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In the era of antiretroviral therapy (ART), mortality among people living with the human immunodeficiency virus (HIV) has significantly decreased, yet the population of people living with HIV remains substantial. Among people living with HIV (PLWH), HIV-associated lymphoma (HAL) has surpassed Kaposi’s sarcoma to become the most common tumor in this population in developed countries. However, there remains a dearth of comprehensive and systematic understanding regarding HIV-associated lymphomas. This review aims to shed light on the changes in the immune system among PLWH and the characteristics of the immune microenvironment in HIV-associated lymphoma, with a specific focus on the immune system’s role in these individuals. Additionally, it seeks to explore recent advancements in immunotherapy for the treatment of HIV-associated lymphoma, intending to enhance strategies for immunotherapy in this specific population.

Data outsourcing has become more and more popular due to its low cost and flexibility. However, there is a problem that the cloud server used to store data is partially trusted. Searchable encryption is an efficient technology that is devoted to helping people conduct accurate searches without leaking information. Nonetheless, most existing schemes cannot support dynamic updates or meet the privacy requirements of all users. There have been some experiments to solve these issues by implementing a dynamically searchable asymmetric encryption scheme. This paper proposes an efficient searchable encryption scheme based on the Authenticator Bloom Filter (ABF). The solution can support dynamic updates and multiple users and meet forward and backward security. This paper uses an ABF to improve the efficiency of searches and updates while playing a significant role in dynamic updates. This paper designs a new token encryption scheme and file set encryption scheme, which not only helps users reduce time in searches and updates but also supports multi-user modes. Experiments show that the proposed scheme takes less time in searching and updating algorithms, especially when the keyword does not exist. The solution also takes into account the problem of history storage when updating, which reduces the unnecessary consumption of memory and avoids multiple storage states for the same file.

With the increasing connectivity between multiple smart devices in the Internet of Vehicles, privacy and security have become stringent threats due to unauthorized access. To overcome this issue, designing continuous authentication systems has become an important research topic because of the advantages of continuous monitoring of users after the initial access to the smart devices. Unfortunately, the existing systems are based on a third-party centralized structure, and most of them suffer storage pressure on equipment, thus resulting in significant security hazards and limited performance. In this paper, we propose a multistage continuous authentication system based on blockchain technology and the IPFS, which achieves decentralization and reduces storage pressure. In the first stage of authentication, we adopt Hyperledger Fabric to implement the underlying technical architecture of the blockchain to enhance the security and reliability of identity parameters. The preoutputs of the first-stage authentication are compared against behavioral biometric characteristics stored in the IPFS that aim to accomplish the final authentication. In particular, we use fuzzy extractors to deal with behavioral biometric feature templates, thus solving the privacy problem caused by user information leakage. To evaluate the security of our system, we prove the correctness of the communication protocol and two-way authentication of the scheme using BAN Logic. Furthermore, we use Hyperledger Caliper to analyze the impact of the sending rate of authentication requests on various performance parameters such as throughput, memory, and CPU utilization of the authentication system. Security and experimental results show that: (i) We solve the problem of centralized authentication and can resist replay attacks. (ii) Our scheme can maintain high throughput and effectively reach consensus. Compared to related works, the throughput is improved by 8.6%.

In light of continual societal advancement and escalating energy consumption, the pursuit of green, low-carbon, and environmentally friendly technologies has become pivotal. Bamboo, renowned for its diverse advantages encompassing swift growth, ecological compatibility, robust regenerative properties, commendable mechanical characteristics, heightened hardness, and abundant availability, has discovered applications across various domains, including furniture and construction. Nevertheless, natural bamboo materials are plagued by inherent limitations, prominently featuring suboptimal hydrophobicity and vulnerability to fracture, thereby constraining their broad-scale application. Thus, the paramount concern is to enhance the performance of bamboo materials through modification. However, prevailing reviews of bamboo modification predominantly concentrate on physical or chemical approaches, resulting in a conspicuous absence of a comprehensive overview of bamboo modification techniques. This review explores an array of bamboo treatment modalities and delivers a valuable assessment of bamboo modification, offering significant guidance for forthcoming bamboo enhancement and utilization endeavors.

Bamboo, with its inherently porous composition and exceptional renewability, stands as a symbolic embodiment of sustainability. The imperative to fortify the utilization of bamboo-based materials becomes paramount for future developments. These materials not only find direct applications in the construction and furniture sectors but also exhibit versatility in burgeoning domains such as adsorption materials and electrode components, thereby expanding their consequential influence. This comprehensive review meticulously delves into both their explicit applications and the nuanced panorama of derived uses, thereby illuminating the multifaceted nature of bamboo-based materials. Beyond their current roles, these materials hold promise for addressing environmental challenges and serving as eco-friendly alternatives across diverse industries. Lastly, we provide some insights into the future prospects of bamboo-based materials, which are poised to lead the way in further development. In conclusion, bamboo-based materials hold immense potential across diverse domains and are set to play an increasingly pivotal role in sustainable development.

In recent years, the utilization of dual-arm robots has gained substantial prominence across various industries owing to their collaborative operational capabilities. In order to achieve collision avoidance and facilitate cooperative task completion, efficient path planning plays a pivotal role. The high dimensionality associated with collaborative task execution in dual-arm robots renders existing path planning methods ineffective for conducting efficient exploration. This paper introduces a multi-agent path planning reinforcement learning algorithm that integrates an experience replay strategy, a shortest-path constraint, and the policy gradient method. To foster collaboration and avoid competition between the robot arms, the proposed approach incorporates a mechanism known as “reward cooperation, punishment competition” during the training process. Our algorithm demonstrates strong performance in the control of dual-arm robots and exhibits the potential to mitigate the challenge of reward sparsity encountered during the training process. The effectiveness of the proposed algorithm is validated through simulations and experiments, comparing the results with existing methods and showcasing its superiority in dual-arm robot path planning.

Objective: This study aims to explore the correlation between the free-triiodothyronine (FT3)-to-free-thyroxine (FT4) ratio (FT3/FT4) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). Methods: This study retrospectively analyzed 1729 patients with T2DM hospitalized in the Department of Endocrinology, Peking University International Hospital, from January 2017 to August 2021, including 1075 males and 654 females. In accordance with the FT3/FT4, the patients were divided into three groups. Results: (1) The levels of glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG) and postprandial blood glucose (PBG) among the three groups were significantly different, with the low FT3/FT4 group having the highest HbA1c, FBG and PBG among the three groups (F = 39.39, p < 0.01; F = 27.04, p < 0.01; F = 5.76, p = 0.03; respectively). (2) The proportion of DKD is the highest in the low FT3/FT4 group and the lowest in the high FT3/FT4 group (χ2 = 25.83, p < 0.01). (3) Logistic regression showed that low FT3/FT4 were independent risk factors for DKD (OR = 2.36, 95 CI% 1.63, 3.43; p = 0.01). Conclusion: A decrease in the FT3/FT4 is an independent predictor of DKD occurrence in patients with T2DM.

Application marketplaces collect ratings and reviews from users to provide references for other consumers. Many crowdturfing activities abuse user reviews to manipulate the reputation of an app and mislead other consumers. To understand and improve the ecosystem of reviews in the app market, we investigate the existence of crowdturfing based on the App Store. This paper reports a measurement study of crowdturfing and its reviews in the App Store. We use a sliding window to obtain the relationship graph between users and the community detection method to binary classify the detected communities. Then, we measure and analyze the crowdturfing obtained from the classification and compare them with genuine users. We analyze several features of crowdturfing, such as ratings, sentiment scores, text similarity, and common words. We also investigate which apps crowdturfing often appears in and reveal their role in app ranking. These insights are used as features in machine learning models, and the results show that they can effectively train classifiers and detect crowdturfing reviews with an accuracy of up to 98.13%. This study reveals malicious crowdfunding practices in the App Store and helps to strengthen the review security of app marketplaces.

(1) Background: To explore the correlation between central and peripheral thyroid sensitivity indices and macrosomia in euthyroid pregnant women and to provide clinical basis for the prevention and treatment of macrosomia. (2) Methods: This study is a prospective study. A total of 1176 euthyroid women in early pregnancy in the obstetrics department of Peking University International Hospital from December 2017 to March 2019 were enrolled. The women were divided into two groups, namely the macrosomia and non-macrosomia groups, according to birth weight. (3) Results: The level of free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), thyroid feedback quantile-based index (TFQI), thyrotropin-T4 resistance index (TT4RI), thyroid-stimulating hormone index (TSHI), and free triiodothyronine/free thyroxine (FT3/FT4) in the macrosomia group was higher than that in the non-macrosomia group (p < 0.05). The multivariate logistic regression model showed that FT3, TFQI, TT4RI, TSHI, and FT3/FT4 were independent risk factors for macrosomia in early pregnancy after adjusting for age, body mass index, parity, blood pressure, blood glucose, and blood lipid levels (p < 0.05, respectively). (4) Conclusions: TFQI, TT4RI, TSHI, and FT3/FT4 are independent risk factors for fetal macrosomia in early pregnancy in euthyroid women.

Microwave-absorbing materials have attracted extensive attention due to the development of electronic countermeasures. In this study, novel nanocomposites with core–shell structures based on the core of Fe-Co nanocrystals and the shell of furan methylamine (FMA)-modified anthracite coal (Coal-F) were designed and fabricated. The Diels-Alder (D-A) reaction of Coal-F with FMA creates a large amount of aromatic lamellar structure. After the high-temperature treatment, the modified anthracite with a high degree of graphitization showed an excellent dielectric loss, and the addition of Fe and Co effectively enhanced the magnetic loss of the obtained nanocomposites. In addition, the obtained micro-morphologies proved the core–shell structure, which plays a significant role in strengthening the interface polarization. As a result, the combined effect of the multiple loss mechanism promoted a remarkable improvement in the absorption of incident electromagnetic waves. The carbonization temperatures were specifically studied through a setting control experiment, and 1200 °C was proved to be the optimum parameter to obtain the best dielectric loss and magnetic loss of the sample. The detecting results show that the 10 wt.% CFC-1200/paraffin wax sample with a thickness of 5 mm achieves a minimum reflection loss of −41.6 dB at a frequency of 6.25 GHz, indicating an excellent microwave absorption performance.

Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication through behavioral biometrics. However, few of them consider continuous user authentication on multiple smart devices. In this paper, we investigate user authentication from a new perspective—continuous authentication on multi-devices, that is, continuously authenticating users after both initial access to one device and transfer to other devices. In contrast to previous studies, we propose a continuous user authentication method that exploits behavioral biometric identification on multiple smart devices. In this study, we consider the sensor data captured by accelerometer and gyroscope sensors on both smartphones and tablets. Furthermore, multi-device behavioral biometric data are utilized as the input of our optimized neural network model, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. In particular, we construct two-dimensional domain images to characterize the underlying features of sensor signals between different devices and then input them into our network for classification. In order to strengthen the effectiveness and efficiency of authentication on multiple devices, we introduce an adaptive confidence-based strategy by taking historical user authentication results into account. This paper evaluates the performance of our multi-device continuous user authentication mechanism under different scenarios, and extensive empirical results demonstrate its feasibility and efficiency. Using the mechanism, we achieved mean accuracies of 99.8% and 99.2% for smartphones and tablets, respectively, in approximately 2.3 s, which shows that it authenticates users accurately and quickly.

Forest cover data are fundamental to sustainable forest management and conservation. Available medium-resolution publicly shared forest-related datasets provide primary information on forest distribution. The evaluation of relevant datasets is of great importance to learn about the differences, characterize the accuracy, and provide a reference for rational use. This study presents an evaluation and analysis of the forest-related datasets in China around 2020, including TreeCover and the forest-related layer (latter referred to as the forest datasets) in WorldCover, Esri land cover, FROM-GLC10, GlobeLand30, and GLC_FCS30. These forest datasets, that are obtained by aggregating forest-related lasses based on the classification schemes, are analyzed from spatial consistency and accuracy comparison. The results illustrate that forest datasets with 10m resolution are generally more precise than those with 30m resolution in China. WorldCover shows the highest accuracy, with producer accuracy and user accuracy of 91.4% and 87.09%, respectively. These datasets exhibit high accuracy but great spatial inconsistency. The more consistent the regions are, the more accurate the accuracy is. High consistency (≥5, i.e., classified into forests by five datasets) areas account for 56.49% of areas of forest classified (AFC), while the area of low consistency (≤2) reach 25.51% of AFC. The analysis delves into the datasets, offering a reliable reference for the usage of these datasets.

Connected and automated vehicles (CAVs) involving massive advanced sensors and electronic control units (ECUs) bring intelligentization to the transportation system and conveniences to human mobility. Unfortunately, these automated vehicles face security threats due to complexity and connectivity. Especially, the existing in-vehicle network protocols (e.g., controller area network) lack security consideration, which is vulnerable to malicious attacks and puts people at large-scale severe risks. In this paper, we propose a novel anomaly detection model that integrates a continuous wavelet transform (CWT) and convolutional neural network (CNN) for an in-vehicle network. By transforming in-vehicle sensor signals in different segments, we adopt CWT to calculate wavelet coefficients for vehicle state image construction so that the model exploits both the time and frequency domain characteristics of the raw data, which can demonstrate more hidden patterns of vehicle events and improve the accuracy of the follow-up detection process. Our model constructs a two-dimensional continuous wavelet transform scalogram (CWTS) and utilizes it as an input into our optimized CNN. The proposed model is able to provide local transient characteristics of the signals so that it can detect anomaly deviations caused by malicious behaviors, and the model is effective for coping with various vehicle anomalies. The experiments show the superior performance of our proposed model under different anomaly scenarios. Compared with related works, the average accuracy and F1 score are improved by 2.51% and 2.46%.

Objective: to analyze the association between blood lipid metabolism and osteoporosis (OP) in older adults with type 2 diabetes mellitus (T2DM). Methods: a total of 1158 older patients with T2DM treated by the Department of Endocrinology, Peking University International Hospital, were retrospectively analyzed, including 541 postmenopausal women and 617 men. Results: (1) Levels of low-density lipoprotein cholesterol (LDL-C) were significantly higher in the OP group, while levels of high-density lipoprotein cholesterol (HDL-C) were higher in the non-osteoporotic group (both p < 0.05). (2) Age, parathyroid hormone (PTH), total cholesterol (TC) and LDL-C were negatively linked to the patients’ bone mineral density (BMD) (all p < 0.05), while the body mass index (BMI), uric acid (UA) level, HDL-C level and glomerular filtration rate (eGFR) were positively related to their BMD (all p < 0.05). (3) In postmenopausal women, after adjustment for other indexes, raised LDL-C is an independent risk factor for OP (OR = 3.38, 95% CI 1.64, 6.98, p < 0.05) while raised HDL-C is protective (OR = 0.49, 95% CI 0.24, 0.96, p < 0.05). However, raised HDL-C was protective against OP (OR = 0.07, 95% CI 0.01, 0.53, p < 0.05). Conclusion: In older T2DM patients, the effect of blood lipid levels is related to sex. Our study conducted a detailed sex stratification. In addition to seeing the traditional risk factors of OP, such as age, sex, and BMI, we comprehensively analyzed the correlation between the blood glucose level, complications, and blood lipids with OP. HDL-C is a protective factor for OP in both men and women, while LDL-C independently predicts OP in postmenopausal women.

Oncolytic viruses are being developed as novel strategies for cancer therapy. Our previous studies have shown that vaccinia viruses armed with marine lectins improved the antitumor efficacy in diverse cancer types. The objective of this study was to assess the cytotoxic effects of oncoVV harboring Tachypleus tridentatus lectin (oncoVV-TTL), Aphrocallistes vastus lectin (oncoVV-AVL), white-spotted charr lectin (oncoVV-WCL), and Asterina pectinifera lectin (oncoVV-APL) on HCC. Our data revealed that the effects of recombinant viruses on Hep-3B cells were oncoVV-AVL > oncoVV-APL > oncoVV-TTL > oncoVV-WCL; oncoVV-AVL showed stronger cytotoxicity than oncoVV-APL, while oncoVV-TTL/WCL had no effect on cell killing in Huh7 cells, and PLC/PRF/5 cells exhibited sensitivity to oncoVV-AVL/TTL but not to oncoVV-APL/WCL. The cytotoxicity of oncoVV-lectins could be enhanced by apoptosis and replication in a cell-type-dependent manner. Further research revealed that AVL may mediate various pathways, including MAPK, Hippo, PI3K, lipid metabolism, and androgen pathways through AMPK crosstalk, to promote oncoVV replication in HCC in a cell-dependent manner. OncoVV-APL replication could be affected by AMPK/Hippo/lipid metabolism pathways in Hep-3B cells, AMPK/Hippo/PI3K/androgen pathways in Huh7 cells, and AMPK/Hippo pathways in PLC/PRF/5 cells. OncoVV-WCL replication was also multi-mechanistic, which could be affected by AMPK/JNK/lipid metabolism pathways in Hep-3B cells, AMPK/Hippo/androgen pathways in Huh7 cells, and AMPK/JNK/Hippo pathways in PLC/PRF/5 cells. In addition, AMPK and lipid metabolism pathways may play critical roles in oncoVV-TTL replication in Hep-3B cells, and oncoVV-TTL replication in Huh7 cells may depend on AMPK/PI3K/androgen pathways. This study provides evidence for the application of oncolytic vaccinia viruses in hepatocellular carcinoma.

Soybean-seed development is controlled in multiple ways, as in many known regulating genes. Here, we identify a novel gene, Novel Seed Size (NSS), involved in seed development, by analyzing a T-DNA mutant (S006). The S006 mutant is a random mutant of the GmFTL4pro:GUS transgenic line, with phenotypes with small and brown seed coats. An analysis of the metabolomics and transcriptome combined with RT-qPCR in the S006 seeds revealed that the brown coat may result from the increased expression of chalcone synthase 7/8 genes, while the down-regulated expression of NSS leads to small seed size. The seed phenotypes and a microscopic observation of the seed-coat integument cells in a CRISPR/Cas9-edited mutant nss1 confirmed that the NSS gene conferred small phenotypes of the S006 seeds. As mentioned in an annotation on the Phytozome website, NSS encodes a potential DNA helicase RuvA subunit, and no such genes were previously reported to be involved in seed development. Therefore, we identify a novel gene in a new pathway controlling seed development in soybeans.

Our previous studies demonstrated that arming vaccinia viruses with marine lectins enhanced the antitumor efficacy in several cancer cells. This study aims to compare the efficacy of oncolytic vaccinia viruses harboring Tachypleus tridentatus lectin (oncoVV-TTL), Aphrocallistes vastus lectin (oncoVV-AVL), white-spotted charr lectin (oncoVV-WCL), and Asterina pectinifera lectin (oncoVV-APL) in breast cancer cells (BC). These results indicated that oncoVV-AVL elicited the highest anti-tumor effect, followed by oncoVV-APL, while oncoVV-TTL and oncoVV-WCL had lower effects in BC. Further studies showed that apoptosis and replication may work together to enhance the cytotoxicity of oncoVV-lectins in a cell-type dependent manner. TTL/AVL/APL/WCL may mediate multiple pathways, including ERK, JNK, Hippo, and PI3K pathways, to promote oncoVV replication in MDA-MB-231 cells. In contrast, these pathways did not affect oncoVV-TTL/AVL/APL/WCL replication in MCF-7 cells, suggesting that the mechanisms of recombinant viruses in MCF-7 (ER+, PR+) and MDA-MB-231 (TNBC) cells were significantly different. Based on this study, we hypothesized that ER or PR may be responsible for the differences in promoting viral replication and inducing apoptosis between MCF-7 and MDA-MB-231 cells, but the specific mechanism needs to be further explored. In addition, small-molecule drugs targeting key cellular signaling pathways, including MAPK, PI3K/Akt, and Hippo, could be conjunction with oncoVV-AVL to promote breast cancer therapy, and key pathway factors in the JNK and PI3K pathways may be related to the efficacy of oncoVV-APL/TTL/WCL. This study provides a basis for applying oncolytic vaccinia virus in breast carcinoma.

In order to explore the methods of recycling waste paper, reduce environment pollution, and develop a circular economy, the application of waste corrugated paper to the strengthening of polylactic acid (PLA) was studied. Plant fiber from waste corrugated paper (WCPF) was used to prepare WCPF/PLA composite via co-extrusion. The WCPF was extracted from the waste corrugated paper by beating in a Valli beating machine and grinding in a disc grinder. KH-550 coupling agent was used to modify the surface of WCPF to improve the interface adhesive strength between the WCPF and PLA matrix. The effects of the contents of WCPF and KH-550 coupling agent on the mechanical properties, microstructure, crystallization properties, and thermostability of the WCPF/PLA composite were studied. The results show that the WCPF can be well separated from each other. The WCPF can be uniformly dispersed in the PLA matrix through a co-extrusion process. WCPF can increase the mechanical strength and deformation resistance ability of WCPF/PLA composite, and KH-550 coupling agent can further improve that of the WCPF/PLA composite. This study is of obvious significance to the recycling of waste paper and the development of a circular economy.

Forests are an essential part of the ecosystem and play an irreplaceable role in maintaining the balance of the ecosystem and protecting biodiversity. The monitoring of forest distribution plays an important role in the conservation and management of forests. This paper analyzes and compares the performance of imagery from GF-1 WFV, Landsat 8, and Sentinel-2 satellites with respect to forest/non-forest classification tasks using the random forest algorithm (RF). The results show that in the classification task of this paper, although the differences in classification accuracy among the three satellite datasets are not remarkable, the Sentinel-2 data have the highest accuracy, GF-1 WFV the second highest, and Landsat 8 the lowest. In addition, it was found that remotely sensed data of different processing levels show little influence on the classification accuracy with respect to the forest/non-forest classification task. However, the classification accuracy of the top of the atmosphere reflectance product was the most stable, and the vegetation index has a marginal effect on the distinction between forest and non-forest areas.

Accurate information on grassland above-ground biomass (AGB) is critical to better understanding the carbon cycle and conserve grassland resources. As a climate-sensitive key ecological function area, it is important to accurately estimate the grassland AGB of the Tibetan Plateau. Sentinel-2 (S2) images have advantages in reducing mixed pixels and the scale effect for remote sensing, while the data volume is correspondingly larger. In order to improve the estimation accuracy while reducing the data volume required for AGB estimation and improving the computational efficiency, this study used the Recursive Feature Elimination (RFE) algorithm to find the optimal feature set and compared the performance of the Cubist, Gradient Boosting Regression Tree (GBRT), random forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms for estimating AGB. In this study, ten S2 bands, ten S2-derived vegetation indexes, 218 pieces of AGB field survey data, four types of meteorological data and three types of topographic data were used as the alternative input features for the AGB estimation model. The impurity and permutation importance were used as the feature importance calculation method input to the RFE, and the Cubist, GBRT, RF and XGBoost algorithms were used to construct the AGB estimation models. The results showed that the RF algorithm based on the monthly average temperature (T), elevation, Normalized Difference Phenology Index (NDPI), Normalized Difference Infrared Index (NDII) and Palmer Drought Severity Index (PDSI) performed best (R2 = 0.8838, RMSE = 35.05 g/m2, LCCC = 2.44, RPPD = 0.91). The above findings suggest that the RF model based on the features related to temperature, altitude, humidity and leaf water content is beneficial to estimate the grassland AGB on the Tibetan Plateau.

The stable genetic transformation of soybean is time-consuming and inefficient. As a simple and practical alternative method, hairy root transformation mediated by Agrobacterium rhizogenes is widely applied in studying root-specific processes, nodulation, biochemical and molecular functions of genes of interest, gene editing efficiency of CRISPR/Cas9, and biological reactors and producers. Therefore, many laboratories have developed unique protocols to obtain hairy roots in composite plants composed of transgenic roots and wild-type shoots. However, these protocols still suffer from the shortcomings of low efficiency and time, space, and cost consumption. To address this issue, we developed a new protocol efficient regeneration and transformation of hairy roots (eR&T) in soybean, by integrating and optimizing the main current methods to achieve high efficiency in both hairy root regeneration and transformation within a shorter period and using less space. By this eR&T method, we obtained 100% regeneration of hairy roots for all explants, with an average 63.7% of transformation frequency, which promoted the simultaneous and comparative analysis of the function of several genes. The eR&T was experimentally verified Promoter:GUS reporters, protein subcellular localization, and CRISPR/Cas9 gene editing experiments. Employing this approach, we identified several novel potential regulators of nodulation, and nucleoporins of the Nup107-160 sub-complex, which showed development-dependent and tissue-dependent expression patterns, indicating their important roles in nodulation in soybean. Thus, the new eR&T method is an efficient and economical approach for investigating not only root and nodule biology, but also gene function.

Ultra-wideband (UWB) technology is used for indoor positioning, but its positioning accuracy is usually degenerated by various obstacles in the indoor environment because of non-line-of-sight (NLOS). Facing the complex and changeable indoor environment, an indoor positioning system with UWB based on a digital twin is presented in this paper. The indoor positioning accuracy is improved with a perception–prediction feedback of cyber-physics space in this indoor positioning system. In addition, an anchor layout method with virtuality–reality interaction and an error mitigation method based on neural networks is put forward in this system. Finally, a case study is presented to validate this indoor positioning system with a significant improvement in positioning accuracy.

Polysaccharide is one of the main components of Codonopsis radix (CR) and has good immune activity. However, the immune activity of CR polysaccharides with different molecular weights has not been systematically screened. In this study, the polysaccharides of CR from Pingshun of Shanxi Province (PSDSs) were first divided into two groups using ultrafiltration: 3.3 kDa (PSDSs-1) and more than 2000 kDa (PSDSs-2). The immunomodulatory effects of PSDSs with different molecular weights were evaluated in vitro and in vivo. In vitro experimental results showed that compared with Lipopolysaccharide-induced macrophages, PSDSs-1 increased TNF-α and IL-6 levels and decreased IL-10. Meanwhile, PSDSs-2 showed the opposite effect, indicating the difference in pro- and anti-inflammatory activities of PSDSs with different molecular weights. The immunosuppressive model of cyclophosphamide proved that PSDSs have immune-promoting function, with PSDSs-1 exhibiting a better effect than PSDSs-2. In vitro and in vivo experiments illustrated the complexity of PSDS immunomodulation. Further research on the functions of PSDs with different molecular weights is needed to lay a foundation for their classification and application.

l-Cysteine is an important sulfur-containing amino acid with numerous applications in the pharmaceutical and cosmetic industries. The microbial production of l-cysteine has received substantial attention, and the supply of the precursor l-serine is important in l-cysteine biosynthesis. In this study, to achieve l-cysteine overproduction, we first increased l-serine production by deleting genes involved in the pathway of l-serine degradation to glycine (serine hydroxymethyl transferase, SHMT, encoded by glyA genes) in strain 4W (with l-serine titer of 1.1 g/L), thus resulting in strain 4WG with l-serine titer of 2.01 g/L. Second, the serine-biosensor based on the transcriptional regulator NCgl0581 of C. glutamicum was constructed in E. coli, and the validity and sensitivity of the biosensor were demonstrated in E. coli. Then 4WG was further evolved through adaptive laboratory evolution (ALE) combined with serine-biosensor, thus yielding the strain 4WGX with 4.13 g/L l-serine production. Moreover, the whole genome of the evolved strain 4WGX was sequenced, and ten non-synonymous mutations were found in the genome of strain 4WGX compared with strain 4W. Finally, 4WGX was used as the starting strain, and deletion of the l-cysteine desulfhydrases (encoded by tnaA), overexpression of serine acetyltransferase (encoded by cysE) and the key enzyme of transport pathway (encoded by ydeD) were performed in strain 4WGX. The recombinant strain 4WGX-∆tnaA-cysE-ydeD can produce 313.4 mg/L of l-cysteine using glycerol as the carbon source. This work provides an efficient method for the biosynthesis of value-added commodity products associated with glycerol conversion.

Oncolytic vaccinia virus has been developed as a novel cancer therapeutic drug in recent years. Our previous studies demonstrated that the antitumor effect of oncolytic vaccina virus harboring Aphrocallistes vastus lectin (oncoVV-AVL) was significantly enhanced in several cancer cells. In the present study, we investigated the underlying mechanisms of AVL that affect virus replication and promote the antitumor efficacy of oncolytic virus in hepatocellular carcinoma (HCC). Our results showed that oncoVV-AVL markedly exhibited antitumor effects in both hepatocellular carcinoma cell lines and a xenograft mouse model. Further investigation illustrated that oncoVV-AVL could activate tumor immunity by upregulating the expression of type I interferons and enhance virus replication by inhibiting ISRE mediated viral defense response. In addition, we inferred that AVL promoted the ability of virus replication by regulating the PI3K/Akt, MAPK/ERK, and Hippo/MST pathways through cross-talk Raf-1, as well as metabolism-related pathways. These findings provide a novel perspective for the exploitation of marine lectins in oncolytic therapy.

In the context of carbon neutrality, forest cover change detection has become a key topic of global environmental monitoring. As a large-scale monitoring technique, remote sensing has received obvious attention in various land cover observation applications. With the rapid development of deep learning, remote sensing change detection combined with deep neural network has achieved high accuracy. In this paper, the deep neural network is used to study forest cover change with Landsat images. The main research ideas are as follows. (1) A Siamese detail difference neural network is proposed, which uses a combination of concatenate weight sharing mode and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and improves the utilization rate of the sample data set. The experimental results on Landsat 8 images show that the proposed method outperforms several Siamese neural network methods in forest cover change extraction.

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

Probiotics have been shown to benefit patients with constipation and depression, but whether they specifically alleviate constipation in patients with depression remains unclear. The aim of this study was to investigate the effect of Lacticaseibacillus paracasei strain Shirota (LcS), formerly Lactobacillus casei strain Shirota, on constipation in patients with depression with specific etiology and gut microbiota and on depressive regimens. Eighty-two patients with constipation were recruited. The subjects consumed 100 mL of a LcS beverage (108 CFU/mL) or placebo every day for 9 weeks. After ingesting beverages for this period, we observed no significant differences in the total patient constipation-symptom (PAC-SYM) scores in the LcS group when compared with the placebo group. However, symptoms/scores in item 7 (rectal tearing or bleeding after a bowel movement) and items 8–12 (stool symptom subscale) were more alleviated in the LcS group than in the placebo group. The Beck Depression Index (BDI) and Hamilton Depression Rating Scale (HAMD) scores were all significantly decreased, and the degree of depression was significantly improved in both the placebo and LcS groups (p < 0.05), but there was no significant difference between the groups. The LcS intervention increased the beneficial Adlercreutzia, Megasphaera and Veillonella levels and decreased the bacterial levels related to mental illness, such as Rikenellaceae_RC9_gut_group, Sutterella and Oscillibacter. Additionally, the interleukin (IL)-1β, IL-6, and tumor necrosis factor-α (TNF-α) levels were significantly decreased in both the placebo and LcS groups (p < 0.05). In particular, the IL-6 levels were significantly lower in the LcS group than the placebo group after the ingestion period (p < 0.05). In conclusion, the daily consumption of LcS for 9 weeks appeared to relieve constipation and improve the potentially depressive symptoms in patients with depression and significantly decrease the IL-6 levels. In addition, the LcS supplementation also appeared to regulate the intestinal microbiota related to mental illness.

Seagrass meadows play critical roles in supporting a high level of biodiversity but are continuously threatened by human activities, such as sea reclamation. In this study, we reported on a large seagrass (Zostera marina L.) meadow in Caofeidian shoal harbor in the Bohai Sea of northern China. We evaluated the environmental impact of sea reclamation activities using Landsat imagery (1974–2019) by mapping seagrass meadow distribution changes. ISODATA was adopted for the unsupervised classification and mapping of seagrass beds. The error matrix developed using the in situ data obtained from acoustic surveys for Landsat 8OLI image classification was 87.20% accurate. The maps showed rapidly increasing changes in seagrass meadows as the amount of reclaimed land increased. Some seagrass meadows experienced large-scale changes, and sea reclamation has been suggested as the main factor responsible for habitat loss, which results from physical damage, excessive sedimentation, and increased turbidity caused by reclamation. In addition, habitat degradation may have resulted from three storm surges induced by typhoons in 1992–1998. Fortunately, land reclamation, forming an artificial “longshore bar”, buffers seagrass meadows from wave actions, providing relatively sheltered conditions, which has allowed a large habitat increase since 2012. These were the largest eelgrass meadows (3,217.32 ha), with a peripheral area of ~100 km2, in the Bohai Sea of northern China in 2019. However, the existing largest eelgrass beds in China are threatened by trawling, clam harvesting (especially clam sucking), channel dredging, and culture pond construction. Our work will help coastal managers monitor the environmental impacts of reclamation activities on seagrass meadows on a large spatio-temporal scale and will also provide information for seagrass restoration using artificial “longshore bars”.

Peeling failure at the interface is one of the main failure modes for CFRP (Carbon Fiber-Reinforced Polymer)-reinforced steel structures. However, there are very few reported studies on the bond-slip relationship at the CFRP-steel interface. A series of simple shear tests were carried out in the present paper. The influence of the fiber fabric’s width and thickness, the surface roughness of the steel sheet, and the thickness of the adhesive layer on the bonding performance of the CFRP fabric to steel interface was considered. The interface constitutive model and bonding strength model were further established under multiple factors. The results show that with the decrease of the surface roughness, the interface’ ultimate peeling load increases gradually, and the failure has a tendency to develop from a glue-steel interface to a glue-CFRP interface. The test pieces that were subjected to sand blasting obtained the peak value of the ultimate peeling load. This indicates that sand blasting can effectively enhance the interface bonding strength. The theoretical values obtained via the interface prediction model are consistent with the experimental values. This proves that the newly developed interface prediction model can effectively predict the local bonding slip and bonding strength of the interface.

The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.

A novel chiral 1,10-phenanthroline-based fluorescent sensor was designed and synthesized from optical active β-amino acids. It used 1,10-phenanthroline moiety as a fluorescent signaling site and binding site, with optically active β-amino acids as a chiral barrier site. Notably, the optically active β-amino acids were obtained by a Lewis base catalyzed hydrosilylation of β-enamino esters according to our former work. The chiral sensor has been used to conduct the enantioselective recognition of chiral mono and dicarboxylic acids derivatives. Using this fluorescent sensor, a moderate “turn-off” fluorescence-diminishment response towards enantiomer of tartaric acids, and proline was observed. It found that l-enantiomers quench the chiral fluorescence sensor more efficiently than d-enantiomers due to the absolute configuration of the β-amino acid.

We report here an efficient and green method for Biginelli condensation reaction of aldehydes, β-ketoesters and urea or thiourea catalyzed by Brønsted acidic ionic liquid [Btto][p-TSA] under solvent-free conditions. Compared to the classical Biginelli reaction conditions, the present method has the advantages of giving good yields, short reaction times, near room temperature conditions and the avoidance of the use of organic solvents and metal catalyst.

A series of poly(thioether)s containing silicon atom with unconventional fluorescence were synthesized via successive thiol click reaction at room temperature. Although rigid π-conjugated structure did not exist in the polymer chain, the poly(thioether)s exhibited excellent fluorescent properties in solutions and showed visible blue fluorescence in living cells. The strong blue fluorescence can be attributed to the aggregation of lone pair electron of heteroatom and coordination between heteroatom and Si atom. In addition, the responsiveness of poly(thioether) to metal ions suggested that the selectivity of poly(thioether) to Fe3+ ion could be enhanced by end-modifying with different sulfhydryl compounds. This study further explored their application in cell imaging and studied their responsiveness to Fe3+ in living cells. It is expected that the described synthetic route could be extended to synthesize novel poly(thioether)s with superior optical properties. Their application in cell imaging and ion detection will broaden the range of application of poly(thioether)s.

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014⁻2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

Phellodendri Amurensis Cortex (PAC) is a well-known herbal medicine in China with complex components, but the previous research has mostly focused on its alkaloids analysis. For the first time, a simpler and more efficient method was proposed in this paper to simultaneously determine the content of three different kinds of compounds—phenolic acids, alkaloids and limonoids—in PAC. The phenolic acids included 3-O-feruloylquinic acid, 4-O-feruloylquinic acid and syringin. The alkaloids include magnoflorine, phellodendrine, jatrorrhizine, palmatine and berberine, while the limonoids include obaculactone and obacunone. An approach combining multi-wavelength and HPLC-DAD was used in this study due to the great difference in maximum absorption wavelength of the various components. Four wavelengths at 215, 275, 280 and 310 nm, respectively, were chosen for monitoring. It has been indicated through appropriate tests that this approach is of high accuracy, good repeatability and stability and provides a scientific basis for the quality assessment of PAC and associated derivatives. In addition, the chromatographic fingerprints method combined with multivariate statistical analysis chosen in this study was proved to be effective and reasonable for an accurate classification of 33 batches of samples collected from different locations.

A Radio Frequency Identification (RFID) grouping-proof protocol is to generate an evidence of the simultaneous existence of a group of tags and it has been applied to many different fields. For current grouping-proof protocols, there still exist some flaws such as low grouping-proof efficiency, being vulnerable to trace attack and information leakage. To improve the secure performance and efficiency, we propose a lightweight RFID grouping-proof protocol based on parallel mode and DHCP (Dynamic Host Configuration Protocol) mechanism. Our protocol involves multiple readers and multiple tag groups. During the grouping-proof period, one reader and one tag group are chosen by the verifier by means of DHCP mechanism. When only a part of the tags of the chosen group exist, the protocol can also give the evidence of their co-existence. Our protocol utilizes parallel communication mode between reader and tags so as to ensure its grouping-proof efficiency. It only uses Hash function to complete the mutual authentication among verifier, readers and tags. It can preserve the privacy of the RFID system and resist the attacks such as eavesdropping, replay, trace and impersonation. Therefore the protocol is secure, flexible and efficient. It only uses some lightweight operations such as Hash function and a pseudorandom number generator. Therefore it is very suitable to some low-cost RFID systems.