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Abstract While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age.

In the construction process of tunnel (roadway) drilling and blasting, intelligent charging can replace manual operation and reduce the occurrence of dangerous accidents in charging operation. However, some factors such as poor light conditions in the tunnel, small blasthole targets, and cracks in the tunnel face will cause the misdetection and missed detection of blastholes during intelligent charging. At the same time, the limited computing power of the vehicle-mounted computer is also a difficulty that restricts the use of large models for blasthole identification. The MCIW-2 deep learning model can solve the problem of high-precision blasthole detection and real-time deployment in the tunnel excavation environment. According to the size characteristics of the collected blasthole images, the model adopts the adaptive anchor frame clustering algorithm module to optimize the aspect ratio size parameters of the detection frame. The loss function WIoU (Wise Intersection over Union) with a dynamic non-monotonic focusing mechanism is used to deal with the challenge of low-quality blasthole images for achieving a high-precision detection. The MobileNetv3-Small network and CBAM (Convolutional Block Attention Module) are used to build a backbone network structure, reducing model parameters to ensure detection accuracy and meet the lightweight deployment requirements of vehicle equipment. Experiments have proved that the MCIW-2 model has reached 96.18% accuracy in blasthole recognition, and the detection speed has reached 59 fps. Compared with the benchmark YOLO (You Only Look Once) series target detection model with the smallest file, the lightweight blasthole intelligent detection model constructed is reduced by 75.86%, and the model file is only 2.80 Mb, which is better than the benchmark target detection model of the YOLO series. The MCIW-2 deep learning model is used to test the live video of the working face, and the rapid and accurate detection of blasthole is realized. The test results show that the model is suitable for the lightweight deployment requirements of intelligent charge engineering, has a good adaptability, and some significant advantages in comprehensive performance.

Abstract Timely and accurate agricultural drought monitoring and drought-driven mechanism analysis in karst basins in the context of global warming are highly important for drought disaster monitoring and sustainable ecological development in a basin. In this study, based on MODIS data, meteorological and topographic data and land use data from 2001 to 2020, we used the Sen slope, the Mann–Kendall test and a geographic detector to explore the driving mechanisms of agricultural drought caused by climate change and human activities in the karst basin of southern China from 2001 to 2020. The results showed that (1) the spatial distribution of the TVDI in the karst basin in southern China has obvious regional characteristics, showing a decreasing trend from west to east. (2) According to the interannual trend of drought, the degree of drought in the South China karst basin exhibited a weakening trend over the last 20 years, with the most severe drought occurring in 2003. Regarding the seasonal change in the TVDI, drought in spring, summer and autumn exhibited a decreasing trend, while that in winter exhibited an increasing trend, and the drought intensity decreased in the following order: spring (0.58) > autumn (0.53) > summer (0.5) > winter (0.48). (3) Single-factor detection the results showed that rainfall, temperature and elevation were the main factors driving aridification in the study area; multifactor coupling (mean) drove drought in descending order: rainfall (q = 0.424) > temperature (q = 0.340) > elevation (q = 0.219) > land use (q = 0.188) > population density (q = 0.061) > slope (q = 0.057). Therefore, revealing the mechanism of agricultural drought in karst basins through the study of this paper has important theoretical significance and provides technical guidance for drought relief in karst areas.

Abstract Compared to earthquakes and volcanoes, drought is one of the most damaging natural disasters and is mainly affected by rainfall losses, especially by the runoff regulation ability of the underlying watershed surface. Based on monthly rainfall runoff data recorded from 1980 to 2020, in this study, the distributed lag regression model is used to simulate the rainfall-runoff process in the karst distribution region of South China, and a time series of watershed lagged-flow volumes is calculated. The watershed lagged effect is analyzed by four distribution models, and the joint probability between the lagged intensity and frequency is simulated by the copula function family. The results show that (1) the watershed lagged effects simulated by the normal, log-normal, P-III and log-logistic distribution models in the karst drainage basin are particularly significant, with small mean square errors (MSEs) and significant time-scale characteristics. (2) Affected by spatiotemporal distribution differences in rainfall and the impacts of different basin media and structures, the lag response of runoff to rainfall differs significantly among different time scales. Especially at the 1-, 3- and 12-month scales, the coefficient of variation (C v ) of the watershed lagged intensity is greater than 1, while it is less than 1 at the 6- and 9-month scales. (3) The lagged frequencies simulated by the log-normal, P-III and log-logistic distribution models are relatively high (with medium, medium–high and high frequencies, respectively), while that simulated by the normal distribution is relatively low (medium–low and low frequencies). (4) There is a significant negative correlation (R

While climate and land use change are commonly known to interactively affect biodiversity worldwide, their interactive effects have rarely been explored, hindering our understanding of the attribution of biodiversity change to each of these two drivers. Here, we used ensemble species distribution models to project the potential distribution of 200 Chinese Theaceae species in the 2070s under three types of climate and land use change scenarios: (1) dynamic climate and constant land use, (2) constant climate and dynamic land use and (3) dynamic climate and dynamic land use. We further assessed the single and combined effects of climate and land use change on Theaceae species assemblages in China, and their interactive effects (i.e., additive, antagonistic and synergistic effects) on species losses and gains. Our results revealed that the mean species richness is predicted to change ranging from − 3.43–0.07 species by the 2070 s in China and the predicted species turnover values range from 1.38% to 18.65%. Additive and antagonistic interactions would dominate species losses and gains in at least 26.54% and 30.17% of the study area, respectively, while synergistical interactions would only occur in

It is widely accepted that anthropogenic climate change is driving species redistribution globally, which would potentially lead to a reshuffling of ecological communities worldwide. Here, we used tuned MaxEnt models to forecast range shifts, changes in species richness and species turnover of the giant panda with its 22 sympatric mammalian species under different future climate change scenarios. We found that more than 78% of these mammalian species will experience range contraction under all future scenarios, and importantly, at least three species were predicted to lose more suitable habitat than the giant pandas. We also predicted net losses of mammalian species under future climate change. In particular, the greatest species richness declines mainly occur in Daxiangling and Liangshan mountains, with high species losses and low species gains. Similarly, we found that the strongest species turnovers mainly occur in Qionglai and Liangshan mountains. Our findings highlight that future climate change will pose a major threat to the distribution and diversity of the giant panda with its sympatric mammalian species, challenging the effectiveness of using umbrella species to guide ecosystem conservation and management under climate change. Therefore, a more holistic ecosystem protection strategy, rather than the single-species rescue approach, should be implemented to protect mammal communities in a changing world.

Blasthole detection is crucial but challenging in tedious underground mining processes, given the diversity of surrounding rock backgrounds and uneven light intensity. However, existing algorithms have limitations in extracting image features and identifying differently sized objects. This study proposes a cascade-network-based blasthole detection method. The proposed method includes a blasthole feature extract transformation (BFET) module and a blasthole detection (BD) module. Firstly, we constructed the BFET module on the improved Cycle Generative Adversarial Network (CycleGAN) by multi-scale feature fusion. Then, we fused the convolution features of the generators in CycleGAN to obtain the enhanced feature map of the blasthole images. Secondly, the BD module was cascaded with the BFET module to accomplish the task of detecting blastholes. Results indicated that the detection accuracy of the blasthole image was significantly improved by strengthening the contrast of the image and suppressing over-exposure. The experimental results also showed that the proposed method enhanced the contrast of the image and could improve the accuracy of blasthole detection in real time. Compared with the YOLOv7 and CycleGAN+YOLOv7 methods, the detection accuracy of our method was improved by 5.34% and 2.38%, respectively.

Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (Emax) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.

Primary central nervous system lymphoma (PCNSL) is a rare subtype of extra-nodal lymphoma. The high relapse rate of PCNSL remains a major challenge to the hematologists, even though patients exhibit high sensitivity to the methotrexate-based chemotherapeutic regimens. Recently, the advent of Bruton’s tyrosine kinase inhibitor (BTKi) and CAR T treatment has made more treatment options available to a proportion of patients. However, whether BTKi monotherapy should be given alone or in combination with conventional chemotherapy is still a clinical question. The status of CAR T therapy for PCNSLs also needs to be elucidated. In this review, we summarized the latest progress on the epidemiology, pathology, clinical manifestation, diagnosis, and treatment options for PCNSLs.

Abstract Cancer is a major public health problem as one of the leading causes of death worldwide. Deciphering the molecular regulation mechanisms of tumor progression can make way for tumor diagnosis and therapy. Paternally expressed gene 10 (PEG10), located on human chromosome 7q21.3, has turned out to be an oncogene implicated in the proliferation, apoptosis and metastasis of tumors. PEG10 has been found to be positively expressed in a variety of cancers with seemingly complex expression regulation mechanisms. In this review, we focus on the most vital factors influencing PEG10 expression and recapitulate some of the currently known and potential mechanisms of PEG10 affecting tumor progression, as understanding the molecular regulatory mechanisms of tumor progression can provide potential PEG10 related diagnosis and biomarker specific targeted therapies.

Abstract Cancer has been a major public health problem that has threatened human life worldwide throughout history. The main causes that contribute to the poor prognosis of cancer are metastasis and recurrence. Cancer stem cells are a group of tumor cells that possess self-renewal and differentiation ability, which is a vital cause of cancer metastasis and recurrence. Long non-coding RNAs refer to a class of RNAs that are longer than 200 nt and have no potential to code proteins, some of which can be specifically expressed in different tissues and different tumors. Long non-coding RNAs have great biological significance in the occurrence and progression of cancers. However, how long non-coding RNAs interact with cancer stem cells and then affect cancer metastasis and recurrence is not yet clear. Therefore, this review aims to summarize recent studies that focus on how long non-coding RNAs impact tumor occurrence and progression by affecting cancer stem cell self-renewal and differentiation in liver cancer, prostate cancer, breast cancer, and glioma.

Acute myeloid leukemia (AML) is a myeloid malignancy characterized by the proliferation of abnormal and immature myeloid blasts in the bone marrow. Circular RNA (circRNA) is a novel class of long non-coding RNA with a stable circular conformation that regulates various biological processes. The aberrant expression of circRNA and its impact on AML progression has been reported by a number of studies. Despite recent advances in circRNA research, our understanding of the leukemogenic mechanism of circRNA remains very limited, and translating the current circRNA-related research into clinical practice is challenging. This review provides an update on the functional roles of and research progress on circRNAs in AML with an emphasis on mechanistic insights. The challenges and opportunities associated with circRNA-based diagonostic and therapeutic development in AML are also outlined.

Allogeneic hematopoietic stem cell transplantation (Allo-HSCT) is the only curative treatment for multiple hematologic malignancies and non-malignant hematological diseases. However, graft-vs.-host disease (GVHD), one of the main complications after allo-HSCT, remains the major reason for morbidity and non-relapse mortality. Emerging evidence has demonstrated that innate lymphoid cells (ILCs) play a non-redundant role in the pathophysiology of GVHD. In this review, we will summarize previously published data regarding the role of ILCs in the pathogenesis of GVHD.

Conflicting results have been obtained regarding the association between X-ray repair cross complementation group 1 (XRCC1) and susceptibility to hepatocellular carcinoma (HCC). In this study, associations between HCC and three polymorphisms (Arg194Trp, Arg280His, and Arg399Gln) were evaluated using a meta-analysis approach. PubMed, Web of Science, Cochrane Library, the Chinese National Knowledge Infrastructure, and the Wanfang standard database were systematically searched to identify all relevant case-control studies published through March 2018. A total of 32 case-control studies, including 13 that evaluated Arg194Trp, 14 that evaluated Arg280His, and 26 that evaluated Arg399Gln, were analyzed. In the entire study population, XRCC1 Arg399Gln was significantly associated not only with overall risk of HCC (homozygous model, OR = 1.61, 95% CI: 1.40-1.85, P < 0.05; recessive model, OR = 1.40, 95% CI: 1.23-1.59, P < 0.05) but also with the risk of HCC in Chinese patients (homozygous model, OR = 1.78, 95% CI: 1.53-2.08, P < 0.05; recessive model, OR = 1.47, 95% CI: 1.27-1.70, P < 0.05). Limiting the analysis to studies demonstrating Hardy-Weinberg equilibrium (HWE), the results were consistent and robust. Similarly, a significant association between XRCC1 Arg399Gln and HCC risk was found in healthy controls in the general population but not in hospital controls. Trial sequential analysis (TSA), false-positive report probabilities (FPRP), and combined genotype analysis revealed that XRCC1 Arg399Gln is mainly associated with susceptibility to liver cancer. However, there was no association between Arg194Trp or Arg280His and the risk of HCC. These results, indicating that the Arg399Gln polymorphism of XRCC1 is associated with the risk of HCC in the Chinese population, provide a basis for the development of improved detection and treatment approaches.

Triggering receptor expressed on myeloid cells-1 (TREM-1) is a potent amplifier of pro-inflammatory innate immune reactions, and it is an essential mediator of death in sepsis. However, the ligand for TREM-1 has not been fully identified. Previous research identified a natural ligand of TREM-1 distributed on platelets that contributed to the development of sepsis. However, the exact signal for TREM-1 recognition remains to be identified. Here, we identified actin as a TREM-1-interacting protein on platelets and found that recombinant actin could interact with recombinant TREM-1 extracellular domain directly. Furthermore, actin co-localized with TREM-1 on the surface of activated mouse macrophage RAW264.7 cells interacting with platelets. In addition, recombinant actin could enhance the inflammatory response of macrophages from wt mice but not from trem1−/− mice, and the enhancement could be inhibited by LP17 (a TREM-1 inhibitor) in a dose-dependent manner. Importantly, extracellular actin showed co-localization with TREM-1 in lung tissue sections from septic mice, which suggested that TREM-1 recognized actin during activation in sepsis. Therefore, the present study identified actin as a new ligand for TREM-1 signaling, and it also provided a link between both essential regulators of death in sepsis.

The production of IFN- I (IFN-α/β) is one of the earliest and most important host-protective responses. Interferon regulatory factor 3 (IRF3) is a critical transcriptional factor in the IFN-β signaling pathway. Although significant progress has been achieved in the regulation of IRF3, the process may be more complicated than previously considered. In the present study, heat shock protein 60 (HSP60, HSPD1) was identified as a novel IRF3-interacting protein. Overexpression of HSPD1 facilitated the phosphorylation and dimerization of IRF3 and enhanced IFN-β induction induced by SeV infection. In contrast, knockdown of endogenous HSPD1 significantly inhibited the signaling pathway. Furthermore, HSPD1 enhanced activation of the IFN-β promoter mediated by RIG-I, MDA-5, MAVS, TBK1 and IKKε but not IRF3/5D, a mock phosphorylated form of IRF3. The present study indicated that HSPD1 interacted with IRF3 and it contributed to the induction of IFN-β.

Despite advances in technology, drug discovery is still a lengthy, expensive, difficult, and inefficient process, with a low rate of success. Today, advances in biomedical science have brought about great strides in therapeutic interventions for a wide spectrum of diseases. The advent of biochemical techniques and cutting-edge bio/chemical technologies has made available a plethora of practical approaches to drug screening and design. In 2010, the total sales of the global pharmaceutical market will reach 600 billion US dollars and expand to over 975 billion dollars by 2013. The aim of this review is to summarize available information on contemporary approaches and strategies in the discovery of novel therapeutic agents, especially from the complementary and alternative medicines, including natural products and traditional remedies such as Chinese herbal medicine.

This paper investigates the impact of the popularization and usage of the Internet on household electricity consumption in China, as well as the mediating role of sleep duration. By employing data from the China Family Panel Studies (CFPS) and employing the basic ordinary least squares (OLS) model, the mediation model, and the instrumental variable (IV) approach, we derive the following conclusions. The results from the basic OLS regression indicate a positive relationship between internet usage and household electricity expenditure, implying that households that use the Internet tend to have higher electricity bills. Subsequently, by introducing sleep duration as a mediating variable, we find that internet usage leads to shorter sleep duration, indirectly resulting in increased household electricity costs. To address potential endogeneity concerns, we employ the instrumental variable approach to correct for the impact of internet usage on household electricity consumption. In addition, through heterogeneity analysis, we found that internet usage impacts households with different characteristics. [ABSTRACT FROM AUTHOR]