Decline in Rehab Transfers Among Rehab-Eligible Stroke Patients During the COVID-19 Pandemic.

OBJECTIVE: To characterize differences in disposition arrangement among rehab-eligible stroke patients at a Comprehensive Stroke Center before and during the COVID-19 pandemic. MATERIALS AND METHODS: We retrospectively analyzed a prospective registry for demographics, hospital course, and discharge dispositions of rehab-eligible acute stroke survivors admitted 6 months prior to (10/2019-03/2020) and during (04/2020-09/2020) the COVID-19 pandemic. The primary outcome was discharge to an inpatient rehabilitation facility (IRF) as opposed to other facilities using descriptive statistics, and IRF versus home using unadjusted and adjusted backward stepwise logistic regression. RESULTS: Of the 507 rehab-eligible stroke survivors, there was no difference in age, premorbid disability, or stroke severity between study periods (p>0.05). There was a 9% absolute decrease in discharges to an IRF during the pandemic (32.1% vs. 41.1%, p=0.04), which translated to 38% lower odds of being discharged to IRF versus home in unadjusted regression (OR 0.62, 95%CI 0.42-0.92, p=0.016). The lower odds of discharge to IRF persisted in the multivariable model (aOR 0.16, 95%CI 0.09-0.31, p<0.001) despite a significant increase in discharge disability (median discharge mRS 4 [IQR 2-4] vs. 2 [IQR 1-3], p<0.001) during the pandemic. CONCLUSIONS: Admission for stroke during the COVID-19 pandemic was associated with a significantly lower probability of being discharged to an IRF. This effect persisted despite adjustment for predictors of IRF disposition, including functional disability at discharge. Potential reasons for this disparity are explored.

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.

Assessing temporal dynamics of nitrogen surplus in Indian agriculture: district scale data from 1966 to 2017.

Nitrogen (N) is essential for agricultural productivity, yet its surplus poses significant environmental risks. Currently, over half of applied nitrogen is lost, resulting in resource wastage, contributing to increased greenhouse gas emissions and biodiversity loss. Excess nitrogen persists in the environment, contaminating soil and water bodies for decades. Quantifying detailed historical N-surplus estimation in India remains limited, despite national and global-scaled assessments. Our study develops a district-level dataset of annual agricultural N-surplus from 1966-2017, integrating 12 different estimates to address uncertainties arising from multiple data sources and methodological choices across major elements of the N surplus. This dataset supports flexible spatial aggregation, aiding policymakers in implementing effective nitrogen management strategies in India. In addition, we verified our estimates by comparing them with previous studies. This work underscores the importance of setting realistic nitrogen management targets that account for inherent uncertainties, paving the way for sustainable agricultural practices in India, reducing environmental impacts, and boosting productivity.

Multifractal characterization of meteorological to agricultural drought propagation over India.

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

Rhythmic Jaw Movements in Amyotrophic Lateral Sclerosis: Is It Clonus or Tremor?

Background: Jaw clonus refers to involuntary, rhythmic jaw contractions induced by a hyperactive trigeminal nerve stretch reflex; however, the movements, when triggered without a stretch, can be confused with a tremor. Phenomenology Shown: This video demonstrates a patient with amyotrophic lateral sclerosis presenting with rapid rhythmic jaw movements seen at rest, alongside a power spectrum analysis revealing a narrow high-frequency peak of 10 Hz. Educational Value: Rhythmic jaw movements are seen in many disorders such as Parkinson's disease, essential tremor, tardive syndromes, and cranial myorhythmias; however, a high-frequency movement, regardless of clonus or tremor, can indicate amyotrophic lateral sclerosis when accompanied by typical upper and lower motor neuron signs. Highlights: The presented video abstract shows a patient with amyotrophic lateral sclerosis with rhythmic jaw movements seen at rest. A power spectrum analysis of the rhythmic movements revealed a 10 Hz peak, a frequency higher than those seen in patients with Parkinson's disease, essential tremor, myorhythmia, and tardive syndromes.

Urban water and electricity demand data for understanding climate change impacts on the water-energy nexus.

As the climate crisis intensifies, it is becoming increasingly important to conduct research aimed at fully understanding the climate change impacts on various infrastructure systems. In particular, the water-electricity demand nexus is a growing area of focus. However, research on the water-electricity demand nexus requires the use of demand data, which can be difficult to obtain, especially across large spatial extents. Here, we present a dataset containing over a decade (2007-2018) of monthly water and electricity consumption data for 46 major US cities (2018 population >250,000). Additionally, we include pre-processed climate data from the North American Regional Reanalysis (NARR) to supplement studies on the relationship between the water-electricity demand nexus and the local climate. This data can be used for a number of studies that require water and/or electricity demand data across long time frames and large spatial extents. The data can also be used to evaluate the possible impacts of climate change on the water-electricity demand nexus by leveraging the relationship between the observed values.

Roussy-Lévy Syndrome: Pes Cavus, Tendon Areflexia, Amyotrophy, Gait Ataxia, and Upper Limb Tremor in a Patient with CMT Neuropathy.

Background: Roussy-Lévy syndrome (RLS) is characterized by postural hand tremor seen in patients with familial autosomal dominant Charcot-Marie-Tooth (CMT) neuropathy. Phenomenology Shown: This video demonstrates irregular, jerky bilateral kinetic, postural, rest tremor affecting the right > left hand, along with pes cavus and gait ataxia in a patient with CMT disease. Educational Value: Pes cavus, tendon areflexia, sensory ataxia, and upper limb tremor should prompt consideration of CMT neuropathy. Highlights: This video abstract depicts a bilateral hand tremor characteristic of Roussy-Lévy syndrome seen in patients with Charcot-Marie-Tooth disease neuropathy. The significance of the abstract lies in the phenomenology and the physiology of the tremor seen in patients with genetically confirmed duplication of PMP22 gene.

High-resolution impact-based early warning system for riverine flooding.

Despite considerable advances in flood forecasting during recent decades, state-of-the-art, operational flood early warning systems (FEWS) need to be equipped with near-real-time inundation and impact forecasts and their associated uncertainties. High-resolution, impact-based flood forecasts provide insightful information for better-informed decisions and tailored emergency actions. Valuable information can now be provided to local authorities for risk-based decision-making by utilising high-resolution lead-time maps and potential impacts to buildings and infrastructures. Here, we demonstrate a comprehensive floodplain inundation hindcast of the 2021 European Summer Flood illustrating these possibilities for better disaster preparedness, offering a 17-hour lead time for informed and advisable actions.

Tracing spatiotemporal changes in agricultural and non-agricultural trade networks of India.

The evolving international economic instability and international trade relationship demand a nation to move towards a self-reliant integrated system at a sub-national scale to address the growing human needs. Given India's role in the global trade network, it is critical to explore the underlying extensive complex trade network at the domestic scale. The potential advantages of complex interaction among the different commodities remain unexplored despite the known importance of trade networks in maintaining food security and industrial sustainability. Here we perform a comprehensive analysis of agricultural flows in contrast with non-agricultural commodities across Indian states. The spatio-temporal evolution of the networks from 2010-2018 was studied by evaluating topological network characteristics of consistent spatially disaggregated trade data. Our results show an increase in average annual trade value by 23.3% and 15.4% for agriculture and non-agriculture commodities, respectively, with no significant increase in connectivity observed in both networks. However, they depict contrasting behavior concerning the spatio-temporal changes, with non-agriculture trade becoming more dependent on production hubs and the agriculture trade progressing toward self-reliance, which signifies the evolution of the diversification in the existing agrarian trade network. Our findings could serve as an important element in deepening the knowledge of practical applications like resilience and recovery by devising design appropriate policy interventions for sustainable development.

Observational evidence of legacy effects of the 2018 drought on a mixed deciduous forest in Germany.

Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site 'Hohes Holz' (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was [Formula: see text] higher in 2018 ([Formula: see text]) and [Formula: see text] lower in 2019 ([Formula: see text]) compared to pre-drought years ([Formula: see text]). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly [Formula: see text]. We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.

Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites.

Eddy covariance sites are ideally suited for the study of extreme events on ecosystems as they allow the exchange of trace gases and energy fluxes between ecosystems and the lower atmosphere to be directly measured on a continuous basis. However, standardized definitions of hydroclimatic extremes are needed to render studies of extreme events comparable across sites. This requires longer datasets than are available from on-site measurements in order to capture the full range of climatic variability. We present a dataset of drought indices based on precipitation (Standardized Precipitation Index, SPI), atmospheric water balance (Standardized Precipitation Evapotranspiration Index, SPEI), and soil moisture (Standardized Soil Moisture Index, SSMI) for 101 ecosystem sites from the Integrated Carbon Observation System (ICOS) with daily temporal resolution from 1950 to 2021. Additionally, we provide simulated soil moisture and evapotranspiration for each site from the Mesoscale Hydrological Model (mHM). These could be utilised for gap-filling or long-term research, among other applications. We validate our data set with measurements from ICOS and discuss potential research avenues.

Shifts in flood generation processes exacerbate regional flood anomalies in Europe.

Anomalies in the frequency of river floods, i.e., flood-rich or -poor periods, cause biases in flood risk estimates and thus make climate adaptation measures less efficient. While observations have recently confirmed the presence of flood anomalies in Europe, their exact causes are not clear. Here we analyse streamflow and climate observations during 1960-2010 to show that shifts in flood generation processes contribute more to the occurrence of regional flood anomalies than changes in extreme rainfall. A shift from rain on dry soil to rain on wet soil events by 5% increased the frequency of flood-rich periods in the Atlantic region, and an opposite shift in the Mediterranean region increased the frequency of flood-poor periods, but will likely make singular extreme floods occur more often. Flood anomalies driven by changing flood generation processes in Europe may further intensify in a warming climate and should be considered in flood estimation and management.

Strong influence of north Pacific Ocean variability on Indian summer heatwaves.

Increased occurrence of heatwaves across different parts of the world is one of the characteristic signatures of anthropogenic warming. With a 1.3 billion population, India is one of the hot spots that experience deadly heatwaves during May-June - yet the large-scale physical mechanism and teleconnection patterns driving such events remain poorly understood. Here using observations and controlled climate model experiments, we demonstrate a significant footprint of the far-reaching Pacific Meridional Mode (PMM) on the heatwave intensity (and duration) across North Central India (NCI) - the high risk region prone to heatwaves. A strong positive phase of PMM leads to a significant increase in heatwave intensity and duration over NCI (0.8-2 °C and 3-6 days; p < 0.05) and vice-versa. The current generation (CMIP6) climate models that adequately capture the PMM and their responses to NCI heatwaves, project significantly higher intensities of future heatwaves (0.5-1 °C; p < 0.05) compared to all model ensembles. These differences in the intensities of heatwaves could significantly increase the mortality (by ≈150%) and therefore can have substantial implications on designing the mitigation and adaptation strategies.

Long-term annual soil nitrogen surplus across Europe (1850-2019).

Worldwide surface waters suffer from the presence of nitrogen (N) compounds causing eutrophication and deterioration of the water quality. Despite many Europe-wide legislation's, we still observe high N levels across many water bodies in Europe. Information on long-term annual soil N surplus is needed to better understand these N levels and inform future management strategies. Here, we reconstructed and analysed the annual long-term N surplus for both agricultural and non-agricultural soils across Europe at a 5 arcmin (≈10 km at the equator) spatial resolution for more than a century (1850-2019). The dataset consists of 16 N surplus estimates that account for the uncertainties resulting from input data sources and methodological choices in major components of the N surplus. We documented the consistency and plausibility of our estimates by comparing them with previous studies and discussed about possible avenues for further improvements. Importantly, our dataset offers the flexibility of aggregating the N surplus at any spatial scale of relevance to support water and land management strategies.

The rise of compound warm-season droughts in Europe.

Drought is one of the main threats to food security and ecosystem productivity. During the past decades, Europe has experienced a series of droughts that caused substantial socioeconomic losses and environmental impacts. A key question is whether there are some similar characteristics in these droughts, especially when compared to the droughts that occurred further in the past. Answering this question is impossible with traditional single-index approaches and also short-term and often spatially inconsistent records. Here, using a multidimensional machine learning-based clustering algorithm and the hydrologic reconstruction of European drought, we determine the dominant drought types and investigate the changes in drought typology. We report a substantial increase in shorter warm-season droughts that are concurrent with an increase in potential evapotranspiration. If shifts reported here persist, then we will need new adaptive water management policies and, in the long run, we may observe considerable alterations in vegetation regimes and ecosystem functioning.

The critical role of humidity in modeling summer electricity demand across the United States.

Cooling demand is projected to increase under climate change. However, most of the existing projections are based on rising air temperatures alone, ignoring that rising temperatures are associated with increased humidity; a lethal combination that could significantly increase morbidity and mortality rates during extreme heat events. We bridge this gap by identifying the key measures of heat stress, considering both air temperature and near-surface humidity, in characterizing the climate sensitivity of electricity demand at a national scale. Here we show that in many of the high energy consuming states, such as California and Texas, projections based on air temperature alone underestimates cooling demand by as much as 10-15% under both present and future climate scenarios. Our results establish that air temperature is a necessary but not sufficient variable for adequately characterizing the climate sensitivity of cooling load, and that near-surface humidity plays an equally important role.

Microplastic particle emission from wastewater treatment plant effluents into river networks in Germany: Loads, spatial patterns of concentrations and potential toxicity.

Wastewater treatment plants (WWTP) are considered to be a point source of microplastic (particles <5 mm) for riverine environments. However, data on microplastic effluent concentrations in WWTPs is collected with a broad range of methods, which impede comparisons across data sets. We provide an estimate of the annual emissions of microplastic particles by WWTPs into the ten major river basins of Germany. We analyze the concentration patterns of microplastics among different stream orders resulting from the spatial organization of WWTPs along the river network. The local in-stream microplastic concentrations are estimated through a network model that accounts for routing of microplastics through the entire fluvial network under the assumption of no losses by sedimentation, entanglement or degradation. Previous studies have observed microplastic concentrations in treated WWTPs effluents ranging several orders of magnitude. In 19 studies reviewed (2016-2020), the concentrations of observed microplastic concentrations (size range between 10 and 5000 µm) in 79 WWTP effluents ranged between 4 ∗ 100 and 4.5 ∗ 105 items/m3 with a median of around 6400 items/m3. The total, median microplastic load emitted by WWTPs in Germany is 7 ∗ 1012 items/year. The simulated microplastic concentrations, on average, tend to increase with increasing stream order suggesting that the WWTP effluent fraction accumulates with a higher rate than discharge. Simulated WWTP-derived in-stream concentrations are higher than observed concentrations with all sources of microplastic, not only those from WWTPs. Observed microplastic concentrations in rivers as well as the considerably higher simulated, WWTP-derived microplastic concentration, even for low flow conditions, are approximately one order of magnitude below currently known toxic effect levels.

Increased future occurrences of the exceptional 2018-2019 Central European drought under global warming.

Since the spring 2018, a large part of Europe has been in the midst of a record-setting drought. Using long-term observations, we demonstrate that the occurrence of the 2018-2019 (consecutive) summer drought is unprecedented in the last 250 years, and its combined impact on the growing season vegetation activities is stronger compared to the 2003 European drought. Using a suite of climate model simulation outputs, we underpin the role of anthropogenic warming on exacerbating the future risk of such a consecutive drought event. Under the highest Representative Concentration Pathway, (RCP 8.5), we notice a seven-fold increase in the occurrence of the consecutive droughts, with additional 40 ([Formula: see text]) million ha of cultivated areas being affected by such droughts, during the second half of the twenty-first century. The occurrence is significantly reduced under low and medium scenarios (RCP 2.6 and RCP 4.5), suggesting that an effective mitigation strategy could aid in reducing the risk of future consecutive droughts.

Asymmetrical response of California electricity demand to summer-time temperature variation.

Current projections of the climate-sensitive portion of residential electricity demand are based on estimating the temperature response of the mean of the demand distribution. In this work, we show that there is significant asymmetry in the summer-time temperature response of electricity demand in the state of California, with high-intensity demand demonstrating a greater sensitivity to temperature increases. The greater climate sensitivity of high-intensity demand is found not only in the observed data, but also in the projections in the near future (2021-2040) and far future periods (2081-2099), and across all (three) utility service regions in California. We illustrate that disregarding the asymmetrical climate sensitivity of demand can lead to underestimating high-intensity demand in a given period by 37-43%. Moreover, the discrepancy in the projected increase in the climate-sensitive portion of demand based on the 50th versus 90[Formula: see text] quantile estimates could range from 18 to 40% over the next 20 years.

Team Approach: Rehabilitation Strategies for Patients After Osteosarcoma Reconstructive Surgery.

The multimodal treatment of osteosarcoma with chemotherapy, surgical resection, and reconstruction has improved outcomes after a limb-salvage surgical procedure. Physical rehabilitation considerations after surgical resection vary, depending on the location of the tumor. Physical medicine and rehabilitation physicians incorporate lymphedema specialists, orthotists, and prosthetists to help to improve limb function. Beyond physical rehabilitation, psychological or behavioral interventions and nutritional rehabilitation are necessary to maximize a patient's return to function.