MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads

Copy number variations (CNVs) are associated with many complex diseases. Next generation sequencing data enable one to identify precise CNV breakpoints to better under the underlying molecular mechanisms and to design more efficient assays. Using the CIGAR strings of the reads, we develop a method that can identify the exact CNV breakpoints, and in cases when the breakpoints are in a repeated region, the method reports a range where the breakpoints can slide. Our method identifies the breakpoints of a CNV using both the positions and CIGAR strings of the reads that cover breakpoints of a CNV. A read with a long soft clipped part (denoted as S in CIGAR) at its 3′(right) end can be used to identify the 5′(left)-side of the breakpoints, and a read with a long S part at the 5′ end can be used to identify the breakpoint at the 3′-side. To ensure both types of reads cover the same CNV, we require the overlapped common string to include both of the soft clipped parts. When a CNV starts and ends in the same repeated regions, its breakpoints are not unique, in which case our method reports the left most positions for the breakpoints and a range within which the breakpoints can be incremented without changing the variant sequence. We have implemented the methods in a C++ package intended for the current Illumina Miseq and Hiseq platforms for both whole genome and exon-sequencing. Our simulation studies have shown that our method compares favorably with other similar methods in terms of true discovery rate, false positive rate and breakpoint accuracy. Our results from a real application have shown that the detected CNVs are consistent with zygosity and read depth information. The software package is available at http://statgene.med.upenn.edu/softprog.html

Characteristics of High Risk People with Cardiovascular Disease in Chinese Rural Areas: Clinical Indictors, Disease Patterns and Drug Treatment

Background and Aims: Current cardiovascular disease (CVD) prevention is based on diagnosis and treatment of specific disease. Little is known for high risk people with CVD at the community level. In rural China, health records of all residents were established after the recent health reforms. This study aims to describe the characters of the rural population with high CVD risk regarding their clinical indicators, disease patterns, drug treatment and adherence. Methods and Results: 17042 (87%) of all the 19500 rural residents in the two townships had valid health records in 2009. We employed a validated tool, the Asian Equation, to screen 8182 (48%) resident health records of those aged between 40-75 years in 2010. Those who were identified with a CVD risk of 20% or higher were selected for a face-to-face questionnaire survey regarding their diagnosed disease and drug treatment. 453 individuals were identified as high risk of CVD, with an average age of 53 years, 62% males, 50% smoking rate and average systolic blood pressure of 161 mmHg. 386 (85%) participated in the survey, while 294 (76%) were diagnosed with and 88 (23%) were suspects of CVD, hypertension, diabetes or hyperlipidaemia. 75 (19%) took drug regularly and 125 (32%) either stopped treatment or missed drugs. The most often used drugs were calcium channel blockers (20%). Only 2% used aspirins and 0.8% used statins. The median costs of drugs were 17 RMB (USD2.66) per month. Conclusion: The majority of the high risk population in our setting of rural China had already been diagnosed with a CVD related disease, but very few took any drugs, and less still took highly effective drugs to prevent CVD. A holistic strategy focused on population with high risk CVD and based on the current China public health reform is suggested in the context of primary care. © 2013 Wei et al.published_or_final_versio

Serial deletion reveals structural basis and stability for the core enzyme activity of human glutaminase 1 isoforms: relevance to excitotoxic neurodegeneration.

BACKGROUND: Glutaminase 1 is a phosphate-activated metabolic enzyme that catalyzes the first step of glutaminolysis, which converts glutamine into glutamate. Glutamate is the major neurotransmitter of excitatory synapses, executing important physiological functions in the central nervous system. There are two isoforms of glutaminase 1, KGA and GAC, both of which are generated through alternative splicing from the same gene. KGA and GAC both transcribe 1-14 exons in the N-terminal, but each has its unique C-terminal in the coding sequence. We have previously identified that KGA and GAC are differentially regulated during inflammatory stimulation and HIV infection. Furthermore, glutaminase 1 has been linked to brain diseases such as amyotrophic lateral sclerosis, Alzheimer\u27s disease, and hepatic encephalopathy. Core enzyme structure of KGA and GAC has been published recently. However, how other coding sequences affect their functional enzyme activity remains unclear. METHODS: We cloned and performed serial deletions of human full-length KGA and GAC from the N-terminal and the C-terminal at an interval of approximately 100 amino acids (AAs). Prokaryotic expressions of the mutant glutaminase 1 protein and a glutaminase enzyme activity assay were used to determine if KGA and GAC have similar efficiency and efficacy to convert glutamine into glutamate. RESULTS: When 110 AAs or 218 AAs were deleted from the N-terminal or when the unique portions of KGA and GAC that are beyond the 550 AA were deleted from the C-terminal, KGA and GAC retained enzyme activity comparable to the full length proteins. In contrast, deletion of 310 AAs or more from N-terminal or deletion of 450 AAs or more from C-terminal resulted in complete loss of enzyme activity for KGA/GAC. Consistently, when both N- and C-terminal of the KGA and GAC were removed, creating a truncated protein that expressed the central 219 AA - 550 AA, the protein retained enzyme activity. Furthermore, expression of the core 219 AA - 550 AA coding sequence in cells increased extracellular glutamate concentrations to levels comparable to those of full-length KGA and GAC expressions, suggesting that the core enzyme activity of the protein lies within the central 219 AA - 550 AA. Full-length KGA and GAC retained enzyme activities when kept at 4 °C. In contrast, 219 AA - 550 AA truncated protein lost glutaminase activities more readily compared with full-length KGA and GAC, suggesting that the N-terminal and C-terminal coding regions are required for the stability KGA and GAC. CONCLUSIONS: Glutaminase isoforms KGA and GAC have similar efficacy to catalyze the conversion of glutamine to glutamate. The core enzyme activity of glutaminase 1 protein is within the central 219 AA - 550 AA. The N-terminal and C-terminal coding regions of KGA and GAC help maintain the long-term activities of the enzymes

Tumor grafts grown on the chicken chorioallantoic membrane are distinctively characterized by MRI under functional gas challenge

Recently, a tumor model based on the chorioallantoic membrane (CAM) was characterized structurally with Magnetic Resonance Imaging (MRI). Yet, capability of MRI to assess vascular functional reserve and potential of oxygenation-sensitive MRI remain largely unexplored in this model. For this purpose, we compared MC-38 colon and A549 lung adenocarcinoma cell grafts grown on the CAM, using quantitative T1 and T2* MRI readouts as imaging markers. These are associated with vascular functionality and oxygenation status when compared between periods of air and carbogen exposure. Our data show that in A549 lung adenocarcinoma cell grafts T2* values increased significantly upon carbogen exposure (p < 0.004, Wilcoxon test; no change in T1), while MC-38 grafts displayed no changes in T1 and T2*), indicating that the grafts differ in their vascular response. Heterogeneity with regard to T1 and T2* distribution within the grafts was noted. MC-38 grafts displayed larger T1 and T2* in the graft centre, while in A549 they were distributed more towards the graft surface. Finally, qualitative assessment of gadolinium-enhancement suggests that A549 grafts display more prominent enhancement compared to MC-38 grafts. Furthermore, MC-38 grafts had 65% larger volumes than A549 grafts. Histology revealed distinct underlying phenotypes of the two tumor grafts, pertaining to the proliferative status (Ki-67) and cellularity (H&E). In sum, a functional gas challenge with carbogen is feasible through gas exchange on the CAM, and it affects MRI signals associated with vascular reactivity and oxygenation status of the tumor graft planted on the CAM. Different grafts based on A549 lung adenocarcinoma and MC-38 colon carcinoma cell lines, respectively, display distinct phenotypes that can be distinguished and characterized non-invasively in ovo using MRI in the living chicken embryo

Predicting Patch Correctness Based on the Similarity of Failing Test Cases

Towards predicting patch correctness in APR, we propose a simple, but novel hypothesis on how the link between the patch behaviour and failing test specifications can be drawn: similar failing test cases should require similar patches. We then propose BATS, an unsupervised learning-based system to predict patch correctness by checking patch Behaviour Against failing Test Specification. BATS exploits deep representation learning models for code and patches: for a given failing test case, the yielded embedding is used to compute similarity metrics in the search for historical similar test cases in order to identify the associated applied patches, which are then used as a proxy for assessing generated patch correctness. Experimentally, we first validate our hypothesis by assessing whether ground-truth developer patches cluster together in the same way that their associated failing test cases are clustered. Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0.557 to 0.718 and a recall between 0.562 and 0.854 in identifying correct patches. Compared against previous work, we demonstrate that our approach outperforms state-of-the-art performance in patch correctness prediction, without the need for large labeled patch datasets in contrast with prior machine learning-based approaches. While BATS is constrained by the availability of similar test cases, we show that it can still be complementary to existing approaches: used in conjunction with a recent approach implementing supervised learning, BATS improves the overall recall in detecting correct patches. We finally show that BATS can be complementary to the state-of-the-art PATCH-SIM dynamic approach of identifying the correct patches for APR tools

The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches

A large body of the literature on automated program repair develops approaches where patches are automatically generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. Our empirical work investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations of patch correctness identification, and assess the possibility of accurate classification of correct patch by combining learned embeddings with engineered features. Experimental results demonstrate the potential of learned embeddings to empower Leopard (a patch correctness predicting framework implemented in this work) with learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based learned embeddings associated with XGBoost achieves an AUC value of about 0.895 in the prediction of patch correctness on a new dataset of 2,147 labeled patches that we collected for the experiments. Our investigations show that deep learned embeddings can lead to complementary/better performance when comparing against the state-of-the-art, PATCH-SIM, which relies on dynamic information. By combining deep learned embeddings and engineered features, Panther (the upgraded version of Leopard implemented in this work) outperforms Leopard with higher scores in terms of AUC, +Recall and -Recall, and can accurately identify more (in)correct patches that cannot be predicted by the classifiers only with learned embeddings or engineered features. Finally, we use an explainable ML technique, SHAP, to empirically interpret how the learned embeddings and engineered features are contributed to the patch correctness prediction.Comment: arXiv admin note: substantial text overlap with arXiv:2008.0294

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.Comment: 14 pages, 8 figure

Exercise Improves Outcomes of Surgery on Fatty Liver in Mice: A Novel Effect Mediated by the AMPK Pathway.

OBJECTIVE To investigate whether exercise improves outcomes of surgery on fatty liver, and whether pharmacological approaches can substitute exercising programs. SUMMARY OF BACKGROUND DATA Steatosis is the hepatic manifestation of the metabolic syndrome, and decreases the liver's ability to handle inflammatory stress or to regenerate after tissue loss. Exercise activates adenosine monophosphate-activated kinase (AMPK) and mitigates steatosis; however, its impact on ischemia-reperfusion injury and regeneration is unknown. METHODS We used a mouse model of simple, diet-induced steatosis and assessed the impact of exercise on metabolic parameters, ischemia-reperfusion injury and regeneration after hepatectomy. The same parameters were evaluated after treatment of mice with the AMPK activator 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR). Mice on a control diet served as age-matched controls. RESULTS A 4-week-exercising program reversed steatosis, lowered insulin levels, and improved glucose tolerance. Exercise markedly enhanced the ischemic tolerance and the regenerative capacity of fatty liver. Replacing exercise with AICAR was sufficient to replicate the above benefits. Both exercise and AICAR improved survival after extended hepatectomy in mice challenged with a Western diet, indicating protection from resection-induced liver failure. CONCLUSIONS Exercise efficiently counteracts the metabolic, ischemic, and regenerative deficits of fatty liver. AICAR acts as an exercise mimetic in settings of fatty liver disease, an important finding given the compliance issues associated with exercise. Exercising, or its substitution through AICAR, may provide a feasible strategy to negate the hepatic consequences of energy-rich diet, and has the potential to extend the application of liver surgery if confirmed in humans

The DNA Methylome of Human Peripheral Blood Mononuclear Cells

Analysis across the genome of patterns of DNA methylation reveals a rich landscape of allele-specific epigenetic modification and consequent effects on allele-specific gene expression