Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic Performance of Diffusion MRI for differentiating Benign and Malignant Nonfatty Musculoskeletal Soft Tissue Tumors: A Systematic Review and Meta-analysis

Objective: To evaluate the diagnostic performance of standard diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for differentiating benign and malignant soft tissue tumors (STTs). Materials and methods: A thorough search was carried out to identify suitable studies published up to September 2020. The quality of the studies involved was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity (SEN), specificity (SPE), and summary receiver operating characteristic (SROC) curve were calculated using bivariate mixed effects models. A subgroup analysis was also performed to explore the heterogeneity. Results: Eighteen studies investigating 1319 patients with musculoskeletal STTs (malignant, n=623; benign, n=696) were enrolled. Thirteen standard DWI studies using the apparent diffusion coefficient (ADC) showed that the pooled SEN and SPE of ADC were 0.80 (95% CI: 0.77-0.82) and 0.63 (95% CI: 0.60-0.67), respectively. The area under the curve (AUC) calculated from the SROC curve was 0.806. The subgroup analysis indicated that the percentage of myxoid malignant tumors, magnet strength, study design, and ROI placement were significant factors affecting heterogeneity. Four IVIM studies showed that the AUCs calculated from the SROC curves of the parameters ADC and D were 0.859 and 0.874, respectively. The AUCs for the IVIM parameters pseudo diffusion coefficient (D*) and perfusion fraction (f) calculated from the SROC curve were 0.736 and 0.573, respectively. Two DKI studies showed that the AUCs of the DKI parameter mean kurtosis (MK) were 0.97 and 0.89, respectively. Conclusion: The DWI-derived ADC value and the IVIM DWI-derived D value might be accurate tools for discriminating musculoskeletal STTs, especially for non-myxoid SSTs, using more than two b values, with maximal b value ranging from 600 to 800 s/mm2, additionally, a high-field strength (3.0 T) optimizes the diagnostic performance.
Analysis of Selected Risk Factors Depending on the Type of Cerebral Palsy

Background: Cerebral palsy (CP) is not a defined, separate disease classification, but a set of etiologically diverse symptoms that change with the child’s age. According to the up-to-date definition, CP is a group of permanent but not unchanging disorders of movement and/or posture and motor function, which are due to a nonprogressive interference, lesion, or abnormality of the developing/immature brain. CP is one of the most frequent causes of motor disability in children. The aim of the present study was to analyze whether selected risk factors may vary depending on particular types of CP. Methods: 181 children with CP (aged 4–17 years), hospitalized at the Department of Pediatrics and Developmental Age Neurology in Katowice in the years 2008–2016 were retrospectively analyzed in the present study. The assumed risk factors of CP were divided into two groups: 1—pre-conception and prenatal (mother’s age, family history of epilepsy, burdened obstetric history, mother’s systemic diseases, pregnancy order, multiple pregnancy, duration of pregnancy, bleedings from the genital tract during gestation, arterial hypertension during pregnancy, infections during pregnancy, preterm contractions, maintained pregnancy, premature rupture of membranes, abruptio placentae, and others), 2—perinatal and postnatal (mode of delivery, birth weight, Apgar score at the first and fifth minute, neonatal convulsions, respiratory failure, infections in neonatal and infant period, and intraventricular bleeding). The division into particular CP types was based on Ingram’s classification. Results: The following risk factors were the most frequent in the total group: respiratory failure, infections, intraventricular bleeding, and prematurity. Among the analyzed preconception and prenatal factors, the duration of pregnancy and preterm contractions during pregnancy significantly differentiated the subgroups of patients depending on the type of CP. The prevalence of almost all analyzed perinatal, neonatal, and infant-related risk factors (i.e., birth weight, Apgar score at the first and fifth minute, neonatal convulsions, respiratory failure, infections in neonatal and infant period, and intraventricular bleeding) significantly differed between CP types, apart from the mode of delivery. However, in multivariate regression, only intraventricular bleeding was an independent predictor for tetraplegic CP type when compared to joined extrapyramidal and ataxic types (OR = 2.801, p = 0.028). Conclusions: As CP is a syndrome of multifactorial etiology, the identification of CP risk factors entails the need for careful observation and comprehensive care of children in the risk group. The presence of certain risk factors may be a prognostic indicator for particular types of CP. The knowledge about the association between the risk factor(s) and the CP type could be a very useful tool for pediatricians looking after the child at risk of developmental disorders.
Curcumin represses lipid accumulation through inhibiting ERK1/2-PPAR-γ signaling pathway and triggering apoptosis in porcine subcutaneous preadipocytes

Objective Excessive lipid accumulation in adipocytes results in prevalence of obesity and metabolic syndrome. Curcumin (CUR), a naturally phenolic active ingredient, has been shown to have lipid-lowering effects. However, its underlying mechanisms have remained largely unknown. Therefore, the study aims to determine the effect of CUR on cellular lipid accumulation in porcine subcutaneous preadipocytes (PSPA) and to clarify novel mechanisms. Methods The PSPA were cultured and treated with or without CUR. Both cell counting Kit-8 and lactate dehydrogenase release assays were used to examine cytotoxicity. Intracellular lipid contents were measured by oil-red-o staining extraction and triglyceride quantification. Apoptosis was determined by flow cytometry and the terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-nick end labelling assay. Adipogenic and apoptosis genes were analyzed by quantitative polymerase chain reaction and Western blot. Results The CUR dose-dependently reduced the proliferation and lipid accumulation of PSPA. Noncytotoxic doses of CUR (10 to 20 μM) significantly inhibited extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation and expression of adipogenic genes peroxisome proliferation-activity receptor-γ (PPAR-γ), CCAAT/enhancer binding protein-α, sterol regulatory element-binding protein-1c, adipocyte protein-2, glucose transporter-4 as well as key lipogenic enzymes fatty acid synthase and acetyl-CoA carboxylase, while ERK1/2 activation significantly reversed CUR-reduced lipid accumulation by increasing PPAR-γ. Furthermore, compared with differentiation induced media treated cells, higher dose of CUR (30 μM) significantly decreased the expression of AKT and B-cell lymphoma-2 (BCL-2), while increased the expression of BCL-2-associated X (BAX) and the BAX/BCL-2 expression ratio, suggesting triggered apoptosis by inactivating AKT and increasing BAX/BCL-2 ratio and Caspase-3 expression. Moreover, AKT activation significantly rescued CUR inhibiting lipid accumulation via repressing apoptosis. Conclusion These results demonstrate that CUR is capable of suppressing differentiation by inhibiting ERK1/2-PPAR-γ signaling pathway and triggering apoptosis via decreasing AKT and subsequently increasing BAX/BCL-2 ratio and Caspase-3, suggesting that CUR provides an important method for the reduction of porcine body fat, as well as the prevention and treatment of human obesity.
Assessment of Anthropometric Indices for Optimal Cut-Offs for Obesity Screening in a South African Adolescent Population

Simple Summary The diagnosis of obesity in sub-Saharan African children relies on cut-off values for body mass index percentile (pBMI) and waist-to-height ratio (WtHR) established in western populations. Hence, this study assessed anthropometric indices to determine optimal cut-off values for obesity screening in the South African adolescent population. Findings from this study showed that the cut-off value for pBMI was p85.2th, which improved the sensitivity of the test by approximately 30% compared to the CDC recommended BMI percentile of p95.0th. Moreover, the optimal cut-off for WHtR was 0.481, which was close to the recommended cut-off value of 0.5. This study reveals a lower pBMI cut-off value, different from the CDC recommended cut-off, for screening obesity in a South African adolescent population and suggests that the optimal pBMI cut-off for obesity screening may be ethnic-specific. Abstract The assessment of obesity in sub-Saharan Africa relies on cut-offs established from western populations. This study assessed anthropometric indices to determine optimal cut-off values for obesity screening in the South African adolescent population. A cross-sectional study involving 1144 (796 females and 348 males) adolescents aged 11–17 years from the Eastern Cape Province of South African was conducted. Anthropometric parameters were measured. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity and specificity of obesity screening tools and establish cut-offs. The optimal cut-offs for obesity in the cohort using waist-to-height ratio (WHtR) as reference were: neck circumference (NC) = 30.6 cm, mid-upper arm circumference (MUAC) = 25.9 cm, waist circumference (WC) = 75.1 cm, hip circumference (HC) = 92.15 cm and body mass index percentile (pBMI) = p85.2th. The new pBMI cut-off value at p85.2th improved the sensitivity of the test by approximately 30% compared to the CDC recommended BMI percentile (pBMIr) of p95.0th. When pBMI was used as reference, the optimal cut-offs in the cohort were: WHtR = 0.481, NC = 30.95 cm, MUAC = 27.95 cm, WC = 76.1 cm and HC = 95.75 cm. The WHtR optimal cut-off of 0.481 was close to the recommended cut-off value of 0.5. The predicted prevalence of obesity obtained using cut-offs from ROC analysis was higher than those from recommended references. All cut-off values for the various anthropometric measures generally increased with age for all percentile ranges. This study reveals a lower pBMI cut-off value, different from the CDC recommended cut-off, for screening obesity in a South African adolescent population. The study has established that the optimal pBMI cut-off for obesity screening may be ethnic-specific.
Robust Ensemble Machine Learning Model for Filtering Phishing URLs: Expandable Random Gradient Stacked Voting Classifier (ERG-SVC)

As cyber-attacks grow fast and complicated, the cybersecurity industry faces challenges to utilize state-of-the-art technology and strategies to battle the consistently present malicious threats. Phishing is a sort of social engineering attack produced technically and classified as identity theft and complicated attack vectors to steal information of internet users. In this perspective, our main objective of this study is to propose a unique, robust ensemble machine learning model architecture that provides the highest prediction accuracy with a low error rate while proposing few other robust machine learning models. Both <italic>supervised</italic> and <italic>unsupervised</italic> techniques were used for the detection process. For our experiments, seven classification algorithms, one clustering algorithm, two ensemble techniques, and two large standard legitimate datasets with 73,575 URLs and 100,000 URLs were used. Two test modes (percentage split, K-Fold cross-validation) were utilized for conducting experiments and final predictions. Mechanisms were developed to (I) identify the best <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>, which is the optimal heuristic-based threshold value for splitting words into subwords for each classifier, (II) tune hyperparameters for each classifier to specify the best parameter combination, (III) select prominent features using various feature selection techniques, (IV) propose a robust ensemble model (classifier) called the <italic>Expandable Random Gradient Stacked Voting Classifier</italic> (<italic>ERG-SVC</italic>) utilizing a voting classifier along with a model architecture, (V) analyze possible clusters of the dataset using k-means clustering, (VI) thoroughly analyze the <italic>gradient boost</italic> classifier (<italic>GB</italic>) with respect to utilizing the “criterion” parameter with the Mean Absolute Error (<italic>MAE</italic>), Mean Squared Error (<italic>MSE</italic>), and <italic>Friendman_MSE</italic>, and(VII) propose a lightweight preprocessor to reduce computational cost and preprocessing time. Initial experiments were carried out with 46 features; the number of features was reduced to 22 after the experiments. The results show that the <italic>GB</italic> classifier outperformed with the least number of <italic>NLP</italic> based features by achieving a 98.118% prediction accuracy. Furthermore, our stacking ensemble model and proposed voting ensemble model (<italic>ERG-SVC</italic>) outperformed other tested approaches and yielded reliable prediction accuracy results in detecting malicious URLs at rates of 98.23% and 98.27%, respectively.
Smooth and Stopping Interval Aware Driving Behavior Prediction at Un-signalized Intersection with Inverse Reinforcement Learning on Sequential MDPs

Driving behavior modeling (DBM) is widely used in the intelligent vehicle field to prevent accidents, which predicts actions that vehicles should take to optimize safe driving behaviors. According to some statistics, accidents easily happen at un-signalized intersections. Modeling driving behavior at such places is of great importance. However, current inverse reinforcement learning-based DBM methods fail to predict proper behaviors at the un-signalized intersections in the aspects of smoothness and stopping behavior by just using a single Markov decision process (MDP). We propose a novel sequential MDPs approach to model the driving behavior at the un-signalized intersections to solve the problems. Our approach decomposes the target behavior through the un-signalized intersections into three parts and models each decomposition's driving behaviors with appropriate time durations by a stopping-time-interval distribution through dynamic programming. Experiments on real driving data show that the proposed method achieved a better result and successfully improved the smoothness and stopping awareness of the planned driving path compared to the baselines.
A Comprehensive Review of Driver’s Attention and the Evaluation Methods

With the increasing number of people killed by distracted driving, drivers' attention has become a fundamental challenge recently. Performing non-driving-related tasks can make drivers manually, visually, and cognitively distracted. Therefore, there are various definitions and categories for distraction based on the distraction source. Yet, detecting manual and visual distractions are easily but cognitive distractions are hard to identify and measure. Thus, detection, assessment, and evaluation of driver’s distraction seem crucial in the safety area. This study aims to (a) present different definitions of driver distraction, (b) introduce various methods to detect driver inattention, and (c) discuss each method's pros and cons and give an insight for an all-inclusive measurement to detect drivers’ distraction.
Environment Prediction to Enhance the Navigation System of Water Pipeline Inspection Platforms

The water distribution network is one of the critical buried infrastructures of a nation. Failure of any water pipes or water mains can disrupt the everyday life of the inhabitants and can lead to significant economic loss. Hence, routine inspections are essential to sustain the water supply among the communities. Nowadays, inspection platforms with cameras, which can record the internal condition of the pipelines emerge to be one of the most attractive solutions. However, these inspection platforms often encounter some problems regarding autonomous navigation. As a result, an interruption occurs in the videotaping process which hampers the condition assessment process for the pipelines. Therefore, this paper presents a deep learning-based environment prediction model, which can predict the next instance of the environment inside the pipelines and enhance the autonomous navigation of the pipelines. The main objective of this paper is to make the inspection platforms intelligent enough to activate the control mechanisms and to pass through branches, curvature, elbows, etc. The results from the study show that integration of the environment prediction model with an embedded device can enhance the autonomous navigation inside the pipelines. This will also aid in the uninterrupted videotaping process and ensure better condition assessment of the water pipelines.
Listening Skills: Important but Difficult to Learn

This study of 87 Saudi female university students aimed to discover whether they experience difficulties learning to listen to spoken English. Acknowledging that listening is an important part not just of learning a new language but also of day-to-day communication, data was collected using an online questionnaire. Participants were asked to respond to a series of statements designed to test four constructs relating to their attitudes to learning English language skills in general (a); learning listening skills specifically (b); their attitudes towards listening activities (c); and (d) their attitudes towards improving their listening skills. The results showed that most of participants felt that speaking and listening were the most important skills to learn, but listening was also the most challenging. Reading was felt to be the most effortless skill to learn, as well as the most commonly used, suggesting that frequency of use contributes to students’ perceptions of the ease of learning a skill. Participants’ difficulties with learning to listen to English were associated with speech rate, pronunciation, nervousness, limited vocabulary, and lack of background information. However, students also expressed positive attitudes towards improving their listening skills. Understanding students’ attitudes to listening skills and their problems with listening may help teachers improve the way they teach these skills. This may in turn improve students’ listening comprehension not only in universities but also in schools.
Development of TPACK for teacher-educators

The purpose of this study is to develop a valid and reliable TPACK (technological pedagogical content knowledge) scale based on the core components of TPACK framework in order to measure teacher-educators’ TPACK. For this purpose, 46 statements were developed and tested by 5-point Likert type scale. Data were collected from 642 teacher educators working in different colleges of education under different universities in state of Punjab. The collected data was analyzed by using SPSS 22.0 software. The Exploratory Factor Analysis (EFA) was carried out in order to investigate the factor structure of the scale. EFA resulted in six factors named as Pedagogical skills, Creative thinking skills, Ethics, Instructional design, Innovativeness and Virtual learning environment. The Cronbach’s Alpha coefficient for the whole scale was calculated as . 848. The KMO value was calculated as . 889 and the Bartlett’s Test of Sphericity examined (chi-square = 17196.73; df = 1035), which is significant (P-Value<.001). Based on result and in comparison with the Cronbach’s alpha coefficient values for other TPACK surveys in the literature as a whole and for their sub-components the TPACK scale established here can be said to have a high level of reliability in terms of its factors and as a whole.
Tunable polydimethylsiloxane-based microsphere for magnetic field sensing

We demonstrate a fiber-optic magnetic field sensor based on a micro-optical resonator. The optical resonator is formed by nanocrystals-incorporated PDMS (Ni-PDMS) microsphere. The external magnetic field sets up a body force within the microsphere and a magnetic pressure acting on its surface. The experimental results show that the sensitivity of the 170-μm-diameter microsphere (the ferromagnetic particle mass fraction is 10%) is 20 pm/mT, and gradually tends to be saturated in the magnetic field range of 0 ~ 15 mT.
Examining the production, export, and immediate fate of kelp detritus on open‐coast subtidal reefs in the Northeast Atlantic

Kelp forests are highly productive coastal habitats and are emerging as important sources of organic matter for other ecosystems. Although their high rates of productivity and detritus release are expected to lead to substantial export of carbon, few studies have actually quantified rates of export or the persistence of detritus. We addressed this in eight subtidal kelp forests (Laminaria hyperborea) spanning the length (9° of latitude) of the United Kingdom. Specifically, we quantified detritus production, retention/export from source and adjacent habitats, and in situ decomposition rates. Detritus released via both dislodgment of whole plants and “May cast” shedding of old growth was highly variable between sites with greatest values recorded in our colder, northern sites. This was attributable to greater plant size biomass in northern regions, rather than plant density or dislodgement rates. On average, the annual production of kelp detritus was 4706 ± 700 g FW m−2 yr−1 or 301 g C m−2 yr−1. Low retention of detritus within the kelp forest and adjacent sedimentary habitats indicated very high rates of export (> 98% across the study). A litterbag experiment showed detritus may take > 4 months to decompose, suggesting great potential for long distance transport. Overall, our findings suggest that L. hyperborea forests export large amounts of detritus subsidies across their range, which can potentially shape the structure of distant benthic communities and constitute a relevant and largely overlooked flux in the coastal carbon cycle, which may represent an important component of natural carbon sequestration.
EFFECT OF SELENIUM SUPPLEMENTATION ON TWINNING INDUCTION IN BARKI EWES

This study aimed to evaluate selenium supplementation effect on induction of twinning of Barki ewes by using two different doses of PMSG in estrus synchronization program during the breeding season. For the experiment, 28 ewes were randomly divided into four groups, 7 ewes in each group, all ewes were treated with intra-vaginal sponges, containing 60 mg medroxy progesterone acetate for 14 days, immediately after sponge withdrawal, PMSG hormone at a doses of 250 IU in groups I, II and 750 IU in groups III and IV were administered intramuscularly. Selenium (Se) levels were 0.05 mg/kg diet DM (without supplementation) for groups I and III and 0.2 mg /kg diet DM for groups II and IV (Se supplementation). The pregnancy and barren rates for groups I, II, III and IV were (57.14%, 100%, 71.43% and 85.71 %) and (42.86%, 0.0%, 28.57% and 14.29%), respectively (P<0.01). At the same time, the twinning rate was (0%, 71.43%, 42.86% and 71.43%), and the lambing rate was (57.14%, 100%, 71.43% and 85.71 %) for the four groups respectively (P<0.01). Meanwhile, the fecundity rate for the same groups was (57.14%, 171.43%, 114.28% and 157.14%), respectively. Decreasing PMSG dose with Se supplementation appear to be a most appropriate program for induction of twinning and increasing of fertility and fecundity rates. Key wards: selenium, twinning rate, fertility, fecundity, ewes.