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Solid-organ transplant recipients exhibiting HLA donor-specific Abs are at risk for graft loss due to chronic Ab-mediated rejection. HLA Abs bind HLA molecules expressed on the surface of endothelial cells (ECs) and induce intracellular signaling pathways, including the activation of the transcriptional coactivator yes-associated protein (YAP). In this study, we examined the impact of lipid-lowering drugs of the statin family on YAP localization, multisite phosphorylation, and transcriptional activity in human ECs. Exposure of sparse cultures of ECs to cerivastatin or simvastatin induced striking relocalization of YAP from the nucleus to the cytoplasm and inhibited the expression of the YAP/TEA domain DNA-binding transcription factor–regulated genes connective tissue growth factor and cysteine-rich angiogenic inducer 61. In dense cultures of ECs, statins prevented YAP nuclear import and expression of connective tissue growth factor and cysteine-rich angiogenic inducer 61 stimulated by the mAb W6/32 that binds HLA class I. Exposure of ECs to either cerivastatin or simvastatin completely blocked the migration of ECs stimulated by ligation of HLA class I. Exogenously supplied mevalonic acid or geranylgeraniol reversed the inhibitory effects of statins on YAP localization either in low-density ECs or high-density ECs challenged with W6/32. Mechanistically, cerivastatin increased the phosphorylation of YAP at Ser127, blunted the assembly of actin stress fiber, and inhibited YAP phosphorylation at Tyr357 in ECs. Using mutant YAP, we substantiated that YAP phosphorylation at Tyr357 is critical for YAP activation. Collectively, our results indicate that statins restrain YAP activity in EC models, thus providing a plausible mechanism underlying their beneficial effects in solid-organ transplant recipients.

Pancreatic ductal adenocarcinoma (PDAC) remains an aggressive disease that is expected to become the second cause of cancer fatalities during the next decade. As therapeutic options are limited, novel targets, and agents for therapeutic intervention are urgently needed. Previously, we identified potent positive crosstalk between insulin/IGF-1 receptors and G protein–coupled (GPCR) signaling systems leading to mitogenic signaling in PDAC cells. Here, we show that a combination of insulin and the GPCR agonist neurotensin induced rapid activation of Src family of tyrosine kinases (SFK) within PANC-1 cells, as shown by FAK phosphorylation at Tyr576/577 and Tyr861, sensitive biomarkers of SFK activity within intact cells and Src416 autophosphorylation. Crucially, SFKs promoted YAP nuclear localization and phosphorylation at Tyr357, as shown by using the SFK inhibitors dasatinib, saracatinib, the preferential YES1 inhibitor CH6953755, siRNA-mediated knockdown of YES1, and transfection of epitogue-tagged YAP mutants in PANC-1 and Mia PaCa-2 cancer cells, models of the aggressive squamous subtype of PDAC. Surprisingly, our results also demonstrate that exposure to SFK inhibitors, including dasatinib or knockdown of YES and Src induces ERK overactivation in PDAC cells. Dasatinib-induced ERK activation was completely abolished by exposure to the FDA-approved MEK inhibitor trametinib. A combination of dasatinib and trametinib potently and synergistically inhibited colony formation by PDAC cells and suppressed the growth of Mia PaCa-2 cells xenografted into the flank of nude mice. The results provide rationale for considering a combination(s) of FDA-approved SFK (dasatinib) and MEK (e.g., trametinib) inhibitors in prospective clinical trials for the treatment of PDAC.

Purpose Alcohol and tobacco consumption are significant public health concerns and considered some of the riskiest behaviors among students. Despite strong indications of heavy consumption of these substances, certain sections of the student population remain unexplored regarding their consumption patterns and associated factors, including research scholars pursuing Doctor of Philosophy degrees. This study aims to explore the patterns and correlations of consumption of alcohol and tobacco among doctoral students in three Indian universities. Design/methodology/approach A cross-sectional study was conducted on a total of 530 scholars from three central universities, Aligarh Muslim University, Banaras Hindu University and Jawahar Lal Nehru University, and focused on two substances (alcohol and tobacco) frequently used by students in India. Bi-variate analysis and penalized logistic regressions were applied to analyse the prevalence and associated factors of alcohol, smoking and smokeless tobacco among doctoral students. Findings Tobacco consumption was observed to be the dominant form of substance use among doctoral students. Findings revealed that 34% of scholars smoked and 25% consumed smokeless tobacco daily and around 14% consumed alcohol at least once a week. Major significant correlates of substance consumption among scholars were found to be gender, religion, parental mortality, dissatisfaction with academic performance and stress about future career/employment. Significant variation in substance use patterns was observed among universities. Originality/value This study provides first-hand evidence on substance use patterns and the correlates among doctoral students in three Indian universities. Findings indicate the implementation of specific intervention programs for mental health and counselling for this section of students to prevent substance use addiction would be beneficial.

These days the construction industry is fully developed due to a massive increase within a short period of time in the use of cement and concrete for multiple construction activities. The same rate is expected to continue in the next decade and this may cause global environmental impacts. The raw materials needed to manufacture cement and produce concrete provide less and require more energy for production. This increasing demand is causing a rapid depletion of resources. To overcome this situation, it is very important to use industrial waste and by-products for the manufacture of cement and concrete construction. Red mud is the extensive amount of industrial waste generated during the production of alumina. It has been found that for every 1 ton of alumina 1.2 to 1.3 tons of red mud waste is generated. As red mud has complex properties, the disposal process becomes quite challenging Disposal of this waste was the first prime problem encountered by the alumina industry after the adoption of the Bayer process. The conventional method of disposal of the red mud in ponds has often adverse environmental impacts as during monsoons the waste may be carried by run off to the surface water courses and as a result of percolating may cause contamination of ground water. Further disposal of large quantities of red mud dumped, poses increasing problems off storage occupying a lot space over the years many attempts have been made to find a use for red mud but nobody have proven to be economically satisfactory. Red mud or red sludge is a waste product generated in the industrial production of Aluminum via Bayer’s process of refining bauxite enrooted to alumina. With about 150 million tons (Globally) & 9 million tons in India of this hazardous material produced annually, red mud is one of the most important disposal problems in the mining industry. In this study, Mix was designed for M25 grade of conversational concrete. 6 mix were made in which Red mud were substituted by weight of cement in varying proportion 0%, 5%, 10% ,15%,20% and 25%. We evaluated the strength of concrete by performing tests which are compressive strength, flexural strength at the age of 7days and 28day. Study results revealed that 15% cement can be the optimally replaced by red mud beyond which compressive strength, Flexural strength deceasing. We find out that Compressive strength increase by 3.80 %, Flexural strength increase by 9.42% and replacing cement with red mud at 15%. However, the optimum level was observed as 15% without deceasing in strength. Study results revealed that 15% cement can be the optimally replaced by red mud beyond which compressive strength, Flexural strength deceasing. We find out that Compressive strength increase by 3.80 %, Flexural strength increase by 9.42% and replacing cement with red mud at 15%. However, the optimum level was observed as 15% without deceasing in strength.

Supplementary Figure from Opposite Effects of Src Family Kinases on YAP and ERK Activation in Pancreatic Cancer Cells: Implications for Targeted Therapy

Pancreatic ductal adenocarcinoma (PDAC) remains an aggressive disease that is expected to become the second cause of cancer fatalities during the next decade. As therapeutic options are limited, novel targets, and agents for therapeutic intervention are urgently needed. Previously, we identified potent positive crosstalk between insulin/IGF-1 receptors and G protein–coupled (GPCR) signaling systems leading to mitogenic signaling in PDAC cells. Here, we show that a combination of insulin and the GPCR agonist neurotensin induced rapid activation of Src family of tyrosine kinases (SFK) within PANC-1 cells, as shown by FAK phosphorylation at Tyr576/577 and Tyr861, sensitive biomarkers of SFK activity within intact cells and Src416 autophosphorylation. Crucially, SFKs promoted YAP nuclear localization and phosphorylation at Tyr357, as shown by using the SFK inhibitors dasatinib, saracatinib, the preferential YES1 inhibitor CH6953755, siRNA-mediated knockdown of YES1, and transfection of epitogue-tagged YAP mutants in PANC-1 and Mia PaCa-2 cancer cells, models of the aggressive squamous subtype of PDAC. Surprisingly, our results also demonstrate that exposure to SFK inhibitors, including dasatinib or knockdown of YES and Src induces ERK overactivation in PDAC cells. Dasatinib-induced ERK activation was completely abolished by exposure to the FDA-approved MEK inhibitor trametinib. A combination of dasatinib and trametinib potently and synergistically inhibited colony formation by PDAC cells and suppressed the growth of Mia PaCa-2 cells xenografted into the flank of nude mice. The results provide rationale for considering a combination(s) of FDA-approved SFK (dasatinib) and MEK (e.g., trametinib) inhibitors in prospective clinical trials for the treatment of PDAC.

Two new mymarid species Dicopomorpha heratyi Anwar & Zeya, sp. nov. and Dicopomorpha mirzai Anwar & Zeya, sp. nov., are described from Himalayan belts of Himachal Pradesh, India and also male of D. albithorax Rameshkumar & Manickavasagam is recorded and described for the first time. The type specimens are deposited in the Insect Collections, Department of Zoology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India.

The real estate industry is the least transparent in our environment. Housing prices fluctuate on a daily basis and are sometimes inflated rather than based on valuation. Since the housing industry is rapidly expanding, forecasting house prices is critical not just for businesses but also for individuals. However, there are other factors that influence house price variations. The purpose of this article is to forecast a real estate property's market value. The goal here is to build a prediction model for evaluating pricing based on characteristics that influence price. We are going to predict data using Machine learning algorithm i.e. linear regression model to predict the house pricing.

Progress towards understanding how social media impacts body image hinges on the use of appropriate measurement tools and methodologies. This review provides an overview of common (qualitative, self-report survey, lab-based experiments) and emerging (momentary assessment, computational) methodological approaches to the exploration of the impact of social media on body image. The potential of these methodologies is detailed, with examples illustrating current use as well as opportunities for expansion. A key theme from our review is that each methodology has provided insights for the body image research field, yet is insufficient in isolation to fully capture the nuance and complexity of social media experiences. Thus, in consideration of gaps in methodology, we emphasise the need for big picture thinking that leverages and combines the strengths of each of these methodologies to yield a more comprehensive, nuanced, and robust picture of the positive and negative impacts of social media.

Depression has become a big problem in our society today. It is also a major reason for suicide, especially among teenagers. In the current outbreak of coronavirus disease (COVID-19), the affected countries have recommended social distancing and lockdown measures. Resulting in interpersonal isolation, these measures have raised serious concerns for mental health and depression. Generally, clinical psychologists diagnose depressed people via face-to-face interviews following the clinical depression criteria. However, often patients tend to not consult doctors in their early stages of depression. Nowadays, people are increasingly using social media to express their moods. In this article, we aim to predict depressed users as well as estimate their depression intensity via leveraging social media (Twitter) data, in order to aid in raising an alarm. We model this problem as a supervised learning task. We start with weakly labeling the Twitter data in a self-supervised manner. A rich set of features, including emotional, topical, behavioral, user level, and depression-related n-gram features, are extracted to represent each user. Using these features, we train a small long short-term memory (LSTM) network using Swish as an activation function, to predict the depression intensities. We perform extensive experiments to demonstrate the efficacy of our method. We outperform the baseline models for depression intensity estimation by achieving the lowest mean squared error of 1.42 and also outperform the existing state-of-the-art binary classification method by more than 2% of accuracy. We found that the depressed users frequently use negative words such as stress and sad, mostly post during late nights, highly use personal pronouns and sometimes also share personal events.

Two new species of Camptoptera Foerster (Hymenoptera: Mymaridae) are described based on the materials collected from Indian states: C. ayezae Anwar & Zeya sp. nov. and C. sadhui Anwar & Zeya sp. nov. The type specimens are deposited in the Insect Collections, Department of Zoology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India.

The present study deals with the new country records and generic records of two genera of family Chalcididae, viz., Epitranus clavatus (Fabricius) and Hockeria tamaricis Bouèek. Both recorded species are detailed with diagnosis, host specificity and geographical distribution.

Cheiloneurus nankingensis Li & Xu, 2020 is recorded for the first time and redescribed from India. The following two species: Aenasius arizonensis (Girault), Blepyrus insularis (Cameron), are recorded with their new hosts from the Indian State of Kerala.

Naturally occurring N-glycans display much diversity in modifications, linkages, and peripheral presentation of the oligosaccharide chain. Despite continued advancements in oligosaccharide synthesis, synthetic access to these natural glycans remains challenging. Biologically relevant complex N-glycan mimetics with various natural and unnatural modifications are an alternate way for investigating glycan-protein interactions. Further supporting this pattern, we report here a new class of sialylated bi- and triantennary pseudo mannose N-glycans reproducing orientation of the underlying glycan chain and branching patterns and replacing the two inner mannopyranosyl units with 1,2,3-triazole rings. Such mimetics are straightforwardly generated by implementing multiple intermolecular Cu(I)-catalyzed azide-alkyne cycloaddition between chemoenzymatically synthesized azido sialosides and rationally designed C-3 and C-6 di-O- or C-2, C-3, and C-6 tri-O-alkynylated mannoside. Human recombinant Siglec-7-Fc fusion protein recognizes almost all sialylated pseudo mannose N-glycans in the microarray. However, a differential Sia-binding pattern was also observed. Given the library size, comparison of pairwise mannose N-glycan combinations showed that biantennary linear α(2,3)α(2,8)- and α(2,6)α(2,8)- or branched α(2,3)α(2,6)-, and triantennary branched α(2,3)α(2,6)-sialyl pseudo N-glycans possess similar binding capabilities and affinity to recombinant Siglec-7-Fc. While the full range of topological mannose arms remain elusive, the bi- and triantennary mimics are simpler structures for interrogating Siglec interactions.

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method.

With a rapid growth in the global population, the modern world is undergoing a rapid expansion of residential areas, especially in urban centres. This continuously demands for increased general services and basic amenities, which are required according to the kind of population associated with the places. The advent of location-based online social networks (LBSNs) has made it much easier to collect voluminous data about users in different locations or spatial regions. The problem of mining location types from the LBSN data is largely unexplored. In this paper, we propose a pattern mining approach, using the geo-social-temporal data collected from LBSNs, to infer types of different locations. The proposed method first mines frequent co-located users and user components from an LBSN and then performs a temporal pattern analysis to finally categorize the locations. Extensive experiments are conducted on two real datasets that demonstrate the efficacy of the proposed method in terms of mean reciprocal rank (MRR), visualisations, and insights. The resulting inference mechanism would be very useful in several application domains including urban planning, billboard placement, tour planning, and geo-social event planning.

Mother needs special medical care during pregnancy, delivery, and after delivery; as a mother, she is more prone to adverse health outcomes or death due to the unsafe and unhygienic methods of managing pregnancy and childbirth. According to the World Health Organization reports, globally, 0.53 million maternal deaths occur annually, out of which 0.12 million (22%) deaths occur only in India. A newborn child needs regular health check-ups as well as nutrition supplements to avoid deficiency diseases and illness. Child health is a foundation for adult health and well-being; therefore, it is imperative to certify good health. Healthy children assure healthy adults who, in turn, ensure good progress and development of the Country (Usmani and Ahmad, 2017). According to NHM, around 81% of under-five child death occurs in one year of birth that marks approximately 10.5 lakh newborn demises; however, 57% of under-five deaths occur in the first one month of life, constituting 7.3 lakh neo-natal deaths annually within the Country. Data and methods: data from National Family Health Survey-4 (2015-2016) on maternal and child health indicators for 75 districts of Uttar Pradesh state were used. Spatial analysis namely Moran\'s-I and LISA were applied to evaluate the maternal and child health indicators through all the districts of the state. Result: Each indicator portrayed prominent coverage variation across the Uttar Pradesh districts in this analysis. Among all the districts, the lowest ANC occurrence was observed mainly in the central part, PNC in the eastern region, SBA in 20 districts mainly of the western and eastern part. The prevalence of full immunization among the children was very high, primarily in the districts of the East region; high PNC among the children was perceived in the districts of eastern, central, and Bundelkhand part of the state. PNC prevalence among the women was found to be highest in the western part of Uttar Pradesh.

Males of the subgenus Eofoersteria Mathot (Hym., Mymaridae, Camptoptera Foerster) are diagnosed, described, and illustrated for the first time, based on examination of specimens from Tamil Nadu and from photographs of the male paratype of Camptoptera matcheta Subba Rao from Karnataka. New distributional records of C. (Eofoersteria) manipurensis (Rehmat & Anis) from Karnataka and Kerala states of India are documented.