Miracle-dz/UniFAR-FTUs huggingface dataset:
Strong associations of a healthy lifestyle with all stages of colorectal carcinogenesis: Results from a large cohort of participants of screening colonoscopy

The risk of developing colorectal cancer (CRC) is associated with a wide range of dietary and lifestyle factors. The individual contribution of single modifiable factors, such as alcohol consumption, physical activity, smoking, body mass index (BMI) or dietary components, to the development of CRC has been investigated extensively, but evidence on their combined effect at various stages of colorectal carcinogenesis is sparse. The aim of our study was to analyze the association of a healthy lifestyle pattern with prevalence of early and advanced colorectal neoplasms. A total of 13,600 participants of screening colonoscopy in Saarland/Germany (mean age 62.9 years) who were enrolled in the KolosSal study (Effektivität der Früherkennungs‐Koloskopie: eine Saarland‐weite Studie) from 2005 until 2013 were included in this cross‐sectional analysis. Dietary and lifestyle data were collected and colonoscopy results were extracted from physicians’ reports. The association of an a priori defined healthy lifestyle score—including dietary intake, alcohol consumption, physical activity, smoking and BMI—with early and advanced colorectal neoplasms was assessed by multiple logistic regression analyses with comprehensive adjustment for potential confounders. Strong inverse dose–response relationships were observed between an overall healthier lifestyle pattern and presence of advanced colorectal neoplasms, nonadvanced adenomas and hyperplastic polyps (p value <0.0001 in all cases), with adjusted odds ratios (95% CI) for the highest compared to the lowest category of the healthy lifestyle score of 0.41 (0.30–0.56), 0.42 (0.33–0.54) and 0.39 (0.29–0.54) respectively. A healthy lifestyle is strongly associated with lower risk of all stages of colorectal neoplasms.
On the Effectiveness of the Block Two-Level Erdős-Rényi Generative Network Model

Complex network models have been continuously improved to better match and understand the structural properties and features of real world networks. Such models are useful to generate sample networks with similar characteristics of real world networks that can then be used to improve algorithms and, at the same time, safeguard real data. Several models have been studied and developed over the last few years aiming at matching features like heavy-tailed degree distributions, low diameter and community structure. However, the BTER model is one of a few which was shown capable of generating synthetic networks with all the main characteristics of a real-world network. BTER is capable to match any real degree distribution and clustering coefficient. However, very few experiments have been carried out to support such a claim. In this work, we examine several different parameter setups and then show that the BTER is capable of matching various degree distributions. However, BTER is not capable to correspond to the desired clustering coefficients when restrictions such as connectivity are added to a given network. Further, we also show that the degree distribution plays an important role in the clustering coefficients, independently of how they are parameterized in the model.
IoT lighting address scheme and profile API design for interoperability

As IoT becomes a hot topic, IoT lighting that can be controlled remotely by smartphones or internet is expanding. Because lighting suppliers are using proprietary CMS and proprietary lighting protocols or existing lighting protocols, interoperability is becoming an important issue when applying IoT lighting. In this paper, we designed the address scheme and profile API and applied it to support interoperability even if IoT lighting and CMS are made by other vendors. In addition, the designed address scheme and profile APIs can be used to support energy savings, user friendliness, control diversity, IoT lighting status consistency and automated maintenance. We have proved that existing lighting and IoT lighting can interoperate by interworking with control devices developed by different companies and five kinds of gateways.
Metabolome-based biomarkers: their potential role in the early detection of lung cancer

Lung cancer is the leading cause of cancer-related death worldwide, and a major problem affecting its mortality is the late diagnosis of the majority of cases, where treatment options are limited and overall prognosis is very bad. Currently, a low-dose computed tomography (LD-CT) screening in the high-risk group is the only available diagnostic strategy that could reduce mortality due to this malignancy. However, the LD-CT screening test suffers from a high false positive rate. Hence, complementation of LD-CT examination with blood-based biomarkers is a rational approach to increase efficacy and reduce the cost of early lung cancer screening programs. Several molecular signatures that discriminate between patients with early lung cancer and healthy individuals have been proposed in recent years, which are based on components of serum/plasma metabolome. However, none of these signatures has been validated by independent studies based on material collected during real lung cancer screening. Therefore, the validation of the real diagnostic value of these otherwise promising candidates remains a critical step in this challenging field of cancer diagnostics.
Multiple components of environmental change drive populations of breeding waders in seminatural grasslands

Abstract Environments are rapidly changing due to climate change, land use, intensive agriculture, and the impact of hunting on predator populations. Here, we analyzed long‐term data recorded during 1928–2014 on the size of breeding populations of waders at two large nature reserves in Denmark, Vejlerne and Tipperne, to determine the effects of components of environmental change on breeding populations of waders. Environmental variables and counts of waders were temporally autocorrelated, and we used generalized least square (GLS) by incorporating the first‐order autoregressive correlation structure in the analyses. We attempted to predict the abundance of waders for short‐term trends for two nature reserves (35 years) and for long‐term trends for one nature reserve (86 years), using precipitation, temperature, nutrients, abundance of foxes Vulpes vulpes, area grazed, and number of cattle. There was evidence of impacts of nutrients, climate (long‐term changes in temperature and precipitation), grazing, mowing, and predation on bird populations. We used standard random effects meta‐analyses weighted by (N–3) to quantify these mean effects. There was no significant difference in effect size among species, while mean effect size differed consistently among environmental factors, and the interaction between effect size for species and environmental factors was also significant. Thus, environmental factors affected the different species differently. Mean effect size was the largest at +0.20 for rain, +0.11 for temperature, −0.09 for fox abundance, and −0.03 for number of cattle, while there was no significant mean effect for fertilizer, area grazed, and year. Effect sizes for two short‐term time series from Tipperne and Vejlerne were positively correlated as were effect sizes for short‐term and long‐term time series at Tipperne. This implies that environmental factors had consistent effects across large temporal and spatial scales.
A Two-Step Server Selection in Hybrid CDN-P2P Mesh-based for Video-on-Demand Streaming

Nowadays, video content takes most of the internet traffic. In Video-on-Demand (VoD), the point of interest of each client is independent of each other which could result in the delay increasing and a significant amount of bandwidth consumption. Mesh-based Peer-to-Peer (P2P) video streaming recently has been employed in many video streaming applications because of its robustness against peer churn. Thus, by integrating both P2P and Content Delivery Network (CDN) advantages, the hybrid CDN-P2P is implemented. Previously, both CDN and P2P have an issue with server selection and neighbor selections, respectively. When the CDN-P2P is integrated, the server and neighbor selection also comes along with the system. In this paper, a two-step server selection algorithm in CDN-P2P VoD streaming is proposed to address this issue. In our proposed algorithm, the central unit of the system which stores the core network topology selects one of the closest servers based on the number of hops. Then, the server which hosts a group of peers locating closest to the incoming client in average distance is selected. Thus, the selected hybrid CDN-P2P overlay is both closest located and consists of nearest located neighbors. The performance of our proposed algorithm is evaluated in terms of startup delay, discontinuity, distortion, server direct connections, server data bandwidth consumption and peer data bandwidth contribution using simulation under a dynamic environment. The results show that our proposed algorithm provides better performance than CDN-P2P regarding all evaluation metrics.
Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise–Cognition Science: A Systematic, Methodology-Focused Review

For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise–cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise–cognition science.
Assessing Sleep-Related Safety Behaviors: Adaptation and Validation of a French Version of the Sleep-Related Behaviors Questionnaire in a Nonclinical Sample

ABSTRACT Objective/Background: Safety behaviors play a prominent role in the development and maintenance of insomnia. The Sleep-Related Behaviors Questionnaire (SRBQ) is a self-report questionnaire designed to assess safety behaviors employed to cope with fatigue or to improve sleep. Despite its frequent use in insomnia, no systematic psychometric validation of the SRBQ has been conducted; its factor structure has never been explored. Furthermore, there is no French version of this scale. The goal of this study was to empirically validate a French version of the SRBQ. Participants/Methods: A total of 539 French-speaking community-dwelling participants from the general population completed a face-to-face clinical interview to determine insomnia disorder against DSM-5 criteria and several questionnaires including the French SRBQ. Results: SRBQ items with poor psychometric properties were removed, thus leading to a 20-item version (SRBQ-20). Exploratory factor analysis and parallel analysis revealed three distinct factors with good internal consistency. The results supported the internal temporal stability of the SRBQ-20. The construct validity of that instrument was underpinned by correlations obtained with various measures of insomnia and related constructs. Adequate discriminative validity was established by comparing individuals with insomnia and individuals without insomnia. Conclusions: This study demonstrated that the French version of the SRBQ-20 has good psychometric properties.
Skeleton-based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module

The skeleton based gesture recognition is gaining more popularity due to its wide possible applications. The key issues are how to extract discriminative features and how to design the classification model. In this paper, we first leverage a robust feature descriptor, path signature (PS), and propose three PS features to explicitly represent the spatial and temporal motion characteristics, i.e., spatial PS (S PS), temporal PS (T PS) and temporal spatial PS (T S PS). Considering the significance of fine hand movements in the gesture, we propose an ”attention on hand” (AOH) principle to define joint pairs for the S PS and select single joint for the T PS. In addition, the dyadic method is employed to extract the T PS and T S PS features that encode global and local temporal dynamics in the motion. Secondly, without the recurrent strategy, the classification model still faces challenges on temporal variation among different sequences. We propose a new temporal transformer module (TTM) that can match the sequence key frames by learning the temporal shifting parameter for each input. This is a learning-based module that can be included into standard neural network architecture. Finally, we design a multi-stream fully connected layer based network to treat spatial and temporal features separately and fused them together for the final result. We have tested our method on three benchmark gesture datasets, i.e., ChaLearn 2016, ChaLearn 2013 and MSRC-12. Experimental results demonstrate that we achieve the state-of-the-art performance on skeleton-based gesture recognition with high computational efficiency.
Prey exploitation and dispersal strategies vary among natural populations of a predatory mite

Abstract When predators commonly overexploit local prey populations, dispersal drives the dynamics in local patches, which together form a metapopulation. Two extremes in a continuum of dispersal strategies are distinguished: the “Killer” strategy, where predators only start dispersing when all prey are eliminated, and the “Milker” strategy, in which predator dispersal occurs irrespective of prey availability. Theory shows that the Milker strategy is not evolutionarily stable if local populations are well connected by dispersal. Using strains of the predatory mite Phytoseiulus persimilis, collected from 11 native populations from coastal areas in Turkey and Sicily, we investigated whether these two strategies occur in nature. In small wind tunnels, we measured dispersal rates and population dynamics of all populations in a system consisting of detached rose leaves, spider mites (Tetranychus urticae) as prey, and P. persimilis. We found significant variation in the exploitation and dispersal strategies among predator populations, but none of the collected strains showed the extreme Killer or Milker strategy. The results suggest that there is genetic variation for prey exploitation and dispersal strategies. Thus, different dispersal strategies in the Milker–Killer continuum may be selected for under natural conditions. This may affect the predator–prey dynamics in local populations and is likely to determine persistence of predator–prey systems at the metapopulation level.
Towards an Intelligent User-Oriented Middleware for Opportunistic Composition of Services in Ambient Spaces

Ambient and mobile systems consist of networked devices and software components surrounding human users and providing services. From the services present in the environment, other services can be composed opportunistically and automatically by an intelligent system and proposed to the user. This article first presents an illustrative use case, then explores the requirements and formulates related research questions. Next, it describes our approach aimed at answering the requirements, based on distributed artificial intelligence and multi-agent systems. It reports on the development of a prototype solution, and analyzes the current status of our work towards the different research questions that we have identified.
MGANet: A Robust Model for Quality Enhancement of Compressed Video

In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames. In this paper, we propose a multi-frame guided attention network (MGANet) to enhance the quality of compressed videos. Our network is composed of a temporal encoder that discovers inter-frame relations, a guided encoder-decoder subnet that encodes and enhances the visual patterns of target frame, and a multi-supervised reconstruction component that aggregates information to predict details. We design a bidirectional residual convolutional LSTM unit to implicitly discover frames variations over time with respect to the target frame. Meanwhile, the guided map is proposed to guide our network to concentrate more on the block boundary. Our approach takes advantage of intra-frame prior information and inter-frame information to improve the quality of compressed video. Experimental results show the robustness and superior performance of the proposed method.Code is available at this https URL
Many phish in the C: A coexisting‐choice‐criteria model of security behavior

Normative decision theory proves inadequate for modeling human responses to the social‐engineering campaigns of advanced persistent threat (APT) attacks. Behavioral decision theory fares better, but still falls short of capturing social‐engineering attack vectors which operate through emotions and peripheral‐route persuasion. We introduce a generalized decision theory, under which any decision will be made according to one of multiple coexisting choice criteria. We denote the set of possible choice criteria by C$\mathcal {C}$ . Thus, the proposed model reduces to conventional Expected Utility theory when |CEU|=1$|\mathcal {C}_{\text{EU}}|=1$ , while Dual‐Process (thinking fast vs. thinking slow) decision making corresponds to a model with |CDP|=2$|\mathcal {C}_{\text{DP}}|=2$ . We consider a more general case with |C|≥2$|\mathcal {C}|\ge 2$ , which necessitates careful consideration of how, for a particular choice‐task instance, one criterion comes to prevail over others. We operationalize this with a probability distribution that is conditional upon traits of the decisionmaker as well as upon the context and the framing of choice options. Whereas existing signal detection theory (SDT) models of phishing detection commingle the different peripheral‐route persuasion pathways, in the present descriptive generalization the different pathways are explicitly identified and represented. A number of implications follow immediately from this formulation, ranging from the conditional nature of security‐breach risk to delineation of the prerequisites for valid tests of security training. Moreover, the model explains the “stepping‐stone” penetration pattern of APT attacks, which has confounded modeling approaches based on normative rationality.
The Safety and Efficacy of Expiratory Muscle Strength Training for Rehabilitation After Supracricoid Partial Laryngectomy: A Pilot Investigation

Objectives: Expiratory muscle strength training (EMST) is a safe, effective intervention that can be performed at home and may be beneficial for individuals with voice and swallowing disorders. To date there have been few studies of EMST in the head and neck cancer population, and there are no previous reports of its use after supracricoid partial laryngectomy (SCPL). The current prospective clinical pilot study was undertaken to determine the safety and efficacy of a 4-week treatment program. Methods: Six participants were recruited who had previously undergone SCPL, were medically stable, and had no contraindications for use of the device. At baseline, objective respiratory measurements were collected, dietary status was recorded, and participants were asked to complete a series of validated self-report instruments relating to voice, swallowing, breathing, and cough. Following the completion of treatment, baseline measures were repeated, and participant feedback was solicited. Results: The majority of individuals found the device easy to use (83%) and beneficial (83%). The side effects of treatment were relatively minor and included dizziness, muscle inflammation, and vocal fatigue. There were improvements in 2 measures from before to after treatment, namely, an average 21% increase in peak cough flow (from 371.67 to 451.33 L/min) and a 38% decrease on the Dyspnea Index (from 6.17 to 3.83). Other measures showed inconsistent changes. Conclusions: EMST appeared to improve cough strength and reduce dyspnea symptoms after SCPL. Further study of the relative efficacy of EMST compared to other rehabilitation protocols after SCPL is needed.
RWAC: A Self-contained Read and Write Access Control Scheme for Group Collaboration

With the development of the Internet and personal digital devices, self-organizing and open-pattern collaborations are becoming popular. In such environments, data are usually outsourced to third-party servers in the cloud, which are out of the control domain of data owners. Hence, traditional access control models, which are enforced relying on data storage servers, will face new security challenges. In this paper, we propose a self- contained read and write access control (RWAC) scheme based on ciphertext-policy attribute-based encryption (CP-ABE) and attribute-based group signature (ABGS) mechanism. By adopting a two-step encryption strategy using CP-ABE and utilizing the write control policy as the signature policy in ABGS, RWAC ensures that fine-grained read and write access control can be enforced during decryption and signature generation without dependence on any third parties. To prevent privacy leakage from RWAC policies, we adopt a CP-ABE scheme with hidden policy. Then, we introduce the policy hiding method into ABGS and propose an ABGS scheme with hidden policy. Moreover, users can trace the edit history of each data object with the signature or a write list. The security analysis indicates that RWAC is able to enforce fine-grained read and write access controls for group collaborations while also ensuring data confidentiality and integrity.
MRI Outperforms [18F]AV‐1451 PET as a Longitudinal Biomarker in Progressive Supranuclear Palsy

Elevated uptake of the [18F]AV‐1451 tau‐PET ligand has been observed cross‐sectionally in subjects with progressive supranuclear palsy (PSP). However, it is unknown how the ligand performs longitudinally in PSP. We aimed to determine how regional measures of change on [18F]AV‐1451 PET perform as longitudinal biomarkers of PSP compared with the more established biomarker of rate of midbrain atrophy.