734 research outputs found
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
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
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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’
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