Miracle-dz/UniFAR-FTUs huggingface dataset:
Sonic Hedgehog Signaling Pathway Mediates Proliferation and Migration of Fibroblast-Like Synoviocytes in Rheumatoid Arthritis via MAPK/ERK Signaling Pathway

Fibroblast-like synoviocytes (FLSs) are the major effector cells that lead to rheumatoid arthritis (RA) synovitis and joint destruction. Our previous studies showed that Sonic Hedgehog (SHH) signaling pathway is involved in aberrant activation of RA-FLSs and inhibition of SHH pathway decreases proliferation and migration of RA-FLSs. The objective of this study was to investigate if the SHH pathway mediates proliferation and migration of RA-FLSs via the mitogen-activated protein kinases/extracellular signal-regulated kinases (MAPK/ERK) signaling pathway. SHH signaling was studied by using SHH agonist (Purmorphamine) and antagonist (Cyclopamine) targeting the Smoothened (SMO) in FLSs. U0126-EtOH was used to inhibit the MAPK/ERK signaling pathway. The phosphorylation of ERK 1/2 (p-ERKl/2) was examined by western blot. Cell viability was detected using cell proliferation and cytotoxicity kit-8 (CCK8), and cell cycle distribution and proliferating cells were evaluated by the flow cytometry. Cell migration was examined by Transwell assay. Results showed that, compared with the control group, Purmorphamine increased the levels of p-ERK1/2 in concentration-and time-dependent manners (P < 0.01). Co-treated with Purmorphamine and U0126-EtOH or Cyclopamine both decreased the levels of p-ERK1/2 (P < 0.05). RA-FLSs treated with Purmorphamine resulted in alteration of cell cycle distribution, increasing of proliferating cells, cell viability, and migration cells compared to controls (P < 0.01). However, the above phenomenon can be abolished by U0126-EtOH (P < 0.05). The findings suggest that SHH signaling pathway mediates proliferation and migration of RA-FLSs via MAPK/ERK pathway and may contribute to progression of RA. Targeting SHH signaling may have a therapeutic potential in patients with RA.
A Two-Stream Variational Adversarial Network for Video Generation

Video generation is an inherently challenging task, as it requires the model to generate realistic content and motion simultaneously. Existing methods generate both motion and content together using a single generator network, but this approach may fail on complex videos. In this paper, we propose a two-stream video generation model that separates content and motion generation into two parallel generators, called Two-Stream Variational Adversarial Network (TwoStreamVAN). Our model outputs a realistic video given an input action label by progressively generating and fusing motion and content features at multiple scales using adaptive motion kernels. In addition, to better evaluate video generation models, we design a new synthetic human action dataset to bridge the difficulty gap between over-complicated human action datasets and simple toy datasets. Our model significantly outperforms existing methods on the standard Weizmann Human Action and MUG Facial Expression datasets, as well as our new dataset.
A Study of the Advances in IoT Security

The Internet-of-things (IoT) holds a lot of benefits to our lives by removing menial tasks and improving efficiency of everyday objects. You are trusting your personal data and device control to the manufactures and you may not be aware of how much risk your putting your privacy at by sending your data over the internet. The internet-of-things may not be as secure as you think when the devices used are constrained by a lot of variables which attackers can exploit to gain access to your data / device and anything they connected to and as the internet-of-things is all about connecting devices together one weak point can be all it takes to gain full access. In this paper we have a look at the current advances in IoT security and the most efficient methods to protect IoT devices.
Prevalence and Factors Associated with Teenage Pregnancy, Northeast Ethiopia, 2017: A Cross-Sectional Study

Introduction Though teen age pregnancy had poor maternal and perinatal health outcomes, its magnitude and determinants are not well understood. Therefore, the aim of this study was to assess the prevalence and associated factors of teenage pregnancy in Wogedi, northeast Ethiopia. Methods A community-based cross-sectional study was conducted among 514 teenagers in Wogedi, northeast Ethiopia, from April to May 2017. Data were collected using a structured questionnaire, entered, and analyzed appropriately. Odds ratios with 95% confidence interval and P-values were computed using appropriate logistic regression models to determine the presence and strength of associations between the dependent and independent variables. Results The prevalence of teenage pregnancy in Wogedi was 28.6% (95% CI: 24.9, 32.5). Age (AOR=2.10; 95% CI: 1.55, 2.88), rural residence (AOR=3.93; 95% CI: 1.20, 12.83), contraceptive nonuse (AOR=10.62; 95% CI: 5.28, 21.36), and parental marital status (divorce) (AOR=1.98; 95%CI: 1.13, 3.93) were found to have statistically significant associations with teenage pregnancy. Conclusions There is high prevalence of teenage pregnancy in the area. Age, residence, contraceptive nonuse, and parental divorce were found to have a statistically significant association. Strengthening contraceptive use by giving special attention to rural dwellers and showing the consequences of divorce to the community are strongly recommended.
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network

We explore a solution for learning disease signatures from weakly, yet easily obtainable, annotated volumetric medical imaging data by analyzing 3D volumes as a sequence of 2D images. We demonstrate the performance of our solution in the detection of emphysema in lung cancer screening low-dose CT images. Our approach utilizes convolutional long short-term memory (LSTM) to "scan" sequentially through an imaging volume for the presence of disease in a portion of scanned region. This framework allowed effective learning given only volumetric images and binary disease labels, thus enabling training from a large dataset of 6,631 un-annotated image volumes from 4,486 patients. When evaluated in a testing set of 2,163 volumes from 2,163 patients, our model distinguished emphysema with area under the receiver operating characteristic curve (AUC) of . 83. This approach was found to outperform 2D convolutional neural networks (CNN) implemented with various multiple-instance learning schemes (AUC=0.69-0.76) and a 3D CNN (AUC=.77).
Musculoskeletal pain and symptoms in pregnancy: a descriptive study

Background: Pregnancy-induced hormonal and physiologic changes increase the risk of musculoskeletal problems in pregnancy. The purpose of this report is to provide a comprehensive look at the musculoskeletal pain and symptoms experienced during pregnancy. Methods: A total of 184 women (mean age 30.9 ± 5.0 years) who gave birth in the obstetrics clinic of a tertiary hospital were included in the study. The participants who had given birth at 37–42 weeks of pregnancy (term pregnancy) and aged over 18 years were selected for participation. Basic demographic and clinical characteristics of the participants including age, body mass index, weight gained during pregnancy, education level, occupation, parity, sex of baby, and exercise habits were collected from the medical chart and face-to-face interviews. Musculoskeletal pain sites were defined as hand–wrist, elbow, shoulder, neck, back, low back, hip, knee, and ankle–foot in a diagram of the human body. The interviews with participants were performed to assess their musculoskeletal pain separately at each trimester follow-up visit. Results: The most frequent musculoskeletal complaints during pregnancy were low back pain (n = 130, 70.7%), back pain (n = 80, 43.5%), hand–wrist (n = 61, 33.2%) and hip pain (n = 59, 32.1%). The participants experienced musculoskeletal pain most in the third trimester except for elbow, shoulder and neck pain compared with the first and second trimesters (p < 0.05). Conclusions: The results of the study suggest that numerous musculoskeletal problems may complicate pregnancy especially in the third trimester.
Expertise Judgment and Communication Accommodation in Linguistic Styles in Computer-Mediated and Face-to-Face Groups

Taking a communication approach to expertise, this study examined emergence of expertise through communication accommodation (CA) in 46 four-person face-to-face (FtF) and text-based computer-mediated communication (CMC) groups. We found that FtF members with a lower pre-discussion task confidence accommodated to the linguistic styles of those with a higher pre-discussion task confidence. Meanwhile, CA influenced post-discussion expertise judgment in CMC groups such that members who accommodated to others were judged as having less expertise. CA also influenced post-discussion expertise judgment in FtF groups after taking into account perceived task-oriented communication and perceived influence. The findings emphasize the important roles of CA and of communication channel in expertise emergence.
Exploring Interaction Patterns in Job Search

We analyze interaction logs from Seek.com, a well-known Australasian employment site, with the goal of better understanding the ways in which users pursue their search goals following the issue of each query. Of particular interest are the patterns of job summary viewing and click-through behaviors that arise, and the differences in activity between mobile/tablet-based users (Android/iOS) and computer-based users.
Breakdown of the Stokes-Einstein relation above the melting temperature in a liquid phase-change material

Failure of classic equation linking diffusion and viscosity points to new thinking on switch kinetics of phase-change materials. The dynamic properties of liquid phase-change materials (PCMs), such as viscosity η and the atomic self-diffusion coefficient D, play an essential role in the ultrafast phase switching behavior of novel nonvolatile phase-change memory applications. To connect η to D, the Stokes-Einstein relation (SER) is commonly assumed to be valid at high temperatures near or above the melting temperature Tm and is often used for assessing liquid fragility (or crystal growth velocity) of technologically important PCMs. However, using quasi-elastic neutron scattering, we provide experimental evidence for a breakdown of the SER even at temperatures above Tm in the high–atomic mobility state of a PCM, Ge1Sb2Te4. This implies that although viscosity may have strongly increased during cooling, diffusivity can remain high owing to early decoupling, being a favorable feature for the fast phase switching behavior of the high-fluidity PCM. We discuss the origin of the observation and propose the possible connection to a metal-semiconductor and fragile-strong transition hidden below Tm.
A Spectral Regularizer for Unsupervised Disentanglement

A generative model with a disentangled representation allows for independent control over different aspects of the output. Learning disentangled representations has been a recent topic of great interest, but it remains poorly understood. We show that even for GANs that do not possess disentangled representations, one can find curved trajectories in latent space over which local disentanglement occurs. These trajectories are found by iteratively following the leading right-singular vectors of the Jacobian of the generator with respect to its input. Based on this insight, we describe an efficient regularizer that aligns these vectors with the coordinate axes, and show that it can be used to induce disentangled representations in GANs, in a completely unsupervised manner.
Detect-To-Retrieve: Efficient Regional Aggregation for Image Search

Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the index that focuses on the most relevant regions. However, due to the lack of bounding-box datasets for objects of interest among retrieval benchmarks, most recent work on regional representations has focused on either uniform or class-agnostic region selection. In this paper, we first fill the void by providing a new dataset of landmark bounding boxes, based on the Google Landmarks dataset, that includes 94k images with manually curated boxes from 15k unique landmarks. Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods. In addition, we introduce a novel regional aggregated selective match kernel (R-ASMK) to effectively combine information from detected regions into an improved holistic image representation. R-ASMK boosts image retrieval accuracy substantially with no dimensionality increase, while even outperforming systems that index image regions independently. Our complete image retrieval system improves upon the previous state-of-the-art by significant margins on the Revisited Oxford and Paris datasets. Code and data will be released.
Evaluation of in vitro anti-oxidant and anti-inflammatory activities of Korean and Chinese Lonicera caerulea

BACKGROUND/OBJECTIVES The honeysuckle berry (HB) contains ascorbic acid and phenolic components, especially anthocyanins, flavonoids, and low-molecular-weight phenolic acids. In order to examine the potential of HB as a hepatoprotective medicinal food, we evaluated the in vitro anti-oxidant and anti-inflammatory activities of Korean HB (HBK) and Chinese HB (HBC). MATERIALS/METHODS Antioxidant and anti-inflammatory effects of the extracts were examined in HepG2 and RAW 264.7 cells, respectively. The anti-oxidant capacity was determined by DPPH, SOD, CAT, and ARE luciferase activities. The production of nitric oxide (NO) as an inflammatory marker was also evaluated. The Nrf2-mediated mRNA levels of heme oxygenase-1 (HO-1), NAD(P)H dehydrogenase [quinone] 1 (Nqo1), and glutamate-cysteine ligase catalytic subunit (Gclc) were measured. The concentrations of HB extracts used were 3, 10, 30, 100, and 300 µg/mL. RESULTS The radical scavenging activity of all HB extracts increased in a concentration-dependent manner (P < 0.01 or P < 0.05). SOD (P < 0.05) and CAT (P < 0.01) activities were increased by treatment with 300 µg/mL of each HB extract, when compared to those in the control. NO production was observed in cells pretreated with 100 or 300 µg/mL of HBC and HBK (P < 0.01). Treatment with 300 µg/mL of HBC significantly increased Nqo1 (P < 0.01) and Gclc (P < 0.05) mRNA levels compared to those in the control. Treatment with 300 µg/mL of HBK (P < 0.05) and HBC (P < 0.01) also significantly increased the HO-1 mRNA level compared to that in the control. CONCLUSIONS Thus, the Korean and Chinese HBs were found to possess favorable in vitro anti-oxidant and anti-inflammatory activities. Nrf2 and its related anti-oxidant genes were associated with both anti-oxidant and anti-inflammatory activities in HB-treated cells. Further studies are needed to confirm these in vivo effects.
Control of Automated Guided Vehicles Without Collision by Quantum Annealer and Digital Devices

We formulate an optimization problem to control a large number of automated guided vehicles in a plant without collision. The formulation consists of binary variables. A quadratic cost function over these variables enables us to utilize certain solvers on digital computers and recently developed purpose-specific hardware such as D-Wave 2000Q and the Fujitsu digital annealer. In the present study, we consider an actual plant in Japan, in which vehicles run, and assess efficiency of our formulation for optimizing the vehicles via several solvers. We confirm that our formulation can be a powerful approach for performing smooth control while avoiding collisions between vehicles, as compared to a conventional method. In addition, comparative experiments performed using several solvers reveal that D-Wave 2000Q can be useful as a rapid solver for generating a plan for controlling the vehicles in a short time although it deals only with a small number of vehicles, while a digital computer can rapidly solve the corresponding optimization problem even with a large number of binary variables.
Analysis of Epicutaneous Patch Test Results in Patients with Contact Dermatitis

Background: Allergic contact dermatitis (ACD) is a delayed type of hypersensitivity from contact with a specific allergen to which the patients has developed a specific sensitivity. The aim of the study was to evaluate the results of epicutaneous patch testing with standard series of contact allergen in patients suspected to have ACD. Methods: 355 cases of ACD were included in the study. Test substances were applied on the upper part of the patient’s back, on clinically uninvolved and untreated skin. All patients were free from therapy with oral antihistamines, steroids and immunosuppressants. The patch test was removed and reaction were evaluated after 48 h and 72 h. Grading of negative (-) to positive (+ to ++++) patch test was done according to the International Contact Dermatitis Research Group. Statistical data analysis was performed by using χ2–test. Results: Of the 355 cases, 146 patients were male (41.1%) and 209 were female (58.9%). The youngest patients in the study was 16 years of age and the oldest was 67 years of age. The commonest age group affected was 41-50 years. Hands were the most common site of involvement. The occupational character of skin lesions was find in 75 (21.1%). The most common positive reactions were recorded to nickel sulphate 99 (27.8%), cobalt chloride 46 (12.9%), thimerosal 31 (8.7%), colophony 23 (6.5%), carba mix 21 (5.9%), potassium dichromate 20 (5.6%), acid chromici 19 (5.3%), fragrance mix 18 (5%), balsam of Peru 13 (3.7%), formaldehyde 9 (2.5%), and other allergens 26 (7.3%). Females were significantly more likely to show a positive response to two or more allergens (p<0.05). There was no statistically significant impact of age, occupation and duration of disease on results of patch testing (p>0.05). Conclusions: Our results indicate that nickel sulphate, cobalt chloride and thimerosal are the most common allergens responsible for induction of ACD. These findings are crucial in the treatment, long term management, an education of patients with ACD.
Disentangling Latent Hands for Image Synthesis and Pose Estimation

Hand image synthesis and pose estimation from RGB images are both highly challenging tasks due to the large discrepancy between factors of variation ranging from image background content to camera viewpoint. To better analyze these factors of variation, we propose the use of disentangled representations and a disentangled variational autoencoder (dVAE) that allows for specific sampling and inference of these factors. The derived objective from the variational lower bound as well as the proposed training strategy are highly flexible, allowing us to handle crossmodal encoders and decoders as well as semi-supervised learning scenarios. Experiments show that our dVAE can synthesize highly realistic images of the hand specifiable by both pose and image background content and also estimate 3D hand poses from RGB images with accuracy competitive with state-of-the-art on two public benchmarks.
Leaders’ Gender, Perceived Abusive Supervision and Health

Purpose: We investigated the role of gender in abusive leadership practices, along with the effects of abusive leadership on employee health. We tested two hypotheses regarding the relationship between abusive leadership practices and subordinates’ health outcomes. Design: At two points of measurement, 663 participants in Germany rated their 158 direct team leaders on abusive supervision and stated their own levels of emotional exhaustion and somatic stress. To test our hypotheses, we used a mixed model approach. Findings: The results show no gender differences between the ratings for female and male leaders regarding abusive supervision but do confirm that the leaders’ gender did play a role in employees’ perceptions of abuse; perceived abusive supervision is more strongly related to increased emotional exhaustion and somatic stress when the leader is male. Limitations: The generalizability of the study is limited due to a majority of females in the sample. Practical Implications: Organizations should review their policies and procedures to first identify abusive supervision, then to offer adequate support programs for both leaders and subordinates. Originality/Value: The study integrates gender into research on leadership and health, shifting the focus from previous studies that investigated constructive to destructive leadership. A further strength of the study is the application of a multilevel design and two separate points of measurement.
Estimating 6D Pose From Localizing Designated Surface Keypoints

In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image. The core of our approach is that we first designate a set of surface points on target object model as keypoints and then train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can recover the 6D pose according to the 2D-3D relationship of keypoints. Different from recent state-of-the-art CNN-based approaches that rely on a time-consuming post-processing procedure, our method can achieve competitive accuracy without any refinement after pose prediction. Meanwhile, we obtain a 30% relative improvement in terms of ADD accuracy among methods without using refinement. Moreover, we succeed in handling heavy occlusion by selecting the most confident keypoints to recover the 6D pose. For the sake of reproducibility, we will make our code and models publicly available soon.
The BMP2/4 ortholog Dpp can function as an inter-organ signal that regulates developmental timing

Increased local trapping of morphogens within tissues as they grow would reduce circulating levels and can therefore provide a systemic readout of the status of their growth and maturation. Developmental transitions are often triggered by a neuroendocrine axis and can be contingent upon multiple organs achieving sufficient growth and maturation. How the neurodendocrine axis senses the size and maturity of peripheral organs is not known. In Drosophila larvae, metamorphosis is triggered by a sharp increase in the level of the steroid hormone ecdysone, secreted by the prothoracic gland (PG). Here, we show that the BMP2/4 ortholog Dpp can function as a systemic signal to regulate developmental timing. Dpp from peripheral tissues, mostly imaginal discs, can reach the PG and inhibit ecdysone biosynthesis. As the discs grow, reduced Dpp signaling in the PG is observed, consistent with the possibility that Dpp functions in a checkpoint mechanism that prevents metamorphosis when growth is insufficient. Indeed, upon starvation early in the third larval instar, reducing Dpp signaling in the PG abrogates the critical-weight checkpoint which normally prevents pupariation under these conditions. We suggest that increased local trapping of morphogen within tissues as they grow would reduce circulating levels and hence provide a systemic readout of their growth status.
Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative data.