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During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human–water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated with potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a multi-objective evolutionary algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing model that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC – with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996–2005), a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration, and baseflow. To further demonstrate the risks associated with the use of such a model, we carry out a climate change impact assessment (for the period 2050–2060), for which we use precipitation and temperature data retrieved from five global circulation models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human–water interactions in large-scale hydrological models.

Long-term planning of low-carbon electricity systems in the Global South involves deeply uncertain infrastructure investments, often undertaken independently from neighboring energy systems. In principle, national grids benefit from greater regional integration, but the nature of these benefits is sensitive to techno-economic uncertainty and natural resource distribution. We examine the value of regional electricity coordination for a South American subregion with abundant, geographically variable renewable resources, under stringent emissions reduction targets and a range of techno-economic assumptions. Results show that decarbonization is achievable with modest cost premiums, which are further mitigated by international coordination. Differences in the deployment of renewables across scenarios tend to offset at the regional level; however, variability at the country level suggests that national decarbonization pathways are sensitive to technology characteristics. Achieving mitigation goals without coordination requires additional generation capacity, at more than triple the added cost of coordinated planning scenarios (14.7-22.8 vs. 3.5-7.0 billion USD). Beyond South America, these results are relevant to regions looking to meet emissions targets through greater international cooperation. The authors model regional electricity grid coordination, which enables access to geographically dispersed resources. Results suggest grid integration can reduce planning uncertainty region-wide but may impact individual countries differently.

Strategic dam planning and the deployment of decentralized renewable technologies are two elements of the same problem, yet normally addressed in isolation. Here, we show that an integrated view of the power system capacity expansion problem could have transformative effects for Southeast Asia’s hydropower plans. We demonstrate that Thailand, Laos, and Cambodia have tangible opportunities for meeting projected electricity demand and CO 2 emission targets with less hydropower than currently planned—options range from halting the construction of all dams in the Lower Mekong to building 82% of the planned ones. The key enabling strategies for these options to succeed are solar PV and regional coordination, expressed in the form of centralized planning and cross-border power trading. The alternative expansion plans would slightly increase the cumulative costs (up to 2.4%), but substantially limit the fragmentation of additional river reaches, thereby offering more sustainable pathways for the Mekong’s ecosystems and riparian people. Hydropower dams in the Lower Mekong basin have profound impact on the riverine ecosystems. Here the authors use strategic dam planning and power system modelling to show that there are economically and technically feasible alternatives to these dams with solar energy and power trading.

The scale at which low-carbon electricity will need to be deployed to meet economic growth, electrification, and climate goals in Africa is unprecedented, yet the potential land use and freshwater impacts from this massive build-out of energy infrastructure is poorly understood. In this study, we characterize low-impact onshore wind, solar photovoltaics, and hydropower potential in Southern Africa and identify the cost-optimal mix of electricity generation technologies under different sets of socio-environmental land use and freshwater constraints and carbon targets. We find substantial wind and solar potential after applying land use protections, but about 40% of planned or proposed hydropower projects face socio-environmental conflicts. Applying land and freshwater protections results in more wind, solar, and battery capacity and less hydropower capacity compared to scenarios without protections. While a carbon target favors hydropower, the amount of cost-competitively selected hydropower is at most 45% of planned or proposed hydropower capacity in any scenario—and is only 25% under socio-environmental protections. Achieving both carbon targets and socio-environmental protections results in system cost increases of 3-6%. In the absence of land and freshwater protections, environmental and social impacts from new hydropower development could be significant. Avoiding the most damaging land use and freshwater impacts of solar PV, wind, and hydropower development while halving carbon emissions by 2040 in the Southern Africa region is not only possible but incurs only modest (3-6%) system cost increases.

Pathological growth of cardiomyocytes (hypertrophy) is a major determinant for the development of heart failure, one of the leading medical causes of mortality worldwide. Here we show that the microRNA (miRNA)-212/132 family regulates cardiac hypertrophy and autophagy in cardiomyocytes. Hypertrophic stimuli upregulate cardiomyocyte expression of miR-212 and miR-132, which are both necessary and sufficient to drive the hypertrophic growth of cardiomyocytes. MiR-212/132 null mice are protected from pressure-overload-induced heart failure, whereas cardiomyocyte-specific overexpression of the miR-212/132 family leads to pathological cardiac hypertrophy, heart failure and death in mice. Both miR-212 and miR-132 directly target the anti-hypertrophic and pro-autophagic FoxO3 transcription factor and overexpression of these miRNAs leads to hyperactivation of pro-hypertrophic calcineurin/NFAT signalling and an impaired autophagic response upon starvation. Pharmacological inhibition of miR-132 by antagomir injection rescues cardiac hypertrophy and heart failure in mice, offering a possible therapeutic approach for cardiac failure. Heart failure is often a consequence of pathological growth of cardiomyocytes or cardiac hypertrophy. Here Ucar and colleagues report that the microRNAs miR-132 and miR-212 promote cardiac hypertrophy and inhibit autophagy in cardiomyocytes by downregulating the transcription factor FoxO3.

Healthcare workers (HCWs) are at increased risk of tuberculosis infection (TBI). We estimated the prevalence and incidence of TBI and risk factors among HCWs in Bangladeshi hospitals to target TB infection prevention and control (IPC) interventions. During 2013-2016, we conducted a longitudinal study among HCWs in four chest disease hospitals. At baseline, we administered a questionnaire on sociodemographic and occupational factors for TB, tuberculin skin tests (TST) in all hospitals, and QuantiFERON ®-TB Gold in-Tube (QFT-GIT) tests in one hospital. We assessed factors associated with baseline TST positivity (induration ≥10mm), TST conversion (induration increase ≥10mm from baseline), baseline QFT-GIT positivity (interferon-gamma ≥0.35 IU/mL), and QFT-GIT conversion (interferon-gamma <0.35 IU/mL to ≥0.35 IU/mL). We included factors with a biologically plausible relationship with TBI identified in prior studies or having an association (p = <0.20) in the bivariate analyses with TST positivity or QFT-GIT positivity in multivariable generalized linear models. The Kaplan-Meier was used to estimate the cumulative TBI incidence rate per 100 person-years. Of the 758 HCWs invited, 732 (97%) consented to participate and 731 completed the one-step TST, 40% had a positive TST result, and 48% had a positive QFT-GIT result. In multivariable models, HCWs years of service 11-20 years had 2.1 (95% CI: 1.5-3.0) times higher odds of being TST-positive and 1.6 (95% CI 1.1-2.5) times higher odds of QFT-GIT-positivity at baseline compared with those working ≤10 years. HCWs working 11-20 years in pulmonary TB ward had 2.0 (95% CI: 1.4-2.9) times higher odds of TST positivity, and those >20 years had 2.5 (95% CI: 1.3-4.9) times higher odds of QFT-GIT-positivity at baseline compared with those working <10 years. TBI incidence was 4.8/100 person-years by TST and 4.2/100 person-years by QFT-GIT. Females had 8.5 (95% CI: 1.5-49.5) times higher odds of TST conversion than males. Prevalent TST and QFT-GIT positivity was associated with an increased number of years working as a healthcare worker and in pulmonary TB wards. The incidence of TBI among HCWs suggests ongoing TB exposure in these facilities and an urgent need for improved TB IPC in chest disease hospitals in Bangladesh.

Wound healing is a complex process that is characterized by an initial inflammatory phase followed by a proliferative phase. This transition is a critical regulatory point; however, the factors that mediate this process are not fully understood. Here, we evaluated microRNAs (miRs) in skin wound healing and characterized the dynamic change of the miRNome in human skin wounds. miR-132 was highly upregulated during the inflammatory phase of wound repair, predominantly expressed in epidermal keratinocytes, and peaked in the subsequent proliferative phase. TGF-β1 and TGF-β2 induced miR-132 expression in keratinocytes, and transcriptome analysis of these cells revealed that miR-132 regulates a large number of immune response- and cell cycle-related genes. In keratinocytes, miR-132 decreased the production of chemokines and the capability to attract leukocytes by suppressing the NF-κB pathway. Conversely, miR-132 increased activity of the STAT3 and ERK pathways, thereby promoting keratinocyte growth. Silencing of the miR-132 target heparin-binding EGF-like growth factor (HB-EGF) phenocopied miR-132 overexpression in keratinocytes. Using mouse and human ex vivo wound models, we found that miR-132 blockade delayed healing, which was accompanied by severe inflammation and deficient keratinocyte proliferation. Together, our results indicate that miR-132 is a critical regulator of skin wound healing that facilitates the transition from the inflammatory to the proliferative phase.

Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.

Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

The identification of the large protein Ambra-1, which regulates autophagy and plays a crucial role in embryogenesis is described. The absence of Ambra-1 function during development results in severe neural tube defects associated with autophagy impairment, accumulation of ubiquitinated proteins, unbalanced cell proliferation and excessive apoptotic cell death. Autophagy is a self-degradative process involved both in basal turnover of cellular components and in response to nutrient starvation or organelle damage in a wide range of eukaryotes 1 , 2 , 3 . During autophagy, portions of the cytoplasm are sequestered by double-membraned vesicles called autophagosomes, and are degraded after fusion with lysosomes for subsequent recycling 4 . In vertebrates, this process acts as a pro-survival or pro-death mechanism in different physiological and pathological conditions, such as neurodegeneration and cancer 2 , 5 , 6 , 7 ; however, the roles of autophagy during embryonic development are still largely uncharacterized 3 . Beclin1 (Becn1; coiled-coil, myosin-like BCL2-interacting protein) is a principal regulator in autophagosome formation, and its deficiency results in early embryonic lethality 8 , 9 . Here we show that Ambra1 (activating molecule in Beclin1-regulated autophagy), a large, previously unknown protein bearing a WD40 domain at its amino terminus, regulates autophagy and has a crucial role in embryogenesis. We found that Ambra1 is a positive regulator of the Becn1-dependent programme of autophagy, as revealed by its overexpression and by RNA interference experiments in vitro . Notably, Ambra1 functional deficiency in mouse embryos leads to severe neural tube defects associated with autophagy impairment, accumulation of ubiquitinated proteins, unbalanced cell proliferation and excessive apoptotic cell death. In addition to identifying a new and essential element regulating the autophagy programme, our results provide in vivo evidence supporting the existence of a complex interplay between autophagy, cell growth and cell death required for neural development in mammals.