Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying migrant remains in South Texas: policy and practice

Abstract In 2012, Texas surpassed Arizona in migrant deaths. The majority of deaths occurred in the Rio Grande Valley, specifically in Brooks County, Texas. Brooks County is one of the poorest in the state and was overwhelmed with deaths, without appropriate resources to follow the state laws pertaining to the investigation of unidentified human remains. Until 2013, most remains that were not immediately identified were buried without collecting DNA samples and the location of burials was not recorded. Our paper outlines the difficulties searching for these burials, the struggles of the families of the missing, and the collaborative approaches to facilitating identifications in South Texas. Community outreach combined with geophysical surveys guide which cemeteries are in need of exhumations. Once cemeteries are surveyed, archaeological methods are employed to exhume remains and document burials. Remains are taken to the Forensic Anthropology Center at Texas State for processing, analysis, and identification efforts. Undergraduate and graduate students clean remains and wash clothing and personal effects. After skeletal analysis, all information regarding the remains, including photographs of personal effects, are uploaded to the National Missing and Unidentified Persons System (NamUs) and a DNA sample is submitted to the University of North Texas for inclusion in the Combined DNA Index System (CODIS) DNA database. However, CODIS lacks DNA family reference samples from many families of the missing due to families living outside the US or because they do not feel comfortable providing a DNA sample in the presence of law enforcement. Therefore, it is necessary to work with non-governmental organizations who specialize in collecting missing persons reports and DNA samples from the families of the missing. Working collaboratively with multiple agencies, identification of migrant remains is possible.
Biolubrication: Structural investigation of lubricin and its glycosylation

The sliding articular cartilage surfaces of the human diarthrodial joints are surrounded by biolubricating synovial fluid (SF), creating a perfect low friction biological biobearing structure with excellent lubrication and wear resistance, even during motion. Lubrication is predominately provided by surface adhered biomolecules including phospholipids, hyaluronic acid and synovial lubricin. Inflammation, such as arthritis and injury, changes the joint assembly resulting in detachment of essential surface molecules, increasing friction and wear of the sliding articular cartilage. Changes in the composition and distribution of these surface molecules are suggested to aggravate the disease. The lubricative, heavily glycosylated mucin-like synovial glycoprotein, lubricin, has previously been observed to contain glycosylation changes related to rheumatoid arthritis and osteoarthritis. Therefore, a structural investigation of lubricin and its glycosylation was initiated in order to better understand the biolubricating ability of lubricin and its pathological involvement in arthritic diseases. An investigation was undertaken to better understand the nature of the dominant glycan structure, sialic acid. Sialidase specific for α2-3 linked sialic acid and subsequent UniCarb-DB fragment spectra comparison of the resultant structure indicated the exclusive 3-linked nature of core 2 sialylation. However, core 1 structures had both 3 and 6 linked sialylation. In arthritis, lubricin has been shown to degrade as identified by its fragments in the synovial fluid. This may open up a new possibility for identification of disease specific biomarker. The mass spectrometric glycopeptide analysis showed that lubricin contains an extended serine, threonine and proline (STP) rich domain composed of imperfect tandem repeats (EPAPTTPK), the target for O-glycosylation. The Nacetylgalactosaminyltransferase (GALNTs) expression analysis of the fibroblastlike synoviocytes showed high expression of the less understood GALNT5 and 15 in addition to the ubiquitously expressed GALNT1 and 2. This indicated that lubricin required a unique combination of transferase genes for its glycosylation. Overall, this study revealed that negatively charged sialic acid in the mucin-like domain makes the lubricin molecule amphoteric in nature, as the arginine and lysine rich protein core is positively charged. The number of glycosylation sites and sialylation were shown to be essential for this amphoteric nature and may be important for its function as an amphoteric biolubricator.
Design and Validation of a New Morphing Camber System by Testing in the Price—Païdoussis Subsonic Wind Tunnel

This paper presents the design and wind tunnel testing of a morphing camber system and an estimation of performances on an unmanned aerial vehicle. The morphing camber system is a combination of two subsystems: the morphing trailing edge and the morphing leading edge. Results of the present study show that the aerodynamics effects of the two subsystems are combined, without interfering with each other on the wing. The morphing camber system acts only on the lift coefficient at a 0° angle of attack when morphing the trailing edge, and only on the stall angle when morphing the leading edge. The behavior of the aerodynamics performances from the MTE and the MLE should allow individual control of the morphing camber trailing and leading edges. The estimation of the performances of the morphing camber on an unmanned aerial vehicle indicates that the morphing of the camber allows a drag reduction. This result is due to the smaller angle of attack needed for an unmanned aerial vehicle equipped with the morphing camber system than an unmanned aerial vehicle equipped with classical aileron. In the case study, the morphing camber system was found to allow a reduction of the drag when the lift coefficient was higher than 0.48.
Visual Analysis Techniques for Dynamic Biological Networks

Due to the complexity of biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying biological networks has increased over the last decades. Visualization provides an efficient way to help biologists understand, communicate, and gain insight into their biological data through visual exploration and analysis. In this dissertation, I propose a set of visual approaches for the analysis of ensemble dynamic biological networks. This set of approaches took shape through the development of multiple prototype visual analytics tools which were aimed to solve complex problems in multiple biological subdomains. The steering of the approach was supported through an in-depth understanding of both the encompassing biological domains and problem space, as well through a survey of existing visual approaches helping to generalize the paradigms used when confronting these problems and the complexity of their data.
Sampling Based Vehicle Motion Planning for Autonomous Valet Parking with Moving Obstacles

2017 ABSTRACT : This paper describes a motion planning algorithm for unstructured dynamic environments with motion prediction for moving obstacles. The proposed algorithm is composed of the four steps: 1) target motion prediction; 2) drivable area decision 3) local path planning and 4) vehicle control. The target motion prediction is crucial parts for realizing autonomous valet parking system because many vehicles which search available parking lot exist simultaneously. To predict future motion of target, the intention of the target should be inferred first. Interacting multiple model (IMM) filter using two models has been used to infer the intention of the target. Based on the inferred intention, most appropriate model’s results are used as a predicted trajectory of the target vehicle. After that, the drivable area is decided to avoid collision with static obstacles and moving targets using potential filed approach to assessment the risk. In this stage, pre-defined parking lot map which contains boundary of the parking lots and waypoints is used to define initial guess of the drivable area. Inside the drivable area, rapidly-exploring random tree (RRT) generate the desired local path while guaranteeing the real-time performance in dynamic environments. Finally, path tracking controller and speed controller calculate desired steering wheel and longitudinal acceleration input. The proposed motion planning algorithm is validated via MATLAB based computer simulation. Simulation results demonstrate the ability of the proposed motion planning algorithm for unconstructed dynamic environments to plan collision-free path which is appropriate in parking lot
How AI Plays Its Tricks: Interpreting the Superior Performance of Deep Learning-Based Approach in Predicting Healthcare Costs

Artificial Intelligence, especially deep learning based approaches, has been criticized for its \black-box" approach in recent years. The lack of interpretability of their performance makes people uncomfortable when applying these models in critical situations. In this study, we aim to contribute to the interpretability of the deep learning model in predicting healthcare costs. We propose to use long short-term memory based recurrent neural network to incorporate the sequential information for more accurate healthcare cost predictions. We first compare the performance of our deep learning approach with traditional machine learning methods including linear regression, lasso regression, ridge regression, and random forest. Among all the methods, deep learning shows the best performance. The superior performance of the deep learning model is further confirmed in subgroup analyses of patients. We then propose a novel interpretation method to examine how the deep learning model performs differently from other methods when facing fluctuations in the monthly costs. We find that while most traditional prediction models are getting worse with greater fluctuation in the data, the deep learning model can incorporate the fluctuation information and gain in prediction accuracy. Our work makes important contributions to the interpretability of deep learning models for more accurate prediction of healthcare costs.
Set-related restrictions for semantic groupings

Author(s): Rundensteiner, Elke; Bic, Lubomir; Gilbert, Jonathan; Yin, Meng-Lai | Abstract: Semantic database models utilize several fundamental forms of groupings to increase their expressive power. In this paper we consider four of the most common of these constructs; basic set groupings, is-a related groupings, power set groupings, and Cartesian aggregation groupings. For each, we define a number of useful restrictions that control its structure and composition. This permits each grouping to capture more subtle distinctions of the concepts or situations in the application environment. The resulting set of restrictions forms a framework which increases the expressive power of semantic models and specifies various set-related integrity constraints.
Obesity is associated with anaemia and iron deficiency indicators among women in the rural Free State, South Africa

Objective: Obesity and anaemia remain global public health problems, holding major consequences for human health. The objective was to determine body composition, prevalence of anaemia and iron deficiency, contraception use and associations between variables in rural women in the Free State, South Africa. Design: A cross-sectional study design was applied. Setting: The study was undertaken in rural Trompsburg, Philippolis and Springfontein. Subjects: A total of 134 HIV-uninfected non-pregnant females, aged between 25 and 49 years participated. Exposure and outcome measures: Data were collected on anthropometry, contraception use and biochemical markers of anaemia, iron deficiency and inflammation. Results: Median body mass index (BMI) (28.7 [24.2, 34.7] kg/m2), waist circumference (90.8 [80.9, 103.0] cm) and body fat percentage (38.8 [34.3, 42.1] %) were classified as unhealthy. Only 2/134 had iron deficiency and 1/134 iron deficiency anaemia. Overall, 3.8% had low red cell folate levels. Almost half (45.0%) had elevated C-reactive protein (CRP) levels. More than half (54.1%) reported that they regularly menstruate and 71.6% currently or had previously used injectable contraceptives. Mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH) and transferrin saturation were significantly associated with BMI, waist circumference and body fat percentage. Conclusion: A predominant pattern of malnutrition, characterised by overweight and obesity, was prevalent. Risk for iron deficiency was associated with obesity. Prevalence of iron deficiency could be underestimated due to the large percentage with elevated CRP levels. Interventions should aim to improve the nutritional status of these women in order to reduce their risk for both chronic diseases and anaemia.
Comparing Content Based and Collaborative Filtering in Recommender Systems

In daily life we need many things to be searched over the internet, for search purpose there are many search engines available. Whenever we search something we try to get the most relevant results, and this can be achieved using Recommender systems.In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to ldquoconsumerdquo (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming userrsquos actions or opinions. The paper presents an overview of the field of recommender systems and describes the difference between two of the most used approaches in recommender systems, i.e. Collaborative Filtering and Content based Filtering Techniques.
DIAGNOSTIC AND ECOLOGICAL STUDIES OF RICE BACTERIAL LEAF BLIGHT, CAUSED BY XANTHOMONAS ORYZAE

Abstract : Contents: History of nomenclature, distribution, and occurrence of the disease; History of research; Symptoms; The pathophyte of rice bacterial leaf blight; Bacteriophage of the pathogen of rice bacterial leaf blight; Experimental methods on the quantitative test of the pathogens of rice bacterial leaf blight; The life cycle of the pathogen of rice bacterial leaf blight during the period of overwintering; Occurrence and life cycle of rice bacterial leaf blight during the rice growing period; Test of primary infection; Test for secondary infection; Relationship between the life cycle of the pathogen phage of rice bacterial leaf blight in irrigation water and disease occurrence.
Vitamin D receptor gene polymorphisms in patients with thyroid cancer

Summary The association between vitamin D receptor (VDR) gene polymorphisms and some diseases such as colorectal cancer, breast cancer, osteoporosis and psoriasis has been extensively investigated during the past few years. This research was performed not only because of the role of vitamin D as an anticancer agent, but also because of the suppressing action of vitamin D on TSH, the major regulator of thyroid cell growth. In this case-control study, comprising 71 thyroid cancer patients and 82 healthy population controls, we investigated the association between altered thyroid cancer risk with four polymorphisms located at the 3' end of the VDR gene detectable by ApaI, TaqI, Tru9 and BsmI restriction enzymes and with a start codon polymorphism located at the 5' end characterized by restriction enzyme FokI. The individual genetic pattern for VDR was evaluated by DNA extraction followed by PCR amplification of the VDR gene and the digestion with the restriction enzymes. For the effect of existence of the mentioned polymorphisms and thyroid cancer the odds ratio and 95% CI were calculated. All the odds ratios were within their CI, representing no relationship between these polymorphisms and risk of thyroid cancer. These observations suggest that VDR gene polymorphisms may not commonly contribute to the risk of thyroid cancer.
Quantification of Bacillus cereus stress responses

The microbial stability and safety of minimally processed foods is controlled by a deliberate combination of preservation hurdles. However, this preservation strategy is challenged by the ability of spoilage bacteria and food-borne pathogens to adapt to stressing environments providing cell robustness. Bacillus cereus is a toxin-producing, spore-forming bacterium, and is able to survive minimal processing conditions. A quantitative approach was followed to gain insight in B. cereus’ stress adaptive behavior at population, individual cell and molecular level. B. cereus’ ability to adapt to salt stress and gain robustness towards subsequent heat challenge-stress exposure was quantified in detail using primary kinetics models. The adaptive salt stress response was influenced by the adaptation-stress concentration, the growth phase of the cells, strain diversity and the culturing temperature during adaptation-stress treatment. The nonlinear nature of the heat inactivation kinetics suggested heterogeneity within the population with respect to stress adaptive behavior. The direct-imaging-based Anopore technology was used to quantitatively describe the population heterogeneity of B. cereus upon mild and severe salt stress treatments and during low temperature growth. Fluorescent labeling of cells provided insights in the origin of stress-induced population heterogeneity. Then, to elucidate adaptive salt stress responses at molecular level, the genome-wide transcriptome profiles of mildly and severely salt-stressed cells were compared. Various transcriptome responses could be correlated to phenotypic features of salt stress-adapted cells. Comparison of the transcriptome profiles of salt stress-adapted cells to those that were exposed to mild heat, acid and oxidative stress, directed to potential cellular biomarkers for stress adaptation. The selected candidate-biomarkers  the transcriptional regulator B (activating general stress responses), catalases (removing reactive oxygen species), and chaperones and proteases (maintaining protein quality)  were measured upon adaptation-stress treatment at transcript, protein and/or activity level, and their induction was correlated to adaptation-stress induced robustness towards challenge-stress. Various candidate-biomarkers were suitable to predict the robustness level of adaptation-stress pretreated cells towards challenge-stress, and are therefore potential predictive cellular indicators for adaptation-stress induced robustness. The predictive potential of transcripts differed from that of proteins and activity level, underlining the significance to evaluate predictive potential of candidate-biomarkers at different functional cell levels. This quantitative understanding of B. cereus’ stress adaptive behavior provides mechanistic insights and opens up avenues to come to a mechanism-based approach for designing mild preservation strategies.
Examining the Transfer of Function Representations

A transfer task was used to test whether people rely on rules or associations to learn a function. The primary function that everyone received was an inverse absolute value function. The secondary transfer functions that had a similar rule were flip conditioned or shift conditioned version of the primary function. The secondary function representing association was a parabola condition shaped function, which had input-output pairs closest to the primary function. It is expected that since the Parabola condition has less deviations from the trained function that if people favor associations then it would be easier, despite it having a very different rule than the primary function. The flip condition is the farthest from the primary function followed by the shift condition, but they have a similar shape, rule-wise to the primary function so if people have the least trouble on these functions then they might favor rules. The subjects had the most trouble with the Parabola condition so seem to favor rules when learning a function. Examining the Transfer of Function Representations
Adaptive VM Allocation and Migration Approach using Fuzzy Classification and Dynamic Threshold

With the growth of Cloud computing, it is important to consider resource management techniques to minimize the overall costs of management. In cloud environments, each host’s utilization and virtual machine’s request based on user preferences are dynamic in nature. To solve this problem, efficient allocation method of virtual machines to hosts where the classification of virtual machines and hosts is undetermined should be studied. In reducing the number of active hosts to reduce energy consumption, thresholds can be implemented to migrate VMs to other hosts. By using Fuzzy logic in classifying resource requests of virtual machines and resource utilization of hosts, we proposed an adaptive VM allocation and migration approach. The allocation strategy classifies the VMs according to their resource request, then assigns it to the host with the lowest resource utilization. In migrating VMs from overutilized hosts, the resource utilization of each host was used to create an upper threshold. In selecting candidate VMs for migration, virtual machines that contributed to the high resource utilization in the host were chosen to be migrated. We evaluated our work through simulations and results show that our approach was significantly better compared to other VM allocation and Migration strategies.
Reducing students’ ‘absent presenteeism’ and mobile misbehaviour in class: an empirical study of teacher perspectives and practices

ABSTRACT Advances in information technology and communication through new social media platforms have enormous benefits in many contexts, including education. At the same time, self-distractive use of technology – or ‘absent presenteeism’ – can have negative effects in the classroom. The main research question of this study is the relationship between organisational leadership, enforcement of discipline and self-distractive use of technology in the secondary school setting. The finding indicated that the permissive style of discipline appeared to be the most effective at reducing self-distractive mobile phone use. However, a more authoritative style of discipline, short teacher tenure and the principal’s transformational leadership style contributed to high self-distractive mobile phone use. The authors rely on empirical data collected in Israeli public high schools from two independent sources: 144 teachers and 591 students, which yielded 4440 teacher-student events. Implications of the findings are discussed in the context of the school setting characterised by rapid changes in technology.
Provenance trails in the Wings/Pegasus system

Our research focuses on creating and executing large‐scale scientific workflows that often involve thousands of computations over distributed, shared resources. We describe an approach to workflow creation and refinement that uses semantic representations to (1) describe complex scientific applications in a data‐independent manner, (2) automatically generate workflows of computations for given data sets, and (3) map the workflows to available computing resources for efficient execution. Our approach is implemented in the Wings/Pegasus workflow system and has been demonstrated in a variety of scientific application domains. This paper illustrates the application‐level provenance information generated Wings during workflow creation and the refinement provenance by the Pegasus mapping system for execution over grid computing environments. We show how this information is used in answering the queries of the First Provenance Challenge. Copyright © 2007 John Wiley & Sons, Ltd.