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With the ubiquity of mobile devices, the availability of mobile health (mHealth) applications for cardiovascular disease (CVD) has markedly increased in recent years. Older adults represent a population with a high CVD burden and therefore have the potential to benefit considerably from interventions that utilize mHealth. Traditional facility‐based cardiac rehabilitation represents one intervention that is currently underutilized for CVD patients and, because of the unique barriers that older adults face, represents an attractive target for mHealth interventions. Despite potential barriers to mHealth adoption in older populations, there is also evidence that older patients may be willing to adopt these technologies. In this review, we highlight the potential for mHealth uptake for older adults with CVD, with a particular focus on mHealth cardiac rehabilitation (mHealth‐CR) and evidence being generated in this field.

The SARS-CoV-2 virus resulted in significant disability and diagnostic challenges among patients with Post-Acute Sequelae of COVID-19 (PASC). Here, we assessed microvascular perfusion, clotting, and autoimmune responses to lung targets in PASC patients compared to healthy controls with the aim of explaining the persistent respiratory symptoms of patients with PASC. We performed a blinded case-control study of 20 PASC patients with persistent respiratory symptoms versus 20 healthy controls previously infected with SARS-CoV-2 virus. We assessed lung perfusion using Technetium-99m macroaggregated albumin (MAA) SPECT-CT scans, clotting using coagulation and thromboelastrogram (TEG) tests, and autoimmunity to vascular and lung antigens using ELISA assays. Subjective respiratory symptoms and quality-of-life measures were significantly worse among the PASC patients compared with healthy controls (p<0.001). Clinical symptoms among PASC patients were inversely correlated with plasma total IgA levels (coefficient: -0.61, p=0.004) and with autoimmune IgA recognizing pulmonary microvascular endothelial cell antigens (coefficient: -0.51, p=0.02). Additionally, levels of total IgA were directly correlated with fibrinogen and fibrin-related clot strength (coefficient: +0.52, p=0.02; coefficient: +0.63, p=0.003). SPECT-CT scans were positive only among 25% of PASC cases versus 10% of healthy controls (p=0.41). TEG tests showed no differences between the groups. Our small study of PASC patients identified that circulating IgA antibodies may correlate inversely with clinical symptoms and directly with clotting parameters, suggesting a possible link between autoimmunity and coagulation. However, many of the study's findings were null, which may mean that tissue-level studies or alternative explanations of PASC need to be explored.

Participation in ambulatory cardiac rehabilitation remains low, especially among older adults. Although mobile health cardiac rehabilitation (mHealth-CR) provides a novel opportunity to deliver care, age-specific impairments may limit older adults' uptake, and efficacy data are currently lacking. This study aims to describe the design of the rehabilitation using mobile health for older adults with ischemic heart disease in the home setting (RESILIENT) trial. RESILIENT is a multicenter randomized clinical trial that is enrolling patients aged ≥65 years with ischemic heart disease in a 3:1 ratio to either an intervention (mHealth-CR) or control (usual care) arm, with a target sample size of 400 participants. mHealth-CR consists of a commercially available mobile health software platform coupled with weekly exercise therapist sessions to review progress and set new activity goals. The primary outcome is a change in functional mobility (6-minute walk distance), which is measured at baseline and 3 months. Secondary outcomes are health status, goal attainment, hospital readmission, and mortality. Among intervention participants, engagement with the mHealth-CR platform will be analyzed to understand the characteristics that determine different patterns of use (eg, persistent high engagement and declining engagement). As of December 2021, the RESILIENT trial had enrolled 116 participants. Enrollment is projected to continue until October 2023. The trial results are expected to be reported in 2024. The RESILIENT trial will generate important evidence about the efficacy of mHealth-CR among older adults in multiple domains and characteristics that determine the sustained use of mHealth-CR. These findings will help design future precision medicine approaches to mobile health implementation in older adults. This knowledge is especially important in light of the COVID-19 pandemic that has shifted much of health care to a remote, internet-based setting. ClinicalTrials.gov NCT03978130; https://clinicaltrials.gov/ct2/show/NCT03978130. DERR1-10.2196/32163.

To study the short-term and long-term effects of combining activity training or lectures to exercise training on quality of life, functional status, and exercise tolerance Randomized clinical trial Outpatient pulmonary rehabilitation center Forty-three outpatients with COPD Patients were randomized to one of three treatment groups: exercise training alone, exercise training plus activity training, and exercise training plus a lecture series. The mean treatment period was 10 weeks The Chronic Respiratory Disease Questionnaire, the modified version of the Pulmonary Functional Status and Dyspnea Questionnaire, and the COPD Self-Efficacy Scale were administered at baseline, and 6, 12, 18, and 24 weeks from the beginning of the rehabilitation program. The 6-min walk test was used to measure exercise tolerance Benefits of activity training combined with exercise included less dyspnea (p ≤ 0.04) and fatigue (p ≤ 0.01), and increased activity involvement (p ≤ 0.02) and total functional status (p ≤ 0.02) in the short term compared to comparison treatment groups for comparatively older participants. Compared to the lecture series adjunct, the activity training adjunct resulted in significantly higher gains in total quality of life (p = 0.04) maintained at 24 weeks. Significantly worse emotional function and functional status resulted from the lecture series adjunct in the oldest participants (p ≤ 0.03). Treatment groups did not differ significantly on exercise tolerance or self-efficacy Evidence for additional benefits of activity-specific training combined with exercise was found. A behavioral method emphasizing structured controlled breathing and supervised physical activity was statistically significantly more effective than didactic instruction in facilitating additional gains and meeting participants’ learning needs

To study the short-term and long-term effects of combining activity training or lectures to exercise training on quality of life, functional status, and exercise tolerance. Randomized clinical trial. Outpatient pulmonary rehabilitation center. Forty-three outpatients with COPD. Patients were randomized to one of three treatment groups: exercise training alone, exercise training plus activity training, and exercise training plus a lecture series. The mean treatment period was 10 weeks. The Chronic Respiratory Disease Questionnaire, the modified version of the Pulmonary Functional Status and Dyspnea Questionnaire, and the COPD Self-Efficacy Scale were administered at baseline, and 6, 12, 18, and 24 weeks from the beginning of the rehabilitation program. The 6-min walk test was used to measure exercise tolerance. Benefits of activity training combined with exercise included less dyspnea (p < or = 0.04) and fatigue (p < or = 0.01), and increased activity involvement (p < or = 0.02) and total functional status (p < or = 0.02) in the short term compared to comparison treatment groups for comparatively older participants. Compared to the lecture series adjunct, the activity training adjunct resulted in significantly higher gains in total quality of life (p = 0.04) maintained at 24 weeks. Significantly worse emotional function and functional status resulted from the lecture series adjunct in the oldest participants (p < or = 0.03). Treatment groups did not differ significantly on exercise tolerance or self-efficacy. Evidence for additional benefits of activity-specific training combined with exercise was found. A behavioral method emphasizing structured controlled breathing and supervised physical activity was statistically significantly more effective than didactic instruction in facilitating additional gains and meeting participants' learning needs.

As microbiome science expands, academic centres scramble to fill many needs, from service provider to industry liaison. A newly created network aims to share strategies and accelerate knowledge transfer, and invites others to join the efforts.

This work explores the potential of the proposed FASER2 experiment at the LHC to determine the properties of a discovered heavy neutral lepton (HNL), including its mass, couplings, and whether it is a Majorana or Dirac fermion. We first consider a Majorana HNL with mass \\(m_N = 1.84\\,GeV\\) that is primarily produced through decays \\(D N\\) at the ATLAS interaction point. Such HNLs may travel macroscopic distances in the far-forward direction and then decay, yielding approximately 8600 \\(N \\) decays in FASER2 at the High-Luminosity LHC. With FASER2 measurements alone, the HNL's mass and couplings can be measured to fractional uncertainties of approximately 0.1% and 3% at 95% CL, respectively, and the Dirac fermion hypothesis can be rejected at 99.8% CL. We then consider a second, more difficult, case of a Majorana HNL with mass \\(m_N = 2.00\\,GeV\\), yielding only 80 \\(N \\) decays in FASER2. With FASER2 alone, measurements of HNL properties are still possible, but somewhat less precise. However, by using FASER2 as a trigger for ATLAS and measuring the charge of the muon produced in association with the HNL at ATLAS to search for lepton number violation, one can precisely measure the HNL's properties and reject the Dirac fermion hypothesis at 99.7% CL. These results show that FASER2, sometimes in coordination with ATLAS, can precisely determine HNL properties, with far-reaching implications for our understanding of neutrino masses, baryogenesis, and the fundamental symmetries of nature.

The availability of challenging benchmarks has played a key role in the recent progress of machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi-Agent Challenge (SMAC) has become a popular testbed for centralised training with decentralised execution. However, after years of sustained improvement on SMAC, algorithms now achieve near-perfect performance. In this work, we conduct new analysis demonstrating that SMAC lacks the stochasticity and partial observability to require complex *closed-loop* policies. In particular, we show that an *open-loop* policy conditioned only on the timestep can achieve non-trivial win rates for many SMAC scenarios. To address this limitation, we introduce SMACv2, a new version of the benchmark where scenarios are procedurally generated and require agents to generalise to previously unseen settings (from the same distribution) during evaluation. We also introduce the extended partial observability challenge (EPO), which augments SMACv2 to ensure meaningful partial observability. We show that these changes ensure the benchmark requires the use of *closed-loop* policies. We evaluate state-of-the-art algorithms on SMACv2 and show that it presents significant challenges not present in the original benchmark. Our analysis illustrates that SMACv2 addresses the discovered deficiencies of SMAC and can help benchmark the next generation of MARL methods. Videos of training are available at https://sites.google.com/view/smacv2.

Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally, RL environments run on the CPU, which limits their scalability with typical academic compute. However, recent advancements in JAX have enabled the wider use of hardware acceleration, enabling massively parallel RL training pipelines and environments. While this has been successfully applied to single-agent RL, it has not yet been widely adopted for multi-agent scenarios. In this paper, we present JaxMARL, the first open-source, Python-based library that combines GPU-enabled efficiency with support for a large number of commonly used MARL environments and popular baseline algorithms. Our experiments show that, in terms of wall clock time, our JAX-based training pipeline is around 14 times faster than existing approaches, and up to 12500x when multiple training runs are vectorized. This enables efficient and thorough evaluations, potentially alleviating the evaluation crisis in the field. We also introduce and benchmark SMAX, a JAX-based approximate reimplementation of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL. The code is available at https://github.com/flairox/jaxmarl.