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This study aims to access the selected satellite remote sensing, climate reanalysis, and land surface models to estimate monthly land surface air temperature (LSAT), solar radiation (SR), and precipitation (P) at the global scale. To this end, we apply six datasets including Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2), European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis-version 5 (ERA-5), ERA-5-Land version (ERA5-Land), Global Land Data Assimilation System (GLDAS), Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FL and Global Precipitation Climatology Project (GPCP). In terms of SR, we compare the selected products against the National Oceanic and Atmospheric Administration (NOAA)-Cooperative Institute for Research in Environmental Sciences (CIRES)-Department of Energy (DOE) Twentieth Century Reanalysis (20CR) (NOAA-CIRES-DOE 20CR) dataset from 1982 to 2015. For LSAT and P, we consider NOAA Climate Prediction Center (CPC) (NOAA-CPC) as the reference dataset in the periods of 1982–2020 and 1983–2019, respectively, based on available data. ERA5-Land, MERRA-2, and GLDAS show the best results with root mean square difference (RMSD) equal to 19.03 W/m2, 1.93 °C, and 37.61 mm/month for SR, LSAT, and P estimates compared to NOAA datasets. Since there are uncertainties in all of the products, here we introduce new datasets based on merging the best products concerning their accuracy. The evaluation results can be used also as feedback to developers to improve the products and to facilitate the users to understand the status of the products and better use them for practical applications on a global scale.

A dark-matter filament connecting the galaxy clusters Abell 222 and Abell 223 has been detected from its weak gravitational lensing signal. Shedding light on dark matter More than half of the cold dark matter in the Universe is thought to exist outside galaxy clusters, connecting them in a filamentous network known as the cosmic web. Dark matter has been 'observed' in galaxy clusters as the deflection of light in its gravitational field, but cosmic-web dark matter has evaded detection until now. This paper presents a direct detection of a dark-matter filament connecting the two main components of the Abell 222/223 supercluster system from its weak gravitational lensing of light from faint background galaxies. The estimated mass of this filament is consistent with models that assume galactic overdensity caused by invisible dark matter. It is a firm prediction of the concordance cold-dark-matter cosmological model that galaxy clusters occur at the intersection of large-scale structure filaments 1 . The thread-like structure of this ‘cosmic web’ has been traced by galaxy redshift surveys for decades 2 , 3 . More recently, the warm–hot intergalactic medium (a sparse plasma with temperatures of 10 5 kelvin to 10 7 kelvin) residing in low-redshift filaments has been observed in emission 4 and absorption 5 , 6 . However, a reliable direct detection of the underlying dark-matter skeleton, which should contain more than half of all matter 7 , has remained elusive, because earlier candidates for such detections 8 , 9 , 10 were either falsified 11 , 12 or suffered from low signal-to-noise ratios 8 , 10 and unphysical misalignments of dark and luminous matter 9 , 10 . Here we report the detection of a dark-matter filament connecting the two main components of the Abell 222/223 supercluster system from its weak gravitational lensing signal, both in a non-parametric mass reconstruction and in parametric model fits. This filament is coincident with an overdensity of galaxies 10 , 13 and diffuse, soft-X-ray emission 4 , and contributes a mass comparable to that of an additional galaxy cluster to the total mass of the supercluster. By combining this result with X-ray observations 4 , we can place an upper limit of 0.09 on the hot gas fraction (the mass of X-ray-emitting gas divided by the total mass) in the filament.

Inhibitors of the mutant isocitrate dehydrogenase 1 ( IDH1 ) entered recently in clinical trials for glioma treatment. Mutant IDH1 produces high levels of 2-hydroxyglurate (2HG), thought to initiate oncogenesis through epigenetic modifications of gene expression. In this study, we show the initial evidence of the pharmacodynamics of a new mutant IDH1 inhibitor in glioma patients, using non-invasive 3D MR spectroscopic imaging of 2HG. Our results from a Phase 1 clinical trial indicate a rapid decrease of 2HG levels by 70% (CI 13%, P  = 0.019) after 1 week of treatment. Importantly, inhibition of mutant IDH1 may lead to the reprogramming of tumor metabolism, suggested by simultaneous changes in glutathione, glutamine, glutamate, and lactate. An inverse correlation between metabolic changes and diffusion MRI indicates an effect on the tumor-cell density. We demonstrate a feasible radiopharmacodynamics approach to support the rapid clinical translation of rationally designed drugs targeting IDH1/2 mutations for personalized and precision medicine of glioma patients. Inhibitors of mutant isocitrate dehydrogenase 1 (IDH1) entered recently clinical trials for treatment of gliomas. Here, the authors apply a MRS imaging method for 2HG detection and assessement of the pharmacodynamic effects of the mutant IDH1 inhibitor (IDH305) in 8 mutant IDH1 glioma patients.

Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps. Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm3 isotropic resolution with acquisition times between 4:11–9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to iterative compressed sensing Total Generalized Variation reconstruction using image and spectral quality metrics. Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial–spectral quality and metabolite quantification with 12%–45% (P<0.05) higher signal-to-noise and 8%–50% (P<0.05) smaller Cramer–Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Deep-ER generalizes reliably to unseen data. Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine. •Acceleration of 3D MRSI acquisition by compressed sense non-cartesian encoding.•Clinically feasible high-resolution metabolic imaging over the whole brain.•End-to-end automated processing pipeline that integrates Deep Learning image reconstruction.•Physics-based model provides high-quality metabolic maps with fast processing times.•High-throughput metabolic imaging that is compatible with radiological workflow.

Since ancient times, dams have been built to store water, control rivers, and irrigate agricultural land to meet human needs. By the end of the 19th century, hydroelectric power stations arose and extended the purposes of dams. Today, dams can be seen as part of the renewable energy supply infrastructure. The word dam comes from French and is defined in dictionaries using words like strange, dike, and obstacle. In other words, a dam is a structure that stores water and directs it to the desired location, with a dam being built in front of river valleys. Dams built on rivers serve various purposes such as the supply of drinking water, agricultural irrigation, flood control, the supply of industrial water, power generation, recreation, the movement control of solids, and fisheries. Dams can also be built in a catchment area to capture and store the rainwater in arid and semi-arid areas. Dams can be built from concrete or natural materials such as earth and rock. There are various types of dams: embankment dams (earth-fill dams, rock-fill dams, and rock-fill dams with concrete faces) and rigid dams (gravity dams, rolled compacted concrete dams, arch dams, and buttress dams). A gravity dam is a straight wall of stone masonry or earthen material that can withstand the full force of the water pressure. In other words, the pressure of the water transfers the vertical compressive forces and horizontal shear forces to the foundations beneath the dam. The strength of a gravity dam ultimately depends on its weight and the strength of its foundations. Most dams built in ancient times were constructed as gravity dams. An arch dam, on the other hand, has a convex curved surface that faces the water. The forces generated by the water pressure are transferred to the sides of the structure by horizontal lines. The horizontal, normal, and shear forces resist the weight at the edges. When viewed in a horizontal section, an arch dam has a curved shape. This type of dam can also resist water pressure due to its particular shape that allows the transfer of the forces generated by the stored water to the rock foundations. This article takes a detailed look at hydraulic engineering in dams over the millennia. Lessons should be learned from the successful and unsuccessful applications and operations of dams. Water resource managers, policymakers, and stakeholders can use these lessons to achieve sustainable development goals in times of climate change and water crisis.

Radiation necrosis, also known as treatment-induced necrosis, has emerged as an important adverse effect following stereotactic radiotherapy (SRS) for brain metastases. The improved survival of patients with brain metastases and increased use of combined systemic therapy and SRS have contributed to a growing incidence of necrosis. The cyclic GMP-AMP (cGAMP) synthase (cGAS) and stimulator of interferon genes (STING) pathway (cGAS-STING) represents a key biological mechanism linking radiation-induced DNA damage to pro-inflammatory effects and innate immunity. By recognizing cytosolic double-stranded DNA, cGAS induces a signaling cascade that results in the upregulation of type 1 interferons and dendritic cell activation. This pathway could play a key role in the pathogenesis of necrosis and provides attractive targets for therapeutic development. Immunotherapy and other novel systemic agents may potentiate activation of cGAS-STING signaling following radiotherapy and increase necrosis risk. Advancements in dosimetric strategies, novel imaging modalities, artificial intelligence, and circulating biomarkers could improve the management of necrosis. This review provides new insights into the pathophysiology of necrosis and synthesizes our current understanding regarding the diagnosis, risk factors, and management options of necrosis while highlighting novel avenues for discovery.

Antiangiogenic therapy has shown clear activity and improved survival benefit for certain tumor types. However, an incomplete understanding of the mechanisms of action of antiangiogenic agents has hindered optimization and broader application of this new therapeutic modality. In particular, the impact of antiangiogenic therapy on tumor blood flow and oxygenation status (i.e., the role of vessel pruning versus normalization) remains controversial. This controversy has become critical as multiple phase III trials of anti-VEGF agents combined with cytotoxics failed to show overall survival benefit in newly diagnosed glioblastoma (nGBM) patients and several other cancers. Here, we shed light on mechanisms of nGBM response to cediranib, a pan-VEGF receptor tyrosine kinase inhibitor, using MRI techniques and blood biomarkers in prospective phase II clinical trials of cediranib with chemoradiation vs. chemoradiation alone in nGBM patients. We demonstrate that improved perfusion occurs only in a subset of patients in cediranib-containing regimens, and is associated with improved overall survival in these nGBM patients. Moreover, an increase in perfusion is associated with improved tumor oxygenation status as well as with pharmacodynamic biomarkers, such as changes in plasma placenta growth factor and sVEGFR2. Finally, treatment resistance was associated with elevated plasma IL-8 and sVEGFR1 posttherapy. In conclusion, tumor perfusion changes after antiangiogenic therapy may distinguish responders vs. nonresponders early in the course of this expensive and potentially toxic form of therapy, and these results may provide new insight into the selection of glioblastoma patients most likely to benefit from anti-VEGF treatments.

Multimodality therapy, including surgery, radiotherapy, and systemic therapy, has significantly improved overall survival for patients with brain metastases. However, treatment-related neurocognitive sequelae remain a major challenge in survivorship. Although advances in radiotherapy delivery techniques have reduced toxicity, the potential interaction with chemotherapy, targeted therapy, and immunotherapy, and the consequent effect on neurocognitive outcomes is poorly characterised. We conducted a systematic review of clinical trials reporting neurocognitive endpoints in patients with brain metastases receiving radiotherapy with or without other concurrent systemic therapies. Neurocognitive outcomes were manually extracted from published reports. 39 studies from 1997 to 2024 involving 6617 patients met inclusion criteria (n=27 whole-brain radiotherapy; n=12 radiosurgery), including six studies evaluating combined-modality therapy. Baseline neurocognitive disability was frequently observed, and the majority of randomised trials evaluating advanced radiotherapy delivery techniques (hippocampal avoidance and radiosurgery) compared with whole-brain radiotherapy reported reduced cognitive decline and improved quality of life. There was no signal for increased toxicity with combined-modality therapy, including radiotherapy with concurrent systemic therapy, although evaluable trials were few in number. Given improvements in survival for patients with brain metastases, characterisation of long-term neurocognitive outcomes is growing in importance. There is an urgent need for targeted research to resolve evidence gaps around modality-specific neurocognitive toxicity and optimal sequencing of therapies. Systemic issues, such as integration of routine neuropsychological screening or assessment and incorporation of rehabilitation strategies into neuro-oncology care pathways, warrant evaluation. Exploration of emerging strategies, ranging from neuroprotectants to dose-sparing radiotherapy techniques, could further mitigate long-term adverse effects.

CAR-T cell therapy is an effective cancer therapy for multiple refractory/relapsed hematologic malignancies but is associated with substantial toxicity, including Immune Effector Cell Associated Neurotoxicity Syndrome (ICANS). Improved detection and assessment of ICANS could improve management and allow greater utilization of CAR-T cell therapy, however, an objective, specific biomarker has not been identified. We hypothesized that the severity of ICANS can be quantified based on patterns of abnormal brain activity seen in electroencephalography (EEG) signals. We conducted a retrospective observational study of 120 CAR-T cell therapy patients who had received EEG monitoring. We determined a daily ICANS grade for each patient through chart review. We used visually assessed EEG features and machine learning techniques to develop the Visual EEG-Immune Effector Cell Associated Neurotoxicity Syndrome (VE-ICANS) score and assessed the association between VE-ICANS and ICANS. We also used it to determine the significance and relative importance of the EEG features. We developed the Visual EEG-ICANS (VE-ICANS) grading scale, a grading scale with a physiological basis that has a strong correlation to ICANS severity (R = 0.58 [0.47–0.66]) and excellent discrimination measured via area under the receiver operator curve (AUC = 0.91 for ICANS ≥ 2). This scale shows promise as a biomarker for ICANS which could help to improve clinical care through greater accuracy in assessing ICANS severity.