Miracle-dz/UniFAR-FTUs huggingface dataset:
A variably imprinted epiallele impacts seed development

The contribution of epigenetic variation to phenotypic variation is unclear. Imprinted genes, because of their strong association with epigenetic modifications, represent an opportunity for the discovery of such phenomena. In mammals and flowering plants, a subset of genes are expressed from only one parental allele in a process called gene imprinting. Imprinting is associated with differential DNA methylation and chromatin modifications between parental alleles. In flowering plants imprinting occurs in a seed tissue – endosperm. Proper endosperm development is essential for the production of viable seeds. We previously showed that in Arabidopsis thaliana intraspecific imprinting variation is correlated with naturally occurring DNA methylation polymorphisms. Here, we investigated the mechanisms and function of allele-specific imprinting of the class IV homeodomain-Leucine zipper (HD-ZIP) transcription factor HDG3. In imprinted strains, HDG3 is expressed primarily from the methylated paternally inherited allele. We manipulated the methylation state of endogenous HDG3 in a non-imprinted strain and demonstrated that methylation of a proximal transposable element is sufficient to promote HDG3 expression and imprinting. Gain of HDG3 imprinting was associated with earlier endosperm cellularization and changes in seed weight. These results indicate that epigenetic variation alone is sufficient to explain imprinting variation and demonstrate that epialleles can underlie variation in seed development phenotypes. Author Summary The contribution of genetic variation to phenotypic variation is well-established. By contrast, it is unknown how frequently epigenetic variation causes differences in organismal phenotypes. Epigenetic information is closely associated with but not encoded in the DNA sequence. In practice, it is challenging to disentangle genetic variation from epigenetic variation, as what appears to be epigenetic variation might have an underlying genetic basis. DNA methylation is one form of epigenetic information. HDG3 encodes an endosperm specific transcription factor that exists in two states in A. thaliana natural populations: methylated and expressed and hypomethylated and repressed. We show that pure epigenetic variation is sufficient to explain expression variation of HDG3 – a naturally lowly expressed allele can be switched to a higher expressed state by adding DNA methylation. We also show that expression of HDG3 in strains where it is normally hypomethylated and relatively repressed causes a seed development phenotype. These data indicate that naturally circulating epialleles have consequences for seed phenotypic variation.
AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

PURPOSE Radiation therapy (RT) is a common treatment option for head and neck (HaN) cancer. An important step involved in RT planning is the delineation of organs-at-risks (OARs) based on HaN computed tomography (CT). However, manually delineating OARs is time-consuming as each slice of CT images needs to be individually examined and a typical CT consists of hundreds of slices. Automating OARs segmentation has the benefit of both reducing the time and improving the quality of RT planning. Existing anatomy autosegmentation algorithms use primarily atlas-based methods, which require sophisticated atlas creation and cannot adequately account for anatomy variations among patients. In this work, we propose an end-to-end, atlas-free three-dimensional (3D) convolutional deep learning framework for fast and fully automated whole-volume HaN anatomy segmentation. METHODS Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it in three important ways: (a) a new encoding scheme to allow autosegmentation on whole-volume CT images instead of local patches or subsets of slices, (b) incorporating 3D squeeze-and-excitation residual blocks in encoding layers for better feature representation, and (c) a new loss function combining Dice scores and focal loss to facilitate the training of the neural model. These features are designed to address two main challenges in deep learning-based HaN segmentation: (a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and (b) training with inconsistent data annotations with missing ground truth for some anatomical structures. RESULTS We collected 261 HaN CT images to train AnatomyNet and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art results from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient by 3.3% on average. AnatomyNet takes about 0.12 s to fully segment a head and neck CT image of dimension 178 × 302 × 225, significantly faster than previous methods. In addition, the model is able to process whole-volume CT images and delineate all OARs in one pass, requiring little pre- or postprocessing. CONCLUSION Deep learning models offer a feasible solution to the problem of delineating OARs from CT images. We demonstrate that our proposed model can improve segmentation accuracy and simplify the autosegmentation pipeline. With this method, it is possible to delineate OARs of a head and neck CT within a fraction of a second.
Biallelic Mutations in LRRC56 encoding a protein associated with intraflagellar transport, cause mucociliary clearance and laterality defects

Defective motile cilia are responsible for a group of heterogeneous genetic conditions characterised by dysfunction of the apparatus responsible for generating fluid flows. Primary ciliary dyskinesia (PCD) is the prototype for such disorders and presents with impaired pulmonary mucus clearance, susceptibility to chronic recurrent respiratory infections, male infertility and laterality defects in about 50 % of patients. Here we report biallelic variants in LRRC56 (also known as ODA8), identified in two unrelated consanguineous families. The phenotype comprises laterality defects and chronic pulmonary infections. High speed video microscopy of cultured patient epithelial cells showed severely dyskinetic cilia, but no obvious ultra-structural abnormalities on routine transmission electron microscopy (TEM). Further investigation revealed that LRRC56 interacts with the intraflagellar transport (IFT) protein IFT88. The link to IFT was interrogated in Trypanosoma brucei. In this protist, LRRC56 is recruited to the cilium during axoneme construction, where it co-localises with IFT trains and facilitates the addition of dynein arms to the distal end of the flagellum. In T. brucei carrying LRRC56 null mutations, or a mutation (p.Leu259Pro) corresponding to the p.Leu140Pro variant seen in one of the affected families, we observed abnormal ciliary beat patterns and an absence of outer dynein arms restricted to the distal portion of the axoneme. Together, our findings confirm that deleterious variants in LRRC56 result in a human disease, and suggest this protein has a likely role in dynein transport during cilia assembly that is evolutionarily important for cilia motility.
Automated Multi-Label Classification based on ML-Plan

Automated machine learning (AutoML) has received increasing attention in the recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and auto-sklearn, mainly deal with single-label classification and regression, there is very little work on other types of machine learning tasks. In particular, there is almost no work on automating the engineering of machine learning applications for multi-label classification. This paper makes two contributions. First, it discusses the usefulness and feasibility of an AutoML approach for multi-label classification. Second, we show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards multi-label classification using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, which sometimes nest another multi-label classifier, up to the selection of a single-label base learner provided by WEKA. In our evaluation, we find that the proposed approach yields superb results and performs significantly better than a set of baselines.
Mitochondrial protective effect of neferine through the modulation of nuclear factor erythroid 2‐related factor 2 signalling in ischaemic stroke

Ischaemic stroke is a leading cause of death and long‐term disability. Promising neuroprotective compounds are urgently needed to overcome clinical therapeutic limitations. Neuroprotective agents are limited to single‐target agents, which further limit their clinical effectiveness. Due to the brain's particular energy requirements, the energy micro‐environment, centred in mitochondria, is a new research hotspot in the complex pathology of ischaemic stroke. Here, we studied the effects of neferine (Nef), a bis‐benzylisoquinoline alkaloid extracted from the seed embryo of Nelumbo nucifera Gaertn, on ischaemic stroke and its underlying mitochondrial protective mechanisms.
Understanding and Predicting Links in Graphs: A Persistent Homology Perspective

Persistent Homology is a powerful tool in Topological Data Analysis (TDA) to capture topological properties of data succinctly at different spatial resolutions. For graphical data, shape, and structure of the neighborhood of individual data items (nodes) is an essential means of characterizing their properties. In this paper, we propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to address the problem of link prediction. We evaluate our approach on seven different real-world datasets and offer directions for future work.
GPRC5A: An Emerging Biomarker in Human Cancer

Aberrant expression of G protein-coupled receptors (GPCRs) is frequently associated with tumorigenesis. G Protein-coupled receptor class C group 5 member A (GPRC5A) is a member of the GPCR superfamily, is expressed preferentially in lung tissues, and is regulated by various entities at multiple levels. GPRC5A exerts a tumor suppressive role in lung cancer and GPRC5A deletion promotes lung tumor initiation and progression. Recent advances have highlighted that GPRC5A dysregulation is found in various human cancers and is related to many tumor-associated signaling pathways, including the cyclic adenosine monophosphate (cAMP), nuclear factor (NF)-κB, signal transducer and activator of transcription (STAT) 3, and focal adhesion kinase (FAK)/Src signaling. This review aimed to summarize our updated view on the biology and regulation of GPRC5A, its expression in human cancers, and the linked signaling pathways. A better comprehension of the underlying cellular and molecular mechanisms of GPRC5A will provide novel insights into its potential diagnostic and therapeutic value.
Multimodal One-shot Learning of Speech and Images

Imagine a robot is shown new concepts visually together with spoken tags, e.g. "milk", "eggs", "butter". After seeing one paired audiovisual example per class, it is shown a new set of unseen instances of these objects, and asked to pick the "milk". Without receiving any hard labels, could it learn to match the new continuous speech input to the correct visual instance? Although unimodal one-shot learning has been studied, where one labelled example in a single modality is given per class, this example motivates multimodal one-shot learning. Our main contribution is to formally define this task, and to propose several baseline and advanced models. We use a dataset of paired spoken and visual digits to specifically investigate recent advances in Siamese convolutional neural networks. Our best Siamese model achieves twice the accuracy of a nearest neighbour model using pixel-distance over images and dynamic time warping over speech in 11-way cross-modal matching.
Semantic and Contrast-Aware Saliency

In this paper, we proposed an integrated model of semantic-aware and contrast-aware saliency combining both bottom-up and top-down cues for effective saliency estimation and eye fixation prediction. The proposed model processes visual information using two pathways. The first pathway aims to capture the attractive semantic information in images, especially for the presence of meaningful objects and object parts such as human faces. The second pathway is based on multi-scale on-line feature learning and information maximization, which learns an adaptive sparse representation for the input and discovers the high contrast salient patterns within the image context. The two pathways characterize both long-term and short-term attention cues and are integrated dynamically using maxima normalization. We investigate two different implementations of the semantic pathway including an End-to-End deep neural network solution and a dynamic feature integration solution, resulting in the SCA and SCAFI model respectively. Experimental results on artificial images and 5 popular benchmark datasets demonstrate the superior performance and better plausibility of the proposed model over both classic approaches and recent deep models.
Crispr/Cas Mediated Deletion of PTPN22 in Jurkat T Cells Enhances TCR Signaling and Production of IL-2

A single nucleotide polymorphism, C1858T, in the gene encoding the protein tyrosine phosphatase nonreceptor type 22 (PTPN22) results in one of the strongest genetic traits associated with autoimmune disease outside of the Major Histocompatibility Complex (MHC) genes. However, the consequences of this polymorphism, which introduces an arginine to tryptophan substitution at amino acid 620, for the function of PTPN22 protein is unclear and conflicting results have been obtained in human compared to mouse cells expressing this variant phosphatase. In mouse the variant appears to be a loss-of-function allele resembling a milder form of the null allele, while studies in human cells have reported it to be a gain-of-function mutation. To address whether the phosphatase has distinct functions in mouse vs. human T cells, we used CRISPR gene-editing to generate the first example of human PTPN22-KnockOut (KO) T cells. By comparing isogenic human T cells which express or lack PTPN22, we showed that PTPN22 KO T cells displayed enhanced expression of IL-2 and CD69 upon stimulation with cognate antigen. PTPN22 KO cells also showed increased Erk phosphorylation upon stimulation with weak antigen, but the difference was diminished in response to strong antigen, indicating that PTPN22 plays a more critical role in regulating weak-antigen responses. These data are in keeping with a role for PTPN22 in determining the threshold of stimulation required to activate T cells, a critical function of autoimmune pathogenesis. Our data indicate that PTPN22 has comparable functions in mouse and human T cells, and that the conflicting results in the literature regarding the impact of the point mutation are not due to differences in the activity of PTPN22 itself, but may be related to interactions with other proteins or splice variation.
G Protein α Subunit 14 Mediates Fibroblast Growth Factor 2‐Induced Cellular Responses in Human Endothelial Cells

During pregnancy, a tremendous increase in fetoplacental angiogenesis is associated with elevated blood flow. Aberrant fetoplacental vascular function may lead to pregnancy complications including pre‐eclampsia. Fibroblast growth factor 2 (FGF2) and vascular endothelial growth factor A (VEGFA) are crucial regulators of fetoplacental endothelial function. G protein α subunit 14 (GNA14), a member of Gαq/11 subfamily is involved in mediating hypertensive diseases and tumor vascularization. However, little is known about roles of GNA14 in mediating the FGF2‐ and VEGFA‐induced fetoplacental endothelial function. Using human umbilical vein endothelial cells (HUVECs) cultured under physiological chronic low oxygen (3% O2) as a cell model, we show that transfecting cells with adenovirus carrying GNA14 complementary DNA (cDNA; Ad‐GNA14) increases (p < 0.05) protein expression of GNA14. GNA14 overexpression blocks (p < 0.05) FGF2‐stimulated endothelial migration, whereas it enhances (p < 0.05) endothelial monolayer integrity (maximum increase of ~35% over the control at 24 hr) in response to FGF2. In contrast, GNA14 overexpression does not significantly alter VEGFA‐stimulated cell migration, VEGFA‐weakened cell monolayer integrity, and intracellular Ca++ mobilization in response to adenosine triphosphate (ATP), FGF2, and VEGFA. GNA14 overexpression does not alter either FGF2‐ or VEGFA‐induced phosphorylation of ERK1/2. However, GNA14 overexpression time‐dependently elevates (p < 0.05) phosphorylation of phospholipase C‐β3 (PLCβ3) at S1105 in response to FGF2, but not VEGFA. These data suggest that GNA14 distinctively mediates fetoplacental endothelial cell migration and permeability in response to FGF2 and VEGFA, possibly in part by altering activation of PLCβ3 under physiological chronic low oxygen.
Manifestations and Outcomes of Patients with Parkinson's Disease and Serious Infection in the Emergency Department

Background Several comorbidities contribute to an increased risk of infections in Parkinson's disease (PD) as the disease progresses. However, few studies have examined the correlation between sepsis and PD. Aim The aim of this study is to disclose the presentation and outcome of serious infection in patients with PD in the emergency department. Methods This retrospective cohort study enrolled patients with PD who had serious infection and were admitted to the emergency department between January 2007 and December 2013. For clinical comparison, we compared the clinical features, laboratory data, and outcomes with those of age- and sex-matched patients who had serious infection but not PD. Results There were a total of 1,200 episodes of infected PD patients and 2,400 age- and sex-matched infected patients without PD as disease controls. PD patients had fewer comorbidities and lower severity of infectious disease but longer hospital stays than control group patients. The incidences of respiratory tract and urinary tract infections were higher in PD patients. The levels of inflammatory and organ dysfunction biomarkers in PD were lower and compatible with the severity of infectious disease. A total of 86 (7.2%) infected PD patients died during the 28-day admission compared to 339 (14.1%) in non-PD patients. Serum C-reactive protein, bandemia, and lactate could be used to predict mortality in infected PD patients. Conclusions In infected patients with PD, respiratory and urinary tract infections were the two most common infectious sources. Empiric therapy based on experience could treat both respiratory and urinary tract infections. Early diagnosis and treatment are essential for survival.
Helicobacter pylori Infection and Its Risk Factors: A Prospective Cross-Sectional Study in Resource-Limited Settings of Northwest Ethiopia

Background Helicobacter pylori (H. pylori) is implicated for the causation of gastrointestinal tract infections including gastric cancer. Although the infection is prevalent globally, the impact is immense in countries with poor environmental and socioeconomic status including Ethiopia. Epidemiological study on the magnitude of H. pylori and possible risk factors has priceless implication. Therefore, in this study, we determined the prevalence and risk factors of H. pylori infection in the resource-limited area of northwest Ethiopia. Methods A prospective cross-sectional study was conducted on northwest Ethiopia among 201 systematically selected dyspeptic patients. Data were collected using a structured and pretested questionnaire, and stool and serum samples were collected and analyzed by SD BIOLINE H. pylori Ag and dBest H. pylori Disk tests, respectively. Chi-square test was performed to see association between variables, and binary and multinomial regression tests were performed to identify potential risk factors. P values <0.05 were taken statistically significant. Result Prevalence of H. pylori was found to be 71.1% (143/201) and 37.3% (75/201) using the dBest H. pylori Test Disk and SD BIOLINE H. pylori Ag test, respectively. H. pylori seropositivity, using dBest H. pylori Disk tests, is significantly associated in age groups <10 years (P=0.044) and married patients (P=0.016). In those patients with H. pylori (a positive result with either the Ab or Ag test), drinking water from well sources had 2.23 times risk of getting H. pylori infection (P=0.017), and drinking coffee (1.51 (0.79–2.96, P=0.025)) and chat chewing (1.78 (1.02–3.46, P=0.008) are the common risk factors. Conclusion The present study discovered considerable magnitude of H. pylori among the dyspeptic patients in the study area. H. pylori infection is frequent in individuals drinking water from well sources, and thus, poor sanitation and unhygienic water supply are contributing factors. Policies aiming at improving the socioeconomic status will reduce potential sources of infection, transmission, and ultimately the prevalence and incidence of H. pylori.
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets

A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of federated learning to GANs, using the MNIST, CIFAR10 and CelebA datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better or identical performances with the adaptation of federated learning. We finally discuss the practical implications of distributing GANs.
Multimodal Grounding for Sequence-to-sequence Speech Recognition

Humans are capable of processing speech by making use of multiple sensory modalities. For example, the environment where a conversation takes place generally provides semantic and/or acoustic context that helps us to resolve ambiguities or to recall named entities. Motivated by this, there have been many works studying the integration of visual information into the speech recognition pipeline. Specifically, in our previous work, we propose a multistep visual adaptive training approach which improves the accuracy of an audio-based Automatic Speech Recognition (ASR) system. This approach, however, is not end-to-end as it requires fine-tuning the whole model with an adaptation layer. In this paper, we propose novel end-to-end multimodal ASR systems and compare them to the adaptive approach by using a range of visual representations obtained from state-of-the-art convolutional neural networks. We show that adaptive training is effective for S2S models leading to an absolute improvement of 1.4% in word error rate. As for the end-to-end systems, although they perform better than baseline, the improvements are slightly less than adaptive training, 0.8 absolute WER reduction in single-best models. Using ensemble decoding, end-to-end models reach a WER of 15% which is the lowest score among all systems.