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Abstract C–C coupling is of utmost importance in the electrocatalytic reduction of CO2, as it governs the selectivity of diverse product formation. Nevertheless, the difficulties to directly observe C–C coupling pathways at a specific nanocavity hinder the advances in catalysts and electrolyzer design for efficient high-value hydrocarbon production. Here we develop a nano-confined Raman technology to elucidate the influence of the local electric field on the evolution of C–C coupling intermediates. Through precise adjustments to the Debye length in nanocavities of a copper catalyst, the overlapping of electrical double layers drives a transition in the C–C coupling pathway at a specific nanocavity from *CHO–*CO coupling to the direct dimerization of *CO species. Experimental evidence and simulations validate that a reduced potential drop across the compact layer promotes a higher yield of CO and promotes the direct dimerization of *CO species. Our findings provide insights for the development of highly selective catalyst materials tailored to promote specific products.

Abstract Background Organoids are approved by the US FDA as an alternative to animal experiments to guide drug development and for sensitivity screening. Stable organoids models of gastric cancer are desirable for personalized medicine and drug screening. Methods Tumor tissues from a primary cancer of the stomach and metastatic cancer of the lymph node were collected for 3D culture. By long-term culture for over 50 generations in vitro, we obtained stably growing organoid lines. We analyzed short tandem repeats (STRs) and karyotypes of cancer cells, and tumorigenesis of the organoids in nude mice, as well as multi-omics profiles of the organoids. A CCK8 method was used to determine the drugs sensitivity to fluorouracil (5-Fu), platinum and paclitaxel. Results Paired organoid lines from primary cancer (SPDO1P) and metastatic lymph node (SPDO1LM) were established with unique STRs and karyotypes. The organoid lines resulted in tumorigenesis in vivo and had clear genetic profiles. Compared to SPDO1P from primary cancer, upregulated genes of SPDO1LM from the metastatic lymph node were enriched in pathways of epithelial-mesenchymal transition and angiogenesis with stronger abilities of cell migration, invasion, and pro-angiogenesis. Based on drug sensitivity analysis, the SOX regimen (5-Fu plus oxaliplatin) was used for chemotherapy with an optimal clinical outcome. Conclusions The organoid lines recapitulate the drug sensitivity of the parental tissues. The paired organoid lines present a step-change toward living biobanks for further translational usage.

Summary: Anoikis resistance allows cancer cells to avoid death caused by detachment from the extracellular matrix’s adhesion, enabling these cells to infiltrate and migrate to regions such as the peritoneum. This study emphasizes GRP94’s involvement in anoikis resistance and peritoneal metastasis in gastric cancer (GC). It’s found that GRP94 overexpression, linked to poor prognosis, was potentially due to SP1 and GRP94 promoter interactions, confirmed through dual luciferase reporter (DLR), chromatin immunoprecipitation (ChIP), and quantitative real-time PCR (real-time qPCR). Increased GRP94 enhanced GC cells’ anoikis resistance and metastasis. Decreasing GRP94 had opposite effects, potentially through yes-associated protein (YAP)/TEAD1 axis inhibition, with raised YAP phosphorylation and decreased TEAD1 levels detected by western blotting (WB). Inhibiting YAP counteracted GRP94’s effects on anoikis resistance and metastasis, while activating YAP reversed the effects of GRP94 reduction. Animal experiments verified GRP94’s contribution to GC’s peritoneal metastasis. In conclusion, our work highlights the effect of GRP94 on anoikis resistance, showing potential value in treating peritoneal metastasis of GC.

This article presents a streamlined, automated classification method to map land-cover-type local climate zones (LCZs). Using a two-phase hybrid approach, we first generated training samples through universal decision rules and subsequently, a machine learning (ML) algorithm was trained on the generated samples to classify LCZs. The proposed model harnesses plant height data, combined with spectral bands and remote sensing indices, to accurately classify various land-cover types, such as dense forest, scattered trees, bush/scrub, low plant/agricultural land, bare rocks/paved surface and bare soil/sands. Targeting global applicability, we tested our method across six diverse locations spanning four continents: Fresno (California), Central Michigan, Western Phoenix (Arizona), Khulna (Bangladesh), Lagos (Nigeria), and Western Sydney. In each location, after generating training samples with the decision rules, a random forest algorithm was employed for LCZ classification. Results showcase that data from sentinels 1 and 2, night-time light, and global ecosystem dynamics investigation relative height are effective in characterizing land-cover-type LCZs and decision-rules can be established. The decision-rules consistently auto-generate training samples, undeterred by varying geographical and climatic conditions. This automated system has achieved promising accuracy across all tested sites, suggesting its potential to map land-cover-type LCZs and vegetation globally with higher accuracy.

Abstract Multi-key membership testing plays a crucial role in computing systems and networking applications, encompassing web search, mail systems, distributed databases, firewalls, and network routing. Traditional approaches, such as the Bloom filter, encounter limitations within this specific context. Addressing these challenges, we propose the Multi-key Learned Bloom Filter (MLBF), a hybrid method that combines machine learning techniques with the Bloom filter. The MLBF introduces a value-interaction-based multi-key classifier and a multi-key Bloom filter. Furthermore, we introduce an Interval-based MLBF approach, which categorizes keys into specific intervals based on data distribution to minimize the False Positive Rate (FPR). Additionally, MLBF incorporates an out-of-distribution (OOD) detection component to identify data shifts. Through extensive experimental evaluations on three authentic datasets, we demonstrate the superiority of the proposed MLBF in terms of FPR and query efficiency.

Objective: Organoids are a powerful tool with broad application prospects in biomedicine. Notably, they provide alternatives to animal models for testing potential drugs before clinical trials. However, the number of passages for which organoids maintain cellular vitality ex vivo remains unclear. Methods: Herein, we constructed 55 gastric organoids from 35 individuals, serially passaged the organoids, and captured microscopic images for phenotypic evaluation. Senescence-associated β-galactosidase (SA-β-Gal), cell diameter in suspension, and gene expression reflecting cell cycle regulation were examined. The YOLOv3 object detection algorithm integrated with a convolutional block attention module (CBAM) was used to evaluate organoid vitality. Results: SA-β-Gal staining intensity; single-cell diameter; and expression of p15, p16, p21, CCNA2, CCNE2, and LMNB1 reflected the progression of aging in organoids during passaging. The CBAM-YOLOv3 algorithm precisely evaluated aging organoids on the basis of organoid average diameter, organoid number, and number × diameter, and the findings positively correlated with SA-β-Gal staining and single-cell diameter. Organoids derived from normal gastric mucosa had limited passaging ability (passages 1–5), before aging, whereas tumor organoids showed unlimited passaging potential for more than 45 passages (511 days) without showing clear senescence. Conclusions: Given the lack of indicators for evaluating organoid growth status, we established a reliable approach for integrated analysis of phenotypic parameters that uses an artificial intelligence algorithm to indicate organoid vitality. This method enables precise evaluation of organoid status in biomedical studies and monitoring of living biobanks.

Human pan-genomics are increasing our knowledge of genomic diversity and genetic factors in disease. Here, the authors built a gastric cancer pan-genome that included the sequences of Chinese Han patients, and predicted putative and previously unaligned genes associated with gastric cancer.

Acute central nervous system injuries (ACNSI), encompassing traumatic brain injury (TBI), non-traumatic brain injury like stroke and encephalomeningitis, as well as spinal cord injuries, are linked to significant rates of disability and mortality globally. Nevertheless, effective and feasible treatment plans are still to be formulated. There are primary and secondary injuries occurred after ACNSI. Most ACNSIs exhibit comparable secondary injuries, which offer numerous potential therapeutic targets for enhancing clinical outcomes. Ferroptosis, a newly discovered form of cell death, is characterized as a lipid peroxidation process that is dependent on iron and oxidative conditions, which is also indispensable to mitochondria. Ferroptosis play a vital role in many neuropathological pathways, and ACNSIs may induce mitochondrial dysfunction, thereby indicating the essentiality of the mitochondrial connection to ferroptosis in ACNSIs. Nevertheless, there remains a lack of clarity regarding the involvement of mitochondria in the occurrence of ferroptosis as a secondary injuries of ACNSIs. In recent studies, anti-ferroptosis agents such as the ferroptosis inhibitor Ferrostain-1 and iron chelation therapy have shown potential in ameliorating the deleterious effects of ferroptosis in cases of traumatic ACNSI. The importance of this evidence is extremely significant in relation to the research and control of ACNSIs. Therefore, our review aims to provide researchers focusing on enhancing the therapeutic outcomes of ACNSIs with valuable insights by summarizing the physiopathological mechanisms of ACNSIs and exploring the correlation between ferroptosis, mitochondrial dysfunction, and ACNSIs.

Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model’s feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads.

BackgroundDiffuse lower-grade glioma (DLGG) in the central lobe is a challenge for safe resection procedures. To improve the extent of resection and reduce the risk of postoperative neurological deficits, we performed an awake craniotomy with cortical-subcortical direct electrical stimulation (DES) mapping for patients with DLGG located primarily within the central lobe. We investigated the outcomes of cortical-subcortical brain mapping using DES in an awake craniotomy for central lobe DLGG resection.MethodsWe performed a retrospective analysis of clinical data of a cohort of consecutively treated patients from February 2017 to August 2021 with diffuse lower-grade gliomas located primarily within the central lobe. All patients underwent awake craniotomy with DES for cortical and subcortical mapping of eloquent brain areas, neuronavigation, and/or ultrasound to identify tumor location. Tumors were removed according to functional boundaries. Maximum safe tumor resection was the surgical objective for all patients.ResultsThirteen patients underwent 15 awake craniotomies with intraoperative mapping of eloquent cortices and subcortical fibers using DES. Maximum safe tumor resection was achieved according to functional boundaries in all patients. The pre-operative tumor volumes ranged from 4.3 cm3 to 137.3 cm3 (median 19.2 cm3). The mean extent of tumor resection was 94.6%, with eight cases (53.3%) achieving total resection, four (26.7%) subtotal and three (20.0%) partial. The mean tumor residue was 1.2 cm3. All patients experienced early postoperative neurological deficits or worsening conditions. Three patients (20.0%) experienced late postoperative neurological deficits at the 3-month follow-up, including one moderate and two mild neurological deficits. None of the patients experienced late onset severe neurological impairments post-operatively. Ten patients with 12 tumor resections (80.0%) had resumed activities of daily living at the 3-month follow-up. Among 14 patients with pre-operative epilepsy, 12 (85.7%) were seizure-free after treatment with antiepileptic drugs 7 days after surgery up to the last follow-up.ConclusionsDLGG located primarily in the central lobe deemed inoperable can be safely resected using awake craniotomy with intraoperative DES without severe permanent neurological sequelae. Patients experienced an improved quality of life in terms of seizure control.

The propagation of two-dimensional cellular detonations is investigated numerically using a one-step reversible reaction model. The effect of the average mole weight ratio WB/WA of the product and reactant on the one-dimensional Zeldovich–von Neumann–Döring (ZND) detonation and cellular detonation behavior is analyzed in detail. Several interesting cellular detonation phenomena are observed in the numerical simulations. These can be divided into five categories according to the cell patterns of detonation, i.e., regular, relatively regular, irregular, half-cell propagating, and decoupled detonation. The results indicate that differences in cell size under different values of WB/WA modify the cellular detonation behavior. The ZND detonation parameters under various WB/WA values are studied and related to the cell size. The results show that the reaction zone length and maximum heat release rate are clearly influenced by WB/WA. Furthermore, for both ZND detonation and cellular detonation, the reaction zone length decreases as WB/WA increases, which effectively reduces the cell size. To elucidate the effects of the reaction zone length on cell size, thermoacoustic instability theory is introduced to investigate the acoustic perturbations in the reaction zone. This allows the correlation between the propagation frequency and cell number along the width of the duct to be determined. Correlation analysis indicates that the cell number has a strong linear dependence on the perturbation frequency.

External short circuit (ESC) of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles. In this study, a novel thermal model is developed to capture the temperature behavior of batteries under ESC conditions. Experiments were systematically performed under different battery initial state of charge and ambient temperatures. Based on the experimental results, we employed an extreme learning machine (ELM)-based thermal (ELMT) model to depict battery temperature behavior under ESC, where a lumped-state thermal model was used to replace the activation function of conventional ELMs. To demonstrate the effectiveness of the proposed model, we compared the ELMT model with a multi-lumped-state thermal (MLT) model parameterized by the genetic algorithm using the experimental data from various sets of battery cells. It is shown that the ELMT model can achieve higher computational efficiency than the MLT model and better fitting and prediction accuracy, where the average root mean squared error (RMSE) of the fitting is 0.65 °C for the ELMT model and 3.95 °C for the MLT model, and the RMES of the prediction under new data set is 3.97 °C for the ELMT model and 6.11 °C for the MLT model.

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

Diffuse low-grade gliomas are a diverse category of neuroepithelial neoplasms, which are characterized by a low proliferation index and an indolent course with a long-term survival in comparison with high-grade gliomas. It is convinced that maximal safe resection can significantly increase survival. Multiple intraoperative techniques help neurosurgeons to maximize the resection with a safe range; however, the tumors involving motor areas are still intractable for many surgeons who believe that these tumors are unresectable. We searched on PubMed database and summarized studies and reviews about diffuse low-grade gliomas within/near motor areas. Moreover, studies about anatomy about motor area were also reviewed. In this article, we discussed the anatomy of the central lobe and supplementary motor area, including the cortex, subcortical fibers, and relevant vessels, as well as the technical details of awake craniotomy and direct electrical stimulation. At last, combined with some cases, we try to demonstrate that tumors within/near motor areas are resectable, which may cause only mild neurological deficits using awake craniotomy with intraoperative monitoring by cortical/subcortical mapping, and, furthermore, it provides a longer and better survival for those young patients.

The articles in this special section focus on the use of digital technology in agricultural research and applications. Sustainability of global agricultural and food systems is one of the prominent factors for peaceful future of the world in next few decades. Although agriculture is the main part of the global food supply chain, it is under rising pressure of global climate change, environmental deterioration, and falling per-capita arable land. Efficiency and sustainability management at all levels of agricultural planning and production appear as the most promising balancing factor for the short and medium terms. For this reason, timely and accurate information about the current conditions and future predictions in agriculture and the related resources become more important than ever. We are living in an age that the annual production number of transistors in microprocessors is more than the number of wheat grains produced in the same year. This is an indication of increasing data processing capability and decreasing cost. On the other hand, the number of Internet connected devices is estimated to be more than 20 billion now and is increasing rapidly, and Intent-of-Things (IoT) devices have the highest share in this rising trend. While it may be not able to solve long-term global food sustainability issue, the rapid increase in data collection and processing capabilities for agricultural monitoring and prediction may remediate the issue at least in short and middle terms.

Rapid Intensification (RI) in Tropical Cyclone (TC) development is one of the most difficult and still challenging tasks in weather forecasting. In addition to the dynamical numerical simulations, commonly used techniques for RI (as well as TC intensity changes) analysis and prediction are the composite analysis and statistical models based on features derived from the composite analysis. Quite a large number of such selected and pre-determined features related to TC intensity change and RI have been accumulated by the domain scientists, such as those in the widely used SHIPS (Statistical Hurricane Intensity Prediction Scheme) database. Moreover, new features are still being added with new algorithms and/or newly available datasets. However, there are very few unified frameworks for systematically distilling features from a comprehensive data source. One such unified Artificial Intelligence (AI) system was developed for deriving features from TC centers, and here, we expand that system to large-scale environmental condition. In this study, we implemented a deep learning algorithm, the Convolutional Neural Network (CNN), to the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data and identified and refined potentially new features relevant to RI such as specific humidity in east or northeast, vorticity and horizontal wind in north and south relative to the TC centers, as well as ozone at high altitudes that could help the prediction and understanding of the occurrence of RI based on the deep learning network (named TCNET in this study). By combining the newly derived features and the features from the SHIPS database, the RI prediction performance can be improved by 43%, 23%, and 30% in terms of Kappa, probability of detection (POD), and false alarm rate (FAR) against the same modern classification model but with the SHIPS inputs only.

In this paper, we propose new inner and outer bounds of the capacity region for multiple access channels in visible light communication (VLC) networks under both peak and average optical power constraints. Specifically, the proposed inner bounds are established by employing the single-user capacity achieving input distribution for each user. The proposed outer bounds are derived by determining single-user capacities for each user and calculating a sum capacity upper bound by relaxing the input constraints. Numerical results show that the proposed new bounds are extremely tight and outperform existing bounds over wide ranges of SNRs.

In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.

Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to limited understanding regarding the intensity change process. We propose an automatic knowledge discovery framework to identify potential spatiotemporal precursors to tropical cyclone rapid intensification from a set of tropical cyclone environmental fields. Specifically, this framework includes (1) formulating RI and non-RI composite environmental fields from historical tropical cyclones using NASA MERRA2 data; (2) utilizing the shared nearest neighbor-based clustering algorithm to detect regions representing relatively homogeneous behavior around tropical cyclone centers; (3) determining candidate precursors from significantly different regions in RI and non-RI groups using a spatiotemporal statistical method; and (4) comparing candidates to existing predictors to select potential precursors. The proposed knowledge discovery framework is applied separately to different factors, including 200 hPa zonal wind, 850–700 hPa relative humidity, and 850–200 hPa vertical shear, to detect potential precursors. Compared to the existing predictors manually labeled, i.e., U200 and U20C, RHLO, and SHRD in the Statistical Hurricane Intensity Prediction Scheme, our automatically discovered precursors have a comparable or better capability for estimating the probability of rapid intensification.

Prediction of tropical cyclone (TC) intensity is one of the ground challenges in weather forecasting, and rapid intensification (RI) is a key part of that prediction. Most of the current RI studies are based on a selected variable (feature) set, which is accumulated based on expert expertise in past studies of TC intensity changes and RI. Are there any more important variables in TC intensity predictions that were not identified in past studies? A systematic and comprehensive search for those variables from vast amounts of gridded data, satellite images, and other historically collected data could be helpful for answering the above question. Artificial intelligence (AI) has the capabilities to distill features in large array data, and it is helpful in identifying new features related to TC intensity changes in general and RI in particular. Here, we leverage the local linear embedding (LLE) dimension reduction techniques to the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis data for identifying new variables related to RI. In addition to the well-known features in the SHIPS (statistical hurricane intensity prediction scheme) database, we identified other significant features, such as 400 and 450 hPa meridional wind, 1000 hPa potential vorticity, and vertical pressure speed, that could help the understanding and prediction of RI occurrences. Furthermore, our AI system outperforms our baseline model with SHIPS data only by 26.6% and 8.4% in kappa and PSS (Peirce’s skill score), respectively.

Currently, most tropical cyclone (TC) rapid intensification (RI) prediction studies are conducted based on a subset of the SHIPS database using a relatively simple model structure. However, variables (features) in the SHIPS database are built upon human expertise in TC intensity studies based on hard and subjective thresholds, and they should be explored thoroughly to make full use of the expertise. Based on the complete SHIPS data, this study constructs a complicated artificial intelligence (AI) system that handles feature engineering and selection, imbalance, prediction, and hyper parameter-tuning, simultaneously. The complicated AI system is used to further improve the performance of the current studies in RI prediction, and to identify other essential SHIPS variables that are ignored by previous studies with variable importance scores. The results outperform most of the earlier studies by approximately 21–50% on POD (Probability Of Detection) with reduced FAR (False Alarm Rate). This study built a baseline for future work on new predictor identification with more complicated AI techniques.

Parkinson’s disease (PD) is characterized by the loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc) and the presence of Lewy bodies (LBs) in the surviving SNc neurons. LBs formation is caused by the accumulation of α-synuclein (α-syn) or phosphorylated α-syn at serine-129 (pSer129-α-syn), which is implicated in the pathological progression of PD. Salidroside (Sal), the main active ingredient of the root of Rhodiola rosea L., has been reported to have potent neuroprotective properties in our previous investigations. Here, we investigated the effects of Sal on 6-OHDA and overexpresssion of WT/A30P-α-syn-induced pathological α-syn increase and the mechanism behind it in SH-SY5Y cells. We found Sal displays neuroprotective effects against 6-hydroxydopamine (6-OHDA)-induced cytotoxicity. Sal decreased the pSer129-α-syn level mainly by maintaining the normal function of ubiquitin-proteasome system (UPS). Furthermore, Sal promoted the clearance of α-syn and protected the cell viability mainly through recovered the 20S proteasome activity in WT/A30P-α-syn-transfected cells. These data provide new mechanistic insights into the neuroprotective effects of Sal and Sal may be a promising therapy to slow neurodegeneration in PD.Highlights: Sal protects cells and decreases the pSer129-α-syn protein level in 6-OHDA-induced impairmental and dysfunctional SH-SY5Y cells.Sal promotes the clearance of α-syn and protects the cell viability mainly through recovering the 20S proteasome activity in WT/A30P-α-syn plasmids transfected cells.Maintaining the normal function of the UPS may be one of the important mechanisms of Sal in neuroprotective effects.

Spontaneous intracerebral hemorrhage (ICH) is a devastating form of stroke, which leads to a high rate of mortality and poor neurological outcomes worldwide. Thrombolytic evacuation with urokinase-type plasminogen activator (uPA) or tissue-type plasminogen activator (tPA) has been showed to be a hopeful treatment for ICH. However, to the best of our knowledge, no clinical trials were reported to compare the efficacy and safety of these two fibrinolytics administrated following minimally invasive stereotactic puncture (MISP) in patients with spontaneous basal ganglia ICH. Therefore, the authors intended here to evaluate the differential impact of uPA and tPA in a retrospective study. In the present study, a total of 86 patients with spontaneous ICH in basal ganglia using MISP received either uPA (uPA group, n = 45) or tPA (tPA group, n = 41), respectively. The clinical baseline characteristics prior to the operation were collected. In addition, therapeutic responses were assessed by the short-term outcomes within 30 days postoperation, as well as long-term outcomes at 1 year postoperation. Our findings showed that, in comparison with tPA, uPA was able to better promote hematoma evacuation and ameliorate perihematomal edema, but the differences were not statistically significant. Moreover, the long-term functional outcomes of both groups were similar, with no statistical difference. In conclusion, these results provide evidence supporting that uPA and tPA are similar in the efficacy and safety for thrombolytic evacuation in combination with MISP in patients with spontaneous basal ganglia ICH.

Land surface temperature (LST) is an important input to the Atmosphere−Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring.

Parkinson’s disease (PD), a complex neurodegenerative disorder, is pathologically characterized by the formation of Lewy bodies and loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Mitochondrial dysfunction is considered to be one of the most important causative mechanisms. In addition, dysfunction of chaperone-mediated autophagy (CMA), one of the lysosomal proteolytic pathways, has been shown to play an important role in the pathogenesis of PD. An exciting and important development is recent finding that CMA and mitochondrial quality control may be linked. This review summarizes the studies revealing the link between autophagy and mitochondrial function. Discussions are focused on the connections between CMA and mitochondrial failure and on the role of MEF2D, a neuronal survival factor, in mediating the regulation of mitochondria in the context of CMA. These new findings highlight the need to further explore the possibility of targeting the MEF2D-mitochondria-CMA network in both understanding the PD pathogenesis and developing novel therapeutic strategies.

MODerate Resolution Imaging Spectroradiometer (MODIS) aerosol retrievals over the North Atlantic spanning seven hurricane seasons are combined with the Statistical Hurricane Intensity Prediction Scheme (SHIPS) parameters. The difference between the current and future intensity changes were selected as response variables. For 24 major hurricanes (category 3, 4 and 5) between 2003 and 2009, eight lead time response variables were determined to be between 6 and 48 h. By combining MODIS and SHIPS data, 56 variables were compiled and selected as predictors for this study. Variable reduction from 56 to 31 was performed in two steps; the first step was via correlation coefficients (cc) followed by Principal Component Analysis (PCA) extraction techniques. The PCA reduced 31 variables to 20. Five categories were established based on the PCA group variables exhibiting similar physical phenomena. Average aerosol retrievals from MODIS Level 2 data in the vicinity of UTC 1,200 and 1,800 h were mapped to the SHIPS parameters to perform Multiple Linear Regression (MLR) between each response variable against six sets of predictors of 31, 30, 28, 27, 23 and 20 variables. The deviation among the predictors Root Mean Square Error (RMSE) varied between 0.01 through 0.05 and, therefore, implied that reducing the number of variables did not change the core physical information. Even when the parameters are reduced from 56 to 20, the correlation values exhibit a stronger relationship between the response and predictors. Therefore, the same phenomena can be explained by the reduction of variables.