Miracle-dz/UniFAR-FTUs huggingface dataset:
Prenatal Air Pollution Exposures, DNA Methyl Transferase Genotypes, and Associations with Newborn LINE1 and Alu Methylation and Childhood Blood Pressure and Carotid Intima-Media Thickness in the Children’s Health Study

Background: Although exposure to ambient air pollutants increases cardiovascular disease risk in adults little is known about the effects of prenatal exposure. Genetic variation and epigenetic alterations are two mechanisms that may influence the effects of early-life exposures on cardiovascular phenotypes. Objectives: We investigated whether genetic and epigenetic variation modify associations between prenatal air pollution on markers of cardiovascular risk in childhood. Methods: We used linear regression analysis to investigate the associations between prenatal pollutants (PM2.5, PM10, NO2, O3), long interspersed nuclear elements (LINE1) and AluYb8 DNA methylation levels measured in newborn blood spot tests, and carotid intima-media thickness (CIMT) and blood pressure (BP) in 459 participants as part of the Children’s Health Study. Interaction terms were also included to test for effect modification of these associations by genetic variation in methylation reprogramming genes. Results: Prenatal exposure to NO2 in the third trimester of pregnancy was associated with higher systolic BP in 11-year-old children. Prenatal exposure to multiple air pollutants in the first trimester was associated with lower DNA methylation in LINE1, whereas later exposure to O3 was associated with higher LINE1 methylation levels in newborn blood spots. The magnitude of associations with prenatal air pollution varied according to genotype for 11 SNPs within DNA methyltransferase 1 (DNMT1), DNA methyltransferase 3 Beta (DNMT3B), Tet methylcytosine dioxygenase 2 (TET2), and Thymine DNA glycosylase (TDG) genes. Although first-trimester O3 exposure was not associated with CIMT and systolic BP overall, associations within strata of DNMT1 or DNMT3B were observed, and the magnitude and the direction of these associations depended on DNMT1 genotypes. Conclusions: Genetic and epigenetic variation in DNA methylation reprogramming genes and in LINE1 retrotransposons may play important roles in downstream cardiovascular consequences of prenatal air pollution exposure. Citation: Breton CV, Yao J, Millstein J, Gao L, Siegmund KD, Mack W, Whitfield-Maxwell L, Lurmann F, Hodis H, Avol E, Gilliland FD. 2016. Prenatal air pollution exposures, DNA methyl transferase genotypes, and associations with newborn LINE1 and Alu methylation and childhood blood pressure and carotid intima-media thickness in the Children’s Health Study. Environ Health Perspect 124:1905–1912; http://dx.doi. org/10.1289/EHP181
Association of Habitual Activity and Body Mass Index in Survivors of Congenital Heart Surgery: A Study of Children and Adolescents With Tetralogy of Fallot, Transposition of the Great Arteries, and Fontan Palliation

Background: Obesity is associated with increased lifelong morbidity and reduced life span and is increasingly prevalent in the congenital heart disease population. Habitual exercise is an important aspect of a healthy lifestyle and primary prevention of obesity in the general population. The association between habitual activity and body mass index (BMI) has not been studied in children with congenital heart disease. Methods: A cross-sectional analysis of two previously collected cohorts was performed, including participants 8 to 18 years old with tetralogy of Fallot, transposition of the great arteries, and single ventricle heart disease after a Fontan operation. The association between BMI and duration of habitual exercise (measured by questionnaire) was studied. Secondary analyses assessing the effect of other possible factors for BMI were performed. Results: In total, 172 participants were studied (45% Tetralogy of Fallot, 12% transposition of the great arteries, and 43% Fontan). Median BMI was 18.2, and 29% of the participants were obese or overweight. Median habitual exercise was 5.9 h/wk. Thirty-eight percent of participants reported having their activity restricted by their cardiologist. Increasing exercise duration was associated with lower BMI (P = .01) in univariate analysis. In secondary analyses, restriction to mild exertion and participation in low-intensity exercise were both associated with increased BMI. Conclusion: Increased habitual activity was associated with lower BMI, emphasizing the potential role of recreational sport in the health of children with congenital heart disease.
High-Productivity and High-Performance Analysis of Filtered Semantic Graphs

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry attributes of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, is customizable in two ways. First, the user can write custom graph algorithms by specifying operations between edges and vertices. These programmer-specified operations are called semiring operations due to KDT's underlying linear-algebraic abstractions. Second, the user can customize existing graph algorithms by writing filters that return true for those vertices and edges the user wants to retain during algorithm execution. For high productivity, both semiring operations and filters are written in a high-level language, resulting in relatively low performance due to the bottleneck of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine, and show our approach enables users to write in high-level languages and still obtain the high performance of low-level code. We also present a new roofline model for graph traversals, and show that our high-performance implementations do not significantly deviate from the roofline. Overall, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs.
DNA barcodes effectively identify the morphologically similar Common Opossum (Didelphis marsupialis) and Virginia Opossum (Didelphis virginiana) from areas of sympatry in Mexico

Two morphologically similar species of opossum from the genus Didelphis–Didelphis virginiana and Didelphis marsupialis–cooccur sympatrically in Mexico. High intraspecific variation complicates their morphological discrimination, under both field and museum conditions. This study aims to evaluate the utility and reliability of using DNA barcodes (short standardized genome fragments used for DNA-based identification) to distinguish these two species. Sequences of the cytochrome c oxidase subunit I (Cox1) mitochondrial gene were obtained from 12 D. marsupialis and 29 D. virginiana individuals and were compared using the neighbor-joining (NJ) algorithm with Kimura's two-parameter (K2P) model of nucleotide substitution. Average K2P distances were 1.56% within D. virginiana and 1.65% in D. marsupialis. Interspecific distances between D. virginiana and D. marsupialis varied from 7.8 to 9.3% and their barcode sequences formed distinct non-overlapping clusters on NJ trees. All sympatric specimens of both species were effectively discriminated, confirming the utility of Cox1 barcoding as a tool for taxonomic identification of these morphologically similar taxa.
Drug-target interaction prediction: databases, web servers and computational models

Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.
Genetic variant of SPARC gene and its association with growth traits in Chinese cattle

Abstract SPARC is a cysteine-rich acidic secreted protein. It is a non-collagen component of bone, which is widely distributed in humans and animals and plays an important role. SPARC has been found in a variety of human cancers (breast cancer, stomach cancer, ovarian cancer, etc.) and diabetes-related research. Especially the muscle and fat metabolism are closely related. In this study, we used a DNA pool to detect a new SNP site (g.12454T > C). A total of 616 samples of four breeds of Qinchuan cattle (QC, n=176), Xianan cattle (XN, n=160), Pinan cattle (PN, n=136) and Jiaxian cattle (JX, n=144) were analyzed and identified with ARMS-PCR. In addition, we correlated SNP with growth traits and showed significant correlation with growth traits such as rump length, hip width, and body length (p<0.05). Moreover, we tested the SPARC gene expression level in different tissues belonging to XN adult cattle (n=3) and found its high expression in muscle tissues (relative to the kidney). Further, we found the SNP is able to increase the SPARC expression level in skeletal muscle (n=12). According to statistical data, this SNP site may be applied to a molecular marker of an early marker-assisted selection for early growth of beef cattle.
Deciphering drought‐induced metabolic responses and regulation in developing maize kernels

Summary Drought stress conditions decrease maize growth and yield, and aggravate preharvest aflatoxin contamination. While several studies have been performed on mature kernels responding to drought stress, the metabolic profiles of developing kernels are not as well characterized, particularly in germplasm with contrasting resistance to both drought and mycotoxin contamination. Here, following screening for drought tolerance, a drought‐sensitive line, B73, and a drought‐tolerant line, Lo964, were selected and stressed beginning at 14 days after pollination. Developing kernels were sampled 7 and 14 days after drought induction (DAI) from both stressed and irrigated plants. Comparative biochemical and metabolomic analyses profiled 409 differentially accumulated metabolites. Multivariate statistics and pathway analyses showed that drought stress induced an accumulation of simple sugars and polyunsaturated fatty acids and a decrease in amines, polyamines and dipeptides in B73. Conversely, sphingolipid, sterol, phenylpropanoid and dipeptide metabolites accumulated in Lo964 under drought stress. Drought stress also resulted in the greater accumulation of reactive oxygen species (ROS) and aflatoxin in kernels of B73 in comparison with Lo964 implying a correlation in their production. Overall, field drought treatments disordered a cascade of normal metabolic programming during development of maize kernels and subsequently caused oxidative stress. The glutathione and urea cycles along with the metabolism of carbohydrates and lipids for osmoprotection, membrane maintenance and antioxidant protection were central among the drought stress responses observed in developing kernels. These results also provide novel targets to enhance host drought tolerance and disease resistance through the use of biotechnologies such as transgenics and genome editing.
Keyframe selection for robust pose estimation in laparoscopic videos

Motion estimation based on point correspondences in two views is a classic problem in computer vision. In the field of laparoscopic video sequences - even with state of the art algorithms - a stable motion estimation can not be guaranteed generally. Typically, a video from a laparoscopic surgery contains sequences where the surgeon barely moves the endoscope. Such restricted movement causes a small ratio between baseline and distance leading to unstable estimation results. Exploiting the fact that the entire sequence is known a priori, we propose an algorithm for keyframe selection in a sequence of images. The key idea can be expressed as follows: if all combination of frames in a sequence are scored, the optimal solution can be described as a weighted directed graph problem. We adapt the widely known Dijkstras Algorithm to find the best selection of frames.1 The framework for keyframe selection can be used universally to find the best combination of frames for any reliable scoring function. For instance, forward motion ensures the most accurate camera position estimation, whereas sideward motion is preferred in the sense of reconstruction. Based on the distribution and the disparity of point correspondences, we propose a scoring function which is capable of detecting poorly conditioned pairs of frames. We demonstrate the potential of the algorithm focusing on accurate camera positions. A robot system provides ground truth data. The environment in laparoscopic videos is reflected by an industrial endoscope and a phantom.
Vitrification after multiple rounds of sample application and blotting improves particle density on cryo-electron microscopy grids

Single particle cryo-electron microscopy (cryoEM) is becoming widely adopted as a tool for structural characterization of biomolecules at near-atomic resolution. Vitrification of the sample to obtain a dense distribution of particles within a single field of view remains a major bottleneck for the success of such experiments. Here, we describe a simple and cost-effective method to increase the density of frozen-hydrated particles on grids with holey carbon support films. It relies on performing multiple rounds of sample application and blotting prior to plunge freezing in liquid ethane. We show that this approach is generally applicable and significantly increases particle density for a range of samples, such as small protein complexes, viruses and filamentous assemblies. The method is versatile, easy to implement, minimizes sample requirements and can enable characterization of samples that would otherwise resist structural studies using single particle cryoEM.
Casein Kinase 1α Mediates the Degradation of Receptors for Type I and Type II Interferons Caused by Hemagglutinin of Influenza A Virus

ABSTRACT Although influenza A virus (IAV) evades cellular defense systems to effectively propagate in the host, the viral immune-evasive mechanisms are incompletely understood. Our recent data showed that hemagglutinin (HA) of IAV induces degradation of type I IFN receptor 1 (IFNAR1). Here, we demonstrate that IAV HA induces degradation of type II IFN (IFN-γ) receptor 1 (IFNGR1), as well as IFNAR1, via casein kinase 1α (CK1α), resulting in the impairment of cellular responsiveness to both type I and II IFNs. IAV infection or transient HA expression induced degradation of both IFNGR1 and IFNAR1, whereas HA gene-deficient IAV failed to downregulate the receptors. IAV HA caused the phosphorylation and ubiquitination of IFNGR1, leading to the lysosome-dependent degradation of IFNGR1. Influenza viral HA strongly decreased cellular sensitivity to type II IFNs, as it suppressed the activation of STAT1 and the induction of IFN-γ-stimulated genes in response to exogenously supplied recombinant IFN-γ. Importantly, CK1α, but not p38 MAP kinase or protein kinase D2, was proven to be critical for HA-induced degradation of both IFNGR1 and IFNAR1. Pharmacologic inhibition of CK1α or small interfering RNA (siRNA)-based knockdown of CK1α repressed the degradation processes of both IFNGR1 and IFNAR1 triggered by IAV infection. Further, CK1α was shown to be pivotal for proficient replication of IAV. Collectively, the results suggest that IAV HA induces degradation of IFN receptors via CK1α, creating conditions favorable for viral propagation. Therefore, the study uncovers a new immune-evasive pathway of influenza virus. IMPORTANCE Influenza A virus (IAV) remains a grave threat to humans, causing seasonal and pandemic influenza. Upon infection, innate and adaptive immunity, such as the interferon (IFN) response, is induced to protect hosts against IAV infection. However, IAV seems to be equipped with tactics to evade the IFN-mediated antiviral responses, although the detailed mechanisms need to be elucidated. In the present study, we show that IAV HA induces the degradation of the type II IFN receptor IFNGR1 and thereby substantially attenuates cellular responses to IFN-γ. Of note, a cellular kinase, casein kinase 1α (CK1α), is crucial for IAV HA-induced degradation of both IFNGR1 and IFNAR1. Accordingly, CK1α is proven to positively regulate IAV propagation. Thus, this study unveils a novel strategy employed by IAV to evade IFN-mediated antiviral activities. These findings may provide new insights into the interplay between IAV and host immunity to impact influenza virus pathogenicity.
LIGHT-WEIGHT CRYPTOGRAPHY TO SECURE MEDICAL DATA IN IOT

Different applications require different level of security where the scarce of resource plays effective role. In order to protect the private medical data in the internet of things (IoT) field, the search for the optimal encryption algorithm is a must. Electronic sensors are used to collect medical data from the patient's body acquiring its transmission to the healthcare system securely. It is essential to ensure trust and data secrecy from the starting point-sensors throughout the medical treatment to prevent any unauthorized access or unneeded interruption. Thus, data encryption from the beginning sensors is necessary but facing all limitations in computing complexity, power consumption and communication bandwidth, where the normal available crypto-algorithms are considered heavyweight is completely unpractical. This work study several realistic lightweight encryption algorithms suitable for IoT medical systems. The paper outlines a comparison between ten applicable cryptographic algorithms resulting fair analysis in terms of memory utilization and speed. The investigation deduces the optimal candidate algorithm for the proposed health care system considering the balance between the optimal requirement and the future threats.
Histone Deacetylase 11 Is a Fatty-Acid Deacylase.

Histone deacetylase 11 (HDAC11) is a sole member of the class IV HDAC subfamily with negligible intrinsic deacetylation activity. Here, we report in vitro profiling of HDAC11 deacylase activities, and our data unequivocally show that the enzyme efficiently removes acyl moieties spanning 8-18 carbons from the side chain nitrogen of the lysine residue of a peptidic substrate. Additionally, N-linked lipoic acid and biotin are removed by the enzyme, although with lower efficacy. Catalytic efficiencies toward dodecanoylated and myristoylated peptides were 77 700 and 149 000 M-1 s-1, respectively, making HDAC11 the most proficient fatty-acid deacylase of the HDAC family. Interestingly, HDAC11 is strongly inhibited by free myristic, palmitic, and stearic acids with inhibition constants of 6.5, 0.9, and 1.6 μM, respectively. At the same time, its deacylase activity is stimulated more than 2.5-fold by both palmitoyl-coenzyme A and myristoyl-coenzyme A, pointing toward metabolic control of the enzymatic activity by fatty-acid metabolites. Our data reveal novel enzymatic activity of HDAC11 that can, in turn, facilitate the uncovering of additional biological functions of the enzyme as well as the design of isoform-specific HDAC inhibitors.
Rapid Heterotrophic Ossification with Cryopreserved Poly(ethylene glycol-) Microencapsulated BMP2-Expressing MSCs

Autologous bone grafting is the most effective treatment for long-bone nonunions, but it poses considerable risks to donors, necessitating the development of alternative therapeutics. Poly(ethylene glycol) (PEG) microencapsulation and BMP2 transgene delivery are being developed together to induce rapid bone formation. However, methods to make these treatments available for clinical applications are presently lacking. In this study we used mesenchymal stem cells (MSCs) due to their ease of harvest, replication potential, and immunomodulatory capabilities. MSCs were from sheep and pig due to their appeal as large animal models for bone nonunion. We demonstrated that cryopreservation of these microencapsulated MSCs did not affect their cell viability, adenoviral BMP2 production, or ability to initiate bone formation. Additionally, microspheres showed no appreciable damage from cryopreservation when examined with light and electron microscopy. These results validate the use of cryopreservation in preserving the viability and functionality of PEG-encapsulated BMP2-transduced MSCs.
Predicting the Risk of Heart Failure With EHR Sequential Data Modeling

Electronic health records (EHRs) contain patient diagnostic records, physician records, and records of hospital departments. For heart failure, we can obtain mass unstructured data from EHR time series. By analyzing and mining these time-based EHRs, we can identify the links between diagnostic events and ultimately predict when a patient will be diagnosed. However, it is difficult to use the existing EHR data directly, because they are sparse and non-standardized. Thus, this paper proposes an effective and robust architecture for heart failure prediction. The main contribution of this paper is to predict heart failure using a neural network (i.e., to predict the possibility of cardiac illness based on patient’s electronic medical data). Specifically, we employed one-hot encoding and word vectors to model the diagnosis events and predicted heart failure events using the basic principles of a long short-term memory network model. Evaluations based on a real-world data set demonstrate the promising utility and efficacy of the proposed architecture in the prediction of the risk of heart failure.