Miracle-dz/UniFAR-FTUs huggingface dataset:
A Biological Model of Acupuncture and its Derived Mathematical Modeling and Simulations

(Aims) Acupuncture was employed since 2 millenaries, but the underlying mechanisms are not globally handled. The present study is aimed at proposing an explanation by pointing out involved processes and a convincing modeling to demonstrate its efficiency when carried out by trained practitioners. (Method) In the absence of global knowledge of any mechanism explaining the acupuncture process, a biological model is first developed, based on stimulation in a given domain around the needle tip of a proper mastocyte population by a mechanical stress, electrical, electromagnetic, or heat field. Whatever the type of mechanical or physical stimuli, mastocytes degranulate. Released messengers either facilitate the transfer of main mediators, or target their cognate receptors of local nerve terminals or after being conveyed by blood their receptors on cerebral cells. Signaling to the brain is fast by nervous impulses and delayed by circulating messengers that nevertheless distribute preferentially in the brain region of interest due to hyperemia. The process is self-sustained due to mastocyte chemotaxis from the nearby dense microcirculatory circuit and surrounding mastocyte pools, which are inadequate for acupuncture, but serve as a signal relay. A simple mathematical model is solved analytically. Numerical simulations are also carried out using the finite element method with mesh adaptivity. (Results) The analytical solution of the simple mathematical model demonstrates the conditions filled by a mastocyte population to operate efficiently. A theorem gives the blow-up condition. This analytical solution serves for validation of numerical experiments. Numerical simulations show that when the needle is positioned in the periphery of the acupoint or outside it, the response is too weak. This explains why a long training is necessary as the needle implantation requires a precision with a magnitude of the order of 1 mm. (Conclusion) The acupoint must contain a highly concentrated population of masto-cytes (e.g., very-high–amplitude, small-width Gaussian distribution) to get an initial proper response. Permanent signaling is provided by chemotaxis and continuous recruitment of mastocytes. Therefore, the density and distribution of mastocytes are crucial factors for efficient acupuncture as well as availability of circulating and neighboring pools of mastocytes.
Repeated intracoronary beta radiation for recurrent in‐stent restenosis

More than 70% of percutaneous coronary interventions are followed by a stent implantation. In‐stent restenosis still occurs in 20‐30% of patients and remains a therapeutic challenge. At present only vascular brachytherapy has been shown to be an effective treatment option. We report here one case of recurrent in‐stent restenosis after vascular brachytherapy that was successfully treated by a second beta radiation treatment. Cathet Cardiovasc Intervent 2002;55:233–236. © 2002 Wiley‐Liss, Inc.
Establishment of a Gene Detection System for Hotspot Mutations of Hearing Loss

Hearing loss is an etiologically heterogeneous trait with a high incidence in China. Though conventional newborn hearing screening program has been widely adopted, gene detection can significantly improve the means of early discovering genetic risk factors. Thus, simple and efficient methods with higher sensitivity and lower cost for detecting hotspot mutations of hearing loss are urgently requested. Here we established a mutation detection system based on multiple fluorescent probe technique, which can detect and genotype nine hotspot mutations of four prominent hearing loss-related genes in two reactions on a four-channel real-time PCR instrument, including GJB2 (rs750188782, rs80338943, rs1110333204, and rs80338939), GJB3 (rs74315319), SLC26A4 (rs111033313 and rs121908362), and mtDNA 12S rRNA (rs267606617 and rs267606619). This system is with high sensitivity that enables detecting as low as 10 DNA copies samples per reaction. A comparison study in 268 clinical samples showed that the detection system had 100% concordance to Sanger sequencing. Besides, blood and saliva samples can be directly detected without DNA extraction process, which greatly simplifies the manipulation. The new system with high sensitivity, accuracy, and specimen type compatibility can be expectedly a reliable tool in clinical application.
Application of the DSM-5 Criteria for Major Neurocognitive Disorder to Vascular MCI Patients

Aims: The DSM-5 introduced the term “major neurocognitive disorders” (NCDs) to replace the previous term “dementia. ” However, psychometric and functional definitions of NCDs are missing. We aimed to apply the DSM-5 criteria for diagnosing the transition to NCD to patients with mild cognitive impairment (MCI) and small vessel disease (SVD), and to define clinically significant thresholds for this transition. Methods: The functional and cognitive features of the NCD criteria were evaluated as change from baseline and operationalized according to hierarchically ordered psychometric rules. Results: According to the applied criteria, out of 138 patients, 44 were diagnosed with major NCD (21 with significant cognitive worsening in ≥1 additional cognitive domain), 84 remained stable, and 10 reverted to normal. Single-domain MCI patients were the most likely to revert to normal, and none progressed to major NCD. The amnestic multiple-domain MCI patients had the highest rate of progression to NCD. Conclusion: We provide rules for the DSM-5 criteria for major NCD based on cognitive and functional changes over time, and define psychometric thresholds for clinically significant worsening to be used in longitudinal studies. According to these operationalized criteria, one-third of the MCI patients with SVD progressed to major NCD after 2 years, but only within the multiple-domain subtypes.
Liquid Chromatographic Determination of Alprazolam with ACE Inhibitors in Bulk, Respective Pharmaceutical Products and Human Serum

Present study describes simple and fast liquid chromatographic method using ultraviolet detector for simultaneous determination of anxiety relief medicine alprazolam with ACE inhibitors i.e; lisinopril, captopril and enalapril employing Prospher Star C18 (25 cm, 0.46 cm, 5 μm). Separation was achieved within 5 min at ambient temperature via methanol:water (8:2 v/v) with pH adjusted to 2.9, monitoring the detector response at 220 nm. Optimum parameters were set up as per ICH (2006) guidelines. Calibration range was found out to be 0.312-10 μg mL -1 for alprazolam and 0.625-20 μg mL 1 for all the ACE inhibitors with correlation coefficients >0.998 and detection limits 85, 37, 68 and 32 ng mL -1 for lisinopril, captopril, enalapril and alprazolam respectively. Intra-day, inter-day precision and accuracy of the assay were in acceptable range of 0.05-1.62% RSD and 98.85-100.76% recovery. Method was determined to be robust and effectively useful for the estimation of studied drugs in dosage formulations and human serum without obstruction of excipients or serum components.
Biological Risk Indicators for Non-specific Low Back Pain in Young Adults of Amritsar, Punjab, India

Abstract The purpose of the present study was to evaluate and correlate the various biological risk indicators of non-specific low back pain (NSLBP) in young adults. The study was based on a total of 100 purposively selected young adults (50 males and 50 females) aged 18 – 25 years with non-specific low back pain and 100 matched controls (50 males and 50 females) asymptomatic with no history of low back pain taken from Amritsar, Punjab, India. To solve the purpose, some anthropometric measurements, viz. height, weight, BMI, four skinfold measurements, (i.e. biceps, triceps, subscapular and suprailiac), back strength, flexibility measure and abdominal muscle endurance were taken on each subject. Results indicated statistically significant differences (p<0.05) in abdominal muscle endurance (t= 2.58) between NSLBP boys and controls and in weight (t=3.22), biceps skinfold (t=3.04), height (t=2.67), triceps skinfold (t=2.83), subscapular skinfold (t=2.32) and in percent lean body mass (t= 2.80) between NSLBP girls and controls. Both in boys and girls with non-specific low back pain, back strength has positively significant correlations (p<0.05) with height (r=0.487 and 0.360 respectively), weight (r=0.495 and 0.213 respectively), BMI (r=0.299 and 0.461 respectively) and flexibility measure (r=0.386 and 0.388 respectively) and negatively significant correlation (r=− 0.417 only in NSLBP girls) with percent body fat.
Engineering Tissues without the Use of a Synthetic Scaffold: A Twenty-Year History of the Self-Assembly Method

Twenty years ago, Dr. François A. Auger, the founder of the Laboratory of Experimental Organogenesis (LOEX), introduced the self-assembly technique. This innovative technique relies on the ability of dermal fibroblasts to produce and assemble their own extracellular matrix, differing from all other tissue-engineering techniques that use preformed synthetic scaffolds. Nevertheless, the use of the self-assembly technique was limited for a long time due to its main drawbacks: time and cost. Recent scientific breakthroughs have addressed these limitations. New protocol modifications that aim at increasing the rate of extracellular matrix formation have been proposed to reduce the production costs and laboratory handling time of engineered tissues. Moreover, the introduction of vascularization strategies in vitro permits the formation of capillary-like networks within reconstructed tissues. These optimization strategies enable the large-scale production of inexpensive native-like substitutes using the self-assembly technique. These substitutes can be used to reconstruct three-dimensional models free of exogenous materials for clinical and fundamental applications.
Makhraj recognition of Hijaiyah letter for children based on Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machines (SVM) method

Makhraj is the most important thing for Muslim to recite the Holy Quran properly besides of Tajweed. This paper describe the Makhraj recognition of Hijaiyah Letter for children education. To make the Makhraj recognition, the feature extraction is used Mel-Frequency Cepstrum Coefficients (MFCC) method and to classify the Hijaiyah letter use Support Vector Machines (SVM) method based on Python 2.7. The waveform analysis of each Hijaiyah Makhraj pronunciation shows the differences of each letter. The database of Hijaiyah Makhraj pronunciation using 12 feature extraction can be classified by SVM process.
Extrusion of Irrigant in Open Apex Teeth with Periapical Lesions Following Laser-Activated Irrigation and Passive Ultrasonic Irrigation

Introduction: Sodium hypochlorite (NaOCl) irrigation is critical for the success of endodontic treatment and several agitation techniques have been developed to improve the efficacy of this irrigation. Using a combination of contrast medium and radiographic examination, this study evaluated NaOCl extrusion during agitation of irrigant. Development of pressure, which may result in apical extrusion of the irrigant, has been described during laser-activated irrigation (LAI) and passive ultrasonic irrigation (PUI). Methods and Materials: We examined 40 single root canals categorized as having open apices with apical lesions in 40 patients. For the final irrigation, the teeth were irrigated with a mixture of radiopaque contrast medium and 2.5% NaOCl in solution. The solution was activated for 60 sec in both groups [the Er, Cr: YSGG laser group (n=20) and the ultrasonic group (n=20)]. The teeth were imaged subsequently using radiography for the evaluation of contrast extrusion. Results: Radiopaque contrast medium was absent from the periapical tissues in all cases. Conclusion: Use of LAI or PUI appears to be safe as used currently in endodontic treatment.
Randomization and Quantization for Average Consensus

Many problems in distributed control reduce to the distributed computation of the average of initial values in a networked system of autonomous agents, known as the average consensus problem. We present a randomized algorithm that solves this problem in networks with directed, time-varying communication topologies, in linear time in the size of the network. This algorithm leverages properties of exponential random variables, which allows for approximating sums by computing minima. It is completely decentralized, in the sense that it does not rely on agent identifiers or global information of any kind. Besides, the agents do not need to know their out-degree; hence, our algorithm demonstrates how randomization can be used to circumvent the impossibility result established in [1]. Using a logarithmic rounding rule, we show that this algorithm can be used under the additional constraints of finite memory and channel capacity. We furthermore extend the algorithm with a termination test, by which the agents can decide irrevocably in finite time — rather than simply converge — on an estimate of the average.
Impact of Health Education on Knowledge and Behaviors toward Infectious Diseases among Students in Gansu Province, China

Objectives Infectious disease knowledge and behaviors are key elements that ensure student health and safety. This study explores the impact of health education on student knowledge and behaviors toward infectious diseases and determines the factors affecting infectious diseases knowledge and behaviors among students in Gansu, China. Methods A cross-sectional study and three sampling methods were used in two counties, 12 schools, and 32 classes in Gansu, China, from 2012 to 2013. Collected data included the following: (1) sociodemographic characteristics of 2002 students (1001 participants in the intervention group and 1001 in the control group); (2) accuracy of student knowledge and behaviors toward infectious diseases based on comparison of intervention and control groups through X2 test; and (3) mean scores on knowledge and behavior of students with different characteristics toward infectious diseases, as analyzed through analysis of variance (ANOVA). Multiple linear regression was conducted to analyze factors affecting student knowledge and behaviors toward infectious diseases. Results Statistically significant differences were observed among eight items of infectious disease transmission and treatment knowledge between intervention and control groups (P < 0.001). Average accuracies of knowledge and behaviors toward infectious diseases reached 72.23% and 60.03%. Significant differences were observed in six items on student behavior in rural and urban areas (P < 0.001). Health education, household register, and county affected scores of student knowledge and behaviors toward infectious diseases (P < 0.05). Gender and education level also affected scores of student behaviors toward infectious diseases (P < 0.001). Conclusions Health education contributes to student knowledge and behaviors toward infectious diseases. Students in the control group need intensified health education on infectious diseases. Health education needs to pay particular attention to rural students, all male students, and students at senior high school level living on campus.
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments

Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a novel approach to train action policies to acquire navigation skills for wheel-legged robots using deep reinforcement learning. The policy maps height-map image observations to motor commands to navigate to a target position while avoiding obstacles. We propose to acquire the multifaceted navigation skill by learning and exploiting a number of manageable navigation behaviors. We also introduce a domain randomization technique to improve the versatility of the training samples. We demonstrate experimentally a significant improvement in terms of data-efficiency, success rate, robustness against irrelevant sensory data, and also the quality of the maneuver skills.
Preventing lower extremity injury in elite orienteerers: study protocol for a randomised controlled trial

Background The high physical load associated with running through uneven terrain contributes toorienteerers being exposed to high injury risk, where the majority of injuries are located in the lower extremities. Specific training programmes have been effective at reducing injury risk in sports. Yet no trial has been conducted in elite orienteering. The aim of this study is to investigate the effectiveness of a specific training programme in preventing lower extremity injury in adult elite orienteerers. Study design Randomised controlled trial (RCT). Methods Seventy-two Swedish elite orienteerers, aged 18–40 years, are allocated to an intervention or control group. The intervention group performs four specific exercises, with three difficult levels intensified every second week over the first 4 weeks, targeting strength, flexibility and coordination of the lower extremity. The exercises are completed four times a week (10 min per session) in conjunction with normal training over 14 weeks. Injury data are collected every second week using a valid injury questionnaire distributed by text messages over 14 weeks. The primary outcome is number of substantial injuries in the lower extremity. The secondary outcomes are incidence of ankle sprains and the average substantial injury prevalence across 14 weeks. Discussion Due to high injury risk and lack of injury prevention trials in orienteering, an RCT investigating the effect of a specific exercise programme in preventing lower extremity injury is warranted. The results of this trial will be beneficial to orienteerers, clubs and federations, and increase our understanding on how lower extremity injuries can be prevented in a physically challenging sport. Trial registration number NCT03408925.
Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping

The manipulator workspace mapping is an important problem in robotics and has attracted significant attention in the community. However, most of the pre-existing algorithms have expensive time complexity due to the reliance on sophisticated kinematic equations. To solve this problem, this paper introduces subspace learning (SL), a variant of subspace embedding, where a set of robot and scope parameters is mapped to the corresponding workspace by a deep neural network (DNN). Trained on a large dataset of around 6 × 104 samples obtained from a MATLAB®implementation of a classical method and sampling of designed uniform distributions, the experiments demonstrate that the embedding significantly reduces run-time from 5.23 × 103 s of traditional discretization method to 0.224 s, with high accuracies (average F-measure is 0.9665 with batch gradient descent and resilient backpropagation). 1MATLAB® Scripts are available at https://github.com//liaopeiyuan/Subspace-Learning
Smiles, turnout, candidates, and the winning of district seats

Abstract. Research has shown that a candidate's appearance affects the support he or she receives in elections. We extend this research in this article in three ways. First, we examine this relationship further in a non-Western context using 2015 local elections in Japan. Next, we show that this positive relationship is more complicated depending on the characteristics of the election under consideration. Specifically, we distinguished election contests by levels of turnout and found that despite a positive relationship between turnout and the extent to which smiling increases a candidate's support levels, the marginal increase in support declined as turnout increased and, in fact, became negative when some high-turnout threshold was crossed. Finally, we show that the number of candidates competing in an election is negatively related to the impact of a candidate smiling, confirming research conducted by the Dartmouth Group.
Runtime Computation of Optimal Offloading Scheduling

Mobile Cloud Computing is an emergent topic that has received increasing attention considering the importance and the practical use of mobile applications. A relevant research direction in this field aims at optimizing the offloading decisions of mobile applications based on different metrics, such as performance and energy consumption, and according to the dynamic environment conditions in which the application is located. In this paper we define and implement two parallel algorithms for supporting offloading decisions at runtime. They are based on the well-known Depth First Search and Dijkstra shortest path algorithms, properly customized for the mobile cloud domain. Considering the mobile characteristics and the application requirements, the algorithms permit to optimize the effectiveness of mobile applications providing an optimal offloading strategy to be followed during the application execution. The proposed algorithms are experimentally validated by comparing their performance and scalability, also with respect to a previously defined approach based on model checking. The comparison is supported by a simulator tool we developed on purpose to assess the potentiality of the algorithms at runtime. The achieved results prove the applicability of the approach into practice, which is also witnessed by a case study focusing on a mobile cloud application for public security.
Performance of p-Norm Detector in Cognitive Radio Networks with Cooperative Spectrum Sensing in Presence of Malicious Users

A cognitive radio network (CRN) with a cooperative spectrum sensing scheme is considered. This CRN has a primary user and multiple secondary users, some of which are malicious secondary users (MSUs). Energy detection at each SU is performed using a -norm detector with , where corresponds to the standard energy detector. The MSUs are capable of perpetrating spectrum sensing data falsification (SSDF) attacks. At the fusion center (FC), an algorithm is used to suppress these MSUs which could be either an adaptive weighing algorithm or one of the following: Tietjen-Moore (TM) test or Peirce’s criterion. This is followed by computation of a test statistic (TS) which is a random variable. In this paper, we assume TS to have either a Gamma or a Gaussian distribution and calculate the threshold accordingly. We provide closed-form expressions of probability of false alarm and probability of miss-detection under both assumptions. We show that Gaussian assumption of TS is more suited in presence of an SSDF attack when compared with the Gamma assumption. We also compare the detection performance for various values of and show that along with the Gaussian assumption is the best amongst all the cases considered.