Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences

The dense depth estimation of a 3D scene has numerous applications, mainly in robotics and surveillance. LiDAR and radar sensors are the hardware solution for real-time depth estimation, but these sensors produce sparse depth maps and are sometimes unreliable. In recent years research aimed at tackling depth estimation using single 2D image has received a lot of attention. The deep learning based self-supervised depth estimation methods from the rectified stereo and monocular video frames have shown promising results. We propose a self-attention based depth and ego-motion network for unrectified images. We also introduce non-differentiable distortion of the camera into the training pipeline. Our approach performs competitively when compared to other established approaches that used rectified images for depth estimation

Role Of Crebh In Endotoxin Mediated Modulation Of Hepatic Metabolism

Bacterial endotoxins can induce a variety of physiological changes in the host. This effect is not only restricted to inflammatory changes but also comprises metabolic changes in the host body. Lipopolysaccharide (LPS), one of the key components of the bacterial cell walls, is capable of triggering host metabolic changes. Hyperlipidemia usually accompanies with high endotoxin levels as well as inflammation. Lipid metabolism disorders are one of the common hallmarks of a patient with sepsis or high levels of endotoxin through diet. Previously, we have identified an endoplasmic reticulum (ER) anchored liver-specific transcription factor CREBH (cAMP-responsive element-binding protein, hepatocyte-specific), which is activated by ER stress, inflammatory stimuli, and metabolic signals. Proinflammatory cytokines TNFα, IL6, and IL1β, bacterial endotoxin lipopolysaccharide, insulin signal, saturated fatty acids, nutrient starvation, or atherogenic high-fat (AHF) feeding, can all induce expression and/or activation of CREBH in the liver. In this study, we demonstrate that CREBH acts a key player in mounting an acute phase response against endotoxemia by modulating apolipoproteins. Endotoxin LPS shock in the body induces activation of the TLR4 signaling pathway in mouse liver. Upon triggering TLR4 signaling pathway, LPS stimulates cleavage and activation of CREBH transcription factor LPS induces the interaction between CREBH and TNF receptor-associated factor 6 (TRAF6), an E3 ubiquitin ligase that plays a key role in mediating TLR signaling. While LPS-induced TRAF6-CREBH interaction relies on MyD88, TRAF6 mediates the ubiquitination of CREBH to facilitate CREBH activation upon LPS challenge. Functionally, CREBH directly activates expression of the gene encoding Apolipoprotein (Apo) A IV and IL6 under LPS challenge, leading to modulation of high-density lipoprotein (HDL) in animal models. In summary, my study suggested that TLR-dependent, LPS-induced CREBH activation may represent a host defense response to bacterial endotoxin by modulating apolipoproteins. Targeting the expression of CREBH under disease condition may represent a novel approach towards alleviating the sepsis-related complications

Prediction of pregnancy in artificial reproductive techniques through evaluation of thickness, morphology and vascularity of endometrium

Background: Prediction of pregnancy in Artificial Reproductive Techniques through evaluation of thickness, morphology and vascularity of endometrium.Methods: Endometrial thickness, morphology and sub endometrial blood flow were assessed using trans-vaginal ultrasound on the day of HCG in 200 undergoing IVF/ICSI treatment in the period between October 2009 and December 2014. Statistical analysis was done.Results: There was no difference in the demographic features between pregnant and non-pregnant women. Overall, 80 patients conceived; 46 (57.5%) of them had blood flow in zone III and 30 (37.5%) in zone II. All patients achieved pregnancy had endometrial thickness >8 mm. There was no significant difference in Doppler indices between pregnant and non-pregnant women.Conclusions: When the endometrial thickness is <8 mm, and if there are non-triple endometrial line, pregnancy rate decreases and the absence of colour mapping of the endometrium and subendometrial areas means and absolute implantation failure or a significant decrease of the implantation rate. Conversely, the pregnancy rate increases when the vessels reach endometrium

Large Pre-trained time series models for cross-domain Time series analysis tasks

Large pre-trained models have been instrumental in significant advancements in domains like language and vision making model training for individual downstream tasks more efficient as well as provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a general time-series model from multiple heterogeneous time-series dataset: providing semantically useful inputs to models for modeling time series of different dynamics from different domains. We observe that partitioning time-series into segments as inputs to sequential models produces semantically better inputs and propose a novel model LPTM that automatically identifies optimal dataset-specific segmentation strategy leveraging self-supervised learning loss during pre-training. LPTM provides performance similar to or better than domain-specific state-of-art model and is significantly more data and compute efficient taking up to 40% less data as well as 50% less training time to achieve state-of-art performance in a wide range of time-series analysis tasks from multiple disparate domain.Comment: 14 pages, 3 Figures, 3 Table

Abruptio placenta and its maternal and fetal outcome

Background: Abruptio placenta is one of the common cause of antepartum haemorrhage and is defined as premature separation of normally implanted placenta. It is more common in second half of pregnancy. Abruptio placenta is serious complication of pregnancy and causes high maternal and neonatal morbidity and mortality.Methods: This retrospective study of abruptio and its maternal and perinatal outcome was carried out between July 2016 and October 2017 at Rama Medical College Hospital and research centre.Results: Incidence of Abruptio placenta is 1.6%. It is most common in the women of age group 30-35 years. 75% of cases were associated with severe pre-eclampsia. Live births were 75% while stillbirths were 25%. PPH occurred in 30% of cases. DIC accounts for 25% of the complication.Conclusions: Abruptio placenta is life threatening complication of pregnancy and it is associated with poor maternal and fetal outcome if not managed appropriately. Hence early diagnosis and prompt resuscitative measures would prevent both perinatal and maternal mortality and morbidity

PEMS: Pre-trained Epidemic Time-series Models

Providing accurate and reliable predictions about the future of an epidemic is an important problem for enabling informed public health decisions. Recent works have shown that leveraging data-driven solutions that utilize advances in deep learning methods to learn from past data of an epidemic often outperform traditional mechanistic models. However, in many cases, the past data is sparse and may not sufficiently capture the underlying dynamics. While there exists a large amount of data from past epidemics, leveraging prior knowledge from time-series data of other diseases is a non-trivial challenge. Motivated by the success of pre-trained models in language and vision tasks, we tackle the problem of pre-training epidemic time-series models to learn from multiple datasets from different diseases and epidemics. We introduce Pre-trained Epidemic Time-Series Models (PEMS) that learn from diverse time-series datasets of a variety of diseases by formulating pre-training as a set of self-supervised learning (SSL) tasks. We tackle various important challenges specific to pre-training for epidemic time-series such as dealing with heterogeneous dynamics and efficiently capturing useful patterns from multiple epidemic datasets by carefully designing the SSL tasks to learn important priors about the epidemic dynamics that can be leveraged for fine-tuning to multiple downstream tasks. The resultant PEM outperforms previous state-of-the-art methods in various downstream time-series tasks across datasets of varying seasonal patterns, geography, and mechanism of contagion including the novel Covid-19 pandemic unseen in pre-trained data with better efficiency using smaller fraction of datasets.Comment: 18 page