Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-KV: Improving Performance of Key-value Caches on Multilevel Cell Non-volatile Memory

∗ In-memory key-value caches are widely used by web applications to reduce latencies of database queries. In recent years, multilevel cell (MLC) technology has been used in various emerging non-volatile memories (NVM), allowing the building of large-scale main memory in a computing system. Building key-value caches on NVM, instead of DRAM, not only prevents cache data loss due to power outage but also improves the cache hit rate. However, it could lead to degraded performance due to the inferior write performance of NVM, compared to that of DRAM. To address this problem, previous studies focus on reducing the amount of NVM writes. In this paper, we propose Dual-KV, a method to improve performance of MLC NVM key-value caches. Different from previous studies, Dual-KV achieves the performance improvement by exploiting the write-latency/retention-time trade-off of MLC NVM. In MLC NVM, write latency increases with the retention time. The basic idea of Dual-KV is to adopt short-latency-and-short-retention writes (i.e., fast writes) for frequently updated data items while using long-latency-and-long-retention writes (i.e., slow writes) for the other data items. The experiment results show that Dual-KV improves throughput by up to 43% and 83% for single-thread and 16-thread YCSB workloads, respectively. Moreover, Dual-KV is designed to be integrated into existing key-value caches easily. Integrating Dual-KV into Memcached, a widely-used key-value cache, requires less than 30 lines of code modifications.
A Poster on Learnings from an Attempt to Build an NLP-based Fake News Classification system for Hindi

Proliferation of “Fake News” and misinformation is resulting in widespread negative social fallout. Scalable Fake News classification techniques for resource poor languages like Hindi are in early stages because of a lack of datasets and lack of robust NLP libraries for these languages. In this exploratory study we curate a dataset of around 13,000 data points of true news articles, and, articles on fake news authored by media organisations which flag fake news. We then use seven ML classification models on this dataset and present the preliminary results. Our results show that concerted efforts need to be made by the research community towards dataset curation and improving the NLP models for resource poor languages in order to make scalable classification systems.
Direction- and Centerline-Aware Joint Learning Network (JLNet) for Vessel Segmentation in X-Ray Angiography Images

Vessel segmentation from X-ray angiography (XRA) images is an important task in the clinical diagnosis of coronary artery disease. The main challenge lies in how to extract continuous and completed vessel masks from XRA images with poor quality and high complexity. Previous state-of-the-art methods are mostly based on hand-crafted features or pixel-wise segmentation and ignore geometric features, thereby resulting in breaks and absence of vessel structure in segmentation masks. In this paper, we propose a geometric feature embedding segmentation network for vessel segmentation in XRA images. This network joins direction- and centerline-related prediction tasks with mask segmentation, which enforces the network to learn the geometric features of vessel connectivity. Besides, a novel joint loss function is proposed to facilitate the joint training of these three tasks. We conduct ablation experiments on XRA images to demonstrate that the two auxiliary tasks can improve the connectivity and completeness of vessel segmentation. We also evaluate our method on XRA images and achieve the value of 85.00±3.66% for vessel segmentation, indicating that our method outperforms the other state-of-the-art methods.
Ecological niche divergence between the brown recluse spiders Loxosceles laeta and L. surca (Sicariidae) in Chile

ABSTRACT The laeta species group is the main source of Loxosceles species in Chile, a genus of medical importance due to the cytotoxic properties of its venom and the synanthropic habits of its dominant species, Loxosceles laeta. However, a poorly studied, cryptic, and putative sister species, Loxosceles surca, is also known to live in Chile at latitudes that overlap with L. laeta. In contrast to L. laeta, L. surca lives in natural areas away from humans. We used DNA sequences from L. surca to infer the phylogenetic history of this species and its congenerics. Additionally, we used ecological niche modelling to define the ecology of this species in contrast to its sister species. Our results show that L. surca does, indeed, belong to the laeta species group, and is likely a sister species to L. laeta. Although these species are fairly cryptic with respect to each other, the substantial genetic divergence between them is consistent with other distinct sister species in this genus. The laeta group is also sister to the large reclusa group that radiated into the Caribbean, Central, and North America. The phylogeny further supports the contention that L. diaguita belongs in its own species group separate from laeta. Our ecological niche analysis shows that L. surca and L. laeta have distinctly different allopatric habitats, and we suggest that these differences explain why one species became synanthropic, while the other did not.
Intergenerational differences in dietary acculturation among Ghanaian immigrants living in New York City: a qualitative study

Abstract Dietary acculturation may explain the increasing risk of diet-related diseases among African immigrants in the United States (US). We interviewed twenty-five Ghanaian immigrants (Youth n 13, Age (Mean ± sd) 20 y ± 5⋅4, Parents (n 6) and Grandparents (n 6) age 58⋅7 ± 9⋅7) living in New York City (NYC) to (a) understand how cultural practices and the acculturation experience influence dietary patterns of Ghanaian immigrants and (b) identify intergenerational differences in dietary acculturation among Ghanaian youth, parents and grandparents. Dietary acculturation began in Ghana, continued in NYC and was perceived as a positive process. At the interpersonal level, parents encouraged youth to embrace school lunch and foods outside the home. In contrast, parents preferred home-cooked Ghanaian meals, yet busy schedules limited time for cooking and shared meals. At the community level, greater purchasing power in NYC led to increased calories, and youth welcomed individual choice as schools and fast food exposed them to new foods. Global forces facilitated nutrition transition in Ghana as fast and packaged foods became omnipresent in urban settings. Adults sought to maintain cultural foodways while facilitating dietary acculturation for youth. Both traditional and global diets evolved as youth and adults adopted new food and healthy social norms in the US.
Research on Position Recognition System of Gantry Hoisting Based on Machine Vision

Abstract During the process of steel casting, the task of worker is to identify the relative position of gantry hook and lifting lug of hot metal ladle so as to judge the appropriate time when gantry crane is to be lifted. Worker needs to stand near hot metal ladle, which does great damage to human’s body if exposed to such a bad environment. For this reason, the author has designed a machine vision system to displace artificial recognition. The machine vision system consists of the vision recognition algorithm based on AlexNet Convolution Neural Network (CNN) and the improved Canny edge detection algorithm. AlexNet CNN completes the preliminary recognition of the image and provides the samples that meet the recognition conditions to the improved Canny algorithm. The improved Canny algorithm completes the extraction of parameters such as the relative distance between the gantry hook and lifting lug and the tilt Angle of gantry hook. The sample images are extracted from two environments: model simulation and field simulation. Experimental data shows that the recognition algorithm based on AlexNet CNN can achieve 100% image recognition rate, which is higher than other traditional machine learning algorithms; the improved Canny edge detection algorithm can improve edge quality of extracting images in harsh environments.
Effects of renin‐angiotensin blockade and APOL1 on kidney function in sickle cell disease

1 Department of Pharmacy Practice, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, USA 2 Division of Hematology andOncology, Department ofMedicine, University of Illinois at Chicago, Chicago, Illinois, USA 3 Department ofMedicine, Jesse Brown VAMedical Center, Chicago, Illinois, USA 4 Division of Nephrology, Department ofMedicine, University of Illinois at Chicago, Chicago, Illinois, USA
Extended TODIM method based on VIKOR for q‐rung orthopair fuzzy information measures and their application in MAGDM problem of medical consumption products

Nowadays, supply chain management (SCM) has achieved considerable attention from all over the world. q‐rung orthopair fuzzy set, developed by Yager, is the entirety of the most prominent tool to express fuzzy data in the decision‐making problems. In this study, the introduction of two new generalised measures (entropy and Jensen–Tsalli divergence measure) of q‐rung orthopair fuzzy information involving one real parameter is given. The proposed measures have satisfied all the necessary mathematical properties of being a measure. Then the introduced entropy and divergence measure is used to obtain the objective weights. Based on the proposed entropy and divergence measure, we proposed a new decision method to deal with multiple‐attribute group decision‐making problems under the q‐rung orthopair fuzzy environment. Then, on the basis of the TODIM and VIKOR techniques, an integrated TODIM‐VIKOR approach is developed to solve multiattribute group decision‐making problem. In this paper, TODIM aims to determine the overall dominance degree and VIKOR aims to determine the compromise solution. Lastly, we handle a supplier selection problem to verify the performance of the proposed q‐rung orthopair fuzzy TODIM‐VIKOR method and results explore the reliability and effectiveness of our proposed methodology by comparing the ranking solution with the ranking results of the existing approaches.
Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

This paper presents a grammar‐based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi‐agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar‐based evolution can significantly improve the performance of an agent system in complex problem domains.
Alpha‐globin gene triplication and its effect in beta‐thalassemia carrier, sickle cell trait, and healthy individual

The genotype and phenotype correlation between coinheritance of heterozygous beta‐thalassemia with the alpha‐globin triplication is unclear. In this study we have investigated and reviewed alpha triplication frequency in beta‐thalassemia carriers, sickle cell trait, and healthy individuals and its effect on hematological and phenotypical changes. In this study, 4005 beta‐thalassemia carriers, 455 sickle cell trait, and 2000 healthy individuals were included. Molecular characterization of beta and alpha‐thalassemia was performed. The frequencies of alpha‐globin triplication in beta‐thalassemia carriers, sickle cell trait, and healthy individuals were 67 (1.67%), 4 (0.88%), and 18 (0.9%), respectively. In total, the frequency of alpha‐triplications is approximately 89 (1.39%) in Khuzestan province, South of Iran population. We have compared the average hematological parameters of beta‐thalassemia carriers, sickle cell trait, and healthy individuals with and without alpha gene triplication.
Pythagorean fuzzy multiattribute group decision making based on risk attitude and evidential reasoning methodology

Two aspects of problems including selection of aggregation operator for extreme fuzzy evaluation value and risk attitude of decision makers cannot be well solved in Pythagorean fuzzy (PF) multiattribute group decision making (MAGDM). This paper extends the evidential reasoning aggregation method in the intuitionistic fuzzy environment, expands the dictionary ranking method by constructing interval‐valued numbers through the proposed credibility functions of PF values and the concept of closeness degree, widens the continuous generalized ordered weighted average ( C ‐ GOWA ) operator to establish a risk attitude ranking measure, and puts forward a PF MAGDM approach based on risk attitude and evidence reasoning methodology (ERM). First, the proposed method utilizes the ERM to aggregate each decision maker's decision matrix and the weights of the attributes to get his/her aggregated decision matrix. Then, it incorporates the obtained aggregated decision matrices of the experts, the weights of the experts and the ERM to accomplish the aggregated PF value of each alternative. Finally, the ranking measure value of risk attitude on each alternative's PF value is calculated, and the sensitivity analysis on the ranking measure function is carried out. The proposed method has overcome the drawbacks of the existing methods for fuzzy MAGDM in PF environments.
Assessing the impact of the European resilience curriculum in preschool, early and late primary school children

Resilience is an individual’s ability to adapt successfully to and persevere during and after significant challenges. Resilience programmes based on a socioemotional learning approach have been associated with an increase in protextive factors (e.g., prosocial competencies), improvements in physical and mental health, and a decrease in internalised and externalised symptoms. The present study aimed to evaluate the impact of the RESCUR curriculum implemented in Portuguese schools on students’ academic, behavioural, and socioemotional outcomes, based on child and teacher reports. Participants included 1,084 children (53.2% male) aged 3-15 (M = 7.24, SD = 2.31). A quasi-experimental study compared outcomes for an experimental intervention group (AIG) with a waiting list control group (WG). The results showed the RESCUR programme decreased mental health difficulties while increasing both prosocial behaviours and well-being. In addition, academic performance increased for those in preschool after implementation. Both teachers and children consistently reported positive behavioural changes in resilience-related competencies after implementing RESCUR. Our findings contribute to the recent research on the potential of RESCUR to address key socioemotional competencies and improve relevant protextive factors. Study limitations and future recommendations are addressed.
High Resolution Ancestry Deconvolution for Next Generation Genomic Data

As genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individual’s genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix.
A genetic compensatory mechanism regulated by Jun and Mef2d modulates the expression of distinct class IIa Hdacs to ensure peripheral nerve myelination and repair

The class IIa histone-deacetylases (HDACs) have pivotal roles in the development of different tissues. Of this family, Schwann cells express HDAC4, 5 and 7 but not HDCA9. Here we show that a transcription factor regulated genetic compensatory mechanism within this family of proteins, blocks negative regulators of myelination ensuring peripheral nerve developmental myelination and remyelination after injury. Thus, when HDAC4 and 5 are knocked-out from Schwann cells, a c-Jun dependent mechanism induces the compensatory overexpression of HDAC7 permitting, although with a delay, the formation of a myelin sheath. When HDAC4,5 and 7 are simultaneously removed, the Myocyte- specific enhancer-factor d (Mef2d) binds to the promoter and induces the de novo expression of HDAC9, and although several melanocytic- lineage genes are mis- expressed and Remak bundle structure is disrupted, myelination proceeds after a long delay. Thus, our data unveil a finely tuned compensatory mechanism within the class IIa HDAC family, coordinated by distinct transcription factors, that guarantees the ability of Schwann cells to myelinate during development and remyelinate after nerve injury.
Mechanistic Insights into the Effects of Key Mutations on SARS-CoV-2 RBD-ACE2 Binding

Some recent SARS-CoV-2 variants appear to have increased transmissibility than the original strain. An underlying mechanism could be the improved ability of the variants to bind receptors on target cells and infect them. In this study, we provide atomic-level insight into the binding of the receptor binding domain (RBD) of the wild-type SARS-CoV-2 spike protein and its single (N501Y), double (E484Q, L452R) and triple (N501Y, E484Q, L452R) mutated variants to the human ACE2 receptor. Using extensive all-atom molecular dynamics simulations and advanced free energy calculations, we estimate the associated binding affinities and binding hotspots. We observe significant secondary structural changes in the RBD of the mutants, which lead to different binding affinities. We find higher binding affinities of the double (E484Q, L452R) and triple (N501Y, E484Q, L452R) mutated variants than the wild type and the N501Y variant, which could contribute to the higher transmissibility of recent variants containing these mutations.
Probing Differences in Gene Essentiality Between the Human and Animal Adapted Lineages of the Mycobacterium tuberculosis Complex Using TnSeq

Members of the Mycobacterium tuberculosis complex (MTBC) show distinct host adaptations, preferences and phenotypes despite being >99% identical at the nucleic acid level. Previous studies have explored gene expression changes between the members, however few studies have probed differences in gene essentiality. To better understand the functional impacts of the nucleic acid differences between Mycobacterium bovis and Mycobacterium tuberculosis we used the Mycomar T7 phagemid delivery system to generate whole genome transposon libraries in laboratory strains of both species and compared the essentiality status of genes during growth under identical in vitro conditions. Libraries contained insertions in 54% of possible TA sites in M. bovis and 40% of those present in M. tuberculosis, achieving similar saturation levels to those previously reported for the MTBC. The distributions of essentiality across the functional categories were similar in both species. 527 genes were found to be essential in M. bovis whereas 477 genes were essential in M. tuberculosis and 370 essential genes were common in both species. CRISPRi was successfully utilised in both species to determine the impacts of silencing genes including wag31, a gene involved in peptidoglycan synthesis and Rv2182c/Mb2204c, a gene involved in glycerophospholipid metabolism. We observed species specific differences in the response to gene silencing, with the inhibition of expression of Mb2204c in M. bovis showing significantly less growth impact than silencing its ortholog (Rv2182c) in M. tuberculosis. Given that glycerophospholipid metabolism is a validated pathway for antimicrobials, our observations suggest that target vulnerability in the animal adapted lineages cannot be assumed to be the same as the human counterpart. This is of relevance for zoonotic tuberculosis as it implies that the development of antimicrobials targeting the human adapted lineage might not necessarily be effective against the animal adapted lineage. The generation of a transposon library and the first reported utilisation of CRISPRi in M. bovis will enable the use of these tools to further probe the genetic basis of survival under disease relevant conditions.
Methodological considerations in analyzing synchronization of resting-state brain networks with the intrinsic electrical rhythm of the stomach: Advantages of weighted phase-locking

Purpose To evaluate the amplitude-weighted phase-locking value (awPLV) as a measure of synchronization of brain resting-state networks (RSNs) with the gastric basal electrical rhythm (BER). Methods A recent study combined rsfMRI with concurrent cutaneous electrogastrography (EGG), in a highly-sampled individual who underwent 22 scanning sessions (two 15-minute runs per session) at 3.0 Tesla. After excluding three sessions due to weak EGG signals, 9.5 hours of data remained, from which 18 RSNs were estimated using spatial independent component analysis. Previously, using the phase-locking value (PLV), three of the 18 RSNs were determined to be synchronized with the BER. However, RSN power fluctuations in the gastric frequency band could reduce sensitivity of PLV. Accordingly, the current reanalysis used awPLV to unweight contributions from low power epochs. Mismatched EGG and rsfMRI data (from different days) served as surrogate data; for each RSN, empirical awPLV was compared with chance-level awPLV using a Wilcoxon rank test. P-values were adjusted using with a false discovery rate of 0.05. Additionally, simulations were performed to compare PLV and awPLV error rates under settings with a known ground truth. Results Simulations show high false-negative rates when using PLV, but not awPLV. Reanalysis of the highly-sampled individual data using awPLV indicates that 11 of the 18 RSNs were synchronized with the BER. Conclusion Simulations indicate that awPLV is a more sensitive measure of stomach/brain synchronization than PLV. Reanalysis results imply communication between the enteric nervous system and brain circuits not typically considered responsive to gastric state or function.