Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify ...robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies.
The agricultural sector uses the largest share of freshwater, accounting for over 70% of the global freshwater withdrawals, and this proportion can be up to 90% in arid and semiarid regions ...
Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use ...without evaluation and often without bias correction. The bias correction of SBP remains a challenge for atmospheric scientists. The present study evaluated the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GSMaP, CHIRPS, PERSIANN-CDS and PERSIANN-CSS, in replicating observed daily rainfall at 364 stations over Peninsular Malaysia. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the rainfall amount during rainfall events. Besides, the study evaluated the performance of different widely used ML algorithms for classification and regression to select the most suitable algorithms for bias correction. IMERG showed better performance, showing a higher correlation coefficient (R
2
) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the k-nearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount during rainfall events with the modified index of agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.
Regional climate change (CC) and land use changes (LUCs) can significantly influence the hydrological processes at watershed scale. Different studies have investigated the impact of climate change in ...the Indus Basin. However, there is a need to investigate the impact of environmental changes on the regional hydrology over a complex topographic region. This study quantitatively assesses the relative contributions of CC and LUC on runoff alterations across Gilgit watershed by using multivariable calibration approach using the Soil and Water Assessment Tool (SWAT). Mann–Kendall (MK) and Pettitt tests are applied to identify the trends and changes in runoff and climatic variables during 1985–2013. The supervised classification is performed to acquire land use maps and other quantitative details required for the analyses. Moreover, Indicators of Hydrologic Alterations (IHA) analyses were performed for the first time in the Gilgit watershed to investigate the impact of CC and LUCs during the pre- and post-impact periods. The results demonstrated that precipitation, temperature, and runoff of the Gilgit watershed presented significant increasing trends. The change point using Pettitt test is depicted in 1999, 1995, and 1998, respectively. The mean annual increasing rate of precipitation, temperature, and runoff is 4.92 mm/year, 0.04 °C/year, and 2.60 m
3
/year, respectively. SWAT model performed well and the relative attributed contribution of CC to runoff change is 97.22% and it is 2.78% for LUC. The IHA results showed that runoff has significantly increased in post-impact (1999–2013) as compared to pre-impact (1985–1998), which was further confirmed by analyzing the IHA results using percent bias (PBIAS). Significant overestimation of runoff (higher runoff in post-impact period) was observed in the wet (maximum runoff) season. This study demonstrated that the high contribution of CC to runoff change is mainly due to the change in climate variables and global warming trends.
The main objective of this study is the flood modeling of the River Jhelum Basin in the Jammu and Kashmir using the Integrated Flood Analysis System (IFAS) hydrological model. The region having an ...area of 33,000 km
2
has a limited rain gauge coverage. The data inaccessibility is accentuated due to the mountainous topography, glaciers, and remote areas. This research makes innovative use of the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) dataset to overcome the data scarcity for the basin. For flood modeling, a regionalized approach was adopted, whereby the four main tributaries of the River Jhelum Basin were separately modeled. The parameters, thus, determined, were later used in the modeling of the River Jhelum, the main river of the basin. The model was calibrated on the medium-high flood of 2010 and validated on the very high flood of 2014, obtaining good results as indicated by Nash–Sutcliffe Model Efficiency Coefficient (NSE) of 0.77. Later, the model was executed using the Global Satellite Mapping of Precipitation (GSMaP). It performed adequately for the high flood event of 2014, while for the flood of 2010, it gave poor results. Besides, this study also demonstrates the efficacy of a new generation of computer models that work by accessing the free global datasets available on the web for hydrological modeling.
Climatic variability and the quantification of climate change impacts on hydrological parameters are persistently uncertain. Remote sensing aids valuable information to streamflow estimations and ...hydrological parameter projections. However, few studies have been implemented using remote sensing and CMIP6 data embedded with hydrological modeling. This research studied how changing climate influences the hydro-climatic parameters based on the earth system models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). GRACE evapotranspiration data were forced into the Soil and Water Assessment Tool (SWAT) to project hydrologic responses to future climatic conditions in the Hongshui River basin (HRB) model. A novel approach based on climate elasticity was utilized to determine the extent to which climate variability affects stream flow. CMIP6 SSPs (shared socio-economic pathways) for the second half of the 20th century (1960–2020) and 21st century (2021–2100) projected precipitation (5–16%) for the whole Hongshui River basin (HRB). The ensemble of GCMs projected an increase of 2 °C in mean temperature. The stream flow is projected to increase by 4.2% under SSP-1.26, 6.2% under SSP-2.45, 8.45% under SSP-3.70, and 9.5% under SSP-5.85, based on the average changes throughout the various long-term future scenarios. We used the climate elasticity method and found that climate change contributes 11% to streamflow variability in the Hongshui River basin (HRB). Despite the uncertainty in projected hydrological variables, most members of the modeling ensemble present encouraging findings for future methods of water resource management.
Aerobic glycolysis supports proliferation through unresolved mechanisms. We have previously shown that aerobic glycolysis is required for the regulated proliferation of cerebellar granule neuron ...progenitors (CGNP) and for the growth of CGNP-derived medulloblastoma. Blocking the initiation of glycolysis via deletion of
(
) disrupts CGNP proliferation and restricts medulloblastoma growth. Here, we assessed whether disrupting
(
), an enzyme that acts in the terminal steps of glycolysis, would alter CGNP metabolism, proliferation, and tumorigenesis. We observed a dichotomous pattern of PKM expression, in which postmitotic neurons throughout the brain expressed the constitutively active PKM1 isoform, while neural progenitors and medulloblastomas exclusively expressed the less active PKM2. Isoform-specific
deletion in CGNPs blocked all
expression.
-deleted CGNPs showed reduced lactate production and increased SHH-driven proliferation.
C-flux analysis showed that
deletion reduced the flow of glucose carbons into lactate and glutamate without markedly increasing glucose-to-ribose flux.
deletion accelerated tumor formation in medulloblastoma-prone
mice, indicating the disrupting PKM releases CGNPs from a tumor-suppressive effect. These findings show that distal and proximal disruptions of glycolysis have opposite effects on proliferation, and that efforts to block the oncogenic effect of aerobic glycolysis must target reactions upstream of PKM.
.
Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization's 2022 worldwide status report on road ...safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver's impairment level by monitoring the driver's blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model.
The robustness of brain structural networks, estimated from diffusion MRI data, may be relevant to cognition. We investigate whether measures of network robustness, such as
, can explain cognitive ...impairment in multiple sclerosis (MS). We assessed whether local (i.e., cortical area) and/or global (i.e., whole brain) robustness, differs between cognitively impaired (MSCI) and non-impaired (MSNI) MS patients. Fifty patients, with Expanded Disability Status Scale mean (m): 3.2, disease duration m: 12 years, and age m: 40 years, were enrolled. Cognitive impairment scores were estimated from the Minimal Assessment of Cognitive Function in Multiple Sclerosis. Images were obtained in a 3T MRI using a diffusion protocol with a 2 min acquisition time. Brain structural networks were created using 333 cortical areas. Local and global robustness was estimated for each individual, and comparisons were performed between MSCI and MSNI patients. 31 MSCI and 10 MSNI patients were included in the analyses. Brain structural network robustness and centrality showed significant correlations with cognitive impairment. Measures of network robustness and centrality identified specific cortical areas relevant to MS-related cognitive impairment. These measures can be obtained on clinical scanners and are succinct yet accurate potential biomarkers of cognitive impairment.
Dam break flows have been a focus of attention for a long in the hydraulic engineering discipline. The interest stems from their high hazard potential to human life and property. This research ...investigates a laboratory dam break flow using a 3D Reynolds-averaged Navier–Stokes (RANS) and a 2D shallow water Eq. (2D SWE) model. The two numerical models have been compared to measured data regarding flow depth, streamwise surface flow velocity, and point velocity. The results show that as far as a simulation of water depths at gauging stations is concerned, both models are almost at par with an error of about 20% of the mean depth value. Further, the comparison of surface velocity field reveals that though both models are capable of reproducing the salient flow features, e.g., oblique hydraulic jump, wake, etc., the 3D RANS model is more accurate in predicting the jump length and the so-called wet–dry fronts. The important contribution of this research lies in clearly enunciating the tradeoffs between a 2D SWE model and a 3D RANS model. The former model is computationally efficient and accurate as far as flow depth and velocity are concerned while the latter model provides a more complete result output, e.g., vertical flow velocity, turbulent kinetic energy and turbulent dissipation, etc. However, for obtaining significant accuracy with a 3D model a more accurate turbulence model than RANS is required which necessitate usage of very refined mesh (~ mm), thus, raising the cost of the simulation manifold.
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