Miracle-dz/UniFAR-FTUs huggingface dataset:
From the Real Towards the Ideal: Risk Prediction in a Better World

Prediction algorithms assign scores in [0 , 1] to individuals, often interpreted as “probabilities” of a positive outcome, for example, of repaying a loan or succeeding in a job. Success, however, rarely depends only on the individual: it is a function of the individual’s interaction with the environment, past and present. Environments do not treat all demographic groups equally. We initiate the study of corrective transformations τ that map predictors of success in the real world to predictors in a better world. In the language of algorithmic fairness, letting p ∗ denote the true probabilities of success in the real, unfair, world, we characterize the transformations τ for which it is feasible to find a predictor ˜ q that is indistinguishable from τ ( p ∗ ). The problem is challenging because we do not have access to probabilities or even outcomes in a better world. Nor do we have access to probabilities p ∗ in the real world. The only data available for training are outcomes from the real world. We obtain a complete characterization of when it is possible to learn predictors that are indistinguishable from τ ( p ∗ ), in the form of a simple-to-state criterion describing necessary and sufficient conditions for doing so. This criterion is inextricably bound with the very existence of uncertainty.
Distributed GPU Joins on Fast RDMA-capable Networks

In this paper, we present a novel pipelined GPU join that accelerates the performance of distributed DBMSs by leveraging GPU resources on fast networks. A key insight is that we enable pipelined join execution by overlapping the network shuffling with the build and probe phases, thereby significantly reducing the GPU idle time. To demonstrate this, we propose novel algorithms for distributed pipelined GPU joins with RDMA and GPUDirect for both arbitrarily large probe- and build-side tables. In our evaluation, we show our pipelined distributed GPU join can reduce the overall runtime of a full query by up to 6× against a state-of-the-art CPU-only join.
PARP-1: a critical regulator in radioprotection and radiotherapy-mechanisms, challenges, and therapeutic opportunities

Background: Since its discovery, poly (ADP-ribose) polymerase 1 (PARP-1) has been extensively studied due to its regulatory role in numerous biologically crucial pathways. PARP inhibitors have opened new therapeutic avenues for cancer patients and have gained approval as standalone treatments for certain types of cancer. With continued advancements in the research of PARP inhibitors, we can fully realize their potential as therapeutic targets for various diseases. Purpose: To assess the current understanding of PARP-1 mechanisms in radioprotection and radiotherapy based on the literature. Methods: We searched the PubMed database and summarized information on PARP inhibitors, the interaction of PARP-1 with DNA, and the relationships between PARP-1 and p53/ROS, NF-κB/DNA-PK, and caspase3/AIF, respectively. Results: The enzyme PARP-1 plays a crucial role in repairing DNA damage and modifying proteins. Cells exposed to radiation can experience DNA damage, such as single-, intra-, or inter-strand damage. This damage, associated with replication fork stagnation, triggers DNA repair mechanisms, including those involving PARP-1. The activity of PARP-1 increases 500-fold on DNA binding. Studies on PARP-1-knockdown mice have shown that the protein regulates the response to radiation. A lack of PARP-1 also increases the organism’s sensitivity to radiation injury. PARP-1 has been found positively or negatively regulate the expression of specific genes through its modulation of key transcription factors and other molecules, including NF-κB, p53, Caspase 3, reactive oxygen species (ROS), and apoptosis-inducing factor (AIF). Conclusion: This review provides a comprehensive analysis of the physiological and pathological roles of PARP-1 and examines the impact of PARP-1 inhibitors under conditions of ionizing radiation exposure. The review also emphasizes the challenges and opportunities for developing PARP-1 inhibitors to improve the clinical outcomes of ionizing radiation damage.
Genome-wide association analysis provides insights into the genetic basis of photosynthetic responses to low-temperature stress in spring barley

Low-temperature stress (LTS) is among the major abiotic stresses affecting the geographical distribution and productivity of the most important crops. Understanding the genetic basis of photosynthetic variation under cold stress is necessary for developing more climate-resilient barley cultivars. To that end, we investigated the ability of chlorophyll fluorescence parameters (FVFM, and FVF0) to respond to changes in the maximum quantum yield of Photosystem II photochemistry as an indicator of photosynthetic energy. A panel of 96 barley spring cultivars from different breeding zones of Canada was evaluated for chlorophyll fluorescence-related traits under cold acclimation and freeze shock stresses at different times. Genome-wide association studies (GWAS) were performed using a mixed linear model (MLM). We identified three major and putative genomic regions harboring 52 significant quantitative trait nucleotides (QTNs) on chromosomes 1H, 3H, and 6H for low-temperature tolerance. Functional annotation indicated several QTNs were either within the known or close to genes that play important roles in the photosynthetic metabolites such as abscisic acid (ABA) signaling, hydrolase activity, protein kinase, and transduction of environmental signal transduction at the posttranslational modification levels. These outcomes revealed that barley plants modified their gene expression profile in response to decreasing temperatures resulting in physiological and biochemical modifications. Cold tolerance could influence a long-term adaption of barley in many parts of the world. Since the degree and frequency of LTS vary considerably among production sites. Hence, these results could shed light on potential approaches for improving barley productivity under low-temperature stress.
The Computer Translation of Interrogatives from English to Malay

This report describes 3 classes of structures for English interro gatives and 4 classes for Malay interrogatives with the view of mapp ing between equivalent structures in the two languages. The result of this study is a computer grammar submodel for translating interro gatives in the English-Malay translation system being developed by the Computer-Aided-Translation research team at Universiti Sains Malaysia. Although only interrrogatives have been discussed in this report, this paper has also illustrated the general approach used in the development of computer translation grammar models. However, it should be pointed out that much simplification has been made in this paper in order to introduce an inherently complex process to the non- MT researcher. For example, only information on syntax is given on the intermediate structures and no information on semantics is men tioned. It is hoped that the omission of such details will not give rise to the false impression that computer translation is a simple process. Further work on interrogative structures will look into the inclu sion of stylistic variations, particularly in the use of the particle '-kah' with the questioned constituent.
Talkitt: toward a new instrument based on artificial intelligence for augmentative and alternative communication in children with down syndrome

Introduction Individuals with Down syndrome (DS) often exhibit a severe speech impairment, with important consequences on language intelligibility. For these cases, the use of Augmentative Alternative Communication instruments, that increase an individual’s communication abilities, becomes crucial. Talkitt is a mobile application created by Voiceitt Company, exploiting speech recognition technology and artificial intelligence models to translate in real-time unintelligible sounds into clear words, allowing individuals with language production impairment to verbally communicate in real-time. Methods The study evaluated the usability and satisfaction related to the Talkitt application use, as well as effects on adapted behavior and communication, of participants with DS. A final number of 23 individuals with DS, aged 5.54 to 28.9 years, participated in this study and completed 6 months of training. The application was trained to consistently recognize at least 20 different unintelligible words (e.g., nouns and/or short phrases)/person. Results Results revealed good usability and high levels of satisfaction related to the application use. Moreover, we registered improvement in linguistic abilities, particularly naming. Discussion These results paves the road for a potential role of Talkitt application as a supportive and rehabilitative tool for DS.
The dissection of tomato flavor: biochemistry, genetics, and omics

Flavor and quality are the major drivers of fruit consumption in the US. However, the poor flavor of modern commercial tomato varieties is a major cause of consumer dissatisfaction. Studies in flavor research have informed the role of volatile organic compounds in improving overall liking and sweetness of tomatoes. These studies have utilized and applied the tools of molecular biology, genetics, biochemistry, omics, machine learning, and gene editing to elucidate the compounds and biochemical pathways essential for good tasting fruit. Here, we discuss the progress in identifying the biosynthetic pathways and chemical modifications of important tomato volatile compounds. We also summarize the advances in developing highly flavorful tomato varieties and future steps toward developing a “perfect tomato”.
Maneuvering on non-Newtonian fluidic terrain: a survey of animal and bio-inspired robot locomotion techniques on soft yielding grounds

Frictionally yielding media are a particular type of non-Newtonian fluids that significantly deform under stress and do not recover their original shape. For example, mud, snow, soil, leaf litters, or sand are such substrates because they flow when stress is applied but do not bounce back when released. Some robots have been designed to move on those substrates. However, compared to moving on solid ground, significantly fewer prototypes have been developed and only a few prototypes have been demonstrated outside of the research laboratory. This paper surveys the existing biology and robotics literature to analyze principles of physics facilitating motion on yielding substrates. We categorize animal and robot locomotion based on the mechanical principles and then further on the nature of the contact: discrete contact, continuous contact above the material, or through the medium. Then, we extract different hardware solutions and motion strategies enabling different robots and animals to progress. The result reveals which design principles are more widely used and which may represent research gaps for robotics. We also discuss that higher level of abstraction helps transferring the solutions to the robotics domain also when the robot is not explicitly meant to be bio-inspired. The contribution of this paper is a review of the biology and robotics literature for identifying locomotion principles that can be applied for future robot design in yielding environments, as well as a catalog of existing solutions either in nature or man-made, to enable locomotion on yielding grounds.
Bayesian Networks in Survey Data: Robustness and Sensitivity Issues

Bayesian networks (BN) implement a graphical model structure known as a directed acyclic graph (DAG) that is popular in statistics, machine learning, and artificial intelligence. They enable an effective representation and computation of a joint probability distribution (JPD) over a set of random variables. The paper focuses on the selection of a robust network structure according to different learning algorithms and the measure of arc strength using resampling techniques. Moreover, it shows how ‘what-if’ sensitivity scenarios are generated with BN using hard and soft evidence in the framework of predictive inference. Establishing a robust network structure and using it for decision support are two essential enablers for efficient and effective applications of BN to improvements of products and processes. A customer-satisfaction survey example is presented and R scripts are provided.
Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability

Introduction Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV). Methods Nine HRV indicators (features) and sleep–wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep–wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated—shallow sleep, deep sleep, and the two types of wake conditions—was also tested. Results and Discussion In the test for predicting three types of sleep–wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82–0.88) and accuracy (0.78–0.81). The test using four types of sleep–wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep–wake conditions. Among the seven features, “the number of interval differences of successive RR intervals greater than 50 ms (NN50)” and “the proportion dividing NN50 by the total number of RR intervals (pNN50)” were useful to predict sleep–wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.
Stacking-based and improved convolutional neural network: a new approach in rice leaf disease identification

Rice leaf diseases are important causes of poor rice yields, and accurately identifying diseases and taking corresponding measures are important ways to improve yields. However, rice leaf diseases are diverse and varied; to address the low efficiency and high cost of manual identification, this study proposes a stacking-based integrated learning model for the efficient and accurate identification of rice leaf diseases. The stacking-based integrated learning model with four convolutional neural networks (namely, an improved AlexNet, an improved GoogLeNet, ResNet50 and MobileNetV3) as the base learners and a support vector machine (SVM) as the sublearner was constructed, and the recognition rate achieved on a rice dataset reached 99.69%. Different improvement methods have different effects on the learning and training processes for different classification tasks. To investigate the effects of different improvement methods on the accuracy of rice leaf disease diagnosis, experiments such as comparison experiments between single models and different stacking-based ensemble model combinations and comparison experiments with different datasets were executed. The model proposed in this study was shown to be more effective than single models and achieved good results on a plant dataset, providing a better method for plant disease identification.
Characterization of nuclear DNA diversity in an individual Leymus chinensis

Intraorganismal genetic heterogeneity (IGH) exists when an individual organism harbors more than one genotype among its cells. In general, intercellular DNA diversity occurs at a very low frequency and cannot be directly detected by DNA sequencing from bulk tissue. In this study, based on Sanger and high-throughput sequencing, different species, different organs, different DNA segments and a single cell were employed to characterize nucleotide mutations in Leymus chinensis. The results demonstrated that 1) the nuclear DNA showed excessive genetic heterogeneity among cells of an individual leaf or seed but the chloroplast genes remained consistent; 2) a high density of SNPs was found in the variants of the unique DNA sequence, and the similar SNP profile shared between the leaf and seed suggested that nucleotide mutation followed a certain rule and was not random; and 3) the mutation rate decreased from the genomic DNA sequence to the corresponding protein sequence. Our results suggested that Leymus chinensis seemed to consist of a collection of cells with different genetic backgrounds.
Incidence and predictors of hemodynamic compromise due to high-grade AV block after TAVI

Background High-grade AV block (HAVB) is the most frequent adverse event after transcatheter aortic valve implantation (TAVI). In rare cases, HAVB is associated with hemodynamic compromise (HC) followed by syncope or application of cardiopulmonary resuscitation (CPR), but data on this severe complication are scarce. The aim of the present study was to investigate the incidence and predictors of HC due to HAVB in patients undergoing TAVI. Methods In this retrospective analysis of 4,602 TAVI cases between 2010 and 2022, 466 developed HAVB. Baseline characteristics and procedural and postprocedural findings were compared for patients with HC versus those without. Univariate and multivariable regression analyses were used to investigate independent predictors of HC. Results Forty-nine of 466 patients (10.5%) had HC due to HAVB after TAVI. Patients with HC had a longer hospital stay [10 (8–13) vs. 13 (9–18) days; p < 0.001], more frequent peripheral artery disease (PAD) (28.6% vs. 15.1%; p = 0.016), and lower hemoglobin levels [11.8 (±) vs. 12.5 (±) g/dl; p = 0.006]. In the HC group, HAVB onset post-TAVI was delayed compared with the non-HC group [2 (1–4) vs. 1 (0–3) days; p < 0.001]. Before HAVB onset, patients in the HC group more frequently developed post-TAVI delirium [18 (4.6%) vs. 11 (25.0%); p < 0.001]. In univariate regression analysis, PAD, hemoglobin, procedural time, contrast agent volume, and post-TAVI delirium were significant predictors of HC. After adjustment, only post-TAVI delirium and contrast agent volume remained independent predictors [OR 3.22 (95% CI: 1.05–9.89); p = 0.042 and OR: 1.01 (95% CI: 1.0–1.01); p = 0.04, respectively]. Conclusion HC due to HAVB after TAVI occurred in over 10% of cases. Development of post-TAVI delirium and contrast agent volume are independent predictors of this severe complication.
Comprehensive analysis of gut microbiome and host transcriptome in chickens after Eimeria tenella infection

Background Coccidiosis is an intestinal parasitic disease caused by Eimeria protozoa, which endangers the health and growth of animals, and causes huge economic losses to the poultry industry worldwide every year. Studies have shown that poultry gut microbiota plays an important role in preventing the colonization of pathogens and maintaining the health of the host. Coccidia infection also affects host gene expression. However, the underlying potential relationship between gut microbiome and host transcriptome during E. tenella infection in chickens remain unclear. Methods In this study, metagenomic and transcriptome sequencing were applied to identify microbiota and genes in cecal contents and cecal tissues of infected (JS) and control (JC) chickens on day 4.5 postinfection (pi), respectively. Results First, microbial sequencing results of cecal contents showed that the abundance of Lactobacillus, Roseburia sp. and Faecalibacterium sp decreased significantly after E. tenella infection (P < 0.05), while the abundance of Alistipes and Prevotella pectinovora increased significantly (P < 0.05). Second, transcriptome sequencing results showed that a total of 434 differentially expressed mRNAs were identified, including 196 up-regulated and 238 down-regulated genes. These differentially expressed genes related to inflammation and immunity, such as GAMA, FABP1, F2RL1 and RSAD2, may play an important role in the process of host resistance to coccidia infection. Functional studies showed that the enriched pathways of differentially expressed genes included the TGF-beta signaling pathway and the ErbB signaling pathways. Finally, the integrated analysis of gut microbiome and host transcriptome suggested that Prevotella pectinovora associated with FABP1, Butyricicoccus porcorum and Colidextribacter sp. associated with RSAD2 were involved in the immune response upon E. tenella infection. Conclusion In conclusion, this study provides valuable information on the microbiota and key immune genes after chicken E. tenella infection, with the aim of providing reference for the impact of coccidia infection on cecal microbiome and host.
Pretreatment with Bifidobacterium longum BAA2573 ameliorates dextran sulfate sodium (DSS)-induced colitis by modulating gut microbiota

Objectives Inflammatory bowel disease (IBD) is a chronic lifelong inflammatory disease. Probiotics such as Bifidobacterium longum are considered to be beneficial to the recovery of intestinal inflammation by interaction with gut microbiota. Our goals were to define the effect of the exclusive use of BAA2573 on dextran sulfate sodium (DSS)-induced colitis, including improvement of symptoms, alleviation of histopathological damage, and modulation of gut microbiota. Methods In the present study, we pretreated C57BL/6J mice with Bifidobacterium longum BAA2573, one of the main components in an over-the-counter (OTC) probiotic mixture BIFOTO capsule, before modeling with DSS. 16S rDNA sequencing and liquid chromatography–tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomic profiling were performed with the collected feces. Results We found that pretreatment of Bifidobacterium longum BAA2573 given by gavage significantly improved symptoms and histopathological damage in DSS-induced colitis mice. After the BAA2573 intervention, 57 genera and 39 metabolites were significantly altered. Pathway enrichment analysis demonstrated that starch and sucrose metabolism, vitamin B6 metabolism, and sphingolipid metabolism may contribute to ameliorating colitis. Moreover, we revealed that the gut microbiome and metabolites were interrelated in the BAA2573 intervention group, while Alistipes was the core genus. Conclusion Our study demonstrates the impact of BAA2573 on the gut microbiota and reveals a possible novel adjuvant therapy for IBD patients.