Miracle-dz/UniFAR-FTUs huggingface dataset:
Duck Interleukin-22: Identification and Expression Analysis in Riemerella anatipestifer Infection

Riemerella anatipestifer is one of the most devastating pathogens affecting the global duck farms. Infection is involved in secretion of proinflammatory cytokines, including interleukin- (IL-) 17A. During the immune response to infection, IL-22 and IL-17A are often produced concurrently and at high levels in inflamed tissues. Little is known about duck IL-22 (duIL-22) during R. anatipestifer infection. We describe the characterization of duIL-22 and its mRNA expression analysis in splenic lymphocytes and macrophages treated with heat-killed R. anatipestifer and in the spleens and livers of R. anatipestifer-infected ducks. Full-length cDNA of duIL-22 encoded 197 amino acids. The deduced amino acid sequence of duIL-22 shared a 30.4–40.5% similarity with piscine counterparts, 57.4–60.1% with mammalian homologs, and 93.4% similarity to the chicken. Duck IL-22 mRNA expression level was relatively high in the skin of normal ducks. It was increased in mitogen-stimulated splenic lymphocytes and in killed R. anatipestifer-activated splenic lymphocytes and macrophages. Compared with healthy ducks, IL-22 transcript expression was significantly upregulated in the livers and spleens on days 1 and 4 postinfection, but not on day 7. IL-17A was significantly increased in the spleens only on day 4 postinfection and in the livers at all time points. When splenic lymphocytes were stimulated with heat-killed R. anatipestifer, CD4+ cells predominantly produced IL-22 while IL-17A was expressed both by CD4+ and CD4− cells. These results suggested that IL-22 and IL-17A are likely expressed in different cell types during R. anatipestifer infection.
GAN‐segNet: A deep generative adversarial segmentation network for brain tumor semantic segmentation

In this study, we present a novel automatic segmentation method using a neural network model named GAN‐segNet, which can not only identify brain tumors from MRI images but also accurately delineate intratumor regions. Since brain tumors with varying shapes and sizes can appear anywhere in the brain and image quality and contrast of MRI could be inadequate, automatic segmentation remains challenging despite its importance in the clinical workflow. The proposed GAN‐segNet is an innovative modification of the Generative Adversarial Network (GAN) and can efficiently and accurately segment brain tumors. One key innovation of our GAN model is an autoencoder learning representation of input data that were added to the generative network of the above‐mentioned GAN. By doing so, information extracted through convolution operations can be meaningfully regularized. As a result, the scales of extracted features can be controlled by the added autoencoder to preserve detail. Additionally, we propose an innovative loss function based on the concept of focal loss to effectively mitigate the impact of label imbalance. The above‐mentioned combination enables the proposed GAN‐segNet model to improve the segmentation of small intratumor region(s). We demonstrate the proposed method using MRI data available from a public database, that is, Brain Tumor Segmentation Challenge 2018 database (BRATS 2018). Using the proposed GAN‐segNet model, the average Dice scores were 0.8280, 0.9022, and 0.814 for segmenting enhancing tumor core, whole tumor, and tumor core, respectively. Furthermore, positive predictive values for segmenting enhanced tumor core, whole tumor, and tumor core were 0.8496, 0.9270, and 0.8610, respectively.
What Are the Most Important Predictors of Subjective Well-Being? Insights From Machine Learning and Linear Regression Approaches on the MIDUS Datasets

What are the most important predictors of subjective well-being? Using a nationally representative publicly available dataset from the Midlife in the United States project (N = 4,378), we applied linear regression, which often relies on assumptions of linearity and a priori interactions, and advanced machine learning approaches, which maximize prediction by thoroughly exploring nonlinear effects and higher-order interactions, to determine the ordering and characteristics of predictors of well-being. Advanced machine learning models generally did not predict well-being more accurately than did regression models, suggesting that many predictors of well-being may be linear and non-interactive. Consistent with this implication, the introduction of product and squared terms in regression models improved prediction, but only nominally. Our findings replicated previous research, with sociability, physical health, disengagement from goals, sex life quality, wealth, and religious activity emerging as the strongest predictors of well-being, and demographic factors emerging as relatively weak predictors. Furthermore, self-reported “aches” (the strongest “objective” predictor of well-being), stress reactivity, and disengagement negatively predicted well-being, reinforcing the role of stress in psychological maladjustment. Finally, unlike prior research, control over one’s life—and control over financial and work matters in particular—strongly predicted well-being.
Image inpainting method based sparse analysis model of synchronous dictionary learning

A novel image inpainting algorithm based on sparse analysis model is proposed. The model is formulated on an analysis dictionary. The dictionary is updated using a least-squares method. The experiments on images demonstrate improved performance in peak signal to noise ratio (PSNR) compared to other image inpainting methods including AMLE Inpainting, Harmonic Inpainting, Mumford-Shah Inpainting, and Transport Inpainting algorithm. The evaluation results showed that our proposed algorithm has better performance.
Disclosure Sentiment: Machine Learning vs. Dictionary Methods

We compare the ability of dictionary-based and machine-learning methods to capture disclosure sentiment at 10-K filing and conference-call dates. Like Loughran and McDonald [Loughran T, McDonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66(1):35–65. ], we use returns to assess sentiment. We find that measures based on machine learning offer a significant improvement in explanatory power over dictionary-based measures. Specifically, machine-learning measures explain returns at 10-K filing dates, whereas measures based on the Loughran and McDonald dictionary only explain returns at 10-K filing dates during the time period of their study. Moreover, at conference-call dates, machine-learning methods offer an improvement over the Loughran and McDonald dictionary method of a greater magnitude than the improvement of the Loughran and McDonald dictionary over the Harvard Psychosociological Dictionary. We further find that the random-forest-regression-tree method better captures disclosure sentiment than alternative algorithms, simplifying the application of the machine-learning approach. Overall, our results suggest that machine-learning methods offer an easily implementable, more powerful, and reliable measure of disclosure sentiment than dictionary-based methods. This paper was accepted by Brian Bushee, management science.
Nederlands-Frans in contrast. 2000 lexicologische valstrikken in het Frans

Nederlands-Frans in contrast wil een wetenschappelijk gefundeerd antwoord geven op de taalvragen van Nederlandstalige leerders en/of (professionele) gebruikers van het Frans. De informatie over de 2000 lexicale en grammaticale valstrikken in het boek vloeit voort uit grondig contrastief onderzoek.
Lived experience of outpatients with Parkinson's disease: an interpretative phenomenological analysis

Introduction: The chronic, degenerative and progressive character of Parkinson’s disease (PD) is reflected in the living experience of patients – and it also has an impact on their dignity. Aim: The aim of the study was to increase the understanding of the lived experience of outpatients with PD and its impact on their dignity. Methods: The study design is explorative using interpretative phenomenological analysis (IPA). Semi-structured interviews were held with 11 participants with PD between February–May 2018. Interviews were conducted as individual face-to-face interviews, in privacy with no involvement of family members according to the interview protocol. Data analysis was performed according to IPA process using the ATLAS.ti 8.0 program. Results: Our study identified five themes that reflect the lived experience of outpatients with PD: Me and my Parkinson’s: losses, acceptance and coping; The Need to Remain Self-sufficient versus the Fear of Dependence; How Do Others See Me: Scorn versus Acceptance; Lack of Information versus the Need to Be Informed; Lack of Respect versus the Accommodating Approach of Healthcare Professionals. We discovered their impact on dignity, especially in the areas concerning autonomy, self-esteem, self-worth, identity, respect, and other people’s appreciation. Conclusions: Patients with PD are confronted with many serious changes on a daily basis, which significantly influence their dignity. This fact must be known and respected by health care professionals when providing patient-oriented care.
P‐60: Reset Waveform for Dark‐Room Contrast‐Ratio Improvement

In this paper, we propose the new reset waveform which shows high dark‐room contrast‐ratio and robustness for cross talk. We apply a ramp pulse to the address electrode after the erase X ramp pulse. And a constant bias voltage is applied to X electrode during the Y rising‐falling ramp and the address period. Using this method, we achieved a great dark‐room contrastratio 2750:1. However, the address minimum voltage was increased about 17∼37V compared with the conventional ramp reset method. We could reduce the minimum address voltage through the waveform optimizing. As a result, when VSB is −30V, we could reduce the address minimum voltage about 14∼28V and improve the dark‐room contrast‐ratio3000:1.
Entrepreneurship in Architectural Robotics: The Simultaneity of Craft, Economics and Design

What do Odico Formwork Robotics, RoboFold, Machineous, ROB Technologies and GREYSHED share in common? They are all architectural robotics startups. Jelle Feringa, Chief Technology Officer at Odico, places the phenomenon of the architectural robotics entrepreneur in a historical and cultural context while highlighting the very practical role startups are poised to play in bridging the gap between academic research and industry, by providing the building industry with much needed new software tools.
Directional Human Fall Recognition Using a Pair of Accelerometer and Gyroscope Sensors

Human fall in the elderly population is one of the major causes of injury or bone fracture: it can be a cause of various injuries (e.g., fracture, concussion, and joint inflammation). It also could be a possible cause of death in a severe case. To detect human fall, various fall detection algorithms have been devised. Most fall detection algorithms rely on signals from a single accelerometer or gyroscope and use a threshold-based method to detect the human fall. However, these algorithms need careful adjustment of a threshold for each subject and cannot detect the direction of falls. In this study, we propose a novel fall recognition algorithm using a pair of a tri-axial accelerometer and a tri-axial gyroscope. Our fall recognition algorithm utilizes a set of augmented features including autoregressive (AR) modeling coefficients of signals, signal magnitude area (SMA), and gradients of angles from the sensors. After Linear Discriminant Analysis (LDA) of the augmented features, an Artificial Neural Nets (ANNs) is utilized to recognize four directional human falls: namely forward fall, backward fall, right-side fall, and left-side fall. Our recognition results show the mean recognition rate of 95.8%. Our proposed fall recognition technique should be useful in the investigation of fall-related injuries and possibly in the prevention of falls for the elderly.
Real-Time Volt/VAr Control Based on the Difference between the Measured and Forecasted Loads in Distribution Systems

This paper proposes a method for real-time control of both capacitors and ULTC in a distribution system to reduce the total power loss and to improve the voltage profile over the course of a day. The multi-stage consists of the off-line stage to determine dispatch schedule based on a load forecast and the on-line stage generates the time and control sequences at each sampling time. It is then determined whether one of the control actions in the control sequence is performed at the present sampling time. The proposed method is presented for a typical radial distribution system with a single ULTC and capacitors.
Structural foam-core panels in Northwest HUD-code manufactured housing: A preliminary assessment of opportunities and obstacles

This investigation of structural foam-core panels (foam panels) in manufactured housing was initiated during the Super Good Cents (SGC) program. The SGC program limited allowable glazing area because of the relatively high thermal losses associated with most windows. Due to their superior thermal performance, foam panels appeared to be a viable option to allow increased glazing area without compromising the thermal integrity of the wall. With the inception of the Manufactured-Housing Acquisition Program (MAP), however, the focus of this study has shifted. MAP permits unlimited glazing area if expensive, super-efficient, vinyl-framed, argon-gas-filled, low-emissivity coated windows are installed. Although MAP permits unlimited glazing area, a foam panel wall could allow the use of less expensive windows, larger window area, or less insulation and still provide the required thermal performance for the building. Bonneville contracted with the Pacific Northwest Laboratory (PNL) to investigate the feasibility of using foam panels in HUD-code manufactured housing. This study presents the results from a product and literature search. The potential barriers and benefits to the use of foam panels are determined from a regional survey of the HUD-code manufacturers and foam panel producers.
A dedicated permanent magnet synchronous motor drive system for electric vehicle

A drive system which consists of rare-earth permanent magnet synchronous motor and insulated gate bipolar transistor (IGBT) three phase inverter for electric vehicle (EV) propulsion is presented. Efforts have been made to ensure efficient operation of the inverter at high pulse width modulation (PWM) switching frequency. A simple and novel control strategy is proposed to achieve regenerative braking of EV while the motor is running below its no-load highest speed. Computer simulation and experimental results are given. The drive system, including a 44 kW, 8 pole, 6000r/min, 240 V battery-powered neodymium iron boron (Nd-Fe-B) permanent magnet synchronous motor and an 85 kVA IGBT inverter, has been applied to a 17-seat bus. Road test running of the bus has verified that the proposed control strategy is practical. <<ETX>>
Modeling the evolution of surface nanobubbles

Surface nanobubbles are gaseous formations on solid-liquid interfaces. They are interesting in that their lifetime can reach several days, although initially it was assumed only a few nanoseconds. We built a mathematical and computer model of the nanobubble and performed a series of simulations to see how the nanobubble behaves when various external factors change.
Safe Navigation of a Car-Like Robot in a Dynamic Environment

This paper addresses the problem of navigation of a car-like robot in dynamic environments. Such environments impose a hard real time constraint. However, computing a complete motion to the goal within a limited time is impossible to achieve in most real situations. Besides, the limited duration validity of the model used for planning requires the model and therefore the plan to be updated. In this paper, we present a Partial Motion Planning (PMP) approach as the answer to this problem. The issue of safety raised by this approach is addressed using the Inevitable Collision State formalism and effectiveness of the approach is demonstrated with several simulation examples. The quality of the generated trajectories is discussed and continuous curvature metric is integrated as a mean to improve it.
New Medical Device and Therapeutic Approvals in Otolaryngology: State of the Art Review 2020

Objectives To evaluate new drugs and devices relevant to otolaryngology–head and neck surgery that were approved by the US Food and Drug Administration (FDA) in 2020. Data Sources Publicly available device and therapeutic approvals from ENT (ear, nose, and throat), anesthesia, neurology (neurosurgery), and plastic and general surgery FDA committees. Review Methods Members of the American Academy of Otolaryngology–Head and Neck Surgery’s Medical Devices and Drugs Committee reviewed new therapeutics and medical devices from a query of the FDA’s device and therapeutic approvals. Two independent reviewers assessed the drug’s or device’s relevance to otolaryngology, classified to subspecialty field, with a critical review of available scientific literature. Conclusions The Medical Devices and Drugs Committee reviewed 53 new therapeutics and 1094 devices (89 ENT, 140 anesthesia, 511 plastic and general surgery, and 354 neurology) approved in 2020. Ten drugs and 17 devices were considered relevant to the otolaryngology community. Rhinology saw significant improvements around image guidance systems; indications for cochlear implantation expanded; several new monoclonal therapeutics were added to head and neck oncology’s armamentarium; and several new approvals appeared for facial plastics surgery, pediatric otolaryngology, and comprehensive otolaryngology. Implications for Practice New technologies and pharmaceuticals offer the promise of improving how we care for otolaryngology patients. However, judicious introduction of innovations into practice requires a nuanced understanding of safety, advantages, and limitations. Working knowledge of new drugs and medical devices approved for the market helps clinicians tailor patient care accordingly.
A novel musculoskeletal modelling approach for non-conforming joints

Most musculoskeletal models presume idealized joints, such as a revolute joint for the knee and a spherical joint for the hip [1,2]. This is convenient in inverse dynamic analysis, where the kinematics of the model is resolved first, i.e. without knowledge of the forces that created the motion. However, several anatomical and prosthetic joints, such as spinal disks, knees and many shoulders are non-conforming to such an extent that the forces significantly influence the detailed joint kinematics and the joint’s internal force equilibrium. For instance in the knee, the internal motions occurring are due to a complex interaction between the muscle actions, cartilage contact mechanics, and soft tissue stiffnesses and deformations. Capturing all these effects in a realistic model using only kinematic constraints is therefore very difficult. To address this problem, this paper proposes a novel modelling approach capable of simultaneously computing the muscle and reaction forces using muscle recruitment typical for inverse dynamics and the resulting motion in user-defined directions as typical for forward dynamics approaches, albeit without many of the dynamic convergence challenges typical for forward dynamics approaches. The method is based on an assumption of quasistatic force equilibrium between all the acting forces in the model and is used to compute the unknown forces as well as the motion. The method is demonstrated on a lower extremity model to resolve the kinematics of the tibiofemoral and patellofemoral joints in a total knee replacement model based on the Twente Lower Extremity Model [1] from the AnyScript Model Repository and the gait data made publically available under the grand challenge competition to predict the knee contact forces.
Parameter Uncertainty of a Watershed Model

Parameter uncertainty of the Hydrologic Simulation Program Fortran (HSPF) was the focus of this work. The model was calibrated using data from the South Nation watershed located in eastern Ontario. Up to 18 parameters were used in independent model calibrations. Three uncertainty methods were used: the method of moments, Monte Carlo (MC) with Latin hypercube sampling (LHS) and induced correlation, and response surface methods. It was observed that as more parameters are simultaneously optimized higher uncertainty is typically observed. The method of moments shows the most conservative uncertainty. The largest uncertainty typically was estimated by the MC method. The 95% confidence intervals of parameter uncertainty correspond to up to 10% variations in spring maximum flows.
Data‐Driven Storytelling: The Missing Link in HR Data Analytics

Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ert.21471 27 is from a C-level executive who had experienced his first ever one-on-one data meeting with an HR data team. After reviewing the newly created dashboard data, he relayed his impression of it to me: “It’s really cute that HR has data now. ” As someone who has been in the HR field for over 30 years, and who is a true believer in the power of data to drive decision making, that remark seemed like a crushing blow to all the good work that has been achieved in the area of HR analytics. All the good work goes down the drain quickly when a senior leader, with a loud corporate voice, has a bad experience. That is the ugly.
Effect of Awake Prone Positioning in non-Intubated COVID-19 Patients with Acute Hypoxemic Respiratory Failure: A Systematic Review and Meta-Analysis

Background Awake prone positioning (APP) has been considered as a feasible treatment for patients with acute hypoxemic respiratory failure in non-intubated coronavirus disease 2019 (COVID-19). However, the efficacy and safety of APP remain uncertain. This meta-analysis aims to assess the effect of APP on intubation rate and mortality in COVID-19 patients with acute respiratory failure. Methods Relevant studies published from January 1, 2020, to June 17, 2022, were systematically searched. The primary outcomes were the intubation rate and mortality; the secondary outcome was the incidence of adverse events. Results Of 5746 identified publications, 22 were eligible for inclusion in the meta-analysis (N = 5146 patients). In comparison to the non-APP group, APP could decrease the intubation rates (OR 0.64; 95% CI 0.48-0.83; P = .001), particularly in the subgroup of the daily median duration of APP > 8 h and in the subgroup of receiving high flow nasal cannula (HFNC) or non-invasive ventilation (NIV). Patients treated with APP were associated with lower mortality rates (OR 0.61; 95% CI 0.45-0.81; P = .0008), but no mortality benefit was found in the APP group in the subgroup of randomized controlled trials (RCTs). No significant difference was found in the incidence of adverse events between the groups (OR 1.13; 95% CI 0.75-1.71; P = .56). Conclusion Our results demonstrated that APP could be an effective strategy to avoid intubation without detrimental effects in non-intubated patients with COVID-19, especially for patients requiring HFNC or NIV, and the daily APP duration with the target of minimally eight hours was suggested. In the subgroup of RCTs, the pooled results did not demonstrate any benefit of APP on mortality. Given the limited number of RCTs, further high-quality RCTs are needed to confirm the results. INPLASY registration number INPLASY2021110037.