Miracle-dz/UniFAR-FTUs huggingface dataset:
D-RDMA: Bringing Zero-Copy RDMA to Database Systems

The DMA part of RDMA stands for Direct Memory Access . It refers to the ability of a network card (among other de-vices) to read and write data from a host’s memory without CPU assistance. RDMA’s performance depends on eﬃcient DMAs in the initiating and target hosts. In turn, a DMA’s cost is almost always proportional to the length of the data transfer. The exception is small DMAs, which suﬀer from high overheads. In this paper, we show that database systems often generate small DMA operations when using RDMA canonically. The reason is that the data they transmit is seldom contiguous by the time transmissions occur. Modern databases avoid this problem by copying data into large transmission buﬀers and issuing RDMAs over these buﬀers instead. How-ever, doing this requires a substantial amount of CPU cycles and memory bandwidth, forfeiting RDMA’s beneﬁts: its zero-copy feature. To solve this issue, we introduce D- RDMA , a declarative extension to RDMA. D-RDMA is declarative in that it speciﬁes what data to transmit but not the DMA schedule to do so. The approach leverages a smart NIC to group data fragments into larger DMAs and produce the same packet stream as regular RDMA. Our experiments show that the network throughput can increase from 18 Gbps per CPU core to up to 98 Gbps (on a 100 Gbps card) with virtually zero CPU usage when replac-ing RDMA with D-RDMA in a typical data shuﬄe scenario. We believe that D-RDMA can enable a new generation of high-performance systems to take full advantage of fast networking without incurring the usual CPU penalties.
3D single-cell shape analysis using geometric deep learning

Aberrations in 3D cell morphogenesis are linked to diseases such as cancer. Yet there is little systems-level understanding of cell shape determination in 3D, largely because there is a paucity of data-driven methods to quantify and describe 3D cell shapes. We have addressed this need using unsupervised geometric deep learning to learn shape representations of over 95,000 melanoma cells imaged by 3D high-throughput light-sheet microscopy. We used a dynamic graph convolutional foldingnet autoencoder with improved deep embedded clustering to simultaneously learn lower-dimensional representations and classes of 3D cell shapes. We describe a landscape of 3D cell morphology using deep learning-derived 3D quantitative morphological signatures (3DQMS) across different substrate geometries, following treatment by different clinically relevant small molecules and systematic gene depletion in high-throughput. By data integration, we predict modes of action for different small molecules providing mechanistic insights and blueprints for biological re-engineering. Finally, we provide explainability and interpretability for deep learning models.
Electrostatic features for the Receptor binding domain of SARS-COV-2 wildtype and its variants. Compass to the severity of the future variants with the charge-rule

Electrostatic intermolecular interactions are important in many aspects of biology. We have studied the main electrostatic features involved in the interaction of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with the human receptor Angiotensin-converting enzyme 2 (ACE2). As the principal computational tool, we have used the FORTE approach, capable to model proton fluctuations and computing free energies for a very large number of protein-protein systems under different physical-chemical conditions, here focusing on the RBD-ACE2 interactions. Both the wild-type and all critical variants are included in this study. From our large ensemble of extensive simulations, we obtain, as a function of pH, the binding affinities, charges of the proteins, their charge regulation capacities, and their dipole moments. In addition, we have calculated the pKas for all ionizable residues and mapped the electrostatic coupling between them. We are able to present a simple predictor for the RBD-ACE2 binding based on the data obtained for Alpha, Beta, Gamma, Delta, and Omicron variants, as a linear correlation between the total charge of the RBD and the corresponding binding affinity. This “RBD charge rule” should work as a quick test of the degree of severity of the coming SARS-CoV-2 variants in the future. Categories and Subject Descriptors:
Genetic parameters for resistance to gastrointestinal nematodes in sheep: a meta-analysis

Gastrointestinal nematodes (GIN) are damaging parasites of global sheep populations. The key weapons in fighting GIN have been anthelmintic drugs, but the emergence of drug-resistant parasites has meant that alternative control methods are needed. One of these alternatives is to breed for enhanced host resistance to GIN, and decades of research have estimated the genetic contribution to different measures of resistance to GIN and their genetic correlations with other desirable performance traits. It is clear that parasite resistance is a heritable trait that can be selected for. Despite this consensus, estimates of both heritability of resistance and genetic correlations with other traits vary widely between studies, and the reasons for this variation have not been examined. This study provides a comprehensive and quantitative meta-analysis of genetic parameters for resistance to GIN in sheep, including measures of worm burden (faecal egg counts, FEC), anti-parasite immunity (GIN-specific antibodies), and parasite-induced pathology (FAMACHA© scores). Analysis of 591 heritability estimates from 121 studies revealed a global heritability estimate for resistance to GIN of 0.25 (95%CI = 0.22 – 0.27) that was stable across breeds, ages, geographical location and analytical methods. Meanwhile, analysis of 559 genetic correlations from 54 studies revealed that resistance to GIN overall has a positive genetic correlation of +0.10 (95%CI = 0.02 – 0.19) with performance traits, and that this was consistent across breeds, ages, sexes and analytical methods. Importantly, the direction of the genetic correlation varied with the resistance trait measured: while FEC and FAMACHA© scores were favourably correlated with performance traits, adaptive immune markers were unfavourably correlated, suggesting that selection for enhanced immune responses to GIN could reduce animal performance. Overall, the results suggest that breeding for resistance to GIN should continue to form part of integrated management programs to reduce the impact of parasites on health and performance, but that selection for enhanced immune responses should be avoided.
Bystander memory-phenotype conventional CD4+ T cells exacerbating autoimmune neuroinflammation

Memory-phenotype (MP) CD4+ T cells are a substantial population of conventional T cells that exist in steady-state mice, and their immunologic functions in autoimmune disease have not yet been studied. In this work, we unveil a unique phenotype of MP CD4+ T cells by analyzing single-cell transcriptomics and T cell receptor (TCR) repertoires. We found that steady-state MP CD4+ T cells exist regardless of germ and food-antigen which are composed of heterogenous effector subpopulations. Distinct subpopulations of MP CD4+ T cells are specifically activated by IL-1 family cytokines and STAT activators, revealing that the cells have TCR-independent effector functions. Especially, CCR6high MP CD4+ T cells are major responders to IL-1β and IL-23 without MOG35-55 antigen reactivity, which gives them pathogenic-Th17 characteristics and allows them to contribute to autoimmune encephalomyelitis. We identified Bhlhe40 in CCR6high MP CD4+ T cells drives the expression of GM-CSF, contributing to CNS pathology in experimental autoimmune encephalomyelitis. Collectively, our findings reveal heterogeneity of MP CD4+ T cells that can contribute to autoimmune neuroinflammation in bystander manner synergistically with antigen-specific T cells.
GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification

Whole genome sequencing of clinical bacterial isolates has the potential to transform the fields of medicine and public health, with particular impact on molecular epidemiology, infection control and assessing the spread of antibiotic resistance. To realize this potential, bioinformatic software needs to be developed that meets the quality standards of a diagnostic test to allow the reporting of identification results. Our research group has developed a methodology (GAMBIT: Genomic Approximation Method for Bacterial Identification and Tracking) using k-mer based strategies for identification of bacteria based on whole genome sequence reads. GAMBIT incorporates this algorithm with a highly curated searchable database of genomes, which is a subset of the NCBI RefSeq assembly database. In this manuscript, we describe the validation of the scoring methodology, robustness to chosen parameters, establishment of confidence thresholds and the curation of the reference database. We discuss a validation set with GAMBIT deployed as a laboratory-developed test at the Alameda County Public Health Laboratory in Oakland, California. Three advancements were required to build upon existing k-mer based strategies to allow GAMBIT to possess the quality control parameters desired for its use as a diagnostic laboratory-developed test. Firstly, we innovated the data structure used to store the database of known bacterial genomes. This allowed us to store 48,224 bacterial genomes in a k-mer based database—a majority of the bacterial genomes in the NCBI RefSeq database at the time of development. Secondly, curation of the NCBI RefSeq database was required to remove ambiguous or potentially incorrectly labeled bacterial genomes to greatly increase confidence in positive matches. Lastly, we used this curated version of the NCBI RefSeq database and our scoring method to generate confidence thresholds for identification. Thus, the end-user does not rely simply on the closest match, but is informed whether that closest match exceeds a threshold for highly confident identification. This method greatly reduces or eliminates false identifications which are often detrimental in a clinical setting.
The virtualome: a computational framework to evaluate microbiome analyses

Microbiomes have been the focus of a substantial research effort in the last decades. The composition of microbial populations is normally determined by comparing DNA sequences sampled from those populations with the sequences stored in genomic databases. Therefore, the amount of information available in databanks should be expected to constrain the accuracy of microbiome analyses. Albeit normally ignored in microbiome studies, this constraint could severely compromise the reliability of microbiome data. To test this hypothesis, we generated virtualomes, virtual bacterial populations that exhibit the ecological structure of real-world microbiomes. Confronting the analyses of virtualomes with their original composition revealed critical issues in the current approach to characterizing microbiomes, issues that were empirically confirmed by analyzing the microbiome of Galleria mellonella larvae. To reduce the uncertainty of microbiome data, the effort in the field must be channeled towards significantly increasing the amount of available genomic information.
Mechano-inhibition of Endocytosis Sensitizes Cancer Cells to Fas-induced Apoptosis

The transmembrane death receptor Fas transduces apoptotic signals upon binding its ligand, FasL. Although Fas is highly expressed in cancer cells, insufficient cell surface Fas expression desensitizes cancer cells to Fas-induced apoptosis. Here, we show that the increase in Fas microaggregate formation on the plasma membrane in response to the inhibition of endocytosis sensitizes cancer cells to Fas-induced apoptosis. We used a clinically accessible Rho-kinase inhibitor, fasudil, that reduces endocytosis dynamics by increasing plasma membrane tension. In combination with exogenous soluble FasL (sFasL), fasudil promoted cancer cell apoptosis, but this collaborative effect was substantially weaker in nonmalignant cells. The combination of sFasL and fasudil prevented glioblastoma cell growth in embryonic stem cell-derived brain organoids and induced tumor regression in a xenograft mouse model. Our results demonstrate that sFasL has strong potential for apoptosis-directed cancer therapy when Fas microaggregate formation is augmented by mechano-inhibition of endocytosis.
ON COGNITIVE ACTIONS AND IMAGERY DEVELOPMENT SKILL: TOWARDS A PATTERN LANGUAGE FOR NOVICE DESIGNERS

The theme of this thesis is to evaluate the possibility of utilizing pattern language to assist designer's preliminary idea. According to Alexander’s proposition, the concept of pattern language is to help novice architects to study design through the systematic process. This study is to investigate whether novices would have a great improvement in idea sketching toward a pattern language. The experiment in this study includes two principal parts. The first part is to find a systematizing method to generalize pattern language by inviting experts to join a design workshop to edit a series of systematic pattern language through the discussion. The pattern language is made by the picture card, containing four key elements: 1.Name, 2.Picture, 3.Question, 4.Solution, it make novice easier to learn by it. The second part is to prove pattern language could promote its picture idea and develop novices’ ability through the process of studying. By dividing into the experiment group and the contrast group, the experiment group sketch toward pattern language; and the contrast group chooses the catalog method by watching a large number of pictures. We would compare and analyze the difference between these two methods through the information what video camera recorded. The result of this investigation finds that the cognitive actions in both groups are similar; it may be because the both groups are all novices. However, there is a huge diversity of the cognitive thinking way organized by the cognitive action in both groups. The novices utilizing pattern language have the continuous and systematic cognitive thinking that consisted of the cognitive action . The thinking of the other novices on catalog method is discontinuous and inefficient. According to the evaluation of the experts’ assessment, the novices utilizing pattern language is more efficient than the novice designers on catalog method.
S-Adenosyl-L-methionine: its role in the treatment of liver disorders1–4

S-Adenosyl-L-methionine (SAMe) exerts many key functions in the liver, including serving as a precursor for cysteine, 1 of 3 amino acids of glutathione—the major physiologic defense mechanism against oxidative stress. SAMe is particularly important in opposing the toxicity of free oxygen radicals generated by various pathogens, including alcohol, which cause oxidative stress largely by the induction of cytochrome P4502E1 (CYP2E1) and by its metabolite acetaldehyde. SAMe also acts as the main methylating agent in the liver. The precursor of SAMe is methionine, one of the essential amino acids, which is activated by SAMe-synthetase (EC 2.5.1.6). Unfortunately, the activity of this enzyme is significantly decreased as a consequence of liver disease. Because of decreased utilization, methionine accumulates and, simultaneously, there is a decrease in SAMe that acquires the status of an essential nutrient and therefore must be provided exogenously as a supernutrient to compensate for its deficiency. Administration of this innocuous supernutrient results in many beneficial effects in various tissues, mainly in the liver, and especially in the mitochondria. This was shown in alcohol-fed baboons and in other experimental models of liver injury and in clinical trials, some of which are reviewed in other articles in this issue. Am J Clin Nutr 2002;76(suppl):1183S–7S.
Analysis of AI Contribution to Improving BPM of E-Commerce in China: Examining the Case of Taobao

The present article is focused on the contribution of Artificial Intelligence (AI) to improve the Business Performance Management (BPM) of China’s E-Commerce. Taking the example of Taobao, one of the growing Chinese companies, some users are facing on unsafe Epurchase and poor quality of some products. This work is a theoretical approach and targeted at an interdisciplinary analysis of AI positive effects on improving the BPM of Taobao to reduce these problems. Despite some critical incidents, the expectation of fully online safety buying and selling products is still pursuing by Taobao Company.
A novel miniaturized log-periodic dipole array antenna using dynamic scaling factor for RFID application

A novel radio frequency identification antenna based on log-periodic dipole array (LPDA) antenna at 5.8 GHz is presented. The antenna design approach consists of two major steps. Firstly, the conventional primitive method is used to design a traditional LPDA antenna. The antenna structure is then miniaturized and its overall performance significantly improved. Dynamic scaling factor along with uniformly separated elements are introduced in this class of antenna, resulting in a 51.5% length reduction. To improve front-to-back (F/B) ratio a symmetrical folding technique is employed, leading to 21.5 dB F/B ratio for the fabricated prototype. An excellent level of input impedance matching (S11 = −28 dB) is achieved using a well-designed feeding system through a proximity coupling technique. The measured gain of the fabricated prototype is 6.2 dBi, and the overall dimension of the antenna substrate is only 30 × 45 mm2 (0.58λ0 × 0.87λ0).
Designing Therapeutic Support for the People with Alzheimer’s disease. A designerly way of Investigation

Abstract Alzheimer’s disease (AD) is incurable but it is treatable; both drug and non-drug treatments of its symptoms are available. Non-drug therapies are consisting of physical and social components, which could be supported and assisted by designerly way of thinking. Besides, literature inquiry corroborates that new ways of interventions/approaches are desperately needed for the wellbeing of both the patients and their caregivers. In this research, person centred design approach is adapted. Accordingly, this research mind-set is set participatory, whereby healthcare professionals and patients’ relatives are considered the experts and equal partners throughout the investigation process. Face to face interviews are conducted. Their real life experiences coping with the problematic symptoms are highlighted. In this direction their recommendations and suggestions are listed as design guidelines for the further investigations. Besides, the findings indicate that the created design concept which focuses on enhancing the patient’s emotional state and keeping these positive emotions as long as possible by suggesting nonverbal commination cues as a tool could lead the upcoming researches in this area.
A COMBINATION OF THE MOST PROBABLE NUMBER TECHNIQUE AND COLORIMETRIC BACTERIOPHAGE-BASED ASSAY FOR QUANTITATIVE DETECTION OF ESCHERICHIA COLI O157:H7 IN CATTLE MANURE

A COMBINATION OF THE MOST PROBABLE NUMBER TECHNIQUE AND COLORIMETRIC BACTERIOPHAGE-BASED ASSAY FOR QUANTITATIVE DETECTION OF ESCHERICHIA COLI O157:H7 IN CATTLE MANURE Hoang A. Hoang, Michiharu Abe, Kiyohiko Nakasaki 1 Faculty of Chemical Engineering, Ho Chi Minh City University of Technology, VNU-HCM, HCMC, Vietnam 2 Department of International Development Engineering, Tokyo Institute of Technology, Tokyo, Japan
Pervasive sensor system for evidence-based nursing care support

This paper introduces a pervasive sensor system for nursing homes, where daily activities of elderly persons are monitored by pervasive sensors all the time. Deterioration in the quality of nursing care for the elderly has become one of the biggest problems in the aging society and the authors have been challenging the problem by sensors embedded in a nursing room. The sensors accumulate position information of a subject person and his wheelchair, then it is utilized for prompt assists for the subject and also used for obtaining his life log. In our experiments, we obtained position data of an elderly person for a month and a half and analyzed his sleeping hours and the number of times of going to a restroom. This paper presents the concept of the system, overview of the current system and experimental results obtained
Boon or Bane? Urban Food Security and Online Food Purchasing during the COVID-19 Epidemic in Nanjing, China

This paper examines the relationship between the rapid growth of online food purchasing and household food security during the first wave of the COVID-19 pandemic in China using the city of Nanjing as a case study. The paper presents the results of an online survey of 968 households in Nanjing in March 2020 focused on their food purchasing behavior and levels of food security during the early weeks of the pandemic. While online food purchasing has increased rapidly in many countries during the COVID-19 pandemic, little research attention has been paid to the relationship between online food purchasing and household food security. This paper provides detailed insights into this relationship in China. The medium- and longer-term food security and other consequences of the pandemic pivot to online food purchasing are a fertile area for future research in China and elsewhere.
Prevalence of Alcohol Use among Medical College Students in Hawler Medical University

Background and objective: Moderate and Heavy/binge alcohol drinking among medical college students have become a major public health problem. There is consistent evidence suggesting that young adults in college are drinking more than their non–college-attending peers, but it is still not clear whether they are more likely to suffer from clinically significant alcohol use disorders. This article reports the first national assessment of patterns of drinking habit among Medical Colleges. The aim of this study was to monitor the use of alcohol and to search for intervention and prevention strategies in Medical college communities. Methods: This is a cross-sectional study. Young adults students (n = 342) in four Medical College in Hawler Medical University were studied from the period between 20th Feb 2007- 20th April 2007 regarding use of alcohol, they were between 17-29 years old, 36% female, 64% male. A semi structured Questionnaires were distributed to students living in the four Medical colleges. The questionnaires asked for information on age, residency (how long they had lived in the college) and what substances they had used in the 6 months prior to the study (cigarettes, alcohol). The students also asked about academic and social activities. The data were expressed as descriptive frequencies and percentages. Results: 19.3% of college students (18, 4% of men, 0.9% of women) has occasional alcohol drink in the in the past 6 month, 4.4% has moderate to binge drinking which was completely among male students, students from College of Medicine form the higher rate of alcohol users (12.8%), while College of Nursing form the lowest rate (0.6%). Bars and restaurant form the major drinking context for the students (44.5%). Conclusion: The problem of alcohol use is increasing among students from medical college / Hawler University. College-based interventions seem desirable, especially in boys.