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General General

Injury severity analysis of pedestrian and bicyclist trespassing crashes at non-crossings: A hybrid predictive text analytics and heterogeneity-based statistical modeling approach.

In Accident; analysis and prevention

Non-motorists involved in rail-trespassing crashes are usually more vulnerable to receiving major or fatal injuries. Previous research has used traditional quantitative crash data for understanding factors contributing to injury outcomes of non-motorists in train involved collisions. However, usually overlooked crash narratives can provide useful and unique contextual crash-specific information regarding factors associated with injury outcomes. The main objective of this study is to harness the rapid advancements in more sophisticated qualitative analysis procedures for identifying thematic concepts in unstructured crash narrative data. A two-staged hybrid approach is proposed where text mining is applied first to extract valuable information from crash narratives followed by inclusion of the new variables derived from text mining in formulation of advanced statistical models for injury outcomes. By using ten-year (2006-2015) non-motorist non-crossing trespassing injury data obtained from the Federal Railroad Administration, statistical procedures and advanced machine learning text analytics are applied to extract unique information on contributory factors of trespassers' injury outcomes. The key concepts are systematically categorized into trespasser, injury, train, medical, and location related factors. A total of 13 unique variables are extracted from the thematic concepts that are not present in traditional tabular crash data. The analysis reveals a positive statistically significant association between presence of crash narrative and trespasser's injury outcome (coded as minor, major, and fatal injury). Compared to crashes with minor injuries, crashes involving major and fatal injuries are more likely to be reported with crash narratives. A crosstabulation of new variables derived from text mining with injury outcomes revealed that trespassers with confirmed suicide attempts, trespassers wearing headphones, or talking on cell phones are more likely to receive fatal injuries. Among other factors identified, trespassers under alcohol influence, trespasser hit by commuter train, and advance warnings by engineer are associated with more severe (major and fatal) trespasser injury outcomes. Accounting for unobserved heterogeneity and controlling for other factors, fixed and random parameter discrete outcome models are developed to understand the heterogeneous correlations between trespasser injury outcomes and the new crash specific explanatory variables derived from text mining - providing deeper insights. Practical implications and future research directions are discussed.

Wali Behram, Khattak Asad J, Ahmad Numan


Concept/Entity extraction, Dynamic factor analysis, Heterogeneity-based discrete outcome modeling, Injury severity, Machine learning, Non-crossings, Non-motorist trespassing crashes, Text analysis

General General

Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs.

In Computers in biology and medicine

Early detection of pneumoconiosis in X-Rays has been a challenging task that leads to high inter- and intra-reader variability. Motivated by the success of deep learning in general and medical image classification, this paper proposes an approach to automatically detect pneumoconiosis using a deep feature based binary classifier. The features are extracted from X-rays using deep transfer learning, comprising both low and high-level feature sets. For this, a CNN model pre-trained with a transfer learning from a CheXNet model was initially used to extract deep features from the X-Ray images, then the deep features were mapped to higher-dimensional feature spaces for classification using Support Vector Machine (SVM) and CNN based feature aggregation methods. In order to cross validate the proposed method, the training and testing images were randomly split into three folds before each experiment. Nine evaluation metrics were employed to compare the performance of the proposed method and state-of-the-art methods from the literature that used the same datasets. The experimental results show that the proposed framework outperformed others, achieving an accuracy of 92.68% in the automated detection of pneumoconiosis.

Devnath Liton, Luo Suhuai, Summons Peter, Wang Dadong


Black lung, “Coal workers pneumoconiosis (CWP)”, Computer-aided diagnosis, Deep transfer learning, Support vector machine, X-rays

Public Health Public Health

A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods.

In Chemosphere

People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM2.5 concentration in indoor environments (e.g., residential households) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM2.5 concentration in residential households. PM2.5 concentration and related parameters (e.g., basic information about the households and ventilation settings) were collected in 116 households during the winter and summer seasons in Hong Kong. Outdoor PM2.5 concentration at households was estimated using a land-use regression model. The random forest machine learning algorithm was then applied to develop indoor PM2.5 prediction models. The results show that the random forest model achieved a promising predictive accuracy, with R2 and cross-validation R2 values of 0.93 and 0.65, respectively. Outdoor PM2.5 concentration was the most important predictor variable, followed in descending order by the household marked number, outdoor temperature, outdoor relative humidity, average household area and air conditioning. The external validation result using an independent dataset confirmed the potential application of the random forest model, with an R2 value of 0.47. Overall, this study shows the value of a combined land-use regression and machine learning approach in establishing indoor PM2.5 prediction models that provide a relatively accurate assessment of exposure for use in epidemiological studies.

Li Zhiyuan, Tong Xinning, Ho Jason Man Wai, Kwok Timothy C Y, Dong Guanghui, Ho Kin-Fai, Yim Steve Hung Lam


Households, Indoor air, PM(2.5), Prediction model, Random forest

General General

T lymphocytes from malignant hyperthermia-susceptible mice display aberrations in intracellular calcium signaling and mitochondrial function.

In Cell calcium

Gain-of-function RyR1-p.R163C mutation in ryanodine receptors type 1 (RyR1) deregulates Ca2+ signaling and mitochondrial function in skeletal muscle and causes malignant hyperthermia in humans and mice under triggering conditions. We investigated whether T lymphocytes from heterozygous RyR1-p.R163C knock-in mutant mice (HET T cells) display measurable aberrations in resting cytosolic Ca2+ concentration ([Ca2+]i), Ca2+ release from the store, store-operated Ca2+ entry (SOCE), and mitochondrial inner membrane potential (ΔΨm) compared with T lymphocytes from wild-type mice (WT T cells). We explored whether these variables can be used to distinguish between T cells with normal and altered RyR1 genotype. HET and WT T cells were isolated from spleen and lymph nodes and activated in vitro using phytohemagglutinin P. [Ca2+]i and ΔΨm dynamics were examined using Fura 2 and tetramethylrhodamine methyl ester fluorescent dyes, respectively. Activated HET T cells displayed elevated resting [Ca2+]i, diminished responses to Ca2+ mobilization with thapsigargin, and decreased rate of [Ca2+]i elevation in response to SOCE compared with WT T cells. Pretreatment of HET T cells with ryanodine or dantrolene sodium reduced disparities in the resting [Ca2+]i and ability of thapsigargin to mobilize Ca2+ between HET and WT T cells. While SOCE elicited dissipation of the ΔΨm in WT T cells, it produced ΔΨm hyperpolarization in HET T cells. When used as the classification variable, the amplitude of thapsigargin-induced Ca2+ transient showed the best promise in predicting the presence of RyR1-p.R163C mutation. Other significant variables identified by machine learning analysis were the ratio of resting cytosolic Ca2+ level to the amplitude of thapsigargin-induced Ca2+ transient and an integral of changes in ΔΨm in response to SOCE. Our study demonstrated that gain-of-function mutation in RyR1 significantly affects Ca2+ signaling and mitochondrial fiction in T lymphocytes, which suggests that this mutation may cause altered immune responses in its carrier. Our data link the RyR1-p.R163C mutation, which causes inherited skeletal muscle diseases, to deregulation of Ca2+ signaling and mitochondrial function in immune T cells and establish proof-of-principle for in vitro T cell-based diagnostic assay for hereditary RyR1 hyperfunction.

Yang Lukun, Dedkova Elena N, Allen Paul D, Jafri M Saleet, Fomina Alla F


Dantrolene sodium, Intracellular Ca(2+), Mitochondrial potential, RYR1-p.R163C knock-in mice, Ryanodine receptor, T lymphocytes

Surgery Surgery

"Robotic surgery: the impact of simulation and other innovative platforms on performance and training".

In Journal of minimally invasive gynecology ; h5-index 40.0

OBJECTIVE : To review the current status of robotic training and the impact of various training platforms on the performance of robotic surgical trainees.

DATA SOURCES : Literature review of Google Scholar and PubMed. Search terms included a combination of the following: "robotic training", "simulation", "robotic curriculum", "obgyn residency robotic training", "virtual reality robotic training", "DaVinci training", "surgical simulation", "gyn surgical training". Sources considered for inclusion included peer reviewed articles, literature reviews, textbook chapters, and statements from various institutions involved in resident training.

METHODS OF STUDY SELECTION : A literature search of Google Scholar and PubMed using terms related to robotic surgery and robotics training, as mentioned above.

RESULTS : Multiple novel platforms that utilize machine learning and real time video feedback to teach and evaluate robotic surgical skills have been developed over recent years. Various training curricula, VR simulators, and other robotic training tools have shown to enhance robotic surgical education and improve surgical skills. Integration of didactic learning, simulation, and intraoperative teaching into more comprehensive training curricula shows positive effects on robotic skills proficiency. Few robotic surgery training curricula have been validated through peer reviewed study, and there is more work to be completed in this area. In addition, there is a lack of information about how skills obtained through robotics curricula and simulation translates into operating room performance and patient outcomes.

CONCLUSION : Data collected to date shows promising advances in training of robotic surgeons. A diverse array of curricula for training robotic surgeons continues to emerge, and existing teaching modalities are evolving to keep up with the rapid demand for proficient robotic surgeons. Futures areas of growth include establishing competency benchmarks for existing training tools, validating existing curricula, and determining how to translate acquired skills in simulation to performance in the operating room and patient outcomes. Many surgical training platforms are beginning to expand beyond discreet robotic skills training to procedure-specific and team training. There is still a wealth of research to be done to understand how to create an effective training experience for gyn surgical trainees and robotics teams.

Azadi Shirin, Green Isabel, Arnold Anne, Truong Mireille, Potts Jacqueline, Martino Martin A


General General

Toward computational modelling on immune system function.

In BMC bioinformatics

The 3rd edition of the computational methods for the immune system function workshop has been held in San Diego, CA, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) from November 18 to 21, 2019. The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. Among these, the best 10 papers have been selected and extended for presentation in this special issue. The covered topics range from computer-aided identification of T cell epitopes to the prediction of heart rate variability to prevent brain injuries, from In Silico modeling of Tuberculosis and generation of digital patients to machine learning applied to predict type-2 diabetes risk.

Pappalardo Francesco, Russo Giulia, Reche Pedro A