Check Point (Israel)

Abstract Strengthening in-country sequencing capacity generated 28 Lassa virus genomes from human clinical cases, expanding our knowledge of Lassa fever in Guinea. Phylogeographic analysis revealed cross-border exchange between Liberia and the N’Zérékoré region, and a Sierra Leone introduction into the Guéckédou area. Enhanced genomic surveillance is crucial to guide future public health actions.

Biological fitness quantifies the efficiency and selective advantage of pathogens and hosts in their bilateral interaction. Key questions—such as how much more infectious an emerging variant is compared with its predecessor, or how much protection vaccination offers relative to no vaccination—require fitness to be measured systematically, in real time, and ideally beyond controlled laboratory settings. We propose an approach that infers biological fitness from mostly non-biological data on infection dynamics and contact levels in a population. Because contact levels are expected to predict infection levels under stable biological conditions, systematic deviations between these trends can indicate changes in the underlying processes of transmission or immunity. Using infection surveillance data and GPS co-location information as a proxy for social contact patterns, we apply Bayesian modeling to detect and quantify biological shifts throughout the SARS-CoV-2 pandemic in Germany. We identify substantial regional variation in both the timing and magnitude of fitness changes. These shifts align with the emergence of major variants and the accumulation of population immunity; across Germany, the Alpha, Delta, and Omicron variants were 29 %, 63 %, and 108 % more transmissible than wild-type SARS-CoV-2, respectively. Early natural infection, primary vaccination, and booster vaccination increased population-level immunity by 13 %, 94 %, and 114 % relative to the pre-pandemic naïve state. Our approach provides an immediately deployable framework for detecting biological change during emerging epidemics, given that relevant behavioral and surveillance data are collected in real time. It is particularly valuable for low-resource settings where direct biological measurements may be limited. Significance Statement Biological changes in pathogen transmission remain difficult to measure in real time during acute epidemic and pandemic situations. Here, we showcase a method to extract biological insights from Bayesian modeling of anonymized mobile phone-based GPS-derived contact data and anonymized infection data. We estimate changes in transmissibility associated with virus evolution and vaccination and find agreement with epidemiological, virological and immunological evidence. Increasingly available mobile phone data makes our approach more easily amenable for use in real time. It enables near real-time detection of biological changes in ongoing (e.g. Influenza) and future epidemics and may be especially valuable in low-resource settings.

Day-to-day variability in social contacts can shape transmission dynamics yet is rarely quantified. We aimed to quantify intraindividual variability (IIV) in non-household contacts during the COVID-19 pandemic in Germany and to assess its associations with sociodemographic characteristics, vaccination, and policy stringency. We analyzed longitudinal contact survey data with 33 waves between April 2020 and December 2021, including 7,845 participants and 59,462 observations. Pearson residuals from a mixed-effects negative binomial model were used to derive the within-person standard deviation (riSD) for participants with at least two observations, as a proxy of IIV. Gamma regression models with log link were fitted to estimate mean ratios (MR). Children and adolescents aged 0–17 years showed higher riSD than other age groups (MR = 1.13, 95% CI 1.09–1.16). Participants living in households with three or more members had higher riSD than those living alone (1.05, 95% CI 1.02–1.07). Retired individuals, homemakers, the unemployed, and students exhibited lower riSD than employed participants. Regarding COVID-19 vaccination, compared with the pre-vaccination window (− 100 to 0 days), riSD was higher in the post-vaccination window (1 to 100 days after the first COVID-19 vaccination dose) (1.13, 95% CI 1.06–1.20). Weaker policy stringency was strongly associated with higher riSD (1.36, 95% CI 1.32–1.39). IIV in non-household contacts was shaped by age, household composition, employment status, vaccination status, and policy context. Analyses relying solely on average contact numbers may misrepresent transmission risk when contact behavior is highly variable. Incorporating IIV alongside mean contact levels may improve infectious disease models and inform public health policies.

Perovskite is a new material which has a special crystal structure, and its chemical formula is ABX3. The working principle of Perovskite light-emitting diode (PeLED) is similar to the traditional LED, but the difference is that PeLED uses perovskite material. This article establishes a dielectric-layered electromagnetic model for the study of electromagnetic losses inside the PeLED devices. Full-wave electromagnetic simulation of the near-field and Poynting vectors are given by Finite Element Method at three different light-band frequencies, expressing for the blue, green and red light, respectively. The light-emitting layer is simulated by the radiation of a horizontal electric dipole antenna. Results indicate the dielectric loss of a PeLED may be subjected to the complex reflection and propagation modes of the field in stratified media. In this case, the study also re-visited the Sommerfeld’s integrals to derive the general reflection coefficients by a muti-layered dielectric structure, which would be an intuitively reflection of their quantitative relations.

Abstract Cognitive care planning (CCP) is an effective intervention to manage and improve health among patients with cognitive impairments like Alzheimer’s Disease and Related Dementias (ADRD). Despite its benefits, CCP is underutilized in clinical settings. The Alzheimer’s Association (AA) has developed paper-based guidelines to promote CCP. Online tools are also emerging to support healthcare providers and patients in implementing CCP, automatically generating assessment results and care plans. This study examines the perceptions and impact of a comprehensive online platform compared to AA’s paper/pdf-based guideline through 17 interviews with healthcare providers, focusing on providers’ perceptions of using these tools in clinical settings. Our findings demonstrate several perceived advantages of the all-inclusive digital cognitive assessment and care planning tool. Such advantages include the necessity and value of a computerized assessment that can be easily adapted to existing electronic medical record systems. Additionally, the time-saving potential of an automated assessment and automatically generated care plan was also mentioned. Healthcare providers also mentioned challenges that may arise from an all-inclusive platform wherein assessments and surveys are carried out uniformly amongst patient populations, causing concerns surrounding the lack of nuance and “humanness”. Finally, concerns about technology acceptance amongst older adult patients and older adult caregivers and reimbursement-related issues were also highlighted. In conclusion, the digital cognitive assessment and care planning tool is acknowledged by providers as beneficial for promoting CCP. Our study provides insights into the impact of digital platforms in supporting older adults with cognitive decline and AD/ADRD and their caregivers in clinical care settings.

This paper presents a Moving Target Defense solution for ESP32-based IoT devices enhanced with task distribution mechanisms. First, we propose a reference architecture integrating deception techniques, active defense, power optimization, and resilient communication based on the MQTT protocol. Second, we include an Atomic Distributed Locking Mechanism (ADLM) to distribute task execution across multiple IoT agents while supporting redundancy and diversity. We implemented the architecture and conducted experiments to validate feasibility and confirm power reduction in MTD operations, with a moderate increase in task claim latency.

Sexual behavior is heterogeneous and dynamic. Characterization of such complexity constitutes evidence for public health authorities and caregivers concerned with the framing of sexual health messages aimed at specific subgroups. We developed a machine-learning-based methodology for inference and characterization of such subgroups from longitudinal data on men who have sex with men (MSM) attending individual sexual health counseling sessions. Because longitudinal data take time to record, we assessed the ability of first visit data to predict subgroups' membership. Our methodology comprised two main steps: (1) Hierarchical clustering to group 2349 HIV-negative MSM based on their self-reported longitudinal sexual behavior during visits to Swiss sexual health counseling centers between November 2016 and April 2019; and (2) Random forest-based classification to predict subgroup membership from first visit data. We found six subgroups with significant differences in behavioral trends, most of which sharply deviated from the overall trends. Two subgroups, which contained 37% of the study population, accounted for over 70% of the overall increases in condomless anal intercourse with non-steady partners, group sex, and having more than five anal intercourse partners. Subgroup-specific trends in online-dating and group sex were heterogeneous with opposing trends across subgroups. Data from first visits predicted trends of sexual behavior with accuracy ranging from 64 to 86%. This study evidenced specific sexual behavioral subgroups that might benefit from customized sexual health messages, demonstrated that first visit registries could predict subgroups, and contributes an algorithmic alternative for establishing subgroups relevant to inform customized sexual health messages that capture sexual behavioral diversity.

To examine the feasibility of high-frequency sampling of symptoms and gather preliminary data on whether daily exercise behavior is related to daily symptom fluctuations. Seven patients with cancer started a 12-week daily home-based walking and resistance exercise program during chemotherapy. Each day, patients rated pain, numbness/tingling, anxiety, and depression; resistance band exercise duration and perceived exertion; and steps for 12 weeks, as well as their pain at pre-, mid-, and postintervention. Overall, daily symptom ratings showed an expected large variation. Participants took more steps on and after days with less pain; performed more minutes of resistance exercises on days with less pain, numbness/tingling, and anxiety; and reported greater exertion on days with more pain and anxiety. It is feasible to collect daily symptom measures throughout an exercise intervention during chemotherapy. Oncology nurses can help support exercise behavior by offering interventions to reduce interfering symptoms and by promoting helpful beliefs about symptoms.

We evaluated 1-year engagement in pre-exposure prophylaxis (PrEP) care and associated factors among gay, bisexual, and other men who have sex with men (GBMSM) in a large cohort of oral PrEP users in the Paris region, France. We included in this analysis cisgender GBMSM enrolled in the ANRS PREVENIR cohort study from 3 May 2017 to 28 February 2019. We categorized 1-year PrEP engagement into 4 categories: high (consistent visits, attendance, and prescription refills at months 3, 6, 9, and 12), low (missed visits or no prescription refills), disengagement (PrEP discontinuation), and lost to follow-up. We used a logistic regression model to identify sociodemographic and behavioral factors associated with high engagement in PrEP care. Of 3211 participants, 2685 GBMSM were included in the analysis. At enrollment, participants had a median age of 36 years, with 88% born in Europe and 52.4% already undergoing PrEP. At month 12, 1612 (60.0%) participants met the high engagement definition, 438 (16.3%) exhibited low engagement, 459 (17.1%) disengaged from PrEP care, and 176 (6.6%) were lost to follow-up. In multivariable analysis, high engagement in PrEP care at 1 year was associated with older age (P < .001), being born in Europe (P = .01), having a higher education level (P = .05), already undergoing PrEP at enrollment (P < .001), having a bacterial sexually transmitted infection in the prior year (P = .01), earlier enrollment in the study (P = .04), and using PrEP daily or switching between PrEP regimens within the first year (P < .001). Younger GBMSM, those born outside Europe, and those who were PrEP naive had lower engagement rates in the cohort, requiring tailored support.