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The United States Veterans Affairs (VA) Health Care System has a strong history of conducting impactful oncology randomized clinical trials (RCTs). We developed a phase II/III RCT to test the use of metastasis-directed therapy in Veterans with oligometastatic prostate cancer (OMPC)—the first VA RCT in OMPC that leverages novel imaging and advanced radiotherapy techniques. To accomplish this, we developed a clinical trial network to conduct the study. In this manuscript, we describe several challenges we encountered in study development/conduct and our strategies to address them, with the goal of helping investigators establish robust study networks to conduct clinical trials. In the study start-up, we encountered challenges in timely site activation, and leveraged project management to maximize efficiency. Additionally, there were several changes in the clinical paradigms in imaging and treatment that led to protocol amendments to ensure maximum equipoise, recruitment, and impact of the study. Specifically, we amended the trial to add de novo OMPC patients (from initially only recurrent OMPC) and expanded the study to allow up to 10 metastases (from initially five). Finally, in order to maintain local study team engagement, we developed initiatives to maximize collaboration and add value to the overall clinical program through study participation.

Abstract Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that uniformly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 88% of library variants on all six predetermined criteria. Furthermore, the models, trained only on mouse in vivo and human in vitro Fit4Function data, accurately predicted AAV capsid variant biodistribution in macaque. Top candidates exhibited production yields comparable to AAV9, efficient murine liver transduction, up to 1000-fold greater human hepatocyte transduction, and increased enrichment relative to AAV9 in a screen for liver transduction in macaques. The Fit4Function strategy ultimately makes it possible to predict cross-species traits of peptide-modified AAV capsids and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits.

Abstract Background Monitoring pyruvate metabolism in the spleen is important for assessing immune activity and achieving successful radiotherapy for cervical cancer due to the significance of the abscopal effect. We aimed to explore the feasibility of utilizing hyperpolarized (HP) [1-13C]-pyruvate magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) to evaluate pyruvate metabolism in the human spleen, with the aim of identifying potential candidates for radiotherapy in cervical cancer. Methods This prospective study recruited six female patients with cervical cancer (median age 55 years; range 39–60) evaluated using HP [1-13C]-pyruvate MRI/MRS at baseline and 2 weeks after radiotherapy. Proton (1H) diffusion-weighted MRI was performed in parallel to estimate splenic cellularity. The primary outcome was defined as tumor response to radiotherapy. The Student t-test was used for comparing 13C data between the groups. Results The splenic HP [1-13C]-lactate-to-total carbon (tC) ratio was 5.6-fold lower in the responders than in the non-responders at baseline (p = 0.009). The splenic [1-13C]-lactate-to-tC ratio revealed a 1.7-fold increase (p = 0.415) and the splenic [1-13C]-alanine-to-tC ratio revealed a 1.8-fold increase after radiotherapy (p = 0.482). The blood leukocyte differential count revealed an increased proportion of neutrophils two weeks following treatment, indicating enhanced immune activity (p = 0.013). The splenic apparent diffusion coefficient values between the groups were not significantly different. Conclusions This exploratory study revealed the feasibility of HP [1-13C]-pyruvate MRS of the spleen for evaluating baseline immune potential, which was associated with clinical outcomes of cervical cancer after radiotherapy. Trial registration ClinicalTrials.gov NCT04951921 , registered 7 July 2021. Relevance statement This prospective study revealed the feasibility of using HP 13C MRI/MRS for assessing pyruvate metabolism of the spleen to evaluate the patients’ immune potential that is associated with radiotherapeutic clinical outcomes in cervical cancer. Key points • Effective radiotherapy induces abscopal effect via altering immune metabolism. • Hyperpolarized 13C MRS evaluates patients’ immune potential non-invasively. • Pyruvate-to-lactate conversion in the spleen is elevated following radiotherapy. Graphical Abstract

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

The Surface Water and Ocean Topography (SWOT) satellite and AirSWOT airborne instrument are the first imaging radar-altimeters designed with near-nadir and low incidence, 35.75 GHz Ka-band InSAR for mapping terrestrial water storage variability. Remotely sensed surface water extents are crucial for assessing such variability but are confounded by emergent and inundated vegetation along shorelines. However, because SWOT-like measurements are novel, there remains some uncertainty in the ability to detect certain land and water classes. This study examines the likelihood of misclassification between 15 land cover types and develops the Ka-band Phenomenology Scattering (KaPS) model to simulate changes to Ka-band backscatter as a result of changing surface water fraction and surface structure, including vegetation morphology and water surface waves. Using a separability metric, the study finds that water is five times more distinct compared with dry land classes, but has the potential to be confused with littoral zone and wet soil cover types. The KaPS scattering model simulates AirSWOT backscatter for incidence angles 1–27°, identifying the conditions under which open water is likely to be confused with littoral zone and wet soil cover types. KaPS characterization of the sensitivity of near-nadir and low incidence Ka-band SAR to small changes in both wet area fraction and surface structure enables a more nuanced classification of inundation area. These results provide additional confidence in the ability of SWOT to classify water inundation extent and open the door for novel hydrological and ecological applications of future Ka-band SAR missions.

Projecting floods and droughts characteristics under climate change is important to formulate an integrative management plan and enhance resiliency of society. However, studies that provide the integration of floods-drought hazards are scarce within literature. This study assessed flood and drought hazards separately and together for future climate in the Mun River basin, a tributary of the Mekong river. A non-modelling and multi-variate approach was used to assess flood and drought hazard respectively. Climate model ensemble suggests that the area under ‘high’ and ‘very high’ drought hazard level will increase from 27% and 4% during baseline period (1981–2010) to 43% and 37%, respectively, during near-future period (2021–2050). Similarly, an increase in ‘high’ and ‘very high’ flood hazard from 11% and 22% during baseline period to 16% and 24% during near-future period is projected. When both hazards are considered together, the total hazard is projected to increase by 155% in the near-future period. 76% of the catchment during the near future period will have combined hazard level from ‘medium’ to ‘very high’ compared to the 30% during the baseline period. The research presents a grim outlook on for the basin, with the area at risk from both hydro-meteorological hazards. HIGHLIGHTS Individual and combined flood and drought hazard assessment for the near-future period.; Non-modelling approach for flood and a multi-variate approach for drought hazards used.; Area under high and very high drought hazards up from 30 to 80% in the near future.; Area under high and very high flood hazards up from 33 to 40% in the near future.; Area under the combined hazard projected to increase by 155% across the catchment.;

During the unprecedented COVID-19 city lockdown, a unique opportunity arose to dissect the intricate dynamics of urban air quality, focusing on ultrafine particles (UFPs) and volatile organic compounds (VOCs). This study delves into the nuanced interplay between traffic patterns and UFP emissions in a subtropical urban setting during the spring-summer transition of 2021. Leveraging meticulous roadside measurements near a traffic nexus, our investigation unravels the intricate relationship between particle number size distribution (PNSD), VOCs mixing ratios, and detailed vehicle activity metrics. The soft lockdown era, marked by a 20–27% dip in overall traffic yet a surprising surge in early morning motorcycle activity, presented a natural experiment. We observed a consequential shift in the urban aerosol regime: the decrease in primary emissions from traffic substantially amplified the role of aged particles and secondary aerosols. This shift was particularly pronounced under stagnant atmospheric conditions, where reduced dilution exacerbated the influence of alternative emission sources, notably solvent evaporation, and was further accentuated with the resumption of normal traffic flows. A distinct seasonal trend emerged as warmer months approached, with aromatic VOCs such as toluene, ethylbenzene, and xylene not only increasing but also significantly contributing to more frequent particle growth events. These findings spotlight the criticality of targeted strategies at traffic hotspots, especially during periods susceptible to weak atmospheric dilution, to curb UFP and precursor emissions effectively. As we stand at the cusp of widespread vehicle electrification, this study underscores the imperative of a holistic approach to urban air quality management, embracing the complexities of primary emission reductions and the resultant shifts in atmospheric chemistry.

Abstract Alzheimer’s disease (AD) pathology and amyloid-beta (Aβ) plaque deposition progress slowly in the cerebellum compared to other brain regions, while the entorhinal cortex (EC) is one of the most vulnerable regions. Using a knock-in AD mouse model (App KI), we show that within the cerebellum, the deep cerebellar nuclei (DCN) has particularly low accumulation of Aβ plaques. To identify factors that might underlie differences in the progression of AD-associated neuropathology across regions, we profiled gene expression in single nuclei (snRNAseq) across all cell types in the DCN and EC of wild-type (WT) and App KI male mice at age 7 months. We found differences in expression of genes associated with inflammatory activation, PI3K–AKT signalling, and neuron support functions between both regions and genotypes. In WT mice, the expression of interferon-response genes in microglia is higher in the DCN than the EC and this enrichment is confirmed by RNA in situ hybridisation, and measurement of inflammatory cytokines by protein array. Our analyses also revealed that multiple glial populations are responsible for establishing this cytokine-enriched niche. Furthermore, homogenates derived from the DCN induced inflammatory gene expression in BV2 microglia. We also assessed the relationship between the DCN microenvironment and Aβ pathology by depleting microglia using a CSF1R inhibitor PLX5622 and saw that, surprisingly, the expression of a subset of inflammatory cytokines was increased while plaque abundance in the DCN was further reduced. Overall, our study revealed the presence of a cytokine-enriched microenvironment unique to the DCN that when modulated, can alter plaque deposition.

Wastewater-based epidemiology (WBE) is a valuable tool for monitoring the circulation of COVID-19. However, while variations in population size are recognised as major sources of uncertainty, wastewater SARS-CoV-2 measurements are not routinely population-normalised. This paper aims to determine whether dynamic population normalisation significantly alters SARS-CoV-2 dynamics observed through wastewater monitoring, and whether it is beneficial or necessary to provide an understanding of COVID-19 epidemiology. Data from 394 sites in England are used, and normalisation is implemented based on ammoniacal nitrogen and orthophosphate concentrations. Raw and normalised wastewater SARS-CoV-2 metrics are evaluated at the site and spatially aggregated levels are compared against indicators of prevalence based on the Coronavirus Infection Survey and Test and Trace polymerase chain reaction test results. Normalisation is shown, on average, to have a limited impact on overall temporal trends. However, significant variability in the degree to which it affects local-level trends is observed. This is not evident from previous WBE studies focused on single sites and, critically, demonstrates that while the impact of normalisation on SARS-CoV-2 trends is small on average, this may not always be the case. When averaged across many sites, normalisation strengthens the correlation between wastewater SARS-CoV-2 data and prevalence indicators; however, confidence in the improvement is low. HIGHLIGHTS 394 sites were analysed to determine the impact of population normalisation on SARS-CoV-2 trends.; Comparison made with the Coronavirus Infection Survey and NHS Test and Trace polymerase chain reaction test results.; Significant variability between sites in the impact of normalisation was revealed.; On average, population normalisation strengthens the correlation between wastewater SARS-CoV-2 data and prevalence indicators.;

Despite the growth in research and applications of nature-based solutions (NBS) within the literature, there are limited applications in South East Asia, moreover studies which quantitatively assess the impacts of NBS could have on hazard reduction are scarce. This paper addresses this gap by developing and validating MCDA-GIS analysis to map how potential nature strategies could mitigate flood hazard if applied within the Mun River Basin, Thailand. Through a literature review, the top three solutions for flood and drought hazards were found: wetlands, re/afforestation, and changing crop types. These strategies were reviewed and validated with a MCDA-GIS methodology, through land use change (LUC) maps to depict different future scenarios. The results found that flood hazard did decrease when NBS were implemented in the catchment, especially for A/Reforestation, and to a greater extent when a combination of NBS were applied. This article provides specific insights into the current gaps of NBS publications, specifically considering the case of the Mun River Basin, Thailand. HIGHLIGHTS Applying nature-based solutions (NBS) to South East Asia Thailand.; Using a MCDA-GIS and land use change modelling to assess the impact of NBS.; Wetlands, re/afforestation, and changing crop types provide optimum solutions in mitigating flood hazard.;

Although land use change (LUC) can have detrimental environmental impacts, very few studies have explored the idea that changes in groundwater conditions and water management directly influence LU. This study uses a socio-hydrogeological approach to model future land use change and help to understand the implication for groundwater availability. The Angas Bremer Prescribed Wells Area (Murray-Darling Basin, Australia) was used as a case study because it provides a rare example of complex and transient groundwater management. This study (i) uses groundwater levels and salinities as drivers for LU; (ii) considers future scenarios based on agricultural development of certain crop types rather than conservation or urban development; and, (iii) takes a socio-hydrogeological approach, using stakeholder expertise in future scenarios design and information gathering on its implications for regional groundwater resources. Predicted changes in climate (rainfall and temperature) will lead to increased uncertainties in agricultural production within the region, which is expected to be especially detrimental for the viticulture industry in Angas Bremer. By using the PLUS model and the development potential (growth possibility) of each LU type through the Land Expansion Analysis Strategy module, we have been able to identify optimum potential growth areas for both vegetables and vineyards. A social-hydrogeology approach is an important aspect to LUC modelling linked to groundwater management. The stakeholder engagement resulted in important inputs and the design of LU scenarios that were consequently more relevant.

In this study we use the Mun river basin to demonstrate how a Multi Criteria Decision Analysis – Geographical Information Systems (MCDA-GIS) methodology can be used to assess drought risk. This paper not only provides a step forward in considering other elements such as land use change, climate within drought risk but also splits annual risk across three seasons (wet, cool and hot), previously not done. We also investigate how land use change, in the form of a/reforestation and changing crop varieties could potentially mitigate future risk.MCDA rankings from experts found that climatic factors such as rainfall, evapotranspiration and maximum temperature were the most significant. By splitting up the seasons we have been able to observe the temporal and spatial changes in drought risk at an increased detail, an important step in mitigating water security issue in the future. Results for cool months found an increased risk in the north and east (Surin, Si Sa Ket and Rio Et). With hot months finding increased risk in the east (Surin and Si Sa Ket especially) and west in Nakon Ratchasima. Whereas the wet season risk was greatest in the West (Nakon Ratchima, Khon Kean and Mara Sarakham). Differences in future land use scenarios compared to 2017 found that if current trends continued (BAU), the areas at risk from drought will increase. However, by changing land use in the form of a/reforestation (COB) or changing crop types (PRO), drought risk will decrease. Thus, the MCDA-GIS methodology serves as a great starting point, providing a high flexibility in data, meaning the methodology can readily applied to other case studies across the world.

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions.

A meta-analysis of 17 cohorts of mitochondrial disease patients reveals that OxPhos defects are associated with signs of hypermetabolism. Experiments in patient-derived fibroblast show that mitochondrial OxPhos defects trigger hypermetabolism in a cell-autonomous manner and this is linked to accelerated telomere shortening and epigenetic aging.

Viruses have evolved the ability to bind and enter cells through interactions with a wide variety of cell macromolecules. We engineered peptide-modified adeno-associated virus (AAV) capsids that transduce the brain through the introduction of de novo interactions with 2 proteins expressed on the mouse blood-brain barrier (BBB), LY6A or LY6C1. The in vivo tropisms of these capsids are predictable as they are dependent on the cell- and strain-specific expression of their target protein. This approach generated hundreds of capsids with dramatically enhanced central nervous system (CNS) tropisms within a single round of screening in vitro and secondary validation in vivo thereby reducing the use of animals in comparison to conventional multi-round in vivo selections. The reproducible and quantitative data derived via this method enabled both saturation mutagenesis and machine learning (ML)-guided exploration of the capsid sequence space. Notably, during our validation process, we determined that nearly all published AAV capsids that were selected for their ability to cross the BBB in mice leverage either the LY6A or LY6C1 protein, which are not present in primates. This work demonstrates that AAV capsids can be directly targeted to specific proteins to generate potent gene delivery vectors with known mechanisms of action and predictable tropisms.

Background: Synaptic loss is considered an early pathological event and major structural correlate of cognitive impairment in Alzheimer’s disease (AD). We used principal component analysis (PCA) to identify regional patterns of covariance in synaptic density using [11C]UCB-J PET and assessed the association between principal components (PC) subject scores with cognitive performance. Methods: [11C]UCB-J binding was measured in 45 amyloid + participants with AD and 19 amyloid– cognitively normal participants aged 55–85. A validated neuropsychological battery assessed performance across five cognitive domains. PCA was applied to the pooled sample using distribution volume ratios (DVR) standardized (z-scored) by region from 42 bilateral regions of interest (ROI). Results: Parallel analysis determined three significant PCs explaining 70.2% of the total variance. PC1 was characterized by positive loadings with similar contributions across the majority of ROIs. PC2 was characterized by positive and negative loadings with strongest contributions from subcortical and parietooccipital cortical regions, respectively, while PC3 was characterized by positive and negative loadings with strongest contributions from rostral and caudal cortical regions, respectively. Within the AD group, PC1 subject scores were positively correlated with performance across all cognitive domains (Pearson r = 0.24–0.40, P = 0.06–0.006), PC2 subject scores were inversely correlated with age (Pearson r = -0.45, P = 0.002) and PC3 subject scores were significantly correlated with CDR-sb (Pearson r = 0.46, P = 0.04). No significant correlations were observed between cognitive performance and PC subject scores in CN participants. Conclusions: This data-driven approach defined specific spatial patterns of synaptic density correlated with unique participant characteristics within the AD group. Our findings reinforce synaptic density as a robust biomarker of disease presence and severity in the early stages of AD.

Abstract Modelling urban inundation and its associated health implications is numerous in its many applications. Flood modelling research contains a broad wealth of material, and microbial risk assessment has gained more popularity over the last decade. However, there is still a relative lack of understanding of how the microbial risk can be quantified from urban sewer flooding. This article intends to review the literature encompassing contemporary urban flood modelling approaches. Hydrodynamic and microbial models that can be applied for quantitative microbial risk assessment will be discussed. Consequently, urban sewer flooding will be the focus. This review found that the literature contains a variety of different hazards posed by urban flooding. Yet, far fewer examples encompass microbial risk from sewer system exceedance. To date, there is no evidence of a perfect model or technique, to carry out a quantitative microbial risk assessment from hydrodynamic simulations. The literature details many different methods. We intend to detail the advantages and limitations of each method. Along similar lines, hydraulic data constitutes a large part of the uncertainty which is inherent to this research field. Many studies in the literature detail data paucity and uncertainty in input data. As such, any advancement in this discipline will very likely aid future research.

BackgroundEarly recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies. ObjectiveIn this study, we aimed to build a machine learning–based model through text mining of emergency calls for the automated identification of severely injured patients after a road accident. MethodsAudio recordings of road accidents in Taipei City, Taiwan, in 2018 were obtained and randomly sampled. Data on call transfers or non-Mandarin speeches were excluded. To predict cases of severe trauma identified on-site by emergency medical technicians, all included cases were evaluated by both humans (6 dispatchers) and a machine learning model, that is, a prehospital-activated major trauma (PAMT) model. The PAMT model was developed using term frequency–inverse document frequency, rule-based classification, and a Bernoulli naïve Bayes classifier. Repeated random subsampling cross-validation was applied to evaluate the robustness of the model. The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. ResultsAlthough the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments. ConclusionsA machine learning–based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment.

Abstract In this article, we evaluate property flood resilience (PFR) to manage pluvial and combined tidal/ fluvial flood risks. We achieve this by evaluating flood risk and intervention targeting strategies across a case study in Bristol (UK) using data types generally available for preliminary option assessment. We investigate opportunities for mitigating flood damages within catchments using PFR and evaluate two targeting strategies: Installing PFR across strategic areas of a catchment and targeting interventions at specific high‐risk properties. We find that individually targeting PFR is more effective than focusing resources on specific high‐risk areas. Targeting pluvial flood measures at individual properties across our case study provides an average annual benefit per property of approximately £750 more than applying zonal targeting, supporting use of high‐resolution modelling in surface water management, and highlighting the applicability of PFR to manage damages at specific high‐risk properties which may not fall under the protection of community level defences. A similar approach provides the best outcomes for fluvial targeting; however, the hazard is more concentrated and so a zonal targeting approach may be more acceptable. Overall, we find resistance based PFR an effective intervention to mitigate damages, however complementary strategies are required when managing extreme flooding.

Study region: A coupled framework, linking subsurface flow and surface hydrodynamics, is developed and applied to a real-world case study of Pingtung coastal aquifer in southwest of Taiwan, in East Asia. Study focus: FEFLOW is adopted to develop a 3-D variable density and transient groundwater model of the Pingtung shallow aquifer lying 250 m below mean sea level (MSL). This model is coupled with a 1-D river network model, comprised of the main river and its two tributaries, using MIKE 11 through the IFM MIKE 11 coupling interface. The model is capable of analysing the relationship between rainfall, surface water and groundwater recharge lag time. Also, the analysis of potential river inundation and maximum river discharge enable the model to choose the best location to apply artificial recharge as a management scenario to mitigate the effect of seawater intrusion (SWI). To the authors’ knowledge, the developed coupled model is the first detailed integrated framework analysing the interaction of surface and subsurface water, with the capability to contribute to the restoration, rehabilitation, and management of the river network. New hydrological insights for the region: The rainfall ratio in the wet season to dry season is significant in this plain comparing with the rest of Taiwan. Also, southern Taiwan experiences the largest sea and river interaction, while Kaoping River playing as a pathway role for inland lead of seawater intrusion.

Rapid population growth, urbanization and high-intensity human activities cause a multitude of extremely serious environmental problems all over the world [...]

COVID-19 CG (covidcg.org) is an open resource for tracking SARS-CoV-2 single-nucleotide variations (SNVs), lineages, and clades using the virus genomes on the GISAID database while filtering by location, date, gene, and mutation of interest. COVID-19 CG provides significant time, labor, and cost-saving utility to projects on SARS-CoV-2 transmission, evolution, diagnostics, therapeutics, vaccines, and intervention tracking. Here, we describe case studies in which users can interrogate (1) SNVs in the SARS-CoV-2 spike receptor binding domain (RBD) across different geographical regions to inform the design and testing of therapeutics, (2) SNVs that may impact the sensitivity of commonly used diagnostic primers, and (3) the emergence of a dominant lineage harboring an S477N RBD mutation in Australia in 2020. To accelerate COVID-19 efforts, COVID-19 CG will be upgraded with new features for users to rapidly pinpoint mutations as the virus evolves throughout the pandemic and in response to therapeutic and public health interventions.

Lactate is now recognized as an important intermediate in brain metabolism, but its role is still under investigation. In this work we mapped the distribution of lactate and bicarbonate produced from intravenously injected 13C-pyruvate over the whole brain using a new imaging method, hyperpolarized 13C MRI (N = 14, ages 23 to 77). Segmenting the 13C-lactate images into brain atlas regions revealed a pattern of lactate that was preserved across individuals. Higher lactate signal was observed in cortical grey matter compared to white matter and was highest in the precuneus, cuneus and lingual gyrus. Bicarbonate signal, indicating flux of [1–13C]pyruvate into the TCA cycle, also displayed consistent spatial distribution. One-way ANOVA to test for significant differences in lactate among atlas regions gave F = 87.6 and p

This paper aims to improve the understanding of environmental and socioeconomic drivers on land use change (LUC) through public participation (PP), and provide recommendations for long-term policy making to support sustainable land use (LU) management. PP was necessary to help understand and address the problem and concerns of stakeholders within the study area. Through two collaboration workshops seven individual future LU scenarios were created. Using the future land use simulation model, LU was projected up till 2060, after which logistic regression analysis took place to find the most significant driver. Results found that LUC within the baseline scenario and the ones chosen by stakeholders were very different, however concluded that Paddy field extent would decrease in the future to be replaced by more drought resilient agriculture; Perennials & Orchards and Field Crops. Outcomes from future scenarios propose that future LUC was driven by environment spatial factors such as elevation and climate, not soil suitability. With, first hand interviews suggesting it is indirect external factors such as, crop price that drive LUC. Overall the study provides steps towards dynamic LUC modelling where future scenarios have been tailored to details specified by the public through their participation.

TraR and its homolog DksA are bacterial proteins that regulate transcription initiation by binding directly to RNA polymerase (RNAP) rather than to promoter DNA. Effects of TraR mimic the combined effects of DksA and its cofactor ppGpp, but the structural basis for regulation by these factors remains unclear. Here, we use cryo-electron microscopy to determine structures of Escherichia coli RNAP, with or without TraR, and of an RNAP-promoter complex. TraR binding induced RNAP conformational changes not seen in previous crystallographic analyses, and a quantitative analysis revealed TraR-induced changes in RNAP conformational heterogeneity. These changes involve mobile regions of RNAP affecting promoter DNA interactions, including the βlobe, the clamp, the bridge helix, and several lineage-specific insertions. Using mutational approaches, we show that these structural changes, as well as effects on σ70 region 1.1, are critical for transcription activation or inhibition, depending on the kinetic features of regulated promoters.

Summary: The deep cerebellar nuclei (DCN) represent output channels of the cerebellum, and they transmit integrated sensorimotor signals to modulate limb movements. But the functional relevance of identifiable neuronal subpopulations within the DCN remains unclear. Here, we examine a genetically tractable population of neurons in the mouse interposed anterior nucleus (IntA). We show that these neurons represent a subset of glutamatergic neurons in the IntA and constitute a specific element of an internal feedback circuit within the cerebellar cortex and cerebello-thalamo-cortical pathway associated with limb control. Ablation and optogenetic stimulation of these neurons disrupt efficacy of skilled reach and locomotor movement and reveal that they control positioning and timing of the forelimb and hindlimb. Together, our findings uncover the function of a distinct neuronal subpopulation in the deep cerebellum and delineate the anatomical substrates and kinematic parameters through which it modulates precision of discrete and rhythmic limb movements. : The deep cerebellar nuclei transmit integrated sensorimotor signals to modify movement. Using a urocortin 3::Cre mouse line, Low et al. identify a subpopulation of glutamatergic neurons in the interposed anterior nucleus required for specific kinematic parameters of rhythmic and discrete movement via connectivity with forelimb-associated nucleocortical and nucleofugal pathways. Keywords: cerebellum, deep cerebellar nuclei, DCN, interposed anterior nucleus, IntA, skilled reach, locomotion, urocortin 3, Ucn3, efference copy

Alterations in metabolism following radiotherapy affect therapeutic efficacy, although the mechanism underlying such alterations is unclear. A new imaging technique—named dynamic nuclear polarization (DNP) carbon-13 magnetic resonance imaging (MRI)—probes the glycolytic flux in a real-time, dynamic manner. The [1-13C]pyruvate is transported by the monocarboxylate transporter (MCT) into cells and converted into [1-13C]lactate by lactate dehydrogenase (LDH). To capture the early glycolytic alterations in the irradiated cancer and immune cells, we designed a preliminary DNP 13C-MRI study by using hyperpolarized [1-13C]pyruvate to study human FaDu squamous carcinoma cells, HMC3 microglial cells, and THP-1 monocytes before and after irradiation. The pyruvate-to-lactate conversion rate (kPL [Pyr.]) calculated by kinetic modeling was used to evaluate the metabolic alterations. Western blotting was performed to assess the expressions of LDHA, LDHB, MCT1, and MCT4 proteins. Following irradiation, the pyruvate-to-lactate conversion rates on DNP 13C-MRI were significantly decreased in the FaDu and the HMC3 cells but increased in the THP-1 cells. Western blot analysis confirmed the similar trends in LDHA and LDHB expression levels. In conclusion, DNP 13C-MRI non-invasively captured the different glycolytic alterations among cancer and immune systems in response to irradiation, implying its potential for clinical use in the future.

How transcription factors (TFs) reprogram one cell lineage to another remains unclear. Here, we define chromatin accessibility changes induced by the proneural TF Ascl1 throughout conversion of fibroblasts into induced neuronal (iN) cells. Thousands of genomic loci are affected as early as 12 hr after Ascl1 induction. Surprisingly, over 80% of the accessibility changes occur between days 2 and 5 of the 3-week reprogramming process. This chromatin switch coincides with robust activation of endogenous neuronal TFs and nucleosome phasing of neuronal promoters and enhancers. Subsequent morphological and functional maturation of iN cells is accomplished with relatively little chromatin reconfiguration. By integrating chromatin accessibility and transcriptome changes, we built a network model of dynamic TF regulation during iN cell reprogramming and identified Zfp238, Sox8, and Dlx3 as key TFs downstream of Ascl1. These results reveal a singular, coordinated epigenomic switch during direct reprogramming, in contrast to stepwise cell fate transitions in development.

Purpose: To compare the effect of 12 versus 18 Gy cranial radiation therapy (RT) on height and weight indices among pediatric patients with acute lymphoblastic leukemia (ALL). Methods and materials: Records of children with ALL who were 2 to 14 years old at the time of RT and were treated at a single institution between 2000 and 2011 were reviewed. Patients' height, weight, and body mass index were converted into z-scores using the Centers for Disease Control growth charts to normalize the values to number of standard deviations from the mean. These values were measured at the pre-RT clinic visit and subsequent yearly intervals. The z-scores of the growth indices were fitted into a generalizing estimating equations model and analyzed by various clinical factors. Results: A total of 48 patients met the study criteria, including 32 boys and 16 girls. The median age at the time of RT was 7 years (range, 2-14 years). Patients were separated into 2 dose groups: 12 Gy (n = 30) and 18 Gy (n = 18). Median follow-up was 4.9 years (range, 3.0-11.8 years) and 6.0 years (range, 3.1-10.5 years) and the median pre-RT height z-scores were −0.55 (range, −2.2 to 1.4) and −0.85 (range, −3.1 to 0.8) for the 2 groups, respectively (P = .65). Patients who received 18 Gy had a significant difference in change in height compared with those who received 12 Gy, who were able to maintain normal growth during the first 3 years of follow-up. This did not appear to be sex-specific, and there was no difference in change in weight or body mass index. Conclusions: Compared with 18 Gy, patients with ALL who received 12 Gy of cranial RT had less height impairment in the first 3 years post-RT, but further prospective studies are needed.

Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization by using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from 13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net.

AirSWOT is an experimental airborne Ka-band radar interferometer developed by NASA-JPL as a validation instrument for the forthcoming NASA Surface Water and Ocean Topography (SWOT) satellite mission. In 2017, AirSWOT was deployed as part of the NASA Arctic Boreal Vulnerability Experiment (ABoVE) to map surface water elevations across Alaska and western Canada. The result is the most extensive known collection of near-nadir airborne Ka-band interferometric synthetic aperture radar (InSAR) data and derivative high-resolution (3.6 m pixel) digital elevation models to produce water surface elevation (WSE) maps. This research provides a synoptic assessment of the 2017 AirSWOT ABoVE dataset to quantify regional WSE errors relative to coincident in situ field surveys and LiDAR data acquired from the NASA Land, Vegetation, and Ice Sensor (LVIS) airborne platform. Results show that AirSWOT WSE data can penetrate cloud cover and have nearly twice the swath-width of LVIS as flown for ABoVE (3.2 km vs. 1.8 km nominal swath-width). Despite noise and biases, spatially averaged AirSWOT WSEs can be used to estimate sub-seasonal hydrologic variability, as confirmed with field GPS surveys and in situ pressure transducers. This analysis informs AirSWOT ABoVE data users of known sources of measurement error in the WSEs as influenced by radar parameters including incidence angle, magnitude, coherence, and elevation uncertainty. The analysis also provides recommended best practices for extracting information from the dataset by using filters for these four parameters. Improvements to data handing would significantly increase the accuracy and spatial coverage of future AirSWOT WSE data collections, aiding scientific surface water studies, and improving the platform’s capability as an airborne validation instrument for SWOT.

GABAergic inhibitory neurons in the cerebellum are subdivided into Purkinje cells and distinct subtypes of interneurons from the same pool of progenitors, but the determinants of this diversification process are not well defined. To explore the transcriptional regulation of the development of cerebellar inhibitory neurons, we examined the role of Tfap2A and Tfap2B in the specification of GABAergic neuronal subtypes in mice. We show that Tfap2A and Tfap2B are expressed in inhibitory precursors during embryonic development and that their expression persists into adulthood. The onset of their expression follows Ptf1a and Olig2, key determinants of GABAergic neuronal fate in the cerebellum; and, their expression precedes Pax2, an interneuron-specific factor. Tfap2A is expressed by all GABAergic neurons, whereas Tfap2B is selectively expressed by interneurons. Genetic manipulation via in utero electroporation (IUE) reveals that Tfap2B is necessary for interneuron specification and is capable of suppressing the generation of excitatory cells. Tfap2A, but not Tfap2B, is capable of inducing the generation of interneurons when misexpressed in the ventricular neuroepithelium. Together, our results demonstrate that the differential expression of Tfap2A and Tfap2B defines subtypes of GABAergic neurons and plays specific, but complementary roles in the specification of interneurons in the developing cerebellum.

Osteochondral allograft (OCA) transplantation is generally effective for treating large cartilage lesions. Cleansing OCA subchondral bone to remove donor marrow elements is typically performed with pulsed lavage. However, the effects of clinical and experimental parameters on OCA marrow removal by pulsed lavage are unknown. The aim of the current study was to determine the effects on marrow cleansing in human osteochondral cores (OCs) of (1) lavage duration, (2) lavage flow intensity, and (3) OC sample type and storage condition.OCs were harvested from human femoral condyles and prepared to a clinical geometry (cylinder, diameter = 20 mm). The OCs were from discarded remnants of Allograft tissues (OCA) or osteoarthritis patients undergoing Total Knee Replacement (OCT). The experimental groups subjected to standard flow lavage for 45 seconds (430 mL of fluid) and 120 seconds (1,150 mL) were (1) OCT/FROZEN (stored at -80°C), (2) OCT/FRESH (stored at 4°C), and (3) OCA/FRESH. The OCA/FRESH group was subsequently lavaged at high flow for 45 seconds (660 mL) and 120 seconds (1,750 mL). Marrow cleansing was assessed grossly and by micro-computed tomography (μCT).Gross and μCT images indicated that marrow cleansing progressed from the OC base toward the cartilage. Empty marrow volume fraction (EMa.V/Ma.V) increased between 0, 45, and 120 seconds of standard flow lavage, and varied between groups, being higher after FROZEN storage (86-92% after 45-120 seconds) than FRESH storage of either OCT or OCA samples (36% and 55% after 45 and 120 seconds, respectively). With a subsequent 120 seconds of high flow lavage, EMa.V/Ma.V of OCA/FRESH samples increased from 61% to 78%.The spatial and temporal pattern of marrow space clearance was consistent with gradual fluid-induced extrusion of marrow components. Pulsed lavage of OCAs with consistent time and flow intensity will help standardize marrow cleansing and may improve clinical outcomes.

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action.

Flood prevention in mixed urban–rural environments has become a greater concern due to climate change. It is a complex task requiring both efficient management of resources and the involvement of multiple stakeholders from diverse backgrounds. As Serious Games (games used for purposes other than mere entertainment) have emerged as an effective means of engaging stakeholders, this work proposes a new Serious Game applied to flood mitigation in the village of Millbrook in the UK. Results show that the game has both an informative and a transformative effect (statistical significance levels from 0.01 to 0.05), improving participants’ understanding of the problem, and helping them to find a new and improved approach to flood risk management in Millbrook, with the potential to improve resilience significantly. Furthermore, the game successfully transformed participants into “citizen scientists” in the purest sense of the term—it led them to use inductive reasoning from data produced by the game to correctly confirm or reject hypotheses and resulted in more than 70% of the participants revising their initial assumptions. Interestingly, the game instigated the formation of new local partnerships and helped to prioritize the discussion of natural flood management measures in Millbrook Parish Council meetings.

Impacts from floods in urban areas can be diverse and wide ranging. These can include the loss of human life, infrastructure and property damages, as well as other kinds of nuisance and inconvenience to urban life. Hence, the ability to identify and quantify wider ranging effects from floods is of the utmost importance to urban flood managers and infrastructure operators. The present work provides a contribution in this direction and describes a methodological framework for analysing cascading effects from floods that has been applied for the Sukhumvit area in Bangkok (Thailand). It demonstrates that the effects from floods can be much broader in their reach and magnitude than the sole impacts incurred from direct and immediate losses. In Sukhumvit, these include loss of critical services, assets and goods, traffic congestion and delays in transportation, loss of business and income, disturbances and discomfort to the residents, and all these can be traced with the careful analysis of cascading effects. The present work explored the use of different visualization options to present the findings. These include a casual loop diagram, a HAZUR resilience map, a tree diagram and GIS maps.