Miracle-dz/UniFAR-FTUs huggingface dataset:
Your WiFi Knows You Fall: A Channel Data-driven Device-free Fall Sensing System

Falls are the second leading cause of injury deaths worldwide, inducing over $0.6$ million accidental deaths per year. Among various prevention strategies, fall-related research has been prioritized. However, conventional fall detection solutions relying on computer vision or wearable sensors embody several inherent limitations such as scalability, coverage, and privacy issues. To this end, we present FallSense, a transparent and real-time fall sensing system driven by wireless channel data. FallSense is built on a Dynamic Template Matching (DTM) algorithm, which can start with a light training set and keep updating on usage. FallSense has been realized on commodity WiFi devices and evaluated in real environments. Experimental results show that FallSense outperforms another state-of-the-art approach WiFall in terms of detection precision, false alarm rate and complexity.
Technical Analysis of Fall Detection Techniques

Fall has become the most imminent public health problem in the world. Each year thousands of people over age 65 experience falls. Because of that a large number of elderly people gets injured. Even some of them die if not rescued on time. With such immense increase in the falls that harms the individuals physically and/or mentally Researchers found ways to tackle the problem by devising different techniques for the purpose of fall detection. Most of techniques being used rely on Threshold based algorithms (TBA). While some of them brought the Machine Learning (ML) into the field to fight detect falls. The use of TBA and ML combinedly for fall detection has been reported. But still every technique has its advantages and disadvantages. We are doing analysis of techniques that are totally based on either TBA’s, ML or the combination of both TBA and ML
On CQI Estimation for Mobility and Correlation Properties of Gaussian Process Regression

Channel quality prediction is an essential function for anticipatory and proactive radio resource allocation. In this paper, we propose a channel quality prediction method based on the concept of Gaussian Process Regression (GPR) in which the spatio-temporal correlation of the wireless channel is used for wireless channel prediction. The objective of the paper is to find the optimal channel quality prediction for non- static users. Furthermore, we propose our analytical optimization in the choice of users which enhances the spatio-temporal correlation of the wireless channel and results in performance improvements in terms of BLER and rate loss. Simulation results show the potential of our proposed method.
Performance Evaluation of Constrained Application Protocol over TCP

The Constrained Application Protocol (CoAP) is specifically designed for constrained IoT devices and is being rapidly deployed for the communication needs of the IoT devices. CoAP has been specified with its own congestion control algorithms because it runs on top of UDP that does not include any congestion control measures. These algorithms aim at taking into account the specific needs of the IoT communication. The need of running CoAP also over TCP has arised recently and is expected to be increasingly deployed alongside with CoAP over UDP. To understand the benefits and shortcomings of both CoAP over TCP and CoAP over UDP, we run an extensive set of experiments in different network settings and compare the performance of CoAP over TCP to the existing congestion control algorithms for CoAP over UDP. Our results reveal that even though CoAP over TCP has its known limitations it scales well and performs even better than expected in certain wireless settings that CoAP over UDP algorithms are specifically designed for, often even outperforming CoAP over UDP.
Naphthenic acid fraction components from oil sands process‐affected water from the Athabasca Oil Sands Region impair murine osteoblast differentiation and function

The extraction of bitumen from surface mining in the Athabasca Oil Sands Region (AOSR) produces large quantities of oil sands process‐affected water (OSPW) that needs to be stored in settling basins near extraction sites. Chemical constituents of OSPW are known to impair bone health in some organisms, which can lead to increased fracture risk and lower reproductive fitness. Naphthenic acid fraction components (NAFCs) are thought to be among the most toxic class of compounds in OSPW; however, the effect of NAFCs on osteoblast development is largely unknown. In this study, we demonstrate that NAFCs from OSPW inhibit osteoblast differentiation and deposition of extracellular matrix, which is required for bone formation. Extracellular matrix deposition was inhibited in osteoblasts exposed to 12.5–125 mg/L of NAFC for 21 days. We also show that components within NAFCs inhibit the expression of gene markers of osteoblast differentiation and function, namely, alkaline phosphatase (Alp), osteocalcin, and collagen type 1 alpha 1 (Col1a1). These effects were partially mediated by the induction of glucocorticoid receptor (GR) activity; NAFC induces the expression of the GR activity marker genes Sgk1 (12.5 mg/L) and p85a (125 mg/L) and inhibits GR protein (125 mg/L) and Opg RNA (12.5 mg/L) expression. This study provides evidence that NAFC concentrations of 12.5 mg/L and above can directly act on osteoblasts to inhibit bone formation and suggests that NAFCs contain components that can act as GR agonists, which may have further endocrine disrupting effects on exposed wildlife.
Patterns of Implicit and Explicit Attitudes: IV. Change and Stability From 2007 to 2020

Using more than 7.1 million implicit and explicit attitude tests drawn from U.S. participants to the Project Implicit website, we examined long-term trends across 14 years (2007–2020). Despite tumultuous sociopolitical events, trends from 2017 to 2020 persisted largely as forecasted from past data (2007–2016). Since 2007, all explicit attitudes decreased in bias between 22% (age attitudes) and 98% (race attitudes). Implicit sexuality, race, and skin-tone attitudes also continued to decrease in bias, by 65%, 26%, and 25%, respectively. Implicit age, disability, and body-weight attitudes, however, continued to show little to no long-term change. Patterns of change and stability were generally consistent across demographic groups (e.g., men and women), indicating widespread, macrolevel change. Ultimately, the data magnify evidence that (some) implicit attitudes reveal persistent, long-term change toward neutrality. The data also newly reveal the potential for short-term influence from sociopolitical events that temporarily disrupt progress toward neutrality, although attitudes eventually return to long-term homeostasis in trends.
Radioimmunotherapy‐induced intratumoral changes in cervical squamous cell carcinoma at single‐cell resolution

Dear Editor, Increasing numbers of studies have revealed the immunomodulating effects of radiotherapy when combinedwith immune checkpoint inhibitors, as radioimmunotherapy has proven to be a promising treatment [1]. Radioimmunotherapy has shown significantly improved tumor responses than radiotherapy or immunotherapy alone in various malignant tumors [2–4]. It has also been applied to cervical cancer in multiple ongoing clinical trials (NCT03612791 [5] and NCT02635360 [6]). However, tumor recurrence andmetastasis are often unavoidable. As such, investigations into radioimmunotherapy-induced tumor ecosystem evolution are essential for guiding improvements in treatment strategies that achieve better long-term disease control. To date, several studies have investigated radiochemotherapy-induced tumor ecosystem evolution using bulk RNA-sequencing and immune staining [7, 8]. However, these findings were limited owing to the cellular heterogeneity in cancers. Single-cell RNA-sequencing (scRNA-seq) enables the characterization of cell compositions and transcriptomic states in the tumor at single-cell resolution. To investigate radioimmunotherapy-induced intratumoral changes, we performed scRNA-seq on a pair of cervical squamous cell carcinoma (CESC) samples before and during radioimmunotherapy. The study protocols are found in the Supplementary Materials. A total of 17,769 cells were obtained, with an average gene number of 1,875 after quality control. Using uniform manifold approximation and projection (UMAP) analysis, we identified nine main cell types: T cells (CD3D, CD3E), plasma cells (IGHG1, IGHG3), macrophages (CD14, CD68), monocytic myeloid-derived suppressor cells (M-MDSCs)
Nanopore Sequencing for Characterization of HIV-1 Recombinant Forms

HIV-1 is characterized by large genetic differences, including HIV-1 recombinant forms (RFs). Conventional genetic analyses require time-consuming pretreatments, i.e., cloning or single-genome amplification, to distinguish RFs from dual- or multiple-infection cases. ABSTRACT High genetic diversity, including the emergence of recombinant forms (RFs), is one of the most prominent features of human immunodeficiency virus type 1 (HIV-1). Conventional detection of HIV-1 RFs requires pretreatments, i.e., cloning or single-genome amplification, to distinguish them from dual- or multiple-infection variants. However, these processes are time-consuming and labor-intensive. Here, we constructed a new nanopore sequencing-based platform that enables us to obtain distinctive genetic information for intersubtype RFs and dual-infection HIV-1 variants by using amplicons of HIV-1 near-full-length genomes or two overlapping half-length genome fragments. Repeated benchmark tests of HIV-1 proviral DNA revealed consensus sequence inference with a reduced error rate, allowing us to obtain sufficiently accurate sequence data. In addition, we applied the platform for sequence analyses of 9 clinical samples with suspected HIV-1 RF infection or dual infection according to Sanger sequencing-based genotyping tests for HIV-1 drug resistance. For each RF infection case, replicated analyses involving our nanopore sequencing-based platform consistently produced long consecutive analogous consensus sequences with mosaic genomic structures consisting of two different subtypes. In contrast, we detected multiple heterologous sequences in each dual-infection case. These results demonstrate that our new nanopore sequencing platform is applicable to identify the full-length HIV-1 genome structure of intersubtype RFs as well as dual-infection heterologous HIV-1. Since the genetic diversity of HIV-1 continues to gradually increase, this system will help accelerate full-length genome analysis and molecular epidemiological surveillance for HIV-1. IMPORTANCE HIV-1 is characterized by large genetic differences, including HIV-1 recombinant forms (RFs). Conventional genetic analyses require time-consuming pretreatments, i.e., cloning or single-genome amplification, to distinguish RFs from dual- or multiple-infection cases. In this study, we developed a new analytical system for HIV-1 sequence data obtained by nanopore sequencing. The error rate of this method was reduced to ~0.06%. We applied this system for sequence analyses of 9 clinical samples with suspected HIV-1 RF infection or dual infection, which were extracted from 373 cases of HIV patients based on our retrospective analysis of HIV-1 drug resistance genotyping test results. We found that our new nanopore sequencing platform is applicable to identify the full-length HIV-1 genome structure of intersubtype RFs as well as dual-infection heterologous HIV-1. Our protocol will be useful for epidemiological surveillance to examine HIV-1 transmission as well as for genotypic tests of HIV-1 drug resistance in clinical settings.
Primary cutaneous peripheral T‐cell lymphomas with a T‐follicular helper phenotype: an integrative clinical, pathological and molecular case series study

Primary cutaneous peripheral T‐cell lymphomas with a T‐follicular helper phenotype (pcTFH‐PTCL) are poorly characterized, and often compared to, but not corresponding with, mycosis fungoides (MF), Sézary syndrome, primary cutaneous CD4+ lymphoproliferative disorder, and skin manifestations of angioimmunoblastic T‐cell lymphomas (AITL).
Aripiprazole vs Risperidone Head-to-Head Effectiveness in First-Episode Non-Affective-Psychosis: A 3-Month Randomized, Flexible-Dose, Open-Label Clinical Trial

Abstract Background Antipsychotic choice for the acute phase of a first episode of psychosis (FEP) is of the utmost importance since it may influence long-term outcome. However, head-to-head comparisons between second-generation antipsychotics remain scarce. The aim of this study was to compare the effectiveness in the short term of aripiprazole and risperidone after FEP outbreak. Methods From February 2011 to October 2018, a prospective, randomized, open-label study was undertaken. Two hundred-sixty-six first-episode drug-naïve patients were randomly assigned to aripiprazole (n = 136) or risperidone (n = 130) and followed-up for 12 weeks. The primary effectiveness measure was all-cause treatment discontinuation. In addition, an analysis based on intention-to-treat principle was conducted to assess clinical efficacy. Results The overall dropout rate at 12 weeks was small (6.39%). Effectiveness measures were similar between treatment arms as treatment discontinuation rates (χ 2 = 0,409; P = .522), and mean time to all-cause discontinuation (log rank χ 2 = −1.009; P = .316) showed no statistically significant differences. Despite no statistically significant differences between groups regarding clinical efficacy, aripiprazole required higher chlorpromazine equivalent dosage (χ 2 = 2.160; P = .032) and extended mean time (W = 8183.5; P = .008) to reach clinical response. Sex-related adverse events and rigidity were more frequent in the risperidone group, whereas sialorrhea was on the aripiprazole group. Conclusions No differences regarding effectiveness were found between aripiprazole and risperidone for the short-phase treatment of FEP. Despite the importance of efficacy during this phase, differences in side effect profiles and patient’s preferences are essential factors that may lead clinical decisions for these patients. Clinicaltrials.gov NCT02532491. Effectiveness of Second-Generation Antipsychotics in First Episode Psychosis Patients: 1-year Follow-up (PAFIP3_1Y).
High-Speed, Directly-Modulated Widely Tunable 1310 nm Coupled Cavity Laser Via Multimode Interference

We discuss the design and demonstration of a widely tunable laser operating at 1310 nm with ∼38 nm tuning range, >35 dB side-mode-suppression-ratio (SMSR), and output powers of ∼3 mW per tuning wavelength. Greater than 50 channels are demonstrated on a material platform that consists of In<sub>0.70</sub>Al<sub>0.17</sub>Ga<sub>0.13</sub>As multiple quantum wells (MQW) and Al<sub>0.52</sub>In<sub>0.48</sub>As electron stop layers for high temperature operation. Various single-side facet power wall-plug-efficiency (WPE) values of 8–1% are reported for operating temperatures of 30–70 °C, respectively. We compare theoretical and experimental data on the impact that multi-mode-interference (MMI) coupling coefficient values have on SMSR. We also discuss the integration of on-chip mode-converters to facilitate low-loss edge coupling to silicon photonic integrated circuits (Si-PICs). Directly-modulated, small-signal frequency measurements show 3-dB modulation bandwidths up to 14 GHz. The absence of epitaxial re-growth and lithographically demanding sub-wavelength gratings lends itself to high operating temperature, reliable and low-cost tunable light sources with device sizes of ∼400 μm × 300 μm.
Implementation Outcomes and Challenges of Partnerships between Resource Parents and Parents with Sick Infants in Intensive Neonatal Care Units: A Scoping Review

Parents with a sick child in a neonatal intensive care unit (NICU) usually experience stress, anxiety, and vulnerability. These precarious feelings can affect early parent–child interactions and have consequences for the child’s neurodevelopment. Parents who have had a sick child in an NICU (veteran parents) can offer helpful interventions for these vulnerable families. This article is a scoping review of parental interventions used with the families of NICU infants, and an overview of French perspectives. Two independent reviewers studied the scientific literature published in English between 2001 to 2021 using Covidence software. The databases used were MEDLINE, ISI Web of Science, the Cochrane Database, and Google Scholar. Themes were identified from the articles’ results using an open coding approach. The data are presented in a narrative format. Ten articles were included, and four major themes addressed: (1) description of activities, (2) recommendations, (3) impact, and (4) barriers (resulting from recruitment, training, remuneration, and organization). Activities were very diverse, and a step-by-step implementation was recommended by all authors. Peer-support interventions might be a potential resource for those anxious parents and improve their NICU experiences. These challenges are described by SOS Préma in France. This article brings together recent studies on partnership in the NICU. It is an innovative topic in neonatology with vast issues to explore.
Occupancy Grid Map Formation and Fusion in Cooperative Autonomous Vehicle Sensing (Invited Paper)

In autonomous driving, sensing is the most fundamental task providing the necessary information for intelligent vehicles. Compared with single-vehicle sensing, cooperative sensing can greatly reduce the cost, increase the accuracy and overcome the vision range limit. In this paper, a cooperative sensing framework is proposed based upon the occupancy grid map. For map formation, we investigate four occupancy probability distribution algorithms to transform the sensor data into a probability model; for map fusion, we design a probability fusion method to combine multi-vehicle maps. Simulations show that the proposed probability distribution algorithms can capture the environment information with different focuses and the map fusion process can expand the vehicles’ sensing range and improve the accuracy.
Time Series Analysis of Multiple Primary User Environment Using HMM-Based Spectrum Sensing

In this paper, we propose a method to estimate the communication parameters and the number of Primary Users (PUs) by using Hidden Markov Model (HMM) from time series data such as received power in the environment. Many researchers focus on a measurement based Radio Environment Database (RED) that utilizes the actual received signal power obtained by spectrum sensing as an enabler for an efficient frequency sharing. In addition, by estimating average received power and channel occupancy, etc. from radio observation information obtained by spectrum sensing, it is possible to realize high precision RED construction. Conventional research on parameter estimation using machine learning for a system like wireless LAN assumes that the number of PUs existing in the sensing environment is known. This assumption is not realistic in an actual environment. In this research, we estimate the number of PUs and parameters of each PU by using HMM and the Bayesian Information Criterion (BIC) for spectrum sensing when the number of PUs existing in the sensing environment is unknown. Thereby, the parameters related to the each PU in the sensing environment can be separated and stored in RED. Moreover, when estimating by using HMM and BIC, we improve the estimation result of the number of PUs by using the threshold for the estimation result of the transition probability matrix which is the parameter of HMM. The simulation results confirm that the number of PUs can be estimated with high precision when targeting wireless LAN.
Application of longitudinal item response theory models to modeling Parkinson’s disease progression

The Movement Disorder Society revised version of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS) parts 2 and 3 reflect patient‐reported functional impact and clinician‐reported severity of motor signs of Parkinson’s disease (PD), respectively. Total scores are common clinical outcomes but may obscure important time‐based changes in items. We aim to analyze longitudinal disease progression based on MDS‐UPRDS parts 2 and 3 item‐level responses over time and as functions of Hoehn & Yahr (H&Y) stages 1 and 2 for subjects with early PD. The longitudinal item response theory (IRT) modeling is a novel statistical method addressing limitations in traditional linear regression approaches, such as ignoring varying item sensitivities and the sum score balancing out improvements and declines. We utilized a harmonized dataset consisting of six studies with 3573 subjects with early PD and 14,904 visits, and mean follow‐up time of 2.5 years (±1.57). We applied both a unidimensional (each part separately) and multidimensional (both parts combined) longitudinal IRT models. We assessed the progression rates for both parts, anchored to baseline H&Y stages 1 and 2. Both the uni‐ and multidimensional longitudinal IRT models indicate significant worsening time effects in both parts 2 and 3. Baseline H&Y stage 2 was associated with significantly higher baseline severities, but slower progression rates in both parts, as compared with stage 1. Patients with baseline H&Y stage 1 demonstrated slower progression in part 2 severity compared to part 3, whereas patients with baseline H&Y stage 2 progressed faster in part 2 than part 3. The multidimensional model had a superior fit compared to the unidimensional models and it had excellent model performance.
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.