Mol-CycleGAN - a generative model for molecular optimization

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results

Target cells of human adenovirus type 12 in subtentorial brain tissue of newborn mice. I. Cyto-histomorphologic and immunofluorescent microscopic studies In vivo

Human adenovirus type 12 (Ad 12) was inoculated through subtentorial route into inbred newborn mice (C3H/BifB/Ki), and sequential changes of the brain and tumor induction were examined by histological and immunofluorescent methods. Two days after virus inoculation, Ad 12 specific tumor antigen (fluorescent T-antigen) appeared in the cells of ependymal and subventricular matrix layers, choroid plexuses and leptomeninges in the subtentorial as well as the supratentorial brains. After 10 days, these fluorescent positive cells decreased gradually in number but still remained focally beneath the ependyma. Sixty days later, early tumor nodules were detected in the same regions in which remained the fluorescent cells. After 107 days, neurological signs and well-developed tumors were noted in 25 of 63 (30.1%) mice examined. In the cerebellum, both of T-antigens and tumors were limited around the IVth ventricle, but not in the granular layers. Histomorphologically, the tumors were of primitive neuroectodermal origin and consisted of the cells resembling immature matrix cells in the subventricular zone. These findings strongly suggest that the virus has a selective affinity to the remaining matrix cells, but not to cerebellar granular cells, at least, in newborn mice.</p

Mol-CycleGAN : a generative model for molecular optimization

During the drug design process, one must develop a molecule, which structure satisfies a number of physicochemical properties. To improve this process, we introduce Mol-CycleGAN – a CycleGAN-based model that generates compounds optimized for a selected property, while aiming to retain the already optimized ones. In the task of constrained optimization of penalized logP of drug-like molecules our model significantly outperforms previous results

Asymmetric localization of DLC1 defines avian trunk neural crest polarity for directional delamination and migration

Following epithelial-mesenchymal transition, acquisition of avian trunk neural crest cell (NCC) polarity is prerequisite for directional delamination and migration, which in turn is essential for peripheral nervous system development. However, how this cell polarization is established and regulated remains unknown. Here we demonstrate that, using the RHOA biosensor in vivo and in vitro, the initiation of NCC polarization is accompanied by highly activated RHOA in the cytoplasm at the cell rear and its fluctuating activity at the front edge. This differential RHOA activity determines polarized NC morphology and motility, and is regulated by the asymmetrically localized RhoGAP Deleted in liver cancer (DLC1) in the cytoplasm at the cell front. Importantly, the association of DLC1 with NEDD9 is crucial for its asymmetric localization and differential RHOA activity. Moreover, NC specifiers, SOX9 and SOX10, regulate NEDD9 and DLC1 expression, respectively. These results present a SOX9/SOX10-NEDD9/DLC1-RHOA regulatory axis to govern NCC migratory polarization.published_or_final_versio

Thermodynamics of a class of non-asymptotically flat black holes in Einstein-Maxwell-Dilaton theory

We analyse in detail the thermodynamics in the canonical and grand canonical ensembles of a class of non-asymptotically flat black holes of the Einstein-(anti) Maxwell-(anti) Dilaton theory in 4D with spherical symmetry. We present the first law of thermodynamics, the thermodynamic analysis of the system through the geometrothermodynamics methods, Weinhold, Ruppeiner, Liu-Lu-Luo-Shao and the most common, that made by the specific heat. The geometric methods show a curvature scalar identically zero, which is incompatible with the results of the analysis made by the non null specific heat, which shows that the system is thermodynamically interacting, does not possess extreme case nor phase transition. We also analyse the local and global stability of the thermodynamic system, and obtain a local and global stability for the normal case for 0<\gamma<1 and for other values of \gamma, an unstable system. The solution where \gamma=0 separates the class of locally and globally stable solutions from the unstable ones.Comment: 18 pages, version accepted for publication in General Relativity and Gravitatio

VeSV- Value at the end of the Sanitation Value Chain: Final Report

Bangladesh is no stranger to composting projects using both green waste and faecal sludge (FS). There have been many initiatives over the years with varying degrees of success. Similarly there have been hundreds, if not thousands of projects to improve access to latrines, latrine use and latrine management. Again there has been a great deal of success, especially in increasing the number of latrines being built. However, a key gap regarding the safe collection and processing of the waste from the pit still remains. In cases where projects have attempted addressing this, the solution has rarely been viable on a large scale. That is where this project—VeSV—is different. The aim of this project is to provide scientific evidence to support the commercial viability of collecting and composting faecal sludge for use in agriculture and horticulture. The gap between a good idea and commercial success is bridged on this project by producing primary scientific data based on qualitative and quantitative research methods and by engaging a number of stakeholders across sectors. A rigorous research was conducted to characterize raw faecal sludge material from single pit latrines in rural Bangladesh, as the starting point to develop value across the sanitation chain from processing FS material, through adding value by recovering nutrient and finally by assessing the potential commercialization of the final product in the fertilizer market. Crucially academics, NGOs, business groups and existing fertilizer, composting and latrine management companies were involved as part of our Reference Group, which helped to develop practical engineering solutions in harmony with the right and relevant context in rural Bangladesh. Our research outcomes include the development of safe methodologies for pit emptying; the assessment of people's intentions to change current operation and maintenance practices of pit latrines at household level and their willingness to participate in commercially viable and sustainable methods for FS management; the assessment of optimised engineering process for FS stabilisation and the production of a safe, high quality fertilizer that is desirable to farmers; and the identification of potential hurdles that may obstruct the widespread adoption of business models for FS fertiliser

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

WormAssay: A Novel Computer Application for Whole-Plate Motion-based Screening of Macroscopic Parasites

Lymphatic filariasis is caused by filarial nematode parasites, including Brugia malayi. Adult worms live in the lymphatic system and cause a strong immune reaction that leads to the obstruction of lymph vessels and swelling of the extremities. Chronic disease leads to the painful and disfiguring condition known as elephantiasis. Current drug therapy is effective against the microfilariae (larval stage) of the parasite, but no drugs are effective against the adult worms. One of the major stumbling blocks toward developing effective macrofilaricides to kill the adult worms is the lack of a high throughput screening method for candidate drugs. Current methods utilize systems that measure one well at a time and are time consuming and often expensive. We have developed a low-cost and simple visual imaging system to automate and quantify screening entire plates based on parasite movement. This system can be applied to the study of many macroparasites as well as other macroscopic organisms

A modified empirical criterion for strength of transversely anisotropic rocks with metamorphic origin

A modified empirical criterion is proposed to determine the strength of transversely anisotropic rocks. In this regard, mechanical properties of intact anisotropic slate obtained from three different districts of Iran were taken into consideration. Afterward, triaxial rock strength criterion introduced by Rafiai was modified for transversely anisotropic rocks. The criterion was modified by adding a new parameter α for taking the influence of strength anisotropy into consideration. The results obtained have shown that the parameter α can be considered as the strength reduction parameter due to rock anisotropy. The modified criterion was compared to the modified Hoek–Brown (Saroglou and Tsiambaos) and Ramamurthy criteria for different anisotropic rocks. It was concluded that the criterion proposed in this paper is a more accurate and precise criterion in predicting the strength of anisotropic rocks

A novel approach to quantify random error explicitly in epidemiological studies

The most frequently used methods for handling random error are largely misunderstood or misused by researchers. We propose a simple approach to quantify the amount of random error which does not require solid background in statistics for its proper interpretation. This method may help researchers refrain from oversimplistic interpretations relying on statistical significance