ROLE OF MITOCHONDRIAL CALCIUM UNIPORTER IN MITOCHONDRIAL MEMBRANE POTENTIAL INSTABILITY IN ISCHEMIA-REPERFUSION INJURY

Mitochondria exhibit non-stationary unstable membrane potential (ΔΨm) when subjected to stress, such as during Ischemia/Reperfusion (I/R). Understanding mitochondrial instability in Ischemia Reperfusion injury is key to determining efficacy of interventions. Excess influx of mitochondrial Ca2+ and reactive oxygen species (ROS) accumulation are thought to be primary triggers of ΔΨm instability, but the underlying molecular mechanisms are still unclear. The goal of this thesis is to understand the contributions of mCa2+ and ROS in triggering ΔΨm instability. For this purpose, it was important to first define and characterize oscillatory patterns of non-stationary mitochondrial ΔΨm instability. A data analysis tool was developed based on wavelet transform functions to automate analysis of time-series data from microscopy images to detect ΔΨm changes in an unbiased and reproducible manner. It is an ImageJ-MATLAB-based workflow called ‘MitoWave’ to unravel dynamic mitochondrial ΔΨm changes that occur during ischemia and reperfusion. Features such as, time-points of ΔΨm depolarization during I/R, area of mitochondrial clusters and time-resolved frequency components during reperfusion were determined per cell and per mitochondrial cluster with this tool. We then used this tool to understand the role of Ca2+ and ROS in triggering ΔΨm instability. Physiologic Ca2+ entry via the Mitochondrial Calcium Uniporter (MCU) participates in energetic adaption to workload but is thought to contribute to cell death during I/R injury. We genetically knocked out the MCU to examine whether MCU-mediated mCa2+ uptake is required to trigger ΔΨm loss or oscillation during reperfusion in neonatal mouse ventricular myocyte (NMVM) monolayers. Our findings demonstrate that MCU knockout does not significantly alter mCa2+ import during I/R, nor does it affect ΔΨm recovery during Reperfusion. In contrast, blocking the mitochondrial sodium-calcium exchange (mNCE) with CGP-37157 suppressed mCa2+ increase during Ischemia but did not affect ΔΨm recovery during reperfusion or the frequency of ∆Ψm oscillations, confirming that mitochondrial ΔΨm instability on reperfusion is not triggered by mCa2+. Interestingly, inhibition of mitochondrial electron transport and supplementation with antioxidants stabilized ΔΨm oscillations. The findings are consistent with mCa2+ overload being mediated by reverse-mode mNCE activity and support ROS-induced ROS release as the primary trigger of ΔΨm instability during reperfusion injury

Hydrological Drought Atlas for the State of Texas For Durations from 3 Months to 36 Months and Return Periods from 5 Years to 100 Years

Maps depicting the spatial variation of hydrological drought severity for Texas are presented in this report. The return periods chosen are 5, 10, 25, 50 and 100 years for drought durations of 3, 6, 9, 12, 18, 24 and 36 months, respectively. The maps were constructed using the drought severity-duration-frequency (S-D-F) relationship derived using a copula-based multivariate probabilistic approach. For purposes of deriving drought properties, monthly stream flow simulations from a large scale land surface model, known as variable infiltration Capacity (VIC) model, were utilized. The stream flow time series obtained was gridded at 1/8th degree resolution over Texas. The marginal distribution most suitable for fitting each of the drought variables was determined after testing commonly used distributions, such as exponential, gamma, log-normal, and Weibull. The marginal distributions of drought severity and duration with the smallest root mean square error (RMSE) value between observed and theoretical probabilities were selected. For modeling the joint distribution of drought characteristics, the following classes of bivariate copulas were considered: Archimedean, extreme value, Plackett and elliptical families. The best performing copula, determined using the RMSE and the Akaike information criterion (AIC), were used to determine the conditional and joint return periods and hence derive the drought SD-F curves. The information obtained from the S-D-F curves was used for the preparation of drought atlas, which depicts the spatial variation of drought severity for specific drought durations and return periods in Texas

High Throughput Microplate Respiratory Measurements Using Minimal Quantities Of Isolated Mitochondria

Recently developed technologies have enabled multi-well measurement of O2 consumption, facilitating the rate of mitochondrial research, particularly regarding the mechanism of action of drugs and proteins that modulate metabolism. Among these technologies, the Seahorse XF24 Analyzer was designed for use with intact cells attached in a monolayer to a multi-well tissue culture plate. In order to have a high throughput assay system in which both energy demand and substrate availability can be tightly controlled, we have developed a protocol to expand the application of the XF24 Analyzer to include isolated mitochondria. Acquisition of optimal rates requires assay conditions that are unexpectedly distinct from those of conventional polarography. The optimized conditions, derived from experiments with isolated mouse liver mitochondria, allow multi-well assessment of rates of respiration and proton production by mitochondria attached to the bottom of the XF assay plate, and require extremely small quantities of material (1–10 µg of mitochondrial protein per well). Sequential measurement of basal, State 3, State 4, and uncoupler-stimulated respiration can be made in each well through additions of reagents from the injection ports. We describe optimization and validation of this technique using isolated mouse liver and rat heart mitochondria, and apply the approach to discover that inclusion of phosphatase inhibitors in the preparation of the heart mitochondria results in a specific decrease in rates of Complex I-dependent respiration. We believe this new technique will be particularly useful for drug screening and for generating previously unobtainable respiratory data on small mitochondrial samples

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Drought Regionalization of Brazos River Basin Using an Entropy Approach

Assessment and understanding of past climate is an important step for drought mitigation and water resources planning. In this study, streamflow simulation obtained from the variable infiltration capacity (VIC) model was used for drought characterization, and subsequently regionalization was done based on the annual severity level, for the Brazos basin in Texas over a time span of 1949-2000. It is important to study drought characteristics within a regional context. Hence, identification of homogenous drought regions is a prerequisite, so that the drought characteristics can be studied within each of these regions. In this study, the concept of entropy was used for formation of homogenous regions based on drought severity. A standardized version of mutual information known as directional information transfer was used for station grouping. Results obtained were compared with the conventional k-means clustering method. The regions obtained were similar in both cases