Proactive case detection of common childhood illnesses by community health workers: a systematic review.

INTRODUCTION: Identifying design features and implementation strategies to optimise community health worker (CHW) programmes is important in the context of mixed results at scale. We systematically reviewed evidence of the effects of proactive case detection by CHWs in low-income and middle-income countries (LMICs) on mortality, morbidity and access to care for common childhood illnesses. METHODS: Published studies were identified via electronic databases from 1978 to 2017. We included randomised and non-randomised controlled trials, controlled before-after studies and interrupted time series studies, and assessed their quality for risk of bias. We reported measures of effect as study investigators reported them, and synthesised by outcomes of mortality, disease prevalence, hospitalisation and access to treatment. We calculated risk ratios (RRs) as a principal summary measure, with CIs adjusted for cluster design effect. RESULTS: We identified 14 studies of 11 interventions from nine LMICs that met inclusion criteria. They showed considerable diversity in intervention design and implementation, comparison, outcomes and study quality, which precluded meta-analysis. Proactive case detection may reduce infant mortality (RR: 0.52-0.94) and increase access to effective treatment (RR: 1.59-4.64) compared with conventional community-based healthcare delivery (low certainty evidence). It is uncertain whether proactive case detection reduces mortality among children under 5 years (RR: 0.04-0.80), prevalence of infectious diseases (RR: 0.06-1.02), hospitalisation (RR: 0.38-1.26) or increases access to prompt treatment (RR: 1.00-2.39) because the certainty of this evidence is very low. CONCLUSION: Proactive case detection may provide promising benefits for child health, but evidence is insufficient to draw conclusions. More research is needed on proactive case detection with rigorous study designs that use standardised outcomes and measurement methods, and report more detail on complex intervention design and implementation. PROSPERO REGISTRATION NUMBER: CRD42017074621

Molecular epidemiological analysis of Escherichia coli sequence type ST131 (O25:H4) and bla CTX-M-15among extended-spectrum-β- lactamase-producing E. coli from the United States, 2000 to 2009

Escherichia coli sequence type ST131 (from phylogenetic group B2), often carrying the extended-spectrum-β-lactamase (ESBL) gene bla , is an emerging globally disseminated pathogen that has received comparatively little attention in the United States. Accordingly, a convenience sample of 351 ESBL-producing E. coli isolates from 15 U.S. centers (collected in 2000 to 2009) underwent PCR-based phylotyping and detection of ST131 and bla . A total of 200 isolates, comprising 4 groups of 50 isolates each that were (i) bla negative non-ST131, (ii) bla positive non-ST131, (iii) bla negative ST131, or (iv) bla positive ST131, also underwent virulence genotyping, antimicrobial susceptibility testing, and pulsed-field gel electrophoresis (PFGE). Overall, 201 (57%) isolates exhibited bla , whereas 165 (47%) were ST131. ST131 accounted for 56% of bla -positive-versus 35% of bla -negative isolates (

Habitat patchiness, ecological connectivity and the uneven recovery of boreal stream ecosystems from an experimental drought

Ongoing climate change is increasing the occurrence and intensity of drought episodes worldwide, including in boreal regions not previously regarded as drought prone, and where the impacts of drought remain poorly understood. Ecological connectivity is one factor that might influence community structure and ecosystem functioning post-drought, by facilitating the recovery of sensitive species via dispersal at both local (e.g. a nearby habitat patch) and regional (from other systems within the same region) scales. In an outdoor mesocosm experiment, we investigated how impacts of drought on boreal stream ecosystems are altered by the spatial arrangement of local habitat patches within stream channels, and variation in ecological connectivity with a regional species pool. We measured basal ecosystem processes underlying carbon and nutrient cycling: (a) algal biomass accrual; (b) microbial respiration; and (c) decomposition of organic matter, and sampled communities of aquatic fungi and benthic invertebrates. An 8-day drought event had strong impacts on both community structure and ecosystem functioning, including algal accrual, leaf decomposition and microbial respiration, with many of these impacts persisting even after water levels had been restored for 3.5 weeks. Enhanced connectivity with the regional species pool and increased aggregation of habitat patches also affected multiple response variables, especially those associated with microbes, and in some cases reduced the effects of drought to a small extent. This indicates that spatial processes might play a role in the resilience of communities and ecosystem functioning, given enough time. These effects were however insufficient to facilitate significant recovery in algal growth before seasonal dieback began in autumn. The limited resilience of ecosystem functioning in our experiment suggests that even short-term droughts can have extended consequences for stream ecosystems in the world's vast boreal region, and especially on the ecosystem processes and services mediated by algal biofilms

Super-Aggregations of Krill and Humpback Whales in Wilhelmina Bay, Antarctic Peninsula

Ecological relationships of krill and whales have not been explored in the Western Antarctic Peninsula (WAP), and have only rarely been studied elsewhere in the Southern Ocean. In the austral autumn we observed an extremely high density (5.1 whales per km2) of humpback whales (Megaptera novaeangliae) feeding on a super-aggregation of Antarctic krill (Euphausia superba) in Wilhelmina Bay. The krill biomass was approximately 2 million tons, distributed over an area of 100 km2 at densities of up to 2000 individuals m−3; reports of such ‘super-aggregations’ of krill have been absent in the scientific literature for >20 years. Retentive circulation patterns in the Bay entrained phytoplankton and meso-zooplankton that were grazed by the krill. Tagged whales rested during daylight hours and fed intensively throughout the night as krill migrated toward the surface. We infer that the previously unstudied WAP embayments are important foraging areas for whales during autumn and, furthermore, that meso-scale variation in the distribution of whales and their prey are important features of this system. Recent decreases in the abundance of Antarctic krill around the WAP have been linked to reductions in sea ice, mediated by rapid climate change in this area. At the same time, baleen whale populations in the Southern Ocean, which feed primarily on krill, are recovering from past exploitation. Consideration of these features and the effects of climate change on krill dynamics are critical to managing both krill harvests and the recovery of baleen whales in the Southern Ocean

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report