Miracle-dz/UniFAR-FTUs huggingface dataset:
Progestin and Nuclear Progestin Receptor Are Essential for Upregulation of Metalloproteinase in Zebrafish Preovulatory Follicles

Ovulation requires proteinases to promote the rupture of ovarian follicles. However, the identity of these proteinases remains unclear. In our previous studies using RNA-seq analysis of differential expressed genes, we found significant down-regulation of five metalloproteinases: adam8b (a disintegrin and metalloproteinase domain 8b), adamts8a (a disintegrin and metalloproteinase with thrombospondin motif 8a), adamts9, mmp2 (matrix metalloproteinase 2), and mmp9 in the nuclear progestin receptor knockout (pgr−/−) zebrafish that have failed to ovulate. We hypothesize that these metalloproteinases are responsible for ovulation and are regulated by progestin and Pgr. In this study, we first determined the expression of these five metalloproteinases and adamts1 in preovulatory follicles at different times within the spawning cycle in pgr−/− and wildtype (wt) zebrafish and under varying hormonal treatments. We found that transcripts of adam8b, adamts1, adamts9, and mmp9 increased drastically in the preovulatory follicular cells of wt female zebrafish, while changes of adamts8a and mmp2 were not significant. This increase of adam8b, adamts9, and mmp9 was significantly reduced in pgr−/−, whereas expression of adamts1 was not affected in pgr−/− zebrafish. Among upregulated metalloproteinases, adamts9 mRNA was found to be expressed specifically in follicular cells. Strong immunostaining of Adamts9 protein was observed in the follicular cells of wt fish, and this expression was reduced drastically in pgr−/−. Interestingly, about an hour prior to the increase of metalloproteinases in wt fish, both Pgr transcript and protein increased transiently in preovulatory follicular cells. The results from in vitro experiments showed that adamts9 expression markedly increased in a dose, time and Pgr-dependent manner when preovulatory follicles were exposed to a progestin, 17α,20β-dihydroxy-4-pregnen-3-one (DHP). Taken together, our results provide the first evidence that upregulation of adamts9 occurs specifically in preovulatory follicular cells of zebrafish prior to ovulation. Progestin and its receptor (Pgr) are essential for the upregulation of metalloproteinases.
Knowledge Sharing in Cooperative Compact Genetic Algorithm

In this study, we present three methods of sharing knowledge between cooperative compact genetic algorithms. The methods exploit the effect of the worse solutions of the two-cooperative compact genetic algorithms to the search space which can prevent premature convergence. The benefit also encourages exploring other areas in solution space which enhance the opportunity to discover the better solutions. The proposed algorithm has a simple structure requires much less execution time than the non-sharing compact genetic algorithm.
LikeNet: A Siamese Motion Estimation Network Trained in an Unsupervised Way

This paper addresses the problem of motion estimation using a Convolutional Neural Network (CNN). We propose a network, called LikeNet, that at test time, receives as input a pair of images and calculates at the output a pixel-wise distribution over motion classes. We adopt a Siamese architecture, each branch of which receives the reference frame and the target frame shifted by a speciﬁc motion vector. Each of the branches calculates at the output a similarity map that at each pixel it could be interpreted as the probability that the image features in the shifted images match, or equivalently, the probability that the motion vector at a pixel in question is equal to the shift by which the target frame was displaced. The individual similarity maps are merged and further processed by RNNs that implement Conditional Random Fields and impose therefore anisotropic smoothing constraints. The proposed network is trained in an unsupervised manner, that is without the need for ground truth motion ﬁelds from synthetic sequences, by optimising at training time the deviation from a feature constancy constraint – this allows for training with unlimited training data. Our method is tested on both synthetic and real image sequences and performs similarly with other state-of-the-art unsupervised methods using a fraction of the number of learned parameters.
Chinese Black Truffle (Tuber indicum) Alters the Ectomycorrhizosphere and Endoectomycosphere Microbiome and Metabolic Profiles of the Host Tree Quercus aliena

Truffles are one group of the most famous ectomycorrhizal fungi in the world. There is little information on the ecological mechanisms of truffle ectomycorrhizal synthesis in vitro. In this study, we investigated the ecological effects of Tuber indicum – Quercus aliena ectomycorrhizal synthesis on microbial communities in the host plant roots and the surrounding soil using high-throughput sequencing and on the metabolic profiles of host plant roots using metabolomics approaches. We observed an increase in the diversity and richness of prokaryotic communities and a decrease in richness of fungal communities in the presence of T. indicum. The microbial community structures in the host roots and the surrounding soil were altered by ectomycorrhizal synthesis in the greenhouse. Bacterial genera Pedomicrobium, Variibacter, and Woodsholea and fungal genera Aspergillus, Phaeoacremonium, and Pochonia were significantly more abundant in ectomycorhizae and the ectomycorrhizosphere soil compared with the corresponding T. indicum-free controls (P < 0.05). Truffle-colonization reduced the abundance of some fungal genera surrounding the host tree, such as Acremonium, Aspergillus, and Penicillium. Putative prokaryotic metabolic functions and fungal functional groups (guilds) were also differentiated by ectomycorrhizal synthesis. The ectomycorrhizal synthesis had great impact on the measured soil physicochemical properties. Metabolic profiling analysis uncovered 55 named differentially abundant metabolites between the ectomycorhizae and the control roots, including 44 upregulated and 11 downregulated metabolites. Organic acids and carbohydrates were two major upregulated metabolites in ectomycorhizae, which were found formed dense interactions with other metabolites, suggesting their crucial roles in sustaining the metabolic functions in the truffle ectomycorrhization system. This study revealed the effects of truffle-colonization on the metabolites of ectomycorrhiza and illustrates an interactive network between truffles, the host plant, soil and associated microbial communities, shedding light on understanding the ecological effects of truffles.
On a Novel Deep-Learning-Based Intelligent Partially Overlapping Channel Assignment in SDN-IoT

Recently, SDN has emerged as a promising technology to cost-effectively provide the scale and flexibility necessary for IoT services. In this article, we consider the wireless SDN for IoT, referred to as SDN-IoT, which is anticipated to smartly route traffic and use underutilized network resources to deliver IoT data to the cloud/ Internet. However, the rapid increase of IoT devices and the subsequent massive surge of the IoT data traffic are expected to place a huge strain on the SDN-IoT. In this article, we focus on this issue and point out the importance of assigning suitable channels to each SDN-IoT switch to avoid potential network congestion. In particular, we consider how to exploit POC assignment in the SDN-IoT. However, our investigation reveals that the conventional fixed POC assignment algorithms are not viable for the highly dynamic large-scale SDN-IoT. Therefore, in this article, we propose a novel deep-learning-based intelligent POC assignment for the wireless SDN-IoT where the IoT data traffic dynamically changes. In particular, we envision two deep-learning-based strategies to predict the future IoT traffic load and to adaptively assign POCs according to predicted traffic load, respectively. Computer-based simulation results demonstrate that with the envisioned deep learning methods carried out at the SDN-IoT controller, our proposal achieves high accuracy of traffic load prediction and quick convergence of the channel assignment process. Additionally, in contrast with the conventional POC assignment algorithms, our proposal significantly improves the network performance.
The efficacy and safety of memantine for the treatment of Alzheimer’s disease

ABSTRACT Introduction: Currently, five pharmacotherapeutic options are available to treat Alzheimer’s disease: memantine; the three cholinesterase inhibitors donepezil, galantamine, and rivastigmine; and combination treatments with memantine and one cholinesterase inhibitor. Selection of the best course of treatment is based upon the evidence gathered by systematic reviews and meta-analyses of randomized controlled trials. Areas covered: This article provides a risk–benefit analysis of these treatments using evidence from meta-analyses on their safety and their efficacy. Expert opinion: Memantine improves cognitive functions and behavioral disturbances more efficiently than the placebo, both as monotherapy and in combination with donepezil. Although memantine monotherapy and combination therapy are associated with a few individual adverse events such as somnolence, it is well-tolerated and its safety (all-cause discontinuation) is comparable or superior to that of the placebo (agitation). Pooled cholinesterase inhibitors are superior to the placebo in the improvement of cognitive functions, but not behavioral disturbances and they are not well-tolerated, as evaluated by the high discontinuation rate. Donepezil (10 mg/day) and oral rivastigmine and galantamine monotherapies carry the risk for some adverse events including gastrointestinal symptoms. Therefore, we consider that combined treatment with memantine and donepezil is the most useful treatment for Alzheimer’s disease.
Digit Elision for Arbitrary-accuracy Iterative Computation

We recently proposed the first hardware architecture enabling the iterative solution of systems of linear equations to accuracies limited only by the amount of available memory. This technique, named ARCHITECT, achieves exact numeric computation by using online arithmetic to allow the refinement of results from earlier iterations over time, eschewing rounding error. ARCHITECT has a key drawback, however: often, many more digits than strictly necessary are generated, with this problem exacerbating the more accurate a solution is sought. In this paper, we infer the locations of these superfluous digits within stationary iterative calculations by exploiting online arithmetic's digit dependencies and using forward error analysis. We demonstrate that their lack of computation is guaranteed not to affect the ability to reach a solution of any accuracy. Versus ARCHITECT, our illustrative hardware implementation achieves a geometric mean 20.1× speedup in the solution of a set of representative linear systems through the avoidance of redundant digit calculation. For the computation of high-precision results, we also obtain an up-to 22.4\times× memory requirement reduction over the same baseline. Finally, we demonstrate that solvers implemented following our proposals can show superiority over conventional arithmetic implementations by virtue of their runtime-tunable precisions.
Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization

We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image. Instead of relying on hand-crafted image priors or explicitly estimating the components of the widely used atmospheric scattering model, our end-to-end system directly generates the clear image from an input hazy image. The proposed network has an encoder-decoder architecture with skip connections and instance normalization. We adopt the convolutional layers of the pre-trained VGG network as encoder to exploit the representation power of deep features, and demonstrate the effectiveness of instance normalization for image dehazing. Our simple yet effective network outperforms the state-of-the-art methods by a large margin on the benchmark datasets.
A Cross-Layer Hybrid Strategy for Congestion Avoidance and Link Quality Routing in Wireless Multi-Hop Networks

The IEEE 802.11s standard was developed for wireless mesh networks. In this standard, the hybrid wireless mesh protocol (HWMP) is the default routing protocol for route selection based on the airtime metric as a measure of link quality for routing. We investigate the performance of HWMP in networks with different load levels and different link conditions. Two cross-layer optimization strategies based on the principle of joint scheduling and routing were developed and their performance compared to HWMP. One technique was a cost function technique that uses weights for the node's load and for the link quality. The second was a probabilistic based selection (PBS) method in which individual nodes in the network perform selection of neighbouring nodes with either better link quality or less load level before routing is performed. We implemented the techniques in OMNeT++ and used the INETMANET framework which has the default HWMP routing protocol implementation. Simulation results showed that for most cases, both techniques improve performance in terms of packet delivery ratio and less end-to-end latency with the PBS technique having better performance in most cases. This work aims to optimise performance by choosing better quality links for routing of data.
Beliefs about Binge Eating: Psychometric Properties of the Eating Beliefs Questionnaire (EBQ-18) in Eating Disorder, Obese, and Community Samples

Binge eating is a core diagnostic feature of bulimia nervosa, binge eating disorder, anorexia nervosa binge/purge type, and is a common feature of “other specified” and “unspecified” feeding and eating disorders. It has been suggested that specific metacognitive beliefs about food, eating, and binge eating may play a key role in the maintenance of binge eating behaviour. The Eating Beliefs Questionnaire (EBQ-18) provides a brief self-report assessment tool measuring three types of metacognitive beliefs: negative, positive, and permissive beliefs about food and eating. This study aimed to build on past research by validating the factor structure and psychometric properties of the EBQ-18 using both a clinical and non-clinical sample. A sample of 688 participants (n = 498 non-clinical participants, n = 161 participants seeking treatment for an eating disorder, and n = 29 participants seeking treatment for obesity) completed a battery of questionnaires, including the EBQ-18 and other measures of eating disorder symptoms and relevant constructs. A subset of 100 non-clinical participants completed the test battery again after an interval of two-weeks, and 38 clinical participants completed the EBQ-18 before and after receiving psychological treatment for their eating disorder. A confirmatory factor analysis (CFA) was conducted and psychometric properties of this measure were assessed. The results of this study provide support for the three-factor model of the EBQ-18. In addition, the EBQ-18 was found to be a valid and reliable measure, with excellent internal consistency, good test-retest reliability in the non-clinical sample, and also demonstrated evidence of sensitivity to treatment in clinical samples with binge eating pathology. Receiver operating characteristic (ROC) curve analyses were used to identify optimal cut-off scores for the EBQ-18. This study provides valuable information about the utility of the EBQ-18 as a measure for use in both clinical and research settings.
An Opportunity Transmission Mechanism in Mobile Crowd Sensing Network based on SSIS Model

Mobile Crowd Sensing Network (MCSN) is a new sensing mode in the Internet of Things by taking advantage of individual's mobile device with multiple sensors to collect some specific data for certain applications. There are two modes of transmission mechanism in the existing systems: one is end-to-end mode via the cellular network and the other is opportunistic transmission via short-range wireless communication technology. Due to the higher cost, the former is not conducive to enlarging users'participation. This paper focuses on the mode of the latter to build a new opportunistic transmission in MCSN. Differently from most existing studies, i.e. preference-aware and energy-aware, this paper proposes an opportunistic data transmission mechanism based on SSIS model (a socialization SIS epidemic model), who can transfer the sensing data to the platform by forwarding step by step without extra cost. Specifically, firstly, SSIS model is defined based on SIS epidemic model by social information in MCSN. Additionally, SSIS model is used to analyze the social relationship of mobile sensing nodes in MCSN to obtain a social relational table. Finally, the social relational table is used to improve the Spray and Wait (SW), which is a typical opportunistic transmission mechanism, to guide the source node which performs the task of sensing to propagate the sensing data selectively to other nodes until it reaches the destination node which can send the data to the platform. Simulation results show that the performance of data transmission can be further improved by using the proposed mechanism in comparison with SW, Epidemic and Prophet.
Academic Social Network Scholars Recommendation Model Based on Community Division

In order to solve the problem of subset community in some community detection algorithms which is based on label propagation, this paper proposes a method that merges the core network in advance, and constructs a scholars recommendation model for academic social network. The model processes the problem of subset community through the GraphChi framework in parallel, and recommends the scholar users within the community according to the relevant recommendation rules to achieve the scholars' recommendation service. Experimental results show that the proposed model effectively reduces the running time of community detection algorithm and achieves good recommendation results.
Robust Principal Component Analysis with Matrix Factorization

Traditional robust principle component analysis (RPCA) has a high computational cost because RPCA needs to calculate the singular value decomposition of large matrices. To address this issue, this paper proposes a matrix-factorization-based RPCA (MFRPCA) model. MFRPCA has high computation efficiency while improving the robustness and flexibility of traditional RPCA using a non-convex low-rank approximation. Experiment results on challenging datasets demonstrate superior performance of MFRPCA compared with several advanced low-rank reconstruction methods.
Frame-Subsampled, Drift-Resilient Video Object Tracking

Performance-cost trade-offs in video object tracking tasks for long video sequences is investigated. A novel frame-subsampled, drift-resilient (FSDR) video object tracking algorithm is presented that would achieve desired tracking accuracy while dramatically reducing computing time by processing only sub-sampled video frames. A new pattern matching score metric is proposed to estimate the probability of drifting. A drift-recovery procedure is developed to enable the algorithm to recover from a drift situation and resume accurate tracking. Compared against state-of-the-art video object tracking algorithms, dramatic performance (accuracy) enhancement and cost (computing time) reduction are observed.
Vision-based Autonomous Landing in Catastrophe-Struck Environments

Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natural disasters such as earthquakes or gas explosions. However, these UAVs have to be able to autonomously land on debris piles in order to accurately locate the survivors. This problem is extremely challenging as the structure of these debris piles is often unknown and no prior knowledge can be leveraged. In this work, we propose a computationally efficient system that is able to reliably identify safe landing sites and autonomously perform the landing maneuver. Specifically, our algorithm computes costmaps based on several hazard factors including terrain flatness, steepness, depth accuracy and energy consumption information. We first estimate dense candidate landing sites from the resulting costmap and then employ clustering to group neighboring sites into a safe landing region. Finally, a minimum-jerk trajectory is computed for landing considering the surrounding obstacles and the UAV dynamics. We demonstrate the efficacy of our system using experiments from a city scale hyperrealistic simulation environment and in real-world scenarios with collapsed buildings.
Hypoplastic left heart syndrome: from fetus to fontan

Abstract The care of children with hypoplastic left heart syndrome is constantly evolving. Prenatal diagnosis of hypoplastic left heart syndrome will aid in counselling of parents, and selected fetuses may be candidates for in utero intervention. Following birth, palliation can be undertaken through staged operations: Norwood (or hybrid) in the 1st week of life, superior cavopulmonary connection at 4–6 months of life, and finally total cavopulmonary connection (Fontan) at 2–4 years of age. Children with hypoplastic left heart syndrome are at risk of circulatory failure their entire life, and selected patients may undergo heart transplantation. In this review article, we summarise recent advances in the critical care management of patients with hypoplastic left heart syndrome as were discussed in a focused session at the 12th International Conference of the Paediatric Cardiac Intensive Care Society held on 9 December, 2016, in Miami Beach, Florida.
Worsening of diabetic retinopathy with rapid improvement in systemic glucose control: A review

Worsening of diabetic retinopathy (DR) is associated with the initiation of effective treatment of glycaemia in some patients with diabetes. It has been associated with risk factors such as poor blood‐glucose control and hypertension, and it manifests prior to the long‐term benefits of optimizing glycaemic control. The majority of evidence supports an association of large and rapid reductions in blood‐glucose levels with early worsening of DR. Despite a general awareness of early worsening within the diabetes community, mechanisms to explain the phenomenon remain speculative. We provide an overview of early worsening of DR and its pathophysiology based on current data. We describe the phenomenon in various settings, including in patients receiving insulin‐ or non‐insulin‐based treatments, in those undergoing bariatric surgery, and in pregnant women. We discuss various mechanisms and theories that have been suggested to explain this paradoxical phenomenon, and we summarize the implications of these in clinical practice.
Rfcm for Data Association and Multitarget Tracking Using 3D Radar

Performance of object classification using 3D automotive radar relies on accurate data association and multitarget tracking' which are greatly affected by data bias and proximity of objects to each other. A regularized fuzzy c-means (RFCM) algorithm is proposed herein to resolve the data association uncertainty problem that has shown to outperform the conventional FCM algorithm. The proposed method exploits results from the companion tracker to increase performance robustness. Simulation results using simulated and field data have proven the efficacy of the proposed method.