Human Ubc9 Contributes to Production of Fully Infectious Human Immunodeficiency Virus Type 1 Virions

Ubc9 was identified as a cellular protein that interacts with the Gag protein of Mason-Pfizer monkey virus. We show here that Ubc9 also interacts with the human immunodeficiency virus type 1 (HIV-1) Gag protein and that their interaction is important for virus replication. Gag was found to colocalize with Ubc9 predominantly at perinuclear puncta. While cells in which Ubc9 expression was suppressed with RNA interference produced normal numbers of virions, these particles were 8- to 10-fold less infectious than those produced in the presence of Ubc9. The nature of this defect was assayed for dependence on Ubc9 during viral assembly, trafficking, and Env incorporation. The Gag-mediated assembly of virus particles and protease-mediated processing of Gag and Gag-Pol were unchanged in the absence of Ubc9. However, the stability of the cell-associated Env glycoprotein was decreased and Env incorporation into released virions was altered. Interestingly, overexpression of the Ubc9 trans-dominant-negative mutant C93A, which is a defective E2-SUMO-1 conjugase, suggests that this activity may not be required for interaction with Gag, virion assembly, or infectivity. This finding demonstrates that Ubc9 plays an important role in the production of infectious HIV-1 virions

Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams

Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importance of utilizing melt pool signatures for real-time defect prediction. While high-quality melt pool image data holds the promise of enabling precise predictions, there has been limited exploration into the utilization of cutting-edge spatiotemporal models that can harness the inherent transient and sequential characteristics of the additive manufacturing process. This research introduces and puts into practice some of the leading deep spatiotemporal learning models that can be adapted for the classification of melt pool image streams originating from various materials, systems, and applications. Specifically, it investigates two-stream networks comprising spatial and temporal streams, a recurrent spatial network, and a factorized 3D convolutional neural network. The capacity of these models to generalize when exposed to perturbations in melt pool image data is examined using data perturbation techniques grounded in real-world process scenarios. The implemented architectures demonstrate the ability to capture the spatiotemporal features of melt pool image sequences. However, among these models, only the Kinetics400 pre-trained SlowFast network, categorized as a two-stream network, exhibits robust generalization capabilities in the presence of data perturbations.Comment: This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-N

Effects of space allocation within a deep-bedded finishing system on pig growth performance, fatty acid composition and pork quality

The objectives of the current study were to determine the degree to which space allocation in a deep-bedded system influences swine performance and pork quality. The deep-bedded method employed was hoop structures, which are large, tent-like shelters with cornstalks or straw for bedding. One hundred gilts ranging in weight from 59 to 71 kg were randomly assigned to treatments of low (0.70 m2 per pig, n = 50) or high (1.13 m2 per pig, n = 50) space allocation. During the 45-day experimental period, gilts were ad libitum fed a two-phase diet. Six gilts per treatment were used for carcass composition and pork quality evaluation for each replication. Five replications were conducted over a period of 4 months. Pigs finished with greater space allocation had smaller longissimusmuscle area and produced pork that appeared to be darker. Variations in fatty acid composition and lipid percentage of subcutaneous adipose and longissimus dorsi muscle were observed when space allocation was changed within hoop structures. Less space resulted in greater proportion of lipid present as polyunsaturated fatty acids. Greater space allocation resulted in lower total lipid in subcutaneous pork adipose tissue. Space allocation did not affect fat firmness. Replications spanned the months of August to November, with temperatures ranging from 32°C to −2°C within the hoop structure. As environmental temperature declined, the proportion of monounsaturated fatty acids increased. Providing more space during finishing in these systems had only a small affect on pig growth and pork quality. Variations observed from replication to replication at fluctuating temperatures provide insight to seasonal differences in growth and adipose tissue composition and firmness. Therefore, finishing pigs in these systems may lead to seasonal variation in lipid composition

The effect of horizontal resolution on Indian monsoon depressions in the Met Office NWP model

Monsoon depressions are synoptic scale features that are responsible for a significant fraction of the rain over northern India during the summer monsoon season, and, as such, it is important to quantify their structure and behaviour in numerical weather prediction models. It is known that increasing model resolution is strongly correlated with improved forecasts in the short term and global circulation in the longer term, as well as better representation of tropical cyclones; here, we explore the sensitivity of depressions to changes in resolution using the Met Office Unified Model. Seven NWP case studies of depressions from 2013-15 were run at eight resolutions corresponding to equatorial grid spacing of between 16 and 208 km, and compared with data for the same events from TRMM and ERA-Interim reanalysis. We found that at the low resolution end of the spectrum, increases in resolution led to improvements in the composite structure, but with diminishing returns. The model also persistently overestimated the depression intensity, in particular the wind speed and the warm core aloft -- with the source appearing to originate in the mid-troposphere. The sensitivity of the diurnal cycle to resolution was also explored: the stratiform component was found to be very well represented by the model, whereas the convective component was described quite poorly. Improvement in most components of structure with increasing model resolution were marginal beyond N320 (63 km) and N512 (39 km) for dynamic and thermodynamic fields respectively

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

Mathematical model insights into arsenic detoxification

<p>Abstract</p> <p>Background</p> <p>Arsenic in drinking water, a major health hazard to millions of people in South and East Asia and in other parts of the world, is ingested primarily as trivalent inorganic arsenic (iAs), which then undergoes hepatic methylation to methylarsonic acid (MMAs) and a second methylation to dimethylarsinic acid (DMAs). Although MMAs and DMAs are also known to be toxic, DMAs is more easily excreted in the urine and therefore methylation has generally been considered a detoxification pathway. A collaborative modeling project between epidemiologists, biologists, and mathematicians has the purpose of explaining existing data on methylation in human studies in Bangladesh and also testing, by mathematical modeling, effects of nutritional supplements that could increase As methylation.</p> <p>Methods</p> <p>We develop a whole body mathematical model of arsenic metabolism including arsenic absorption, storage, methylation, and excretion. The parameters for arsenic methylation in the liver were taken from the biochemical literature. The transport parameters between compartments are largely unknown, so we adjust them so that the model accurately predicts the urine excretion rates of time for the iAs, MMAs, and DMAs in single dose experiments on human subjects.</p> <p>Results</p> <p>We test the model by showing that, with no changes in parameters, it predicts accurately the time courses of urinary excretion in mutiple dose experiments conducted on human subjects. Our main purpose is to use the model to study and interpret the data on the effects of folate supplementation on arsenic methylation and excretion in clinical trials in Bangladesh. Folate supplementation of folate-deficient individuals resulted in a 14% decrease in arsenicals in the blood. This is confirmed by the model and the model predicts that arsenicals in the liver will decrease by 19% and arsenicals in other body stores by 26% in these same individuals. In addition, the model predicts that arsenic methyltransferase has been upregulated by a factor of two in this population. Finally, we also show that a modification of the model gives excellent fits to the data on arsenic metabolism in human cultured hepatocytes.</p> <p>Conclusions</p> <p>The analysis of the Bangladesh data using the model suggests that folate supplementation may be more effective at reducing whole body arsenic than previously expected. There is almost no data on the upregulation of arsenic methyltransferase in populations chronically exposed to arsenic. Our model predicts upregulation by a factor of two in the Bangladesh population studied. This prediction should be verified since it could have important public health consequences both for treatment strategies and for setting appropriate limits on arsenic in drinking water. Our model has compartments for the binding of arsenicals to proteins inside of cells and we show that these comparments are necessary to obtain good fits to data. Protein-binding of arsenicals should be explored in future biochemical studies.</p

Predictors of complications in gynaecological oncological surgery: a prospective multicentre study (UKGOSOC-UK gynaecological oncology surgical outcomes and complications)

Background: There are limited data on surgical outcomes in gynaecological oncology. We report on predictors of complications in a multicentre prospective study. / Methods: Data on surgical procedures and resulting complications were contemporaneously recorded on consented patients in 10 participating UK gynaecological cancer centres. Patients were sent follow-up letters to capture any further complications. Post-operative (Post-op) complications were graded (I–V) in increasing severity using the Clavien-Dindo system. Grade I complications were excluded from the analysis. Univariable and multivariable regression was used to identify predictors of complications using all surgery for intra-operative (Intra-op) and only those with both hospital and patient-reported data for Post-op complications. / Results: Prospective data were available on 2948 major operations undertaken between April 2010 and February 2012. Median age was 62 years, with 35% obese and 20.4% ASA grade ⩾3. Consultant gynaecological oncologists performed 74.3% of operations. Intra-op complications were reported in 139 of 2948 and Grade II–V Post-op complications in 379 of 1462 surgeries. The predictors of risk were different for Intra-op and Post-op complications. For Intra-op complications, previous abdominal surgery, metabolic/endocrine disorders (excluding diabetes), surgical complexity and final diagnosis were significant in univariable and multivariable regression (P<0.05), with diabetes only in multivariable regression (P=0.006). For Post-op complications, age, comorbidity status, diabetes, surgical approach, duration of surgery, and final diagnosis were significant in both univariable and multivariable regression (P<0.05). / Conclusions: This multicentre prospective audit benchmarks the considerable morbidity associated with gynaecological oncology surgery. There are significant patient and surgical factors that influence this risk