Towards Sensor Enhanced Virtual Reality Teleoperation in a Dynamic Environment

A teleoperation interface is introduced featuring an integrated virtual reality based simulation augmented by sensors and image processing capabilities on-board the remotely operated vehicle. The virtual reality system addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view, allowing the operator to navigate in a continuous fashion. The vehicle incorporates an on-board computer and a stereo vision system to facilitate obstacle detection. It also enables temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. Finally, the system provides real time update to the virtual environment based on anomalies encountered by the vehicle. System architecture and preliminary implementation results are discussed, and future work focused on incorporating dynamic moving objects in the environment is described

Mammalian Target of Rapamycin (mTOR) Activity Dependent Phospho-Protein Expression in Childhood Acute Lymphoblastic Leukemia (ALL)

Modern treatment strategies have improved the prognosis of childhood ALL; however, treatment still fails in 25–30% of patients. Further improvement of treatment may depend on the development of targeted therapies. mTOR kinase, a central mediator of several signaling pathways, has recently attracted remarkable attention as a potential target in pediatric ALL. However, limited data exists about the activity of mTOR. In the present study, the amount of mTOR activity dependent phospho-proteins was characterized by ELISA in human leukemia cell lines and in lymphoblasts from childhood ALL patients (n = 49). Expression was measured before and during chemotherapy and at relapses. Leukemia cell lines exhibited increased mTOR activity, indicated by phospho-S6 ribosomal protein (p-S6) and phosphorylated eukaryotic initiation factor 4E binding protein (p-4EBP1). Elevated p-4EBP1 protein levels were detected in ALL samples at diagnosis; efficacy of chemotherapy was followed by the decrease of mTOR activity dependent protein phosphorylation. Optical density (OD) for p-4EBP1 (ELISA) was significantly higher in patients with poor prognosis at diagnosis, and in the samples of relapsed patients. Our results suggest that measuring mTOR activity related phospho-proteins such as p-4EBP1 by ELISA may help to identify patients with poor prognosis before treatment, and to detect early relapses. Determining mTOR activity in leukemic cells may also be a useful tool for selecting patients who may benefit from future mTOR inhibitor treatments

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study

Download PDFPDF Download PDF + Supplemental DataPDF + Supplementary Material Epidemiology/Health services research Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case–control study http://orcid.org/0000-0002-9905-4855Nikki L B Freeman1, Rashmi Muthukkumar2, Ruth S Weinstock3, M Victor Wickerhauser4, http://orcid.org/0000-0003-2701-101XAnna R Kahkoska5,6 Correspondence to Dr Nikki L B Freeman; [email protected] Abstract Introduction Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. Research design and methods Data from a case–control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. Results Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score. Conclusions Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics

A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

Background: The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge.Methods: This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node.Results: Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.peer-reviewe

Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias

Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data. In this study, we introduce Cross-Care, the first benchmark framework dedicated to assessing biases and real world knowledge in LLMs, specifically focusing on the representation of disease prevalence across diverse demographic groups. We systematically evaluate how demographic biases embedded in pre-training corpora like $ThePile$ influence the outputs of LLMs. We expose and quantify discrepancies by juxtaposing these biases against actual disease prevalences in various U.S. demographic groups. Our results highlight substantial misalignment between LLM representation of disease prevalence and real disease prevalence rates across demographic subgroups, indicating a pronounced risk of bias propagation and a lack of real-world grounding for medical applications of LLMs. Furthermore, we observe that various alignment methods minimally resolve inconsistencies in the models' representation of disease prevalence across different languages. For further exploration and analysis, we make all data and a data visualization tool available at: www.crosscare.net.Comment: Submitted for review, data visualization tool available at: www.crosscare.ne

Radiation Induced Apoptosis of Murine Bone Marrow Cells is Independent of Early Growth Response 1 (EGR1)

An understanding of how each individual 5q chromosome critical deleted region (CDR) gene contributes to malignant transformation would foster the development of much needed targeted therapies for the treatment of therapy related myeloid neoplasms (t-MNs). Early Growth Response 1 (EGR1) is a key transcriptional regulator of myeloid differentiation located within the 5q chromosome CDR that has been shown to regulate HSC (hematopoietic stem cell) quiescence as well as the master regulator of apoptosis—p53. Since resistance to apoptosis is a hallmark of malignant transformation, we investigated the role of EGR1 in apoptosis of bone marrow cells; a cell population from which myeloid malignancies arise. We evaluated radiation induced apoptosis of Egr1+/+ and Egr1-/- bone marrow cells in vitro and in vivo. EGR1 is not required for radiation induced apoptosis of murine bone marrow cells. Neither p53 mRNA (messenger RNA) nor protein expression is regulated by EGR1 in these cells. Radiation induced apoptosis of bone marrow cells by double strand DNA breaks induced p53 activation. These results suggest EGR1 dependent signaling mechanisms do not contribute to aberrant apoptosis of malignant cells in myeloid malignancies

Effect of cyclosporin A on murine experimental Salmonellosis

Investigations were undertaken to evaluate the effect of cyclosporin A (CyA) on primary infection of mice with Salmonella typhimurium. Further, its effect on delayed-type hypersensitivity (DTH) and antibody response to porin, an outer membrane protein of S. typhimurium, has been characterized under different CyA regimen. When mice were infected i.p. with 5 * 10(4) live medium virulent organisms, the bacterial growth increased by about 2 log10 in CyA treated mice. CyA administered daily for 2 weeks following immunization with porin caused profound suppression of DTH and both IgM and IgG antibody responses. Given a single dose of CyA 24 h before DTH elicitation, the drug had a marked suppressive effect on DTH, but not on the antibody response to porin. Treatment on days 0-5 following immunization had no effect on the manifestation of the DTH response nor on the anti-porin IgM response whereas an enhanced level of IgG was observed in this group