STR-927: SHEAR RESISTANCE OF LIGHTWEIGHT SELF-CONSOLIDATING CONCRETE BEAMS

This paper presents the shear behavior of lightweight self-consolidating concrete (LWSCC) beams without shear reinforcement compared to those made with normal weight self-consolidating concrete (SCC). The variables in this experimental and Code based study was shear span to depth ratio, concrete types and longitudinal reinforcement. The performance of LWSCC was compared with normal SCC beams based on load-deformation response, stress-strain development, and shear strength and failure modes. LWSCC beams showed lower post-cracking shear resistance and the shear strength of LWSCC/SCC beams increased with the decrease of shear span to depth ratio. LWSCC beams showed higher number of cracks and wider crack width at failure than their SCC counterparts. American, Canadian and British Codes were conservative in predicting shear strength of LWSCC beams

Antagonism of histamine H3 receptors alleviates pentylenetetrazole-induced kindling and associated memory deficits by mitigating oxidative stress, central neurotransmitters, and c-Fos protein expression in rats

Histamine H3 receptors (H3Rs) are involved in several neuropsychiatric diseases including epilepsy. Therefore, the e ects of H3R antagonist E177 (5 and 10 mg/kg, intraperitoneal (i.p.)) were evaluated on the course of kindling development, kindling-induced memory deficit, oxidative stress levels (glutathione (GSH), malondialdehyde (MDA), catalase (CAT), and superoxide dismutase (SOD)), various brain neurotransmitters (histamine (HA), acetylcholine (ACh), -aminobutyric acid (GABA)), and glutamate (GLU), acetylcholine esterase (AChE) activity, and c-Fos protein expression in pentylenetetrazole (PTZ, 40 mg/kg) kindled rats. E177 (5 and 10 mg/kg, i.p.) significantly decreased seizure score, increased step-through latency (STL) time in inhibitory avoidance paradigm, and decreased transfer latency time (TLT) in elevated plus maze (all P < 0.05). Moreover, E177 mitigated oxidative stress by significantly increasing GSH, CAT, and SOD, and decreasing the abnormal level of MDA (all P < 0.05). Furthermore, E177 attenuated elevated levels of hippocampal AChE, GLU, and c-Fos protein expression, whereas the decreased hippocampal levels of HA and ACh were modulated in PTZ-kindled animals (all P < 0.05). The findings suggest the potential of H3R antagonist E177 as adjuvant to antiepileptic drugs with an added advantage of preventing cognitive impairment, highlighting the H3Rs as a potential target for the therapeutic management of epilepsy with accompanied memory deficits

Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks

Segmenting the boundary between tumor and healthy tissue during surgical cancer resection poses a significant challenge. In recent years, Hyperspectral Imaging (HSI) combined with Machine Learning (ML) has emerged as a promising solution. However, due to the extensive information contained within the spectral domain, most ML approaches primarily classify individual HSI (super-)pixels, or tiles, without taking into account their spatial context. In this paper, we propose an improved methodology that leverages the spatial context of tiles for more robust and smoother segmentation. To address the irregular shapes of tiles, we utilize Graph Neural Networks (GNNs) to propagate context information across neighboring regions. The features for each tile within the graph are extracted using a Convolutional Neural Network (CNN), which is trained simultaneously with the subsequent GNN. Moreover, we incorporate local image quality metrics into the loss function to enhance the training procedure's robustness against low-quality regions in the training images. We demonstrate the superiority of our proposed method using a clinical ex vivo dataset consisting of 51 HSI images from 30 patients. Despite the limited dataset, the GNN-based model significantly outperforms context-agnostic approaches, accurately distinguishing between healthy and tumor tissues, even in images from previously unseen patients. Furthermore, we show that our carefully designed loss function, accounting for local image quality, results in additional improvements. Our findings demonstrate that context-aware GNN algorithms can robustly find tumor demarcations on HSI images, ultimately contributing to better surgery success and patient outcome.Comment: 11 pages, 6 figure

Low-cost hardware in the loop for intelligent neural predictive control of hybrid electric vehicle

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation

Serum Islet Cell Autoantibodies During Interferon α Treatment in Patients With HCV-Genotype 4 Chronic Hepatitis

Chronic hepatitis C virus (HCV) infection is a leading cause of end-stage liver disease worldwide and HCV genotype 4 (HCV4) is predominant in African and Middle Eastern countries. It is well established that interferon-α (IFNa) treatment for HCV may trigger serum autoantibodies against pancreatic islet cells (ICA) in a subgroup of patients. Available data on the incidence of ICA during IFNa therapy for chronic HCV4 infection are not conclusive. We investigated the appearance of ICA in 40 naïve Egyptian patients (38 males, 32 ± 6 years) with histologically defined chronic HCV4 infection undergoing IFNa treatment at a dose of 9-million U/week for 24 weeks. Serum samples were collected at baseline and following IFNa therapy and ICA were detected using indirect immunofluorescence. Baseline evaluation indicated that 2/40 (5%) patients had detectable serum ICA. After the completion of the treatment scheme, 12/38 (32%) previously ICA negative patients became ICA positive; however, no patient developed impaired glucose tolerance (IGT) or diabetes during follow-up. In conclusion, we submit that IFNa treatment for chronic hepatitis C (CHC) may induce serum ICA in one-third of Egyptian patients with HCV4. These autoantibodies, however, do not lead to alterations in glucose metabolism

Interactions of malnutrition and immune impairment, with specific reference to immunity against parasites

KEY POINTS: 1. Clinical malnutrition is a heterogenous group of disorders including macronutrient deficiencies leading to body cell mass depletion and micronutrient deficiencies, and these often coexist with infectious and inflammatory processes and environmental problems. 2. There is good evidence that specific micronutrients influence immunity, particularly zinc and vitamin A. Iron may have both beneficial and deleterious effects depending on circumstances. 3. There is surprisingly slender good evidence that immunity to parasites is dependent on macronutrient intake or body composition

Recent Progress in the Use of Glucagon and Glucagon Receptor Antagonists in the Treatment of Diabetes Mellitus

Glucagon is an important pancreatic hormone, released into blood circulation by alpha cells of the islet of Langerhans. Glucagon induces gluconeogenesis and glycogenolysis in hepatocytes, leading to an increase in hepatic glucose production and subsequently hyperglycemia in susceptible individuals. Hyperglucagonemia is a constant feature in patients with T2DM. A number of bioactive agents that can block glucagon receptor have been identified. These glucagon receptor antagonists can reduce the hyperglycemia associated with exogenous glucagon administration in normal as well as diabetic subjects. Glucagon receptor antagonists include isoserine and beta-alanine derivatives, bicyclic 19-residue peptide BI-32169, Des-His1-[Glu9] glucagon amide and related compounds, 5-hydroxyalkyl-4-phenylpyridines, N-[3-cano-6- (1,1 dimethylpropyl)-4,5,6,7-tetrahydro-1-benzothien-2-yl]-2-ethylbutamide, Skyrin and NNC 250926. The absorption, dosage, catabolism, excretion and medicinal chemistry of these agents are the subject of this review. It emphasizes the role of glucagon in glucose homeostasis and how it could be applied as a novel tool for the management of diabetes mellitus by blocking its receptors with either monoclonal antibodies, peptide and non-peptide antagonists or gene knockout techniques

Subjectivity in a context of environmental change: opening new dialogues in mental health research

In a period of unstable experimentation with challenges of globalization of associated risks, and disenchantment with ‘enduring injustice’, we bring forward a consideration of subjectivity to the study of environmental change and mental health. We begin by identifying how mainstream climate change and mental health studies are unable to explain the emergent and co-evolutionary pathways of agency. As a means of freeing these studies of their objective dimensions of linear-causation, we argue in favour of a re-positioning of subjectivity within an appreciation of recognition conflicts and beyond the over-deterministic interpretations of power centres—state, market or religion. We draw on one example of scientific research that was conducted in a region undergoing strong environmental, social and cultural changes, in the state of São Paulo/Brazil, with the aim to open mental health research to new dialogues, to which we contribute with the notion of the ‘pluriversal subject’

A Portrait of the Transcriptome of the Neglected Trematode, Fasciola gigantica—Biological and Biotechnological Implications

Fasciola gigantica (Digenea) is an important foodborne trematode that causes liver fluke disease (fascioliasis) in mammals, including ungulates and humans, mainly in tropical climatic zones of the world. Despite its socioeconomic impact, almost nothing is known about the molecular biology of this parasite, its interplay with its hosts, and the pathogenesis of fascioliasis. Modern genomic technologies now provide unique opportunities to rapidly tackle these exciting areas. The present study reports the first transcriptome representing the adult stage of F. gigantica (of bovid origin), defined using a massively parallel sequencing-coupled bioinformatic approach. From >20 million raw sequence reads, >30,000 contiguous sequences were assembled, of which most were novel. Relative levels of transcription were determined for individual molecules, which were also characterized (at the inferred amino acid level) based on homology, gene ontology, and/or pathway mapping. Comparisons of the transcriptome of F. gigantica with those of other trematodes, including F. hepatica, revealed similarities in transcription for molecules inferred to have key roles in parasite-host interactions. Overall, the present dataset should provide a solid foundation for future fundamental genomic, proteomic, and metabolomic explorations of F. gigantica, as well as a basis for applied outcomes such as the development of novel methods of intervention against this neglected parasite