BcCluster: a bladder cancer database at the molecular level

Background: Bladder Cancer (BC) has two clearly distinct phenotypes. Non-muscle invasive BC has good prognosis and is treated with tumor resection and intravesical therapy whereas muscle invasive BC has poor prognosis and requires usually systemic cisplatin based chemotherapy either prior to or after radical cystectomy. Neoadjuvant chemotherapy is not often used for patients undergoing cystectomy. High-throughput analytical omics techniques are now available that allow the identification of individual molecular signatures to characterize the invasive phenotype. However, a large amount of data produced by omics experiments is not easily accessible since it is often scattered over many publications or stored in supplementary files. Objective: To develop a novel open-source database, BcCluster (http://www.bccluster.org/), dedicated to the comprehensive molecular characterization of muscle invasive bladder carcinoma. Materials: A database was created containing all reported molecular features significant in invasive BC. The query interface was developed in Ruby programming language (version 1.9.3) using the web-framework Rails (version 4.1.5) (http://rubyonrails.org/). Results: BcCluster contains the data from 112 published references, providing 1,559 statistically significant features relative to BC invasion. The database also holds 435 protein-protein interaction data and 92 molecular pathways significant in BC invasion. The database can be used to retrieve binding partners and pathways for any protein of interest. We illustrate this possibility using survivin, a known BC biomarker. Conclusions: BcCluster is an online database for retrieving molecular signatures relative to BC invasion. This application offers a comprehensive view of BC invasiveness at the molecular level and allows formulation of research hypotheses relevant to this phenotype

An Encoder-Decoder Approach for Packing Circles

The problem of packing smaller objects within a larger object has been of interest since decades. In these problems, in addition to the requirement that the smaller objects must lie completely inside the larger objects, they are expected to not overlap or have minimum overlap with each other. Due to this, the problem of packing turns out to be a non-convex problem, obtaining whose optimal solution is challenging. As such, several heuristic approaches have been used for obtaining sub-optimal solutions in general, and provably optimal solutions for some special instances. In this paper, we propose a novel encoder-decoder architecture consisting of an encoder block, a perturbation block and a decoder block, for packing identical circles within a larger circle. In our approach, the encoder takes the index of a circle to be packed as an input and outputs its center through a normalization layer, the perturbation layer adds controlled perturbations to the center, ensuring that it does not deviate beyond the radius of the smaller circle to be packed, and the decoder takes the perturbed center as input and estimates the index of the intended circle for packing. We parameterize the encoder and decoder by a neural network and optimize it to reduce an error between the decoder's estimated index and the actual index of the circle provided as input to the encoder. The proposed approach can be generalized to pack objects of higher dimensions and different shapes by carefully choosing normalization and perturbation layers. The approach gives a sub-optimal solution and is able to pack smaller objects within a larger object with competitive performance with respect to classical methods

ANALYSIS OF EFFECT OF AMLA RASA (SOUR FOOD) IN DIFFERENT CONCENTRATIONS ON MAMSA DHATU (MUSCLE TISSUE)

It is said that one should consume Shad-Rasayuktha Ahara for better nourishment. The nutritive value of the 6 Rasas (taste) has been explained in the classics of Ayurveda along with their harmful effects when used in excess. When Amla Rasa is taken in excess it produces Dantaharsha (Morbid sensation of teeth), Trishna (thirst) etc.Objectives - The present study is designed by administering Amla Rasa Pradhana Drvya given in different concentration 10% and 15% and effect on the Mamsa Dhatu is assessed by certain blood parameters.Material and Methods -Wistar strain albino rats of either sex, weighing of 200-300 g. Obtained from S.D.M Centre for Research in Ayurveda, Udupi, Karnataka. 5Groups were taken and Group 1 is Control, Group-2 is 15% Chincha, Group-3 is 15% Vrukshamla, Group-4 is 10% Chincha, Group-5 is 10% Vrukshamla which receives drug and laboratory food for 28days, next day blood was collected by supra-orbital puncture with the help of micro capillary tubes under mild ether anesthesia.Results - There was increase in the Total Protein, Serum Albumin and serum creatinine levels and decrease in Serum globulin, Serum Urea level and Body weight.Conclusion – the study suggests that there is catabolic action of the Amla Rasa on the Mamsa Dhatu and anabolic act action on serum protein especially at the level of liver, so there will be a loss of muscle tissue which suggests Mamsa Vidhahyati action

EFFECT OF PHYTOESTROGENS ON LIPID PROFILE: MINI REVIEW

Phytoestrogens are structurally similar to its mammalian counterparts and are capable of producing estrogen-like effects and non-steroidal in nature. Their affinity to bind to the estrogen receptors, a class of cytoplasmic proteins, is far less in comparison to the natural estrogen hormones found in humans and other animals or even the synthetically prepared estrogen and, therefore, are far less potent. Even so, the usage of phytoestrogen in either dietary or supplemental form in postmenopausal women is gaining interest, as opposed to the conventional estrogen replacement therapy (ERT), which was so far being used to alleviate the postmenopausal symptoms and believed, with little experimental/clinical evidence, to cause a significant reduction in the threat of coronary heart diseases (CHD). This usage of phytoestrogen follows from the fact that the overall health risks exerted by ERT outweigh the benefits, and to a little extent, the perception that synthetically prepared, so-called prescription estrogen†interferes with what is otherwise a natural process. It is crucial to explore the alternatives that mimic the beneficial effects of ERT, especially with regard to the minimization of the risk of developing CHD and further evaluation of the aforementioned risks in terms of lipid profiling while simultaneously keeping the health risks to a minimum. This review discusses the scope of use of phytoestrogen specifically pertaining to lipid profiling, their possible health benefits, and adverse effects if any. Keywords: Phytoestrogens, Postmenopause, Lipid profile, Isoflavones

Predicting Compressive Strength of Alkali-Activated Systems based on the Network Topology and Phase Assemblages using Tree-Structure Computing Algorithms

Alkali-activated system is an environment-friendly, sustainable construction material utilized to replace ordinary Portland cement (OPC) that contributes to 9% of the global carbon footprint. Moreover, the alkali-activated system has exhibited superior strength at early ages and better corrosion resistance compared to OPC. The current state of analytical and machine learning models cannot produce highly reliable predictions of the compressive strength of alkali-activated systems made from different types of aluminosilicate-rich precursors owing to substantive variation in the chemical compositions and reactivity of these precursors. In this study, a random forest model with two constraints (i.e., topological network and thermodynamic constraints) is employed to predict the compressive strength of alkali-activated systems made from 26 aluminosilicate-rich precursors and distinct processing parameters. Results show that once the model is rigorously trained and optimized, the RF model can yield a priori, high-fidelity predictions of the compressive strength in relation to the physicochemical properties of aluminosilicate-rich precursors; processing parameters; and constraints. The topological network constraint provides the chemo structural properties and reactivity of the aluminosilicate-rich precursors. Whereas the thermodynamic constraint estimates the phase assemblages at different degrees of reaction of the aluminosilicate-rich precursors. Finally, the correlations between topological network constraint; phase assemblage; and compressive strength are demonstrated. When the topological network constraint equals 3.4, the alkali-activated systems can achieve their optimal compressive strength

Comprehensive Network Analysis Reveals Alternative Splicing-Related lncRNAs in Hepatocellular Carcinoma

© Copyright © 2020 Wang, Wang, Bhat, Chen, Xu, Mo, Yi and Zhou. It is increasingly appreciated that long non-coding RNAs (lncRNAs) associated with alternative splicing (AS) could be involved in aggressive hepatocellular carcinoma. Although many recent studies show the alteration of RNA alternative splicing by deregulated lncRNAs in cancer, the extent to which and how lncRNAs impact alternative splicing at the genome scale remains largely elusive. We analyzed RNA-seq data obtained from 369 hepatocellular carcinomas (HCCs) and 160 normal liver tissues, quantified 198,619 isoform transcripts, and identified a total of 1,375 significant AS events in liver cancer. In order to predict novel AS-associated lncRNAs, we performed an integration of co-expression, protein-protein interaction (PPI) and epigenetic interaction networks that links lncRNA modulators (such as splicing factors, transcript factors, and miRNAs) along with their targeted AS genes in HCC. We developed a random walk-based multi-graphic (RWMG) model algorithm that prioritizes functional lncRNAs with their associated AS targets to computationally model the heterogeneous networks in HCC. RWMG shows a good performance evaluated by the ROC curve based on cross-validation and bootstrapping strategies. As a conclusion, our robust network-based framework has derived 31 AS-related lncRNAs that not only validates known cancer-associated cases MALAT1 and HOXA11-AS, but also reveals new players such as DNM1P35 and DLX6-AS1with potential functional implications. Survival analysis further provides insights into the clinical significance of identified lncRNAs

Deep Learning to Predict the Hydration and Performance of Fly Ash-Containing Cementitious Binders

Fly ash (FA) – an industrial byproduct – is used to partially substitute Portland cement (PC) in concrete to mitigate concrete\u27s environmental impact. Chemical composition and structure of FAs significantly impact hydration kinetics and compressive strength of concrete. Due to the substantial diversity in these physicochemical attributes of FAs, it has been challenging to develop a generic theoretical framework – and, therefore, theory-based analytical models – that could produce reliable, a priori predictions of properties of [PC + FA] binders. In recent years, machine learning (ML) – which is purely data-driven, as opposed to being derived from theorical underpinnings – has emerged as a promising tool to predict and optimize properties of complex, heterogenous materials, including the aforesaid binders. That said, there are two issues that stand in the way of widespread use of ML models: (1) ML models require thousands of data-records to learn input-output correlations and developing such a large, yet consistent database is impractical; and (2) ML models – while good at producing predictions – are unable to reveal the underlying correlation between composition/structure of material and its properties. This study employs a deep forest (DF) model to predict composition- and time-dependent hydration kinetics and compressive strength of [PC + FA] binders. Data dimensionality-reduction and segmentation techniques – premised on theoretical understanding of composition-structure correlations in FAs, and hydration mechanism of PC – are used to boost the DF model\u27s prediction performance. And, finally, through inference of the intermediate and final outputs of the DF model, a simple, closed-form analytical model is developed to predict compressive strength, and reveal the correlations between mixture design and compressive strength of [PC + FA] binders

Social Cognition for Human-Robot Symbiosis—Challenges and Building Blocks

The next generation of robot companions or robot working partners will need to satisfy social requirements somehow similar to the famous laws of robotics envisaged by Isaac Asimov time ago (Asimov, 1942). The necessary technology has almost reached the required level, including sensors and actuators, but the cognitive organization is still in its infancy and is only partially supported by the current understanding of brain cognitive processes. The brain of symbiotic robots will certainly not be a “positronic” replica of the human brain: probably, the greatest part of it will be a set of interacting computational processes running in the cloud. In this article, we review the challenges that must be met in the design of a set of interacting computational processes as building blocks of a cognitive architecture that may give symbiotic capabilities to collaborative robots of the next decades: (1) an animated body-schema; (2) an imitation machinery; (3) a motor intentions machinery; (4) a set of physical interaction mechanisms; and (5) a shared memory system for incremental symbiotic development. We would like to stress that our approach is totally un-hierarchical: the five building blocks of the shared cognitive architecture are fully bi-directionally connected. For example, imitation and intentional processes require the “services” of the animated body schema which, on the other hand, can run its simulations if appropriately prompted by imitation and/or intention, with or without physical interaction. Successful experiences can leave a trace in the shared memory system and chunks of memory fragment may compete to participate to novel cooperative actions. And so on and so forth. At the heart of the system is lifelong training and learning but, different from the conventional learning paradigms in neural networks, where learning is somehow passively imposed by an external agent, in symbiotic robots there is an element of free choice of what is worth learning, driven by the interaction between the robot and the human partner. The proposed set of building blocks is certainly a rough approximation of what is needed by symbiotic robots but we believe it is a useful starting point for building a computational framework

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease