Miracle-dz/UniFAR-FTUs huggingface dataset:
Imputation-Based Whole-Genome Sequence Association Study Reveals Constant and Novel Loci for Hematological Traits in a Large-Scale Swine F2 Resource Population

The whole-genome sequences of progenies with low-density single-nucleotide polymorphism (SNP) genotypes can be imputed with high accuracy based on the deep-coverage sequences of key ancestors. With this imputation technology, a more powerful genome-wide association study (GWAS) can be carried out using imputed whole-genome variants and the phenotypes of interest to overcome the shortcomings of low-power detection and the large confidence interval derived from low-density SNP markers in classic association studies. In this study, 19 ancestors of a large-scale swine F2 White Duroc × Erhualian population were deeply sequenced for their genome with an average coverage of 25×. Considering 98 pigs from 10 different breeds with high-quality deep sequenced genomes, we imputed the whole genomic variants of 1020 F2 pigs genotyped by the PorcineSNP60 BeadChip with high accuracy and obtained 14,851,440 sequence variants after quality control. Based on this, 87 novel quantitative traits loci (QTLs) for 18 hematological traits at three different physiological stages of the F2 pigs were identified, among which most of the novel QTLs have been repeated in two of the three stages. Literature mining pinpointed that the FGF14 and LCLAT1 genes at SSC11 and SSC3 may affect the MCH at day 240 and MCV at day 18, respectively. The present study shows that combining high-quality imputed genomic variants and correlated phenomic traits into GWAS can improve the capability to detect QTL considerably. The large number of different QTLs for hematological traits identified at multiple growth stages implies the complexity and time specificity of these traits.
Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach

In personality neuroscience, the openness-brain association has been a topic of interest. Previous studies usually started from difference in openness trait and used it to infer brain functional activity characteristics, but no study has used a “brain-first” research strategy to explore that association based on more objective brain imaging data. In this study, we used a fully data-driven approach to discover and validate the association between openness and the resting-state brain network. We collected data of 120 subjects as a discovery sample and 56 subjects as a validation sample. The Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) was used to measure the personality characteristics of all the subjects. Using an exploratory approach based on independent component analysis of resting-state functional magnetic resonance imaging (fMRI) data, we identified a parietal network that consisted of the precuneus and inferior parietal lobe. The inter-subject similarity of the parietal memory network exhibited significant associations with openness trait, and this association was validated using the 56-subject independent sample. This finding connects the openness trait to the characteristics of a neural network and helps to understand the underlying biology of the openness trait.
Impact of the Uridine–Cytidine Kinase Like-1 Protein and IL28B rs12979860 and rs8099917 SNPs on the Development of Hepatocellular Carcinoma in Cirrhotic Chronic Hepatitis C Patients—A Pilot Study

Background and objectives: The hepatitis C virus (HCV) is the major causative agent of hepatocellular carcinoma (HCC) in the western world. The efficacy of surveillance programs for early detection of HCC is not satisfactory: many tumors are diagnosed at the late, incurable stages. Therefore, there is a need in reliable prognostic markers for the proper follow-up of HCV-positive patients. The aim of the present study was to assess the prognostic value of the uridine–cytidine kinase-like protein 1 (UCKL-1), a putative oncoprotein, together with genetically determined polymorphisms in the interleukin 28B (IL28B) gene (rs12979860, rs8099917) in the development of HCC in HCV-positive cirrhotic patients. Materials and Methods: We included 32 HCV cirrhotic patients, 21 (65.6%) of whom had HCC. The expression of UCKL-1 was assessed in liver tissue sections, using immunohistochemistry. For IL28B rs12979860 and rs8099917 genotype analysis, the corresponding genomic regions were amplified by polymerase chain reaction (PCR) with appropriate primers. Results: We have found that UCKL-1 expression was significantly increased in HCC (p = 0.003). The presence of rs8099917 TT single-nucleotide polymorphism (SNP) elevated the chances of HCC manifestation more than sevenfold (OR = 7.3, p = 0.0273). The presence of rs12979860 CC SNP also heightened HCC chances more than sevenfold (OR = 7.5, p = 0.0765). Moreover, in the HCC group, a combination of IL28B rs12979860 non-TT and rs8099917 TT genotypes was observed more often, compared with the non-HCC group. Other combinations of IL28B rs12979860 and rs8099917 SNIPs were associated with a reduced risk of HCC development, approximately at the same extent. Conclusions: The presence of IL28B rs8099917 TT and rs12979860 CC SNPs, but not the intensity of UCKL-1 expression, is strongly associated with increased chances of HCC development in HCV-positive cirrhotic patients.
Expected Communication Cost of Distributed Quantum Tasks

A central question in classical information theory is that of source compression, which is the task where Alice receives a sample from a known probability distribution and needs to transmit it to the receiver Bob with small error. This problem has a one-shot solution due to Huffman, in which the messages are of variable length and the expected length of the messages matches the asymptotic and i.i. d. compression rate of the Shannon entropy of the source. In this work, we consider a quantum extension of above task, where Alice receives a sample from a known probability distribution and needs to transmit a part of a pure quantum state (that is associated to the sample) to Bob. We allow entanglement assistance in the protocol, so that the communication is possible through classical messages, for example using quantum teleportation. The classical messages can have a variable length and the goal is to minimize their expected length. We provide a characterization of the expected communication cost of this task, by giving a lower bound that is near optimal up to some additive factors. A special case of above task, and the quantum analogue of the source compression problem, is when Alice needs to transmit the whole of her pure quantum state. Here we show that there is no one-shot interactive scheme which matches the asymptotic and i.i. d. compression rate of the von Neumann entropy of the average quantum state. This is a relatively rare case in quantum information theory where the cost of a quantum task is significantly different from its classical analogue. Further, we also exhibit similar results for the fully quantum task of quantum state redistribution, employing some different techniques. We show implications for the one-shot version of the problem of quantum channel simulation. A full version of this paper is accessible at: https://arxiv.org/abs/1605.04601
Enhancing Smallholder Access to Agricultural Machinery Services: Lessons from Bangladesh

Abstract Resource poor smallholders in developing countries often lack access to capital goods such as farm machinery. Enabling adequate access through machinery services can thereby significantly contribute to food security and farm incomes. At the core of the service provision model is the lead farmer, who makes the initial investment in agricultural machinery, and provides services to others on a fee-for-service basis. Profiling the lead farmers can thereby provide important lessons and scaling implications. The present paper provides a case study of Bangladesh, using primary data to characterise the lead farmers. General education, credit availability and risk taking attitude play significant roles in whether or not a farm household will be a lead farmer in Bangladesh.
Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical. In the variational approach, the reconstructed signal is solution to an optimization problem that establishes a tradeoff between fidelity to the data and smoothness conditions via a quadratic regularization associated with a linear operator. In the statistical approach, the signal is modeled as a stationary random process defined from a Gaussian white noise and a whitening operator; one then looks for the optimal estimator in the mean-square sense. We give a generic form of the reconstructed signals for both approaches, allowing for a rigorous comparison of the two. We fully characterize the conditions under which the two formulations yield the same solution, which is a periodic spline in the case of sampling measurements. We also show that this equivalence between the two approaches remains valid on simulations for a broad class of problems. This extends the practical range of applicability of the variational method.
CARDIOPULMONARY EXERCISE TESTING WITH SUPRAMAXIMAL VERIFICATION PRODUCES A SAFE AND VALID ASSESSMENT OF V̇O2max IN PEOPLE WITH CYSTIC FIBROSIS.

INTRODUCTION The validity and safety of using supramaximal verification (Smax) to confirm a maximal effort during cardiopulmonary exercise testing (CPET) in people with cystic fibrosis (CF) and/or those with severe disease has been questioned. Therefore, this study aimed to investigate these concerns in children, adolescents and adults with mild-to-severe CF lung disease. METHODS Retrospective analysis of 17 pediatric and 28 adult participants with CF (age range: 9.2-62.9 y; forced expiratory volume in 1 s: 66.7% [range: 29.9-102.3%]; 30 males) who completed a routine ramp incremental cycling test to determine peak oxygen uptake (V̇O2peak). Maximal oxygen uptake (V̇O2max) was subsequently confirmed by Smax at 110% of peak power output. RESULTS All participants satisfied the criteria to verify a maximal effort during CPET. However, Smax-V̇O2peak exceeded ramp-V̇O2peak in 3/14 (21.4%) of pediatric and 6/28 (21.4%) adult exercise tests. A valid measurement of V̇O2max was attained in 85.7% of pediatric and 96.4% of adult exercise tests, as Smax-V̇O2peak did not exceed ramp-V̇O2peak by > 9%. Nine adults experienced a {greater than or equal to} 5% reduction in arterial O2 saturation during CPET; 4 during both the ramp and Smax, 3 during only the ramp and 2 during Smax only. Smax did not significantly worsen perceived breathing effort, chest tightness, throat narrowing or exertion compared with ramp incremental testing. CONCLUSIONS Given the clinical importance of aerobic fitness in people with CF, incorporating Smax is recommended to provide a safe and valid measure of V̇O2max in children, adolescents and adults who span the spectrum of CF disease severity.
Generalized Conditional Domain Adaptation: A Causal Perspective With Low-Rank Translators

Learning domain adaptive features aims to enhance the classification performance of the target domain by exploring the discriminant information from an auxiliary source set. Let <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math></inline-formula> denote the feature and <inline-formula> <tex-math notation="LaTeX">${Y}$ </tex-math></inline-formula> as the label. The most typical problem to be addressed is that <inline-formula> <tex-math notation="LaTeX">${P} _{\textit {XY}}$ </tex-math></inline-formula> has a so large variation between different domains that classification in the target domain is difficult. In this paper, we study the generalized conditional domain adaptation (DA) problem, in which both <inline-formula> <tex-math notation="LaTeX">${P} _{Y}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${P} _{X|Y}$ </tex-math></inline-formula> change across domains, in a causal perspective. We propose transforming the class conditional probability matching to the marginal probability matching problem, under a proper assumption. We build an intermediate domain by employing a regression model. In order to enforce the most relevant data to reconstruct the intermediate representations, a low-rank constraint is placed on the regression model for regularization. The low-rank constraint underlines a global algebraic structure between different domains, and stresses the group compactness in representing the samples. The new model is considered under the discriminant subspace framework, which is favorable in simultaneously extracting the classification information from the source domain and adaptation information across domains. The model can be solved by an alternative optimization manner of quadratic programming and the alternative Lagrange multiplier method. To the best of our knowledge, this paper is the first to exploit low-rank representation, from the source domain to the intermediate domain, to learn the domain adaptive features. Comprehensive experimental results validate that the proposed method provides better classification accuracies with DA, compared with well-established baselines.
The association between social cohesion and community disaster resilience: A cross‐sectional study

A cross-sectional study design was created, using the Index of Perceived Community Resilience (IPCR) and Buckner's Index of Cohesion (BIC) to survey 386 flood evacuees from six communities in Kelantan, Malaysia, in 2015. The respondents were mostly female (54.7%); lived in basic housing (95.6%); average income (55.9%); secondary level schooling (81.1%); not involved with community organisations (95.1%), volunteering activities (91.2%), or emergency teams (96.9%); inexperience with injury during flooding (94%); experienced the emergency disaster (61.6%); and their mean age was 49 years old. Overall, respondents scored a high level of community disaster resilience (CDR) (mean 3.9) and social cohesion (mean 3.79). Also, respondents' housing type, event of injury during disaster, volunteering in post-disaster activities, and emergency team participation were significantly associated with CDR (p = 0.001-0.002), organisational involvement (p = 0.016), and emergency disaster experience (p = 0.028) were significantly associated with social cohesion. The Pearson correlation coefficient results mostly showing a moderate, weak, and one with a strong relationship. There is a strong relationship between community participation (CDR) in events and BIC variables (r = 0.529, p = 0.001). Other analysis shows a moderate but significant relationship with BIC; is open to ideas (r = 0.332, p = 0.001); community has similar values/ideas (r = 0.421, p = 0.001); sense of pride (r = 0.389, p = 0.001); strong leadership (r = 0.339, p = 0.001); positive change (r = 0.484, p = 0.001); and able to handle problems (r = 0.454, p = 0.001). Overall, the results show that respondents had high levels of CDR and social cohesion, while the demographic characteristics show the impact of CDR and social cohesion. In conclusion, the data gives original insight into the level of association between social cohesion and disaster resilience, which could be used as a building block in sustainable disaster recovery. There is a need to explore this further on programmes designed to improve social cohesion across communities.
Lack of combination effects of soy isoflavones and taxane chemotherapy of castration‐resistant prostate cancer

Patients with cancer, including prostate cancer, often use dietary supplements, such as soy or isoflavones, before, during, or after therapy. There is little information about possible interactions between supplements and cancer chemotherapy. There are some reports suggesting enhancement by genistein of taxane chemotherapy for castrate‐resistant prostate cancer (CRPC).
Video Deflickering Using Multi - Frame Optimization

In this paper, we propose an approach of removing flickering artifacts in a video. The video is obtained via applying the image-based processing methods to an original non-flickering video. In traditional video deflickering methods, we always reconstruct flickering frames with nonflickering frames, which fail to keep video spatial consistency and are always designed to address certain video flickering artifact under specific condition. On the contrary, we propose a general multiple frames based video deflickering approach, where we take both temporal and spatial coherence into account. Instead of reconstructing a flickering frame only from its last frame, we warp multiple corresponding frames to reconstruct the flickering frame, so the warp inaccuracy in the reconstruction process can be reduced. By taking the advantage of video fidelity, temporal coherence and spatial coherence, we formulate video deflickeing objective as a least-squares energy. A non-flickering output video can be obtained via solving the constructed energy formulation with the least angle regression. Results of visual quality, objective measurement and user study demonstrate the efficiency of our proposed multiple frames based video deflickering approach.
Network-Based Video Quality Assessment for Encrypted HTTP Adaptive Streaming

End-to-end encryption brings new challenges for mobile network operators to obtain key quality indicators (KQIs) of the HTTP adaptive streaming (HAS), which are vital for quality of experience (QoE) of the end users. Targeting on this issue, this paper proposes a network-based solution to extract KQI-related features from encrypted traffic; then, the video quality is assessed through machine-learning-based methods. First, content-independent features are extracted resorting to traffic characteristics and a typical HAS player model. Then, back-propagation neural network and random forest are applied to evaluate stalling and estimate initial buffering delay, which are known as the most important KQIs for QoE. In addition, to evaluate the performance of our solution, we construct a data acquisition platform, and 4733 encrypted video sessions are collected from YouTube by crowdsourcing. Experiment results show that the proposed solution outperforms the previous methods, where initial buffering delay is effectively estimated with an absolute error less than 1 s for up to 80% videos, and the accuracy of stalling detection reaches 88.5%.
Regularized LSTM Based Deep Learning Model: First Step towards Real-Time Non-Intrusive Load Monitoring

Residential and commercial buildings consume more electricity than any other sector. Thus, energy saving through smart electrification in those buildings is the best way to reduce overall energy demand. Smart electrification includes end-use appliance energy consumption monitoring in real-time, which can be achieved through Non-Intrusive Load Monitoring (NILM). Recently, deep learning algorithms have been introduced for energy disaggregation but their effectiveness in real-time appliance load monitoring is questionable. In this paper, we have presented two deep recurrent neural networks models: LSTM and GRU. We have introduced regularization to improve our proposed model’s performance and have tested them on unseen buildings during training. We have achieved promising results with proposed Regularized LSTM model in terms of accuracy, f1 score and mean absolute error. These results suggest using this model in real-time energy disaggregation of end-use appliances.
Sampling of Signals Bandlimited to a Disc in the Linear Canonical Transform Domain for IEEE Signal Processing Letters

We derive sampling formula for signals that are bandlimited to a disc of radius <inline-formula> <tex-math notation="LaTeX">$R$</tex-math></inline-formula> in the linear canonical transform (LCT) domain. By bandlimitedness in a disc <inline-formula><tex-math notation="LaTeX">$D$</tex-math></inline-formula> in the LCT domain, we mean that the LCT <inline-formula><tex-math notation="LaTeX">$F(\omega)$</tex-math></inline-formula> of a signal <inline-formula><tex-math notation="LaTeX">$f(t)$</tex-math></inline-formula> vanishes outside the disc <inline-formula><tex-math notation="LaTeX">$D. $</tex-math></inline-formula> We first express the signal in polar coordinates and then obtain the sampling formula. The samples of the angle <inline-formula><tex-math notation="LaTeX"> $\theta$</tex-math></inline-formula> are taken at <inline-formula><tex-math notation="LaTeX">$2N+1$</tex-math> </inline-formula> uniformly distributed points on the unit circle while the samples of the radial distance <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> are taken at the zeros of the Bessel function. As a special case, we obtain sampling formula for signals that are bandlimited to a disc in the fractional Fourier transform domain.
Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries

Objective: Segmentation of anatomical structures in ultrasound images requires vast radiological knowledge and experience. Moreover, the manual segmentation often results in subjective variations, therefore, an automatic segmentation is desirable. We aim to develop a fully convolutional neural network (FCNN) with attentional deep supervision for the automatic and accurate segmentation of the ultrasound images. Method: FCNN/CNNs are used to infer high-level context using low-level image features. In this paper, a sub-problem specific deep supervision of the FCNN is performed. The attention of fine resolution layers is steered to learn object boundary definitions using auxiliary losses, whereas coarse resolution layers are trained to discriminate object regions from the background. Furthermore, a customized scheme for downweighting the auxiliary losses and a trainable fusion layer are introduced. This produces an accurate segmentation and helps in dealing with the broken boundaries, usually found in the ultrasound images. Results: The proposed network is first tested for blood vessel segmentation in liver images. It results in $F1$ score, mean intersection over union, and dice index of 0.83, 0.83, and 0.79, respectively. The best values observed among the existing approaches are produced by U-net as 0.74, 0.81, and 0.75, respectively. The proposed network also results in dice index value of 0.91 in the lumen segmentation experiments on MICCAI 2011 IVUS challenge dataset, which is near to the provided reference value of 0.93. Furthermore, the improvements similar to vessel segmentation experiments are also observed in the experiment performed to segment lesions. Conclusion: Deep supervision of the network based on the input-output characteristics of the layers results in improvement in overall segmentation accuracy. Significance: Sub-problem specific deep supervision for ultrasound image segmentation is the main contribution of this paper. Currently the network is trained and tested for fixed size inputs. It requires image resizing and limits the performance in small size images.
The Keywords Spotting with Context for Multi -Oriented Chinese Scene Text

Scene text spotting is currently a popular research topic in the computer vision community. However, it is a challenging task due to the variations of texts and clutter backgrounds. In some situation, especially in the natural scene, it is not necessary to understand the whole text information in an image if we have acquired the key information. In this paper, we propose a bottom-to-up framework for keywords spotting and context extraction for multi-oriented Chinese in scene images. The proposed framework includes three modules which are Chinese character detection, keywords spotting and context extractor. We adopt the object detection methods to detect and recognize the Chinese text simultaneously in character-level. The geometric relationship between keywords and their context is analyzed, which is robust to multi-oriented text. The proposed framework classifies the valuable images, extracts possible keywords context and achieves a good performance in the CTW dataset.