased. Maximum accumulation of carbohydrates occurred in leaves and flavedo at 10° ambient air. Both sucrose and reducing sugars increased in leaves at all acclimating temperatures, but only reducing sugars increased in the flavedo at temperatures below 15°. The concentration of proline was the greatest in the leaves and flavedo at 5°. Both total soluble carbohydrates and proline decreased during temperature deacclimation at 25°.
Diversity of endophytic bacteria of mulberry (Morus L.) under cold conditions

Endophytic bacteria are known to impact the growth and fitness of agriculturally relevant plants. However, there are limited reports describing endophytic bacteria related to mulberry (Morus L.). The present study used Illumina-based 16S rRNA gene sequencing to investigate the endophytic bacterial communities of two mulberry cultivars with differing resistance to low temperature, under cold conditions. In most cases, the bacterial communities of endophytes in the root exhibited higher richness compared with those in the stem, and the communities in resistant cultivar X792 exhibited higher richness compared with those of the sensitive cultivar “Da Shi” (DS). The difference in the proportion of unique operational taxonomic units showed the same trend. The number of genera with significant differences in abundance was greater between organs than between months, and greater between months than between cultivars. Microbial diversity analysis showed that Proteobacteria and Actinobacteria were the dominant phyla in all samples, while Pseudomonas, Steroidobacter, and Rhodococcus were the dominant genera in different samples. There were significant differences between cultivars DS and X792 in the relative abundance of Pseudomonas, Acidibacter, Frigoribacterium, Gaiella, and Pseudokineococcus. PICRUSt predictions indicated that the relative abundances of endophytic bacteria in membrane transport and signal transduction were significantly higher in the stem of resistant cultivar X792 in January compared with that of sensitive cultivar DS. Analysis of β-Diversity also revealed distinct differences in endophytic bacterial communities of stem and root, and communities of the stem in January and February. The complex correlation of the endophytic communities was higher in sensitive mulberry cultivar DS compared with resistant cultivar X792, in the stem compared with the root, and in January compared with February. Overall, findings from this study suggested that the diversity and community structure of endophytic bacteria in mulberry were significantly influenced by organs and months, followed by the host cultivar. The study provides insight into the complex microbial diversity of mulberry under cold conditions.
A Study on Persistence of GAN-Based Vision-Induced Gustatory Manipulation

Vision-induced gustatory manipulation interfaces can help people with dietary restrictions feel as if they are eating what they want by modulating the appearance of the alternative foods they are eating in reality. However, it is still unclear whether vision-induced gustatory change persists beyond a single bite, how the sensation changes over time, and how it varies among individuals from different cultural backgrounds. The present paper reports on a user study conducted to answer these questions using a generative adversarial network (GAN)-based real-time image-to-image translation system. In the user study, 16 participants were presented somen noodles or steamed rice through a video see-through head mounted display (HMD) both in two conditions; without or with visual modulation (somen noodles and steamed rice were translated into ramen noodles and curry and rice, respectively), and brought food to the mouth and tasted it five times with an interval of two minutes. The results of the experiments revealed that vision-induced gustatory manipulation is persistent in many participants. Their persistent gustatory changes are divided into three groups: those in which the intensity of the gustatory change gradually increased, those in which it gradually decreased, and those in which it did not fluctuate, each with about the same number of participants. Although the generalizability is limited due to the small population, it was also found that non-Japanese and male participants tended to perceive stronger gustatory manipulation compared to Japanese and female participants. We believe that our study deepens our understanding and insight into vision-induced gustatory manipulation and encourages further investigation.
Multi-Task Learning of Relative Height Estimation and Semantic Segmentation from Single Airborne RGB Images

The generation of topographic classification maps or relative heights from aerial or remote sensing images represents a crucial research tool in remote sensing. On the one hand, from auto-driving, three-dimensional city modeling, road design, and resource statistics to smart cities, each task requires relative height data and classification data of objects. On the other hand, most relative height data acquisition methods currently use multiple images. We find that relative height and geographic classification data can be mutually assisted through data distribution. In recent years, with the rapid development of artificial intelligence technology, it has become possible to estimate the relative height from a single image. It learns implicit mapping relationships in a data-driven manner that may not be explicitly available through mathematical modeling. On this basis, we propose a unified, in-depth learning structure that can generate both estimated relative height maps and semantically segmented maps and perform end-to-end training. Compared with the existing methods, our task is to perform both relative height estimation and semantic segmentation tasks simultaneously. We only need one picture to obtain the corresponding semantically segmented images and relative heights simultaneously. The model’s performance is much better than that of equivalent computational models. We also designed dynamic weights to enable the model to learn relative height estimation and semantic segmentation simultaneously. At the same time, we have conducted good experiments on existing datasets. The experimental results show that the proposed Transformer-based network architecture is suitable for relative height estimation tasks and vastly outperforms other state-of-the-art DL (Deep Learning) methods.
Experiences and Perceptions in Dyads about Ostomy Care. Meta-synthesis of Qualitative Studies

Abstract Objective. To understand the experiences and perceptions of care dyads (person and caregiver) when having a permanent discharge ostomy. Methods. Meta-synthesis that followed the ENTREQ standards and was registered in PROSPERO CRD42020221755; It was developed in three phases: (i). Search for studies in academic search engines, with MeSH terms: [(Patients) AND (Ostomy)) AND (Caregivers)], including qualitative primary studies published between 2000 and 2020; (ii). Assessment of the methodological quality with the CORE-Q instrument and the credibility of qualitative findings under the guidelines of the Johanna Briggs Institute; and (iii). Comparative analysis according to the guidelines by Sandelowski and Barroso. Results. The work identified 664 studies; in screening, 35 passed to full-text analysis; 22 to methodological quality evaluation; and 10 to meta-synthesis. The study found 43 qualitative findings that constituted four categories: dyads perform instrumental and emotional care; ambivalent family caregiving feelings and actions; assertive and effective family care; and rejection of bodily changes and sexual dysfunction. These categories constitute the central meta-theme: “The dyads experience a life rupture, which is restored in a sea of ambivalent emotions and learning; at the same time, affective, instrumental and assertive care is constructed”. Conclusions. People who experience having a permanent discharge ostomy express their rejection to the change in bodily image, alterations in sexual life and as a couple. Caregivers and families are the main source of support by being facilitators in self-care, through relationships of mutuality and reciprocity.
A Paradigm Shift in the Treatment and Management of Onychomycosis

There is an increase in the incidence of onychomycosis, especially in at-risk populations. Onychomycosis is difficult to treat, as the efficacy of most antifungal agents is relatively low. Nondermatophyte molds (NDMs) and mixed infection (dermatophyte plus NDM) onychomycosis are contributing to growing antifungal resistance, as they are often underestimated and ignored due to incorrect diagnosis. There is a need for a paradigm shift in the management of onychomycosis to a patient-centered, holistic approach with an emphasis on laboratory diagnosis prior to initiating treatment, which enables the rational choice of the antifungal agent. Additionally, in the case of resistant infections, antifungal susceptibility testing is recommended. Strategies for effective management of onychomycosis include disinfection of fungal reservoirs in shoes and socks and prophylaxis posttreatment using topical antifungal agents. These measures may reduce the recurrence of onychomycosis and improve long-term clinical success.
ACiS: Lightweight and Robust Homomorphic Block Cipher Additive Scheme

Recent IoT emerging systems and applications require processing over encrypted data to preserve confidentiality. However, designing efficient Homomorphic Encryption (HE) algorithm to respond better to real-time requirements of IoT applications and tiny IoT devices is primordial. Unfortunately, existing homomorphic asymmetric schemes do not provide the efficiency implementation. While, on the other hand, homomorphic symmetric approaches suffer from a low level of security. In this paper, an efficient flexible additive homomorphic symmetric cipher scheme is proposed and it is called “ACiS”. Moreover, ACiS is based on the dynamic key approach to reach a high level of security, where different cryptographic primitives are used for different sessions. In addition, it can achieve a good performance and robustness against confidentiality attacks. It is based on a simple round function that should be iterated for nr iterations and $nr > 1$. Furthermore, the proposed solution is analyzed and evaluated in terms of security and performance levels. Experimental results prove that the proposed solution reaches a good balance between performance and security level compared to the Paillier cipher scheme (a well-known asymmetric additive HE scheme).
Pyroptotic Patterns in Blood Leukocytes Predict Disease Severity and Outcome in COVID-19 Patients

The global coronavirus disease 2019 (COVID-19) pandemic has lasted for over 2 years now and has already caused millions of deaths. In COVID-19, leukocyte pyroptosis has been previously associated with both beneficial and detrimental effects, so its role in the development of this disease remains controversial. Using transcriptomic data (GSE157103) of blood leukocytes from 126 acute respiratory distress syndrome patients (ARDS) with or without COVID-19, we found that COVID-19 patients present with enhanced leukocyte pyroptosis. Based on unsupervised clustering, we divided 100 COVID-19 patients into two clusters (PYRcluster1 and PYRcluster2) according to the expression of 35 pyroptosis-related genes. The results revealed distinct pyroptotic patterns associated with different leukocytes in these PYRclusters. PYRcluster1 patients were in a hyperinflammatory state and had a worse prognosis than PYRcluster2 patients. The hyperinflammation of PYRcluster1 was validated by the results of gene set enrichment analysis (GSEA) of proteomic data (MSV000085703). These differences in pyroptosis between the two PYRclusters were confirmed by the PYRscore. To improve the clinical treatment of COVID-19 patients, we used least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model based on differentially expressed genes between PYRclusters (PYRsafescore), which can be applied as an effective prognosis tool. Lastly, we explored the upstream transcription factors of different pyroptotic patterns, thereby identifying 112 compounds with potential therapeutic value in public databases.
Neural Cascade Architecture for Multi-Channel Acoustic Echo Suppression

Traditional acoustic echo cancellation (AEC) works by identifying an acoustic impulse response using adaptive algorithms. This paper proposes a neural cascade architecture for joint acoustic echo and noise suppression to address both single-channel and multi-channel AEC (MCAEC) problems. The proposed cascade architecture consists of two modules. A convolutional recurrent network (CRN) is employed in the first module for complex spectral mapping. Its output is fed as an additional input to the second module, where a long short-term memory network (LSTM) is utilized for magnitude mask estimation. The entire architecture is trained in an end-to-end manner with the two modules optimized jointly using a single loss function. The final output is generated using the enhanced phase and magnitude obtained from the first and the second module, respectively. The cascade architecture enables the proposed method to obtain robust magnitude estimation as well as phase enhancement. The proposed method is investigated under different AEC setups. We find that the deep learning based approach avoids the no-uniqueness problem in traditional MCAEC. For MCAEC setups with multiple microphones, combining deep MCAEC with supervised beamforming further improves the system performance. Evaluation results show that the proposed approach effectively suppresses acoustic echo and noise while preserving speech quality, and consistently outperforms related methods under different setups.
Electrospun Hydrophobic Interaction Chromatography (HIC) Membranes for Protein Purification

Responsive membranes for hydrophobic interaction chromatography have been fabricated by functionalizing poly(N-vinylcaprolactam) (PVCL) ligands on the substrate of electrospun regenerated cellulose nanofibers. Both static and dynamic binding capacities and product recovery were investigated using bovine serum albumin (BSA) and Immunoglobulin G (IgG) as model proteins. The effects of ligand chain length and chain density on static binding capacity were also studied. A static binding capacity of ~25 mg/mL of membrane volume (MV) can be achieved in optimal ligand grafting conditions. For dynamic binding studies, protein binding capacity increased with protein concentration from 0.1 to 1.0 g/L. Dynamic binding capacity increased from ~8 mg/mL MV at 0.1 g/L BSA to over 30 mg/mL at 1.0 g/L BSA. However, BSA recovery decreased as protein concentration increased from ~98% at 0.1 g/L BSA to 51% at 1 g/L BSA loading concentration. There is a clear trade-off between binding capacity and recovery rate. The electrospun substrate with thicker fibers and more open pore structures is superior to thinner fibrous membrane substrates.