Intelligent Head-Mounted Obstacle Avoidance Wearable for the Blind and Visually Impaired.

Individuals who are Blind and Visually Impaired (BVI) take significant risks and dangers on obstacles, particularly when they are unaccompanied. We propose an intelligent head-mount device to assist BVI people with this challenge. The objective of this study is to develop a computationally efficient mechanism that can effectively detect obstacles in real time and provide warnings. The learned model aims to be both reliable and compact so that it can be integrated into a wearable device with a small size. Additionally, it should be capable of handling natural head turns, which can generally impact the accuracy of readings from the device's sensors. Over thirty models with different hyper-parameters were explored and their key metrics were compared to identify the most suitable model that strikes a balance between accuracy and real-time performance. Our study demonstrates the feasibility of a highly efficient wearable device that can assist BVI individuals in avoiding obstacles with a high level of accuracy.

Investigating chimeric antigen receptor T cell therapy and the potential for cancer immunotherapy (Review).

Immunotherapy has emerged as a crucial treatment option, particularly for types of cancer that display resistance to conventional therapies. A remarkable breakthrough in this field is the development of chimeric antigen receptor (CAR) T cell therapy. CAR T cells are generated by engineering the T cells of a patient to express receptors that can recognize specific tumor antigens. This groundbreaking approach has demonstrated impressive outcomes in hematologic malignancies, including diffuse large B cell lymphoma, B cell acute lymphoblastic leukemia and multiple myeloma. Despite these significant successes, CAR T cell therapy has encountered challenges in its application against solid tumors, leading to limited success in these cases. Consequently, researchers are actively exploring novel strategies to enhance the efficacy of CAR T cells. The focus lies on augmenting CAR T cell trafficking to tumors while preventing the development of CAR T cell exhaustion and dysfunction. The present review aimed to provide a comprehensive analysis of the achievements and limitations of CAR T cell therapy in the context of cancer treatment. By understanding both the successes and hurdles, further advancements in this promising area of research can be developed. Overall, immunotherapy, particularly CAR T cell therapy, has opened up novel possibilities for cancer treatment, offering hope to patients with previously untreatable malignancies. However, to fully realize its potential, ongoing research and innovative strategies are essential in overcoming the challenges posed by solid tumors and maximizing CAR T cell efficacy in clinical settings.

A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems.

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method.

Brain Tissue Oxygenation in Patients with Septic Shock: a Feasibility Study.

BACKGROUND: Delirium is common in critically ill patients and its presence is associated with increased mortality and increased likelihood of poor cognitive function among survivors. However, the cause of delirium is unknown. The purpose of this study was to demonstrate the feasibility of using near-infrared spectroscopy (NIRS) to assess brain tissue oxygenation in patients with septic shock, who are at high risk of developing delirium. METHODS: This prospective observational study was conducted in a 33-bed general medical surgical intensive care unit (ICU). Patients with severe sepsis or septic shock were eligible for recruitment. The FORESIGHT NIRS monitor was used to assess brain tissue oxygenation in the frontal lobes for the first 72 hours of ICU admission. Physiological data was also recorded. We used the Confusion Assessment Method-ICU to screen for delirium. RESULTS: From March 1st 2014-September 30th 2014, 10 patients with septic shock were recruited. The NIRS monitor captured 81% of the available data. No adverse events were recorded. Brain tissue oxygenation demonstrated significant intra- and inter-individual variability in the way it correlated with physiological parameters, such as mean arterial pressure, heart rate, and peripheral oxygen saturation. Mean brain tissue oxygen levels were significantly lower in patients who were delirious for the majority of their ICU stay. CONCLUSION: It is feasible to record brain tissue oxygenation with NIRS in patients with septic shock. This study provides the infrastructure necessary for a larger prospective observational study to further examine the relationship between brain tissue oxygenation, physiological parameters, and acute neurological dysfunction.

Texture segmentation by genetic programming.

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.