Assessing gait dysfunction severity in Parkinson's Disease using 2-Stream Spatial-Temporal Neural Network.

Parkinson's Disease (PD), a neurodegenerative disorder, significantly impacts the quality of life for millions of people worldwide. PD primarily impacts dopaminergic neurons in the brain's substantia nigra, resulting in dopamine deficiency and gait impairments such as bradykinesia and rigidity. Currently, several well-established tools, such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Hoehn and Yahr (H&Y) Scale, are used for evaluating gait dysfunction in PD. While insightful, these methods are subjective, time-consuming, and often ineffective in early-stage diagnosis. Other methods using specialized sensors and equipment to measure movement disorders are cumbersome and expensive, limiting their accessibility. This study introduces a hierarchical approach to evaluating gait dysfunction in PD through videos. The novel 2-Stream Spatial-Temporal Neural Network (2S-STNN) leverages the spatial-temporal features from the skeleton and silhouette streams for PD classification. This approach achieves an accuracy rate of 89% and outperforms other state-of-the-art models. The study also employs saliency values to highlight critical body regions that significantly influence model decisions and are severely affected by the disease. For a more detailed analysis, the study investigates 21 specific gait attributes for a more nuanced quantification of gait disorders. Parameters such as walking pace, step length, and neck forward angle are found to be strongly correlated with PD gait severity categories. This approach offers a comprehensive and convenient solution for PD management in clinical settings, enabling patients to receive a more precise evaluation and monitoring of their gait impairments.

Conditional Generative Models for Dynamic Trajectory Generation and Urban Driving.

This work explores methodologies for dynamic trajectory generation for urban driving environments by utilizing coarse global plan representations. In contrast to state-of-the-art architectures for autonomous driving that often leverage lane-level high-definition (HD) maps, we focus on minimizing required map priors that are needed to navigate in dynamic environments that may change over time. To incorporate high-level instructions (i.e., turn right vs. turn left at intersections), we compare various representations provided by lightweight and open-source OpenStreetMaps (OSM) and formulate a conditional generative model strategy to explicitly capture the multimodal characteristics of urban driving. To evaluate the performance of the models introduced, a data collection phase is performed using multiple full-scale vehicles with ground truth labels. Our results show potential use cases in dynamic urban driving scenarios with real-time constraints. The dataset is released publicly as part of this work in combination with code and benchmarks.

Navigating the perils and pitfalls throughout the consent process in hematopoietic cell transplantation.

Hematopoietic cell transplantation (HCT) is a complex treatment used in malignancies and some non-malignant diseases. The informed consent process for HCT can also be complex due to patient- and process-related barriers. The informed consent process needs to be a dynamic and ongoing process, not simply a checklist. As a result of the realities of HCT, we highlight some potential pitfalls to the informed consent process including uncertainty, sociocultural and communication barriers, and decisional regret. The purpose of this comprehensive review is to highlight unique situations which can result in failure of the informed consent process. We also offer potential solutions to these pitfalls, primarily making the informed consent more patient focused through dynamic and continuous processes to mitigate decisional regret.

Detection of Strawberry Diseases Using a Convolutional Neural Network.

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30-40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases-leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

The Effect of One-Abutment at One-Time on Marginal Bone Loss Around Implants Placed in Healed Bone: A Systematic Review of Human Studies.

OBJECTIVES: The primary aim of the present article was to review the effect and the clinical significance of abutment dis- and reconnection on the peri-implant marginal bone levels. MATERIALS AND METHODS: English articles published from 2009 to April 2019 were identified on the MEDLINE, Cochrane Library, and PubMed databases, according to the PRISMA guidelines. Comparative in vivo studies on humans were included. RESULTS: A total of 4 studies with different levels of bias were included in this review. A significant heterogeneity of the reported data was observed, which limited the comparison of the findings. The only parameter that was homogenous throughout all 4 studies was the marginal bone level measurement. CONCLUSION: Within the limitations of the present review, it can be suggested that minimizing the number of abutment dis- and reconnections would be beneficial to ensure minimal disruption to the peri-implant tissue and marginal bone level. However, the clinical significance of the marginal bone level changes is still inconclusive.

A genome-based functional screening approach to vaccine development that combines in vitro assays and DNA immunization.

A two-step screening strategy was developed to identify strong immunogenic polypeptides with putative vaccine and/or adjuvant activity. In the first step, a mycobacterial genomic DNA library was screened in vitro to identify plasmids encoding polypeptides that stimulate splenocytes from mycobacteria-immunized mice and T cells from PPD-positive healthy donors to produce interferon-gamma. In the second step, plasmids were selected for their ability to induce protective immunity in a mouse model of tuberculosis following DNA immunization. The potential of this approach is illustrated by the identification of a panel of immunogenic polypeptides that may be used to engineer a new generation of vaccines.