A Structure-Aware Convolutional Neural Network for Automatic Diagnosis of Fungal Keratitis with In Vivo Confocal Microscopy Images.

Fungal keratitis (FK) is a common and severe corneal disease, which is widely spread in tropical and subtropical areas. Early diagnosis and treatment are vital for patients, with confocal microscopy cornea imaging being one of the most effective methods for the diagnosis of FK. However, most cases are currently diagnosed by the subjective judgment of ophthalmologists, which is time-consuming and heavily depends on the experience of the ophthalmologists. In this paper, we introduce a novel structure-aware automatic diagnosis algorithm based on deep convolutional neural networks for the accurate diagnosis of FK. Specifically, a two-stream convolutional network is deployed, combining GoogLeNet and VGGNet, which are two commonly used networks in computer vision architectures. The main stream is used for feature extraction of the input image, while the auxiliary stream is used for feature discrimination and enhancement of the hyphae structure. Then, the features are combined by concatenating the channel dimension to obtain the final output, i.e., normal or abnormal. The results showed that the proposed method achieved accuracy, sensitivity, and specificity of 97.73%, 97.02%, and 98.54%, respectively. These results suggest that the proposed neural network could be a promising computer-aided FK diagnosis solution.

RNNCon: Contribution Coverage Testing for Stacked Recurrent Neural Networks.

Recurrent Neural Networks (RNNs) are applied in safety-critical fields such as autonomous driving, aircraft collision detection, and smart credit. They are highly susceptible to input perturbations, but little research on RNN-oriented testing techniques has been conducted, leaving a threat to a large number of sequential application domains. To address these gaps, improve the test adequacy of RNNs, find more defects, and improve the performance of RNNs models and their robustness to input perturbations. We aim to propose a test coverage metric for the underlying structure of RNNs, which is used to guide the generation of test inputs to test RNNs. Although coverage metrics have been proposed for RNNs, such as the hidden state coverage in RNN-Test, they ignore the fact that the underlying structure of RNNs is still a fully connected neural network but with an additional "delayer" that records the network state at the time of data input. We use the contributions, i.e., the combination of the outputs of neurons and the weights they emit, as the minimum computational unit of RNNs to explore the finer-grained logical structure inside the recurrent cells. Compared to existing coverage metrics, our research covers the decision mechanism of RNNs in more detail and is more likely to generate more adversarial samples and discover more flaws in the model. In this paper, we redefine the contribution coverage metric applicable to Stacked LSTMs and Stacked GRUs by considering the joint effect of neurons and weights in the underlying structure of the neural network. We propose a new coverage metric, RNNCon, which can be used to guide the generation of adversarial test inputs. And we design and implement a test framework prototype RNNCon-Test. 2 datasets, 4 LSTM models, and 4 GRU models are used to verify the effectiveness of RNNCon-Test. Compared to the current state-of-the-art study RNN-Test, RNNCon can cover a deeper decision logic of RNNs. RNNCon-Test is not only effective in identifying defects in Deep Learning (DL) systems but also in improving the performance of the model if the adversarial inputs generated by RNNCon-Test are filtered and added to the training set to retrain the model. In the case where the accuracy of the model is already high, RNNCon-Test is still able to improve the accuracy of the model by up to 0.45%.

Discovery of a Novel Inhibitor Structure of Mycobacterium tuberculosis Isocitrate Lyase.

Tuberculosis remains a global threat to public health, and dormant Mycobacterium tuberculosis leads to long-term medication that is harmful to the human body. M. tuberculosis isocitrate lyase (MtICL), which is absent in host cells, is a key rate-limiting enzyme of the glyoxylic acid cycle and is essential for the survival of dormant M. tuberculosis. The aim of this study was to evaluate natural compounds as potential MtICL inhibitors through docking and experimental verification. Screening of the TCMSP database library was done using Discovery Studio 2019 for molecular docking and interaction analysis, with the putative inhibitors of MtICL, 3-BP, and IA as reference ligands. Daphnetin (MOL005118), with a docking score of 94.8 and -CDOCKER interaction energy of 56 kcal/mol, was selected and verified on MtICL in vitro and M. smegmatis; daphnetin gave an IC50 of 4.34 µg/mL for the MtICL enzyme and an MIC value of 128 µg/mL against M. smegmatis, showing enhanced potential in comparison with 3-BP and IA. The interactions and essential amino acid residues of the protein were analyzed. In summary, natural daphnetin may be a promising new skeleton for the design of inhibitors of MtICL to combat dormant M. tuberculosis.

Identification of Crop Type in Crowdsourced Road View Photos with Deep Convolutional Neural Network.

In situ ground truth data are an important requirement for producing accurate cropland type map, and this is precisely what is lacking at vast scales. Although volunteered geographic information (VGI) has been proven as a possible solution for in situ data acquisition, processing and extracting valuable information from millions of pictures remains challenging. This paper targets the detection of specific crop types from crowdsourced road view photos. A first large, public, multiclass road view crop photo dataset named iCrop was established for the development of crop type detection with deep learning. Five state-of-the-art deep convolutional neural networks including InceptionV4, DenseNet121, ResNet50, MobileNetV2, and ShuffleNetV2 were employed to compare the baseline performance. ResNet50 outperformed the others according to the overall accuracy (87.9%), and ShuffleNetV2 outperformed the others according to the efficiency (13 FPS). The decision fusion schemes major voting was used to further improve crop identification accuracy. The results clearly demonstrate the superior accuracy of the proposed decision fusion over the other non-fusion-based methods in crop type detection of imbalanced road view photos dataset. The voting method achieved higher mean accuracy (90.6-91.1%) and can be leveraged to classify crop type in crowdsourced road view photos.

Method for monitoring environmental flows with high spatial and temporal resolution satellite data.

Monitoring environmental flows is crucial to maintaining the function and stability of river and lake ecosystems. However, current methods for monitoring environmental flows are expensive and ground based, and the accuracy of the results needs to be verified to evaluate the environmental flows. This evaluation is hampered by the problem of data shortages, such as hydrological and ecological data. In this study, a method for monitoring environmental flows is proposed using multisource high spatial and temporal resolution satellite data. A case study in the Yongding River Basin demonstrates that the method is feasible for monitoring the environmental flows of rivers in semiarid and arid areas. The results show that the environmental flows and months with large water discharges and shortages in the three control sections of the Yongding River Basin were different. Moreover, the downstream river width rarely met the environmental water demand, achieving this only for one period from 2017 to 2019 according to the three typical types of years (an average water year, a dry year, and an extremely dry year). This method and the results have applications in planning environmental flows and could improve the comprehensive management of the ecological environment in river basins.

CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture.

CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; www.cropwatch.com.cn , Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5's spatial resolution and at MODIS's temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R² of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R² of the SOS ranging from 0.68 to 0.86 and with an R² of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.

Knowledge domains and emerging trends in radiotherapy in oesophageal cancer from 2004 to 2023: a bibliometric analysis and visualization study.

Esophageal cancer (EC) is a malignant tumour with high morbidity and mortality rates. Recent studies have shown that much progress has been made in the research of radiotherapy in EC. This study aims to provide a comprehensive overview of the knowledge structure and research hotspots of radiotherapy in EC through bibliometrics. Publications related to radiotherapy in EC from 2014 to 2023 were searched on the web of science core collection database. VOSviewers, CiteSpace and R package 'bibliometrix' were used to conduct this bibliometric analysis. In total, 4258 articles from 76 countries led by China and the USA were included. The Chinese Academy of Medical Sciences-Peking Union Medical College has the highest number of publications. International Journal of Radiation Oncology Biology Physics is the most popular journal and also the most co-cited journal in this field. These publications come from 21 972 authors among which Liao Zhongxing had published the most papers and Cooper JS was co-cited most often. Neoadjuvant chemoradiotherapy and strategies based on it are the main topics in this research field. 'IMRT' and 'immunotherapy' are the primary keywords of emerging research hotspots. This is a bibliometric study that comprehensively summarizes the research trends and developments of radiotherapy in EC. This information identifies recent research frontiers and hot directions, which will provide a reference for scholars studying radiotherapy in EC.

Network pharmacology in combination with bibliometrics analysis on the mechanism of compound Kushen injection in the treatment of radiation pneumonia and lung cancer.

Radiation pneumonia is a common adverse reaction during radiotherapy in lung cancer patients, which negatively impacts the quality of life and survival of patients. Recent studies have shown that compound Kushen injection (CKI), a traditional Chinese medicine (TCM), has great anti-inflammatory and anticancer potential, but the mechanism is still unclear. We used CiteSpace, the R package "bibliometrix," and VOSviewers to perform a bibliometrics analysis of 162 articles included from the Web of Science core collection. A network pharmacology-based approach was used to screen effective compounds, screen and predict target genes, analyze biological functions and pathways, and construct regulatory networks and protein interaction networks. Molecular docking experiments were used to identify the affinity of key compounds and core target. The literature metrology analysis revealed that over 90% of the CKI-related studies were conducted by Chinese scholars and institutions, with a predominant focus on tumors, while research on radiation pneumonia remained limited. Our investigation identified 60 active ingredients of CKI, 292 genes associated with radiation pneumonia, 533 genes linked to lung cancer, and 37 common targets of CKI in the treatment of both radiation pneumonia and lung cancer. These core potential targets were found to be significantly associated with the OS of lung cancer patients, and the key compounds exhibited a good docking affinity with these targets. Additionally, GO and KEGG enrichment analysis highlighted that the bioinformatics annotation of these common genes mainly involved ubiquitin protein ligase binding, cytokine receptor binding, and the PI3K/Akt signaling pathway. Our study revealed that the main active components of CKI, primarily quercetin, luteolin, and naringin, might act on major core targets, including AKT1, PTGS2, and PPARG, and further regulated key signaling pathways such as the PI3K/Akt pathway, thereby playing a crucial role in the treatment of radiation pneumonia and lung cancer. Moreover, this study had a certain promotional effect on further clinical application and provided a theoretical basis for subsequent experimental research.

Network Pharmacology Analysis on the Mechanism of Xihuangwan in Treating Rectal Cancer and Radiation Enteritis.

BACKGROUND: Recent studies have shown that XihuangWan (XHW) is a kind of Chinese medicine with significant anti-tumor and anti-inflammatory activities. However, its mechanism for preventing and treating radiation proctitis in rectal cancer patients during radiotherapy remains unclear. METHODS: This study employed the network pharmacology to establish a "drug-active ingredient-target genedisease" network via using TCMSP, SymMap, GeneCard, and OMIM databases. The PPI network was conducted by the String tool. The core targets of XHW in the treatment of rectal cancer and radiation enteritis were identified by topological analysis, and the functional annotation analysis and pathway enrichment analysis were performed. RESULTS: A total of 61 active ingredients of XHW ingredients, 4607 rectal cancer-related genes, 5803 radiation enteritis-related genes, and 68 common targets of XHW in the treatment of rectal cancer and radiation enteritis were obtained. PTGS1 and NR3C2, as identified potential targets, were significantly associated with OS of colorectal cancer patients. GO and KEGG enrichment analysis showed that bioinformatics annotation of these common genes was mainly involved in DNA-binding transcription factor, PI3K/Akt, TNF, HIF-1 signaling pathway, and colorectal cancer pathway. CONCLUSION: The active ingredients of XHW, mainly including Quercetin, Ellagic acid, and Stigmasterol, might act on common targets of rectal cancer and radiation enteritis, such as PTGS1, NR3C2, IL-6, EGFR, HIF-1A, CASP3, BCL2, ESR1, MYC, and PPARG, and regulate multiple signaling pathways like PI3K-Akt, TNF, and HIF-1 to inhibit tumor proliferation, tumor angiogenesis, inflammatory responses, and oxidative stress, thereby achieving prevention and treatment of radiation enteritis in rectal cancer patients during radiotherapy. It provided an important reference for further elucidating the anti-inflammation and anti-tumor mechanism and clinical application of XHW.

Challenges and opportunities in remote sensing-based crop monitoring: a review.

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.

Assessment of environmentally sensitive areas to desertification in the Blue Nile Basin driven by the MEDALUS-GEE framework.

Assessing environmentally sensitive areas (ESA) to desertification and understanding their primary drivers are necessary for applying targeted management practices to combat land degradation at the basin scale. We have developed the MEditerranean Desertification And Land Use framework in the Google Earth Engine cloud platform (MEDALUS-GEE) to map and assess the ESA index at 300 m grids in the Blue Nile Basin (BNB). The ESA index was derived from elaborating 19 key indicators representing soil, climate, vegetation, and management through the geometric mean of their sensitivity scores. The results showed that 43.4%, 28.8%, and 70.4% of the entire BNB, Upper BNB, and Lower BNB, respectively, are highly susceptible to desertification, indicating appropriate land and water management measures should be urgently implemented. Our findings also showed that the main land degradation drivers are moderate to intensive cultivation across the BNB, high slope gradient and water erosion in the Upper BNB, and low soil organic matter and vegetation cover in the Lower BNB. The study presented an integrated monitoring and assessment framework for understanding desertification processes to help achieve land-related sustainable development goals.

Rapid RNA detection through intra-enzyme chain replacement-promoted Cas13a cascade cyclic reaction without amplification.

The clinical methods to detect RNA viruses and disease-related RNAs suffer from time-consuming processes, high false-positive rates, or limited sensitivity. Here, we propose a strategy for rapid RNA detection through intra-enzyme chain replacement-mediated Cas13a cascade cyclic reaction without target amplification. A hairpin RNA mediator (a cleavage substrate for target-activated Cas13a) and a guiding RNA recognized by the cleavage product through intra-enzyme chain replacement were designed and optimized. Upon the recognition and binding of the target RNA to the Cas13a/CrRNA complex, Cas13a is initially activated to cleave the mediator, and the cleavage products recognize the corresponding Cas13a/CrRNA complex by intra-enzyme chain replacement and initiate the circular cascade of Cas13a cleavage and activation. The accumulated active Cas13a cleaves fluorescent reporter probe for achieving target RNA detection. This "mix & read" RNA detection at room temperature was performed in total 30 min. Using miRNA-21 as the target, the changes in fluorescence intensity were linearly correlated to the concentrations from 10 fM to 50 pM with the detection limit of 75 aM, while no significant changes in fluorescence intensity were detected for non-targets. This method applied to the clinical sputum respiratory syncytial virus-positive samples gave results consistent with those from the clinical fluorescence immunoassay. Thus, intra-enzyme chain replacement-promoted Cas13a cascade cyclic reaction for detection of RNA viruses in the "mix & read" mode at room temperature is rapid, simple, convenient, and efficient for RNA detection and can be adapted to point-of-care testing for high throughput screening of RNA virus infections.

One-pot synthesis of stable and functional hydrophilic CsPbBr3 perovskite quantum dots for "turn-on" fluorescence detection of Mycobacterium tuberculosis.

All-inorganic CsPbBr3 perovskite quantum dots (QDs) are widely studied owing to their excellent optoelectronic properties; however, they are usually hydrophobic and unstable in water and thus their biomedical applications are seriously limited. In this study, stable and hydrophilic CsPbBr3 QDs functionalized with carboxyl groups (CsPbBr3-COOH QDs) were prepared in one-pot with the aid of new ligands amino-poly(ethylene glycol)-carboxyl and perfluorooctyltriethoxylsilane. The aqueous solution of CsPbBr3-COOH QDs maintained the initial fluorescence intensity after 8 days of storage; the free carboxyl groups on the surface of CsPbBr3-COOH QDs were covalently conjugated with amino-terminal DNA to construct CsPbBr3 QDs-DNA probes for subsequent application. Then, a biosensing platform utilizing fluorescence resonance energy transfer between hydrophilic CsPbBr3 QDs-DNA and MoS2 nanosheets was developed for the sensitive and selective detection of the Mycobacterium tuberculosis DNA with a low limit of detection of 51.9 pM and the identification of drug-resistant clinical strains. This study advances the preparation of hydrophilic carboxyl-functionalized CsPbBr3 QDs with enhanced stability and extends their application in biomolecule detection.

A framework for separating natural and anthropogenic contributions to evapotranspiration of human-managed land covers in watersheds based on machine learning.

Actual EvapoTranspiration (ET) represents the water consumption in watersheds; distinguishing between natural and anthropogenic contributions to ET is essential for water conservation and ecological sustainability. This study proposed a framework to separate the contribution of natural and anthropogenic factors to ET of human-managed land cover types using the Random Forest Regressor (RFR). The steps include: (1) classify land cover into natural and human-managed land covers and then divide ET, meteorological, topographical, and geographical data into two parts corresponding to natural and human-managed land cover types; (2) construct a natural ET (ETn) prediction model using natural land cover types of ET, and the corresponding meteorological, topographical and geographical factors; (3) the constructed ETn prediction model is used to predict the ETn of human-managed land cover types using the corresponding meteorological, topographical and geographical data as inputs, and (4) derive the anthropogenic ET (ETh) by subtracting the natural ET from the total ET (ETt) for human-managed land cover types. Take 2017 as an example, ETn and ETh for rainfed agriculture, mosaic agriculture, irrigated agriculture, and settlement in Colorado, Blue Nile, and Heihe Basin were separated by the proposed framework, with R2 and NSE of predicted ETn above 0.95 and RB within 1% for all three basins. In the semi-arid Colorado River Basin and arid Heihe Basin, human activities on human-managed land cover types tended to increase ET higher than humid Blue Nile Basin. The anthropogenic contribution to total water consumption is approaching 53.68%, 66.47%, and 6.14% for the four human-managed land cover types in Colorado River Basin, Heihe Bain and Blue Nile Basin, respectively. The framework provides strong support for the disturbance of water resources by different anthropogenic activities at the basin scale and the accurate estimation of the impact of human activities on ET to help achieve water-related sustainable development goals.

Clinical significance of MMP-9 overexpression in endometrial cancer: A PRISMA-compliant meta-analysis.

Objective: Several studies have found that MMP-9, one of the extracellular matrix-degrading proteinases, was involved in EC's (endometrial cancer) clinical progression and prognosis. However, the results involving the associations of MMP-9 expression with risk, clinical features and prognosis of EC were conflicting. Therefore, we performed a systematic review and meta-analysis to clarify the correlation of MMP-9 expression with EC. Methods: Relative studies involving the associations between MMP-9 expression and EC were retrieved from PubMed, Embase, Web of Science and CNKI (China National Knowledge Infrastructure) electronic databases. OR (odds ratio) with 95% CI (confidence interval) was applied to evaluate the associations of MMP-9 expression with risk and clinical features of EC. Furthermore, we evaluated the role of MMP-9 expression in prognosis of EC using HR and 95% CI. The funnel plots and Begg test were used to assess the publication bias. Results: A total of 28 eligible studies were acquired from Pubmed, Embase, Web of science and CNKI databases. We found MMP-9 overexpression was significantly associated with the risk of EC (OR = 11.02, 95% CI = 7.51-16.16, P < 0.05). In the meantime, MMP-9 overexpression was significantly associated with the tumor grade, FIGO stage, lymph node metastasis and myometrial invasion (Tumor grade: OR = 1.68, 95% CI = 1.09-2.58, P < 0.05; FIGO stage: OR = 3.25, 95% CI = 1.73-6.08, P < 0.05; Lymph node metastasis: OR = 2.98, 95% CI = 1.27-7.03, P < 0.05; Myometrial invasion: OR = 2.42, 95% CI = 1.42-4.12, P < 0.05) in Asians. In addition, the overall results showed that MMP-9 overexpression predicted a worse prognosis of EC (OR = 1.82, 95% CI = 1.01-2.62, P < 0.05). Conclusions: MMP-9 overexpression might be a potential predictor of poor clinical progression and prognosis of EC.

A sensing strategy combining T7 promoter-contained DNA probe with CRISPR/Cas13a for detection of bacteria and human methyltransferase.

Abnormal DNA methylation is closely related to the occurrence and development of many diseases. The determination of human DNA methyltransferase activity and the screening of its inhibitors are extreme important for the diagnosis and the treatment of methylation-related diseases in clinic. Most of the current detection methods have the disadvantages of sophisticated design, high cost and low detection limit. By combining T7 promoter-contained DNA probe as the substrate for methyltransferase with CRISPR/Cas13a sensing strategy, a novel fluorescent sensing platform is designed to achieve simple, specific, sensitive detection of bacteria DNA methyltransferase (DNA-(N-6-adenine)-methyltransferase, Dam MTase) and also human methyltransferase (DNA (cytosine-5)-methyltransferase 1, Dnmt1). A hairpin DNA probe designed for Dam MTase and a double strand DNA probe for Dnmt1 are both methylated followed by the methylation-dependent site-specific cleavage, which result a T7 promoter-contained product and a T7 promoter-free one to respectively open and close the transcription and subsequent CRISPR/Cas13a target-initiated cleavage of fluorescence-labeled reporter RNA. In virtue of the specificity of methylation-dependent cleavage of probe, the efficient transcription amplification and CRISPR/Cas13a sequence-specific sensing, this strategy exhibited remarkable specificity and sensitivity, with the limit of detection of 3.10 × 10-5 U/mL for Dam MTase. Moreover, Dnmt1 activity in MCF-7 cells was detected and the inhibition of Apt. #9 was evaluated. This strategy for methyltransferase detection is convenient and efficient for inhibitor discovery and early cancer diagnosis.

Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework.

Assessment of soil loss and understanding its major drivers are essential to implement targeted management interventions. We have proposed and developed a Revised Universal Soil Loss Equation framework fully implemented in the Google Earth Engine cloud platform (RUSLE-GEE) for high spatial resolution (90 m) soil erosion assessment. Using RUSLE-GEE, we analyzed the soil loss rate for different erosion levels, land cover types, and slopes in the Blue Nile Basin. The results showed that the mean soil loss rate is 39.73, 57.98, and 6.40 t ha-1 yr-1 for the entire Blue Nile, Upper Blue Nile, and Lower Blue Nile Basins, respectively. Our results also indicated that soil protection measures should be implemented in approximately 27% of the Blue Nile Basin, as these areas face a moderate to high risk of erosion (>10 t ha-1 yr-1). In addition, downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 km to 1 km spatial resolution significantly impacts rainfall erosivity and soil loss rate. In terms of soil erosion assessment, the study showed the rapid characterization of soil loss rates that could be used to prioritize erosion mitigation plans to support sustainable land resources and tackle land degradation in the Blue Nile Basin.

Variation in actual evapotranspiration following changes in climate and vegetation cover during an ecological restoration period (2000-2015) in the Loess Plateau, China.

The spatial distribution of water resources largely influences Earth ecosystems and human civilization. Being a major component of the global water cycle, evapotranspiration (ET) serves as an indicator of the availability of water resources. Understanding the actual ET (ETa) variation mechanism at different spatial and temporal scales can improve management of water use within the sustainable development limits. In this study, remote sensing derived ETa data were used to study water resource fluctuations in the Loess Plateau, China. This region covers diverse climate types from humid to arid and experienced large changes in vegetation cover during a revegetation project between 2000 and 2015. The relations between spatiotemporal variation of ETa, climate factors and vegetation change were explored using statistical methods. The results show that cropland, forestland and grassland take the largest percentage of total ETa. Total ETa exhibited a marginally increasing trend (p < 0.1) during 2000-2010 and no trend during 2011-2015. Windspeed and vegetation cover index highly influenced ETa, followed by atmospheric pressure, air humidity, precipitation, bright sunshine duration and temperature. Temperature has little effect on ETa throughout the Loess Plateau. The monitoring of water resources based upon water balance between precipitation, ETa and river flow changes shows that water consumption deficit is consistent with vegetation changes: it was large during 2000-2010 when vegetation increased rapidly and decreased after 2010. These results could help to develop different water saving strategies across the Loess Plateau and build a better monitoring system of water resources.