A Multidomain Intervention Program for Older People with Dementia: A Pilot Study

Background: Multidomain interventions have been shown to be effective in improving cognition, quality of life, reducing neuropsychiatric symptoms and delaying progression of functional impairment or disability in dementia patients. To investigate the multidomain intervention in other populations and diverse cultural and geographical settings, this pilot study will assess the feasibility of a multidomain intervention for older people with dementia in nursing homes in Vietnam. Methods: Participants will be randomized into two equal groups, to receive either a multidomain intervention (intervention group) or regular health advice (control group). The intervention will include physical, cognitive, and social interventions as well as management of metabolic and vascular risk factors. We will hypothesize that the multidomain intervention will be feasible in Vietnam, and participants who receive the intervention will show improvement in quality of life, behaviors, functional ability, cognitive function, sleep, and in reduction of falls, use of healthcare services, and death rate compared to those in the control group during the 6 months intervention period and after the 6 months extended follow-up. Discussion: This is the first study to evaluate the feasibility of a multidomain intervention program for older people with dementia in nursing homes in Vietnam. The results from the trial will inform clinicians and the public of the possibility of comprehensive treatment beyond simply drug treatments for dementia. This paves the way for further studies to evaluate the long-term effects of multidomain interventions in dementia patients. Furthermore, the research results will provide information on the effectiveness of multidomain interventions which will inform policy development on dementia. Trial Registration: The trial is registered with ClinicalTrials.gov identifier: NCT04948450 on 02/07/2021

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M <; N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance

Salmonella typhimurium Suppresses Tumor Growth via the Pro-Inflammatory Cytokine Interleukin-1 beta

Although strains of attenuated Salmonella typhimurium and wild-type Escherichia coli show similar tumor-targeting capacities, only S. typhimurium significantly suppresses tumor growth in mice. The aim of the present study was to examine bacteria-mediated immune responses by conducting comparative analyses of the cytokine profiles and immune cell populations within tumor tissues colonized by E. coli or attenuated Salmonellae. CT26 tumor-bearing mice were treated with two different bacterial strains: S. typhimurium defective in ppGpp synthesis (Delta ppGpp Salmonellae) or wild-type E. coli MG1655. Cytokine profiles and immune cell populations in tumor tissue colonized by these two bacterial strains were examined at two time points based on the pattern of tumor growth after Delta ppGpp Salmonellae treatment: 1) when tumor growth was suppressed (&apos;suppression stage&apos;) and 2) when they began to re-grow (&apos;re-growing stage&apos;). The levels of IL-1 beta and TNF-alpha were markedly increased in tumors colonized by Delta ppGpp Salmonellae. This increase was associated with tumor regression; the levels of both IL-1 beta and TNF-alpha returned to normal level when the tumors started to re-grow. To identify the immune cells primarily responsible for Salmonellae-mediated tumor suppression, we examined the major cell types that produce IL-1 beta and TNF-alpha. We found that macrophages and dendritic cells were the main producers of TNF-alpha and IL-1 beta. Inhibiting IL-1 beta production in Salmonellae-treated mice restored tumor growth, whereas tumor growth was suppressed for longer by local administration of recombinant IL-1 beta or TNF-alpha in conjunction with Salmonella therapy. These findings suggested that IL-1 beta and TNF-alpha play important roles in Salmonella-mediated cancer therapy. A better understanding of host immune responses in Salmonella therapy may increase the success of a given drug, particularly when various strategies are combined with bacteriotherapy.111715Ysciescopu

Quality assessment of an interferon-gamma release assay for tuberculosis infection in a resource-limited setting

<p>Abstract</p> <p>Background</p> <p>When a test for diagnosis of infectious diseases is introduced in a resource-limited setting, monitoring quality is a major concern. An optimized design of experiment and statistical models are required for this assessment.</p> <p>Methods</p> <p>Interferon-gamma release assay to detect tuberculosis (TB) infection from whole blood was tested in Hanoi, Viet Nam. Balanced incomplete block design (BIBD) was planned and fixed-effect models with heterogeneous error variance were used for analysis. In the first trial, the whole blood from 12 donors was incubated with nil, TB-specific antigens or mitogen. In 72 measurements, two laboratory members exchanged their roles in harvesting plasma and testing for interferon-gamma release using enzyme linked immunosorbent assay (ELISA) technique. After intervention including checkup of all steps and standard operation procedures, the second trial was implemented in a similar manner.</p> <p>Results</p> <p>The lack of precision in the first trial was clearly demonstrated. Large within-individual error was significantly affected by both harvester and ELISA operator, indicating that both of the steps had problems. After the intervention, overall within-individual error was significantly reduced (<it>P </it>< 0.0001) and error variance was no longer affected by laboratory personnel in charge, indicating that a marked improvement could be objectively observed.</p> <p>Conclusion</p> <p>BIBD and analysis of fixed-effect models with heterogeneous variance are suitable and useful for objective and individualized assessment of proficiency in a multistep diagnostic test for infectious diseases in a resource-constrained laboratory. The action plan based on our findings would be worth considering when monitoring for internal quality control is difficult on site.</p

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Antenna Selection in Rank-Deficient MIMO Systems

In a rank-deficient MIMt> system, the number of non-zero eigen-modes is smaller than min In such a system, it is desirable to be able to identify then eliminate the most 'inactive' antennas or equivalently to select the most 'active' antennas for operation. In this paper we use the incremental algorithm for the successive selection technique for receive antenna selection applied to a rank-deficient indoor MIMO link transmitting through a small window between two rooms. It is shown that there is a close corrcspondence betwecn the rank of the ill-conditioned MIMO channel and the minimum number of receive antennas that can be selected for a given small reduction in capacity

Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks

In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively

Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis

This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve