Miracle-dz/UniFAR-FTUs huggingface dataset:
Professional and Amateur Pitchers’ Perspective on the Ulnar Collateral Ligament Injury Risk

Background: Medial ulnar collateral ligament (UCL) injury is increasingly prevalent in professional baseball pitchers, and significant research has been devoted to understanding the risk factors and prevention strategies associated with it. To date, no study has investigated what the players themselves believe causes and prevents the injury. Purpose: To evaluate the opinions of UCL injuries among pitchers, including professional athletes. Study Design: Cross-sectional study. Methods: A total of 214 baseball pitchers (45 high school/college, 169 Major League Baseball [MLB]/Minor League Baseball) completed a 52-item questionnaire designed to evaluate their opinions on the cause of UCL injuries, injury prevention, and Tommy John surgery. Overall, 51 of the 214 pitchers had previously experienced a UCL injury. The frequency of the selection of each answer option was measured. Additionally, chi-square tests were used to compare (1) responses between professional and nonprofessional pitchers and (2) responses between pitchers with and without a previous UCL injury. Results: Only 45% of pitchers thought that UCL injuries are avoidable in MLB. Additionally, 55% of pitchers with a UCL injury had a history of elbow injuries as an adolescent/child, compared with 18% in the uninjured group (P < .0001). Also, 72% of all surveyed pitchers agreed that fatigue over the course of a season increases the risk of UCL injuries, and the majority of pitchers agreed that inadequate rest from throwing both during the off-season (61%) and the season (59%) increases the risk of UCL injuries. Moreover, 59% of pitchers believed that a 6-man starting rotation would decrease the incidence of UCL injuries. Professional and nonprofessional pitchers significantly differed (P = .005) in the type of pitch most prone to causing UCL injuries. Conclusion: Pitchers with a previous childhood elbow injury had a significantly higher incidence of UCL injuries during their adult career, suggesting possible predisposition to UCL injury and warranting further research. Fatigue and inadequate rest were of greatest concern among all pitchers for an increased risk of UCL injuries. Understanding and acknowledging the opinions that players have regarding UCL injuries are important to improve UCL education, prevention, and treatment.
A Consideration of Mesopredator Release and New Zealand's Predator Free Plan: A Literature Review

2 Introduction . 2 Methods . 3 Literature Review . 3 Mesopredator Release . 3 Trophic Control: Top-Down and Bottom-Up Effect . 4 Predicting Mesopredator Responses . 4 Island Specific Mesopredator Release . 5 Mesopredator Release in New Zealand.6 Linear and Triangular Interactions to Predict Cascading Effects in New Zealand . 6 New Zealand Island Mesopredator Release . 7 New Zealand Mainland Mesopredator Release . 8 Mesopredator Release Effect Informing Lethal Eradication . 8 Considerations before Lethal Eradication is Implemented . 8 Conclusion . 9 Acknowledgements . 10 Literature Cited . 10
Asset Pricing with Structural Uncertainty and Structural Disagreement

We study a pure exchange economy with incomplete information in which two agents are uncertain and disagree about the length of business cycles. That is, the agents do not question whether the economy is growing or not, but instead continuously estimate how long economic cycles will last. Learning about persistence generates high and persistent stock-return volatility mostly during recessions, but also (to a smaller extent) during economic booms. Disagreement among agents fluctuates and earns a risk premium. A clear risk-return tradeoff appears only when conditioning on the sign and magnitude of disagreement. We confirm these predictions empirically.
Microorganisms, Tryptophan Metabolism, and Kynurenine Pathway: A Complex Interconnected Loop Influencing Human Health Status

The kynurenine pathway is important in cellular energy generation and limiting cellular ageing as it degrades about 90% of dietary tryptophan into the essential co-factor NAD+ (nicotinamide adenine dinucleotide). Prior to the production of NAD+, various intermediate compounds with neuroactivity (kynurenic acid, quinolinic acid) or antioxidant activity (3-hydroxykynurenine, picolinic acid) are synthesized. The kynurenine metabolites can participate in numerous neurodegenerative disorders (Alzheimer disease, amyotrophic lateral sclerosis, Huntington disease, and Parkinson disease) or other diseases such as AIDS, cancer, cardiovascular diseases, inflammation, and irritable bowel syndrome. Recently, the role of gut in affecting the emotional and cognitive centres of the brain has attracted a great deal of attention. In this review, we focus on the bidirectional communication between the gut and the brain, known as the gut-brain axis. The interaction of components of this axis, namely, the gut, its microbiota, and gut pathogens; tryptophan; the kynurenine pathway on tryptophan availability; the regulation of kynurenine metabolite concentration; and diversity and population of gut microbiota, has been considered.
Changes in Expression of Multiple Checkpoint Molecules and Infiltration of Tumor Immune Cells after Neoadjuvant Chemotherapy in Gastric Cancer

It remains unclear that how tumor immune micro-environment will change following neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). In this study, we aimed to characterize the changes in tumor-infiltrating immune cells and checkpoint molecules following NACT and investigate the prognostic value of these changes in LAGC. Paired tumor samples (pre-NACT and post-NACT) of 60 patients were retrospectively identified and analyzed by multiplex immunohistochemistry with a panel including CD4, CD8, FOXP3, PD-1, PD-L1, and TIM3. Following NACT, the overall median expression levels of CD4, CD8, PD1, PD-L1 and TIM3 were significantly increased (P = 0.008 for PD-L1 and P < 0.001 for all the other markers), while the median FOXP3 expression level remained stable (P = 0.120). Individually, the majority of patients presented increased expression of the markers, while 8.5%, 11.9%, 16.9%, 25.4%, 22.0% and 42.2% of patients had decreased expression of CD4, CD8, PD-1, PD-L1, TIM3 and FOXP3, respectively. Changes in expression between baseline and post-NACT of TIM3, PD-1, and PD-L1 showed strongly positive pairwise correlations with each other (P < 0.001). Multivariate analysis demonstrated that high upregulation levels of CD8 (HR = 0.73, P = 0.028), PD-1 (HR = 0.76, P = 0.027), and PD-L1 (HR = 0.67, P = 0.038) following NACT were beneficial prognostic factors of OS. NACT increase the expression of multiple checkpoint molecules and infiltration of CD4+, CD8+ immune cells in LAGC with the levels of changes in checkpoint molecules positively related with each other. This may raise the possibility of applying immunotherapy with chemotherapy or even dual checkpoint inhibitors in LAGC.
Perceptions of Parenting Practices and Psychological Variables of Elite and Sub-Elite Youth Athletes

Drawing from the model of parental involvement in sport, the overall purpose was to examine the associations of perceptions of parenting practices (encouragement, reinforcement, instruction, and role modeling) and athletes’ psychological variables (self-efficacy, social self-efficacy, self-regulation, and intrinsic motivation) of elite and sub-elite youth athletes. Participants were elite (n = 210) and sub-elite (n = 635) athletes aged between 14 and 18 years (Mage = 16.58, SD = 1.33). Structural equation modeling analysis revealed that young elite athletes’ perceptions of sport-related parenting practices are associated with their psychological skills and performance level in sport. Specifically, in comparison with their sub-elite peers, perceptions of parental encouragement had a significantly different strong effect on intrinsic motivation. Moreover, perceptions of parental modeling revealed different effects on performance level, as well as on intrinsic motivation, and self-regulation. These perceptions of parenting practices may promote a positive learning environment, resulting in an increased likelihood of achieving a high level of sport performance in comparison with their sub-elite peers.
A Trainable Markov Random Field for Low-Level Image Feature Matching with Spatial Relationships Ein trainierbares Markoff-Zufallsfeld für die Zuordnung lokaler Bildmerkmale unter Berücksichtigung ihrer räumlichen Beziehungen

Many vision applications rely on local features for image analysis, notably in the areas of object recognition, image registration and camera calibration. One important example in photogrammetry are fully automatic algorithms for relative image orientation. Such applications rely on a matching algorithm to extract a sufficient number of correct feature correspondences at acceptable outlier rates, which is most often based on the similarity of feature descriptions. When the number of detected features is low, it is advisable to use multiple feature detectors with complementary properties. When feature similarity is not sufficient for matching, spatial feature relationships provide valuable information. In this work, a highly generic matching algorithm is proposed which is based on a trainable Markov random field (MRF). It is able to incorporate almost arbitrary combinations of features, similarity measures and pairwise spatial relationships, and has a clear statistical interpretation. A major novelty is its ability to compensate for weaknesses in one information cue by implicitely exploiting the strengths of others. Zusammenfassung: Ein trainierbares MarkoffZufallsfeld für die Zuordnung lokaler Bildmerkmale unter Berücksichtigung ihrer räumlichen Beziehungen. Viele Anwendungen im Bereich des maschinellen Sehens nutzen lokale Merkmale für die Bildanalyse, insbesondere in den Bereichen Objekterkennung, Bildregistrierung und Kamerakalibrierung. Ein wichtiges Beispiel in der Photogrammetrie sind vollautomatische Algorithmen für die relative Kameraorientierung. Dazu muss aus den Bildmerkmalen verschiedener Bilder anhand eines Matchingalgorithmus eine ausreichende Anzahl von Zuordnungen mit vertretbarem Ausreißeranteil gewonnen werden. Die Suche nach Zuordnungen basiert dabei meist auf der Ähnlichkeit von Merkmalsbeschreibungen. Wenn die Anzahl der extrahierten Merkmale gering ist, macht es Sinn, mehrere möglichst komplementäre Merkmalsdetektoren gleichzeitig einzusetzen. Ist die Ähnlichkeit von Bildmerkmalen kein ausreichendes Kriterium für die Zuordnung, liefern räumliche Beziehungen von Merkmalen zusätzlich wertvolle Information. In dieser Arbeit stellen wir ein allgemeines Matchingverfahren vor, das auf einem trainierbaren Markoff-Zufallsfeld basiert. Es ermöglicht die gleichzeitige Berücksichtigung nahezu beliebiger Arten von Bildmerkmalen, Ähnlichkeitsmaßen und paarweisen räumlichen Beziehungen, und lässt sich statistisch klar interpretieren. Eine Besonderheit dieses Verfahrens ist seine Eigenschaft, Schwachpunkte einer Informationsquelle durch die Stärken einer anderen implizit auszugleichen.
Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach

In this paper, a hybrid electricity price forecasting method which is composed of two-stage feature selection method and optimized adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine is proposed to accurately forecast electricity price. A multi-objective feature selection approach comprises of multi-objective binary-valued backtracking search algorithm (MOBBSA) as an efficient evolutionary search algorithm and ANFIS method is developed in this paper to extract the most influential subsets of input variables with maximum relevancy and minimum redundancy. Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. Real-world electricity demand and price dataset from Ontario power market; which is reported as among the most volatile market worldwide, has been used as case study to validate the performance of the proposed approach. From the simulation results, it has been seen that the proposed hybrid forecasting method was effective in accurately forecast the Ontario electricity price. In addition, to prove the superiority of the proposed hybrid forecasting method the simulation results obtained using ANN and ANFIS models optimized by other well-known optimization methods have been compared with that of proposed method.
Improving Drone's Command and Control Link Reliability through Dual-Network Connectivity

In this work, we analyze the end-to-end latency measured in a client-server application that emulates the traffic requirements for the Unmanned Aerial Vehicle (UAV)â€™s Command and Control (C2) link. The connectivity is provided by two real LTE-A networks to a client attached to a flying UAV. Measurements are performed at 4 different heights: ground level, 15 m, 40 m and 100 m. In single operator scenarios, the reliability measured at the target latency, 50 ms, was between 99.6 % and 97.6 % in downlink, and 91.3% and 99.4% in uplink. These results are below the 99.9 % target reliability defined for UAVs and they show that several consecutive packets can be missed when the radio link connectivity degrades, leading to high (> 1 s) values for the 99.9%-ile of latency. To circumvent this, a dualoperator hybrid access scheme is proposed in this paper. The results show that the hybrid access strategy managed to reach the performance requirements in most cases. The solution shows potential to enable C2 over cellular networks, without requiring optimization or modifications in the network.
Disturbance-Observer-Based Composite Hierarchical Antidisturbance Control for Singular Markovian Jump Systems

Antidisturbance control issue for singular Markovian jump systems under infinitely unobservable states and multiple disturbances is investigated. One of the disturbances is norm bounded, whereas the other is written in a state-space expression. Under assumption that some of the states of the system are infinitely unobservable, a novel mode-dependent observer is proposed to estimate the modeling disturbance and all the states by equivalent transforming and reformulating the system into a reduced-order system where the observable states are included and infinitely unobservable states are regarded as an unknown input. Then, we construct a composite hierarchical controller based on the outputs of the proposed observer. Sufficient conditions for the existence of the desired observer gains and controller matrices are derived. Finally, a simulation example is presented to demonstrate the effectiveness of our scheme.
Application of Hybrid Switch Method to Quantify Oil Spills

Oil spill occurs across large landscape in a variety of soils which causes serious concern to the environment, therefore, it is important to identify and quantify spills at an early stage. Linear umixing methods are primary used due to their simplicity and computational cost with respect to non linear to monitor oil spills. However, the effectiveness of the different methods to quantify oil spills has not been assessed yet. Here we propose to robustly choose the most suitable method among linear and non linear spectral unmixing approaches to quantify different Hydrocarbon (HCs) substances in different soils. Hypersectral data sets have been acquired using mixtures of different HCs and soils. Then Artificial Neural Networks was used to switch between linear and non-linear methods to assess the most suitable method in quantifying the amount of spills. Results are presented for Vertex Component Analysis (VCA) and Fully Constrained Least Square Method (FCLS) for the linear models, and the Polynomial Post Nonlinear Mixing Model (PPNMM) and Generalised Bilinear Model (GBM).
Machine Learning-Driven Optimal Proactive Edge Caching in Wireless Small Cell Networks

In this paper, we proposed a novel approach for proactive edge caching in wireless small cell networks. Specifically, we propose using a recurrent neural network for predicting the content popularity with low computational complexity. The mean estimation error of the adopted recurrent neural network could be very close to that of the optimal linear prediction filter utilizing all past history. Based on the predicted content popularity, we formulate and solve a minimum cost flow problem in order to optimally place content files at edge caches. Since the computational complexity of the adopted recurrent neural network is relatively low and the minimum cost flow problem can be solved in polynomial time, the proposed approach is feasible in practice. Simulation results show that the proposed approach outperforms a greedy approach and can significantly reduce the bandwidth consumption of the backhaul network.
Observations over SPROBES Mechanism on the TrustZone Architecture

In this paper we try to reproduce the results described in the paper "SPROBES: Enforcing Kernel Code Integrity on the TrustZone Architecture" [1]. The implementation revolves around the idea of protecting the operating system against rootkits with minimum overhead. This is described as an introspection mechanism implemented through TrustZone Secure Monitor Calls (SMC) and handled in secure world. It can instrument any operating system instruction. Since the challenge with this implementation was to avoid being removed by a rootkit, five invariants were enforced and considered to be enough to protect the operating system. This implementation was done on a Linux kernel 2.6. 38 and used only 12 such SPROBES. As the original article was written the ARM market is expanding further and further with TrustZone extension now available even on the M series cortex systems. In addition to smartphones as described in previous article, the TrustZone extension is found in a lot of IoT device, such as RaspberryPi and in high-end devices. We try to reimplement the above-mentioned work on a new 4.9 Linux kernel and latest arm-trusted-firmware implementation for a reference ARM 64 hardware in order to identify if it could prove a viable software solution Also we provide feedback and observations for this whole process.
Adaptive control of combustion instability using extremum-seeking

We present results of experiment with two distinct extremum-seeking adaptive algorithms for control of combustion instability suitable for reduction of acoustic pressure oscillations in gas turbine over large range of operating conditions. The algorithms consists of a frequency tracking extended Kalman filter to determine the in-phase component, the quadrature component, and the magnitude of the acoustic mode of interest, and a phase shifting controller with the controller phase tuned using an extremum-seeking algorithms. Even though the algorithms were designed specifically for control of combustion instability in gas turbine engines, they are applicable for control of oscillations of systems whose oscillation frequency and optimal control phase shift depends on operating conditions, and which are driven by strong broad-band disturbance. The algorithms have been tested in combustion experiments involving full-scale engine hardware and during simulated fast engine transients.
Incremental Hopping-Window Pose-Graph Fusion for Real-Time Vehicle Localization

In this work, we research and evaluate incremental hopping-window pose-graph fusion strategies for vehicle localization. Pose-graphs can model multiple absolute and relative vehicle localization sensors, and can be optimized using non-linear techniques. We focus on the performance of incremental hopping-window optimization for on- line usage in vehicles and compare it with global off-line optimization. Our evaluation is based on 180 Km long vehicle trajectories that are recorded in highway, urban, and rural areas, and that are accompanied with post-processed Real Time Kinematic GNSS as ground truth. The results exhibit a 17% reduction in the error's standard deviation and a significant reduction in GNSS outliers when compared with automotive-grade GNSS receivers. The incremental hopping-window pose- graph optimization bounds the computation cost, when compared to global pose-graph fusion, which increases linearly with the size of the pose- graph, whereas the difference in accuracy is only 1%. This allows real-time usage of non-linear pose-graph fusion for vehicle localization.
Assessment of Albanian adults psychopathology through cross-informant agreement

Aim: Adult psychopathology is principally assessed with self-report measures. The person assessed frequently offers a biased perspective of self. People knowing the person well (informants) usually view the person being assessed more realistically. The aim of this study was to compare respondents and informants perspectives in the Albanian adult population. Methods: Adult psychopathology in this study was assessed using two widely known psychometric measures of Achenbach System of Empirically Based Assessment (ASEBA): Adult Self Report for Ages 18-59 (ASR) and Adult Behavior Checklist for Ages 18-59 (ABCL). Normative data were collected in an epidemiological cross-sectional design including a nationwide representative sample of 1500 individuals (750 respondents and 750 informants). Results: We calculated correlations between adult respondents and informants reports of problems for the full sample. High correlations were obtained both for the global scale (r=0.83) and for the eight syndrome scales (range from r=0.58 to r=0.84) in the sample. Tests of mean level differences showed that respondents self-reported significantly more Internalizing Problems (P<0.05) compared to informants reports. In contrary, informants reported significantly higher levels of Externalizing Problems (P<0.001) compared to respondents self-reports. Conclusion: Our findings indicate that both parties perceive more their own discomfort and suffering than that of the other one. These findings can be described, among other things, by the traditional behavioral rules in the Albanian society. This study also confirmed the ASEBA instruments as valid and suitable tools for practical use in Albania.
MicroRNA-18 b mediates the inhibitory effects of nicotine on periodontal ligament-derived stem cell

Objective: To investigate the level change of miR-18b in PDLSCs with nicotine exposure and its effect on PDLSCs’ proliferation, migration and osteogenic differentiation. Methods: PDLSCs were isolated from periodontal ligament tissues which obtained from smoker patients and age-matched non-smoker. miR-18b levels in PDLSCs from different sources were detected using qRT-PCR. PDLSCs from non-smoker were treated with nicotine and the miR-18b level changes were confirmed at different time point after exposure. miR-18b was downregulated by miR-18b inhibitor transfection in PDLSCs and the influence of miR-18b knockdown on proliferation, apoptosis and migration of nicotine exposed PDLSCs were estimated using Cell Titer-Blue Assay, flow cytometry and time-lapse microscope, respectively. The changes of osteogenic differentiation markers including ALP, RUNX2, OCN and OPN were also analyzed using qRT-PCR and western blot. Furthermore, bioinformatics analysis was performed to predict the target genes of miR-18 followed with gene ontology analysis. Result: Higher miR-18b level was observed in PDLSCs from smoker and nicotine-treated PDLSCs. Restoring of miR-18b level in PDLSCs could relieve the inhibitory effect of nicotine on proliferation, migration and decrease apoptosis; however, it could not change the osteogenic differentiation markers. miR-18b may target the genes involved with protein localization and cellular localization. Conclusion: Nicotine has an inhibitory effect on proliferation, migration and osteogenic differentiation in PDLSCs; miR-18b may be a crucial regulator in these nicotine-associated functional changes.