Miracle-dz/UniFAR-FTUs huggingface dataset:
Gendered Language in Letters of Recommendation for Applicants to Pulmonary Critical Care Fellowships

Background Previous work has demonstrated letters of recommendation for women in academic medicine are shorter and emphasize communal traits over grindstone or agentic traits. Objective To determine if there are sex-based differences in letters of recommendation written for applicants applying to pulmonary critical care medicine fellowships and if the sex of the letter writer impacts these differences. Methods All fellowship applications submitted to a pulmonary critical care medicine fellowship program in 2020 were included in this study. The applicant demographics and self-reported accomplishments were extracted from their application. The sex of letter writers was identified through public online searches. Word count and language differences in the letters of recommendation were analyzed for each applicant using the Linguistic Inquiry and Word Count (LIWC2015) program. Multivariable linear regressions were performed controlling for applicant characteristics to identify if applicant sex was associated with total word counts and total agentic word counts. Results Of the 529 complete applications, 2,024 letters of recommendation were reviewed. A majority of the applicants (70%, n = 370/530) and letter writers (75%, n = 1,515/2,024) were male. When adjusting for applicant demographic and accomplishments, female applicants had longer letters of recommendation (30.91 words longer, 95% confidence interval [CI], 1.53–60.29; P = 0.04) and more supportive letters (3.27 words longer, 95% CI, 1.59–4.95; P < 0.01) as compared with male applicants. Female letter writers wrote longer and more supportive letters than male letter writers, and this difference was greatest for female applicants. Conclusion Female applicants received longer and more supportive letters of recommendation. Further work is needed to understand if this finding is the beginning of a change in the letters of recommendation for female applicants.
Towards Depth-based Respiratory Rate Estimation with Arbitrary Camera Placement

Newborn patients in the neonatal intensive care unit (NICU) require continuous monitoring of vital signs. Non-contact patient monitoring is preferred in this setting, due to fragile condition of neonatal patients. Depth-based approaches for estimating the respiratory rate (RR) can operate effectively in conditions where an RGB-based method would typically fail, such as low-lighting or where a patient is covered with blankets. Many previously developed depth-based RR estimation techniques require careful camera placement with known geometry relative to the patient, or manual definition of a region of interest (ROI). We here present a framework for depth-based RR estimation where the camera position is arbitrary and the ROI is determined automatically and directly from the depth data. Camera placement is addressed through perspective transformation of the scene, which is accomplished by selecting a small number of registration points known to lie in the same plane. The chest ROI is determined automatically from examining the morphology of progressive depth slices in the corrected depth data. We demonstrate the effectiveness of this RR estimation pipeline using actual neonatal patient depth data collected from an RGB-D sensor. RR estimation accuracy is measured relative to gold standard RR captured from the bedside patient monitor. Perspective transformation is shown to be critical to effectively achieve automated ROI segmentation algorithm. Furthermore, the automated ROI segmentation algorithm is shown to improve both time- and frequency-domain based RR estimation accuracy. When combined, these pre-processing stages are shown to substantially improve the depth-based RR estimation pipeline, with a percentage of acceptable estimates (where the mean absolute error is less than 5 breaths per minute) increasing from 3.60% to 13.47% in the frequency domain and 6.12% to 8.97% in the time domain. Further development will focus on RR estimation from the perspective-corrected depth data and segmented ROI.
Who Is Treating the Children? Training Child and Family Psychoanalytic Psychotherapists in China

ABSTRACT This article traces the development and evolution of The Psychodynamic Couple and Family Therapy Continuous Training Program in Beijing, China since its founding in 2009 as a two-year series of four immersion experiences in couple therapy training. The program’s learning components include a live clinical demonstration, didactic lectures, and small process learning groups. In the past 10 years, program faculty opted to include a child and family focus to address the relative absence of child psychodynamic psychotherapy training in China and the increasing mental health needs of Chinese children. Research on the training’s effectiveness is presented, highlighting the importance of the live clinical demonstration to the group’s learning, and supporting the importance of showing clinical theory and technique in action. Required adaptations from an in-person to an online training format owing to COVID are discussed. Examples drawn from the clinical demonstration, small process learning groups, and later emerging consultation groups are offered to show applications of the training model, challenges in learning, and cultural differences. The author contends that this training program and its extensions rely on the strong working alliance of its founders and illustrate the value of cross-cultural collaboration in training and treatment efforts.
TMEM16F may be a new therapeutic target for Alzheimer’s disease

TMEM16F is involved in many physiological processes such as blood coagulation, cell membrane fusion and bone mineralization. Activation of TMEM16F has been studied in various central nervous system diseases. High TMEM16F level has been also found to participate in microglial phagocytosis and transformation. Microglia-mediated neuroinflammation is a key factor in promoting the progression of Alzheimer’s disease. However, few studies have examined the effects of TMEM16F on neuroinflammation in Alzheimer’s disease. In this study, we established TMEM16F-knockdown AD model in vitro and in vivo to investigate the underlying regulatory mechanism about TMEM16F-mediated neuroinflammation in AD. We performed a Morris water maze test to evaluate the spatial memory ability of animals and detected markers for the microglia M1/M2 phenotype and NLRP3 inflammasome. Our results showed that TMEM16F was elevated in 9-month-old APP/PS1 mice. After TMEM16F knockdown in mice, spatial memory ability was improved, microglia polarization to the M2 phenotype was promoted, NLRP3 inflammasome activation was inhibited, cell apoptosis and Aβ plaque deposition in brain tissue were reduced, and brain injury was alleviated. We used amyloid-beta (Aβ25–35) to stimulate human microglia to construct microglia models of Alzheimer’s disease. The levels of TMEM16F, inducible nitric oxide synthase (iNOS), proinflammatory cytokines and NLRP3 inflammasome-associated biomarkers were higher in Aβ25–35 treated group compared with that in the control group. TMEM16F knockdown enhanced the expression of the M2 phenotype biomarkers Arg1 and Socs3, reduced the release of proinflammatory factors interleukin-1, interleukin-6 and tumor necrosis factor-α, and inhibited NLRP3 inflammasome activation through reducing downstream proinflammatory factors interleukin-1β and interleukin-18. This inhibitory effect of TMEM16F knockdown on M1 microglia was partially reversed by the NLRP3 agonist Nigericin. Our findings suggest that TMEM16F participates in neuroinflammation in Alzheimer’s disease through participating in polarization of microglia and activation of the NLRP3 inflammasome. These results indicate that TMEM16F inhibition may be a potential therapeutic approach for Alzheimer’s disease treatment.
A Fast Point Cloud Recognition Algorithm Based on Keypoint Pair Feature

At present, PPF-based point cloud recognition algorithms can perform better matching than competitors and be verified in the case of severe occlusion and stacking. However, including certain superfluous feature point pairs in the global model description would significantly lower the algorithm’s efficiency. As a result, this paper delves into the Point Pair Feature (PPF) algorithm and proposes a 6D pose estimation method based on Keypoint Pair Feature (K-PPF) voting. The K-PPF algorithm is based on the PPF algorithm and proposes an improved algorithm for the sampling point part. The sample points are retrieved using a combination of curvature-adaptive and grid ISS, and the angle-adaptive judgment is performed on the sampling points to extract the keypoints, therefore improving the point pair feature difference and matching accuracy. To verify the effectiveness of the method, we analyze the experimental results in scenes with different occlusion and complexity levels under the evaluation metrics of ADD-S, Recall, Precision, and Overlap rate. The results show that the algorithm in this paper reduces redundant point pairs and improves recognition efficiency and robustness compared with PPF. Compared with FPFH, CSHOT, SHOT and SI algorithms, this paper improves the recall rate by more than 12.5%.
Pan-Cancer Analysis of PDIA3: Identifying It as a Potential Biomarker for Tumor Prognosis and Immunotherapy

Protein disulfide isomerase A3 (PDIA3) is a kind of thiol oxidoreductase with a wide range of functions, and its expression is elevated in a variety of tumors, which is closely related to the invasion and metastasis of tumor cells, and has a significant impact on the immunogenicity of tumor cells. Although more and more studies have shown that PDIA3 plays an important role in the occurrence and development of many tumors, there is no systematic pan-cancer study on PDIA3. Therefore, in this study, the differential expression of PDIA3 in 33 kinds of tumors was analyzed to explore its ability to regulate tumor immunity as a biomarker and evaluate its role in different cancer onset stages or clinical prognosis. In this paper, by analyzing the multilevel data including 33 kinds of cancers in the databases of Cancer Genome Atlas (TCGA), UCSC Xena, Cancer Cell Encyclopedia (CCLE), Genotypic Tissue Expression (GTEx), Human Protein Atlas (HPA), cBioPortal, and GDC; the differential expression level of PDIA3 in different types of malignant tumors and its relationship with prognosis and the potential correlation between PDIA3 expression and microsatellite instability (MSI), tumor mutation load (TMB), mismatch repair gene (MMR), DNA methylation level, and immune infiltration level were analyzed with bioinformatics. The results showed that PDIA3 was highly expressed in 19 types of cancers, but downregulated only in THCA. Next, PDIA3 in different tumors was positively or negatively correlated with patient outcome, Kaplan-Meier survival analysis showed that PDIA3 plays an important role in the prognosis of patients with KIRP, KICH, and CESC and may be used as a prognostic biomarker, and the methylation level of PDIA3 promoter region was closely related to patient outcome in eight tumors. The expression level of PDIA3 was correlated with TMB in 13 tumors and MSI in 9 tumors. Among them, the expression level of PDIA3 in THYM has the strongest correlation with TMB, and the expression level of PDIA3 in READ has the strongest correlation with MSI. In addition, the expression of PDIA3 in eight kinds of tumors, including BRCA, HNSC, THYM, LGG, LUAD, LUSC, PRAD, and THCA, had the highest correlation with the infiltration degree of immune cells, and the expression of PDIA3 had the highest correlation with the infiltration degree of 11 kinds of immune cells, including regulatory T cell and macrophages. And LGG is the tumor most likely to be affected by the tumor microenvironment to affect its development and prognosis. To sum up, this study suggests that PDIA3 plays an important role in the occurrence and development of KIRP, KICH, and CESC and in the immunotherapeutic response of THYM, READ, and LGG and can be used as a prognostic biomarker for these tumors.
Online and Graph-Based Temporal Alignment for Monocular-Inertial SLAM Applied on Android Smart Phones

In this paper, we propose a monocular, visual-inertial simultaneous localization and mapping (SLAM) system which can be used in augmented reality (AR) applications on Android smart phones. The most challenging point of applying SLAM systems on low-cost smart phone devices is that the timestamps of the camera and inertial measurement unit (IMU) are not aligned. The misalignment will lead to a severe decline in accuracy and even frequent tracking failures. To address the above issue, we introduce the camera-IMU timestamp delay (denoted as td) to the IMU pre-integration residual and deduce the Jacobian coefficients with respect to td. Furthermore, we propose an online method for td estimation and calibration based on the graph optimization framework (g2o). The experimental results demonstrate our method can achieve superior accuracy of trajectory and robustness compared with the existing systems in unsynchronized conditions on EuRoC dataset and Android platform applications.
Multi-Factors Cooperatively Actuated Photonic Hydrogel Aptasensors for Facile, Label-Free and Colorimetric Detection of Lysozyme

Responsive two-dimensional photonic crystal (2DPC) hydrogels have been widely used as smart sensing materials for constructing various optical sensors to accurately detect different target analytes. Herein, we report photonic hydrogel aptasensors based on aptamer-functionalized 2DPC poly(acrylamide-acrylic acid-N-tert-butyl acrylamide) hydrogels for facile, label-free and colorimetric detection of lysozyme in human serum. The constructed photonic hydrogel aptasensors undergo shrinkage upon exposure to lysozyme solution through multi-factors cooperative actuation. Here, the specific binding between the aptamer and lysozyme, and the simultaneous interactions between carboxyl anions and N-tert-butyl groups with lysozyme, increase the cross-linking density of the hydrogel, leading to its shrinkage. The aptasensors’ shrinkage decreases the particle spacing of the 2DPC embedded in the hydrogel network. It can be simply monitored by measuring the Debye diffraction ring of the photonic hydrogel aptasensors using a laser pointer and a ruler without needing sophisticated apparatus. The significant shrinkage of the aptasensors can be observed by the naked eye via the hydrogel size and color change. The aptasensors show good sensitivity with a limit of detection of 1.8 nM, high selectivity and anti-interference for the detection of lysozyme. The photonic hydrogel aptasensors have been successfully used to accurately determine the concentration of lysozyme in human serum. Therefore, novel photonic hydrogel aptasensors can be constructed by designing functional monomers and aptamers that can specifically bind target analytes.
Optimised fusion rule in cluster-based energy-efficient CSS for cognitive radio networks

ABSTRACT Cooperative spectrum sensing (CSS) can solve the problem of the hidden terminal in cognitive radio (CR). Energy consumption and sensing time are high in CSS due to a large number of users. In CSS, the parameters that affect the energy efficiency (EE) of the system are fusion rule, sensing time, transmission power and number of users. In this paper, four fusion rules OR-OR, AND-OR, OR-AND and AND-AND are proposed for cluster-based CSS and optimum fusion rule is determined. It is found that in terms of EE metric, AND-OR rule outperforms the other fusion rules. An iterative algorithm has been proposed which finds the optimum number of CR users in a cluster and total number of clusters that maximises the EE. Quantitatively at SNR = −20 dB, EE is maximum when number of clusters equals to 4 and number of users in the cluster equals to 3 for 12 fixed number of users. The maximum EE is 3.69 Mbits/Hz/joule at sensing time 1.5 ms.
Asymptotic Stability of Combination of Viscous Contact Wave with Rarefaction Waves for 1-D Compressible Navier-Stokes System (Mathematical Analysis in Fluid and Gas Dynamics)

for $x\in \mathbb{R}=(-\infty, +\infty),$ $t>0$ , where $v(x, t)>0,$ $u(x, t),$ $\theta(x, t)>0,$ $e(x, t)>0$ and $p(x, t)$ are the specific volume, fluid velocity, internal energy, absolute temperature, and pressure respectively, while the positive constants $\mu$ and $\kappa$ denote the viscosity and heat conduction coefficients respectively. Here we study $t\}_{1}e$ ideal polytropic fluids so $tIiatp$ and $e$ are given by the state equatioris
Magnetic CuFe2O4 Nanoparticles with Pseudocapacitive Properties for Electrical Energy Storage

This investigation is motivated by increasing interest in the development of magnetically ordered pseudocapacitors (MOPC), which exhibit interesting magnetocapacitive effects. Here, advanced pseudocapacitive properties of magnetic CuFe2O4 nanoparticles in negative potential range are reported, suggesting that CuFe2O4 is a promising MOPC and advanced negative electrode material for supercapacitors. A high capacitance of 2.76 F cm−2 is achieved at a low electrode resistance in a relatively large potential window of 0.8 V. The cyclic voltammograms and galvanostatic charge–discharge data show nearly ideal pseudocapacitive behavior. Good electrochemical performance is achieved at a high active mass loading due to the use of chelating molecules of ammonium salt of purpuric acid (ASPA) as a co-dispersant for CuFe2O4 nanoparticles and conductive multiwalled carbon nanotube (MCNT) additives. The adsorption of ASPA on different materials is linked to structural features of ASPA, which allows for different interaction and adsorption mechanisms. The combination of advanced magnetic and pseudocapacitive properties in a negative potential range in a single MOPC material provides a platform for various effects related to the influence of pseudocapacitive/magnetic properties on magnetic/pseudocapacitive behavior.
Root finding method for problems of elastodynamics

This paper presents a simple and efficient method for finding complex roots of dispersion equations occurring in many problems of elastodynamics. The method is characterized by high accuracy in root finding and absence of restrictions on function representation. The essence of the method is explained geometrically; initial guesses are found as the solutions to the appropriate problems of elastostatics. Numerical solutions to dispersion equations are obtained for two elastic isotropic waveguides: a plate of infinite cross-section and a rod of rectangular cross-section. For an infinite plate, the calculated results are in full conformity with those obtained by Newton-Raphson and bisection methods. For a waveguide of rectangular cross-section, the earlier unsolved problem of finding complex roots of dispersion equations is solved by the proposed method.
Artificial Intelligence and Journalism: Current Situation and Expectations in the Portuguese Sports Media

With an important presence in various sectors of activity, Artificial Intelligence has also reached journalism, mostly in the field of natural-language processing, in the detection of informational trends or in the automatic production of texts. The fact that sport is one of the first to test AI is not a coincidence: it is a subject in which there is a lot of information online and where data is essential, so it is simpler to resort to natural language processing to transform them in texts with little or no human intervention; this work sought to understand if Artificial Intelligence is already used in Portuguese sports media, but also in mainstream media sports sections, whether they are newspapers, radios, TVs or online natives. A survey was sent only to decision-makers, that is, editors and coordinators. The study seeks to understand to what extent journalists expect AI to help journalism, what are the greatest difficulties of its use and what threats it represents. We can conclude that Portuguese sports journalism is aware of the potential of AI, although for now it is not used in newsrooms due to economic and professional constraints.
Secured Optimized Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.
Emergence pattern of horseweed (Erigeron canadensis L.) accessions across Nebraska

Abstract Horseweed is a North American indigenous plant species commonly found in Nebraska cropping systems. Horseweed management is challenging because of horseweed's prolific seed production, long-distance seed dispersal via wind, competitiveness, and rapid evolution of herbicide resistance. Understanding the horseweed emergence pattern across Nebraska can contribute to implementing effective and more sustainable tactics to minimize its impact on cropping systems. Field studies were conducted during fall and spring from 2016 to 2018 in Lincoln (corn and soybean), North Platte (wheat stubble and soybean), and Scottsbluff (corn and fallow) to investigate the emergence pattern of horseweed accessions from Lincoln, North Platte, and Scottsbluff, NE. Results show that most horseweed seedling emergence occurred in fall (99%) and only a few seedlings emerged in spring across locations, except in the wheat stubble experiment at North Platte, where higher spring emergence was detected (3% to 22%). In four out of six experiments, the density of total emerged seedlings of each accession was greatest when established in their site of origin. Our results suggest that late fall and/or early spring is likely the best timing for horseweed management across Nebraska. Nomenclature: horseweed; Erigeron canadensis L. ; corn; Zea mays L. ; soybean; Glycine max (L.) Merr. ; wheat; Triticum aestivum L.
CLASSIFICATION OF VISUALIZATION EXUDATES FUNDUS IMAGES RESULTS USING SUPPORT VECTOR MACHINE

This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes. I mage normalization was performed on 149 fundus images (81 normal and 68 exudates) from MESSIDOR databases to standardize the colours in the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings; linear, polynomial, quadratic and RBF have an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using SVM2 to determine its accuracy. It has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.