Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Expression of Bone Marrow Angiogenic Factors, Cell-Cell Adhesion Molecules and Matrix-Metallo-Proteinases in Plasmacellular Disorders: a Molecular Panel to İnvestigate Disease Progression

Increasing levels of angiogenesis play an important role in the pathogenesis and progression of multiple myeloma (MM). Malignant plasma cells promote a gradual increase in the degree of angiogenesis, modulation of specific cell-cell adhesion molecules and secretion of matrix-metallo-proteinases (MMPs), changing the BM composition from benign conditions, such as MGUS, to smouldering multiple myeloma (SM) and to active MM. We aimed to identify a gene expression profile, helpful to discriminate the “angiogenic potential” in BM and PB plasma cells from MGUS, SMM and active MM patients analyzed at diagnosis. We analyzed the expression of cell-cell adhesion molecules such as VE-Cadherin, E-Cadherin MCAM/MUC18/CD146 and of the MMP-2 and MMP-9. MCAM/MUC18 expression resulted mostly associated with that of the pivotal angiogenic factors VEGF and Ang2, and in MGUS the pattern was different in steady state, compared to progression towards SM. Furthermore, E-Cadherin, the main epithelial cell-cell-adhesion molecule, unexpectedly resulted overexpressed in MM.
Spatio-Temporal Signal Recovery Based on Low Rank and Differential Smoothness

The analysis of spatio-temporal signals plays an important role in various fields including sociology, climatology, and environmental studies, etc. Due to the abrupt breakdown of the sensors in the sensor network, there always are missing entries in the observed spatio-temporal signals. In this paper, we study the problem of recovering spatio-temporal signals from partially known entries. Based on both the global and local correlated property of spatio-temporal signals, we propose a low rank and differential smoothness based recovery method (LRDS), which novelly introduces the differential smooth prior of time-varying graph signals to the field of spatio-temporal signal analysis. The performance of the proposed method is analyzed theoretically. Considering the case where a priori information about the signal's global pattern is available, we propose prior LRDS to further improve the reconstruction accuracy. Such improvement is also verified by synthetic experiments. Besides, experiments on several real-world datasets demonstrate the improvement on recovery accuracy of the proposed LRDS over the state-of-the-art spatio-temporal signal recovery methods.
Energy crops affecting farmland birds in Central Europe: insights from a miscanthus-dominated landscape

The expansion of energy crops such as miscanthus Miscanthus x giganteus has changed the habitat of European farmland birds. However, most studies on the subject are based in Western Europe. We surveyed the avian community in a sample bioenergy landscape in Poland to investigate the pattern of use of the crop by birds in Central Europe. During a year-long survey, 80 species were noted, with 32 sighted in miscanthus. The number of bird sightings was negatively correlated with the area covered by miscanthus in study plots. In contrast to results from Western Europe, most woodland-generalist bird species tended to avoid miscanthus. Farmland species (e.g. the corn bunting Miliaria calandra; the tree sparrow Passer montanus; the starling Sturnus vulgaris; the yellowhammer Emberiza citrinella) were less frequently sighted in the crop than in other habitats. Only a few species were sighted more often in miscanthus than in other habitats, e.g. the marsh warbler Acrocephalus palustris; the reed bunting Emberiza schoeniclus; the whinchat Saxicola rubetra. The potential impact of expanded miscanthus cropping on bird communities in Central and Eastern Europe may diverge from predictions derived from studies based in Western Europe due to differing habitat preferences and bird densities between the regions. For a majority of farmland species, the crop may constitute only a suboptimal, supplementary habitat, with only a few birds potentially adapting to the exploitation of bioenergy plantations.
Probability Weighting, Stop-Loss and the Disposition Effect

In this paper we study a continuous-time, optimal stopping model of an asset sale with prospect theory preferences under pre-commitment. We show for a wide range of value and probability weighting functions, including those of Tversky and Kahneman (1992), that the optimal prospect takes the form of a stop-loss threshold and a distribution over gains. It is skewed with a long right tail. This is consistent with both the widespread use of stop-loss strategies in financial markets, and recent experimental evidence. Moreover, our model with probability weighting in tandem with the S-shaped value function makes predictions for the disposition effect which match in magnitude that calculated by Odean (1998).
Knowledge and Experiences of Safety and Health Occupation Risks among Students

Occupational safety and health should be the subject of education, as it prepares students for their future occupation, and further adult education, including retraining. Nowadays, as society supports technical education via dual learning, this issue is becoming particularly necessary. Schools ensure students’ safety and health in education, training and other related activities as well as in the provision of school services. In order to secure this task the schools adopt preventive measures based on risk assessment associated with the school activities and environment. When establishing specific measures the school takes into account predominantly the possible threat to students in education of individual subjects, movement within school, and the participation of students in various school-organised events. Subsequently, the educational institution provides continuous education in this area for its students and employees and requires the observance of relevant legal standards by systematic control. It takes into account the age of students, their abilities, and their physical and mental maturity. The aim of this article was to find out how students at different age levels (students of secondary school and elementary schools) understand and observe the principles of safety and health which is a central prerequisite in preparedness for future occupation. In the conclusion part of this paper, we present some solution suggestions; highlight the importance of suitable curricula at schools and educational institutions. Education methods must reflect current, valid and enforceable safety and health regulations at work. It is therefore essential that risk assessment is included in the education process.
Pneumococcal carriage among sickle cell disease patients in Accra, Ghana: Risk factors, serotypes and antibiotic resistance

Background Pneumococcal carriage is the precursor for development of pneumococcal disease, and is also responsible for transmission of the organism from person-to-person. Individuals with Sickle Cell Disease (SCD) are more likely to develop invasive disease with S. pneumoniae compared to their healthy counterparts and the presentation of disease in the former is usually abrupt and severe. In Africa, little is known about the pneumococcus in relation to people with SCD Sickle Cell Disease (SCD). The aim of the study was to investigate the epidemiology of pneumococcal carriage among SCD patients including the carriage prevalence, risk factors, serotypes and antibiotic resistance. Method This was a cross sectional study involving 402 SCD patients recruited from Korle Bu Teaching Hospital and Princess Marie Louis Hospital in Accra from October 2016 to March 2017. The study subjects included 202 children of the age groups: ≤5 years (94), >5–9 years (75), ≥10–13 years (33) and 200 adults of the age groups: 14–20 years (46), 21–40 years (112), 41–60 years (25), ≤ 61 years (17). Nasopharyngeal (NP) swabs were collected from the study participants as well as epidemiological data on demographic, household and clinical features. The NP specimens were cultured for S. pneumoniae and the isolates were serotyped by latex agglutination. Antimicrobial susceptibility tests of the isolates were done by the disc diffusion test and E-test. Results Prevalence of S. pneumoniae carriage among children and adult SCD patients enrolled in the study were 79/202 (39.1%; 95% CI: 32.3 to 46.2) and 20/200 (10.0%; 95% CI: 6.2 to 15.0) respectively. Risk factors associated with pneumococcal carriage were age (OR = 1.137; 95% CI: 1.036–1.248; p = 0.007) and runny nose (OR = 5.371; 95% CI: 1.760–16.390; p = 0.003). Overall, twenty-six pneumococcal serotypes were isolated from the study participants and the predominant serotype was 6B (10.6%), followed by 23B (8.2%). Among the children, serotype coverage of the 13-valent Pneumococcal Conjugate Vaccine, which is currently used in Ghana was 32.4%. Prevalence of penicillin resistance among the pneumococcal isolates was 37.4% (37/99) and all the penicillin-resistant isolates exhibited intermediate penicillin resistance with the exception of one isolate that showed full resistance and was susceptible to ceftriaxone. Prevalence of resistance to the other antibiotics ranged from 2.5% (levofloxacin) to 85% (cotrimoxazole). Multidrug resistance occurred among 34.3% (34/99) of the pneumococcal isolates. Conclusion Pneumococcal carriage was four-fold higher in SCD children than adults and was characterized by predominance of non-vaccine serotypes and considerable level of multidrug resistance, though penicillin, cefotaxime and levofloxacin resistance appeared to be very low.
Inhibitory killer cell immunoglobulin-like receptors strengthen CD8+ T cell–mediated control of HIV-1, HCV, and HTLV-1

Inhibitory killer cell immunoglobulin-like receptors (iKIRs) help maintain CD8+ T cell responses to human chronic viral infections. Fine-tuning CD8+ T cell responses Human cytotoxic CD8+ T cells are important for defense against viral infections. Boelen et al. investigated whether inhibitory killer cell immunoglobulin-like receptors (iKIRs) carried by patients with chronic viral infections affected the efficacy of their CD8+ T cell responses. Possession of an iKIR gene along with a gene encoding a KIR ligand enhanced protective and detrimental HLA class I associations for HIV-1, HCV, and HTLV-1. Analysis of virus dynamics, in vitro survival assays, and mathematical modeling suggested that iKIR ligation enhances HLA associations by increasing T cell survival. In contrast to many reported iKIR-disease associations, these observations applied to all iKIRs and the three viral infections studied. This analysis reveals a consistent ability of iKIRs to influence clinical outcomes in human chronic viral infection. Killer cell immunoglobulin-like receptors (KIRs) are expressed predominantly on natural killer cells, where they play a key role in the regulation of innate immune responses. Recent studies show that inhibitory KIRs can also affect adaptive T cell–mediated immunity. In mice and in human T cells in vitro, inhibitory KIR ligation enhanced CD8+ T cell survival. To investigate the clinical relevance of these observations, we conducted an extensive immunogenetic analysis of multiple independent cohorts of HIV-1–, hepatitis C virus (HCV)–, and human T cell leukemia virus type 1 (HTLV-1)–infected individuals in conjunction with in vitro assays of T cell survival, analysis of ex vivo KIR expression, and mathematical modeling of host-virus dynamics. Our data suggest that functional engagement of inhibitory KIRs enhances the CD8+ T cell response against HIV-1, HCV, and HTLV-1 and is a significant determinant of clinical outcome in all three viral infections.
Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis

Bearing faults are one of the most common inducements for machine failures. Therefore, it is very important to perform bearing fault diagnosis reliably and rapidly. However, it is fundamental but difficult to extract impulses buried in heavy background noise for bearing fault diagnosis. In this paper, a novel adaptive enhanced sparse period-group lasso (AdaESPGL) algorithm for bearing fault diagnosis is proposed. The algorithm is based on the proposed enhanced sparse group lasso penalty, which promotes the sparsity within and across groups of the impulsive feature of bearing faults. Moreover, a periodic prior is embedded and updated dynamically through each iteration of the optimization procedure. Additionally, we formed a deterministic rule about how to set the parameters adaptively. The main advantage over conventional sparse representation methods is that AdaESPGL is parameter free (forming a deterministic rule) and rapid (extracting the impulsive information directly from the time domain). Finally, the performance of AdaESPGL is verified through a series of numerical simulations and the diagnosis of a motor bearing. Results demonstrate its superiority in extracting periodic impulses in comparison to other state-of-the-art methods.
A study of relationship between perceived stress, coping pattern, burnout, and general psychopathology among the postgraduate medical students

Introduction: Medical postgraduate student experience considerable stress during their training, which may cause agony to the individual or affect care rendered to the patient. Significant burnout and psychopathology has not been uncommon. Materials and Methods: A study was done to assess the relation between perceived stress, coping pattern, burnout, and general psychopathology among the postgraduate medical students. Results: Perceived stress was associated with higher scores on general psychopathology and burnout. Postgraduate students who displayed positive coping strategies had lesser perceived stress. Females had higher scores on perceived stress and psychopathology. Conclusion: Stress is one of the major growing mental problems among highly educated health professional, and it should not be ignored as it can cause many other health issues.
An evidence‐based approach to object pose estimation from LiDAR measurements in challenging environments

This paper addresses the problem of estimating object pose from high‐density LiDAR measurements in unpredictable field robotic environments. Point‐cloud measurements collected in such environments do not lend themselves to providing an initial estimate or systematic segmentation of the point‐cloud. A novel approach is presented that evaluates measurements individually for the evidence they provide to a collection of pose hypotheses. A maximum evidence strategy is constructed that is based in the idea that the most likely pose must be that which is most consistent with the observed LiDAR range measurements. This evidence‐based approach is shown to handle the diversity of range measurements without an initial estimate or segmentation. The method is robust to dust. The approach is demonstrated by two pose estimation problems associated with the automation of a large mining excavator.
Watermarking Deep Neural Networks for Embedded Systems

Deep neural networks (DNNs) have become an important tool for bringing intelligence to mobile and embedded devices. The increasingly wide deployment, sharing and potential commercialization of DNN models create a compelling need for intellectual property (IP) protection. Recently, DNN watermarking emerges as a plausible IP protection method. Enabling DNN watermarking on embedded devices in a practical setting requires a black-box approach. Existing DNN watermarking frameworks either fail to meet the black-box requirement or are susceptible to several forms of attacks. We propose a watermarking framework by incorporating the author's signature in the process of training DNNs. While functioning normally in regular cases, the resulting watermarked DNN behaves in a different, predefined pattern when given any signed inputs, thus proving the authorship. We demonstrate an example implementation of the framework on popular image classification datasets and show that strong watermarks can be embedded in the models.
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering

We posed the shared task of assigning sentence-level quality scores for a very noisy corpus of sentence pairs crawled from the web, with the goal of sub-selecting 1% and 10% of high-quality data to be used to train machine translation systems. Seventeen participants from companies, national research labs, and universities participated in this task.
Effectiveness of casein phosphopeptide-amorphous calcium phosphate and xylitol chewing gums on salivary pH, buffer capacity, and Streptococcus mutans levels: An interventional study

Aim: The aim of the study is to compare the anticariogenic effectiveness of Casein phosphopeptide- Amorphous Calcium phosphate (CPP-ACP) and xylitol chewing gums based on salivary pH, buffer capacity, and Streptococcus mutans levels. Materials and Methods: A group of twenty individuals in the age group of 18–25 years were randomly divided into two Groups A and B. Test arm A received xylitol gums and test arm B received CPP-ACP gums and they were instructed to use the gums thrice daily for 2 weeks. Unstimulated salivary samples were collected before they began the use of the gums for baseline values, 24 h after beginning the usage of chewing gums and at the end of 14 days. The samples were analyzed for pH, buffer capacity, and S. mutans levels. Results: A statistically significant reduction of salivary S. mutans levels, improvement in salivary pH, and buffer capacity were displayed in both groups 24 h and 14 days after the intervention when compared with baseline. Group B showed more statistically significant improvement in pH than group A after 24 h (P = 0.028) and at the end of 2 weeks (P = 0.041). Conclusion: CPP-ACP has better ability than xylitol in improving the pH of saliva. Both CPP-ACP and xylitol gums individually have remarkable ability in bringing down S. mutans levels while simultaneously improving the pH and buffer of saliva.
Nonlinear Polynomial Graph Filter for Signal Processing With Irregular Structures

Processing of signals with irregular structures is a fundamental challenge to the conventional signal processing due to the complex structures. Since most of the relationships among such data from engineering applications are inherently nonlinear, those tools that do not take the nonlinearity into account may have limitations in dealing with such relationships. In this paper, a novel nonlinear polynomial graph filter (NPGF) is proposed to facilitate the nonlinear processing of signals with irregular structures. First of all, the nonlinear relationship between the input and output graph signals is constructed, which is further developed to utilize the structure information of the irregular structures (i.e., the graph shift ${\boldsymbol S}$ ). Specifically, the nonlinear kernels and the nonlinear input terms are both designed to be polynomials of the graph shift ${\boldsymbol S}$ . Such nonlinear polynomial relationship is then defined as the NPGF. Note that shift invariance is a fundamental property in signal processing but is not straightforward in the existing nonlinear graph signal processing techniques. Due to the special expression of the NPGF, the shift-invariance is extended to our NPGF, which can conveniently facilitate the analysis and design of graph signals using NPGF and is one of the most important contributions in this paper. Numerical studies that characterize the nonlinear temperature distribution and GDP prediction based on real datasets are presented. Compared with other existing techniques, our NPGF achieves superior capability in characterizing the nonlinear relationship between the input and output graph signals, especially when only very few graph signals can be observed.
Modifications of the Dmax method in comparison to the maximal lactate steady state in young male athletes

ABSTRACT Objective: To investigate the influence of different approaches for first-rise determination on the accuracy of Dmax as an estimate of the maximal lactate steady state (MLSS). Methods: Seventeen male cyclists and 18 male runners with different levels of endurance performance completed graded exercise tests either on a cycle ergometer or treadmill to determine Dmax, calculated by the final data point and five modifications of the first rise in blood lactate concentration. Two or more constant load tests over 30 min were performed to determine MLSS. Differences between the modifications of the first rise in blood lactate concentration as well as the corresponding Dmax variants and MLSS were tested, using one-way repeated measure ANOVA with Bonferroni post-hoc tests, and illustrated, using the Bland–Altman method. The absolute agreement was observed, using intra-class correlation coefficients, based on a single measure, absolute agreement, 2-way mixed effects model. Results: The peak power output/running velocity of the groups averaged 275 ± 43 W and 4.3 ± 0.4 m · s−1, respectively. The mean power output/running velocity at MLSS was 229 ± 38 W and 3.77 ± 0.38 m · s−1. For both running and cycling the original Dmax described by Cheng et al. was significantly lower than MLSS (p < 0.01). All modifications showed good agreement with MLSS (ICC ≥0.75). According to the Bland–Altman method the mean differences of the modifications compared to MLSS in cycling ranged from −7 (43) to 2 (41) W. In running the mean differences ranged from −0.12 (0.34) to −0.08 (0.35) m· s−1. Conclusion: We suggest using the first rise in blood lactate concentration for calculating Dmax instead of the first data point of a lactate curve as originally described. The approach of first rise determination has no substantial influence on the accuracy of Dmax compared to MLSS in cycling and running.
Identification of an energy metabolism-related signature associated with clinical prognosis in diffuse glioma

Now, numerous exciting findings have been yielded in the field of energy metabolism within glioma cells. In addition to aerobic glycolysis, multiple catabolic pathways are employed for energy production. However, the prognostic significance of energy metabolism in glioma remains obscure. Here, we explored the relationship between energy metabolism gene profile and outcome of diffuse glioma patients using The Cancer Genome Altas (TCGA) and Chinese Glioma Genome Altas (CGGA) datasets. Based on the gene expression profile, consensus clustering identified two robust clusters of glioma patients with distinguished prognostic and molecular features. With the Cox proportional hazards model with elastic net penalty, an energy metabolism-related signature was built to evaluate patients’ prognosis. Kaplan-Meier analysis found that the acquired signature could differentiate the outcome of low and high-risk groups of patients in both cohorts. Moreover, the signature, significantly associated with the clinical and molecular features, could serve as an independent prognostic factor for glioma patients. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) showed that gene sets correlated with high-risk group were involved in immune and inflammatory response, with the low-risk group were mainly related to glutamate receptor signaling pathway. Our results provided new insight into energy metabolism role in diffuse glioma.