Search

Litter decomposition is a crucial biochemical process regulated by microbial activities in the forest ecosystem. However, the dynamic response of the fungal community during litter decomposition to vegetation changes is not well understood. Here, we investigated the litter decomposition rate, extracellular enzyme activities, fungal community, and nutrient cycling-related genes in leaf and twig litters over a three-year decomposition period in a pure Liquidamabar formosana forest and a mixed L. formosana/Pinus thunbergii forest. The result showed that during the three-year decomposition, twig litter in the mixed forest decomposed faster than that in the pure forest. In both leaf litter and twig litter, β-cellobiosidase and N-acetyl-glucosamidase exhibited higher activities in the mixed forest, whereas phosphatase, β-glucosidase, and β-xylosidase were higher in the pure forest. The fungal α-diversity were higher in both litters in the pure forest compared to the mixed forest, with leaf litter showing higher α-diversity than twig litter. Fungal species richness and α-diversity within leaf litter increased as decomposition progressed. Within leaf litter, Basidiomycota dominated in the mixed forest, while Ascomycota dominated in the pure forest. Funguild analysis revealed that Symbiotroph and ectomycorrhizal fungi were more abundant in the mixed forest compared to the pure forest. In the third-year decomposition, genes related to phosphorus cycling were most abundant in both forests, with the pure forest having a higher abundance of cex and gcd genes. Fungal community structure, predicted functional structure, and gene composition differed between the two forest types and between the two litter types. Notably, the fungal functional community structure during the first-year decomposition was distinct from that in the subsequent two years. These findings suggest that dominant tree species, litter quality, and decomposition time all significantly influence litter decomposition by attracting different fungal communities, thereby affecting the entire decomposition process.

Microwave coincidence imaging (MCI) represents a novel forward-looking radar imaging method with high-resolution capabilities. Most MCI methods rely on random frequency modulation to generate stochastic radiation fields, which introduces the complexity of radar systems and imposes limitations on imaging quality under noisy conditions. In this paper, microwave coincidence imaging with phase-coded stochastic radiation fields is proposed, which generates spatio-temporally uncorrelated stochastic radiation fields with phase coding. Firstly, the radiation field characteristics are analyzed, and the coding sequences are designed. Then, pulse compression is applied to achieve a one-dimensional range image. Furthermore, an azimuthal imaging model is built, and a reference matrix is derived from the frequency domain. Finally, sparse Bayesian learning (SBL) and alternating direction method of multipliers (ADMM)-based total variation are implemented to reconstruct targets. The methods have better imaging performance at low signal-to-noise ratios (SNRs), as shown by the imaging results and mean square error (MSE) curves. In addition, compared with the SBL and ADMM-based total variation methods based on the direct frequency-domain solution, the proposed method’s computational complexity is reduced, giving it great potential in forward-looking high-resolution scenarios, such as autonomous obstacle avoidance with vehicle-mounted radar and terminal guidance.

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades.

The fluid pump was the key component of the formation tester; the pump cylinder, piston, and piston rod of the fluid pump often suffer from wear scratches and seal failure, which greatly reduces the service reliability of the instrument. To improve the wear resistance of the fluid pump, 17-4PH steel specimens were treated by boronizing at 750 °C for 20 h. Specimens with and without boronizing were studied by OM, SEM, XRD, microhardness test, and wear resistance test. Layers of about 60 μm thickness formed during boronization contain a mixture of FeB, CrB, and α(B)-Fe phases, which leads to a significant improvement in microhardness (from 336 to 980 HV) and wear rate (from 16.4 × 10−5 mm3/Nm to 3.3 × 10−5 mm3/Nm). The pump cylinder and the fluid-pump piston rod were boronized and assembled into the pumping module, which passed the indoor durability test for 90 h and did not show obvious surface wear after 60 h of field experience. For the first time, the boronization process extends the service time of the fluid pump, improving the wear resistance of the pump cylinder and piston rod.

Gastrodia elata, commonly known as Tian Ma, is a perennial mycoheterotrophic orchid. Qianyang Tian Ma (QTM), a geographical indication agricultural product from Hongjiang City, Hunan Province, China, is primarily characterized by the red variety, G. elata f. elata. A severe outbreak of tuber brown rot disease was documented in QTM during the harvesting season in Hunan. The fungal pathogen associated with the disease was isolated on potato saccharose agar (PSA) and identified through morphological and phylogenetic analyses. Pathogenicity tests were performed on healthy tubers of G. elata f. elata. The results showed that the representative isolate, named TMB, produced white hyphal colonies with a ring structure, broom-like phialides, partially curved ellipsoidal conidia, and orange–yellow spherical ascocarps on PSA. Phylogenetic analysis of the ITS, LSU, rpb2 and tub2 sequences using Bayesian and maximum-likelihood methods identified the isolate TMB as Clonostachys rosea, based on morphological and phylogenetic data. Pathogenicity tests revealed typical disease symptoms on healthy G. elata tubers 15 days post-inoculation with the isolate TMB. C. rosea is known to cause diseases in economically important crops, but there are no reports of its occurrence on G. elata f. elata in China. This study provides valuable insights into the occurrence, prevention, and control of brown rot disease in G. elata f. elata.

Bi3+ doped Ti/Sb-SnO2/PbO2 electrode materials were fabricated by electrodeposition to improve their electrochemical performance in zinc electrowinning. The surface morphology, chemical composition, and hydrophilicity of the as-prepared electrodes were characterized using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and contact angle. An electrochemical measurement and an accelerated lifetime experiment were also conducted to investigate the electrocatalytic performance and stability of the electrodes. The results show that the Bi3+ modification electrode has an important effect on the coating morphology, the crystal structure, the surface hydrophilicity, the electrocatalytic activity, and the stability. The electrode prepared from the solution containing 2 mmol·L−1 Bi(NO3)3 (marked as the Ti/Sb-SnO2/2Bi-PbO2 electrode) exhibits the best hydrophilicity performance (θ = 21.6°) and the longest service life (1196 h). During the electrochemical characterization analysis, the Ti/Sb-SnO2/2Bi-PbO2 electrode showed the highest oxygen evolution activity, which can be attributed to it having the highest electroactive surface (qT* = 21.20 C·cm−2) and the best charge-transfer efficiency. The DFT calculation demonstrated that the doping of Bi3+ leads to a decrease in the OER reaction barrier and an increase in the DOS of the electrode, which further enhances the catalytic activity and the conductivity of the electrode. Moreover, the simulated zinc electrowinning experiment demonstrated that the Ti/Sb-SnO2/2Bi-PbO2 electrode consumes less energy than other electrodes. Therefore, it is expected that the Bi3+ modified electrode will become a very promising electrode material for zinc electrowinning in the future.

Background: Oxidative stress (OS) is involved in the development of diabetes, but the genetic mechanisms are not completely understood. We integrated multi-omics data in order to explore the genetic relations between OS-related genes, diabetes mellitus, and microvascular complications using Mendelian randomization and colocalization analysis. Methods: Summary-level data related to OS were acquired from respective studies of methylation, expression, and protein abundance quantitative trait loci. Genetic associations concerning diabetes, diabetic nephropathy (DN), and diabetic retinopathy (DR) were derived from the FinnGen study. Summary-data-based Mendelian randomization (SMR) analysis was conducted to evaluate the correlations between molecular features concerned with OS-related genes and diabetes mellitus, along with its microvascular complications. Additionally, we performed colocalization analysis to determine if the detected signal pairs shared a causal genetic variant. Results: At the genetic level, we identified ten potential causal associations of oxidative stress genes with diabetes, along with microvascular complications, through SMR and colocalization analysis. After integrating the DNA methylation quantitative trait loci (mQTL) and expression QTL (eQTL) data, our analyses revealed a correlation between the methylation site cg26343298 and reduced expression of TP53INP1, supporting the protective role of cg26343298 methylation on type 2 diabetes (T2D) and diabetic nephropathy. Similarly, an inverse association was observed between gene methylation and expression in CHEK1 (cg07110182), confirming the beneficial effect of modification of CHEK1 by cg07110182 in diabetic retinopathy. In addition, upregulation of SUOX expression by cg22580629 was linked to a reduced risk of diabetic retinopathy. At circulating protein levels, genetically predicted a higher level of ICAM1 (OR 1.05, 95%CI 1.03–1.08) was positively connected with the risk of diabetic retinopathy. Conclusions: This SMR study elucidated that the TP53INP1 gene was putatively associated with T2D and DN risk, while the SUOX and CHEK1 genes were associated with DR risk through oxidative stress mechanisms. Additionally, our study showed a positive correlation between the ICAM-1 protein and DR. These findings may enhance our understanding of their pathogenesis and suggest new therapeutic targets for clinical practice.

Climate change has triggered a series of global problems, posing a huge threat to the distribution of many plants and animals, especially endangered species such as the giant panda. Therefore, predicting the distribution of habitat quality under climate change conditions is of great significance for protecting these species. In this study, we examined the correlation between suitable habitat index and ecosystem services using 260 occurrence records and 13 environmental factors with giant pandas as the model species. The species distribution models can also be employed to predict and compare the potential geographical distribution of giant pandas at present and in the 2050s and 2090s in the Qinling Mountains of Shaanxi Province. The results show the following: (1) The relationship between suitable habitat index and ecosystem services of giant panda is not uniform. (2) From 2040 to 2100, the existing habitats may decrease by 47.8% to 98.5%. (3) The main direction of change in the center of the distribution of the giant panda’s habitat is to migrate first eastward and then northwestward. Our results regarding the potential distribution pattern of giant pandas in the Qinling Mountains and their response to climate change can provide important references for optimizing the conservation and habitat management of wild giant pandas in the Qinling Mountains.

In the present study, a new degraded konjac glucomannan (DKGM) was prepared using a crude enzyme from abalone (Haliotis discus hannai) viscera, and its physicochemical properties were investigated. After enzymatic hydrolysis, the viscosity of KGM obviously decreased from 15,500 mPa·s to 398 mPa·s. The rheological properties analysis of KGM and DKGMs revealed that they were pseudoplastic fluids, and pseudoplasticity, viscoelasticity, melting temperature, and gelling temperature significantly decreased after enzymatic hydrolysis, especially for KGM-180 and KGM-240. In addition, the molecular weight of KGM decreased from 1.80 × 106 Da, to 0.45 × 106 Da and the polydispersity index increased from 1.17 to 1.83 after 240 min of degradation time. Compared with natural KGM, the smaller particle size distribution of DKGM further suggests enzyme hydrolysis reduces the aggregation of molecular chains with low molecular weight. FT-IR and FESEM analyses showed that the fragmented KMG chain did not affect the structural characteristics of molecular monomers; however, the dense three-dimensional network microstructure formed by intermolecular interaction changed to fragment microstructure after enzyme hydrolysis. These results revealed that the viscosity and rheological properties of KGM could be controlled and effectively changed using crude enzymes from abalone viscera. This work provides theoretical guidance for the promising application of DKGM in the food industry.

To address the coordination issue of connected autonomous vehicles (CAVs) at unsignalized intersections, this paper proposes a game-theory-based distributed strategy equalization algorithm. To begin, the vehicles present in the scene are conceptualized as participants in a game theory. The decision-payoff function takes into account three critical performance indicators: driving safety, driving comfort, and driving efficiency. Then, virtual logic lines connect the front and rear extremities of vehicles with odd and even numbers at the intersection to create a virtual logic ring. By dividing the virtual logic ring into numerous overlapping game groups, CAVs can engage in negotiation and interaction within their respective game groups. This enables the revision of action strategies and facilitates interaction between the overlapping game groups. A further application of the genetic algorithm (GA) is the search for the optimal set of strategies in constrained multi-objective optimization problems. The proposed decision algorithm is ultimately assessed and certified through a collaborative simulation utilizing Python and SUMO. In comparison to the first-come, first-served algorithm and the cooperative driving model based on cooperative games, the average passing delay is decreased by 40.7% and 6.17%, respectively, resulting in an overall improvement in the traffic system’s passing efficiency.

In this study, we introduce a novel collaborative federated learning (FL) framework, aiming at enhancing robustness in distributed learning environments, particularly pertinent to IoT and industrial automation scenarios. At the core of our contribution is the development of an innovative grouping algorithm for edge clients. This algorithm employs a distinctive ID distribution function, enabling efficient and secure grouping of both normal and potentially malicious clients. Our proposed grouping scheme accurately determines the numerical difference between normal and malicious groups under various network scenarios. Our method addresses the challenge of model poisoning attacks, ensuring the accuracy of outcomes in a collaborative federated learning framework. Our numerical experiments demonstrate that our grouping scheme effectively limits the number of malicious groups. Additionally, our collaborative FL framework has shown resilience against various levels of poisoning attack abilities and maintained high prediction accuracy across a range of scenarios, showcasing its robustness against poisoning attacks.

In the original publication [...]

To reduce the damage to reinforced concrete shear walls in earthquakes and repair costs, a self-centering wall with sloped plane friction dampers (SPFDs) is proposed. In addition to the SPFDs, the proposed wall includes a precast composite wall, steel wall toes, and post-tensioned (PT) tendons. The steel wall toes embedded in the base of the precast wall were used to improve its strength, and the SPFDs installed in the steel toes were used to increase its energy dissipation capacity. To investigate the effect of the initial PT force and prestressing clamping force of the friction bolt on the seismic performance of the wall, quasistatic cyclic loading tests were carried out on three precast wall specimens. The damage to the self-centering walls was slight, the residual drift was small, and the energy dissipation met the specification requirements. The wall with the greater initial PT force showed higher self-centering and bearing capacity, and the wall with higher prestressing clamping forces showed greater energy dissipation capacity. Additionally, a calculation method for the bearing capacity of the precast wall, which was verified by comparison with the test results, is suggested.

We present an unusual case of uveitis secondary to avelumab and pembrolizumab in a 39-year-old Taiwanese male with stage IV clear cell renal cell carcinoma (ccRCC) and lung metastasis, who initially received pembrolizumab as his primary treatment. However, the patient experienced skin and liver immune-related adverse events (irAEs) after the seventh dose of pembrolizumab, which prompted a switch to avelumab. The patient began to experience gradual blurring of vision after completing the fifth cycle of avelumab immunotherapy. Ophthalmic examinations revealed findings consistent with bilateral anterior uveitis. Despite an initial lack of significant improvement with steroid treatment, the patient’s vision and inflammation improved upon discontinuation of avelumab. Due to the occurrence of uveitis, avelumab was switched back to pembrolizumab. However, three months after initiating pembrolizumab, the patient developed foggy vision and bilateral anterior uveitis with cystoid macular edema (CME). The administration of topical, oral, and subconjunctival steroids resulted in an improvement in vision and the resolution of CME, without the need to discontinue pembrolizumab. Over the subsequent eighteen months, there has been no recurrence of uveitis, and there is no evidence of relapse or further metastasis in his ccRCC.

In order to improve the real-time performance of gesture recognition by a micro-Doppler map of mmWave radar, the point cloud based gesture recognition for mmWave radar is proposed in this paper. Two steps are carried out for mmWave radar-based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave radar sensor IWR1642 is used as a benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx radar sensor is developed in our lab, and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real time with limited training data in comparison with the existing methods.

Objective: To evaluate the clinical outcomes of UTUC patients with or without concurrent bladder tumor. Design, Setting, and Participants: The Clinical Research Office of the Endourology Society-Urothelial Carcinomas of the Upper Tract (CROES-UTUC) Registry included 1134 UTUC patients with or without concurrent bladder tumor treated between 2014 and 2019. Results: In 218 (19.2%) cases, concurrent bladder tumor was present, while in 916 (80.8%) patients, no bladder cancer was found. In the multivariable Cox regression analysis, concomitant bladder tumor (hazard ratio (HR) 1.562, 95% confidence interval (CI) 0.954–2.560, p = 0.076) indicated a trend associated with recurrence-free survival for UTUC. Further data dissection confirmed that concomitant bladder tumor is a risk factor of bladder recurrence (HR 1.874, 95% CI 1.104–3.183, p = 0.020) but not UTUC recurrence (HR 0.876, 95% CI 0.292–2.625, p = 0.812). Kidney-sparing surgery (KSS) (HR 3.940, 95% CI 1.352–11.486, p = 0.012), pathological T staging ≥ pT2 (HR 2.840, 95% 1.039–7.763, p = 0.042) were significantly associated with UTUC recurrence. KSS does not affect bladder recurrence (HR 0.619, 95% CI 0.242–1.580, p = 0.315). A limitation is the retrospective nature of the present study analysis. Conclusions: The presence of concomitant bladder tumor does not increase risk of UTUC recurrence, but it results in an increased risk of bladder recurrence. KSS does not affect bladder recurrence and can still be considered in patients with concomitant bladder tumor.

As a new concept for systematically evaluating ecosystem services, Gross Ecosystem Product (GEP) provides an effective means to comprehensively reveal the overall status of the ecosystem, the impact of economic activities on the ecological environment, and the effectiveness of ecological protection efforts. GEP accounting has been conducted in various regions; however, GEP’s application in natural reserves still requires further exploration. Taking the Qinling Mountains as the research area, this paper aims to assess the relationship between GEP and economic development on the basis of the GEP accounting system. The results indicated that: (1) From 2010 to 2020, GEP tended to increase continuously and exhibited a distribution pattern with high value regions in the east and west, and low value regions in the north and south. (2) Over the years, the coupling coordination degree between GEP and GDP was in a consistent upward trend. In 2020, a good coupling coordination state between GEP and GDP was achieved in most districts and counties. (3) With the relative development between GEP and GDP, the social economy of most districts and counties lagged behind GEP in 2010. The number of districts and counties lagging in GEP in 2020 increased, while the number of regions with a balanced development of GEP and GDP was still relatively discouraging. (4) In general, elevation, contagion, temperature, population density, and precipitation were the main drivers of coupling coordination degree between GEP and GDP. If the relationship between economic development and ecological environmental protection can be reasonably balanced, it will further promote the sustainable development of nature reserves, and provide a scientific basis for sustainable policy-making in other similar areas.

Dealing with municipal sludge in an effective way is crucial for urban development and environmental protection. Co-processing the sludge by burning it in a decomposition furnace in the cement production line has been found to be a viable solution. This work aims to analyze the effects of the co-disposal of municipal sludge on the decomposition reactions and NOx emissions in the decomposing furnaces. Specifically, a practical 6000 t/d decomposition furnace was taken as the research object. To achieve this, ANSYS FLUENT with a UDF (user-defined function) was applied to establish a numerical model coupling the limestone decomposition reaction, fuel combustion, and NOx generation and reduction reactions. The flow, temperature, and component field distributions within the furnace with no sludge were firstly simulated with this model. Compared with site test results, the model was validated. Then, with sludge involved, the structure and operation parameters of the decomposition furnace for the co-disposal of municipal sludge were investigated by simulating the flow field, temperature field, and component field distributions. Parametric studies were carried out in three perspectives, i.e., sludge mixing ratio, preheating furnace arrangement height, and sludge particle size. The results show that all three aspects have great importance in the discomposing process. A set of preferable values, including a sludge mixing ratio of 10%, preheating furnace height of 21.5 m, and sludge particle diameter of 1.0 mm, was obtained, which resulted in a raw material decomposition rate of 89.9% and a NO volume fraction of 251 ppm at the furnace outlet.

Product appearance is essential in shaping consumers’ preferences. Thus, we predicted consumer preference by establishing a feasible threshold for appearance innovation with a novel method. We used deep learning (DL)-based image classification and the Waikato Environment for Knowledge Analysis (WEKA) as the implementation tool. Advanced DL algorithms were adopted to classify images of disparate products according to their aesthetics. The results of the classification presented that the method could explain the level of innovation in appearance that consumers favored. The result can be used as a guideline for industrial designers in the creation of new products. Moreover, the method can be used to assess existing products that need enhancement to meet consumer preferences. The proposed method assists industrial designers in understanding consumer preferences and product innovation. Future research is required to ensure the successful design of new products.

Staphylococcus aureus (S. aureus), commonly known as “superbugs”, is a highly pathogenic bacterium that poses a serious threat to human health. There is an urgent need to replace traditional antibiotics with novel drugs to combat S. aureus. Sortase A (SrtA) is a crucial transpeptidase involved in the adhesion process of S. aureus. The reduction in virulence and prevention of S. aureus infections have made it a significant target for antimicrobial drugs. In this study, we combined virtual screening with experimental validation to identify potential drug candidates from a drug library. Three hits, referred to as Naldemedine, Telmisartan, and Azilsartan, were identified based on docking binding energy and the ratio of occupied functional sites of SrtA. The stability analysis manifests that Naldemedine and Telmisartan have a higher binding affinity to the hydrophobic pockets. Specifically, Telmisartan forms stable hydrogen bonds with SrtA, resulting in the highest binding energy. Our experiments prove that the efficiency of adhesion and invasion by S. aureus can be decreased without significantly affecting bacterial growth. Our work identifies Telmisartan as the most promising candidate for inhibiting SrtA, which can help combat S. aureus infection.

Multi-modal sensors are the key to ensuring the robust and accurate operation of autonomous driving systems, where LiDAR and cameras are important on-board sensors. However, current fusion methods face challenges due to inconsistent multi-sensor data representations and the misalignment of dynamic scenes. Specifically, current fusion methods either explicitly correlate multi-sensor data features by calibrating parameters, ignoring the feature blurring problems caused by misalignment, or find correlated features between multi-sensor data through global attention, causing rapidly escalating computational costs. On this basis, we propose a transformer-based end-to-end multi-sensor fusion framework named the adaptive fusion transformer (AFTR). The proposed AFTR consists of the adaptive spatial cross-attention (ASCA) mechanism and the spatial temporal self-attention (STSA) mechanism. Specifically, ASCA adaptively associates and interacts with multi-sensor data features in 3D space through learnable local attention, alleviating the problem of the misalignment of geometric information and reducing computational costs, and STSA interacts with cross-temporal information using learnable offsets in deformable attention, mitigating displacements due to dynamic scenes. We show through numerous experiments that the AFTR obtains SOTA performance in the nuScenes 3D object detection task (74.9% NDS and 73.2% mAP) and demonstrates strong robustness to misalignment (only a 0.2% NDS drop with slight noise). At the same time, we demonstrate the effectiveness of the AFTR components through ablation studies. In summary, the proposed AFTR is an accurate, efficient, and robust multi-sensor data fusion framework.

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0–1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: (1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. (2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations of two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR).

The coal and coal-bearing measures in the Jungar Coalfield in Inner Mongolia are characterized by rare earth element (REE) enrichment. Combustion in coal-fired power plants can lead to further enrichment of REEs in coal ash, which serves as a new potential source for REE extraction and smelting. Further, investigating the content, modes of occurrence, and transformation behavior of REEs during coal combustion may help in better understanding REE differentiation during coal combustion and facilitate the development of economically feasible REE recovery technologies. Therefore, in this study, we analyzed coal ash from the Jungar Energy Gangue Power Plant in Inner Mongolia via inductively coupled plasma mass spectrometry, X-ray diffraction, and scanning electron microscopy combined with energy-dispersive spectroscopy. Our results showed that the REE content of the feed coal was 220 μg/g, slightly higher than the average for global coal. Additionally, fly ash had a higher REE content (898 μg/g) than bottom ash, and its rare earth oxide content was approximately 1152 μg/g, which meets the industrial requirements. Bottom and fly ashes contained similar minerals; however, their relative abundances were different. Specifically, mullite, quartz, calcite, and gypsum were slightly more abundant in fly ash than in bottom ash, whereas amorphous solids were slightly more abundant in bottom ash than in fly ash. Furthermore, fly ash, dominated by Si- and Al-rich minerals, was composed of irregular particles of different shapes and sizes. It also contained monazite and REE fluoro-oxides, which possibly originated from the feed coal and had mineral structures that remained unchanged during coal combustion. Thus, the REE fluoro-oxides possibly resulted from the conversion of bastnaesite in the feed coal during combustion and thereafter became attached to the edge of the Si–Al minerals in the fly ash.

The construction hoist drive system is a critical component of the construction hoist, and high speed and low vibration noise are essential development directions. In order to improve the NVH level of the construction hoist drive system, this paper carries out research and analysis of construction hoist drive system excitation, establishes the drive system rigid-flexible coupling dynamics model, and completes the establishment of the vibration and noise model of the drive system, simulation analysis, and optimization work. Ansys Motor CAD 2020 was used to establish the parametric model of the asynchronous motor and it was combined with the virtual work method to calculate Maxwell’s electromagnetic force to arrive at the radial electromagnetic force as the main cause of electromagnetic noise. For the mechanical excitation generated by the gearbox, the time-varying stiffness excitation, mesh shock excitation, and transmission error excitation are considered, and the transmission error of helical gears under different working conditions is calculated by combining it with Romax software 2020. The rigid-flexible coupling model of the construction hoist drive system is established. The load distribution analysis of the unit length of the tooth surface is completed for the first- and third-stage helical gears under different working conditions. The primary source of the drive system excitation is the tooth surface bias load. Based on the dynamic response analysis theory of the vibration superposition method, the maximum vibration speed of the drive system was analyzed by Romax. The maximum noise value of 78.8 dB was calculated from the acoustic power simulation of the drive system using Actran acoustic software 2022 in combination with acoustic theory, and the magnetic density amplitude of the stator teeth of the asynchronous motor was optimized based on the microscopic shaping design of the helical gear by Romax. The vibration and noise simulation of the optimized drive system shows that the vibration value is reduced to 0.75 mm/s, and the maximum noise is reduced to 70.2 dB, which is 10.9% lower than before the optimization. The overall NVH level has been improved. The optimization method to reduce the vibration noise of the drive system is explored, which can be used for vibration noise prediction and control during the development of the construction hoist drive system.

Steel surface defect detection is crucial for ensuring steel quality. The traditional detection algorithm has low detection probability. This paper proposes an improved algorithm based on the YOLOv5 model to enhance detection probability. Firstly, deformable convolution is introduced in the backbone network, and a traditional convolution module is replaced by deformable convolution; secondly, the CBAM attention mechanism is added to the backbone network; then, Focal EIOU is used instead of the CIOU loss function in YOLOv5; lastly, the K-means algorithm is used to cluster the Anchor box, and the Anchor box parameters that are more suitable for this paper are obtained. The experimental results show that using deformable convolution instead of traditional convolution can get more feature information, which is more conducive to the learning of the network. This paper uses the CBAM attention mechanism, and the heat map of the attention mechanism shows that the CBAM attention mechanism is beneficial for feature extraction. Focal EIOU is optimized in high and wide loss compared with the CIOU loss function, which accelerates the convergence of the model. The Anchor box is more favorable for feature extraction. The improved algorithm achieved a detection probability of 78.8% in the NEU-DET dataset, which is 4.3% better than the original YOLOv5 network, and the inference time of each image is only increased by 1 ms; therefore, the optimized algorithm proposed in this paper is effective.

To address the problems of low productivity and sizeable dispensing positioning errors in manual and semi-automatic dispensing processes in small- and medium-sized electronic enterprises, this study proposes a fully automatic dispensing method based on visual positioning with RJDNEL-type PCBs as the research object. The fully automatic dispensing system is constructed through the construction of a mechanical structure, the selection of optical equipment, and the debugging of the control system. The method is based on optical imaging technology. Firstly, the dispensing area is extracted through image preprocessing; then, the edge is detected by the Sobel operator, and the Hilditch operator optimizes the dispensing sharpness; then, the minimum outer rectangle algorithm is used to calculate the positioning information of the dispensing area by using the relationship between geometric transformations, which provides the database support for the realization of automatic dispensing; finally, the simulated annealing algorithm is adopted to optimize the dispensing path. Through the image acquisition matching experiments and positioning accuracy experimental analysis, it is concluded that the matching success rate is more than 99%, and the image positioning information repeated extraction accuracy error is less than 0.02 mm. Analysis of the total path of dispensing, as well as computing time, was performed as follows: for the complete dispensing process, 20 PCB boards were used; the path optimization of the dispensing path was reduced by 723.4 mm, and the dispensing efficiency was improved by 20% to 30%; after dispensing, the dispensing area measurement was performed on the PCB boards. PCB boards meet the quality requirements of dispensing, and the proposed method for meeting the quality of dispensing at the same time effectively improves the dispensing accuracy and efficiency.

Small-object detection is a challenging task in computer vision due to the limited training samples and low-quality images. Transfer learning, which transfers the knowledge learned from a large dataset to a small dataset, is a popular method for improving performance on limited data. However, we empirically find that due to the dataset discrepancy, directly transferring the model trained on a general object dataset to small-object datasets obtains inferior performance. In this paper, we propose TranSDet, a novel approach for effective transfer learning for small-object detection. Our method adapts a model trained on a general dataset to a small-object-friendly model by augmenting the training images with diverse smaller resolutions. A dynamic resolution adaptation scheme is employed to ensure consistent performance on various sizes of objects using meta-learning. Additionally, the proposed method introduces two network components, an FPN with shifted feature aggregation and an anchor relation module, which are compatible with transfer learning and effectively improve small-object detection performance. Extensive experiments on the TT100K, BUUISE-MO-Lite, and COCO datasets demonstrate that TranSDet achieves significant improvements compared to existing methods. For example, on the TT100K dataset, TranSDet outperforms the state-of-the-art method by 8.0% in terms of the mean average precision (mAP) for small-object detection. On the BUUISE-MO-Lite dataset, TranSDet improves the detection accuracy of RetinaNet and YOLOv3 by 32.2% and 12.8%, respectively.

The characteristics of flexible production lines, i.e., “multiple steps and few processes”, increase the complexity of the process and the difficulty of process quality control, but are not conducive for improving the quality and efficiency of a production line. In this study, we use a flexible production line processing spindle box as the research object. Using an extensive data analysis method, we study the key influencing factors of process quality and the prediction and optimization of process quality characteristics, aiming to accurately predict the machining accuracy of flexible production line processes and to achieve efficient quality control. A fuzzy hierarchical analysis-based impact factor model is developed to obtain a process quality impact factor model consistent with the spindle box of a production line. By verifying the prediction accuracy of 24 sets of quality spindle bore data, a prediction model with a relative error of less than 0.01 is obtained, which provides a prediction sample for analyzing potential problems of process quality in a production line. The SPC control principle is used to monitor process quality by using the standard control method, and the change trends between the actual and predicted values of quality characteristics are compared to achieve predictive control of the process quality. The product qualification rate of this control scheme under this monitoring method is 96%.

Tinidazole (TNZ), a 5-nitroimidazole derivative, has received increasing attention due to its pharmacological activities in treatment for amebic and parasitic infections. In this paper, we synthesized three novel drug supramolecular compounds successfully based on TNZ. The three compounds discussed were formed by TNZ and 2,6-dihydroxybenzoic acid (2,6-DHBA), 4-methylsalicylic acid (4-MAC), and 5-chloro-2-hydroxybenzoic acid (5-C-2-HBA). The N-H···O and O-H···O hydrogen bonds and weak C-H···O hydrogen bonds are the primary intermolecular forces in the construction of the three compounds. Crystal structure analysis revealed that all the compounds exhibit three-dimensional frameworks consisting of non-covalent interactions. Furthermore, six primary synthons, Ⅰ R22 (8), Ⅱ R21(6), Ⅲ R22(12), Ⅳ R33(9), Ⅴ R22(12), Ⅵ R33(9), formed through various hydrogen bonds are found in the three compounds. Moreover, the resulting pharmaceutical supramolecular compounds show improved stability. Single-crystal X-ray diffraction analysis, infrared spectroscopy (IR), element analysis, and thermogravimetric analysis (TGA) are reported.

Aiming at the problems of low detection efficiency and poor detection accuracy caused by texture feature interference and dramatic changes in the scale of defect on steel surfaces, an improved YOLOv5s model is proposed. In this study, we propose a novel re-parameterized large kernel C3 module, which enables the model to obtain a larger effective receptive field and improve the ability of feature extraction under complex texture interference. Moreover, we construct a feature fusion structure with a multi-path spatial pyramid pooling module to adapt to the scale variation of steel surface defects. Finally, we propose a training strategy that applies different kernel sizes for feature maps of different scales so that the receptive field of the model can adapt to the scale changes of the feature maps to the greatest extent. The experiment on the NEU-DET dataset shows that our model improved the detection accuracy of crazing and rolled in-scale, which contain a large number of weak texture features and are densely distributed by 14.4% and 11.1%, respectively. Additionally, the detection accuracy of inclusion and scratched defects with prominent scale changes and significant shape features was improved by 10.5% and 6.6%, respectively. Meanwhile, the mean average precision value reaches 76.8%, compared with the YOLOv5s and YOLOv8s, which increased by 8.6% and 3.7%, respectively.

Terrestrial ecosystems play an important role in carbon reduction and sequestration, and it is important to explore the carbon sequestration potential of terrestrial ecosystems under different land use scenarios to enhance the regional carbon storage potential. We analysed land use changes in the Loess Plateau, an important ecological barrier in China, from 2000 to 2020, used the PLUS model to predict land use patterns under different scenarios in 2035, and applied the InVEST model to assess carbon storage from 2000 to 2035. The findings were as follows: (1) Cropland in the study area decreased significantly from 2000 to 2020, and forests, waters, and construction land showed an increasing trend. The area of cropland further decreased under the natural growth and ecological protection scenarios, the area of grassland decreased significantly under the cropland protection scenario, and forests and waters were effectively protected under the natural development, ecological protection, and cropland protection scenarios. (2) Carbon storage in the Loess Plateau has increased by 28 Tg (0.56%) over the past 20 years. Compared with those in 2020, by 2035, carbon storage under the natural development, ecological protection, and cropland protection scenarios will increase by 30, 44, and 21 Tg, respectively. (3) Carbon storage has obvious spatial heterogeneity, with high carbon density in the northern Qinling Mountains, Taihang Mountains, and Lvliang Mountains and low carbon density in Erdos City and its surrounding areas. Regional differences in carbon density are closely related to the spatial distribution of land use types. (4) Carbon storage showed an inverted V-shaped trend with the increase in elevation. Land use change is the main reason for the increase or decrease in carbon storage under different scenarios. Compared with the other two scenarios, the ecological protection scenario not only protects the ecological environment but also has a strong carbon storage potential, which may be significant for guiding the formulation of future land use planning on the Loess Plateau.

Protein probes, including ultrafiltrates from the placenta (UPla) and lung (ULu) of postnatal rabbits, were investigated in premature senescent HEK293 and HepG2 cells to explore whether they could modulate cellular senescence. Tris-Tricine–PAGE, gene ontology (GO), and LC–MS/MS analysis were applied to describe the characteristics of the ultrafiltrates. HEK293 and HepG2 cells (both under 25 passages) exposed to a sub-toxic concentration of hydrogen peroxide (H2O2, 300 μM) became senescent; UPla (10 μg/mL), ULu (10 μg/mL), as well as positive controls lipoic acid (10 μg/mL) and transferrin (10 μg/mL) were added along with H2O2 to the cells. Cell morphology; cellular proliferation; senescence-associated beta-galactosidase (SA-β-X-gal) activity; expression of senescence biomarkers including p16 INK4A (p16), p21 Waf1/Cip1 (p21), HMGB1, MMP-3, TNF-α, IL-6, lamin B1, and phospho-histone H2A.X (γ-H2AX); senescence-related gene expression; reactive oxygen species (ROS) levels; and mitochondrial fission were examined. Tris-Tricine–PAGE revealed prominent detectable bands between 10 and 100 kDa. LC–MS/MS identified 150–180 proteins and peptides in the protein probes, and GO analysis demonstrated a distinct enrichment of proteins associated with “extracellular space” and “proteasome core complex”. UPla and ULu modulated senescent cell morphology, improved cell proliferation, and decreased beta-galactosidase activity, intracellular and mitochondrial ROS production, and mitochondrial fission caused by H2O2. The results from this study demonstrated that UPla and Ulu, as well as lipoic acid and transferrin, could protect HEK293 and HepG2 cells from H2O2-induced oxidative damage via protecting mitochondrial homeostasis and thus have the potential to be explored in anti-aging therapies.

COVID-19 opened a window of opportunity to change the green development of the hospitality industry. For many years, Chinese tourists have been the world’s largest source of outbound tourists. Therefore, this study attempted to improve built-environment strategies for green rooms at B&Bs using the empirical statistics of Chinese tourists after the end of COVID-19 control measures and different green B&B standards, combining IPA (importance-performance analysis). For the lack of a green built-environment study from a tourism perspective, this study can be used mainly for improving the green satisfaction of urban B&Bs as it attempted to fill the gaps in research on green B&B rooms. This study will significantly help improve the quality of green rooms for the B&B industry in the future, and it also provides an improved green B&B room sample for other countries and regions. Moreover, it is an optimistic attempt at hospitality and tourism recovery.

Today, multi-sensor fusion detection frameworks in autonomous driving, especially sequence-based data-level fusion frameworks, face high latency and coupling issues and generally perform worse than LiDAR-only detectors. On this basis, we propose PMPF, point-cloud multiple-pixel fusion, for 3D object detection. PMPF projects the point cloud data onto the image plane, where the region pixels are processed to correspond with the points and decorated to the point cloud data, such that the fused point cloud data can be applied to LiDAR-only detectors with autoencoders. PMPF is a plug-and-play, decoupled multi-sensor fusion detection framework with low latency. Extensive experiments on the KITTI 3D object detection benchmark show that PMPF vastly improves upon most of the LiDAR-only detectors, e.g., PointPillars, SECOND, CIA-SSD, SE-SSD four state-of-the-art one-stage detectors, and PointRCNN, PV-RCNN, Part-A2 three two-stage detectors.

The Tian Shan is one of the most active intracontinental orogenic belts in the world. It has undergone complex deformation that has resulted in the formation of several fold-and-thrust belts (FTBs) in the piedmonts and intermontane basins. Investigating the deformation histories of these FTBs is important for understanding the tectonic propagation processes of the Tian Shan. Here, we gain insight into these crustal shortening processes by deciphering the Houyanshan structure, a typical fold-thrust belt in the eastern Chinese Tian Shan. We first describe a curved thrust ramp and related fold pairs of the structure using high-resolution remote sensing photography, deformation of fluvial terraces, and field-based geological cross-section. Combined with deformed terrace records and optically stimulated luminescence (OSL) dating results, the kinematic style allows us to yield a geologic shortening rate of 1.6 ± 0.2 mm/a since ~52 ka. Second, to reduce uncertainty in the seismic interpretation and quantify the amount and time of crustal shortening, we interpret three seismic reflection profiles by using the theory of quantitative fault-related fold, area-depth-strain (ADS), and reverse modeling analyses. These profiles provide direct evidence that this structure connects by means of a listric thrust ramp to a shallow detachment level. ADS analysis reveals that the maximum shortening of the Huoyanshan structure is ~4.5 km, which is consistent with the result of quantitative inverse modeling. Each of the structural analysis methods gives similar parameters, and the high consistency of results greatly improves the soundness of a given geologic interpretation. Finally, the shortening rate and total shortening amount suggest that the structure may have formed at 1.8–3.7 Ma, which is nearly synchronous around the Tibetan Plateau. Together, these results indicate that this combined geological and geomorphological analysis provides greater insight into deformation information than can be achieved by any individual technique in studying fold-and-thrust belts worldwide.

CNC machine tools are a measure of national manufacturing and comprehensive national strength and how to reduce CNC machine errors in modern industry has received much attention from all walks of life. The thermal deformation of the feed axis of CNC machine tools is affected by the machine’s structure, installation, material properties, motion position, working conditions, and environmental temperature. Therefore, adopting a single thermal error model to the compensation implementation needs is difficult under changing motion parameters. In this paper, VMC655H is used as the research object and an embedded system is used as the control core. The model classification method is based on the approximate matching of the motion parameters of the thermal error model. Dynamic loading technology is used to fit multiple thermal error models as a technical means to reduce the impact of the machine feed axis’s thermal deformation on the positioning accuracy during the manufacturing process. The thermal error classification model for machine tool feed axes with variable motion parameters in an embedded environment can classify, identify, and load compensation device models within a limited range of motion parameters. This strategy can determine the current operating environment and compensate for the poor adaptability of a single thermal error model. The study of the actual operation effect of the compensation device under this strategy shows that the compensation strategy proposed in this paper can compensate for the thermal deformation of the ball screw feed axis under variable motion, and the X-axis positioning accuracy of its feed system can be improved by 53.11%. This study provides a new idea for the compensation method for thermogenic errors in machine tools.

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a serious threat to global public health. In an effort to develop novel anti-coronavirus therapeutics and achieve prophylactics, we used gene set enrichment analysis (GSEA) for drug screening and identified that Astragalus polysaccharide (PG2), a mixture of polysaccharides purified from Astragalus membranaceus, could effectively reverse COVID-19 signature genes. Further biological assays revealed that PG2 could prevent the fusion of BHK21-expressing wild-type (WT) viral spike (S) protein and Calu-3-expressing ACE2. Additionally, it specifically prevents the binding of recombinant viral S of WT, alpha, and beta strains to ACE2 receptor in our non-cell-based system. In addition, PG2 enhances let-7a, miR-146a, and miR-148b expression levels in the lung epithelial cells. These findings speculate that PG2 has the potential to reduce viral replication in lung and cytokine storm via these PG2-induced miRNAs. Furthermore, macrophage activation is one of the primary issues leading to the complicated condition of COVID-19 patients, and our results revealed that PG2 could regulate the activation of macrophages by promoting the polarization of THP-1-derived macrophages into an anti-inflammatory phenotype. In this study, PG2 stimulated M2 macrophage activation and increased the expression levels of anti-inflammatory cytokines IL-10 and IL-1RN. Additionally, PG2 was recently used to treat patients with severe COVID-19 symptoms by reducing the neutrophil-to-lymphocyte ratio (NLR). Therefore, our data suggest that PG2, a repurposed drug, possesses the potential to prevent WT SARS-CoV-2 S-mediated syncytia formation with the host cells; it also inhibits the binding of S proteins of WT, alpha, and beta strains to the recombinant ACE2 and halts severe COVID-19 development by regulating the polarization of macrophages to M2 cells.

Herein, three novel cocrystals of 1-hydroxy-2-naphthoic acid: tetramethylpyrazine, 1-hydroxy-2-naphthoic acid:1,10-phenanthroline, and 1-hydroxy-2-naphthoic acid:1,4-bis(imidazol-1-ylmethyl)benzene (L2) were obtained by crystallization in methanol–water mixed solvent via a slow evaporation method. The cocrystalline products 1−3 were carried out by a range of techniques, including single-crystal X-ray diffraction, Fourier transform–infrared spectroscopy, elemental analysis, and thermogravimetric testing. We analyzed the crystal structures of the cocrystals 1−3 and found that weak interactions C–H···X (X = O or π) were of great importance in the process of self-assembly as well as strong and conventional hydrogen bonds (N–H···O, O–H···N, O–H···O), leading to a stable and diverse multidimensional supramolecular architecture. It is worth noting that a series of ring motifs with different sizes were explored in the crystal structures of the above complexes, such as R22(5), R22(7), R22(8), R23(13), R24(16), R44(16), R44(22), and so on. The classical and robust supramolecular synthon intermolecular bond between acid and pyridine (acid···pyridine) heterosynthon R22(7), commonly found in organic solids containing carboxylic acids with other N-containing heteroaromatics, was further demonstrated to be involved in the construction of the hydrogen-bond networks of cocrystal 1. The thermogravimetric technique used in this study proved that the mass losses of these three cocrystals were closely related to the strength of the hydrogen bonds in the package fraction.

A 25-year-old female with diabetes and hypertension presented with progressive painless blurred vision in her left eye ten days after she received her third dose of the SARS-CoV-2 mRNA vaccine BNT162b2 (Pfizer-BioNTech). The clinical examination confirmed the diagnosis of Central Retinal Vein Occlusion (CRVO) complicated with macular edema. Three doses of anti-vascular endothelial growth factor (VEGF) were injected intravitreally. Visual acuity was improved from 20/100 to 20/30, but recurrence was noted at 6 months. Several cases of retinal vein occlusion (RVO) after COVID-19 vaccination have been reported. However, the present case is the youngest female individual documented to have CRVO after SARS-CoV-2 vaccination. This case demonstrates that the macular edema might be recurrent in patients with risk factors for CRVO who receive SARS-CoV-2 vaccination, suggesting the need for careful consideration of the treatment strategy and close follow-up. Although the definite pathogenesis still needs to be carefully determined, this report highlights the possible association between RVO and mRNA-based COVID-19 vaccination, even in young individuals.

Accurate state of health (SOH) estimation is critical to the operation, maintenance, and replacement of lithium-ion batteries (LIBs), which have penetrated almost every aspect of our life. This paper introduces a new approach to accurately estimate the SOH for rechargeable lithium-ion batteries based on the corresponding charging process and long short-term memory recurrent neural network (LSTM-RNN). In order to learn the mapping function without employing battery models and filtering techniques, the LSTM-RNN is initially fed into the health indicators (HIs) extracted from the charging process and trained to encode the dependencies of the related data sequence. Subsequently, the trained LSTM-RNN can properly estimate online SOHs of LIBs using extracted HIs. We experiment on two public datasets for model construction, validation, and comparison. Conclusively, the trained LSTM-RNN achieves an overall root mean square error (RMSE) lower than 1% on the cases with the same discharging current rate and an RMSE of 1.1198% above 80% SOH on another testing case that underwent a different discharging current rate.

Due to the high incidence of diabetes mellitus (DM) and poor response to the first-line treatment of DM-induced erectile dysfunction (DMED), new therapeutic strategies for DMED are needed. Adipose-derived stem cell (ADSC) transplantation is considered a promising treatment modality for DMED but is limited by poor survival and efficacy after transplantation. In this study, we aimed to increase the therapeutic effect of DMED by overexpressing the relaxin family peptide receptor 1 (RXFP1) using a clustered regularly interspaced short palindromic repeats activation (CRISPRa) system in ADSCs. Two lentiviruses carrying the CRISPRa system transfected ADSCs to overexpress RXFP1 (RXFP1-ADSCs). The intracavernous injection of ADSCs was performed in DMED rats induced by the intraperitoneal injection of streptozotocin. Four weeks after transplantation, we measured erectile function and collected specimens of the corpus cavernosum for follow-up detection. The results showed that ADSCs improved erectile function in diabetic rats, and the RXFP1-ADSCs were more significant. We detected reduced levels of oxidative stress, apoptosis and fibrosis together with relative normalization of endothelial and smooth muscle cell function in the penis after ADSC transplantation. RXFP1-ADSCs had more potent efficacy in the above alterations compared to negative control ADSCs due to the high levels of survival and paracrine capacity in RXFP1-ADSCs. The results revealed that RXFP1-ADSC transplantation could partially preserve erectile function in DMED rats associated with the regulation of oxidative stress, apoptosis, fibrosis and endothelial and smooth muscle cell dysfunction. RXFP1 may be the new target for the genetic modification of ADSCs, which benefits the management of DMED.

A phytochemical investigation of the leaves and twigs of Glycosmis pentaphylla (Rutaceae), collected in Vietnam, yielded three new compounds named glyfuran (1), glyphyllamide (2), and glyphyllazole (3), along with twenty-five known compounds (4–28). The structures of isolates were determined by IR, MS, NMR, and UV data analyses. In the anti-diabetic activity screening, (+)-isoaltholacton (4), glycoborinine (17), 2′,4′-dihydroxy-4,6′-dimethoxychalcone (24), and flavokawain A (25) simultaneously exhibited inhibition of dipeptidyl peptidase-4 (DPP4) and stimulation of the glucagon-like peptide-1 (GLP-1) secretion on the murine intestinal secretin tumor cell line (STC-1).

Supplementing with vitamin B3 has been reported to protect against retinal ganglion cell (RGC) damage events and exhibit multiple neuroprotective properties in a mouse model of optic nerve injury. In this study, a rat model of anterior ischemic optic neuropathy was used to assess the neuroprotective benefits of vitamin B3 (rAION). Vitamin B3 (500 mg/kg/day) or phosphate-buffered saline (PBS) was administered to the rAION-induced rats every day for 28 days. The vitamin B3-treated group had significantly higher first positive and second negative peak (P1-N2) amplitudes of flash visual-evoked potentials and RGC densities than the PBS-treated group (p < 0.05). A terminal deoxynucleotidyl transferase dUTP nick end labeling assay conducted on vitamin B3-treated rats revealed a significant reduction in apoptotic cells (p < 0.05). Superoxide dismutase and thiobarbituric acid reactive substance activity showed that vitamin B3 treatment decreased reactive oxygen species (p < 0.05). Therefore, vitamin B3 supplementation preserves vision in rAION-induced rats by reducing oxidative stress, neuroinflammation, and mitochondrial apoptosis.

Excessive zinc (Zn) is toxic to plants, but the effect of zinc-stressed plants on herbivorous insects is still unclear. Hence, we assessed the effect of zinc-stressed rice plants on its feeding pest, Nilaparvata lugens. The soil–rice–N. lugens system was treated with Zn2+ solution. Sugar content in rice was measured, and the reproduction and trehalose metabolism in N. lugens were assessed. The trehalase activity in rice significantly decreased at 100 mg·kg−1 Zn2+, and the trehalose content increased. The glucose and starch content increased at higher Zn2+ concentrations. The fecundity and trehalose content of N. lugens decreased after feeding on zinc-stressed rice, and the glucose content in the high Zn2+ group was significantly higher than that in the low Zn2+ group. In addition, the soluble trehalase activity of N. lugens significantly decreased under the 125 mg·kg−1 treatment, while the activity of membrane-bound trehalase significantly increased under the 150 mg·kg−1 treatment. Quantitative RT-PCR indicated significantly lower expressions of NlTre1-1, NlTre2, and NITps after Zn treatment. In conclusion, Zn2+ treatment significantly altered the sugar content in rice plants; it also decreased the fecundity of N. lugens, which may be mediated by alterations in trehalose metabolism.

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.

A heterogeneous distribution of nutrients in the soil and the root interactions of crops coexist in agroecosystems. A ‘root splitting’ approach, i.e., splitting the root system into different nutrient compartments, was used to study nutrient heterogeneity distributions and root interactions. We evaluated root foraging behavior, individual growth, and root development mechanisms in response to soil nitrogen heterogeneity, and intraspecific competition in maize. A heterogeneous distribution of nitrogen increased the foraging precision of roots at flowering and mature stages (Foraging precision > 1), and significantly increased root biomass and surface area on the intraspecific competition and no competition on the high nitrogen side. The heterogeneous distribution had no effect on yield, total root biomass, specific root length, total surface area, or average diameter. Intraspecific competition increased inter-crop competition (RII < 0) and reduced total root biomass, total plant biomass, and the root/shoot ratio. These results inform root trait development studies and provide valid evidence for optimizing and managing fertilizer application in agroecosystems, helping to maximize crop yield and nutrient use efficiency.

Congenital stationary night blindness (CSNB) is an inherited retinal disease (IRD) that causes night blindness in childhood with heterogeneous genetic, electrophysical, and clinical characteristics. The development of sequencing technologies and gene therapy have increased the ease and urgency of diagnosing IRDs. This study describes seven Taiwanese patients from six unrelated families examined at a tertiary referral center, diagnosed with CSNB, and confirmed by genetic testing. Complete ophthalmic exams included best corrected visual acuity, retinal imaging, and an electroretinogram. The effects of identified novel variants were predicted using clinical details, protein prediction tools, and conservation scores. One patient had an autosomal dominant CSNB with a RHO variant; five patients had complete CSNB with variants in GRM6, TRPM1, and NYX; and one patient had incomplete CSNB with variants in CACNA1F. The patients had Riggs and Schubert–Bornschein types of CSNB with autosomal dominant, autosomal recessive, and X-linked inheritance patterns. This is the first report of CSNB patients in Taiwan with confirmed genetic testing, providing novel perspectives on molecular etiology and genotype–phenotype correlation of CSNB. Particularly, variants in TRPM1, NYX, and CACNA1F in our patient cohort have not previously been described, although their clinical significance needs further study. Additional study is needed for the genotype–phenotype correlation of different mutations causing CSNB. In addition to genetic etiology, the future of gene therapy for CSNB patients is reviewed and discussed.

Two-dimensional molybdenum disulfide (MoS2) has been extensively investigated in the field of optoelectronic devices. However, most reported MoS2 phototransistors are fabricated using the mechanical exfoliation method to obtain micro-scale MoS2 flakes, which is laboratory- feasible but not practical for the future industrial fabrication of large-scale pixel arrays. Recently, wafer-scale MoS2 growth has been rapidly developed, but few results of uniform large-scale photoelectric devices were reported. Here, we designed a 12 × 12 pixels pixel array image sensor fabricated on a 2 cm × 2 cm monolayer MoS2 film grown by chemical vapor deposition (CVD). The photogating effect induced by the formation of trap states ensures a high photoresponsivity of 364 AW−1, which is considerably superior to traditional CMOS sensors (≈0.1 AW−1). Experimental results also show highly uniform photoelectric properties in this array. Finally, the concatenated image obtained by laser lighting stencil and photolithography mask demonstrates the promising potential of 2D MoS2 for future optoelectrical applications.