Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse intervertebral fence composition for 3D cervical vertebra segmentation

Statistical shape models are capable of extracting shape prior information, and are usually utilized to assist the task of segmentation of medical images. However, such models require large training datasets in the case of multi-object structures, and it also is difficult to achieve satisfactory results for complex shapes. This study proposed a novel statistical model for cervical vertebra segmentation, called sparse intervertebral fence composition (SiFC), which can reconstruct the boundary between adjacent vertebrae by modeling intervertebral fences. The complex shape of the cervical spine is replaced by a simple intervertebral fence, which considerably reduces the difficulty of cervical segmentation. The final segmentation results are obtained by using a 3D active contour deformation model without shape constraint, which substantially enhances the recognition capability of the proposed method for objects with complex shapes. The proposed segmentation framework is tested on a dataset with CT images from 20 patients. A quantitative comparison against corresponding reference vertebral segmentation yields an overall mean absolute surface distance of 0.70 mm and a dice similarity index of 95.47% for cervical vertebral segmentation. The experimental results show that the SiFC method achieves competitive cervical vertebral segmentation performances, and completely eliminates inter-process overlap.
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning task as the problem of sequentially testing how the minimum mean among a finite set of distributions compares to a given threshold. We develop refined non-asymptotic lower bounds, which show that optimality mandates very different sampling behavior for a low vs high true minimum. We show that Thompson Sampling and the intuitive Lower Confidence Bounds policy each nail only one of these cases. We develop a novel approach that we call Murphy Sampling. Even though it entertains exclusively low true minima, we prove that MS is optimal for both possibilities. We then design advanced self-normalized deviation inequalities, fueling more aggressive stopping rules. We complement our theoretical guarantees by experiments showing that MS works best in practice.
Modeling Realistic Degradations in Non-Blind Deconvolution

Most image deblurring methods assume an over-simplistic image formation model and as a result are sensitive to more realistic image degradations. We propose a novel variational framework, that explicitly handles pixel saturation, noise, quantization, as well as non-linear camera response function due to e.g., gamma correction. We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR. Furthermore, we show that incorporating the nonlinear response in both the data and the regularization terms of the proposed energy leads to a more detailed restoration than a naive inversion of the non-linear curve. The minimization of the proposed energy is performed using stochastic optimization. A dataset consisting of realistically degraded images is created in order to evaluate the method.
Biomimetic Layer-by-Layer Self-Assembly of Nanofilms, Nanocoatings, and 3D Scaffolds for Tissue Engineering

Achieving surface design and control of biomaterial scaffolds with nanometer- or micrometer-scaled functional films is critical to mimic the unique features of native extracellular matrices, which has significant technological implications for tissue engineering including cell-seeded scaffolds, microbioreactors, cell assembly, tissue regeneration, etc. Compared with other techniques available for surface design, layer-by-layer (LbL) self-assembly technology has attracted extensive attention because of its integrated features of simplicity, versatility, and nanoscale control. Here we present a brief overview of current state-of-the-art research related to the LbL self-assembly technique and its assembled biomaterials as scaffolds for tissue engineering. An overview of the LbL self-assembly technique, with a focus on issues associated with distinct routes and driving forces of self-assembly, is described briefly. Then, we highlight the controllable fabrication, properties, and applications of LbL self-assembly biomaterials in the forms of multilayer nanofilms, scaffold nanocoatings, and three-dimensional scaffolds to systematically demonstrate advances in LbL self-assembly in the field of tissue engineering. LbL self-assembly not only provides advances for molecular deposition but also opens avenues for the design and development of innovative biomaterials for tissue engineering.
Applying fecal microbiota transplantation (FMT) to treat recurrent Clostridium difficile infections (rCDI) in children

Background Fecal Microbiota Transplantation (FMT) is an innovative means of treating recurrent Clostridium difficile infection (rCDI), through restoration of gut floral balance. However, there is a lack of data concerning the efficacy of FMT and its impact on the gut microbiome among pediatric patients. This study analyzes clinical outcomes and microbial community composition among 15 pediatric patients treated for rCDI via FMT. Methods This is a prospective, observational, pilot study of 15 children ≤18 years, who presented for rCDI and who met inclusion criteria for FMT at a pediatric hospital and pediatric gastroenterology clinic. Past medical history and demographics were recorded at enrollment and subsequent follow-up. Specimens of the donors’ and the patients’ pre-FMT and post-FMT fecal specimen were collected and used to assess microbiome composition via 16S rRNA gene sequencing. Results FMT successfully prevented rCDI episodes for minimum of 3 months post-FMT in all patients, with no major adverse effects. Three patients reported continued GI bleeding; however, all three also had underlying Inflammatory Bowel Disease (IBD). Our analyses confirm a significant difference between pre-and post-FMT gut microbiome profiles (Shannon diversity index), whereas no significant difference was observed between post-FMT and donor microbiome profiles. At the phyla level, post-FMT profiles showed significantly increased levels of Bacteroidetes and significantly decreased levels of Proteobacteria. Subjects with underlying IBD showed no difference in their pre-and post-FMT profiles. Conclusion The low rate of recurrence or re-infection by C. difficile, coupled with minimal adverse effects post-FMT, suggests that FMT is a viable therapeutic means to treat pediatric rCDI. Post-FMT microbiomes are different from pre-FMT microbiomes, and similar to those of healthy donors, suggesting successful establishment of a healthier microbiome.
Efficient Intra Mode Selection for Depth-Map Coding Utilizing Spatiotemporal, Inter-Component and Inter-View Correlations in 3D-HEVC

3D-high efficiency video coding (HEVC) is developed for the compression of the multi-view video plus depth format, which is based on the latest generation of video coding standard, HEVC. It further adopts several new intra prediction modes, depth-modeling modes (DMMs) in intra candidate modes for a better representation of edges in depth maps, which introduces a drastic increase in the computational complexity. The procedure of depth intra mode decision together with DMMs and existing intra modes is a very time consuming part due to huge complexity of full rate distortion (RD) cost calculation. In this paper, a low complexity intra mode selection algorithm is proposed to reduce complexity of depth intra prediction in both intra-frames and inter-frames. An experimental analysis is first performed to study the inter-view correlation and the inter-component (texture video and its associated depth) correlation in intra coding information such as the intra mode and RD cost. All intra modes available in 3D-HEVC are classified into three activity classes assigned with different mode-weight factors, and the coding mode complexity of a coding unit (CU) is defined according to the intra mode information from available spatiotemporal, inter-view, and inter-component neighboring coded CUs. The coding mode complexity analysis is utilized to assign different candidate intra modes for different types of CUs. The optimal intra prediction mode and the RD cost value in current CU depth level are further used to skip unnecessary intra prediction sizes. Experimental results show that the proposed fast depth intra coding algorithm achieves 61% complexity reduction on intra prediction, while incurring a 0.2% Bjontegaard metric increase for coded and synthesized views compared to the test model of 3D-HEVC.
Autoantibodies to Cytosolic 5′-Nucleotidase 1A in Primary Sjögren’s Syndrome and Systemic Lupus Erythematosus

Introduction Autoantibodies to cytosolic 5′-nucleotidase 1A (cN-1A; NT5C1A) have a high specificity when differentiating sporadic inclusion body myositis from polymyositis and dermatomyositis. In primary Sjögren’s syndrome (pSS) and systemic lupus erythematosus (SLE) anti-cN-1A autoantibodies can be detected as well. However, various frequencies of anti-cN-1A reactivity have been reported in SLE and pSS, which may at least in part be explained by the different assays used. Here, we determined the occurrence of anti-cN-1A reactivity in a large number of patients with pSS and SLE using one standardized ELISA. Methods Sera from pSS (n = 193) and SLE patients (n = 252) were collected in five European centers. Anti-cN-1A, anti-Ro52, anti-nucleosome, and anti-dsDNA reactivities were tested by ELISA (Euroimmun AG) in a single laboratory. Correlations of anti-cN-1A reactivity with demographic data and clinical data (duration of disease at the moment of serum sampling, autoimmune comorbidity and presence of muscular symptoms) were analyzed using SPSS software. Results Anti-cN-1A autoantibodies were found on average in 12% of pSS patients, with varying frequencies among the different cohorts (range: 7–19%). In SLE patients, the anti-cN-1A positivity on average was 10% (range: 6–21%). No relationship was found between anti-cN-1A reactivity and the presence or absence of anti-Ro52, anti-nucleosome, and anti-dsDNA reactivity in both pSS and SLE. No relationship between anti-cN-1A reactivity and duration of disease at the moment of serum sampling and the duration of serum storage was observed. The frequency of muscular symptoms or viral infections did not differ between anti-cN-1A-positive and -negative patients. In both disease groups anti-cN-1A-positive patients suffered more often from other autoimmune diseases than the anti-cN-1A-negative patients (15 versus 5% (p = 0.05) in pSS and 50 versus 30% (p = 0.02) in SLE). Conclusion Our results confirm the relatively frequent occurrence of anti-cN-1A in pSS and SLE patients and the variation in anti-cN-1A reactivity between independent groups of these patients. The explanation for this variation remains elusive. The correlation between anti-cN-1A reactivity and polyautoimmunity should be evaluated in future studies. We conclude that anti-cN-1A should be classified as a myositis-associated-, not as a myositis-specific-autoantibody based on its frequent presence in SLE and pSS.
Fast Locality Sensitive Hashing for Beam Search on GPU

We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed up beam search for sequence models. We utilize the winner-take-all (WTA) hash, which is based on relative ranking order of hidden dimensions and thus resilient to perturbations in numerical values. Our algorithm is designed by fully considering the underling architecture of CUDA-enabled GPUs (Algorithm/Architecture Co-design): 1) A parallel Cuckoo hash table is applied for LSH code lookup (guaranteed O(1) lookup time); 2) Candidate lists are shared across beams to maximize the parallelism; 3) Top frequent words are merged into candidate lists to improve performance. Experiments on 4 large-scale neural machine translation models demonstrate that our algorithm can achieve up to 4x speedup on softmax module, and 2x overall speedup without hurting BLEU on GPU.
Distance-dependent defensive coloration in the poison frog Dendrobates tinctorius, Dendrobatidae

Significance Poison dart frogs are well known for their deadly toxins and bright colors; they are a classic example of warning coloration. However, conspicuousness is not the only consideration; defensive coloration must be effective against a diverse predator community with a variety of different visual systems, and variable knowledge of prey defenses and motivation to attack. We found that the bright colors of Dendrobates tinctorius are highly salient at close-range but blend together to match the background when viewed from a distance. D. tinctorius combines aposematism and camouflage without necessarily compromising the efficacy of either strategy, producing bright colors while reducing encounters with predators. These data highlight the importance of incorporating viewing distance and pattern distribution into studies of signal design. Poison dart frogs provide classic examples of warning signals: potent toxins signaled by distinctive, conspicuous coloration. We show that, counterintuitively, the bright yellow and blue-black color of Dendrobates tinctorius (Dendrobatidae) also provides camouflage. Through computational modeling of predator vision, and a screen-based detection experiment presenting frogs at different spatial resolutions, we demonstrate that at close range the frog is highly detectable, but from a distance the colors blend together, forming effective camouflage. This result was corroborated with an in situ experiment, which found survival to be background-dependent, a feature more associated with camouflage than aposematism. Our results suggest that in D. tinctorius the distribution of pattern elements, and the particular colors expressed, act as a highly salient close range aposematic signal, while simultaneously minimizing detectability to distant observers.
Transcranial direct current stimulation over the right DLPFC selectively modulates subprocesses in working memory

Background Working memory, as a complex system, consists of two independent components: manipulation and maintenance process, which are defined as executive control and storage process. Previous studies mainly focused on the overall effect of transcranial direct current stimulation (tDCS) on working memory. However, little has been known about the segregative effects of tDCS on the sub-processes within working memory. Method Transcranial direct current stimulation, as one of the non-invasive brain stimulation techniques, is being widely used to modulate the cortical activation of local brain areas. This study modified a spatial n-back experiment with anodal and cathodal tDCS exertion on the right dorsolateral prefrontal cortex (DLPFC), aiming to investigate the effects of tDCS on the two sub-processes of working memory: manipulation (updating) and maintenance. Meanwhile, considering the separability of tDCS effects, we further reconfirmed the causal relationship between the right DLPFC and the sub-processes of working memory with different tDCS conditions. Results The present study showed that cathodal tDCS on the right DLPFC selectively improved the performance of the modified 2-back task in the difficult condition, whereas anodal tDCS significantly reduced the performance of subjects and showed an speeding-up tendency of response time. More precisely, the results of discriminability index and criterion showed that only cathodal tDCS enhanced the performance of maintenance in the difficult condition. Neither of the two tDCS conditions affected the performance of manipulation (updating). Conclusion These findings provide evidence that cathodal tDCS of the right DLPFC selectively affects maintenance capacity. Besides, cathodal tDCS also serves as an interference suppressor to reduce the irrelevant interference, thereby indirectly improving the working memory capacity. Moreover, the right DLPFC is not the unique brain regions for working memory manipulation (updating).
New polymer construction materials for applications in electrical engineering

Polymeric materials and composites on their matrix are increasingly used in the electronics and electrotechnical industry, mainly as insulating elements, construction and housing electronic equipment. Modification of classic plastics using fillers and polymer recyclate allows the production of new composite materials with good electrical, exploitive and processing properties. It also creates opportunities for the rational use of recyclates generated from waste. The article describes the electrical properties of the new polymeric composites of polypropylene (PP), recycled of polyvinyl butyral (PVB rec), and fillers such as glass microspheres (GB) and glass powder (GP). On the basis of studies, the potential use of new materials in electronics and electrical engineering were determined.
Fault Ride-Through Capability of Voltage-Controlled Inverters

An auxiliary voltage controller is proposed for the fault ride-through (FRT) control of voltage-controlled inverters used in distributed energy resources. The proposed auxiliary controller can be added to different existing voltage control strategies with minimum or no need to alter the original structure of the voltage controller. The proposed controller prevents the transient overcurrent during grid faults and controls the exchange of real and reactive powers according to the governing grid standard. A fault detection algorithm is also implemented to detect the fault instant and determine the interval of fault during which the real and reactive power references are adjusted accordingly. Complete stability analysis of the proposed controller is performed through a new model. Successful performance of the proposed controller in achieving FRT requirements of German and Danish codes are illustrated by simulations. Extensive real-time and experimental results are also presented and discussed.
Dual-Primal Graph Convolutional Networks

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.
Design of a Seed-Specific Chimeric Promoter with a Modified Expression Profile to Improve Seed Oil Content

Increasing the yield of plant oil is an important objective to meet the demand for sustainable resources and energy. Some attempts to enhance the expression of genes involved in oil synthesis in seeds have succeeded in increasing oil content. In many cases, the promoters of seed-storage protein genes have been used as seed-specific promoters. However, conventional promoters are developmentally regulated and their expression periods are limited. We constructed a chimeric promoter that starts to express in the early stage of seed development, and high-level expression is retained until the later stage by connecting the promoters of the biotin carboxyl carrier protein 2 (BCCP2) gene encoding the BCCP2 subunit of acetyl-CoA carboxylase and the fatty acid elongase 1 (FAE1) gene from Arabidopsis. The constructed promoter was ligated upstream of the TAG1 gene encoding diacylglycerol acyltransferase 1 and introduced into Arabidopsis. Seeds from transgenic plants carrying AtTAG1 under the control of the chimeric promoter showed increased oil content (up by 18–73%) compared with wild-type seeds. The novel expression profile of the chimeric promoter showed that this could be a promising strategy to manipulate the content of seed-storage oils and other compounds.
A stress-induced response complex (SIRC) shuttles miRNAs, siRNAs, and oligonucleotides to the nucleus

Significance The deregulation of miRNA function is critical in the pathogenesis of cancer and other diseases. miRNAs and other noncoding RNAs (ncRNAs) tightly regulate gene expression, often in the cell nucleus. Heretofore, there has been no understanding that there exists a general shuttling mechanism that brings miRNAs, in addition to therapeutic oligonucleotides and siRNAs, from the cytoplasm into the nucleus. We have identified this shuttling mechanism, which occurs in response to cell stress. Nuclear imported miRNAs are functional, can potentially alter gene expression, and participate in cell stress response mechanisms. This shuttling mechanism can be augmented to target specific RNAs, including miRNA sponges, and long ncRNAs like Malat-1, which have been implicated in promoting tumor metastasis. Although some information is available for specific subsets of miRNAs and several factors have been shown to bind oligonucleotides (ONs), no general transport mechanism for these molecules has been identified to date. In this work, we demonstrate that the nuclear transport of ONs, siRNAs, and miRNAs responds to cellular stress. Furthermore, we have identified a stress-induced response complex (SIRC), which includes Ago-1 and Ago-2 in addition to the transcription and splicing regulators YB1, CTCF, FUS, Smad1, Smad3, and Smad4. The SIRC transports endogenous miRNAs, siRNAs, and ONs to the nucleus. We show that cellular stress can significantly increase ON- or siRNA-directed splicing switch events and endogenous miRNA targeting of nuclear RNAs.