Screening of Autophagy-Related Prognostic Genes in Metastatic Skin Melanoma

Cutaneous melanoma refers to a common skin tumor that is dangerous to health with a great risk of metastasis. Previous researches reported that autophagy is associated with the progression of cutaneous melanoma. Nevertheless, the role played by genes with a relation to autophagy (ARG) in the prediction of the course of metastatic cutaneous melanoma is still largely unknown. We observed that thirteen ARGs showed relations to overall survival (OS) in the Cox regression investigation based on a single variate. We developed 2-gene signature, which stratified metastatic cutaneous melanoma cases to groups at great and small risks. Cases suffering from metastatic cutaneous melanoma in the group at great risks had power OS compared with cases at small risks. The risk score, T phase, N phase, and age were proved to be individual factors in terms of the prediction of OS. Besides, the risk scores identified by the two ARGs were significantly correlated with metastatic cutaneous melanoma. Receiver operating characteristic (ROC) curve analysis demonstrated accurate predicting performance exhibited by the 2-gene signature. We also found that the immunization and stromal scores achieved by the group based on large risks were higher compared with those achieved by the group based on small risks. The metastatic cutaneous melanoma cases achieving the score based on small risks acquired greater expression of immune checkpoint molecules as compared with the high-risk group. In conclusion, the 2-ARG gene signature indicated a novel prognostic indicator for prognosis prediction of metastatic cutaneous melanoma, which served as an important tool for guiding the clinical treatment of cutaneous melanoma.

Recruiting for Resilience

Bringing a safe and effective pharmaceutical product or medical device to market requires an astonishing amount of time and money. This research features interviews with the Chief Executive Officers (CEOs), Chief Scientific Officers (CSOs) and Chief Medical Officers (CMOs) of many of the most successful life science firms in the USA with the goal of to capturing their thoughts on the recruitment of new hires. The executives screened candidates for emotional commitment as an essential quality to complete the long process of bench science, regulatory clearance and product positioning in the market. They sought to hire experienced team members who thought of set-backs as problems to be solved on the way to providing life-altering options for patients. These C-suite leaders needed to create a productive workplace culture, enhanced by a diverse group of professionals with a variety of experiences and temperaments. Participants noted that shared vision and resilience played a greater role in predicting performance than any particular skill-set discernible from a resume.

Assessing lexical-semantic naming deficits using an automated machine learning approach

After left hemisphere stroke, 20-50% of people experience language deficits, including difficulties in naming. Naming errors that are semantically related to the intended target (e.g., producing “violin” for picture HARP) indicate a potential impairment in accessing knowledge of word forms and their meanings. Understanding the cause of naming impairments is crucial to better modeling of language production as well as for tailoring individualized rehabilitation. However, evaluation of naming errors is typically by subjective and laborious dichotomous classification. As a result, these evaluations do not capture the degree of semantic similarity and are susceptible to lower inter-rater reliability because of subjectivity. We investigated whether a computational linguistic measure, word2vec (Mikolov, Chen, Corrado, & Dean, 2013) addressed these limitations by evaluating errors during object naming in a group of patients during the acute stage of a left-hemisphere stroke (N=105). Pearson correlations demonstrated excellent convergent validity of word2vec’s semantically related estimates of naming errors and independent tests of access to lexical-semantic knowledge (p’s < .0001). Further, multiple regression analysis showed word2vec’s semantically related estimates were significantly better than human error classification at predicting performance on tests of lexical-semantic knowledge (p < .001). Useful to both theorists and clinicians, word2vec provides an automated, continuous, and objective psychometric measure of access to lexical-semantic knowledge during naming.

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

USING RELATIVE VISUAL PERFORMANCE TO PREDICT THE ABILITY TO MAKE INTERPERSONAL EVALUATIONS

In research of lighting for pedestrians, many experiments have been conducted to determine how changes in lighting affect the ability to make interpersonal evaluations. Here we consider an alternative approach, predicting performance using a model - Relative Visual Performance. The results show that face evaluation ability is affected by adaptation luminance and also by personal characteristics; observer age and skin tone of the observed person. While 2 lx is sufficient for a young observer to evaluate a Caucasian face, the typical situation in laboratory trials, higher illuminances are needed for older observers and for darker skin tones.

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.

Mental rotation ability predicts the acquisition of basic endovascular skills

Due to the increasing complexity of diseases in the aging population and rapid progress in catheter-based technology, the demands on operators’ skills in conducting endovascular interventions (EI) has increased dramatically, putting more emphasis on training. However, it is not well understood which factors influence learning and performance. In the present study, we examined the ability of EI naïve medical students to acquire basic catheter skills and the role of pre-existing cognitive ability and manual dexterity in predicting performance. Nineteen medical students practised an internal carotid artery angiography during a three-day training on an endovascular simulator. Prior to the training they completed a battery of tests. Skill acquisition was assessed using quantitative and clinical performance measures; the outcome measures from the test battery were used to predict the learning rate. The quantitative metrics indicated that participants’ performance improved significantly across the training, but the clinical evaluation revealed that participants did not significantly improve on the more complex part of the procedure. Mental rotation ability (MRA) predicted quantitative, but not clinical performance. We suggest that MRA tests in combination with simulator sessions could be used to assess the trainee’s early competence level and tailor the training to individual needs.