Explore the vast range of titles available.

Soil moisture is an important prognostic variable within a soil and climate system. Soil moisture is often used in the analysis of soil and crop health, determining the probability of natural hazard occurrence, and of the overall climatology. However, obtaining soil moisture measurements that are comprehensive with respect to a study area is often a tedious and costly endeavor. Globally available satellite-based soil moisture retrievals yield a unique solution to this problem. Although globally available, these estimates are typically at too coarse a resolution for use in site-specific analyses. For this reason, this study presents a comparative analysis upon the efficacy of methods used to remotely obtain site-specific moisture estimates from these satellite-based moisture data sets. In the geoscience and remote-sensing communities, downscaling or assimilation methods are traditionally used to obtain desired site-specific moisture estimates. This study investigated Random Forest and Soil Evaporative Efficiency (SEE) downscaling methods as well as an Ensemble Kalman Filter (EnKF)-based assimilation method to obtain site-specific moisture data. This study also proposes a less intensive approach which was observed to effectively yield site-specific soil moisture estimates from satellite-based moisture datasets. The proposed approach developed a multivariate regression analysis which characterized relationships between site-specific soil texture data and SMAP L4_SM root zone soil moisture correction factors. This approach was conducted over various in-situ sites across the Commonwealth of Kentucky to yield site-specific L4_SM soil moisture estimates. These sites served as control sites, whereas the developed regression approach was able to be validated. Through qualitative and quantitative analyses, it was found that the EnKF and proposed multivariate regression approaches performed strongly when compared to site-specific in-situ measurements. These analyses accounted for both the accuracy of the site-specific products as compared to in-situ data and the efforts required to complete the approach. The study presented herein shows that the proposed multivariate regression approach is far less intensive, yet still yields site-specific moisture estimates comparable to that of downscaling or assimilated approaches.

Rainfall-induced landslides threaten residential and civil infrastructure. As extreme rainfall events increase with climatological variability, so does the need to effectively monitor these occurrences. However, physical monitoring of landslide occurrence requires costly instrumentation over vast areas. Therefore, a means for large scale spatial monitoring is desired. This study conducts infinite slope stability analyses on known spatially distributed rainfall-induced shallow colluvial landslides. Infinite slope analyses were chosen due to applicability to the investigated shallow landslides. These analyses were investigated as functions of spatial geomorphologic and spatiotemporal soil moisture data. The underlying assumption of these analyses was that soil moisture would act as a hydro-mechanical precursor for rainfall-induced landslides. A majority of geomorphologic data for these analyses was obtained via web databases. Contrarily, it was observed that measurements of friction angle were not spatially available. To remedy this, an Artificial Neural Network (ANN) machine learning workflow was developed to yield these requisite measurements. For spatiotemporal soil moisture, the Land Information System (LIS) was utilized to conduct assimilation of NOAH 3.6 LSM and NASA SMAP L3SMP_E moisture estimates. The LIS workflow yielded soil moisture estimates at various depths and fine resolutions. With spatial geomorphologic and spatiotemporal soil moisture available, this study moved towards the associated stability analyses. These analyses were focused upon a region of Eastern Kentucky, USA, which experienced an extreme rainfall and subsequent landslide event. Through these analyses, a majority of occurred landslides were able to be detected in areas observed to experience increases in soil moisture. Therefore, this study confirmed the underlying assumption that soil moisture can serve as a hydro-mechanical precursor for rainfall-induced landslide occurrence.

Landslides, specifically those triggered through an increase of soil moisture either during or after a rainfall event, pose severe threats to surrounding infrastructure. Herein, the term “landslide” refers primarily to translational movements of shallow colluvial soil upon a hillslope. These landslides are assumed to adhere to infinite slope approximations. Potential landslide occurrences are monitored through identification of areas susceptible to occurrence, through susceptibility analyses, or areas likely to experience a landslide at a given time, through hazard analyses. Traditional landslide susceptibility systems are created as a function of static geomorphologic data. This is to say that, while spatially differing, susceptibility via this system does not change with time. Landslide hazard analyses consider dynamic data, such as that of precipitation, and provide warnings of when landslide occurrences are likely. However, these hazard analysis systems typically only provide warnings in near real time (i.e., over the next few days). Therefore, dynamic susceptibility (susceptibility that is seen to change with time rather than remain static) as well as the ability to forecast landslide hazard analyses beyond real time is desired. The study herein presents a novel workflow for the creation of dynamic landslide susceptibility and forecastable hazard analyses over a domain within Eastern Kentucky. Dynamic susceptibility was developed through inclusion of static geomorphic parameters and dynamic vegetation levels over sites of interest. These susceptibility data were used in the development of a logistic regression classification machine learning approach which yielded susceptibility classifications with an accuracy of 89%. Forecastable hazard analyses were developed as a function of forecasted soil moisture, assumed to be a controlling factor in landslide occurrence, over a site. Forecasting of soil moisture was conducted through development of a Long Short-Term Memory (LSTM) forecasting machine learning system. Forecasts of soil moisture were then assimilated into an infinite slope stability equation to provide forecasts of hazard analyses. These forecasted hazard analyses were investigated over known landslides with satisfactory results obtained. Therefore, this study presents a novel workflow for both dynamic and forecastable hazard analyses that will undoubtedly provide greater warning and preparation periods to those within landslide prone regions.

Variation in susceptibility to infectious disease often has a substantial genetic component in animal and plant populations. We have used genome-wide association studies (GWAS) in Drosophila melanogaster to identify the genetic basis of variation in susceptibility to viral infection. We found that there is substantially more genetic variation in susceptibility to two viruses that naturally infect D. melanogaster (DCV and DMelSV) than to two viruses isolated from other insects (FHV and DAffSV). Furthermore, this increased variation is caused by a small number of common polymorphisms that have a major effect on resistance and can individually explain up to 47% of the heritability in disease susceptibility. For two of these polymorphisms, it has previously been shown that they have been driven to a high frequency by natural selection. An advantage of GWAS in Drosophila is that the results can be confirmed experimentally. We verified that a gene called pastrel--which was previously not known to have an antiviral function--is associated with DCV-resistance by knocking down its expression by RNAi. Our data suggest that selection for resistance to infectious disease can increase genetic variation by increasing the frequency of major-effect alleles, and this has resulted in a simple genetic basis to variation in virus resistance.

PROpel met its primary endpoint showing statistically significant improvement in radiographic progression-free survival with olaparib plus abiraterone versus placebo plus abiraterone in patients with first-line metastatic castration-resistant prostate cancer (mCRPC) unselected by homologous recombination repair mutation (HRRm) status, with benefit observed in all prespecified subgroups. Here we report the final prespecified overall survival analysis. This was a randomised, double-blind, phase 3 trial done at 126 centres in 17 countries worldwide. Patients with mCRPC aged at least 18 years, Eastern Cooperative Oncology Group performance status 0–1, a life expectancy of at least 6 months, with no previous systemic treatment for mCRPC and unselected by HRRm status were randomly assigned (1:1) centrally by means of an interactive voice response system–interactive web response system to abiraterone acetate (orally, 1000 mg once daily) plus prednisone or prednisolone with either olaparib (orally, 300 mg twice daily) or placebo. The patients, the investigator, and study centre staff were masked to drug allocation. Stratification factors were site of metastases and previous docetaxel at metastatic hormone-sensitive cancer stage. Radiographic progression-free survival was the primary endpoint and overall survival was a key secondary endpoint with alpha-control (alpha-threshold at prespecified final analysis: 0·0377 [two-sided]), evaluated in the intention-to-treat population. Safety was evaluated in all patients who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03732820, and is completed and no longer recruiting. Between Oct 31, 2018 and March 11, 2020, 1103 patients were screened, of whom 399 were randomly assigned to olaparib plus abiraterone and 397 to placebo plus abiraterone. Median follow-up for overall survival in patients with censored data was 36·6 months (IQR 34·1–40·3) for olaparib plus abiraterone and 36·5 months (33·8–40·3) for placebo plus abiraterone. Median overall survival was 42·1 months (95% CI 38·4–not reached) with olaparib plus abiraterone and 34·7 months (31·0–39·3) with placebo plus abiraterone (hazard ratio 0·81, 95% CI 0·67–1·00; p=0·054). The most common grade 3–4 adverse event was anaemia reported in 64 (16%) of 398 patients in the olaparib plus abiraterone and 13 (3%) of 396 patients in the placebo plus abiraterone group. Serious adverse events were reported in 161 (40%) in the olaparib plus abiraterone group and 126 (32%) in the placebo plus abiraterone group. One death in the placebo plus abiraterone group, from interstitial lung disease, was considered treatment related. Overall survival was not significantly different between treatment groups at this final prespecified analysis. Supported by AstraZeneca and Merck Sharp & Dohme.

People with chronic tetraplegia, due to high-cervical spinal cord injury, can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as functional electrical stimulation (FES). Users typically command FES systems through other preserved, but unrelated and limited in number, volitional movements (eg, facial muscle activity, head movements, shoulder shrugs). We report the findings of an individual with traumatic high-cervical spinal cord injury who coordinated reaching and grasping movements using his own paralysed arm and hand, reanimated through implanted FES, and commanded using his own cortical signals through an intracortical brain–computer interface (iBCI). We recruited a participant into the BrainGate2 clinical trial, an ongoing study that obtains safety information regarding an intracortical neural interface device, and investigates the feasibility of people with tetraplegia controlling assistive devices using their cortical signals. Surgical procedures were performed at University Hospitals Cleveland Medical Center (Cleveland, OH, USA). Study procedures and data analyses were performed at Case Western Reserve University (Cleveland, OH, USA) and the US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center (Cleveland, OH, USA). The study participant was a 53-year-old man with a spinal cord injury (cervical level 4, American Spinal Injury Association Impairment Scale category A). He received two intracortical microelectrode arrays in the hand area of his motor cortex, and 4 months and 9 months later received a total of 36 implanted percutaneous electrodes in his right upper and lower arm to electrically stimulate his hand, elbow, and shoulder muscles. The participant used a motorised mobile arm support for gravitational assistance and to provide humeral abduction and adduction under cortical control. We assessed the participant's ability to cortically command his paralysed arm to perform simple single-joint arm and hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralysed arm with that of a virtual three-dimensional arm. This study is registered with ClinicalTrials.gov, number NCT00912041. The intracortical implant occurred on Dec 1, 2014, and we are continuing to study the participant. The last session included in this report was Nov 7, 2016. The point-to-point target acquisition sessions began on Oct 8, 2015 (311 days after implant). The participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80–100% accuracy), using first a virtual arm and second his own arm animated by FES. Using his paralysed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session 463 days after implant) and feed himself (717 days after implant). To our knowledge, this is the first report of a combined implanted FES+iBCI neuroprosthesis for restoring both reaching and grasping movements to people with chronic tetraplegia due to spinal cord injury, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoration of reaching and grasping after paralysis. National Institutes of Health, Department of Veterans Affairs.

Data from millions of trees in thousands of locations are used to show that certain key traits affect competitive ability in predictable ways, and that there are trade-offs between traits that favour growth with and without competition. Key traits affect the competitive ability of trees The properties of plants affect their physiology in predictable and consistent ways, but it is not clear if this can be extended to effects on ecological competitiveness. Georges Kunstler et al . assemble data from three million trees, 140,000 forest growth plots, and many vegetation types worldwide to show that certain key traits affect competitive ability in predictable ways, and that there are trade-offs between traits that favour growth with, and without, competition. Elsewhere in this issue of Nature , Sandra Díaz et al . analyse a comprehensive database mapping worldwide variation in six traits critical to growth, survival and reproduction of vascular plants and arrive at a detailed quantitative global picture of plant functional diversity. Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions 1 , 2 , 3 , but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear 4 . Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits—wood density, specific leaf area and maximum height—consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies 5 . Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.

Overfishing is the primary cause of marine defaunation, yet declines in and increasing extinction risks of individual species are difficult to measure, particularly for the largest predators found in the high seas 1 – 3 . Here we calculate two well-established indicators to track progress towards Aichi Biodiversity Targets and Sustainable Development Goals 4 , 5 : the Living Planet Index (a measure of changes in abundance aggregated from 57 abundance time-series datasets for 18 oceanic shark and ray species) and the Red List Index (a measure of change in extinction risk calculated for all 31 oceanic species of sharks and rays). We find that, since 1970, the global abundance of oceanic sharks and rays has declined by 71% owing to an 18-fold increase in relative fishing pressure. This depletion has increased the global extinction risk to the point at which three-quarters of the species comprising this functionally important assemblage are threatened with extinction. Strict prohibitions and precautionary science-based catch limits are urgently needed to avert population collapse 6 , 7 , avoid the disruption of ecological functions and promote species recovery 8 , 9 . The global abundance of oceanic sharks and rays has decreased by 71% since 1970 and 24 species are threatened with extinction owing to a concomitant increase in fishing pressure.

The formation of sinking particles in the ocean, which promote carbon sequestration into deeper water and sediments, involves algal polysaccharides acting as an adhesive, binding together molecules, cells and minerals. These as yet unidentified adhesive polysaccharides must resist degradation by bacterial enzymes or else they dissolve and particles disassemble before exporting carbon. Here, using monoclonal antibodies as analytical tools, we trace the abundance of 27 polysaccharide epitopes in dissolved and particulate organic matter during a series of diatom blooms in the North Sea, and discover a fucose-containing sulphated polysaccharide (FCSP) that resists enzymatic degradation, accumulates and aggregates. Previously only known as a macroalgal polysaccharide, we find FCSP to be secreted by several globally abundant diatom species including the genera Chaetoceros and Thalassiosira . These findings provide evidence for a novel polysaccharide candidate to contribute to carbon sequestration in the ocean. The fate of ocean carbon is determined by the balance between primary productivity and heterotrophic breakdown of that photosynthate. Here the authors show that diatoms produce a polysaccharide that resists bacterial degradation, accumulates, aggregates and stores carbon during spring blooms.

Tissue-resident memory T (Trm) cells permanently localize to portals of pathogen entry, where they provide immediate protection against reinfection. To enforce tissue retention, Trm cells up-regulate CD69 and down-regulate molecules associated with tissue egress; however, a Trm-specific transcriptional regulator has not been identified. Here, we show that the transcription factor Hobit is specifically up-regulated in Trm cells and, together with related Blimp1, mediates the development of Trm cells in skin, gut, liver, and kidney in mice. The Hobit-Blimp1 transcriptional module is also required for other populations of tissue-resident lymphocytes, including natural killer T (NKT) cells and liver-resident NK cells, all of which share a common transcriptional program. Our results identify Hobit and Blimp1 as central regulators of this universal program that instructs tissue retention in diverse tissue-resident lymphocyte populations.