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Background: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. Methods: Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test–retest reproducibility ADPKD patients. Results: Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test–retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. Conclusions: Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted.

Oily sludge (OS) has long been regarded as a hazardous waste, and improper disposal may lead to serious environmental concerns and human health risks. Despite various methods having been proposed and applied to the treatment of OS, the oil occurrence states and properties in sludge are rarely characterized, which may directly link to the selection and effectiveness of treatment methods. Here, confocal laser scanning microscopy (CLSM), X-ray diffraction (XRD), gas chromatography (GC), and four components (SARA) analysis were utilized to characterize the changes in the oil occurrence states and compositions in OS samples before and after high-speed stirring (HSS) treatment. Our results show a substantial reduction in the oil concentration of OS after HSS treatment (from 32.98% to 1.65%), while SARA analysis reveals a similar oil composition before and after treatment, suggesting the broad applicability of HSS in removing oil and its insignificant selectivity towards various hydrocarbon components. This is further supported by the total petroleum hydrocarbon (TPH) analysis results, which show that the separated oil phase has a hydrocarbon composition similar to that of the original OS sample. The CLSM and fluorescence analysis suggest a homogeneous distribution of oil in the sludge, with relatively light components more concentrated in the pore systems between coarse mineral particles, whereas relatively heavy components tend to coexist with clay minerals. After HSS cleaning, both light and heavy components are removed to varying degrees, but light components are preferentially removed while heavy components tend to be retained in the sludge due to adsorption by clay minerals. This is consistent with TPH analysis, where a significant decrease in n-alkanes with lower carbon numbers (n-C14 to n-C20) was observed in the residual sample. Our findings demonstrate the dynamic response of oil occurrence states and compositions to the OS treatment process and highlight the importance of characterizing these fundamental properties prior to the selection of OS treatment methods.

Crop rotation and diversification (CRD) are crucial strategies in sustainable agriculture, offering multiple benefits to both farmers and the environment. By alternating crops or introducing diverse plant species, CRD practices improve soil fertility, reduce pest populations, and enhance nutrient availability. For example, legume-based rotations increase soil nitrogen levels through biological nitrogen fixation, reducing the need for synthetic fertilizers. Moreover, these practices promote more efficient water and nutrient use, reducing the reliance on synthetic fertilizers and minimizing the risk of pests and diseases. This review synthesizes findings from recent research on the role of CRD in enhancing sustainable agriculture and resilience, highlighting the potential contributions of these practices towards climate change mitigation and adaptation. Specific crop rotation systems, such as the cereal–legume rotation in temperate regions and the intercropping of maize with beans in tropical environments, are reviewed to provide a comprehensive understanding of their applicability in different agroecological contexts. The review also addresses the challenges related to implementing CRD practices, such as market demand and knowledge transfer, and suggests potential solutions to encourage broader adoption. Lastly, the potential environmental benefits, including carbon sequestration and reduced greenhouse gas emissions, are discussed, highlighting the role of CRD in building resilient agricultural systems. Collectively, this review paper emphasizes the importance of CRD methods as sustainable agricultural practices and provides key insights for researchers and farmers to effectively integrate these practices into farming systems.

This paper studied the flexural behavior of bridge deck link slabs made with polyvinyl alcohol–strain-hardening cementitious composites (PVA-SHCC). The tensile and flexural properties of the self-compacting PVA-SHCC with four volume fractions, i.e., 0%, 1%, 1.5%, and 2%, were evaluated first. Next, using the similarity theory, composite models with a geometric similarity ratio of 1:5 were designed to represent the bridge deck with the link slabs. The models considered three materials for link slabs, including concrete, cement mortar, and self-compacting PVA-SHCC, and two different curing ages at 7 and 56 days. Bending tests were performed to investigate the flexural behavior of the models. Based on the fractal theory, the cracking characteristics of the models with different types of link slabs were compared, and the relationship between fractal dimensions and the flexural behavior of the models was studied. Numerical models were built to correlate with the results from the bending tests. It was illustrated that the flexural behavior of the self-compacting PVA-SHCC link slab is better than that of concrete and cement mortar link slabs, where the crack initiation and propagation can be postponed. The results can provide theoretical support and design guidance for the self-compacting PVA-SHCC bridge deck.

Precise nitrogen supply is crucial for ensuring the quality of cut chrysanthemums (Chrysanthemum morifolium Ramat.). The nitrogen nutrition index (NNI) serves as an important indicator for diagnosing crop nitrogen (N) nutrition. Hyperspectral remote sensing (HRS) technology has been widely used in monitoring crop N status due to its rapid, accurate, and non-destructive capabilities. However, its application in estimating the NNI of cut chrysanthemums has received limited attention. Therefore, this study aimed to use HRS to accurately determine the cut chrysanthemum NNI, thereby providing valuable guidance for managing N fertilization. During several key growth stages, a hyperspectral spectroradiometer was used to capture hyperspectral reflectance data (350–2500 nm) from three leaf layers. Subsequently, cut chrysanthemum canopies were sampled for aboveground biomass (AGB) and plant nitrogen concentration (PNC). The collected AGB and PNC data were then utilized to fit the critical N (Nc) dilution curve of cut chrysanthemums using a Bayesian hierarchical model, enabling the calculation of the NNI. Finally, spectral indices and partial least squares regression (PLSR) were used to establish the NNI estimation model for cut chrysanthemums. The results showed that the Nc dilution curve of the cut chrysanthemums was Nc = 5.401 × AGB−0.468. The first leaf layer (L1) proved to be optimal for estimating cut chrysanthemum NNI. Additionally, a newly proposed two-band spectral index, DVI-L1 (R1105, R700), demonstrated moderate predictive capabilities for the NNI of cut chrysanthemums (R2 = 0.5309, RMSE = 0.3210). Compared with the spectral index-based NNI estimation model, PLSR-L1 showed the best performance in estimating the cut chrysanthemum NNI (R2 = 0.8177, RMSE = 0.2000). Our results highlight the rapid NNI prediction potential of HRS and its significance in facilitating precise N management in cut chrysanthemums.

In this paper, the three inorganic flocculants polymeric chloride PAC, FeCl3, and Al2(SO4)3 and two organic flocculants anionic polyacrylamide APAM and cationic polyacrylamide CPAM were screened to determine the most efficient flocculants and the optimal dosage, optimizing the flocculation operating conditions through the orthogonal test and then proving the experimental effect according to a comparison study of the high-concentration method and the traditional activated sludge method. The results show that the addition of CPAM achieves the best flocculation for high-concentration activated sludge suspension, and that the sludge interface descent rate, sludge volume index, and sludge settling ratio are better than those of other flocculants. The orthogonal test was used on the sludge volume index to perform evaluations and analyses: mixing section mixing intensity > Flocculation Stage 1 section mixing intensity > Flocculation Stage 2 section mixing intensity > mixing section residence time > flocculation section hydraulic residence time. In the comparison test, the settling performance of the high-concentration method was higher than that of the traditional activated sludge method. In terms of pollutant removal, the removal rates of COD, ammonia nitrogen, and total nitrogen of the traditional activated sludge method were 90.85%, 95.74%, and 71.6%, respectively. The average removal rates of COD, ammonia nitrogen, and total nitrogen of high-concentration activated sludge method were 92.24%, 97.28%, and 80.97%—higher than that of the traditional activated sludge method.

High concentrations of activated sludge are an excellent biological treatment; in particular, simultaneous nitrification and denitrification play a huge role in nitrogen removal. However, the influencing factors of SND have not been fully elucidated. The effects of sludge concentration and dissolved oxygen (DO) concentration on the performance of SND in a high-concentration activated sludge reactor assisted by chemical agents were investigated, and the SND reaction effect was de-termined by analyzing the along-stream changes of elemental nitrogen in the reactor. The results showed that the SND phenomenon in the reactor was most obvious when the system activated sludge concentration (MLSS) was maintained at 7–9 g/L and DO concentration at 1–2 mg/L. When MLSS decreases within the range of 5–9 g/L, the nitrification reaction improves, but the SND phe-nomenon decreases or even disappears; the SND phenomenon diminishes with increasing DO con-centration. Thus, high sludge concentrations and low dissolved oxygen concentrations are im-portant influences associated with SND and promote unconventional nitrogen removal pathways. In addition, the average value of MLVSS/MLSS for the high-concentration activated sludge process was 0.586, which indicates that the system has a higher activated sludge volume and better sludge activity, which is very effective in enhancing SND. In addition, this study also further investigated the influencing factors of SND in the high-concentration method by exploring the kinetic modeling of the SND reaction in the high-concentration method.

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions.

The development of a piezoelectric linear motor is presented in this paper, based on the principle of alternating motion, to meet the acting stroke and accuracy requirements for the probe station’s motion servo system. By partitioning the stator into tangential and normal components, two tangential actuators are affixed to the base, while two normal actuators are fixed on the preloading mechanism, thereby proposing a novel approach for connecting the stator and base. First, the construction and the working principle of the motor were introduced. Subsequently, the motor’s major components were designed through finite element simulation, followed by modeling the motor dynamics and deriving its displacement transfer function. Finally, an experimental prototype was fabricated, and a prototype test system was constructed. The driving method can realize a large stroke operation at a low frequency. The minimum operating frequency of the motor is 1 Hz, the minimum step is 12.55 μm, and the stroke is 105 mm. The study results will promote the development of high-performance probe systems.

Climate change has been regarded as a primary threat to biodiversity at local, regional, and global scales. Populus euphratica Oliv. is one of the main constructive species in dryland regions and has a key role in regulating ecosystem processes and services. However, there is a knowledge gap regarding the spatial distribution of habitat suitability of P. euphratica and how it will be affected by future climate change. Based on the distribution records collected from an online database and specialized literature, we applied an optimized MaxEnt model to predict the distribution range of P. euphratica in China under four climate change scenarios (SSP126, SSP245, SSP370, and SSP585) for both current and future (2090s) conditions. We found that (1) future climate change would reduce the adaptability of P. euphratica, resulting in a significant decrease in its distribution area; (2) water availability had the most important effect on P. euphratica distribution; (3) the habitat for P. euphratica would shift northwestward and contract towards lower elevations closer to rivers in the future. These findings can provide a reference for developing long-term biodiversity conservation and management strategies in arid regions.

The kelp gel edible granules developed utilizing the gel properties of alginate are prone to quality deterioration if improperly stored during the storage process. This study comprehensively investigated the quality changes of kelp gel edible granules stored at 4 °C and 25 °C by evaluating indicators such as total bacterial count, coliform bacteria, pH, relaxation time, color difference, appearance, texture characteristics, gel strength, and sensory scoring. The results showed that during the storage at 4 °C, the total bacterial count remained within the national standard range, the hardness and chewiness increased, the gel strength first increased and then decreased, the partial exudation of the bound water in the product occurred, and the sensory score slightly decreased, with an overall minor change in quality. During the storage at 25 °C, significant quality changes were observed, with the total bacterial count exceeding the national standard on the 20th day; additionally, the hardness, chewiness, and gel strength all initially increased and then decreased, both the bound water and the restrained water in the product exuded, the moisture stability decreased, and the sensory score significantly decreased between 16 to 20 days. The spoilage of the product was characterized by a significant water loss, reduction in volume, color change from bright green to dark yellow-brown, and a distinct smell of decaying algae. No coliform bacteria was detected in all products during the storage period. In summary, the shelf life endpoint of the product stored at 25 °C is 16 days, and the shelf life of the product stored at 4 °C is greater than 20 days. Storage at 4 °C can better maintain product quality, extend the shelf life, and effectively maintain the overall color of the product.

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

Nitrogen is one of the important factors restricting the development of sesame planting and industry in China. Cultivating sesame varieties tolerant to low nitrogen is an effective way to solve the problem of crop nitrogen deficiency. To date, the mechanism of low nitrogen tolerance in sesame has not been elucidated at the transcriptional level. In this study, two sesame varieties Zhengzhi HL05 (ZZ, nitrogen efficient) and Burmese prolific (MD, nitrogen inefficient) in low nitrogen were used for RNA-sequencing. A total of 3964 DEGs (differentially expressed genes) and 221 DELs (differentially expressed lncRNAs) were identified in two sesame varieties at 3d and 9d after low nitrogen stress. Among them, 1227 genes related to low nitrogen tolerance are mainly located in amino acid metabolism, starch and sucrose metabolism and secondary metabolism, and participate in the process of transporter activity and antioxidant activity. In addition, a total of 209 pairs of lncRNA-mRNA were detected, including 21 pairs of trans and 188 cis. WGCNA (weighted gene co-expression network analysis) analysis divided the obtained genes into 29 modules; phenotypic association analysis identified three low-nitrogen response modules; through lncRNA-mRNA co-expression network, a number of hub genes and cis/trans-regulatory factors were identified in response to low-nitrogen stress including GS1-2 (glutamine synthetase 1–2), PAL (phenylalanine ammonia-lyase), CHS (chalcone synthase, CHS), CAB21 (chlorophyll a-b binding protein 21) and transcription factors MYB54, MYB88 and NAC75 and so on. As a trans regulator, lncRNA MSTRG.13854.1 affects the expression of some genes related to low nitrogen response by regulating the expression of MYB54, thus responding to low nitrogen stress. Our research is the first to provide a more comprehensive understanding of DEGs involved in the low nitrogen stress of sesame at the transcriptome level. These results may reveal insights into the molecular mechanisms of low nitrogen tolerance in sesame and provide diverse genetic resources involved in low nitrogen tolerance research.

Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment’s efficacy. Deep learning for segmenting the kidneys has improved these measurements’ speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.

In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition’s complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced query methodologies, this study synthesizes findings from 28 relevant documents, unveiling a landscape marked by significant advancements and persistent challenges. Through a methodological lens grounded in the PRISMA framework (Version 2020) and Machine Learning-driven tools like VosViewer (Version 1.6.20) and Litmaps (Free Version), the research delineates the high variability in speech patterns, the intricacies of speech recognition, and the hurdles posed by limited and diverse datasets as core obstacles. Innovative solutions such as specialized deep learning models, data augmentation strategies, and the pivotal role of interdisciplinary collaboration in dataset annotation emerge as vital contributions to this field. The analysis culminates in identifying theoretical and practical pathways for surmounting these barriers, highlighting the potential of deep learning technologies to revolutionize aphasia assessment and treatment. This review not only consolidates current knowledge but also charts a course for future research, emphasizing the need for comprehensive datasets, model optimization, and integration into clinical workflows to enhance patient care. Ultimately, this work underscores the transformative power of deep learning in advancing aphasia diagnosis, treatment, and support, heralding a new era of innovation and interdisciplinary collaboration in addressing this challenging disorder.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

In drug discovery, selecting targeted molecules is crucial as the target could directly affect drug efficacy and the treatment outcomes. As a member of the CCN family, CTGF (also known as CCN2) is an essential regulator in the progression of various diseases, including fibrosis, cancer, neurological disorders, and eye diseases. Understanding the regulatory mechanisms of CTGF in different diseases may contribute to the discovery of novel drug candidates. Summarizing the CTGF-targeting and -inhibitory drugs is also beneficial for the analysis of the efficacy, applications, and limitations of these drugs in different disease models. Therefore, we reviewed the CTGF structure, the regulatory mechanisms in various diseases, and drug development in order to provide more references for future drug discovery.

About 2.5 billion people rely on groundwater as their main drinking water source, and arsenic pollution within the groundwater system can cause serious short- and long-term health issues. Within the natural environment, arsenic generally exists as oxyanions which have two oxidation states, As(III) and As(V). Under ambient pH conditions, As(V) is primarily present as an anion (i.e., H2AsO4−) while As(III) tends to be uncharged (i.e., H3AsO3), making it much more difficult to remove As(III) through existing treatment techniques such as adsorption and reverse osmosis (RO). In Eastern Wisconsin, the dolomite and dolomite/sandstone aquifers represent a major drinking water source and high arsenic concentrations have been observed. Previous studies showed that arsenic can be released into private drinking water wells due to the oxidation of sulfide-bearing minerals with arsenic impurities that are usually dispersed within the dolomite and sandstone bedrock. However, there is a lack of information on the concentrations of each arsenic species as well as arsenic dynamics during water pumping/usage. The primary goals of this research were (1) to quantify the baseline concentrations of each arsenic species within selected Eastern Wisconsin private drinking water wells, and (2) to determine how the arsenic concentrations and species could be affected by continuous water usage. Our results showed that As(III) was the dominant species of arsenic, and during continuous water usage, there could be an upward trend in arsenic concentration (there was minimal change in arsenic speciation). Upon the completion of water pumping, arsenic concentrations decreased over time and returned to pre-pumping levels. Our findings highlighted the importance of quantifying the speciation and dynamics of arsenic during water use to the assessment of public health risks and the design of appropriate water treatment techniques.

An important component contributing to the onset of epilepsy is the death of hippocampal neurons. Several studies have shown that Dravet syndrome model mice: Scn1a KO mice have a high number of apoptotic neurons following seizures, but the precise mechanism underlying this remains unclear. The aim of this research was to elucidate the potential molecular mechanism of neuronal apoptosis in Scn1a KO mice by integrating proteomics and transcriptomics, with the ultimate goal of offering better neuroprotection. We found that apoptotic processes were enriched in both proteomic and transcriptomic GO analyses, and KEGG results also indicated that differential proteins and genes play a role in neurotransmission, the cell cycle, apoptosis, and neuroinflammation. Then, we examined the upstream and downstream KGML interactions of the pathways to determine the relationship between the two omics, and we found that the HIF-1 signaling pathway plays a significant role in the onset and apoptosis of epilepsy. Meanwhile, the expression of the apoptosis-related protein VHL decreased in this pathway, and the expression of p21 was upregulated. Therefore, this study suggests that VHL/HIF-1α/p21 might be involved in the apoptosis of hippocampal neurons in Scn1a KO mice.

Qinghai spruce forests, found in the Qilian mountains, are a typical type of water conservation forest and play an important role in regulating the regional water balance and quantifying the changes and controlling factors for evapotranspiration (ET) and its components, namely, transpiration (T), evaporation (Es) and canopy interceptions (Ei), of the Qinghai spruce, which may provide rich information for improving water resource management. In this study, we partitioned ET based on the assumption that total ET equals the sum of T, Es and Ei, and then we analyzed the environmental controls on ET, T and Es. The results show that, during the main growing seasons of the Qinghai spruce (from May to September) in the Qilian mountains, the total ET values were 353.7 and 325.1 mm in 2019 and 2020, respectively. The monthly dynamics in the daily variations in T/ET and Es/ET showed that T/ET increased until July and gradually decreased afterwards, while Es/ET showed opposite trends and was mainly controlled by the amount of precipitation. Among all the ET components, T always occupied the largest part, while the contribution of Es to ET was minimal. Meanwhile, Ei must be considered when partitioning ET, as it accounts for a certain percentage (greater than one-third) of the total ET values. Combining Pearson’s correlation analysis and the boosted regression trees method, we concluded that net radiation (Rn), soil temperature (Ts) and soil water content (SWC) were the main controlling factors for ET. T was mainly determined by the radiation and soil hydrothermic factors (Rn, photosynthetic active radiation (PAR) and TS30), while Es was mostly controlled by the vapor pressure deficit (VPD), atmospheric precipitation (Pa), throughfall (Pt) and air temperature (Ta). Our study may provide further theoretical support to improve our understanding of the responses of ET and its components to surrounding environments.

Rhodium-catalyzed cycloaddition reactions are a powerful tool for the construction of polycyclic compounds. Combined experimental and DFT studies were used to investigate the temperature-controlled chemoselectivity of cationic rhodium-catalyzed intramolecular cycloaddition reactions of ene-vinylidenecyclopropanes. After a series of mechanistic studies, it was found that trace amounts of water in the reaction system play an important role in generating the product with endo double bond located on a five-membered ring and revealed that trace amounts of water in the reaction system, including the rhodium catalyst, substrate and solvent, were sufficient to promote the formation of the product with endo double bond located on a five-membered ring, and additional water could not further accelerate the reaction. DFT calculation results show that the addition of water indeed significantly lowers the energy barrier of the proton transfer step, making the formation of the product with endo double bond located on a five-membered ring more likely to occur and confirming the rationality of water-assisted proton transfer occurring in the selective access to the product with endo double bond located on a five-membered ring.

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.

Protamine is a cationic peptide derived from fish sperm and has several important functional properties: antibacterial properties, acting as a carrier for injectable insulin and as a heparin antagonist, combatting fatigue, etc. Thus, it has been widely used in medicinal applications and food products. Cultured Takifugu flavidus is a type of pufferfish with a delicious taste that is popular in China, and its production is increasing significantly. Therefore, protamine was extracted via acid extraction from the sperm of Takifugu flavidus and further isolated and purified via sephadex gel chromatography, ion exchange chromatography, and desalination chromatography. Furthermore, the physicochemical properties of protamine were investigated. The results showed that the sperm of the cultured T. flavidus were non-toxic, and the extracted and purified protamine had high contents of arginine (36.90%) and lysine (27.02%), respectively. The secondary structure of protamine was mainly β-folded and irregularly curled. Additionally, protamine exhibited high thermal stability with a denaturation temperature of 176 °C. This study would provide a theoretical basis for the structural analysis, bioactivity, and resource development of pufferfish protamine and help to promote the development of the pufferfish industry.

The reliable observation and accurate estimates of land–atmosphere water vapor (H2O) flux is essential for ecosystem management and the development of Earth system models. Currently, the most direct measurement method for H2O flux is eddy covariance (EC), which depends on the development of fast-response H2O sensors. In this study, we presented a cost-efficient open-path H2O analyzer (model: HT1800) based on the tunable diode laser absorption spectroscopy (TDLAS) technique, and investigated its applicability for measuring atmospheric turbulent flux of H2O using the EC method. We prepared two HT1800 analyzers with lasers that operate at wavelengths of 1392 nm and 1877 nm, respectively. The field performance of the two analyzers was evaluated through inter-comparative experiments with LI-7500RS and IRGASON, two of the most commonly used H2O analyzers in the EC community. Water vapor densities measured by the three types of analyzers had high overall agreement with the reference sensor; however, they all experienced drift. The mean density drifts of HT1800, LI-7500 and IRGASON were 3.7–5.2%, 4.0% and 3.8%, respectively. Even so, the half-hourly H2O fluxes measured by HT1800 were highly consistent with those by LI-7500RS and IRGASON (with a difference of less than 2%), suggesting that HT1800 can obtain H2O fluxes with high confidence. The HT1800 was also proved to be suitable for EC application in terms of data availability, flux detection limit and response to the high-frequency turbulent variation. Furthermore, we investigated how the spectroscopic effect influences the measurements of H2O density and flux. Despite the fact that the 1392 nm laser was much more susceptible to the spectroscopic effect, the fluxes after correcting for this bias showed excellent agreement with the IRGASON fluxes. Considering the cost advantage in laser and photodetector, the HT1800 analyzer using a 1392 nm infrared laser is a promising and economical solution for EC measurement studies of water vapor.

The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm.

This paper analyzes the deformation responses of single-cylinder and four-cylinder suction pile foundations in soft clay under horizontal cyclic loading. Based on the existing stiffness degradation model of soft clay, a more suitable model for marine soft clay is proposed by carrying out dynamic triaxial experiments under different stress levels. The stiffness degradation and cumulative displacement of soil under cyclic loading are implemented in ABAQUS by compiling a USDFLD subroutine. The rationality is verified by comparison with the displacement response of the pile measured by the centrifugal model test and the scaled model test reported in the literature. With the aid of the current model, the stress state and deformation of suction pile foundations under horizontal cyclic load are further analyzed. The numerical results reveal the path of soil stiffness degradation and the deformation response of foundations during horizontal cycling. The cumulative displacement generated by the cyclic load is quantitatively displayed. It is proved that this model has the capability to reflect the cumulative displacement under cyclic loading and shows promise in analyzing the long-term deformation response of offshore foundations.

The development of stiffness theory is constrained by its contradiction with engineering experience. Several easily overlooked details of stiffness theory were clarified, and a qualitative evaluation formula for the risk of coal burst was provided. Then a novel structure factor called uneven stiffness coal seam structure (USCS), which consists of high stiffness zone (HSZ), low stiffness zone (LSZ), and contiguous roof and floor, was proposed. Many areas prone to coal bursts, such as thinning zones, bifurcating areas, magmatic intrusion areas, and remnant pillar affected areas of coal seam, are the HSZs of USCSs. Comparative analysis of the uneven stiffness coal seam under different roof conditions and examination of the simplified trisection model of the USCS were conducted. Then 6 groups of 14 simplified 2D models using COMSOL5.2 was constructed based on controlled variable method to simulate different responses of the USCS with varying parameters under same working conditions. The results demonstrate the following: (1) coal bursts occur only when both the failure criterion and the stiffness criterion are simultaneously satisfied, the risk of coal burst (rCB) is the product of the risk of failure (rF) and the risk of instability (rI). (2) The pressure concentration function of USCS facilitates stress concentration from LSZ to HSZ, thus raising the rF in HSZ. The stiffness reduction function of USCS reduces the local mine stiffness (LMS) of the HSZ, allowing the system to meet the stiffness criterion even with a hard roof, thereby raising the rI in HSZ and reconciling the contrast between stiffness theory and engineering experience. Failures within HSZ of the USCS enables the roof strata to release bending deformation energy without roof breakage. (3) The normal stress of HSZ is positively correlates with the value of ERHRKHSL/KLSH; The LMS of the HSZ is positively correlated with the value of ERKL/KHHRSLSH. The USCS boasts significant advantages in integrating and harmonizing various existing theories and explaining multiple specific types of coal bursts. By applying relevant USCS findings, new explanations can be provided for engineering phenomena such as the time-delayed coal bursts, the inefficient pressure relief in ultra thick coal seams, and the “microseism deficiency” observed prior to certain coal bursts.

CO gas is not only lethal but also a significant forecasting indicator for the spontaneous combustion of coal mines. It is imperative that monitoring modules for CO gas that work well in the coal mine environment are available. A feasible solution is the detection of CO by using monitoring modules based on tunable diode laser absorption spectroscopy (TDLAS) over a mid-infrared waveband near 4.6 μm. However, in most cases, the mid-infrared TDLAS-based CO monitoring module tends to introduce severe interference fringe noise into the TDLAS spectral backgrounds which is difficult to filter out using traditional spectral filtering methods, reducing the detection performance of the module. In order to filter out the noise and improve the stability of the module in complex coal mine environments, this work proposed an algorithm based on support vector regression (SVR) to extract the TDLAS spectral backgrounds. Spectral analysis indicates that the TDLAS spectral background can be predicted over the entire scanning spectrum range by using this algorithm, and the noise in the spectral background can be effectively filtered out when calculating the absorbance spectrum based on the Lambert–Beer law. Compared to extracting spectral backgrounds using the traditional least square polynomial fit, the obtained correlation coefficients between regression models of spectral backgrounds and corresponding training point datasets were increased from below 0.998 to above 0.999. The peak-to-peak value of the obtained N2 absorbance spectrum was suppressed below 0.022 from nearly 0.045. The signal-to-noise ratio of the obtained 25 ppm CO absorbance spectrum was increased to 13.35 from 6.95. A CO monitoring module polluted by dust was used to conduct experiments to further test the SVR-based algorithm. The experiment results showed that after programming the SVR-based algorithm to the module, the estimated limit of detection of the module was reduced to 5.46 ppm from 29.08 ppm, and all the absolute measuring errors of the standard CO gases with different low concentrations were reduced to less than 4 ppm from a majority of the errors of more than 10 ppm, compared to least square polynomial fit. The CO monitoring module could still maintain the performance of high-precision quantitative detection when using the SVR-based algorithm even if it had been polluted severely. So, the CO monitoring module has good adaptability to harsh field environments, and its operation stability can be effectively improved by using the algorithm proposed in this work.

Objective: To conduct a systematic review to determine if there are exercise mobile applications (apps) that can produce evidence-based, individualized exercise plans. Materials and Methods: We searched the Apple Store and Google Play for exercise apps with terms related to exercise and health. Exercise apps were eligible if they: (1) had a ≥4 out of 5 overall rating with ≥1000 reviews; (2) were free to download; and (3) were not gender specific. Exercise apps were evaluated via the evidence-based exercise prescription (ExRx) standards of the American College of Sports Medicine (ACSM) and American Heart Association. For the exercise app evaluation criteria, an app was included if it (1) was evidence-based; (2) contained a preparticipation health screening protocol; (3) built a cardiovascular disease (CVD) risk factor profile; (4) prioritized one chronic disease or health condition to focus on; (5) framed the exercise plan by the frequency, intensity, time, and type principle (FITT) of ExRx; and (6) specified special considerations. Results: Of the 531 potentially qualifying apps, 219 qualified. The qualifying apps were rarely evidenced-based (0.5%) or had a preparticipation screening protocol (3.7%). Only 27.7% built CVD risk factor profiles. Most apps (64.8%) focused on body image and/or athletic performance. Only 4.3% focused on chronic diseases or health conditions, while the remainder (34.5%) did not disclose a focus. No app framed the exercise plans by the FITT of ExRx. Only 1.4% of the apps specified special considerations. Conclusion: There are no commercially available exercise apps that generate an evidence-based, individualized exercise plan for adults with CVD risk factors.

(1) Lipases are catalysts widely applied in industrial fields. To sustain the harsh treatments in industries, optimizing lipase activities and thermal stability is necessary to reduce production loss. (2) The thermostability of Thermomyces lanuginosus lipase (TLL) was evaluated via B-factor analysis and consensus-sequence substitutions. Five single-point variants (K24S, D27N, D27R, P29S, and A30P) with improved thermostability were constructed via site-directed mutagenesis. (3) The optimal reaction temperatures of all the five variants displayed 5 °C improvement compared with TLL. Four variants, except D27N, showed enhanced residual activities at 80 °C. The melting temperatures of three variants (D27R, P29S, and A30P) were significantly increased. The molecular dynamics simulations indicated that the 25-loop (residues 24–30) in the N-terminus of the five variants generated more hydrogen bonds with surrounding amino acids; hydrogen bond pair D254-I255 preserved in the C-terminus of the variants also contributes to the improved thermostability. Furthermore, the newly formed salt-bridge interaction (R27…E56) in D27R was identified as a crucial determinant for thermostability. (4) Our study discovered that substituting residues from the 25-loop will enhance the stability of the N-terminus and C-terminus simultaneously, restrict the most flexible regions of TLL, and result in improved thermostability.

Background: Normal tension glaucoma (NTG) is considered a neurodegenerative disease with glaucomatous damage extending to diffuse brain areas. Therefore, this study aims to explore the abnormalities in the NTG structural network to help in the early diagnosis and course evaluation of NTG. Methods: The structural networks of 46 NTG patients and 19 age- and sex-matched healthy controls were constructed using diffusion tensor imaging, followed by graph theory analysis and correlation analysis of small-world properties with glaucoma clinical indicators. In addition, the network-based statistical analysis (NBS) method was used to compare structural network connectivity differences between NTG patients and healthy controls. Results: Structural brain networks in both NTG and NC groups exhibited small-world properties. However, the small-world index in the severe NTG group was reduced and correlated with a mean deviation of the visual field (MDVF) and retinal nerve fiber layer (RNFL) thickness. When compared to healthy controls, degree centrality and nodal efficiency in visual brain areas were significantly decreased, and betweenness centrality and nodal local efficiency in both visual and nonvisual brain areas were also significantly altered in NTG patients (all p < 0.05, FDR corrected). Furthermore, NTG patients exhibited increased structural connectivity in the occipitotemporal area, with the left fusiform gyrus (FFG.L) as the hub (p < 0.05). Conclusions: NTG exhibited altered global properties and local properties of visual and cognitive-emotional brain areas, with enhanced structural connections within the occipitotemporal area. Moreover, the disrupted small-world properties of white matter might be imaging biomarkers for assessing NTG progression.

An affinity chromatography filler of CNBr-activated Sepharose 4B-immobilized ACE was used to purify ACE-inhibitory peptides from Takifugu flavidus protein hydrolysate (50 = 93.5 µmol·L−1) was selected. Molecular docking revealed that TLRFALHGME may interact with the active site of ACE through H-bond, hydrophobic, and electrostatic interactions. The total binding energy (ΔGbinding) of TLRFALHGME was estimated to be −82.7382 kJ·mol−1 by MD simulations, indicating the favorable binding of peptides with ACE. Furthermore, the binding affinity of TLRFALHGME to ACE was determined by surface plasmon resonance (SPR) with a Kd of 80.9 µmol, indicating that there was a direct molecular interaction between them. TLRFALHGME has great potential for the treatment of hypertension.

Applications of haloalkane dehalogenase DhaA in biocatalysis are limited by its unfavorable performance in organic solvents. Our previous work proved that mutations of surface positive-charged residues enhanced the organic solvent resistance of DhaA, which inspired us to explore the effect of cationic polymers on DhaA in organic solvents. Remarkably boosted performance was achieved in different organic solvent solutions by introducing cationic polymers, for example, there was a 6.1-fold activity increase with poly(allylamine hydrochloride) and a 5.5-fold activity increase with poly(ethylene imine) in 40 vol.% dimethylsulfoxide. The presence of cationic polymers protected DhaA from damage by organic solvents and increased the substrate concentration around the enzyme-polymer complex. Fluorescence spectroscopy and molecular dynamics simulations revealed that the binding of cationic polymers onto DhaA weakened the interactions between organic solvents and DhaA, decreased the organic solvent solvation level around DhaA, and enhanced the structural stability of DhaA in organic solvents. This comprehensive understanding of the effect of cationic polymers on DhaA can help to broaden the applications of DhaA in organic solvent-involved biocatalysis.

Large-amplitude internal waves (IWs) with strong lateral shear forces can cause destructive effects on marine engineering structures. In this study, a large eddy simulation (LES) method was employed to simulate the generation and propagation of IWs in a three-dimensional numerical wave tank, and the pressure distribution, flow field characteristics, and force behaviors of two tandem cylinders under the coupling effect of the IWs and slope terrain were studied. The influence mechanism of the normalized value of center-to-center spacing (L) and the diameter of the cylinder (D), i.e., (L/D), on the strength of the vortex disturbance between cylinders was studied by comparing the simulation results of two tandem cylinders with those of a single cylinder (SC) to further explore the mechanical response characteristics of the upstream cylinder (P1) and downstream cylinder (P2). The simulation results showed that the vortex interaction between cylinders is the critical factor that affects the forces acting on the cylinders. The strength of the vortex disturbance could be distinguished by the dimensionless critical center-to-center spacing between cylinders (Lc/D = 3.0). When L/D ≤ Lc/D, the vortex disturbance was severe, causing P1 and P2 to experience significant horizontal positive forces and negative forces, respectively. In the case of L/D > Lc/D, the forces acting on both cylinders gradually returned to those on a single cylinder.

Soil salinization is a common abiotic stress that seriously affects soybean growth and yield, underscoring the need to enhance plant salt tolerance for sustainable agriculture development. Selenium is a beneficial element that has been shown to promote plant growth, development and stress resistance. This study employed pot experiments to investigate the effects of different salt levels (0, 50, 100 and 150 mM NaCl) on salt-tolerant (Zhonghuang 13) and salt-sensitive soybean (Dongnong 63) varieties. Additionally, the critical salt concentration (100 mM NaCl) was selected to explore the effects of exogenous selenium (0, 0.5, 1 and 3 mg·kg−1) on improving salt tolerance in salt-tolerant and salt-sensitive soybeans under salt stress. Results showed that as salt concentration increased, plant height, shoot and root fresh weight, SPAD value and enzyme activity of both salt-tolerant and salt-sensitive soybeans significantly decreased. The increasing concentration of exogenous selenium significantly decreased the proline content of salt-sensitive and salt-tolerant soybeans by 40.65–58.87% and 38.51–50.46%, respectively, and the MDA content by 19.33–30.36% and 16.94–37.48%, respectively. Selenium supplementation also reduced the content of Na+ in salt-sensitive and salt-tolerant soybeans and improved K+ absorption in soybeans, which increased the K+/Na+ ratio. Moreover, high-throughput sequencing of the 16S ribosomal RNA gene demonstrated that selenium application optimized the rhizosphere microecology structure of salt-tolerant and salt-sensitive soybean varieties and enhanced functional genes related to lipid metabolism, energy metabolism and cell motility of rhizosphere microorganisms. In summary, selenium application improved the salt tolerance of the two soybean varieties by enhancing the physiological resistance to salt stress and optimizing the structure and function of the rhizosphere microbial community.

Taking a wind farm in the Qinghai–Tibet Plateau as the experimental site, the ZephiR Dual Mode (ZDM) LiDAR and ground-based laser LiDAR were used to scan the incoming flow and wake of the wind turbine separately. Based on wavelet analysis, the experimental study was conducted on the influence of different incoming wind speeds on the power and wake of the wind turbine. It is found that the incoming wind speeds have a great influence on the wind turbine power, and the fluctuation frequency of the wind speed is obviously higher than that of the power, that is, the scale effects of turbulence are magnified. The rotation of the wind wheel can accelerate the collapse of the large-scale turbulent structures of the incoming flow, and large-scale vortices continue to collapse into small-scale vortices, that is, the energy cascade evolution occurs. And in the wake diffusion process, the dissipation degree of the upper blade tip vortex is greater than that of the lower blade tip vortex caused by the rotation of the wind turbine. Under the same incoming flow conditions, due to the influence of tower and ground turbulence structure, the energy level connection phenomenon of the measuring points below the hub height is stronger than that above the hub height, and it weakens with the increase of the measuring distance. That is, the energy cascade of the measuring points below the hub height at 1.5 D (D is the diameter of the wind wheel) of the wake is weaker than that at 1 D of the wake. With the increase of the measuring distance of the wake, the influx of the external flow field further aggravates the momentum exchange and energy transport between the vortex clusters, that is, the influence of the external flow field gradually increases in the wake vortex pulsation.

With the strengthening of regional and urban–rural interactions, farmers’ livelihood activities are becoming increasingly complex, and environmental factors that influence farmers’ livelihoods have multi-spatial effects. Consequently, comprehending farmers’ livelihoods from a multi-spatial perspective is imperative. Based on surveys conducted in 65 villages and 451 households in Jia County on the Loess Plateau, China, rural locations were deconstructed into natural, traffic, and positional advantages to explore the relationships and mechanisms between the rural environment and farmers’ livelihood stability from local, urban–rural, and interconnected multi-spatial perspectives. We found that 77% of the villages achieved a moderate or high Rural Location Advantage Index (RLAI) rating; 45% still lack natural advantages and are mainly located in hilly and sandy areas because of the fragile ecological environment of the Loess Plateau. Additionally, the Livelihood Stability Index (LSI) was moderate overall, but with significant spatial heterogeneity, and 72% of farmers possess strong transition capacity and have shifted away from relying on monoculture as their primary livelihood strategy. While a certain coupling correspondence exists between the LSI and RLAI, the interaction is intricate rather than a simple linear agglomeration process. The spatial variation in the LSI results from the superposition or interaction of multi-spatial location factors. The rural–urban spatial location factors are the key control element of the LSI and the interaction between rural–urban and local spatial location factors has the greatest influence on the LSI. It is simple for interconnected spatial location factors to produce a scale correlation effect, and have non-negligible effects on farmers’ livelihoods when they interact with other spatial location factors. Understanding the impact of rural location on farmers’ livelihood from a multi-spatial perspective is of great practical significance for identifying the causes of spatial heterogeneity in livelihoods and enhancing multi-level policy coordination on rural revitalization and livelihood security.

The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.

Osmotolerant yeasts are considered one of the major contaminants responsible for spoilage in honey. To address the signature volatile components of jujube honey contaminated by Zygosaccharomyces rouxii, headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and chemometrics analyses were used to analyze the variation of volatile substances during early contamination of mature and immature jujube honey. Undecanal, methyl butyrate, methyl 2-nonenoate, methyl hexanoate, and 2-methyl-3-pentanone were identified as signature volatiles of jujube honey contaminated with Z. rouxii. In addition, methyl heptanoate, 2,6,10-trimethyltetradecane, and heptanal were identified as potential volatile signatures for immature jujube honey. The R2 and Q2 of OPLS-DA analyses ranged from 0.736 to 0.955, and 0.991 to 0.997, which indicates that the constructed model was stable and predictive. This study has demonstrated that HS-SPME-GC-MS could be used to distinguish Z. rouxii-contaminated jujube honey from uncontaminated honey based on variation in VOCs, and could provide theoretical support for the use of HS-SPME-GC-MS for the rapid detection of honey decomposition caused by Z. rouxii, which could improve nutritional quality and reduce economic losses.

To address the potential risks to food safety, headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) were used to analyze the volatile organic compounds (VOCs) generated from chilled chicken contaminated with Staphylococcus aureus during early storage. Together with the KEGG database, we analyzed differential metabolites and their possible biotransformation pathways. Orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to characterize VOCs and identify biomarkers associated with the early stage of chicken meat contamination with S. aureus. The results showed 2,6,10,15-tetramethylheptadecane, ethyl acetate, hexanal, 2-methylbutanal, butan-2-one, 3-hydroxy-2-butanone, 3-methylbutanal, and cyclohexanone as characteristic biomarkers, and 1-octen-3-ol, tetradecane, 2-hexanol, 3-methyl-1-butanol, and ethyl 2-methylpropanoate as potential characteristic biomarkers. This provides a theoretical basis for the study of biomarkers of Staphylococcus aureus in poultry meat.

Cd2+, a major environmental pollutant, is heavily toxic to human health. Many traditional techniques are high-cost and complicated; thus, developing a simple, sensitive, convenient, and cheap monitoring approach is necessary. The aptamer can be obtained from a novel method called SELEX, which is widely used as a DNA biosensor for its easy acquisition and high affinity of the target, especially for heavy metal ions detection, such as Cd2+. In recent years, highly stable Cd2+ aptamer oligonucleotides (CAOs) were observed, and electrochemical, fluorescent, and colorimetric biosensors based on aptamers have been designed to monitor Cd2+. In addition, the monitoring sensitivity of aptamer-based biosensors is improved with signal amplification mechanisms such as hybridization chain reactions and enzyme-free methods. This paper reviews approaches to building biosensors for inspecting Cd2+ by electrochemical, fluorescent, and colorimetric methods. Finally, many practical applications of sensors and their implications for humans and the environment are discussed.

Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to install or cause different degrees of damage. Here, we propose a novel Internet of Things (IoT) monitoring system that includes wireless acoustic emission sensor nodes (WASNs) and underground soil MC sensor nodes to efficiently detect and diagnose the MC level of living tree trunks. After the characteristic parameters were collected by the two sensors, a feature selection and multi-sensory global fusion method for MC diagnosis was designed and developed and several statistical parameters were selected as the input variables to predict the heartwood MC level with a support vector machine (SVM) model. Moreover, to achieve the highest prediction accuracy, an improved sparrow search algorithm (ISSA) is applied to ensure the most suitable parameter combinations in a two-objective optimization model. Extensive experiments result in a fusion of the environment, and AE signals show that the proposed mechanism has better diagnostic performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions.

The current standards used for nitrogen pollution evaluation are lacking, and scientific classification methods are needed for nitrogen pollution to improve water quality management capabilities. This study addresses the important issue of assessing surface water nitrogen pollution by utilizing two advanced multivariate statistical techniques: self-organizing maps (SOMs) obtained using the K-means algorithm and the Hasse diagram technique (HDT). The research targets of this study are the rivers of the megacity Chengdu, China. Samples were collected on a monthly basis in 2017–2020 from different sites along the rivers, and their nitrogen pollution parameters were determined. The grouping of nitrogen pollution parameters and the clustering of sampling events using SOMs facilitate the preprocessing required for the HDT, wherein clusters are ordered according to the pre-clustered water sampling events. The results indicate that nitrogen pollution in the Chengdu River Basin, which is prominent and mainly driven by nitrate nitrogen, can be categorized into five levels. The nitrogen pollution in Tuo River is serious. Although the degree of ammonia nitrogen pollution in Jin River is higher, the pollution range is smaller. Furthermore, these results were evaluated by the SOMs and HDT to be clear and reliable. Overall, these findings can provide a basis for local environmental legislation.

Glutamate is excitotoxic to neurons. The entry of glutamine or glutamate from the blood into the brain is limited. To overcome this, branched-chain amino acids (BCAAs) catabolism replenishes the glutamate in brain cells. Branched-chain amino acid transaminase 1 (BCAT1) activity is silenced by epigenetic methylation in IDH mutant gliomas. However, glioblastomas (GBMs) express wild type IDH. Here, we investigated how oxidative stress promotes BCAAs’ metabolism to maintain intracellular redox balance and, consequently, the rapid progression of GBMs. We found that reactive oxygen species (ROS) accumulation promoted the nuclear translocation of lactate dehydrogenase A (LDHA), which triggered DOT1L (disruptor of telomeric silencing 1-like)-mediated histone H3K79 hypermethylation and enhanced BCAA catabolism in GBM cells. Glutamate derived from BCAAs catabolism participates in antioxidant thioredoxin (TxN) production. The inhibition of BCAT1 decreased the tumorigenicity of GBM cells in orthotopically transplanted nude mice, and prolonged their survival time. In GBM samples, BCAT1 expression was negatively correlated with the overall survival time (OS) of patients. These findings highlight the role of the non-canonical enzyme activity of LDHA on BCAT1 expression, which links the two major metabolic pathways in GBMs. Glutamate produced by the catabolism of BCAAs was involved in complementary antioxidant TxN synthesis to balance the redox state in tumor cells and promote the progression of GBMs.

Black phosphorus (BP), an emerging two-dimensional material with unique optical, thermoelectric, and mechanical properties, has been proposed as bioactive material for tissue engineering. However, its toxic effects on physiological systems remain obscure. The present study investigated the cytotoxicity of BP to vascular endothelial cells. BP nanosheets (BPNSs) with a diameter of 230 nm were fabricated via a classical liquid-phase exfoliation method. Human umbilical vein endothelial cells (HUVECs) were used to determine the cytotoxicity induced by BPNSs (0.31–80 μg/mL). When the concentrations were over 2.5 μg/mL, BPNSs adversely affected the cytoskeleton and cell migration. Furthermore, BPNSs caused mitochondrial dysfunction and generated excessive intercellular reactive oxygen species (ROS) at tested concentrations after 24 h. BPNSs could influence the expression of apoptosis-related genes, including the P53 and BCL-2 family, resulting in the apoptosis of HUVECs. Therefore, the viability and function of HUVECs were adversely influenced by the concentration of BPNSs over 2.5 μg/mL. These findings provide significant information for the potential applications of BP in tissue engineering.

Pyrolysis is an emerging and effective means for sludge disposal. Biochar derived from sludge has broad application prospects, however, is limited by heavy metals. In this study, the fate of heavy metals (HMs) in pyrolysis coupling with acid washing treatment for sewage sludge was comprehensively investigated for the first time. Most of the HMs redistributed in the pyrolyzed residues (biochar) after pyrolysis, and the enrichment order of the HMs was: Zn > Cu > Ni > Cr. Compared with various washing agents, phosphoric acid presented a superior washing effect on most heavy metals (Cu, Zn, and Cr) in biochars derived at low pyrolysis temperature and Ni in biochars derived at high pyrolysis temperature. The optimal washing conditions for heavy metals (including Cu, Zn, Cr, and Ni) removal by H3PO4 were obtained by batch washing experiments and the response surface methodology (RSM). The total maximum HM removal efficiency was 95.05% under the optimal washing specifications by H3PO4 (acid concentration of 2.47 mol/L, L/S of 9.85 mL/g, and a washing temperature of 71.18 °C). Kinetic results indicated that the washing process of heavy metals in sludge and biochars was controlled by a mixture of diffusion and surface chemical reactions. After phosphoric acid washing, the leaching concentrations of HMs in the solid residue were further reduced compared with that of biochar, which were below the USEPA limit value (5 mg/L). The solid residue after pyrolysis coupling with acid washing resulted in a low environmental risk for resource utilization (the values of the potential ecological risk index were lower than 20). This work provides an environmentally friendly alternative of pyrolysis coupling with acid washing treatment for sewage sludge from the viewpoint of the utilization of solid waste.

Keystone species are critical to preserving community stability and species diversity. Identifying key species and investigating their ecological regulation will help to prioritize important species and gain a better understanding of community stability mechanisms. It is based on this understanding that the present study tried to elucidate major keystone species in an important ecosystem in eastern Chinese waters. Therefore, data were collected from fisheries surveys conducted between 2016 and 2021 in the East China Sea. We identified Muraenesox cinereus, Leptochela gracilis, and Trichiurus lepturus as keystone species in the region based on the results of principal component analysis of ten network indices. The removal analysis performed suggested that the loss of keystone species might have a negative impact on the complexity and stability of the food web in the East China Sea. As a result, keystone species should be prioritized in marine ecosystems.

Phages, which are often used therapeutically, have begun to receive interest as alternatives to antibiotic growth promoters (AGPs) for enhancing chicken growth. Another option that has been extensively studied as a growth promoter in chickens is probiotics. To the best of our knowledge, there is currently no study available on the use of phages and probiotics in combination as potential feed additives for broiler chickens. Therefore, this study demonstrated the effects of a phage cocktail, probiotics, and their combination on the growth performance and gut microbiota of broiler chickens. A total of 288 one-day-old male Cobb 500 broilers were randomly allotted to one of six treatments in a completely randomised design. The treatments were (i) C (basal diet (BD) only), (ii) 1ϕ (BD + 0.1% phage cocktail), (iii) 2ϕ (BD + 0.2% phage cocktail), (iv) P (BD + 0.1% probiotic), (v) 1ϕP (BD + 0.1% phage cocktail + 0.1% probiotic), and (vi) 2ϕP (BD + 0.2% phage cocktail + 0.1% probiotic). The 1ϕP treatment had significantly (p < 0.05) better BW (35 days), BWG (22–35 days, 1–35 days), and FCR (1–21 days, 22–35 days, 1–35 days) compared to C. Unique gut microbiota diversity was also found between the ϕP (1ϕP and 2ϕP) and non-ϕP groups (C, 1ϕ, 2ϕ, and P) in ilea, particularly in the 35-day-old chickens. Microorganisms associated with short-chain fatty acid (SCFA) producers were significantly (p < 0.05) more present in the ϕP group than in the non-ϕP group. The predicted genes related to carbohydrate and amino acid metabolism were significantly upregulated in ϕP groups compared to non-ϕP groups. These genes were involved in the digestion and absorption of nutrients, as well as the production of energy. Our findings showed that the 1ϕP treatment could be a potential alternative to AGPs for poultry, as growth performance was enhanced, and gut microbiota was positively modulated.

NADC34-like porcine reproductive and respiratory syndrome virus first appeared in 2017 in a herd of pigs in Liaoning Province, China. The virus was subsequently found in other provinces. Given the potential for this virus to cause an epidemic, rapid, sensitive, and specific detection of NADC34-like PRRSV is required. The virus’ ORF5 gene was artificially synthesized based on a Chinese reference strain, and specific primers/probes for the ORF5 gene were designed. Then, the amplified target fragment was cloned into the pMD19-T vector, and a series of diluted recombinant plasmids were used to generate a standard curve. An optimized real-time TaqMan RT-PCR method was established. The method was highly specific for NADC34-like PRRSV, without cross-reactions with other non-targeted pig viruses. The detection limit of this assay was 101 copies/μL. The method had an efficiency of 98.8%, a squared regression value (R2) of 0.999, and showed a linear range of 103–108 copies/μL of DNA per reaction. This method was shown to be analytically specific and sensitive with a low intra- and inter-assay coefficient of variation (