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Global dysregulation of RNA splicing and imbalanced sphingolipid metabolism has emerged as promoters of cancer cell transformation. Here, we present specific signature of alternative splicing (AS) events of sphingolipid genes for each breast cancer subtype from the TCGA-BRCA dataset. We show that ceramide synthase 2 (CERS2) undergoes a unique cassette exon event specifically in Luminal B subtype tumors. We validated this exon 8 skipping event in Luminal B cancer cells compared to normal epithelial cells, and in patient-derived tumor tissues compared to matched normal tissues. Differential AS-based survival analysis shows that this AS event of CERS2 is a poor prognostic factor for Luminal B patients. As Exon 8 corresponds to catalytic Lag1p domain, overexpression of AS transcript of CERS2 in Luminal B cancer cells leads to a reduction in the level of very-long-chain ceramides compared to overexpression of protein-coding (PC) transcript of CERS2. We further demonstrate that this AS event-mediated decrease of very-long-chain ceramides leads to enhanced cancer cell proliferation and migration. Therefore, our results show subtype-specific AS of sphingolipid genes as a regulatory mechanism that deregulates sphingolipids like ceramides in breast tumors, and can be explored further as a suitable therapeutic target.

Relational databases are among the most widely used architectures to store massive amounts of data in the modern world. However, there is a barrier between these databases and the average user. The user often lacks the knowledge of a query language such as SQL required to interact with the database. The NL2SQL task aims at finding deep learning approaches to solve this problem by converting natural language questions into valid SQL queries. Given the sensitive nature of some databases and the growing need for data privacy, we have presented an approach with data privacy at its core. We have passed RoBERTa embeddings and data-agnostic knowledge vectors into LSTM based submodels to predict the final query. Although we have not achieved state of the art results, we have eliminated the need for the table data, right from the training of the model, and have achieved a test set execution accuracy of 76.7%. By eliminating the table data dependency while training we have created a model capable of zero shot learning based on the natural language question and table schema alone., 8 Pages, 2 figures

The task of recognising an object and estimating its 6d pose in a scene has received considerable attention in recent years. The accessibility and low-cost of consumer RGB-D cameras, make object recognition and pose estimation feasible even for small industrial businesses. An example is the industrial assembly line, where a robotic arm should pick a small, textureless and mostly homogeneous object and place it in a designated location. Despite all the recent advancements of object recognition and pose estimation techniques in natural scenes, the problem remains challenging for industrial parts. In this paper, we present a framework to simultaneously recognise the object&rsquo, s class and estimate its 6d pose from RGB-D data. The proposed model adapts a global approach, where an object and the Region of Interest (ROI) are first recognised from RGB images. The object&rsquo, s pose is then estimated from the corresponding depth information. We train various classifiers based on extracted Histogram of Oriented Gradient (HOG) features to detect and recognize the objects. We then perform template matching on the point cloud based on surface normal and Fast Point Feature Histograms (FPFH) to estimate the pose of the object. Experimental results show that our system is quite efficient, accurate and robust to illumination and background changes, even for the challenging objects of Tless dataset.

The VapBC22 toxin-antitoxin system regulates adaptation to oxidative stress and virulence in Mycobacterium tuberculosis., Virulence-associated protein B and C toxin-antitoxin (TA) systems are widespread in prokaryotes, but their precise role in physiology is poorly understood. We have functionally characterized the VapBC22 TA system from Mycobacterium tuberculosis. Transcriptome analysis revealed that overexpression of VapC22 toxin in M. tuberculosis results in reduced levels of metabolic enzymes and increased levels of ribosomal proteins. Proteomics studies showed reduced expression of virulence-associated proteins and increased levels of cognate antitoxin, VapB22 in the ΔvapC22 mutant strain. Furthermore, both the ΔvapC22 mutant and VapB22 overexpression strains of M. tuberculosis were susceptible to killing upon exposure to oxidative stress and showed attenuated growth in guinea pigs and mice. Host transcriptome analysis suggests upregulation of the transcripts involved in innate immune responses and tissue remodeling in mice infected with the ΔvapC22 mutant strain. Together, we demonstrate that the VapBC22 TA system belongs to a key regulatory network and is essential for M. tuberculosis pathogenesis.

У роботі наночастинки оксиду олова (SnO2), відпалені при температурах 400, 500 та 600 °C, були підготовлені методом твердотільної реакції. Структурні та оптичні властивості підготовлених наночастинок SnO2 аналізували за допомогою рентгенівського дифрактометра (XRD) та скануючого електронного мікроскопа (SEM). Елементне дослідження було виконано енергетично-дисперсійною рентгенівською спектроскопією (EDAX), інфрачервоною спектроскопією з використанням перетворення Фур'є (FTIR), методами динамічного розсіювання світла (DLS) та поглинання світла в ультрафіолетовому та видимому діапазонах (UV-Vis). Рентгенівська дифрактограма підтверджує чисту тетрагональну структуру рутилу наночастинок SnO2 без будь-якої домішкової фази. SEM зображення відображають утворення нанострижнів при всіх температурах відпалу 400, 500 і 600 °C, тоді як EDAX аналіз підтверджує наявність Sn і O без будь-яких домішок. UV-Vis спектри показують, що ширина забороненої зони підготовлених наночастинок SnO2 спочатку зменшується з 3,44 еВ при температурі 400 °С до 3,03 еВ при 500 °С, а потім збільшується до 3,39 еВ при температурі 600 °С. Спектр FTIR підтверджує наявність зв'язку O–Sn–O разом з одним небажаним піком поглинання. In the present work, nanoparticles of tin oxide (SnO2) annealed at temperatures of 400 °C, 500 °C, and 600 °C were prepared via solid-state reaction method. Structural and optical properties of prepared SnO2 nanoparticles were analyzed by X-ray diffractometer (XRD), scanning electron microscope (SEM), elemental study was performed by energy-dispersive X-ray spectroscopy (EDAX), Fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS) and ultraviolet-visible (UV-Vis) absorption characterizing techniques. The X-ray diffraction pattern confirms the pure tetragonal rutile structure of tin oxide nanoparticles without any impurity phase. SEM images depict the formation of nanorods at all annealing temperatures of 400 °C, 500 °C, and 600 °C while EDAX analysis confirms the presence of Sn and O without any impurities. UV-Vis absorption spectra show that the band gap of prepared tin oxide nanoparticles decreases initially from 3.44 eV at 400 °C to 3.03 eV at 500 °C and further increases to 3.39 eV at 600 °C. FTIR spectrum confirms the presence of O–Sn–O bond along with one unwanted absorption peak.

Background Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. Methods Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. Results Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84–3.50) and 2.48 (95% CrI: 1.27–5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37–10.61). Conclusions For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.