Miracle-dz/UniFAR-FTUs huggingface dataset:
Unraveling the Complexity of Amyotrophic Lateral Sclerosis Survival Prediction

Objective: The heterogeneity of amyotrophic lateral sclerosis (ALS) survival duration, which varies from <1 year to >10 years, challenges clinical decisions and trials. Utilizing data from 801 deceased ALS patients, we: (1) assess the underlying complex relationships among common clinical ALS metrics; (2) identify which clinical ALS metrics are the “best” survival predictors and how their predictive ability changes as a function of disease progression. Methods: Analyses included examination of relationships within the raw data as well as the construction of interactive survival regression and classification models (generalized linear model and random forests model). Dimensionality reduction and feature clustering enabled decomposition of clinical variable contributions. Thirty-eight metrics were utilized, including Medical Research Council (MRC) muscle scores; respiratory function, including forced vital capacity (FVC) and FVC % predicted, oxygen saturation, negative inspiratory force (NIF); the Revised ALS Functional Rating Scale (ALSFRS-R) and its activities of daily living (ADL) and respiratory sub-scores; body weight; onset type, onset age, gender, and height. Prognostic random forest models confirm the dominance of patient age-related parameters decline in classifying survival at thresholds of 30, 60, 90, and 180 days and 1, 2, 3, 4, and 5 years. Results: Collective prognostic insight derived from the overall investigation includes: multi-dimensionality of ALSFRS-R scores suggests cautious usage for survival forecasting; upper and lower extremities independently degenerate and are autonomous from respiratory decline, with the latter associating with nearer-to-death classifications; height and weight-based metrics are auxiliary predictors for farther-from-death classifications; sex and onset site (limb, bulbar) are not independent survival predictors due to age co-correlation. Conclusion: The dimensionality and fluctuating predictors of ALS survival must be considered when developing predictive models for clinical trial development or in-clinic usage. Additional independent metrics and possible revisions to current metrics, like the ALSFRS-R, are needed to capture the underlying complexity needed for population and personalized forecasting of survival.
Screening of Potential Probiotic Lactic Acid Bacteria with Anticancer Properties.

Background Probiotics have shown to reduce cancer recurrence and side effects in colorectal cancer patients. Objective To isolate the lactic acid bacteria from Thai healthy newborn feces and screen good probiotics with anticancer properties. Material and Method Lactic acid bacteria were isolated from newborn feces and selected for the cytotoxicity property against human cancer cell lines by MTT assay and probiotics property by acid and bile tolerance tests. Results Among 200 lactic acid bacteria isolated, 3 and 1 isolates significantly demonstrated strong and moderate antiproliferative effect against Caco-2 cells, respectively. Likewise, 4 and 5 isolates showed significant strong and moderate inhibitory effect on U937 cells, respectively. Seven candidate strains did not displayed cytotoxic to normal cells (Vero), except MSMC 112-2. All candidates showed good probiotics properties in resistance to acidic condition (pH 2-4) and to 1-4% bile concentrations, except MSMC105-3 showed intolerance at 4% bile concentration. The nucleotide sequence homology showed that MSMC95-4, MSMC104-2, MSMC111-2, MSMC112-2 and MSMC215-1 strains belong to Enterococcus faecalis (99.4%, 98.8%, 99.5%, 98.7 and 98.9%, respectively), MSMC105-3 is Lactobacillus salivarius (99.1%) and MSMC171-1 is Enterococcus faecium (99.3%). Conclusion The authors have isolated lactic acid bacteria which have anticancer and good probiotics properties.
Interdependence in Health and Functioning Among Older Spousal Caregivers and Care Recipients

Older spousal caregiving relationships involve support that may be affected by the health of either the caregiver or care recipient. We conducted a longitudinal analysis using pooled data from 4,632 community-dwelling spousal care recipients and caregivers aged ⩾50 from the 2002 to 2014 waves of the Health and Retirement Study. We specified logistic and negative binomial regression models using lagged predictor variables to assess the role of partner health status on spousal caregiver and care recipient health care utilization and physical functioning outcomes. Care recipients’ odds of hospitalization, odds ratio (OR): 0.83, p<. 001, decreased when caregivers had more ADL difficulties. When spouses were in poorer versus better health, care recipients’ bed days decreased (4.69 vs. 2.54) while caregivers’ bed days increased (0.20 vs. 0.96). Providers should consider the dual needs of caregivers caring for care recipients and their own health care needs, in adopting a family-centered approach to management of older adult long-term care needs.
IEEE 802.11 aa GATS mechanisms performance study for very high throughput WLANs

Multimedia applications and in particular video streaming and video conferencing over IEEE 802.11 wireless networks are becoming more and more common, such applications do not only require large bands but also impose strict QoS. Very High Throughput (VHT) amendments have permitted to boost data rates and provide larger bandwidth. However, this haven't resolve problems related to multicast transmission. Indeed, the IEEE 802.11aa amendment has been defined. The latter introduces new multicast transport policies, the Group Addressed Transmission Service (GATS), at the aim of ensuring a reliable multicast transport. In this paper we perform the first evaluation of the performance of the GATS in the context of an IEEE 802.11ac based network. Analysis show that the IEEE 802.11ac amendment have permitted to significantly enhance the performance of the aforementioned mechanisms. However, it raises problems related to reliability.
Local allergic rhinitis: Implications for management

A significant proportion of rhinitis patients without systemic IgE‐sensitisation tested by skin prick test and serum allergen‐specific IgE (sIgE) display nasal reactivity upon nasal allergen provocation test (NAPT). This disease phenotype has been termed local allergic rhinitis (LAR). LAR is an underdiagnosed entity affecting children and adults from different parts of the world, with moderate‐to‐severe symptoms, impairment of quality of life and rapid progression to symptom worsening. LAR is a stable phenotype and not merely an initial state of AR. Allergic rhinitis and LAR share many clinical features including a positive NAPT response, markers of type 2 nasal inflammation including sIgE in nasal secretions and a significant rate of asthma development. LAR should be considered as a differential diagnosis in those subjects of any age with symptoms suggestive of AR but no evidence of systemic atopy. Although LAR pathophysiology is partially unknown, in some patients sIgE can be demonstrated directly in the nasal secretions and/or indirectly via positive responses in basophil activation test (BAT). LAR can coexist with other rhinitis phenotypes, especially AR. The diagnosis currently relies on the positivity of NAPT to a single or multiple allergens. NAPT has high sensitivity, specificity and reproducibility, and it is considered the gold standard. BAT and the measurement of nasal sIgE can also contribute to LAR diagnosis. LAR patients benefit from the same therapeutic strategies than AR individuals, including the avoidance of allergen exposure and the pharmacotherapy. Moreover, several recent studies support the effectiveness and safety of allergen immunotherapy for LAR, which opens a window of treatment opportunity in these patients.
Doppler Radar Techniques for Accurate Respiration Characterization and Subject Identification

A low distortion dc coupled CW radar system with high signal to noise ratio is capable of accurate representation of respiration in human subjects. We propose to test the hypothesis that a non-contact physiological radar monitoring system which measures and characterizes subtle body kinematics, can be made to resolve patterns accurately enough to recognize an individual’s identity. This paper investigates a technique to attain the requisite signal to noise ratio by dc offset management. Detailed exploration of the unique features in respiration signals using noncontact CW Doppler radar are presented. A proposed dynamic segmentation technique allowed detection of various unique features and patterns. KMN nearest neighbor and majority vote algorithms were implemented in software for this radar-based unique identification system. The system was tested and validated for six test subjects with 95% success rate. Fractal analysis of minor components of linearly demodulated radar signal was also presented for additional improvement in accuracy. This paper is believed to be significant as radar unique identification of human subjects has many potential applications, including security, health monitoring, IoT applications, and virtual reality.
Evaluating air navigation service efficiency of European airports utilizing DEA

The rising air traffic volume in Europe, and beyond, is demanding. Accordingly, the need for safe but also efficient air traffic management asks for approaches to evaluate service productions by more than just univariate measures. Data Envelopment Analysis (DEA) is a non-parametric method to assess the efficiency of organizations and processes, so-called Decision-Making Units (DMUs). The major advantage of DEA is to reduce multivariate data to a single key performance indicator (KPI). However, this KPI is just one element to analyze the (economic) situation of a DMU, as shown in the contribution at hand. While almost all DEA applications in the field of air transport refer to airports or airlines as complete units, we concentrate on the efficiency measurement of air navigation services (ANS), especially the arrival and departure phases. The set of DMUs comprises 32 major European airports. Due to a considerable amount of data for potential DEA studies of ANS, this contribution discusses the process of data-selection in a first step; the Performance Review Unit of EUROCONTROL provided all data. In a second step, graphical projections of the DEA results are determined by applying the multivariate method of Multidimensional Scaling (MDS). Finally, possible interpretations of underlying latent variables are given. Thus, the proposed approach provides new insights: for air navigation service providers as well as for authorities, i.e. for regulatory purposes. With regard to a liberalized market for air navigation services in Europe, the overall project goal is to develop a powerful tool for ANS regulation.
Mini-Mesh: Practical assessment of a miniaturized IEEE 802.11n/s mesh testbed

WLAN mesh networks are one of the key technologies for upcoming smart city applications and characterized by a flexible and low-cost deployment. The amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. On the physical layer, IEEE 802.11n introduced major improvements such as HT data rates, MIMO techniques, and frame aggregation. However, building large-scale 802.11n/s testbeds and reproducible setups is challenging and costly. On the other hand, existing attempts for down-scaling real-world setups are limited to works without support for 802.11n and 802.11s. We therefore present Mini-Mesh, a miniaturized indoor 802.11n/s testbed. Following a transmission range scaling approach, we deploy a 6×6-node mesh grid on an area of only 1 m2. We validate the applicability of our method via comparative measurements, exhibiting a deviation of less than 6 % between a scaled indoor and unscaled outdoor setup. Based on these results, we parameterize a path loss model helping us to estimate outdoor dimensions for arbitrary indoor mesh topologies.
Effective sensor fusion of a mobile robot for SLAM implementation

The Simultaneous Localization and Mapping (SLAM) is the process of building a map of an environment with an unknown topography by a mobile robot. The purpose of this paper is to build a mapping of an unknown environment by the mobile robot which we designed through the help of sensor fusion algorithms we have established. The mobile robot performs its mapping process by using the combination of ultrasonic, optical encoder and IMU sensors. Determining the position of the obstacles and its own location, for the mobile robot, is the core of this study. Inertial and rotational sensors are utilized to calculate the distance and position of the mobile robot. Due to low cost, the ultrasonic sensor is used instead of a Lidar laser, and the real-like results were provided. In this study, the robot's direction and movement is performed by an algorithm developed on the Raspberry Pi processor. This algorithm controls the movement of the wheels with the information received from the optical encoder and protractor. The data received from the gyroscope and the accelerometer is very affected from many external factors such as vibrational motion and the noise, eventhough, we used moving average filter and complementary filter to reduce the effect of the noise and measurement error problems. However, they still produce faulty results when calculating distance values. Therefore, the distance computation is carried out by using optical encoder instead of the accelerometer. The algorithm of the distance computation is written in Python programming language. In this study, it is established that the comparative usage of several detectors provide more accurate results. At the same time, the system is quite efficiently developed by using open structure software (Raspberry Pi, Linux etc.) and writing authentic libraries. The robot's coordinate information are combined under simulation medium by using Pygame library and by computing the coordinates of its location and the coordinates of the objects' locations it detects during its navigation. The mobile robot executes its mapping process according to these data derived. Also, the effects of margin of error in the information obtained during the comparable usage of detectors are studied within the scope of this study.
Flame detection using deep learning

Flame detection is an increasingly important issue in intelligent surveillance. In fire flame detection, we need to extract visual features from video frames for training and test. Based on them, a group of shallow learning models have been developed to detect flames, such as color-based model, fuzzy-based model, motion and shape-based model, etc. Deep learning is a novel method which could be much efficient and accurate in flame detection. In this paper, we use YOLO model to implement flame detection and compare it with those shallow learning methods so as to determine the most efficient one for flame detection. Our contribution of this paper is to make use of the optimized YOLO model for flame detection from video frames. We collected the dataset and trained them using Google platform TensorFlow, the obtained accuracy of our proposed flame detection is up to 76%.
Metagenomic insights into diazotrophic communities across Arctic glacier forefields

Abstract Microbial nitrogen fixation is crucial for building labile nitrogen stocks and facilitating higher plant colonisation in oligotrophic glacier forefield soils. Here, the diazotrophic bacterial community structure across four Arctic glacier forefields was investigated using metagenomic analysis. In total, 70 soil metagenomes were used for taxonomic interpretation based on 185 nitrogenase (nif) sequences, extracted from assembled contigs. The low number of recovered genes highlights the need for deeper sequencing in some diverse samples, to uncover the complete microbial populations. A key group of forefield diazotrophs, found throughout the forefields, was identified using a nifH phylogeny, associated with nifH Cluster I and III. Sequences related most closely to groups including Alphaproteobacteria, Betaproteobacteria, Cyanobacteria and Firmicutes. Using multiple nif genes in a Last Common Ancestor analysis revealed a diverse range of diazotrophs across the forefields. Key organisms identified across the forefields included Nostoc, Geobacter, Polaromonas and Frankia. Nitrogen fixers that are symbiotic with plants were also identified, through the presence of root associated diazotrophs, which fix nitrogen in return for reduced carbon. Additional nitrogen fixers identified in forefield soils were metabolically diverse, including fermentative and sulphur cycling bacteria, halophiles and anaerobes.
A DMPs-Based Framework for Robot Learning and Generalization of Humanlike Variable Impedance Skills

One promising approach for robots efficiently learning skills is to learn manipulation skills from human tutors by demonstration and then generalize these learned skills to complete new tasks. Traditional learning and generalization methods, however, have not well considered human impedance features, which makes the skills less humanlike and restricted in physical human–robot interaction scenarios. In particular, stiffness generalization has not been well considered. This paper develops a framework that enables the robot to learn both movement and stiffness features from the human tutor. To this end, the upper limb muscle activities of the human tutor are monitored to extract variable stiffness in real time, and the estimated human arm endpoint stiffness is properly mapped into the robot impedance controller. Then, a dynamic movement primitives model is extended and employed to simultaneously encode the movement trajectories and the stiffness profiles. In this way, both position trajectory and stiffness profile are considered for robot motion control in this paper to realize a more complete skill transfer process. More importantly, stiffness generalization and movement generalization can be efficiently realized by the proposed framework. Experimental tests have been performed on a dual-arm Baxter robot to verify the effectiveness of the proposed method.
A review of image reconstruction algorithms in electrical capacitance tomography

In order to learn the fundamental physics of complex multiphase flow systems using the electrical capacitance tomography (ECT) approach as an advanced measurement technique, we write a literature review of the ECT method from a perspective of the optimization algorithm design with domain constraints. In the first place we introduce the physical laws governing the ECT system. Then we will focus on optimization techniques in terms of various hardware settings, and signal processing algorithms that are capable to recover the cross sectional image of the internal characteristics of a specified region based on the measuring capacitances data of multi-electrode sensors. Among these approaches, we will investigate their advantages and limitations, and analyze the algorithms which have been examined through simulations or physical experiments.
Robust precoding design for indoor MU-MISO visible light communication

Visible light communication (VLC) is recognized as a promising technology to complement existing wireless communication systems due to its main advantages such as ease of deployment, low cost and large unlicensed bandwidth. This paper considers the precoding design for a multi-user multiple-input-single-output VLC system. Two major concerns need to be considered while solving such a problem. The first one is related to the inter-user interference, basically inherent to our consideration of a multi-user system, while the second results from the users' mobility, causing imperfect channel estimates. To address both concerns, we propose robust precoding designs that solve both max-min SINR and minimal illumination level problems. The first problem allows users' rates maximization while ensuring fairness and the second problem decides the feasibility of a certain set of target SINRs. The proposed robust designs are studied under different conditions, and are shown to achieve a high gain over their nonrobust counterparts.
Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes

Internet of Things (IoT) technology provides a competent and structured approach to handle service deliverance aspects of healthcare in terms of mobile health and remote patient monitoring. IoT generates an unprecedented amount of data that can be processed using cloud computing. But for real-time remote health monitoring applications, the delay caused by transferring data to the cloud and back to the application is unacceptable. Relative to this context, we proposed the remote patient health monitoring in smart homes by using the concept of fog computing at the smart gateway. The proposed model uses advanced techniques and services, such as embedded data mining, distributed storage, and notification services at the edge of the network. Event triggering-based data transmission methodology is adopted to process the patient’s real-time data at fog layer. Temporal mining concept is used to analyze the events adversity by calculating the temporal health index of the patient. In order to determine the validity of the system, health data of 67 patients in IoT-based smart home environment was systematically generated for 30 days. Results depict that the proposed Bayesian belief network classifier-based model has high accuracy and response time in determining the state of an event when compared with other classification algorithms. Moreover, decision making based on real-time healthcare data further enhances the utility of the proposed system.
A Tonoplast-Associated Calcium-Signaling Module Dampens ABA Signaling during Stomatal Movement1

Arabidopsis PAT10 and its downstream tonoplast calcineurin B-like proteins (CBLs) and CBL-interacting protein kinases negatively mediate ABA signaling in guard cells by promoting vacuolar potassium influx and preventing fast vacuolar convolution. Stomatal movement, critical for photobiosynthesis, respiration, and stress responses, is regulated by many factors, among which abscisic acid (ABA) is critical. Early events of ABA signaling involve Ca2+ influx and an increase of cytoplasmic calcium ([Ca2+]cyt). Positive regulators of this process have been extensively studied, whereas negative regulators are obscure. ABA-induced stomatal closure involves K+ flux and vacuolar convolution. How these processes are connected with Ca2+ is not fully understood. We report that pat10-1, a null mutant of Arabidopsis (Arabidopsis thaliana) PROTEIN S-ACYL TRANSFERASE10 (PAT10), is hypersensitive to ABA-induced stomatal closure and vacuolar convolution. A similar phenotype was observed in cbl2;cbl3, the double mutant of CBL2 and CBL3, whose tonoplast association depends on PAT10. Functional loss of the PAT10-CBL2/CBL3 system resulted in enhanced Ca2+ influx and [Ca2+]cyt elevation. Promoting vacuolar K+ accumulation by overexpressing NHX2 suppressed ABA-hypersensitive stomatal closure and vacuolar convolution of the mutants, suggesting that PAT10-CBL2/CBL3 positively mediates vacuolar K+ accumulation. We have identified CBL-interacting protein kinases (CIPKs) that mediate CBL2/CBL3 signaling during ABA-induced stomatal movement. Functional loss of the PAT10-CBL2/3-CIPK9/17 system in guard cells enhanced drought tolerance. We propose that the tonoplast CBL-CIPK complexes form a signaling module that negatively regulates ABA signaling during stomatal movement.