Explore the vast range of titles available.

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

IntroductionArm weakness after stroke is one of the leading causes of adult-onset disability. Invasive vagus nerve stimulation (VNS) paired with rehabilitation has been shown to improve arm recovery in chronic stroke. Small studies of non-invasive or transcutaneous VNS (tVNS) suggest it is safe and tolerable. However, it is not known whether tVNS paired with rehabilitation is effective in promoting arm recovery in chronic stroke and what the mechanisms of action are.Methods and analysisTRICEPS is a UK multicentre, double-blinded, superiority, parallel-group, three-arm two-stage with an option to select promising arm(s) at 50% accrual, individually randomised, sham-controlled trial. Up to 243 participants will be randomised (1:1:1) using minimisation via a restricted, web-based centralised system. tVNS will be delivered by a movement-activated tVNS system (TVNS Technologies), which delivers stimulation during repetitive task practice. Rehabilitation will consist of repetitive task training for 1 hour a day, 5 days per week for 12 weeks. Participants will be adults with anterior circulation ischaemic stroke between 6 months and 10 years prior with moderate-severe arm weakness. The primary outcome measure will be the change in Upper Limb Fugl-Meyer total motor score at 91 days after the start of treatment. Secondary outcome measures include the Wolf Motor Function Test, the Modified Ashworth Scale to assess spasticity in the affected arm and the Stroke-Specific Quality of Life Scale. A mechanistic substudy including 40 participants will explore the mechanisms of active versus sham tVNS using multimodal MRI and serum inflammatory cytokine levels. Participant recruitment started on 30 November 2023.Ethics and disseminationThe study has received ethical approval from the Cambridge Central Research Ethics Committee (REC reference: 22/NI/0134). Dissemination of results will be via publications in scientific journals, meetings, written reports and articles in stakeholder publications.Trial registration numberISRCTN20221867.

IntroductionAutologous haematopoietic stem cell transplantation (aHSCT) is increasingly used as treatment for patients with active multiple sclerosis (MS), typically after failure of disease-modifying therapies (DMTs). A recent phase III trial, ‘Multiple Sclerosis International Stem Cell Transplant, MIST’, showed that aHSCT resulted in prolonged time to disability progression compared with DMTs in patients with relapsing remitting MS (RRMS). However, the MIST trial did not include many of the current high-efficacy DMTs (alemtuzumab, ocrelizumab, ofatumumab or cladribine) in use in the UK within the control arm, which are now offered to patients with rapidly evolving severe MS (RES-MS) who are treatment naïve. There remain, therefore, unanswered questions about the relative efficacy and safety of aHSCT over these high-efficacy DMTs in these patient groups. The StarMS trial (Autologous Stem Cell Transplantation versus Alemtuzumab, Ocrelizumab, Ofatumumab or Cladribine in Relapsing Remitting Multiple Sclerosis) will assess the efficacy, safety and long-term impact of aHSCT compared with high-efficacy DMTs in patients with highly active RRMS despite the use of standard DMTs or in patients with treatment naïve RES-MS.Methods and analysisStarMS is a multicentre parallel-group rater-blinded randomised controlled trial with two arms. A total of 198 participants will be recruited from 19 regional neurology secondary care centres in the UK. Participants will be randomly allocated to the aHSCT arm or DMT arm in a 1:1 ratio. Participants will remain in the study for 2 years with follow-up visits at 3, 6, 9, 12, 18 and 24 months postrandomisation. The primary outcome is the proportion of patients who achieve ‘no evidence of disease activity’ during the 2-year postrandomisation follow-up period in an intention to treat analysis. Secondary outcomes include efficacy, safety, cost-effectiveness and immune reconstitution of aHSCT and the four high-efficacy DMTs.Ethics and disseminationThe study was approved by the Yorkshire and Humber—Leeds West Research Ethics Committee (20/YH/0061). Participants will provide written informed consent prior to any study specific procedures. The study results will be submitted to a peer-reviewed journal and abstracts will be submitted to relevant national and international conferences.Trial registration numberISRCTN88667898.

Monsoon climate over India has high degree of spatio-temporal heterogeneity characterized by the existence of multi-climatic zones along with strong intra-seasonal, seasonal, and inter-annual variability. Vegetation growth of Indian forests relates to this climate variability, though the dependence structure over space and time is yet to be explored. Here, we present a comprehensive analysis of this association with quality-controlled satellite-based remote sensing dataset of vegetation greenness and radiation along with station based gridded precipitation datasets. A spatio-temporal time-frequency analysis using wavelets is performed to understand the relative association of vegetation growth with precipitation and radiation at different time scales. The inter-annual variation of forest greenness over the Tropical India are observed to be correlated with the seasonal monsoon precipitation. However, at inter and intra-seasonal scales, vegetation has a strong association with radiation in regions of high precipitation like the Western Ghats, Eastern Himalayas, and Northeast hills. Forests in Western Himalayas were found to be correlated more on the winter precipitation from western disturbances than the south west monsoon precipitation. Our results provide new and useful region-specific information for dynamic vegetation modelling in the Indian monsoon region that may further be used in understanding global vegetation-land-atmosphere interactions.

Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists’ judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. Thus, for grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with median CSI of 0.04 for HRRR. However, despite hydrologically-relevant improvements in point-by-point forecasts from NowcastNet, caveats include overestimation of spatially aggregate precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists and river managers suggest the possibility of improved flood emergency response and hydropower management.

Internal climate variability (ICV) remains a major source of uncertainty in climate projections, complicating impact assessments across critical sectors, especially at stakeholder-relevant scales. Given that ICV emerges from the nonlinear interactions of the climate system, we argue that nonlinear dynamical (NLD) approaches can improve its characterization, providing physically interpretable insights that strengthen adaptation strategies and support multisector decision-making. However, despite their suitability for such problems, NLD approaches remain largely underutilized in the analysis of initial condition large ensembles (LEs). We argue that a diverse suite of NLD approaches offers a promising pathway for systematically extracting robust insights from LEs. If effectively applied and systematically integrated, these methods could fully harness the potential of LEs, uncovering underlying patterns and variability across ensemble members to refine fundamental insights from climate projections. This will help bridge the gap between complex climate dynamics and practical resilience strategies, ensuring that decision-makers, resource managers, and infrastructure planners have a more reliable foundation for navigating irreducible uncertainty.

Climate-mediated changes in thermal stress can destabilize animal populations and promote extinction risk. However, risk assessments often focus on changes in mean temperatures and thus ignore the role of temporal variability or structure. Using Earth System Model projections, we show that significant regional differences in the statistical distribution of temperature will emerge over time and give rise to shifts in the mean, variability and persistence of thermal stress. Integrating these trends into mathematical models that simulate the dynamical and cumulative effects of thermal stress on the performance of 38 globally distributed ectotherm species revealed complex regional changes in population stability over the twenty-first century, with temperate species facing higher risk. Yet despite their idiosyncratic effects on stability, projected temperatures universally increased extinction risk. Overall, these results show that the effects of climate change may be more extensive than previously predicted on the basis of the statistical relationship between biological performance and average temperature.The authors project changes in mean thermal stress, as well as its persistence and variability. They show complex impacts on species stability but universal increases in extinction risk, and highlight the need to go beyond average-temperature-based projections of biological performance.

Little research has focused on systematically integrating clinical treatment within existing drug court procedures. This could be particularly useful for clients with substance use disorders, who comprise those on court dockets and often have co-existing mental health issues. This article reports on the preliminary outcomes of integrating MISSION-Criminal Justice (MISSION-CJ), a co-occurring mental health and substance use wraparound intervention, within two Massachusetts drug courts. In this open pilot, clients completed intake and 6-month follow-up assessments. The participants were primarily Caucasian (86%), male (82%), had at least 2 prior arrests, and received outpatient treatment for mental health (54%), alcohol use (51%), or drug use (88%) prior to enrolling in MISSION-CJ. Six-month follow-up data suggested that participants showed statistically significant reductions in average number of nights spent in jail, alcohol use, and drug use, as well as an increase in full time employment.