Miracle-dz/UniFAR-FTUs huggingface dataset:
Advance care planning for frail elderly: are we missing a golden opportunity? A mixed-method systematic review and meta-analysis

Objective The aim is to integrate quantitative and qualitative evidence to understand the effectiveness and experience of advance care planning (ACP) for frail elderly. Design A mixed-methods systematic review and meta-analysis was conducted. Quality evaluation was conducted using critical appraisal tools from the Joanna Briggs Institute. Data were synthesised and pooled for meta-analysis or meta-aggregation as needed. Data sources An electronic search of MEDLINE, CINAHL, Embase, PubMed, PsycINFO, and Cochrane Library databases from January 2003 to April 2022. Eligibility criteria for selecting studies We included experimental and mixed-methods studies. The quantitative component attempts to incorporate a broader study design. The qualitative component aids in comprehending the participant’s experience with ACP and its efficacy. Data extraction and synthesis Two independent reviewers undertook screening, data extraction and quality assessment. The quantitative and qualitative data were synthesised and integrated using a convergent segregated approach. Results There were 12 158 articles found, and 17 matched the inclusion criteria. The quality of the quantitative component of most included studies (6/10) was rated as low, and the qualitative component of half included studies (4/8) was rated as moderate. The meta-analysis showed that the intervention of ACP for frail elderly effectively increases readiness, knowledge and process of ACP behaviours. The meta-aggregation showed that the participants hold a positive attitude towards ACP and think it facilitates expressing their preferences for the medical decision. Conclusion ACP is an effective and feasible strategy to facilitate frail elderly to express their healthcare wishes timely and improve their outcomes. This study could provide proof for a better understanding of the subject and help direct future clinical practice. More well-designed randomised controlled trials evaluating the most effective ACP interventions and tools are needed for the frail elderly population. PROSPERO registration number CRD42022329615.
A Review of Routing Optimization Techniques for Quality of Service Assurance in Software-Defined Networks

The traditional military communication network is based on IP architecture, which has the problems of rigid architecture and challenging quality of service guarantee. The rapid development of various new applications has put differentiated demands on the whole network's service quality. In recent years, software-defined network technology has been developing, which has the characteristics of decoupling the control and data planes. Its control plane has excellent global control capability and network equipment information collection capability, which has natural advantages for optimizing the quality of service. Firstly, the SDN network architecture, quality of service performance parameters, and service model are introduced; secondly, the different requirements of quality of service for standard and typical service application scenarios are analyzed, and the current research status of quality of service enhancement through routing optimization is described; finally, the outlook is summarized, and two new ideas for quality of service enhancement in SDN networks and the development trend of quality of service research in military communication networks are proposed.
Elastic Detection Mechanism Aimed at Hybrid DDoS Attack

In Distributed Denial of Service(DDoS) attack, the attacker uses a remotely controlled botnet to attack the target server at the same time to prevent legitimate users from obtaining information services. Previous studies focused on the detection of DDoS attacks on offline datasets, but ignored the detection of specific DDoS types, and some reports showed that the number of DDoS hybrid attacks was increasing significantly. In this paper, we propose an elastic detection mechanism(EDM), which can economize the server’s idle computing power. The framework integrates a variety of pre-trained lightweight CNN detect models, which are suitable for online rapid detection of DDoS hybrid attacks. We focus on evaluating the response accuracy and the detection speed of the EDM. The experimental results show that the model can achieve excellent hybrid attack detection performance, and meet the actual requirements of real-time detection.
Federated Learning-Based Intrusion Detection Method for Smart Grid

Power systems have revealed serious security problems in the process of gradual opening, and intrusion detection as an important security defense measure can detect potential intrusions in a timely manner. In the big data environment of electric power, there are information silos between different electric power data owners, and in order to obtain intrusion detection models with better performance, traditional methods need to fuse data from all parties, which often brings difficulties in information security and data privacy protection. In this paper, we propose a distributed intrusion detection framework based on federated learning and apply it to network traffic data analysis. The framework aims to ensure the information security of each local power data while establishing a collection of decentralized data and completing the joint training of models from multiple data sources. The experimental results show that the scheme achieves 98.1% accuracy on the simulated data set, which is better than other commonly used intrusion detection algorithms. In addition, the method well ensures the security and privacy of data because the data are not interoperable among each participant under the federated learning mechanism.
MM-UNet: Multi-attention mechanism and multi-scale feature fusion UNet for tumor image segmentation

To address the problems of many parameters and loss of spatial information in traditional Unet networks, this paper proposes a U-Net-based brain tumor segmentation model named MM-UNet to solve the problem of 3D image segmentation. Firstly, the U-Net model performs three times downsampling to extract the image features for the changing characteristics of brain tumor 3D images, which reduces the number of model parameters while maximally preserving the target edge features; then, a structure similar to FPN was used to achieve the fusion of multi-scale predictions; we introduce the channel attention mechanism and pixel attention mechanism to establish the relationship between global features; meanwhile, to improve the generalization ability of the model, data augmentation techniques are used to enhance the information. The experimental results show that the model proposed in this paper has improved the accuracy of brain tumor segmentation compared with U- Net, PSPNet, ICNet, and Fast- SCNN, suggesting 3.9%, 1.3%, 5%, and 3.9%, respectively.
RhySpeech: A Deployable Rhythmic Text-to-Speech Based on Feed-Forward Transformer for Reading Disabilities

Dyslexia was first proposed in 1877, but this century-old problem still troubles many people today [1]. Dyslexia is marked by difficulty in reading despite having normal or superior conditions in their environment and intellectual ability, is curable using multi-sensory learning, which involves providing audio stimulus, sometimes generated from expressive text-to-speech. However, such generated audio lacks rhythmic features, marked by inadequate insertion of pauses. In response to such technological difficulty, this paper proposes RhySpeech, which models rhythm using feed-forward transformer neural networks and an LRV (Latent Rhythm Vector). The LRV receives input from the pitch, energy, and duration features encoded using a Transformers network along with the numeric encoding of the previous 16 phonemes, which together build a strong sense of context for the pause prediction. This LRV is trained to generate adequate lengths and positions of pa uses, allowing the synthesized audio to have more accurate pausing
An Interpretable Brain Network Atlas-Based Hybrid Model for Mild Cognitive Impairment Progression Prediction

The process of Alzheimer’s disease (AD) is irreversible, but reasonable medical intervention for preclinical AD can delay AD’s onset. Progressive mild cognitive impairment (pMCI) is the most critical stage for AD preclinical intervention. Therefore, accurate identification of pMCI will significantly improve patient benefits. Functional MRI is a neuroimaging modality that has been widely utilized to study brain activity related to AD. However, it is challenging to obtain functional MRI data, and a small amount of data will easily lead to the overfitting of the identification model. In addition, the current pMCI identification model lack interpretability leads to difficulty in acceptance by clinicians. In this work, we propose an interpretable hybrid model based on a brain network atlas to identify pMCI subjects. First, the hybrid model utilizes multi-layer perceptron to obtain categorical global features to help graph neural networks reduce overfitting. Second, the attention mechanism is introduced into the model to explain the recognition behavior of the model. The results show that our model outperforms the comparison models on multiple metrics.
Pose Estimation of Space Targets Based on Geometry Structure Features

The pose estimation of space targets is of great significance for space target state assessment, anomaly detection, fault diagnosis, etc. With the development of adaptive optics technology, the imaging quality of ground-based optical systems has been greatly improved, and we can use the observed images to estimate the pose of space targets. However, the imaging process of the ground-based optical system is still affected by various noises and disturbances, which makes the images degrade. Aiming at the space target pose estimation with these degraded images, we propose a new pose estimation pipeline based on robust geometry structure features. By associating the corresponding geometry structure feature between consecutive frames, we can get the target pose by optimization method. This paper will explain the definition and extraction of the proposed geometry structure feature. We propose a geometry structure feature prediction method base on set prediction in a multi-task way with target components classification and segmentation. Experiments show that our structure feature prediction network achieves competitive results on the simulated photo-realistic SpaceShuttle dataset which is rendered according to the physics imaging process.
Increased suicide risk of psychiatric patients following the recent utilization of health care services: results from a nationwide cohort study in South Korea

Purpose This study aimed to examine whether and to what degree the suicide risk of psychiatric patients is associated with psychiatric and non-psychiatric health service utilization. Methods We selected incident psychiatric patients, including schizophrenia, bipolar disorders, borderline personality disorder, depressive disorders, other affective disorders, and post-traumatic stress disorder patients, in 2007–2010 and followed them up until 2017 based on the data linkage between the Korean National Health Insurance and National Death Registry. We analyzed the time-dependent association between suicide and four types of health service (psychiatric vs. non-psychiatric and outpatient vs. inpatient) utilization using a time-dependent Cox regression. Results The suicide risk of psychiatric patients was significantly increased with recent psychiatric and non-psychiatric admission and psychiatric outpatient visits. The adjusted suicide hazard ratios for recent outpatient visits were similar to or even higher than those for recent psychiatric admission. The adjusted suicide hazard ratios of schizophrenia patients for psychiatric admission, psychiatric outpatient visits, and non-psychiatric admission within the recent 6 months were 2.34 (95% confidence interval [CI]: 2.12–2.58, p < 0.001), 2.96 (95% CI: 2.65–3.30, p < 0.001), and 1.55 (95% CI: 1.39–1.74, p < 0.001), respectively. Suicide risk was not associated with recent non-psychiatric outpatient visits in patients, except for the depressive disorders group showing a negative association. Conclusion Our results highlight the priority of suicide prevention for psychiatric patients in the clinical setting. Additionally, our results warrant the precaution against increased suicide risk of psychiatric patients after psychiatric and non-psychiatric discharge.
Exploring professionals' practices and perspectives on supporting parents with intellectual disabilities: a qualitative study

Introduction Evidence suggests that parents with intellectual disabilities require appropriate parenting support. However, professional practices vary widely, and several barriers and challenges persist in supporting parents with intellectual disabilities. To identify effective and collaborative practices, this study investigated practices reported by professionals and their roles in providing services to parents with intellectual disabilities. Methods Semi-structured interviews were conducted with 22 professionals from three sectors (disability, early childhood, and healthcare), and the content was analysed using inductive thematic analysis. Results Thematic analyses yielded four main themes: (1) Perceived professional practices, (2) professional stances, (3) the frame of reference and the ethics of support, (4) the experience of providing support. They are described in terms of content and distribution across sectors to provide an overview of practices as well as potential discrepancies. Conclusions This study concludes by developing recommendations on good practices for support professionals to respond as adequately as possible to meet the needs of parents and future parents with intellectual disabilities, which include structural support and guidelines for professionals to provide sensitive, family-centred, and enabling support.
Eliciting brain waves of people with cognitive impairment during meditation exercises using portable electroencephalography in a smart-home environment: a pilot study

Objectives Meditation imparts relaxation and constitutes an important non-pharmacological intervention for people with cognitive impairment. Moreover, EEG has been widely used as a tool for detecting brain changes even at the early stages of Alzheimer’s Disease (AD). The current study investigates the effect of meditation practices on the human brain across the AD spectrum by using a novel portable EEG headband in a smart-home environment. Methods Forty (40) people (13 Healthy Controls—HC, 14 with Subjective Cognitive Decline—SCD and 13 with Mild Cognitive Impairment—MCI) participated practicing Mindfulness Based Stress Reduction (Session 2-MBSR) and a novel adaptation of the Kirtan Kriya meditation to the Greek culture setting (Session 3-KK), while a Resting State (RS) condition was undertaken at baseline and follow-up (Session 1—RS Baseline and Session 4—RS Follow-Up). The signals were recorded by using the Muse EEG device and brain waves were computed (alpha, theta, gamma, and beta). Results Analysis was conducted on four-electrodes (AF7, AF8, TP9, and TP10). Statistical analysis included the Kruskal–Wallis (KW) nonparametric analysis of variance. The results revealed that both states of MBSR and KK lead to a marked difference in the brain’s activation patterns across people at different cognitive states. Wilcoxon Signed-ranks test indicated for HC that theta waves at TP9, TP10 and AF7, AF8 in Session 3-KK were statistically significantly reduced compared to Session 1-RS Z = –2.271, p = 0.023, Z = −3.110, p = 0.002 and Z = −2.341, p = 0.019, Z = −2.132, p = 0.033, respectively. Conclusion The results showed the potential of the parameters used between the various groups (HC, SCD, and MCI) as well as between the two meditation sessions (MBSR and KK) in discriminating early cognitive decline and brain alterations in a smart-home environment without medical support.
Fisheries and aquaculture by-products modulate growth, body composition, and omega-3 polyunsaturated fatty acid content in black soldier fly (Hermetia illucens) larvae

The black soldier fly (BSF) (Hermetia illucens L.) is one of the most promising species for the production of ingredients, mainly protein, useful for animal feed formulation, owing to its ability to convert organic waste into biomass with a high nutritional value. However, the low percentage of n-3 series polyunsaturated fatty acids (PUFAs) in its fatty acid profile is a limiting factor for the utilization of BSF in fish feed. Recent studies have highlighted that wastes derived from different agro-food value chains could affect the nutritional composition of BSF larvae, depending on the composition of the wastes used as larvae-growing substrate. Due to the significant amount of n-3 PUFA in marine products, both in edible produce and in by-products, in this study, different sources of fish by-products were included in experimental diets for BSF to evaluate the effect of its addition on the final nutritional profile of BSF pre-pupae. One control diet and five experimental diets were prepared to feed the BSF larvae: wheat bran as the control diet (diet B), bycatch from Mediterranean trawl fisheries (diet F), Parapenaeus longirostris processing by-products (diet S), aquaculture processing by-products (diet R), Thunnus albacares processing by-products (diet T), and Engraulis encrasicolus processing by-products (diet A). In this study, the effects of the different diets were analyzed on the growth, body composition, and fatty acid profile of BSF larvae and pre-pupae. The obtained results showed that the different experimental diets affected total lipids content and fatty acids composition, when compared with the control. A significant increase in eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) in BSF larvae and pre-pupae fed with all fish by-products was observed when compared with those fed with diet B, in particular in larvae and pre-pupae fed with diet A, demonstrating that the utilization of fish processing by-products is a suitable solution for improving the nutritional value of insects as ingredients in aqua feeds. The reuse of marine by-products can contribute to the industry’s “zero waste” goal, increasing the sustainability of the fishery value chain and the formulation of new valuable products.
Power quality signals' de-noising method based on singular value decomposition (SVD)

A power quality signals' de-noising method based on singular value decomposition(SVD)is proposed in this paper.A track matrix of attractor is reconstructed based on time series,then according to the singular values' percent of contribution to total energy(PCTE),disturbance signal is reconstructed,which is the de-noised power quality signal.Simulation results indicate that SVD can improve SNR and keep original disturbances' characters.The proposed algorithm's theory foundation is consummate and is easy to be carried out,so it has nice developmental foreground.
Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions

A sparse inertial sensor setup, with a small number of inertial measurement units (IMUs), could enable a comprehensive motion analysis, including spatiotemporal, kinematic, and kinetic variables, with little obtrusion to the user, which is critical for clinical and sports applications. Therefore, we aimed to systematically investigate the relationship between sensor setup and the resulting accuracy of spatiotemporal, kinematic, and kinetic variables. We calculated these variables by solving optimal control problems for three walking and three running speeds on a sagittal plane lower-body musculoskeletal model. In these problems, we minimized an objective that combined tracking of accelerometer and gyroscope data with minimizing muscular effort. We applied this approach to seven different sensor setups, with between two and seven sensors located at the feet, shank, thighs, and pelvis. We compared the IMU-based analysis to an inverse analysis from optical motion capture (OMC) and force plate data for 10 participants. We found that correlations between IMU- and OMC-based variables were high for all tested sensor setups, that setups without a pelvis sensor led to too much forward trunk lean and inaccurate spatiotemporal variables, that including all sensors did not necessarily lead to the smallest root mean square deviations (RMSDs), and that RMSDs were highest for the setup with two foot-worn IMUs. The smallest setup that estimated kinematics as accurately as the full setup (less than 1 degree difference in RMSD) was the setup with IMUs at the feet and thighs. The mean correlations for kinematics and kinetics were at least 0.8 for walking and 0.9 for running when either a pelvic sensor or thigh sensors were included. Therefore, we conclude that we can accurately perform a comprehensive sagittal-plane motion analysis with sparse sensor setups, when sensors are placed on the feet and on either the pelvis or the thighs.
FUS Facilitates Gene Body Export from Nuclear Condensates

Membraneless nuclear condensates are an emerging model of transcriptional regulation that involves spatially clustered enhancers and genes forming transcription condensates (TCs). We used a physical isolation approach and DNA/nascent RNA analysis to investigate the global organization of the genome in TCs. Comparative analysis of the two revealed the dynamic nature of the genome, with stable localization of promoters, dynamic recruitment of enhancers, and dynamic export of gene bodies out of TCs upon gene activation. Our findings also suggest that the RNA binding protein FUS undergoes decondensation upon nascent RNA binding, facilitating gene body export out of TCs.
Neuroanatomical and functional dissociations between variably present anterior lateral prefrontal sulci

The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid-specific. While recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from 72 young adult humans aged 22-36 and show that dorsal and ventral components of the paraintermediate frontal sulcus (pimfs) present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in anatomy and function in LPFC, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and highlight the importance of considering individual anatomy when investigating structural and functional features of the cortex.
splicekit: a comprehensive toolkit for splicing analysis from short-read RNA-seq

Motivation Analysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. Results We introduce splicekit, a python package that provides a comprehensive set of tools for conducting splicing analysis. Availability and implementation https://github.com/bedapub/splicekit and over PyPI.