DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization

The key method for determining the structure of a protein to date is X-ray crystallography, which is a very expensive technique that suffers from high attrition rate. On the contrary, a sequence-based predictor that is capable of accurately determining protein crystallization property, would not only overcome such limitations, but also would reduce the trial-and-error settings required to perform crystallization. In this work, to predict protein crystallizability, we have developed a novel sequence-based hybrid method that employs two separate, yet fully automated, concepts for extracting features from protein sequences. Specifically, we use a deep convolutional neural network on a publicly available dataset to extract descriptive features directly from the sequences, then fuse such feature with structural-and-physio-chemical driven features (such as amino-acid composition or AAIndex-based physicochemical properties). Dimentionality reduction is then performed on the resulting features and the output vectors are applied to train optimized gradient boosting machine (XGBoostt). We evaluate our method through three publicly available test sets, and show that our proposed DHS-Crystallize algorithm outperforms state-of-the-art methods, and achieves higher performance compared to using DCNN-deriven features, or structural-and-physio-chemical driven features alone.

Comparative Microsomal Proteomics of A Model Lung Cancer Cell Line NCI-H23 Reveals Distinct Differences Between Molecular Profiles of 3D and 2D Cultured Cells

Background: Lung cancer is the leading cause of cancer-related deaths in the USA and worldwide. Yet, about 95% of new drug candidates validated in preclinical phase eventually fail in clinical trials. Such a high attrition rate is attributed mostly to the inability of conventional two-dimensionally (2D) cultured cancer cells to mimic native three-dimensional (3D) growth of malignant cells in human tumors. Thus, it is expected that 3D cell culture systems would more accurately represent the phenotype of cancer cells growing in tumors. To ascertain phenotypical differences between these two distinct culture conditions, we carried out a comparative proteomic analysis of membrane fraction obtained from 3D- and 2D-cultured NSCLC model cell line NCI-H23. Methods: Global shotgun membrane (SGM) proteomics that relies on strong cation exchange (SCX)-based peptide fractionation and accurate-mass, liquid chromatography mass spectrometry (HR/AM LC-MS) was employed to analyze microsomal fractions obtained from the NCI-H23 cells grown in both 2D and 3D culture conditions. Results: Comparative proteomics revealed a map of 1,166 (24%) nonredundant protein species regulated in culture dependent manner in NCI-H23 cell line. Of these, a subset of 234 (i.e., 21 %) proteins were found significantly dysregulated (p-value ≤ 0.05) under both culture conditions whereas a total of 334 (27.8%) and 598 (51,2%) proteins were uniquely identified in 3D and 2D culture, respectively. The Ingenuity Pathway Analysis revealed extensive metabolic changes and differential regulation of a subset of CD molecules in culture-dependent manner. Using western blotting we verified exclusive 3D-culture expression of CD99, CD146 and CD239, involved in development of malignant stroma extracellular matrix, neo-angiogenesis and metastasis. Furthermore, using label-free quantitation we unambiguously confirmed upregulation of wild type and monoallelic KRas4B G12C mutant in 3D cultured NCI-H23 cells, targeting exclusively proteoform-specific tryptic peptides. Conclusions: In this study we generated a large-scale proteomic resource/atlas of a preclinical testing model NCI-H23 cell line grown in 3D- and 2D-culture, providing insight into phenotypical/proteomic changes unique to each culture type, that would not have been discovered using only conventional 2D-culture. To reduce high attrition rate of new drug candidates it is critical to profile a large number of patient-derived NSCLC cell lines.

Computational Drug Repositioning: Current Progress and Challenges

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

Effects of an Interactive Web Training to Support Parents in Reducing Challenging Behaviors in Children with Autism

Many children with autism spectrum disorder (ASD) engage in challenging behaviors, which may interfere with their daily functioning, development, and well-being. To address this issue, we conducted a four-week randomized waitlist control trial to examine the effects of a fully self-guided interactive web training (IWT) on (a) child engagement in challenging behaviors and (b) parental intervention. After 4 weeks, parents in the treatment group reported lower levels of challenging behaviors in their children and more frequent use of behavioral interventions than those in the waitlist groups. Furthermore, within-group analyses suggest that these changes persisted up to 12 weeks following completion of the IWT. Our results highlight the potential utility of web training, but our high attrition rate and potential side effects prevent us from recommending the training as a standalone treatment.

Stress Management to Enhance Teaching Quality and Teaching Effectiveness

Teaching has been identified as one of the most stressful professions, with a high attrition rate resulting from teacher stress and burnout. This chapter addresses the problem of how to enhance teaching quality and effectiveness by providing teachers with professional development in stress management, specific to the stressors of teaching. Existing research has clearly identified the key stressors for teachers, and evidence-based stress management approaches have been shown to be effective in mitigating teacher stress and improving teaching quality. However, there is little evidence that such professional development approaches have become integrated into the teacher training or continuing professional development curricula for teachers. Consequently, the aim of this chapter is to provide an overview of how teaching quality can be improved with a professional development framework of targeted approaches in stress management, which are aligned with the needs of individual teachers and whole schools.

Implications of Similarities in Instructional Design, Learner Interface Design and User Interface Design in Designing a User-Friendly Online Module

The development of a user-friendly online module depends on the inputs, the processes and the outcomes from the user interface design, the learner interface design and the instructional design. The online module includes the user interface design, the learner interface and the instructional design. This chapter would examine the theories behind these three designs. What guidelines can be garnered from the theories of these three designs? How can these guidelines be used to develop a user-friendly online module? In addition, it would examine their similarities and how they can be used to develop a user-friendly online module. Further, the chapter recommended an alignment of the garnered guidelines from the three designs to explore the plausible reasons for the high attrition rate in Massive Open Online Courses (MOOC).

An Analysis on Attrition Rate and Job Satisfaction of Employees in Insurance Sector

Man has found out himself many things to make himself and his near dear ones happy. Insurance is one such invention of man. It is not just the reluctant entry and the periodical reminders for paying the premium and the last receipt of the claim money which may look large or a mere pittance depending upon the policy was in force earning handsome bonus or increase in capital. Insurance industry is one of the better tools to protect personal benefits as well as economical benefits to country. Insurance benefits society as a whole, not just who hold insurance. It is one of the largest industries in the field of employment too. Insurance industry has given employment to millions of employees. It has been well said that great achievements come with great responsibility and so with great ups and downs. This sector has witnessed high attrition rate and lower degree of job satisfaction in its employees. The purpose of this paper is to identify the factors causing these problems and to suggest some possible solutions.

Attrition of Employees of Information Technology (It) Companies in Bangalore City

To manage their business in the competitive environment, management should focus on the company’s internal power, this internal power is proven based on their attrition rates. Bangalore is called as IT capital of India, hundreds and thousands of people from different parts of the country come to Bangalore for their career growth and development. There are nearly 12000+ companies in Bangalore hence the possibility of employees switching companies is high and hence the HR professionals are challenged more. It becomes ineluctable for the IT companies of Bangalore to tackle high attrition rate issue. This study is to with-stand the challenges faced due to high attrition and how to address them. In this study, the opinions of 200 respondents were taken for the analysis purpose. In this research, structured questionnaire has been incorporated for collecting data and chi-square test, percentage analysis and ANOVA were used for analysis.

Eigen Value ANalySis (EVANS) ‒ A Tool to Address Pharmacodynamic, Pharmacokinetic and Toxicity Issues

Drug discovery is a continuously evolving area, essential for mankind albeit a very expensive process with a high attrition rate. The main challenge faced by pharmaceutical researchers today is to identify the major hurdles and validate the “developability” of a compound in the initial stages of drug development in order to select superior drug candidates with the best chances of success. This motivated us to introduce a “universal” approach for analyzing the pharmacodynamics, pharmacokinetics and toxicity profile of compounds based on the philosophy of QSAR. The present work deals with the development, validation and application of a novel QSPR formalism entitled EigenValue ANalySis (EVANS). This methodology encodes 3D structural information in terms of the atom pair distances along with molecular physicochemical properties to generate a set of unique hybrid descriptors, termed as “Eigenvalues.” The present article deals with the intricacies of the methodology and explores its applicability on a series of datasets for building pharmacodynamic QSAR models.