The influence of expertise and experimental paradigms on the visual behavior of tennis athletes in returning a serve

To return a serve, one must pick up information from the server’s kinematics and anticipate the ball trajectory. Although the perceptual requirements are important, the literature diverges in terms of the differences between experts and novices as well as the importance of the experimental paradigm (in-situ vs. video-based) for the results. This study aimed to address both concerns. We compared experts’ (n=7, 20.6±1.1 years of age) and novices’ (n=7, 20.0±0.4 years of age) visual pattern when returning a serve (Experiment 1) and the influence of the experimental paradigm in experts (Experiment 2). Experts fixated more and longer the upper body and ball, while novices showed a more distributed pattern and with longer fixations outside of the server’s body. Also, the pattern was different when comparing in-situ and laboratory settings, differing mainly in fixation frequency. The influence of expertise was observed in qualitative (relative) and quantitative (absolute) measures of visual behavior with the setting having an important influence. Thus, studies should be as close to the actual situation if trying to understand experts’ behavior

Recombinant human insulin-like growth factor-1 promotes osteoclast formation and accelerates orthodontic tooth movement in rats

Background: IGF-1 may be an important factor in bone remodeling, but its mechanism of action on osteoclasts during orthodontic tooth movement is complex and unclear. Methodology: The closed-coil spring was placed between the left maxillary first molar and upper incisors with a force of 50 g to establish an orthodontic movement model. Eighty SD rats were randomized to receive phosphate buffer saline or 400 ng rhIGF-1 in the lateral buccal mucosa of the left maxillary first molar every two days. Tissue sections were stained for tartrate-resistant acidic phosphatase (TRAP), the number of TRAP-positive cells was estimated and tooth movement measured. Results: The rhIGF-1 group exhibited evidential bone resorption and lacuna appeared on the alveolar bone compared to the control group. Moreover, the number of osteoclasts in compression side of the periodontal ligament in the rhIGF-1 group peaked at day 4 (11.37±0.95 compared to 5.28±0.47 in the control group) after the orthodontic force was applied and was significantly higher than that of the control group (p<0.01). Furthermore, the distance of tooth movement in the rhIGF-1 group was significantly larger than that of the control group from day 4 to day 14 (p<0.01), suggesting that rhIGF-1 accelerated orthodontic tooth movement. Conclusion: Our study has showed that rhIGF-1 could stimulate the formation of osteoclasts in the periodontal ligament, and accelerate bone remodeling and orthodontic tooth movement

Survival Prediction of Initial Blood pH for Nontraumatic Out-of-hospital Cardiac Arrest Patients in the Emergency Department

SummaryBackgroundMost nontraumatic out-of-hospital cardiac arrest (NTOHCA) patients who fail in prehospital resuscitation receive continued cardiopulmonary resuscitation in the emergency department (ED). Initial blood pH, which can be assessed rapidly in the ED, was examined to see whether it is a strong survival predictor for these patients.MethodsA 1-year retrospective study included consecutive 225 NTOHCA patients at a medical center in northern Taiwan who presented through the emergency medical services system. On arrival at the ED, these patients received continued cardiopulmonary resuscitation, and their initial blood pH data were assessed.ResultsThe pH value was positively correlated with variables such as return of spontaneous circulation, witnessed arrest, short prehospital time (≤20 minutes), and survival. The best cut-off value of initial blood pH, revealed by the receiver operating characteristic curve, was 7.068. The lowest pH value of the survivors was 6.856. The results of logistic regression model analysis shows that the odds ratios of survival was 10.0 (95% confidence interval [CI], 2.1–47.7) for patients with initial blood pH ≥ 7.068, 5.3 (95% CI, 1.48–18.9) for those with nonasystole rhythm, 4.0 (95% CI, 1.1–14.8) for those with prehospital time ≤20 minutes, and 9.1 (95% CI, 2.3–35.2) for those without NaHCO3 administration during resuscitation, respectively.ConclusionA cut-off value of an initial blood pH of 7.068 can serve as a predictor for survival among NTOHCA patients. In addition, patients whose initial blood pH is lower than 6.85 in the ED may not survive until hospital discharge

Attention Allocation for Human Multi-Robot Control: Cognitive Analysis based on Behavior Data and Hidden States

Human multi-robot interaction exploits both the human operator’s high-level decision-making skills and the robotic agents’ vigorous computing and motion abilities. While controlling multi-robot teams, an operator’s attention must constantly shift between individual robots to maintain sufficient situation awareness. To conserve an operator’s attentional resources, a robot with self reflect capability on its abnormal status can help an operator focus her attention on emergent tasks rather than unneeded routine checks. With the proposing self-reflect aids, the human-robot interaction becomes a queuing framework, where the robots act as the clients to request for interaction and an operator acts as the server to respond these job requests. This paper examined two types of queuing schemes, the self-paced Open-queue identifying all robots’ normal/abnormal conditions, whereas the forced-paced shortest-job-first (SJF) queue showing a single robot’s request at one time by following the SJF approach. As a robot may miscarry its experienced failures in various situations, the effects of imperfect automation were also investigated in this paper. The results suggest that the SJF attentional scheduling approach can provide stable performance in both primary (locate potential targets) and secondary (resolve robots’ failures) tasks, regardless of the system’s reliability levels. However, the conventional results (e.g., number of targets marked) only present little information about users’ underlying cognitive strategies and may fail to reflect the user’s true intent. As understanding users’ intentions is critical to providing appropriate cognitive aids to enhance task performance, a Hidden Markov Model (HMM) is used to examine operators’ underlying cognitive intent and identify the unobservable cognitive states. The HMM results demonstrate fundamental differences among the queuing mechanisms and reliability conditions. The findings suggest that HMM can be helpful in investigating the use of human cognitive resources under multitasking environments

GeneAlign: a coding exon prediction tool based on phylogenetical comparisons

GeneAlign is a coding exon prediction tool for predicting protein coding genes by measuring the homologies between a sequence of a genome and related sequences, which have been annotated, of other genomes. Identifying protein coding genes is one of most important tasks in newly sequenced genomes. With increasing numbers of gene annotations verified by experiments, it is feasible to identify genes in the newly sequenced genomes by comparing to annotated genes of phylogenetically close organisms. GeneAlign applies CORAL, a heuristic linear time alignment tool, to determine if regions flanked by the candidate signals (initiation codon-GT, AG-GT and AG-STOP codon) are similar to annotated coding exons. Employing the conservation of gene structures and sequence homologies between protein coding regions increases the prediction accuracy. GeneAlign was tested on Projector dataset of 491 human–mouse homologous sequence pairs. At the gene level, both the average sensitivity and the average specificity of GeneAlign are 81%, and they are larger than 96% at the exon level. The rates of missing exons and wrong exons are smaller than 1%. GeneAlign is a free tool available at

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods

Biphasic Effect of Curcumin on Morphine Tolerance: A Preliminary Evidence from Cytokine/Chemokine Protein Array Analysis

The aim of this study was to evaluate the effect of curcumin on morphine tolerance and the corresponding cytokine/chemokine changes. Male ICR mice were made tolerant to morphine by daily subcutaneous injection for 7 days. Intraperitoneal injections of vehicle, low-dose or high-dose curcumin were administered 15 min after morphine injection, either acutely or chronically for 7 days to test the effect of curcumin on morphine-induced antinociception and development of morphine tolerance. On day 8, cumulative dose-response curves were generated and the 50% of maximal analgesic dose values were calculated and compared among groups. Corresponding set of mice were used for analyzing the cytokine responses by antibody-based cytokine protein array. Acute, high-dose curcumin enhanced morphine-induced antinociception. While morphine tolerance was attenuated by administration of low-dose curcumin following morphine injections for 7 days, it was aggravated by chronic high-dose curcumin following morphine injection, suggesting a biphasic effect of curcumin on morphine-induced tolerance. Of the 96 cytokine/chemokines analyzed by mouse cytokine protein array, 14 cytokines exhibited significant changes after the different 7-day treatments. Mechanisms for the modulatory effects of low-dose and high-dose curcumin on morphine tolerance were discussed. Even though curcumin itself is a neuroprotectant and low doses of the compound serve to attenuate morphine tolerance, high-doses of curcumin might cause neurotoxicity and aggravate morphine tolerance by inhibiting the expression of antiapoptotic cytokines and neuroprotective factors. Our results indicate that the effect of curcumin on morphine tolerance may be biphasic, and therefore curcumin should be used cautiously

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure

The anaphase promoting complex impacts repair choice by protecting ubiquitin signalling at DNA damage sites

Double-strand breaks (DSBs) are repaired through two major pathways, homology-directed recombination (HDR) and non-homologous end joining (NHEJ). While HDR can only occur in S/G2, NHEJ can happen in all cell cycle phases (except mitosis). How then is the repair choice made in S/G2 cells? Here we provide evidence demonstrating that APCCdh1 plays a critical role in choosing the repair pathways in S/G2 cells. Our results suggest that the default for all DSBs is to recruit 53BP1 and RIF1. BRCA1 is blocked from being recruited to broken ends because its recruitment signal, K63-linked poly-ubiquitin chains on histones, is actively destroyed by the deubiquitinating enzyme USP1. We show that the removal of USP1 depends on APCCdh1 and requires Chk1 activation known to be catalysed by ssDNA-RPA-ATR signalling at the ends designated for HDR, linking the status of end processing to RIF1 or BRCA1 recruitment.We thank S.-Y. Lin (MD Anderson Cancer Center) for cell lines; J. Rosen (Baylor College of Medicine) for reagents; H. Masai (Tokyo Metropolitan Institute of Medical Science) for U2OS-Fucci cell line; D. Durocher (University of Toronto) for HeLa-Fucci cell line; E. Citterio (Netherlands Cancer Institute) for GFP-USP3 construct; M.S.Y. Huen (The University of Hong Kong) for RNF168 antibody. This work was performed with facilities and instruments in the Imaging Core of National Center for Protein Science (Beijing), the Cytometry and Cell Sorting Core at Baylor College of Medicine with funding from the NIH (P30 AI036211, P30 CA125123 and S10 RR024574), the Integrated Microscopy Core at Baylor College of Medicine with funding from the NIH (HD007495, DK56338 and CA125123), and the John S. Dunn Gulf Coast Consortium for Chemical Genomics. We also thank other members of the Zhang lab for helpful discussion and support. This work was supported in part by an international collaboration grant (# 2013DFB30210) and a 973 Project grant (# 2013CB910300) from Chinese Minister of Science and Technology, in part by a Chinese National Natural Science Foundation grant (# 81171920), in part by a grant from The Committee of Science and Technology of Beijing Municipality, China (# Z141100000214015), and in part by NIH grants CA116097 and CA122623 to P.Z. J.J. is supported by grants from National Institutes of Health (R01GM102529) and the Welch Foundation (AU-1711). S.H. is supported by grants (# 81272488 and 81472795) from Chinese National Natural Science Foundation. Y.Z. is supported by grants from the National Natural Scientific Foundation of China (No. 81430055), Programs for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R13).S