Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bristle-Less Tooth Brush Reduces Recession Risk Over One YearCompared to a Conventional Soft Manual Brush

Background: Aggressive tooth brushing can contribute to gingival recession, and we tested in a randomized clinical trial if use of a bristle-less tooth brush results in less recession compared to using a conventional soft brush. Methods: We recruited 23 subjects with gingival recession who received regular periodontal maintenance care at Western University of Health Sciences Dental Center, and who did not exhibit signs of acute dental and systemic disease, occlusal discrepancies and para-functional habits. We randomly assigned these subjects to two groups, one using a soft nylon-bristled tooth brush, and the other group using the experimental tooth brush that contains a brush head with short, soft, rubbery cones. Both groups received regular periodontal maintenance and periodontal exams by blinded examiners every 3-4 months measuring probing depth, bleeding on probing, plaque indices. Tooth brush use was monitored by phone follow-up and measuring tooth brush wear of the tooth brushes assigned to subjects. Gingival recession was assessed clinically and through use of a stent on diagnostic casts obtained at each visit. Results: Average probing depths, plaque levels, and the number of sites with bleeding on probing did not change over one year. Subjects were compliant with tooth brush use as tooth brushes showed consistent wear patterns, but with much less tooth brush damage in the bristle-less brushes. There was a small, but statistically significant improvement of gingival recession (0.4 mm, p<0.01) at sites with gingival recession in the experimental tooth brush group compared to the control group and compared to baseline gingival level. Conclusion: A bristle-less brush appears to be more suitable in preventing additional gingival recession in patients with a history of aggressive tooth brushing.
The open bar is closed: restructuration of a native parasitoid community following successful control of an invasive pest

The rise of the Asian chestnut gall wasp Dryocosmus kuriphilus in France has benefited the native community of parasitoids originally associated with oak gall wasps by becoming an additional trophic subsidy and therefore perturbing population dynamics of local parasitoids. However, the successful biological control of this pest has then led to significant decreases in its population densities. Here we investigate how the invasion of the Asian chestnut gall wasp Dryocosmus kuriphilus in France and its subsequent control by the exotic parasitoid Torymus sinensis has impacted the local community of native parasitoids. We explored 5 years of native community dynamics within 26 locations during the rise and fall of the invasive pest. In an attempt to understand how mechanisms such as local extinction or competition come into play, we analyzed how the patterns of co-occurrence between the different native parasitoid species changed through time. Our results demonstrate that native parasitoid communities experienced increased competition as the D. kuriphilus levels of infestation decreased. During the last year of the survey, two alternative patterns were observed depending on the sampled location: either native parasitoid communities were represented by an extremely limited number of species occurring at low densities, in some cases no native parasitoid species at all, or they were dominated by one main parasitoid: Mesopolobus sericeus. These two patterns seemed to correlate with the habitat type, M. sericeus being more abundant in semi-natural habitats compared to agricultural lands, the former known to be natural reservoirs for native parasitoids. These results highlight how the “boom-and-bust” dynamics of an invasive pest followed by successful biological control can deeply alter the structure of native communities of natural enemies. This article has been peer-reviewed and recommended by Peer Community in Zoology https://doi.org/10.24072/pci.zool. 100004
Reducing Wait Time Prediction In Hospital Emergency Room: Lean Analysis Using a Random Forest Model

Most of the patients visiting emergency departments face long waiting times due to overcrowding which is a major concern across the hospital in the United States. Emergency Department (ED) overcrowding is a common phenomenon across hospitals, which leads to issues for the hospital management, such as increased patient's dissatisfaction and an increase in the number of patients choosing to terminate their ED visit without being attended to by a medical healthcare professional. Patients who have to Leave Without Being Seen (LWBS) by doctors often leads to loss of revenue to hospitals encouraging healthcare professionals to analyze ways to improve operational efficiency and reduce the operational expenses of an emergency department. To keep patients informed of the conditions in the emergency room, recently hospitals have started publishing wait times online. Posted wait times help patients to choose the ED which is least overcrowded thus benefiting patients with shortest waiting time and allowing hospitals to allocate and plan resources appropriately. This requires an accurate and efficient method to model the experienced waiting time for patients visiting an emergency medical services unit. In this thesis, the author seeks to estimate the waiting time for low acuity patients within an ED setting; using regularized regression methods such as Lasso, Ridge, Elastic Net, SCAD and MCP; along with tree-based regression (Random Forest). For accurately capturing the dynamic state of emergency rooms, queues of patients at various stage of ED is used as candidate predictor variables along with time patient's arrival time to account for diurnal variation. Best waiting time prediction model is selected based on the analysis of historical data from the hospital. Tree-based regression model predicts wait time of
The analysis of English sentential subjects in the spontaneous production of English/Spanish simultaneous bilingual children

This dissertation presents a study on the analysis of the production of English sentential subjects on the part of simultaneous English/Spanish bilingual children and on the examination of the children’s development on the realization of the subject element in English from the age of 2;11 to 5;11. The data collected are spontaneous and have been selected from CHILDES. Data analysis focuses on three variables: referentiality, overtness and grammatical correctness; and the objective is to test the children’s linguistic production of sentential subjects, both overall and by developmental stages. The results obtained are in line with what previous studies have found: the children reveal an early consciousness on how subjects are used in English; and the children present some patterns in their subject production which could be related to crosslinguistic influence from Spanish into English.
The Solitary Fibrous Tumor with Unusual Retroperitoneal Localization

Solitary fibrous tumors (SFTs) are rare mesenchymal tumors, usually described at the pleura level, but since they can develop in different extra pleural locations. Retroperitoneal location is rarely described. The anatomopathological study remains the key to the diagnosis of SFT, the curative treatment is based on surgical excision. Most extra-thoracic SFT are benign, but may recur or metastasize after complete removal, even in the absence of histopathological evidence of malignancy. Careful longterm monitoring is therefore recommended. We report the case of retroperitoneal SFT in a 70-year-old male who was consulting for generalized abdominal pain. The aspects of imaging, anatomopathology and immunohistochemistry are
Parameterized Algorithms for Finding a Collective Set of Items

We extend the work of Skowron et al. (AIJ, 2016) by considering the parameterized complexity of the following problem. We are given a set of items and a set of agents, where each agent assigns an integer utility value to each item. The goal is to find a set of k items that these agents would collectively use. For each such collective set of items, each agent provides a score that can be described using an OWA (ordered weighted average) operator and we seek a set with the highest total score. We focus on the parameterization by the number of agents and we find numerous fixed-parameter tractability results (however, we also find some W[1]-hardness results). It turns out that most of our algorithms even apply to the setting where each agent has an integer weight.
A 24.88 nV/√Hz Wheatstone Bridge Readout Integrated Circuit with Chopper-Stabilized Multipath Operational Amplifier

This paper proposes a low noise readout integrated circuit (IC) with a chopper-stabilized multipath operational amplifier suitable for a Wheatstone bridge sensor. The input voltage of the readout IC changes due to a change in input resistance, and is efficiently amplified using a three-operational amplifier instrumentation amplifier (IA) structure with high input impedance and adjustable gain. Furthermore, a chopper-stabilized multipath structure is applied to the operational amplifier, and a ripple reduction loop (RRL) in the low frequency path (LFP) is employed to attenuate the ripple generated by the chopper stabilization technique. A 12-bit successive approximation register (SAR) analog-to-digital converter (ADC) is employed to convert the output voltage of the three-operational amplifier IA into digital code. The Wheatstone bridge readout IC is manufactured using a standard 0.18 µm complementary metal-oxide-semiconductor (CMOS) technology, drawing 833 µA current from a 1.8 V supply. The input range and the input referred noise are ±20 mV and 24.88 nV/√Hz, respectively.
Common genetic variants shape broad patterns of within-population variation in gene expression

Understanding the persistence of genetic variation within populations has long been a goal of evolutionary biology. One promising route towards achieving this goal is using population genetic approaches to describe how selection acts on the loci associated with trait variation. In particular, gene expression provides a model trait for addressing the challenge of the maintenance of variation because it can be measured genome-wide without information about how gene expression affects traits. Previous work has shown that loci affecting the expression of nearby genes (cis-eQTL) tend to be under purifying selection, but we lack a clear understanding of the selective forces acting on variants that affect the expression of large numbers of genes across the genome (large-effect trans-eQTL). Here, we identify loci that affect the expression of coexpression networks using genomic and transcriptomic data from one population of the obligately outcrossing plant, Capsella grandiflora. We identify nine loci associated with the expression of 10s to 1000s of genes. One of these loci is also associated with trait variation, but we do not detect evidence of balancing selection acting on sequence variation surrounding these loci.
Electrical resistance tomography with conditional generative adversarial networks

Electrical resistance tomography (ERT) can reconstruct measured field images non-invasively and is therefore widely used in the field of engineering technology. However, blurred reconstructed images and the lack of detailed features limit the application of electrical tomography. In this paper, a reconstruction method based on a conditional generative adversarial network (CGAN) is proposed to mitigate these problems. CGAN, a deep learning algorithm, can generate images based on condition information, making the generated images more stable and trustable. To adapt the training to the characteristics of ERT, the CGAN structure has been improved. In addition, loss judgement is added to the discriminator training to improve the training efficiency. We further propose a data generation method to simulate various conductivity distributions. The method is tested by using a testing set without repetition data in the training set. The results indicate that the method can effectively improve the sharpness of the reconstructed images and the reconstruction of the image details and reduce reconstructed image error. Using real experimental data for reconstruction, the results also show that this method can effectively improve the quality of reconstruction.
To Be Happy: A Case Study of Entrepreneurial Motivation and Entrepreneurial Process from the Perspective of Positive Psychology

The pursuit of wealth maximization is considered to be the greatest driving force of entrepreneurship. However, this economic rational perspective cannot sufficiently answer why potential or continuous entrepreneurs still choose entrepreneurship or even continuous entrepreneurship in the face of high failure rate and tremendous uncertainty. On the basis of the dynamic process of entrepreneurship and the perspective of positive psychology, this study attempts to interpret the sustained motivation mechanism of entrepreneurs. This study uses multiple cases to investigate the emotion, cognition, and behavior of entrepreneurial process. Through NVivo software and emotion dictionary, more than 27,000 micro blogs (Weibo) of six entrepreneurs were analyzed, and the model of positive emotion in entrepreneurial process was constructed. The findings are as follows. (1) In the process of establishing a business, entrepreneurs can persist in a highly uncertain environment by acquiring positive emotions. That is, the motivation of sustainable entrepreneurship originates from the emotion of happiness and satisfaction that entrepreneurs obtain. (2) Positive emotions affect the formation and expansion of key activities of entrepreneurship through cognition and then persist with entrepreneurship. Specifically, positive emotion promotes the formation of entrepreneurial intention by expanding cognitive structure, intuitive processing, and analytical processing to promote the acquisition of entrepreneurial resources and the expansion of entrepreneurial ability. (3) In the process of entrepreneurship, emotional return is a performance dimension parallel to economic return. This conclusion provides a new perspective towards revealing the entrepreneurial motivation of entrepreneurs in highly ambiguous environments.
Prevalence and Clinical Characteristics of Dual Diagnosis (Substance Abuse and Mental Illness) Among Egyptian Psychiatric Patients

Background: The co-occurrence of substance abuse and mental illness is common in Egypt, yet enough studies for the prevalence or management are available. This co-occurrence adversely affects the course and outcome. Separate and parallel mental health and substance abuse treatment systems do not offer interventions that are accessible, integrated, and tailored for the presence of such co-occurrence. Aim: The aim of this study was to screen for prevalence of dual diagnosis (co-occurring mental illness and substance abuse) among inpatients of the Institute of Psychiatry, Ain Shams University, and their clinical characteristics. Methods: All cases referred for admission at the Institute of Psychiatry, Ain Shams University, were examined by Structured Clinical Interview for DSM-IV TR Axis I Disorders, addiction severity index, and then according to diagnosis reassessed by Positive and Negative Affect Scale, young mania scale, or Beck Depression Inventory. Results: An overall 55.5% of referred cases were having dual diagnosis, of which 42% comprised schizophrenia, 38% BAD, and 20% depression; all were male individuals with cannabis being the most-used drug followed by polysubstance use of cannabis and tramadol. Discussion: Dual diagnosis is a very common neglected condition among hospitalized psychiatric patients, with more severe illness and more impact on all aspects.
Dynamic Temperature Measurements of a GaN DC–DC Boost Converter at MHz Frequencies

For reliability predictions, gallium nitride transistors require accurate estimations of the peak operating temperatures within the device. This article presents a new application of thermoreflectance-based temperature measurements performed on a gallium nitride high electron mobility transistor. The submicron spatial and nanosecond temporal resolutions of the measurement system enables for the first time, the dynamic temperature measurement of a transistor operating up to 5 MHz. The gallium nitride transistor is first biased in class-A and excited with a 1 MHz ac signal to demonstrate the dynamic temperature measurement. The transistor is, then, incorporated in a 20–40 V dc–dc boost converter to measure the dynamic temperature distributions across the semiconductor die operating under real loading conditions at 1 and 5 MHz switching frequencies. This technique captures the temperature variations that occur during the switching of the transistor and the recorded peak temperatures are 7.4 $^{\circ }$C higher compared with conventional measurement and simulation approaches.
Integrating the Digital and the Traditional to Deliver Therapy for Depression: Lessons from a Pragmatic Study

Traditional approaches to psychotherapy emphasise face-to-face contact between patients and therapists. In contrast, current computerised approaches tend to minimise this contact. This can limit the range of mental health difficulties for which computerised approaches are effective. Here, we explore an alternative approach that integrates face-to-face contact, electronic contact, online collaboration, and support for between-session activities. Our discussion is grounded in the design of a platform to deliver psychotherapy for depression. We report findings of an 11-month pragmatic study in which 17 patients received treatment for depression via the platform. Results show how design decisions had a significant impact on the dynamics of therapeutic sessions and the establishment of patient-therapist relationships. For example, the use of instant messaging for synchronous, in-session contact slowed communication, but also provided a valuable space for reflection and helped to maintain session focus. We discuss the impact of flexibility and the potential of integrated approaches to both enhance and reduce patient engagement.
Diagnostics via partial residual plots in inverse Gaussian regression

Regression diagnostics is the basic requirement to apply regression analysis to reach reliable conclusions. Generalized linear models also required diagnostics for its implementation. The construction of partial residuals using response residuals for the inverse Gaussian regression model is carried out to explore the structure and usefulness for visualizing diagnostics and curvature as a function of selected predictors. The current study established the performance of partial residual plots over conventional diagnostic methods. The comparison has been made using aerial biomass data and with the help of simulation study. It has been observed that partial residual plots provide much better diagnosis than do conventional methods. Moreover, multiple diagnostics in a single display provide better perceptive towards lack of fit, specification, and data anomalies.
False Color Method for Retinal Oximetry

Oximetry is a method for measuring the oxygen saturation of haemoglobin in blood. Particularly, retinal oximetry based in the measurement of oxygen saturation in retinal vessels has acquired great interest to gather information on blood oxygenation from said vessels within inner and outer retina. Non-invasive spectrophotometric retinal oximetry has been studied for over five decades based on imaging spectroscopy. However, Optical Coherence Tomography (OCT) is an alternative to analyze the absorption difference between oxyhaemoglobin (HbO2) and deoxyhaemoglobin (Hb) in the retinal vessels and the choroidal structure. We propose in this paper an alternative process to manipulate conventional OCT images to evaluate changes in the relative haemoglobin oxygen saturation. Conventional OCT images from 570 nm and 600 nm in gray scale are converted to a corresponding color scale to be compared to the oxygenation information involved in the original gray scale OCT images.
A Time Domain Artificial Intelligence Radar System Using 33-GHz Direct Sampling for Hand Gesture Recognition

This article introduces a time-domain-based artificial intelligence (AI) radar system for gesture recognition using 33-GS/s direct sampling technique. High-speed sampling using a time-extension method allows AI learning to be applied to a time-domain radar signal reflecting information on both dynamic and static gestures, and thus can recognize not only dynamic but also static gestures. The Vernier clock generators and high-speed active samplers applied with the time-extension technique makes sampling at 33 GS/s possible. A 1-D convolutional neural network and long short-term memory are employed for both static and dynamic gestures and recognition rates of 93.2% and 90.5% are obtained, respectively. The radar system is implemented using a 65-nm CMOS process with a power consumption of 95 mW.
Pathogenic Variants in Cancer Predisposition Genes and Prostate Cancer Risk in Men of African Ancestry.

PURPOSE In studies of men of European ancestry, rare pathogenic variants in DNA repair pathway genes have been shown to be associated with risk of aggressive prostate cancer. The contribution of rare coding variation to prostate cancer risk in men of African ancestry has not been established. METHODS We sequenced a panel of 19 DNA repair and cancer predisposition genes in 2,453 African American and 1,151 Ugandan prostate cancer cases and controls. Rare variants were classified as pathogenic or putatively functionally disruptive and examined in association with prostate cancer risk and disease aggressiveness in gene and pathway-level association analyses. RESULTS Pathogenic variants were found in 75 out of 2,098 cases (3.6%) and 31 out of 1,481 controls (2.1%) (OR=1.82, 95% CI=1.19 to 2.79, P=0.0044) with the association being stronger for more aggressive disease phenotypes (OR=3.10, 95% CI=1.54 to 6.23, P=0.0022). The highest risks for aggressive disease were observed with pathogenic variants in the ATM, BRCA2, PALB2 and NBN genes, with odds ratios ranging from ~4 to 15 in the combined study sample of African American and Ugandan men. Rare, non-pathogenic, non-synonymous variants did not have a major impact on risk of overall prostate cancer or disease aggressiveness. CONCLUSIONS Rare pathogenic variants in DNA repair genes have appreciable effects on risk of aggressive prostate cancer in men of African ancestry. These findings have potential implications for panel testing and risk stratification in this high-risk population.
Hydrolytic Process of Proteins in Moringa oleifera Seeds in Varied Concentrations of Sodium Hydroxide and Hydrochloric Acid

Indonesia is endowed with an immense biodiversity that can be used as protein sources. One of these is Moringa oleifera tree that is locally known as Kelor. The seeds of this plant can be used as a protein source, an effective coagulant for water purification, a natural absorbent, and an antimicrobial treatment. Kelor seeds are known to contain fibers, proteins, carbohydrates, and vitamins. The objectives of this study were to identify the optimal solution concentration, determine the yield percentage, and determine the optimal protein content from hydrolytic processes of Moringa seed extraction using Sodium Hydroxide (NaOH) and Hydrochloric Acid (HCl). The hydrolysis took place for 30 minutes at 60 o C. The proteins extracted from Moringa seeds were identified with biuret and Braford tests. The NaOH extractions resulted in the highest yield of 12.1% and protein content of 0.43% with 2% NaOH. Whereas those of HCl produced the highest yield of 11.1% and protein content of 9.63% with 1% HCl.