Miracle-dz/UniFAR-FTUs huggingface dataset:
Genealogy databases and the future of criminal investigation

The police can access your online family-tree research—and use it to investigate your relatives The 24 April 2018 arrest of Joseph James DeAngelo as the alleged Golden State Killer, suspected of more than a dozen murders and 50 rapes in California, has raised serious societal questions related to personal privacy. The break in the case came when investigators compared DNA recovered from victims and crime scenes to other DNA profiles searchable in a free genealogical database called GEDmatch. This presents a different situation from the analysis of DNA of individuals arrested or convicted of certain crimes, which has been collected in the U.S. National DNA Index System (NDIS) for forensic purposes since 1989. The search of a nonforensic database for law enforcement purposes has caught public attention, with many wondering how common such searches are, whether they are legal, and what consumers can do to protect themselves and their families from prying police eyes. Investigators are already rushing to make similar searches of GEDmatch in other cases, making ethical and legal inquiry into such use urgent.
Head Movements in Context of Speech during Stress Induction

This paper focuses on the analysis of head movements in the context of speech during stress and neutral conditions. An experimental protocol was followed including tasks of neutral and stressful emotional states induced by different types of stressors. Translational and rotational head movements and velocities were computationally estimated from 2-dimensional facial landmarks in order to assess stress states. In parallel, the effect of speech production on head motility was investigated. The results indicate that stress conditions increase head motility, in both translational or rotational movement features. Besides, there is a clear involvement of speech in the increase of head motility. However, the intensity of head motility can be attributed to the combined effect of stress or arousal and speech and not just the effect of speech production.
Pannexin-1 in Human Lymphatic Endothelial Cells Regulates Lymphangiogenesis

The molecular mechanisms governing the formation of lymphatic vasculature are not yet well understood. Pannexins are transmembrane proteins that form channels which allow for diffusion of ions and small molecules (<1 kDa) between the extracellular space and the cytosol. The expression and function of pannexins in blood vessels have been studied in the last few decades. Meanwhile, no studies have been conducted to evaluate the role of pannexins during human lymphatic vessel formation. Here we show, using primary human dermal lymphatic endothelial cells (HDLECs), pharmacological tools (probenecid, Brilliant Blue FCF, mimetic peptides [10Panx]) and siRNA-mediated knockdown that Pannexin-1 is necessary for capillary tube formation on Matrigel and for VEGF-C-induced invasion. These results newly identify Pannexin-1 as a protein highly expressed in HDLECs and its requirement during in vitro lymphangiogenesis.
Impact of the pretransplant dialysis modality on kidney transplantation outcomes: a nationwide cohort study

Objective Most patients with uraemia must undergo chronic dialysis while awaiting kidney transplantation; however, the role of the pretransplant dialysis modality on the outcomes of kidney transplantation remains obscure. The objective of this study was to clarify the associations between the pretransplant dialysis modality, namely haemodialysis (HD) or peritoneal dialysis (PD), and the development of post-transplant de novo diseases, allograft failure and all-cause mortality for kidney-transplant recipients. Design Retrospective nationwide cohort study. Setting Data retrieved from the Taiwan National Health Insurance Research Database. Participants The National Health Insurance database was explored for patients who received kidney transplantation in Taiwan during 1998–2011 and underwent dialysis >90 days before transplantation. Outcome measures The pretransplant characteristics, complications during kidney transplantation and post-transplant outcomes were statistically analysed and compared between the HD and PD groups. Cox regression analysis was used to evaluate the HR of the dialysis modality on graft failure and all-cause mortality. The primary outcomes were long-term post-transplant death-censored allograft failure and all-cause mortality started after 90 days of kidney transplantation until the end of follow-up. The secondary outcomes were events during kidney transplantation and post-transplant de novo diseases adjusted by propensity score in log-binomial model. Results There were 1812 patients included in our cohort, among which 1209 (66.7%) and 603 (33.3%) recipients received pretransplant HD and PD, respectively. Recipients with chronic HD were generally older and male, had higher risks of developing post-transplant de novo ischaemic heart disease, tuberculosis and hepatitis C after adjustment. Pretransplant HD contributed to higher graft failure in the multivariate analysis (HR 1.38, p<0.05) after adjustment for the recipient age, sex, duration of dialysis and pretransplant diseases. There was no significant between-group difference in overall survival. Conclusions Pretransplant HD contributed to higher risks of death-censored allograft failure after kidney transplantation when compared with PD.
Toward Better Statistical Validation of Machine Learning-Based Multimedia Quality Estimators

Objective assessment of multimedia quality using machine learning (ML) has been gaining popularity especially in the context of both traditional (e.g., terrestrial and satellite broadcast) and advance (such as over-the-top media services, IPTV) broadcast services. Being data-driven, these methods obviously rely on training to find the optimal model parameters. Therefore, to statistically compare and validate such ML-based quality predictors, the current approach randomly splits the given data into training and test sets and obtains a performance measure (for instance mean squared error, correlation coefficient etc.). The process is repeated a large number of times and parametric tests (e.g., ${t}$ test) are then employed to statistically compare mean (or median) prediction accuracies. However, the current approach suffers from a few limitations (related to the qualitative aspects of training and testing data, the use of improper sample size for statistical testing, possibly dependent sample observations, and a lack of focus on quantifying the learning ability of the ML-based objective quality predictor) which have not been addressed in literature. Therefore, the main goal of this paper is to shed light on the said limitations both from practical and theoretical perspectives wherever applicable, and in the process propose an alternate approach to overcome some of them. As a major advantage, the proposed guidelines not only help in a theoretically more grounded statistical comparison but also provide useful insights into how well the ML-based objective quality predictors exploit data structure for learning. We demonstrate the added value of the proposed set of guidelines on standard datasets by comparing the performance of few existing ML-based quality estimators. A software implementation of the presented guidelines is also made publicly available to enable researchers and developers to test and compare different models in a repeatable manner.
A survey of group activity recognition in smart building

Human activity recognition (HAR) has an important role in various areas of research, including security, health, daily activity, elderly, energy consumption in the smart building, etc. Most survey papers on HAR discuss single activity recognition. This study explained the research survey on group activity recognition (GAR) and linked it to the advancement of IoT technology in smart building. Object usage sensor activity recognition (OUAR) has a higher chance than video and wearable sensor-based because of its compatibility with the activity recognition system while preserving user privacy and convenience. OUAR can benefit HAR systems when IoT is used (all devices and sensors are integrated into the Internet). This is in line with the advancement of smart building. Also, many data sensors in AR and smart building systems when using OUAR can integrate big data to generate HAR systems in future smart buildings.
Performance Evaluation of NoSQL and SQL Queries in Response Time for the E-government

Society demands accurate, right, quickly and easy access information. Therefore, governments are forced to implements or improve information systems with modern technologies. This research work is focused on the research of responses times on relational and non-relational data base models in an experimental data base system provided by the Función Judicial del Ecuador. The data is migrated for load in NoSQL allotment, and the performance tests is obtained a document-oriented data model. With these tests we could know the best database, focusing all on response time of data queries that have been used on performance tests. In addition, we evaluate the performance before and after the relational database administration system (RDBMS), obtaining as a result that the first test of the queries made to MongoDB substantially surpass the queries made to SQL Server, but in the subsequent tests the times they tend to be similar.
Comparison of two significant development methods applied to the design of real-time robot controllers

This paper presents the principal functionalities of robot-manipulator controllers and a comparative study of two development methods for real-time systems applied to this controller. The selected techniques are SART and the unified method associated to UML, because they represent two classical types of modelling techniques: structured design with functional decomposition and object-oriented methodology. We analyse seven design issues associated to the robot controller development process and its utilisation comparing these two methods.
Targets Classification on Multispectral Images using Connectionists Methods

Identification of targets on remote sensing images depends mainly on the databases representing different classes. In this context, this paper proposes a connectionist system using a two-dimensional Kohonen self-organizing map to build a database of some identified targets on a multi-band satellite image. After an enhancing process, essentially based on a non-linear filtering, the system performs a non-supervised classification of a reference image in order to extract the sequence of samples of the reflectance coefficients related to the desired targets. This classification will be used later for automatic extraction of these targets on other stages.
Fumonisin-Exposure Impairs Age-Related Ecological Succession of Bacterial Species in Weaned Pig Gut Microbiota

Pigs are highly affected by dietary mycotoxin contamination and particularly by fumonisin. The effects of fumonisin on pig intestinal health are well documented, but little is known regarding its impact on gut microbiota. We investigate the effects of the fumonisin (FB1, 12 mg/kg feed) on the fecal microbiota of piglets (n = 6) after 0, 8, 15, 22, and 29 days of exposure. A control group of six piglets received a diet free of FB1. Bacterial community diversity, structure and taxonomic composition were carried out by V3–V4 16S rRNA gene sequencing. Exposure to FB1 decreases the diversity index, and shifts and constrains the structure and the composition of the bacterial community. This takes place as early as after 15 days of exposure and is at a maximum after 22 days of exposure. Compared to control, FB1 alters the ecological succession of fecal microbiota species toward higher levels of Lactobacillus and lower levels of the Lachnospiraceae and Veillonellaceae families, and particularly OTUs (Operational Taxonomic Units) of the genera Mitsuokella, Faecalibacterium and Roseburia. In conclusion, FB1 shifts and constrains age-related evolution of microbiota. The direct or indirect contribution of FB1 microbiota alteration in the global host response to FB1 toxicity remains to be investigated.
Aging-Aware Workload Management on Embedded GPU Under Process Variation

Graphics Processing Units (GPUs) have been employed in embedded systems to handle increased amounts of computation and to satisfy the timing requirement. Due to the small feature size, chip aging and within-die parameter variations have been considered to be among the challenging problems for state-of-the-art processors, including GPUs. In order to deal with the process variation, several processors use chip-level guardbanding, which uses the lowest operating frequency that results in a significant chip-level performance drop. Other processors improve their performance efficiency through core-level guardbanding that may use a different operating frequency for each core. Existing aging management techniques are based on the chip-level guardbanding, which assigns the same number of instructions to the cores that have the same aging status. However, in the presence of the process variation, existing aging management techniques have a limitation in minimizing the aging effect because each core has a different amount of stress for the same number of instructions. In order to tackle this problem, we propose a low-overhead aging and process variation aware workload management technique for embedded GPUs. The proposed technique considers the process variation and the current aging status together, and assigns a different number of instructions to clusters to minimize the aging effect in the presence of process variation. Results show that our technique improves the GPU aging in over 95 percent of cases whereas the state-of-the-art compiler-based technique improves the GPU aging in 72.25 percent of cases. Moreover, compared to the compiler-based technique, our technique reduces the performance overhead by 40 percent while achieving almost the same GPU aging improvement.
Cross-cultural differences in language markers of depression online

Depression is a global mental health condition that affects all cultures. Despite this, the way depression is expressed varies by culture. Uptake of machine learning technology for diagnosing mental health conditions means that increasingly more depression classifiers are created from online language data. Yet, culture is rarely considered as a factor affecting online language in this literature. This study explores cultural differences in online language data of users with depression. Written language data from 1,593 users with self-reported depression from the online peer support community 7 Cups of Tea was analyzed using the Linguistic Inquiry and Word Count (LIWC), topic modeling, data visualization, and other techniques. We compared the language of users identifying as White, Black or African American, Hispanic or Latino, and Asian or Pacific Islander. Exploratory analyses revealed cross-cultural differences in depression expression in online language data, particularly in relation to emotion expression, cognition, and functioning. The results have important implications for avoiding depression misclassification from machine-driven assessments when used in a clinical setting, and for avoiding inadvertent cultural biases in this line of research more broadly.
Collective asset display for commander's decision making

Despite the level of computer automation, commanders and their support staff still manually integrate enormous amounts of battle information from a number of sensor sources. On the digital battlefield, it is important that decisions that concern movement of troops, resource management, courses of action (COA), terrain, weather, etc. be presented in a timely, concise, and meaningful manner that enhances the commander's reliance on his/her mental models and knowledge repertoire. This can be achieved by the development of intelligent decision support display tools that can help the decision maker to increase battlefield situation awareness, provide decision support way points for real-time cognitive validation of the battle situation, and allow him/her to adapt to acquired battle information to known and unknown contexts. Our current experiments and theoretical validations of existing display design paradigms suggest the possibility of designing a collective asset display and visualization tool for the commander's decision support.
Belatacept/CTLA4Ig: an update and critical appraisal of preclinical and clinical results

ABSTRACT Introduction: The B7/CD28/CTLA4 signaling cascade is the most thoroughly studied costimulatory pathway and blockade with CTLA4Ig (abatacept) or its derivative belatacept has emerged as a valuable option for pharmacologic immune modulation. Several clinical studies have ultimately led to the approval of belatacept for immunosuppression in kidney transplant recipients. Areas covered: This review will discuss the immunological background of costimulation blockade and recent preclinical data and clinical results of CTLA4Ig/belatacept. Expert commentary: The development of belatacept is a major advance in clinical transplantation. However, in spite of promising results in preclinical and clinical trials, clinical use remains limited at present, in part due to increased rates of acute rejection. Recent efforts showing encouraging progress in refining such protocols might be a step toward harnessing the full potential of costimulation blockade-based immunosuppression.
Techology trend of edge AI

Artificial intelligence (AI), defined as intelligence exhibited by machines, has many applications in today's society, including robotics, mobile devices, smart transportation, healthcare service, and more. Recently, lots of AI investment in both big companies and startups have launched. Besides cloud-based solution, AI on the edge devices (Edge AI) takes the advantages of rapid response with low latency, high privacy, more robustness, and better efficient use of network bandwidth. To enable Edge AI, new embedded system technologies are desired, including machine learning, neural network acceleration and reduction, and heterogeneous run-time mechanism. This paper introduces challenges and technologies trend of Edge AI. In addition, it illustrates edge AI solutions from MediaTek, including the dedicated AI processing unit (APU) and NeuroPilot technology, which provides superior Edge AI ability in a wide range of applications.
Probiotic potential of Lactobacillus isolates of chicken origin with anti-Campylobacter activity

Campylobacteriosis is currently the most frequent zoonosis in humans and the main source of infection is contaminated poultry meat. As chickens are a natural host for Campylobacter species, one strategy to prevent infection in humans is to eliminate these bacteria on poultry farms. A study was conducted to evaluate the probiotic potential of 46 Lactobacillus isolates from chickens faeces or cloacae. All lactobacilli were able to produce active compounds on solid media with antagonistic properties against C. jejuni and C. coli, with L. salivarius and L. reuteri exhibiting particularly strong antagonism. The cell-free culture supernatants had a much weaker inhibitory effect on the growth of Campylobacter, and the neutralization of organic acids caused them to completely lose their inhibitory properties. The ability to produce H2O2 was exhibited by 93% of isolates; most of isolates had a hydrophobic surface, showed excellent survival at pH 2.0 or 1.5, and displayed tolerance to bile; 50% isolates displayed the ability to biofilm formation. Determination of MICs of various antibiotics showed that as much as 80.4% of Lactobacillus isolates were resistant to at least one antimicrobial agent. Seven ultimately selected isolates that met all the basic criteria for probiotics may have potential application in reducing Camylobacter spp. in chickens and thus prevent infections in both birds and humans.
Use of Haploid Model of Candida albicans to Uncover Mechanism of Action of a Novel Antifungal Agent

Antifungal agents for the treatment of Candida albicans infections are limited. We recently discovered a novel antifungal small molecule, SM21, with promising in vivo activity. Herein, we employed the newly developed C. albicans haploid toolbox to uncover the mechanism of action of SM21. Comprehensive RNA-Seq analyses of the haploid susceptible GZY803 strain revealed significant gene expression changes related to mitochondria when exposed to SM21. Mitochondrial structure visualization and measurement of ATP generation, reactive oxygen species (ROS) levels, and the antioxidant potential of SM21-treated and untreated GZY803, mitochondrial structure defective haploid mutant (dnm1Δ), and wild-type diploid SC5314 strains confirmed defects in mitochondria. Exploiting the advantage of C. albicans haploids as a single ploidy model, we further exposed GZY803 to repetitive treatments of SM21 in order to generate resistant mutants. Three colonies designated S3, S5 and S6, which displayed resistance to SM21, were isolated. All resistant strains exhibited enhanced transcriptomic responses for peptide and protein metabolism and secreted aspartate proteases (SAPs) activity under SM21 treatment compared to the parent strain GZY803. Consistently, supplementing the resistant strains, GZY803, and SC5314 with peptone, a form of digested peptides, decreased susceptibility to SM21. The present study demonstrates the usefulness of haploid C. albicans model in antifungal drug discovery. The findings will be invaluable to develop SM21 as a novel antifungal agent, which will benefit millions of patients suffering from Candida infections.
A High-Performing Similarity Measure for Categorical Dataset with SF-Tree Clustering Algorithm

Tasks such as clustering and classification assume the existence of a similarity measure to assess the similarity (or dissimilarity) of a pair of observations or clusters. The key difference between most clustering methods is in their similarity measures. This article proposes a new similarity measure function called PWO “Probability of the Weights between Overlapped items ”which could be used in clustering categorical dataset; proves that PWO is a metric; presents a framework implementation to detect the best similarity value for different datasets; and improves the F-tree clustering algorithm with Semi-supervised method to refine the results. The experimental evaluation on real categorical datasets, such as “Mushrooms, KrVskp, Congressional Voting, Soybean-Large, Soybean-Small, Hepatitis, Zoo, Lenses, and Adult-Stretch” shows that PWO is more effective in measuring the similarity between categorical data than state-of-the-art algorithms; clustering based on PWO with pre-defined number of clusters results a good separation of classes with a high purity of average 80% coverage of real classes; and the overlap estimator perfectly estimates the value of the overlap threshold using a small sample of dataset of around 5% of data size.