Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimally invasive mediastinal staging of non-small cell lung cancer

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive procedure widely used for the diagnosis and staging of primary lung cancer. This review focuses on the role of EBUS-TBNA in minimally invasive mediastinal staging, restaging after induction therapy, and procedure-related issues. To better understand the role of EBUS-TBNA, one must consider issues of sedation and rapid onsite examination, sonographic features during the procedure, the number of aspirations per lymph node, and the thoroughness of the procedure. A literature review indicated that EBUS-TBNA showed equivalent or even superior performance to mediastinoscopy in the mediastinal staging of non-small cell lung cancer (NSCLC). Combining endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) or the transesophageal approach using an EBUS bronchoscope (EUS-B-FNA) with EBUS-TBNA can provide additional diagnostic benefits. A recent guideline recommended endosonography over mediastinoscopy as the initial procedure for mediastinal nodal staging in patients with NSCLC with abnormal mediastinal and/or hilar lymph nodes on chest computed tomography (CT) or positron emission tomography/CT. The diagnostic sensitivity of EBUS-TBNA for restaging after induction therapy in patients with stage IIIA-N2 NSCLC was lower than that of initial staging. It appears reasonable to perform EBUS-TBNA first for initial mediastinal staging and reserve mediastinoscopy for restaging after induction therapy.
EXTENDED TECHNOLOGY ACCEPTANCE OF MOBILE TECHNOLOGY IN HIGHER ED: A RELIABILITY & VALIDITY TEST OF A SCALE

The objective of this research study is to study the mobile technology acceptance of educators in the higher education industry in the United States. This study utilized the Chen et al. (2013) extended technology acceptance model, that extended the original Davis (1989) TAM. The study incorporated path analysis to determine estimates of the magnitude and significance of hypothesized causal connections between sets of the study variables. The researchers investigated the reliability of the survey instrument for the sample with Cronbach’s alpha. There were five variables of interest in the study, perceived resources, perceived ease of use, perceived usefulness, attitude toward use, behavioral intention, and technology use with two dimensions, incorporating frequency of use and duration of use. The research question, hypotheses, and current state of research are presented and discussed.
Entropy method of constructing a combined model for improving loan default prediction: A case study in China

Abstract In recent years, credit scoring has become an efficient tool to assist financial institutions in identifying potential default borrowers, and the combined model is widely viewed as a useful vehicle. In this study, after pre-processing based on random forest, we propose a combined logistic regression algorithm and artificial neural network model to improve the predictive performance based on actual data from a rural commercial bank under the condition that loan quality directly affects the profitability of the bank. The combined model requires a step with an entropy method to determine the entropy weights of the logistic regression model and artificial neural network model. The experimental results reveal that the proposed combined model outperforms the two base models on four evaluation metrics: accuracy (ACC), area under the curve (AUC), Kolmogorov-Smirnov statistic (KS), and Brier score (BS). Moreover, the model is superior to a state-of-the-art ensemble model, stacking.
Three-DoF Flapping-Wing Robot with Variable-Amplitude Link Mechanism

This paper describes the development of a three-degrees-of-freedom flapping-wing robot with a variable-amplitude link mechanism for controlling the lift and thrust forces acting on it. The variable-amplitude link mechanism comprises a lever-crank mechanism driven by a brushless DC motor and a linear actuator to control the amplitude of the flapping angle. The robot also comprises two DC motors with reduction gears for feathering and lead-lag motion. In our experiments, the measurement of force-torque revealed the effects of the motion of each wing. We found that the flapping-amplitude difference between the left and right wings causes a roll and yaw moment.
Disentangling microbial associations from hidden environmental and technical factors via latent graphical models

Detecting community-wide statistical relationships from targeted amplicon-based and metagenomic profiling of microbes in their natural environment is an important step toward understanding the organization and function of these communities. We present a robust and computationally tractable latent graphical model inference scheme that allows simultaneous identification of parsimonious statistical relationships among microbial species and unobserved factors that influence the prevalence and variability of the abundance measurements. Our method comes with theoretical performance guarantees and is available within the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework (‘SpiecEasi’ R-package). Using simulations, as well as a comprehensive collection of amplicon-based gut microbiome datasets, we illustrate the method’s ability to jointly identify compositional biases, latent factors that correlate with observed technical covariates, and robust statistical microbial associations that replicate across different gut microbial data sets.
Shot detection combining Bayesian and structural information

There are a number of shots in a video, each of which has boundary types, such as cut, fade, dissolve and wipe. Many previous approaches can find the cut boundary without difficulty. However, most of them often produce false alarms for the videos with large motions of camera and objects. We propose a shot boundary detection method combining Bayesian and structural information. In the Bayesian approach, a probability distribution function models each transition type, e.g., normal, abrupt, gradual transition, and also models shot length. But inseparability between those distributions causes unwanted results and degrades the precision. In this paper, we demonstrate that the shape of the filtered frame difference, called the structural information, provides an important cue to distinguish fade and dissolve effects form cut effects and gradual changes caused by motion of camera and objects. The proposed method has been tested for a few golf video segments and shown good performances in detecting fade and dissolve effects as well as cut.
A comparison of 0.075% and 0.15% of ropivacaine with fentanyl for postoperative patient controlled epidural analgesia after laparoscopic gynecologic surgery

Background: A motor blockade of lower limbs interferes with early ambulation and limits the usefulness of patient-controlled epidural analgesia (PCEA). The concentration of local anesthetic solution is a major determinant for motor block with PCEA. We compared the effects of epidural infusion of 0.075% ropivacaine with 0.15% epidural ropivacaine on postoperative analgesia, motor block of lower limbs, and other side effects. Methods: A total of 70 patients undergoing laparoscopic gynecologic surgery received epidural infusions (group R1, 0.15% ropivacaine with fentanyl; group R2, 0.075% ropivacaine with fentanyl). Pain score, motor block, and side effects (hypotension, nausea, vomiting, pruritus, urinary retention, dizziness, and numbness) were measured. Results: There were no significant differences in the demographic profiles between the groups. Pain scores of the group R1 and the group R2 were not significantly different. Motor block was more frequent in the group R1 (0.15% ropivacaine with fentanyl) than in the group R2 (0.075% ropivacaine with fentanyl). Conclusion: Lower concentration of ropivacaine (0.075%), when compared with higher concentration of ropi-vacaine (0.15%), seemed to provide similar analgesia with less motor blockade of the lower limbs for the purpose
RESPONSE 2: A phase 3b study evaluating the efficacy and safety of ruxolitinib in patients with hydroxyurea (HU)-resistant/intolerant polycythemia vera (PV) versus best available therapy (BAT).

TPS7128^ Background: PV is a myeloproliferative neoplasm characterized by clonal stem cell proliferation, hyperviscous blood, significant morbidity, and reduced life span. Advanced PV results in splenomegaly, severe constitutional symptoms, and possible evolution to myelofibrosis and acute myeloid leukemia (AML). Nearly all patients (pts) have a mutation in JAK2, and > 95% have the JAK2 V617F allele. HU is the myelosuppressive agent of choice in high-risk pts, but it often loses efficacy over time and may cause unacceptable skin toxicities. Therapeutic options in the case of HU resistance are limited, and survival is significantly reduced. A phase 2 study in HU-resistant/-intolerant PV pts showed that ruxolitinib, a potent and selective JAK1/JAK2 inhibitor, was well tolerated and achieved rapid and durable clinical responses, including hematocrit (Hct) normalization, phlebotomy independence, resolution of splenomegaly, and improvements in symptoms (Verstovsek et al, Cancer, 2013). RESPONSE, a phase 3 tria.
50-GHz Repetition Gain Switching Using a Cavity-Enhanced DFB Laser Assisted by Optical Injection Locking

We demonstrate pulse generation at a repetition rate of 50 GHz by gain switching an injection-locked distributed feedback (DFB) laser. The small-signal BW of the DFB laser was enhanced from an intrinsic BW of 30 GHz to 52 GHz due to the joint effects of photon-photon resonance (PPR) and detuned loading. The detuned loading effect is achieved by exploiting the frequency-dependent cavity loss, which results in an increase of the effective differential gain, and correspondingly, the relaxation oscillation frequency. In addition to small-signal measurements, we show that the PPR and the detuned loading effects can also be utilized to improve large signal gain-switching, despite the dynamic changes of the detuned loading condition due to the large signal chirp. By modulating the laser with 50-GHz RF signals, we obtained 50-GHz repetition rate pulses from the gain-switched laser, confirming that the BW enhancement effects are still valid for large-signal modulation. Subsequently, we optically injection lock the gain-switched laser with strong external seeding light, which suppresses the chirp and creates coherent frequency tones with 50 GHz spacing. The optical injection locking (OIL) further enhances the large-signal BW, resulting in narrower pulse width of 9.5 ps (6.2 ps after deconvolution) in the time domain and three sideband peaks above 70% from the peak in the frequency domain (full width half maximum of 130 GHz). Assuming linear chirp, the pulse could be further compressed to 2.4 ps.
Combining Harvest and Genetics to Estimate Reproduction in Wolves

Parameters of reproductive success are important to the management of wildlife populations. Genetic monitoring can be an effective approach for acquiring this important demographic information when traditional methods are unsuccessful, inefficient, or too expensive. This study demonstrates a novel application of genetic data opportunistically collected from harvested game to estimate a minimum annual count of breeding packs of gray wolves (Canis lupus) and to provide a coarse index of harvest vulnerability of young of the year (YOY) across packs. We used 18 microsatellite loci to genotype 98 gray wolf YOY from 2014 and 105 from 2015 harvested in Idaho, USA. Using this genotype data, we reconstructed sibling groups for each cohort using the program COLONY and treated full‐sibling litters as proxies for unique packs. In addition to evaluating our marker panel using simulations, we assessed the accuracy of empirical relationship assignments by adding YOY of known relationship from long‐term study packs to the dataset (27 individuals from 2014 and 61 from 2015) and tracking correctly reconstructed relationships. We varied COLONY input parameters to evaluate the power of relationship assignments under conditions that may be encountered when working with empirical data. We also compared COLONY's estimates of effective number of breeders based on sibship frequency to estimates based on a commonly used linkage‐ disequilibrium method. All COLONY runs for both cohorts correctly identified the known sibling relationships. Among the other individuals, changes in the geographic clustering of putative siblings, probabilities of inclusion and exclusion for reconstructed sibling groups, and consistency of relationship assignments across COLONY runs suggested that marker number had a larger effect on accuracy than access to population‐level genetic data. Our estimates of breeding packs subjected to harvest within the state (52 for 2014 and 63 for 2015) differed from estimates reported by Idaho Department of Fish and Game by ≤6 for both years. Among packs that had pups harvested, most packs had 1–2 YOY harvested, whereas other packs had as many as 5 YOY harvested. All estimates of the number of effective breeders had overlapping confidence intervals regardless of method, though sibship frequency‐based estimates had larger confidence intervals than estimates using the linkage disequilibrium method. Our study shows that sibling relationships can be accurately and reliably reconstructed from harvested gray wolves, and demonstrates a valuable new use of samples collected through harvest. © 2020 The Wildlife Society.
Managements of Patients with Malignant Gastric Outlet Obstruction: Prospective Study

INTRODUCTION: Gastric outlet obstruction (GOO, also known as pyloric obstruction) is the clinical and pathophysiological consequence of any disease process that produces a mechanical impediment to gastric emptying (1) . GOO is categorized into benign and malignant (2) . The incidence of gastric outlet obstruction has been reported to be less than 5% in patients with peptic ulcer disease, while distal gastric cancer accounting for up to 35 % and peripancreatic malignancy, has been reported as 15-
The Effectiveness of Problem-Solving Therapy Program Intervention in Reducing Depression of Older Cancer Patients

Background: Appropriate intervention has not been developed and implemented because depression has been overlooked for older cancer patients. However, because depression is prevalent among this population, the need for the intervention is high. The objective of this study was to verify the effectiveness of the problem-solving therapy program in reducing depression level for older cancer patients. Methods: The experimental participants were recruited by social workers in five university hospitals in Seoul, Gyeonggi, and Gangwon. Using Patient Health Questionnaire-9 (PHQ-9), older cancer patients who scored between 10-19 points are selected. The subjects were assigned to the experimental group (30 patients) and the control group (30 patients) according to their will, and the experimental group participated in the problem solving therapy program for 6 weeks. The Center for Epidemiological Studies Depression 10 Scale and the Social Problem Solving Ability Scale were used to verify the effectiveness of the problem-solving program. Results: The group homogeneity test indicated that the experimental group and the control group are homogeneous. The results of this study showed that the depression of older cancer patients had a significant positive correlation with the negative attitude toward the problem. The effectiveness of the problem solving program was significantly reduced in the experimental group (Z=-3.534, P<0.001). And the social problem solving ability of experimental group was significantly improved (Z=-2.908, P=0.003). Conclusions: The problem-solving therapy program is effective for depression in geriatric cancer patients and this result suggests that it can be implemented as an alternative medical treatment. Korean J Health Promot 2018;18(1):60-70
Impact of Bolsa Família Program on the nutritional status of children and adolescents from two Brazilian regions

Objetivo: Avaliar e comparar o impacto do Programa Bolsa Familia no estado nutricional de criancas e adolescentes do Nordeste e Sudeste brasileiro. Metodos: A base de dados procedeu de uma subamostra da Pesquisa de Orcamento Familiar de 2008-2009. Calculou-se a proporcao de criancas e adolescentes com baixo peso, baixa estatura e excesso de peso. A analise da medida de impacto foi precedida de tecnica (Propensity Score Matching) que assemelha, familias beneficiarias e nao beneficiarias, em relacao a um conjunto de caracteristicas socioeconomicas. O impacto do programa foi estimado atraves do algoritmo de pareamento do vizinho mais proximo. Resultados: No Nordeste, a proporcao de criancas e adolescentes abaixo do peso foi em media, 1,1% menor nas familias beneficiarias, em comparacao as nao beneficiarias. No Sudeste, a proporcao de criancas e adolescentes acima do peso foi em media, 4,2% menor nas familias beneficiarias. Nao foi observado impacto do programa sobre o deficit de estatura em ambas as regioes. Conclusao: Os resultados retrataram impacto positivo e boa focalizacao do programa, que se atrelado a acoes estruturais pode contribuir para melhoria do estado nutricional e qualidade de vida dos beneficiarios.
Advanced optimization fot the thruster layout problem in a challenging space mission

L'oggetto di studio riguardera l'orientamento degli attuatori a bordo di sistemi spaziali caratterizzati da requisiti di controllo oltremodo ristretti, in riferimento a programmi attualmente in corso di sviluppo presso l'azienda ospitante. Obiettivo dell'analisi sara prevalentemente la riduzione dei consumi durante la missione. Le metodologie finalizzate a tale scopo prevedono l'applicazione di modelli matematici ed algoritmi di ottimizzazione non-lineare, globale e mista intera.
Enact, Discard, Transform: An Impact Agenda

In her 2019 Earle F. Zeigler address, Jennifer McGarry drew on the 2017 Academy of Management Report “Measuring and Achieving Scholarly Impact” to examine how the field of sport management and the North American Society for Sport Management operationalize impact. She pointed to a broader, more inclusive, and critical examination of impact. McGarry highlighted impact on practice and impact through being explicit, particularly about the ways gender and race affect what we deem to have impact. Finally, she spoke to impact through individual and collective action, such as educating students, scholarship, and policy and advocacy. She provided examples of where we could disrupt the structures that work to maintain the status quo in terms of impact—the in-groups and the out-groups, the metrics and evaluations. She also gave examples of impact that have happened, that are happening, and that can happen even more.
Optimal feature extraction and classification-oriented medical insurance prediction model: machine learning integrated with the internet of things

This paper plans to develop an effective machine learning system integrated with the Internet of Things (IoT) to predict the health insurance amount. IoT in healthcare enables interoperability, machine-to-machine communication, information exchange, and data movement that make healthcare service delivery effective. The model includes three phases (a) Feature Extraction, and (b) Weighted Feature Extraction, and (c) Prediction. The feature extraction process computes two statistical measures: First Order Statistics like mean, median, standard deviation, the maximum value of entire data, and minimum value of entire data, and Second-Order Statistics like Kurtosis, skewness, correlation, and entropy. The prediction process deploys a renowned machine learning algorithm called Neural Network (NN). As the main contribution, the weighted feature vector is developed here, where the weight optimally tuned by Modified Whale Optimization Algorithm (WOA). Also, the contribution relies on NN, where the training algorithm replaced with the same modified WOA for weight update. The modified WOA developed here is termed as Fitness dependent Randomized Whale Optimization Algorithm (FR-WOA). At last, the valuable experimental analysis using three datasets confirms the efficient performance of the suggested model.
OsRuvB transgene induces salt tolerance in pigeon pea

ABSTRACT Salinity of cultivable land is a growing global concern that has been affecting the yields of major crops worldwide such as pigeon pea. In the current study, transgenic pigeon pea plants were developed using an in-planta Agrobacterium-mediated genetic transformation method wherein OsRuvB, a rice DNA helicase gene, was incorporated to induce salt tolerance in pigeon pea plants. Transformation efficiency of 35.7% was achieved with stable insertion of OsRuvB in transgenic lines. When subjected to salinity stress induced by 75 mM NaCl increase in chlorophyll content, relative water content, peroxidase and catalase activity in transgenic lines was observed over the wild type plants. Membrane injury index and lipid peroxidation were significantly reduced in transgenic lines. Proline and Total Soluble Sugar content were enhanced in both transgenic plants and wild type strains. It was inferred that transgenic lines were tolerant to salinity stress and tolerance may be imparted through an alternative unknown pathway. GRAPHICAL ABSTRACT
Intelligent Personalized Abnormality Detection for Remote Health Monitoring

Machine learning algorithms are extensively used in healthcare analytics to learn normal and abnormal patterns automatically. The detection and prediction accuracy of any machine learning model depends on many factors like ground truth instances, attribute relationships, model design, the size of the dataset, the percentage of uncertainty, the training and testing environment, etc. Prediction models in healthcare should generate a minimal false positive and false negative rate. To accomplish high classification or prediction accuracy, the screening of health status needs to be personalized rather than following general clinical practice guidelines (CPG) which fits for an average population. Hence, a personalized screening model (IPAD – Intelligent Personalized Abnormality Detection) for remote healthcare is proposed that tailored to specific individual. The severity level of the abnormal status has been derived using personalized health values and the IPAD model obtains an area under the curve (AUC) of 0.907.