279 research outputs found
Eliminating read barriers through procrastination and cleanliness
Managed languages use read barriers to interpret forwarding pointers introduced to keep track of copied objects. For example, in a split-heap managed runtime for a multicore environment, an object initially allocated on a local heap may be copied to a shared heap if it becomes the source of a store operation whose target location resides on the shared heap. As part of the copy operation, a forwarding pointer may be established to allow existing references to the local object to reference the copied version. In this paper, we consider the design of a managed runtime that avoids the need for read barriers. Our design is premised on the availability of a sufficient degree of concurrency to stall operations that would otherwise necessitate the copy. Stalled actions are deferred until the next local collection, avoiding exposing forwarding pointers to the mutator. In certain important cases, procrastination is unnecessary- lightweight runtime techniques can sometimes be used to allow objects to be eagerly copied when their set of incoming references is known, or when it can be determined that having multiple copies would not violate program semantics. Experimental results over a range of parallel benchmarks on a number of different architectural platforms including an 864 core Azul Vega 3, and a 48 core Intel SCC, indicate that our approach leads to notable performance gains (20- 32 % on average) without incurring any additional complexity
Version Control Is for Your Data Too
Programmers regularly use distributed version control systems (DVCS) such as Git to facilitate collaborative software development. The primary purpose of a DVCS is to maintain integrity of source code in the presence of concurrent, possibly conflicting edits from collaborators. In addition to safely merging concurrent non-conflicting edits, a DVCS extensively tracks source code provenance to help programmers contextualize and resolve conflicts. Provenance also facilitates debugging by letting programmers see diffs between versions and quickly find those edits that introduced the offending conflict (e.g., via git blame).
In this paper, we posit that analogous workflows to collaborative software development also arise in distributed software execution; we argue that the characteristics that make a DVCS an ideal fit for the former also make it an ideal fit for the latter. Building on this observation, we propose a distributed programming model, called carmot that views distributed shared state as an entity evolving in time, manifested as a sequence of persistent versions, and relies on an explicitly defined merge semantics to reconcile concurrent conflicting versions. We show examples demonstrating how carmot simplifies distributed programming, while also enabling novel workflows integral to modern applications such as blockchains. We also describe a prototype implementation of carmot that we use to evaluate its practicality
pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model
Background: Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results: Using an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. Conclusion: Together, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available at https://github.com/KCLabMTU/pLMSNOSite
DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides
With the technological advancement in recent years and the widespread use of magnetism in every sector of the current technology, a search for a low-cost magnetic material has been more important than ever. The discovery of magnetism in alternate materials such as metal chalcogenides with abundant atomic constituents would be a milestone in such a scenario. However, considering the multitude of possible chalcogenide configurations, predictive computational modeling or experimental synthesis is an open challenge. Here, we recourse to a stacked generalization machine learning model to predict magnetic moment (µB) in hexagonal Fe-based bimetallic chalcogenides, FexAyB; A represents Ni, Co, Cr, or Mn, and B represents S, Se, or Te, and x and y represent the concentration of respective atoms. The stacked generalization model is trained on the dataset obtained using first-principles density functional theory. The model achieves MSE, MAE, and R2 values of 1.655 (µB)2, 0.546 (µB), and 0.922 respectively on an independent test set, indicating that our model predicts the compositional dependent magnetism in bimetallic chalcogenides with a high degree of accuracy. A generalized algorithm is also developed to test the universality of our proposed model for any concentration of Ni, Co, Cr, or Mn up to 62.5% in bimetallic chalcogenides
Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction
O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we compared the performance of three protein sequence-based large protein language models (pLMs), Ankh, ESM-2, and ProtT5, for prediction of O-GlcNAc sites and also evaluated various ensemble strategies to integrate embeddings from these protein language models. Upon investigation, the decision-level fusion approach that integrates the decisions of the three embedding models, which we call LM-OGlcNAc-Site, outperformed the models trained on these individual language models as well as other fusion approaches and other existing predictors in almost all of the parameters evaluated. The precise prediction of O-GlcNAc sites will facilitate the probing of O-GlcNAc site-specific functions of proteins in physiology and diseases. Moreover, these findings also indicate the effectiveness of combined uses of multiple protein language models in post-translational modification prediction and open exciting avenues for further research and exploration in other protein downstream tasks. LM-OGlcNAc-Site’s web server and source code are publicly available to the community
Improving protein succinylation sites prediction using embeddings from protein language model
Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease
Functional divergence in the role of N-linked glycosylation in smoothened signaling
The G protein-coupled receptor (GPCR) Smoothened (Smo) is the requisite signal transducer of the evolutionarily conserved Hedgehog (Hh) pathway. Although aspects of Smo signaling are conserved from Drosophila to vertebrates, significant differences have evolved. These include changes in its active sub-cellular localization, and the ability of vertebrate Smo to induce distinct G protein-dependent and independent signals in response to ligand. Whereas the canonical Smo signal to Gli transcriptional effectors occurs in a G protein-independent manner, its non-canonical signal employs Gαi. Whether vertebrate Smo can selectively bias its signal between these routes is not yet known. N-linked glycosylation is a post-translational modification that can influence GPCR trafficking, ligand responsiveness and signal output. Smo proteins in Drosophila and vertebrate systems harbor N-linked glycans, but their role in Smo signaling has not been established. Herein, we present a comprehensive analysis of Drosophila and murine Smo glycosylation that supports a functional divergence in the contribution of N-linked glycans to signaling. Of the seven predicted glycan acceptor sites in Drosophila Smo, one is essential. Loss of N-glycosylation at this site disrupted Smo trafficking and attenuated its signaling capability. In stark contrast, we found that all four predicted N-glycosylation sites on murine Smo were dispensable for proper trafficking, agonist binding and canonical signal induction. However, the under-glycosylated protein was compromised in its ability to induce a non-canonical signal through Gαi, providing for the first time evidence that Smo can bias its signal and that a post-translational modification can impact this process. As such, we postulate a profound shift in N-glycan function from affecting Smo ER exit in flies to influencing its signal output in mice
Extended 2D myotube culture recapitulates postnatal fibre type plasticity
Background: The traditional problems of performing skeletal muscle cell cultures derived from mammalian or avian species are limited myotube differentiation, and transient myotube persistence which greatly restricts the ability of myotubes to undergo phenotypic maturation. We report here on a major technical breakthrough in the establishment of a simple and effective method of extended porcine myotube cultures (beyond 50 days) in two-dimension (2D) that recapitulates key features of postnatal fibre types.
Results: Primary porcine muscle satellite cells (myoblasts) were isolated from the longissimus dorsi of 4 to 6 weeks old pigs for 2D cultures to optimise myotube formation, improve surface adherence and characterise myotube maturation. Over 95 % of isolated cells were myoblasts as evidenced by the expression of Pax3 and Pax7. Our relatively simple approach, based on modifications of existing surface coating reagents (Maxgel), and of proliferation and differentiation (Ultroser G) media, typically achieved by 5 days of differentiation fusion index of around 80 % manifested in an abundance of discrete myosin heavy chain (MyHC) slow and fast myotubes. There was little deterioration in myotube viability over 50 days, and the efficiency of myotube formation was maintained over seven myoblast passages. Regular spontaneous contractions of myotubes were frequently observed throughout culture. Myotubes in extended cultures were able to undergo phenotypic adaptation in response to different culture media, including the adoption of a dominant postnatal phenotype of fast-glycolytic MyHC 2x and 2b expression by about day 20 of differentiation. Furthermore, fast-glycolytic myotubes coincided with enhanced expression of the putative porcine long intergenic non-coding RNA (linc-MYH), which has recently been shown to be a key coordinator of MyHC 2b expression in vivo.
Conclusions: Our revised culture protocol allows the efficient differentiation and fusion of porcine myoblasts into myotubes and their prolonged adherence to the culture surface. Furthermore, we are able to recapitulate in 2D the maturation process of myotubes to resemble postnatal fibre types which represent a major technical advance in opening access to the in vitro study of coordinated postnatal muscle gene expression
Silkworm Thermal Biology: A Review of Heat Shock Response, Heat Shock Proteins and Heat Acclimation in the Domesticated Silkworm, Bombyx mori
Heat shock proteins (HSPs) are known to play ecological and evolutionary roles in this postgenomic era. Recent research suggests that HSPs are implicated in cardiovascular biology and disease development, proliferation and regulation of cancer cells, cell death via apoptosis, and several other key cellular functions. These activities have generated great interest amongst cell and molecular biologists, and these biologists are keen to unravel other hitherto unknown potential functions of this group of proteins. Consequently, the biological significance of HSPs has led to cloning and characterization of genes encoding HSPs in many organisms including the silkworm, Bombyx mori L. (Lepidoptera: Bombycidae). However, most of the past investigations in B. mori were confined to expression of HSPs in tissues and cell lines, whereas information on their specific functional roles in biological, physiological, and molecular processes is scarce. Naturally occurring or domesticated polyvoltines (known to be the tropical race) are more resistant to high temperatures and diseases than bi- or univoltines (temperate races). The mechanism of ecological or evolutionary modification of HSPs during the course of domestication of B. mori - particularly in relation to thermotolerance in geographically distinct races/strains - is still unclear. In addition, the heat shock response, thermal acclimation, and hardening have not been studied extensively in B. mori compared to other organisms. Towards this, recent investigations on differential expression of HSPs at various stages of development, considering the concept of the whole organism, open ample scope to evaluate their biological and commercial importance in B. mori which has not been addressed in any of the representative organisms studied so far. Comparatively, heat shock response among different silkworm races/strains of poly-, bi-, and univoltines varies significantly and thermotolerance increases as the larval development proceeds. Hence, this being the first review in this area, an attempt has been made to collate all available information on the heat shock response, HSPs expression, associated genes, amino acid sequences, and acquired/unacquired thermotolerance. The aim is to present this as a valuable resource for addressing the gap in knowledge and understanding evolutionary significance of HSPs between domesticated (B. mori) and non-domesticated insects. It is believed that the information presented here will also help researchers/breeders to design appropriate strategies for developing novel strains for the tropics
Head and Neck Lymphomas: Tip of the Iceberg?
ABSTRACT Background: Lymphomas comprise around 5% of all head and neck neoplasms and is the second most common extra nodal non hodgkin's lymphoma (NHL). However there is sporadic data on this entity from the subcontinent and hence we undertook this study. Methodology: This retrospective observational study was conducted at a tertiary care oncology center in India on diagnosed cases of NHL between January 2007 and December 2013. All patients were diagnosed based on histopathology and immunohistochemistry. Staging work up was done in all patients. Patients were considered as primary Head and Neck lymphomas if there was head and neck as the predominant site with or without regional lymph node involvement. Results: A total of 39 patients were studied. The age at presentation ranged from 29 to 78 years. The most common site of presentation was oral cavity (26%; n=10), followed by parotid and thyroid (18% each; n=7), eye (12%, n=5), maxilla (8%; n=3), paranasal sinuses (8%; n-=3) cheek (8%, n=3), and nasal cavity (2%, n=1). 41% (n=16) cases were in stage I, 43% (n=17) in stage II, 3% (n=1) in stage III, and 13% (n=5) were in stage IV. Most common histology was DLBCL (71%; n=28), followed by plasmablastic (10%; n=4), marginal zone (8%, n=3), mantle cell (3%; n=1), follicular lymphomas (5%; n=2), and NK/T cell lymphoma (3%; n=1). Most of the patients were of low risk (67%; n=26), followed by intermediate (23%; n=9), and high risk (10%; n=4). Patients were treated with anthracycline based chemotherapy +/-radiotherapy. In this study, stage I and stage II patients had a better prognosis and overall survival, median OS 28 months and 11 months, respectively. In stage III and IV, it was 7 and 3 months, respectively. According to site, the best median overall survival was seen with parotid (27 m), paranasal sinus (26m), and oral cavity (23 m), followed by thyroid (18 m) nasal cavity (17 m), maxilla (11 m), eye (8 m), and cheek (7 m)
- …