An Implementation of the Parallel K-core Decomposition Algorithm in GraphBLAS

The k-core of an undirected graph is the largest subgraph in which every vertex has a degree of at least some number k. Computing the k-core, also known as the k-core decomposition algorithm, has significant applications in network analysis, visualization, bioinformatics, and community detection. There exists a sequential procedure, developed by Batagelj and Zaversnik in 2003, that accurately performs k-core decomposition. This implementation has been consistently referenced as the gold standard, due to its O(n + m) runtime. However, due to its large working set and lack of parallelism, its performance suffers on modern big-data graph problems where sheer size tends to overwhelm runtime due to cache misses. A 2014 algorithm designed by Dasari, Desh and Zubair M implements a parallel version of k-core decomposition (ParK) with significant speedup on multithreaded architectures. This paper aims to describe the development and implementation of ParK using the SuiteSparse:GraphBLAS API in C, a robust framework that defines a set of matrix and vector operations based on an algebra of semirings to perform computations on graphs. We show that while the GraphBLAS algorithm underperforms versus the sequential implementation in a full decomposition, a modified version of the algorithm that only computes a partial decomposition given some value k is significantly faster

KritiSamhita: A machine learning dataset of South Indian classical music audio clips with tonic classification

There are currently a limited number of Indian classical music datasets, especially those large enough and with useful annotations, particularly the subtler ones, such as the tonic, for training classification or prediction models. The dataset described in this paper is created with useful tonic annotations, to fill this gap. The tonic pitch, or base pitch, plays an important role in music, so much so that it is sometimes called the keynote. The vocalists and the accompanying instrumental ensemble are fine-tuned to this keynote to render the composition. The first and second authors of this paper, who are vocalists themselves, recorded songs in four different tonics: F#, G, G#, and A. Using the Python library pydub, each 3+ minute song was segmented into 20-second snippets, including the remainder as a separate snippet. The raw audio snippet data is available in folders separated by tonic, and a directory contains each snippet\u27s file path and tonic. This dataset can be reused for tonic classification work in the future, as well as for training other automated systems targeting higher-level attributes of ICM, such as melodic framework, as a tonic can be the basis for them all

Expression and methylation status of tissue factor pathway inhibitor-2 gene in non-small-cell lung cancer

Tissue factor pathway inhibitor-2 (TFPI-2) is a Kunitz-type serine proteinase inhibitor that inhibits plasmin-dependent activation of several metalloproteinases. Downregulation of TFPI-2 could thus enhance the invasive potential of neoplastic cells in several cancers, including lung cancer. In this study, TFPI-2 mRNA was measured using a real-time PCR method in tumours of 59 patients with non-small-cell lung cancer (NSCLC). Tumour TFPI-2 mRNA levels appeared well correlated with protein expression evaluated by immunohistochemistry and were 4–120 times lower compared to those of nonaffected lung tissue in 22 cases (37%). Hypermethylation of the TFPI-2 gene promoter was demonstrated by restriction enzyme-polymerase chain reaction in 12 of 40 cases of NSCLC (30%), including nine of 17 for whom tumour TFPI-2 gene expression was lower than in noncancerous tissue. In contrast, this epigenetic modification was shown in only three of 23 tumours in which no decrease in TFPI-2 synthesis was found (P=0.016). Decreased TFPI-2 gene expression and hypermethylation were more frequently associated with stages III or IV NSCLC (eight out of 10, P=0.02) and the TFPI-2 gene promoter was more frequently hypermethylated in patients with lymph node metastases (eight out of 16, P=0.02). These results suggest that silencing of the TFPI-2 gene by hypermethylation might contribute to tumour progression in NSCLC

D-4F, an apoA-1 mimetic, decreases airway hyperresponsiveness, inflammation, and oxidative stress in a murine model of asthma

Asthma is characterized by oxidative stress and inflammation of the airways. Although proinflammatory lipids are involved in asthma, therapies targeting them remain lacking. Ac-DWFKAFYDKVAEKFKEAFNH2 (4F) is an apolipoprotein (apo)A-I mimetic that has been shown to preferentially bind oxidized lipids and improve HDL function. The objective of the present study was to determine the effects of 4F on oxidative stress, inflammation, and airway resistance in an established murine model of asthma. We show here that ovalbumin (OVA) -sensitization increased airway hyperresponsiveness, eosinophil recruitment, and collagen deposition in lungs of C57BL/6J mice by a mechanism that could be reduced by 4F. OVA sensitization induced marked increases in transforming growth factor (TGF)β-1, fibroblast specific protein (FSP)-1, anti-T15 autoantibody staining, and modest increases in 4-hydroxynonenal (4-HNE) Michael\u27s adducts in lungs of OVA-sensitized mice. 4F decreased TGFβ-1, FSP-1, anti-T15 autoantibody, and 4-HNE adducts in the lungs of the OVA-sensitized mice. Eosinophil peroxidase (EPO) activity in bronchial alveolar lavage fluid (BALF), peripheral eosinophil counts, total IgE, and proinflammatory HDL (p-HDL) were all increased in OVA-sensitized mice. 4F decreased BALF EPO activity, eosinophil counts, total IgE, and p-HDL in these mice. These data indicate that 4F reduces pulmonary inflammation and airway resistance in an experimental murine model of asthma by decreasing oxidative stress

Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning

Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convolutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies

Impact of Intracranial Volume and Brain Volume on the Prognostic Value of Computed Tomography Perfusion Core Volume in Acute Ischemic Stroke

Background: Computed tomography perfusion (CTP)-estimated core volume is associated with functional outcomes in acute ischemic stroke. This relationship might differ among patients, depending on brain volume. Materials and Methods: We retrospectively included patients from the MR CLEAN Registry. Cerebrospinal fluid (CSF) and intracranial volume (ICV) were automatically segmented on NCCT. We defined the proportion of the ICV and total brain volume (TBV) affected by the ischemic core as ICVcore and TBVcore. Associations between the core volume, ICVcore, TBVcore, and functional outcome are reported per interquartile range (IQR). We calculated the area under the curve (AUC) to assess diagnostic accuracy.Results: In 200 patients, the median core volume was 13 (5–41) mL. Median ICV and TBV were 1377 (1283–1456) mL and 1108 (1020–1197) mL. Median ICVcore and TBVcore were 0.9 (0.4–2.8)% and 1.7 (0.5–3.6)%. Core volume (acOR per IQR 0.48 [95%CI 0.33–0.69]), ICVcore (acOR per IQR 0.50 [95%CI 0.35–0.69]), and TBVcore (acOR per IQR 0.41 95%CI 0.33–0.67]) showed a lower likelihood of achieving improved functional outcomes after 90 days. The AUC was 0.80 for the prediction of functional independence at 90 days for the CTP-estimated core volume, the ICVcore, and the TBVcore. Conclusion:Correcting the CTP-estimated core volume for the intracranial or total brain volume did not improve the association with functional outcomes in patients who underwent EVT.</p

Nitric oxide synthases in infants and children with pulmonary hypertension and congenital heart disease

Nitric oxide is an important regulator of vascular tone in the pulmonary circulation. Surgical correction of congenital heart disease limits pulmonary hypertension to a brief period. The study has measured expression of endothelial (eNOS), inducible (iNOS), and neuronal nitric oxide synthase (nNOS) in the lungs from biopsies of infants with pulmonary hypertension secondary to cardiac abnormalities (n = 26), compared to a control group who did not have pulmonary or cardiac disease (n = 8). eNOS, iNOS and nNOS were identified by immunohistochemistry and quantified in specific cell types. Significant increases of eNOS and iNOS staining were found in pulmonary vascular endothelial cells of patients with congenital heart disease compared to control infants. These changes were confined to endothelial cells and not present in other cell types. Patients who strongly expressed eNOS also had strong expression of iNOS. Upregulation of eNOS and iNOS occurs at an early stage of pulmonary hypertension, and may be a compensatory mechanism limiting the rise in pulmonary artery pressure