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The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring.

Hippophae rhamnoides subsp. sinensis Rousi (Abbrev. H. rhamnoides) stands as a vital botanical asset in ameliorating the ecological landscape of the arid regions in Northwest China, where its rhizospheric microorganisms serve as linchpins in its growth and developmental dynamics. This study aimed to explore the community structure characteristics and origin differences of root endophytic fungi in H. rhamnoides. Samples were collected from 25 areas where H. rhamnoides is naturally distributed along an altitude gradient in the northwest region. Then, endophytic fungi from different regions were analyzed by using high-throughput sequencing technology to compare the structural characteristics of endophytic fungi and examine their association with environmental factors. FUNGuild was employed to analyze the community structure and functions of endophytic fungi, and the results showed that each region had its own dominant endophytic fungal flora, demonstrating the differences in origin of endophytic fungi, and the specific endophytic flora acquired from the original soil in the growing season of H. rhamnoides will help us construct the microecological community structure. Furthermore, the study identified and assessed the diversity of fungi, elucidating the species structure and highlighting dominant species. The RDA analysis revealed that available phosphorus (AP), available potassium (AK), and total nitrogen (TN) exhibit significant correlations with the composition and diversity of root-associated fungi. In conclusion, the fungal community structure is similar within the same region, while significant differences exist in the taxonomic structure and biodiversity among different regions. These findings shed light on the intricate interplay and mechanisms governing the ecological restoration of H. rhamnoides, offering a valuable framework for advancing green ecology initiatives and harnessing the potential of root-associated microorganisms in this species.

ABSTRACTWe previously established a hepatocellular carcinoma (HCC) targeting system of conditionally replicative adenovirus (CRAd) delivered by human umbilical cord-derived mesenchymal stem cells (HUMSCs). However, this system needed to be developed further to enhance the antitumor effect and overcome the limitations caused by the alpha-fetoprotein (AFP) heterogeneity of HCC. In this study, a bispecific T cell engager (BiTE) targeting programmed death ligand 1 controlled by the human telomerase reverse transcriptase promoter was armed on the CRAd of the old system. It was demonstrated on orthotopic transplantation model mice that the new system had a better anti-tumor effect with no more damage to extrahepatic organs and less liver injury, and the infiltration and activation of T cells were significantly enhanced in the tumor tissues of the model mice treated with the new system. Importantly, we confirmed that the new system eliminated the AFP-negative cells on AFP heterogeneous tumor models efficiently. Conclusion: Compared with the old system, the new system provided a more effective and safer strategy against HCC.

Abstract Background Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. Results In this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR Conclusions The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

Wetlands are important CO2 sinks and methane sources, and their seasonality and phenological cycle play an essential role in understanding the carbon budget. However, given the spatial heterogeneity of wetland landscapes and the coarser spatial resolution of satellites, the phenological retrievals of wetlands are challenging. Here we examined the phenology of wetlands from 30 m harmonized Landsat/Sentinel-2 (LandSent30) and 500 m MODIS satellite observations using the ground phenology network PhenoCam as a benchmark. This study used all 11 available wetland PhenoCam sites (about 30 site years), covering diverse wetland types from different climate zones. We found that the LandSent30-based phenology results were in overall higher consistency with the PhenoCam results compared to MODIS, which could be related to the better explanation capacity of LandSent30 data in the heterogeneous landscapes of wetlands. This also means that the LandSent30 has an advantage over the 500 m MODIS regarding wetland vegetation phenological retrievals. It should be noted that the LandSent30 did not show a greatly improved performance, which could be related to the specificity and complexity of the wetlands landscape. We also illustrated the potential effects of the location and observation direction of PhenoCam cameras, the selection of Region of Interest (ROI), as well as the landscape composition of the site. Overall, this study highlights the complexity of wetland phenology from both ground and remote sensing observations at different scales, which paves the road for understanding the role of wetlands in global climate change and provides a basis for understanding the real phenological changes of wetland surfaces.

Background. Lamin family members play crucial roles in promoting oncogenesis and cancer development. The values of lamin family in predicting prognosis and immunotherapy response remain largely unclarified. Our research is aimed at comprehensively estimating the clinical significance of lamin family in hepatocellular carcinoma and constructing a novel lamin family-based signature to predict prognosis and guide the precise immunotherapy. Methods. The expression features and prognostic value of LMNA, LMNB1, and LMNB2 were explored in the TCGA and GEO databases. The biological functions of LMNB1 and LMNB2 were validated by in vitro assays. A lamin family-based signature was built using the TCGA training set. The TCGA test set, entire TCGA set, and GSE14520 set were used to validate its predictive power. Univariate and multivariate analyses were performed to evaluate the independence of the lamin family-based signature from other clinicopathological characteristics. A nomogram was constructed using the lamin family-based signature and TNM stage. The associations of this signature with molecular pathways, clinical characteristics, immune cell infiltration, and immunotherapy response were analyzed. Results. Lamin family members were upregulated in HCC. Upregulation of LMNB1 and LMNB2 promoted HCC proliferation, migration, and invasion. The predictive signature was initially established based on LMNB1 and LMNB2 which could effectively identify differences in overall survival, immune cell infiltration, and clinicopathological characteristics of high- and low-risk patients. The nomogram showed high prognostic predictive accuracy. Importantly, the lamin family-based signature was correlated with immune suppression and expression of immune checkpoint molecules. Conclusions. The lamin family-based signature is a robust biomarker to predict overall survival and immunotherapy response in HCC. High-risk score patients have a poorer overall survival and might be more sensitive to immunotherapy. This signature may contribute to improving individualized prognosis prediction and precision immunotherapy for HCC patients.

Abstract The 1T phase of MoS2 has been widely reported to be highly active toward the hydrogen evolution reaction (HER), which is expected to restrict the competitive nitrogen reduction reaction (NRR). However, in this work, a prototype of active sites separation over 1T‐MoS2 is proposed by DFT calculations that the Mo‐edge and S atoms on the basal plane exhibit different catalytic NRR and HER selectivity, and a new role‐playing synergistic mechanism is also well enabled for the multistep NRR, which is further experimentally confirmed. More importantly, a self‐sacrificial strategy using g‐C3N4 as templates is proposed to synthesize 1T‐MoS2 with an ultrahigh 1T content (75.44%, named as CNMS, representing the composition elements of C, N, Mo, and S), which yields excellent NRR performances with an ammonia formation rate of 71.07 µg h–1 mg–1cat. at −0.5 V versus RHE and a Faradic efficiency of 21.01%. This work provides a promising new orientation of synchronizing the selectivity and activity for the multistep catalytic reactions.

This study describes the preparation of a lignin-based expandable flame retardant (Lignin-N-DOPO) using grafting melamine and covering 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) using the Mannich reaction. Then, through in situ growth, a metal-organic framework (MOF) HKUST-1 (e.g., Cu3(BTC)2, BTC = benzene-1,3,5-tricarboxylate)/lignin-based expandable flame retardant (F-lignin@HKUST-1) was created. Before that, lignin epoxy resin containing phosphorus (P) and nitrogen (N) components had been created by combining epoxy resin (EP) with F-lignin@HKUST-1. Thermogravimetric analysis was used to examine the thermal characteristics of epoxy resin (EP) composite. The findings indicate that the thermal stability of EP is significantly affected by the presence of F-lignin@HKUST-1. Last but not least, the activation energy (E) of EP/15% F-lignin@HKUST-1 was examined using four different techniques, including the Kissinger-SY iteration method, the Ozawa-SY iteration method, the Lee-Beck approximation-iteration method, and the Gorbatchev approximation-iteration method. It was discovered that the activation energy was significantly higher than that of lignin. Higher activation energy suggests that F-lignin@HKUST-1 pyrolysis requires more energy from the environment, which will be significant about the application of lignin-based flame retardants.

Water splitting is regarded as among the most prospective methods of generating green hydrogen. Switching electrolytes of water electrolysis from acidic to non-acidic ones will enable the use of noble-metal-free electrocatalysts and mitigate material corrosion, thus lowering the capital cost of water electrolyzers and improving their operational stability. However, increasing electrolyte pH will degrade the hydrogen evolution reaction (HER) activity because of the reduced concentration of H _3 O ^+ as reactants, making non-acidic HER sluggish. To accelerate HER, MoS _2 -based materials with the advantages of unique atomistic structure, low cost, and high abundance have been considered prospective electrocatalysts to substitute for Pt in acid. Great efforts are being spent on extending MoS _2 -based materials into the catalysis of non-acidic HER, and their further development requires clarification of the existing challenges and current progress. However, it has not yet been discussed for non-acidic HER on MoS _2 -based electrocatalysts. To mitigate the disparity, we systematically overview MoS _2 -based electrocatalysts for non-acidic HER, covering catalytic mechanisms, modulation strategies, materials development, current challenges, research progress, and perspectives. This review will contribute to the rational design of MoS _2 -based materials for high-performance HER in non-acidic conditions.

The livestock product consumption per capita in China has almost doubled in the past three decades. The planting of green fodder has increased in the agropastoral ecotone of China to meet the increasing demand for livestock feed, and the green fodder expansion can have subsequent consequences on the environment. However, information on the area and distribution of green fodder is very limited. Here, we developed a pixel- and phenology-based algorithm to map green fodder and track its dynamics in the northeastern Tibetan Plateau, a typical alpine pasture region in China, using all the available Landsat images and Google Earth Engine (GEE). We developed a simple approach for the rapid identification of green fodder fields by using a new green fodder index that considers the unique phenology of green fodder, which has higher greenness and water content in the late growing season than other vegetation. A total of 858 Landsat images were used to generate green fodder maps in northeastern Tibetan Plateau (including Zeku, Guinan, and Tongde Counties) in three periods (circa 2010, 2015, and 2019). The overall accuracies of our green fodder maps were 93–97% and the Matthews correlation coefficients were 0.76–0.83. We found a rapid expansion of green fodder from 16.3 km2 in 2010 to 136.1 km2 in 2019. Newly cultivated green fodder occurred in both existing croplands and natural grasslands. Our study demonstrated the potential of the phenology-based approach, all the available Landsat images, and GEE for tracing the historical dynamics of green fodder at 30-m resolution in alpine regions. Our findings advance our understanding of changes in forage area, production, the supply–demand gap, and the ecological and climatic consequences of green fodder expansion.

Abstract Background Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods require sufficient case reports to generate signals, limiting their usages for newly approved drugs with few (or even no) reports. Methods In this study, we propose a label propagation framework to enhance drug safety signals by combining drug chemical structures with FDA Adverse Event Reporting System (FAERS). First, we compute original drug safety signals via common signal detection algorithms. Then, we construct a drug similarity network based on chemical structures. Finally, we generate enhanced drug safety signals by propagating original signals on the drug similarity network. Our proposed framework enriches post-market safety reports with pre-clinical drug similarity network, effectively alleviating issues of insufficient cases for newly approved drugs. Results We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR, MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines. In addition, our framework identifies potential ADRs for newly approved drugs, thus paving the way for early detection of ADRs. Conclusions The proposed label propagation framework combines pre-clinical drug structures with post-market safety reports, generates enhanced drug safety signals, and can potentially help to accurately detect ADRs ahead of time. Availability The source code for this paper is available at: https://github.com/ruoqi-liu/LP-SDA.

Macrophages as components of the innate immune system play a critical role in antitumor responses. Strategies for targeting CD47 are becoming a hot spot for cancer therapy. The expression of CD47 is exercised by macrophages to make a distinction between “self” or “nonself.” Anti-CD47 antibodies block the interaction between macrophage signal regulatory protein-α (SIRPα) and tumor surface CD47. In this study, we report and assess a novel anti-CD47 blocking antibody named 2C8, which exhibits high affinity and tremendous anticancer effects. More concretely, 2C8 significantly induces macrophages, including protumorigenic subtype M2 macrophages killing tumor cells in vitro, and is revealed to be more effective than commercially available anti-CD47 mAb B6H12.2. In vivo, 2C8 controls tumor growth and extends survival of xenograft mice. The antitumor ability of 2C8 might be applicable to many other cancers. The generation of a novel CD47 antibody contributes to consolidating clinical interest in targeting macrophages for the treatment of malignancy and, moreover, as a supplement therapy when patients are resistant or refractory to other checkpoint therapies or relapse after such treatments.

The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.

Abstract Background Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task. Results In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing. Conclusion We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/. The case studies show that the server can find out novel associations, which are not included in the CTD database.

The Tibetan Plateau (TP), known as “The Roof of World”, has expansive alpine grasslands and is a hotspot for climate change studies. However, cropland expansion and increasing anthropogenic activities have been poorly documented, let alone the effects of agricultural activities on food security and environmental change in the TP. The existing cropland mapping products do not depict the spatiotemporal characteristics of the TP due to low accuracies and inconsistent cropland distribution, which is affected by complicated topography and impedes our understanding of cropland expansion and its associated environmental impacts. One of the biggest challenges of cropland mapping in the TP is the diverse crop phenology across a wide range of elevations. To decrease the classification errors due to elevational differences in crop phenology, we developed two pixel- and phenology-based algorithms to map croplands using Landsat imagery and the Google Earth Engine platform along the Brahmaputra River and its two tributaries (BRTT) in the Tibet Autonomous Region, also known as the granary of TP, in 2015–2019. Our first phenology-based cropland mapping algorithm (PCM1) used different thresholds of land surface water index (LSWI) by considering varied crop phenology along different elevations. The second algorithm (PCM2) further offsets the phenological discrepancy along elevational gradients by considering the length and peak of the growing season. We found that PCM2 had a higher accuracy with fewer images compared with PCM1. The number of images for PCM2 was 279 less than PCM1, and the Matthews correlation coefficient for PCM2 was 0.036 higher than PCM1. We also found that the cropland area in BRTT was estimated to be 1979 ± 52 km2 in the late 2010s. Croplands were mainly distributed in the BRTT basins with elevations of 3800–4000 m asl. Our phenology-based methods were effective for mapping croplands in mountainous areas. The spatially explicit information on cropland area and distribution in the TP aid future research into the effects of cropland expansion on food security and environmental change in the TP.