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Advances in artificial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reflective practice of educators in a time when online teaching has become the norm. Having achieved a ‘proof of concept’, this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices.

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each recognized load pattern. The numerical results on the public load dataset show that our proposed method effectively forecasts the residential load with a high accuracy.

Seawater intrusion significantly affects the microbial communities within coastal aquifers. Investigating the spatial distribution of groundwater microbial communities in coastal regions is crucial for understanding seawater intrusion. The primary objective of this study is to develop a novel microbial index-based method for detecting seawater intrusion. Groundwater microbial samples were collected and sent to the laboratory in the west coastal area of Longkou City, Shandong Province. By characterizing the microbial community within the whole interval of seawater intrusion into fresh groundwater and discussing the effects of salt-freshwater displacement intensities on groundwater microbial communities, including diversity, structure, and function, using indoor domestication experiments, we reveal the response of microorganisms to the seawater intrusion process under in situ environmental conditions. The results show that the microbial community diversity is highest in environments with a seawater mixing proportion (P(sm)) of 2.5% and lowest in those with a P(sm) of 75%. When considering species abundance and evolutionary processes, the microbial community structure is similar at higher P(sm) levels, while it is similar at lower P(sm) levels based on the presence or absence of species. Tenericutes, Flavobacteriia, Rhodobacterales, Flavobacteriales, Rhodobacteraceae, Flavobacteriaceae, Cohaesibacteraceae, and Cohaesibacter are significantly positively correlated with the P(sm). Strong salt-freshwater displacement enhanced the richness and evenness of the microbial community, whereas weak displacement showed the opposite trend. Strong displacement affects the functional profiles of the microbial community. This study effectively addressed the challenge of obtaining samples in coastal areas and also incorporated salt-freshwater displacement intensities, which can more comprehensively describe the microbial community characteristics within the groundwater of coastal aquifers.

Fibroblast growth factor 5 (FGF5) plays key roles in promoting the transition from the anagen to catagen during the hair follicle cycle. The sheep serves as an excellent model for studying hair growth and is frequently utilized in various research processes related to human skin diseases. We used the CRISPR/Cas9 system to generate four FGF5-edited Dorper sheep and only low levels of FGF5 were detected in the edited sheep. The density of fine wool in GE sheep was markedly increased, and the proportion of fine wool with a diameter of 14.4–20.0 μm was significantly higher. The proliferation signal in the skin of gene-edited (GE) sheep was stronger than in wild-type (WT) sheep. FGF5 editing decreased cortisol concentration in the skin, further activated the activity of antioxidant enzymes such as Glutathione peroxidase (GSH-Px), and regulated the expression of Wnt signaling pathways containing Wnt agonists (Rspondins, Rspos) and antagonists (Notum) in hair regeneration. We suggest that FGF5 not only mediates the activation of antioxidant pathways by cortisol, which constitutes a highly coordinated microenvironment in hair follicle cells, but also influences key signals of the Wnt pathway to regulate secondary hair follicle (SHF) development. Overall, our findings here demonstrate that FGF5 plays a significant role in regulating SHF growth in sheep and potentially serves as a molecular marker of fine wool growth in sheep breeding.

Due to the overlap of initial traveling wave signals, the traveling wave propagation process in hybrid distribution lines is complicated to analyze. The most significant challenge posed by the traditional passive traveling wave-locating method for hybrid distribution lines lies in identifying the fault section and distinguishing the reflected wave from the fault point or the hybrid connection points. Based on this approach, with the application of the aerial-mode component of the pulse signal generated at the fault point, a fault-section-identification and fault-locating scheme for hybrid distribution feeders with active pulse injection is proposed. When power in a line is cut after a single-to-line ground (SLG) fault occurs, the same pulse is injected into the three phases from the neutral point of the coupling capacitor bank to construct the zero-mode component, which propagates to the SLG fault three-phase asymmetrical point, producing an aerial-mode component that is reflected back to the first end of the line. With the application of the arrival time of an aerial-mode wavefront, it is simple to locate the SLG fault for arbitrary forms of hybrid lines. The simulation results confirm the feasibility of the fault-locating scheme under different feeders, different fault locations, and fault resistances. The results of the experiments confirm the high practical value of the proposed method.

Currently, little is known about the characteristics of polyphenol oxidase from wheat bran, which is closely linked to the browning of wheat product. The wheat PPO was purified by ammonium sulfate precipitation, DEAE-Sepharose ion-exchange column, and Superdex G-75 chromatography column. Purified wheat PPO activity was 11.05-fold higher, its specific activity was 1365.12 U/mg, and its yield was 8.46%. SDS-PAGE showed that the molecular weight of wheat PPO was approximately 21 kDa. Its optimal pH and temperature were 6.5 and 35 °C for catechol as substrate, respectively. Twelve phenolic substrates from wheat and green tea were used for analyzing the substrate specificity. Wheat PPO showed the highest affinity to catechol due to its maximum Vmax (517.55 U·mL−1·min−1) and low Km (6.36 mM) values. Docking analysis revealed strong affinities between catechol, gallic acid, EGCG, and EC with binding energies of −5.28 kcal/mol, −4.65 kcal/mol, −4.21 kcal/mol, and −5.62 kcal/mol, respectively, for PPO. Sodium sulfite, ascorbic acid, and sodium bisulfite dramatically inhibited wheat PPO activity. Cu2+ and Ca2+ at 10 mM were considered potent activators and inhibitors for wheat PPO, respectively. This report provides a theoretical basis for controlling the enzymatic browning of wheat products fortified with green tea.

Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.

Locoweed is a collective name for a variety of plants, such as Oxytropis and Astragalus L. When these plants are infected by some fungi or endophytes, they will produce an alkaloid (swainsonine) that is harmful to livestock. Chronic toxicity characterized by neurological disorders occurs in livestock overfed on locoweed, and swainsonine (SW) is considered a major toxic component. The mechanism of the SW synthesis of endophytic fungi from locoweed remains unknown. In order to further discover the possible synthetic pathway of SW, in this study, a mycotoxin (SW) producer, Alternaria oxytropis isolate, UA003, isolated from Locoweed plants, and its mutant were subjected to transcriptomic analyses to ascertain the genes involved in the synthesis of this toxin. Mutant strain A. oxytropis E02 was obtained by ethyl methanesulfonate (EMS) mutagenesis treatment, and the strains were sequenced with different culture times for transcriptomic analysis and screening of differentially expressed genes. The results show a highly significant (p < 0.01) increase in SW yield in the A. oxytropis E02 strain obtained by EMS mutagenesis treatment compared to A. oxytropis UA003. A total of 637 differentially expressed genes were screened by transcriptome sequencing analysis, including 11 genes potentially associated with SW biosynthesis. These genes were screened using GO and KEGG data annotation and analysis. Among the differential genes, evm.TU.Contig4.409, evm.TU.Contig19.10, and evm.TU.Contig50.48 were associated with L-lysine biosynthesis, the L-pipecolic acid pathway, and the α-aminoadipic acid synthesis pathway. This study provides new insights to elucidate the mechanism of SW synthesis of endophytic fungi in locoweed and provides data support for further exploration of A. oxytropis genomics studies.

As the number of modalities in biomedical data continues to increase, the significance of multi-modal data becomes evident in capturing complex relationships between biological processes, thereby complementing disease classification. However, the current multi-modal fusion methods for biomedical data require more effective exploitation of intra- and inter-modal interactions, and the application of powerful fusion methods to biomedical data is relatively rare. In this paper, we propose a novel multi-modal data fusion method that addresses these limitations. Our proposed method utilizes a graph neural network and a 3D convolutional network to identify intra-modal relationships. By doing so, we can extract meaningful features from each modality, preserving crucial information. To fuse information from different modalities, we employ the Low-rank Multi-modal Fusion method, which effectively integrates multiple modalities while reducing noise and redundancy. Additionally, our method incorporates the Cross-modal Transformer to automatically learn relationships between different modalities, facilitating enhanced information exchange and representation. We validate the effectiveness of our proposed method using lung CT imaging data and physiological and biochemical data obtained from patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Our method demonstrates superior performance compared to various fusion methods and their variants in terms of disease classification accuracy.

The Arctic’s navigation and utilization have garnered global interest due to the economic and strategic significance of its open shipping routes. The feasibility and cost-effectiveness of Arctic wind power generation depend largely on the potential of wind energy, which in turn influences the construction of new ports. In order to effectively harness polar wind energy, we must understand and adapt to its ever-changing rules. This study leverages ERA5′s 40-year wind field data to estimate the Theil–Sen Median slope and perform Mann–Kendall trend analysis. We consider factors such as wind power density, effective wind speed occurrence, energy level frequency, stability, and resource reserves to comprehensively analyze the intergenerational variations in Arctic wind energy resources. Our findings indicate that Northeast Passage, Davis Strait, and Baffin Bay possess favorable wind power density (1~2 W/m2·yr−1), effective wind speed occurrence (0.1~0.2%·yr−1), energy level frequency (0.1~0.2%·yr−1), stability (−0.005 yr−1), and resource reserves (1 kWh/m2·yr−1). However, these indicators are inferior in the Barents Sea, Canada’s northern archipelagos, and Greenland’s vicinity, where wind energy is relatively poor and unfavorable for development. Autumn dominates the annual change trend of Arctic wind energy, while spring and summer show no significant trends.

The indolizidine alkaloid-swainsonine (SW) is the main toxic component of locoweeds and the main cause of locoweed poisoning in grazing animals. The endophytic fungi, Alternaria Section Undifilum spp., are responsible for the biosynthesis of SW in locoweeds. The swnK gene is a multifunctional complex enzyme encoding gene in fungal SW biosynthesis, and its encoding product plays a key role in the multistep catalytic synthesis of SW by fungi using pipecolic acid as a precursor. However, the transcriptional regulation mechanism of the swnK gene is still unclear. To identify the transcriptional regulators involved in the swnK gene in endophytic fungi of locoweeds, we first analyzed the upstream non-coding region of the swnK gene in the A. oxytropis UA003 strain and predicted its high transcriptional activity region combined with dual-luciferase reporter assay. Then, a yeast one-hybrid library of A. oxytropis UA003 strain was constructed, and the transcriptional regulatory factors that may bind to the high-transcriptional activity region of the upstream non-coding region of the swnK gene were screened by this system. The results showed that the high transcriptional activity region was located at −656 bp and −392 bp of the upstream regulatory region of the swnK gene. A total of nine candidate transcriptional regulator molecules, including a C2H2 type transcription factor, seven annotated proteins, and an unannotated protein, were screened out through the Y1H system, which were bound to the upstream high transcriptional activity region of the swnK gene. This study provides new insight into the transcriptional regulation of the swnK gene and lays the foundation for further exploration of the regulatory mechanisms of SW biosynthesis in fungal endophytic locoweeds.

This paper verified through experiments that change in ambient temperature are the main cause of dark output noise drift. Additionally, the impact of dark output noise drift in fiber optic spectrometers on emissivity measurements has been investigated in this work. Based on an improved fiber optic spectrometer, two methods were proposed for characterizing and correcting the dark output noise offset in fiber optic spectrometers: the mean correction scheme and the linear fitting correction scheme. Compared to the mean correction scheme, the linear fitting correction scheme is more effective in solving the problem of dark output noise drift. When the wavelength is greater than 1600 nm, the calibration relative error of silicon carbide (SIC) emissivity is less than 0.8% by the mean correction scheme, while the calibration relative error of silicon carbide emissivity is less than 0.62% by the linear fitting correction scheme. This work solves the problem of dark output noise drift in prolonged measurement based on fiber optic spectrometers, improving the accuracy and reliability of emissivity and quantitative radiation measurement.

Accurate and timely traffic forecasting is an important task for the realization of urban smart traffic. The random occurrence of social events such as traffic accidents will make traffic prediction particularly difficult. At the same time, most of the existing prediction methods rely on prior knowledge to obtain traffic maps and the obtained map structure cannot be guaranteed to be accurate for the current learning task. In addition, traffic data is highly non-linear and long-term dependent, so it is more difficult to achieve accurate prediction. In response to the above problems, this paper proposes a new integrated unified architecture for traffic prediction based on heterogeneous graph attention network combined with residual-time-series convolutional network, which is called HGA-ResTCN. First, the heterogeneous graph attention is used to capture the changes in the relationship between the traffic graph nodes caused by social events, so as to learn the link weights between the target node and its neighbor nodes; at the same time, by introducing the timing of residual links convolutional network to capture the long-term dependence of complex traffic data. These two models are integrated into a unified framework to learn in an end-to-end manner. Through testing on real-world data sets, the results show that the accuracy of the model in this paper is better than other proposed baselines.

Infrared selective emitters are attracting more and more attention due to their modulation ability of infrared radiance, which provides an efficient ability to blend objects into the surrounding environment. In this paper, an Ag/ZnS/Si/Ag/Si multilayered emitter is proposed by virtue of impedance matching as well as Fabry-Perot cavity effect to achieve selective radiation in the infrared band. The emissivity of the fabricated selective emitter is measured to be ε3–5μm = 0.16 and ε8–14μm = 0.23 in the atmosphere windows, respectively, meeting the requirements of infrared stealth. Meanwhile, the emissivity at the non-atmospheric window (5–8 μm) is as high as 0.78, which allows efficient heat dissipation to achieve radiative cooling. Furthermore, the selective emitter maintains excellent stealth performance until 350 °C, indicating its good heat resistance and dissipation at medium temperature. The proposed emitter with spectral selectivity provides a new strategy for the facile fabrication of mid-/low-temperature infrared stealth devices.

To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR).

Meiyu-front rainstorm is one of the main disastrous weather events in summer in East China. In this study, seven assimilation experiments of multi-type observation data such as wind profile data, microwave radiometer data and radiosonde sounding data are designed to forecast the Meiyu-front rainstorm on 15 June 2020. The results show that the seven experiments can basically simulate the orientation of rain belt. The comprehensive experiment which assimilates all types of observations performs the best in simulating the location of heavy rainstorm and shows good performance in simulating the precipitation above moderate rain. For the comprehensive experiment, the forecast deviation of rainstorm and heavy rainstorm is small, and the equitable threat score has also been greatly improved compared with other experiments. It is found that the convective available potential energy is enhanced after the assimilation of surface observation data. In addition, the wind convergence and water vapor transportation are modified after assimilating wind profile data. Accordingly, the precipitation efficiency is improved in the comprehensive experiment. The profiles of pseudo-equivalent potential temperature, vorticity and divergence show that, the assimilation of new-types observation data from wind profiler radar and microwave radiometer increases the instability of atmospheric stratification and enhances the ascending motion in the heavy precipitation center. The above results show that the introduction of various some new-type data before the numerical simulation can reduce the forecast deviation. In addition, the combined assimilation of microwave radiometer and sounding data presents better performance than single data assimilation, which indicates that data mutual complementation is essential to improving forecast accuracy.

Anthocyanins, soluble sugars, and organic acids play a vital role in the color and flavor of grape berries. N and KH2PO4 are essential nutrients for grape growth and development. However, the research on the effects of foliar spraying of KH2PO4 on the skin color and flavor of grapes under different N levels were not systematic. In this study, “Flame seedless” grapes were used as the test material. There were six treatments in this experiment, including low nitrogen (LN), low nitrogen + KH2PO4 (LNK), moderate nitrogen (MN), moderate nitrogen + KH2PO4 (MNK), high nitrogen (HN), and high nitrogen + KH2PO4 (HNK). Foliar spraying of KH2PO4 on grapes significantly increased total K, anthocyanin contents, and the color index of red grapes (CIRG) in LN, MN, and HN. In the N and KH2PO4 treatments, foliar spraying of KH2PO4 significantly increased the content of methylated, acetylated, and coumarylated anthocyanins under MN treatment. The glucose and fructose contents of MNK were the highest compared to other treatments. The sole use of N showed the highest glucose and fructose contents with MN application. Anthocyanin had a significant positive correlation with soluble sugars; and showed a significant negative correlation with organic acids. Overall, foliar spraying of 0.5% KH2PO4 improved the color and flavor of “Flame seedless” grapes under all N levels, with the most significant effect at MN.

Tumor metastasis is an important reason for the difficulty of tumor treatment. Besides the tumor cells themselves, the tumor microenvironment plays an important role in the process of tumor metastasis. Tumor infiltrating immune cells (TIICs) are one of the main components of TME and plays an important role in every link of tumor metastasis. This article mainly reviews the role of tumor-infiltrating immune cells in epithelial mesenchymal transformation, extracellular matrix remodeling, tumor angiogenesis and formation of pre-metastatic niche. The value of TIICs in the prognosis of cervical cancer, lung cancer and breast cancer was also discussed. We believe that accurate prognosis of cancer treatment outcomes is conducive to further improving treatment regimens, determining personalized treatment strategies, and ultimately achieving successful cancer treatment. This paper elucidates the relationship between tumor and TIICs in order to explore the function of immune cells in different diseases and provide new ideas for the treatment of cancer.

Solar energy is a clean and renewable energy source and solves today’s energy and climate emergency. Near-perfect broadband solar absorbers can offer necessary technical assistance to follow this route and develop an effective solar energy-harvesting system. In this work, the metamaterial perfect absorber operating in the ultraviolet to the near-infrared spectral range was designed, consisting of a periodically aligned titanium (Ti) nanoarray coupled to an optical cavity. Through numerical simulations, the average absorption efficiency of the optimal parameter absorber can reach up to 99.84% in the 200–3000 nm broadband range. We show that the Ti pyramid’s localized surface plasmon resonances, the intrinsic loss of the Ti material, and the coupling of resonance modes between two neighboring pyramids are highly responsible for this broadband perfect absorption effect. Additionally, we demonstrate that the absorber exhibits some excellent features desirable for the practical absorption and harvesting of solar energy, such as precision tolerance, polarization independence, and large angular acceptance.

Realizing high-precision real-time underwater detection has been a pressing issue for intelligent underwater robots in recent years. Poor quality of underwater datasets leads to low accuracy of detection models. To handle this problem, an improved YOLOV4-Tiny algorithm is proposed. The CSPrestblock_body in YOLOV4-Tiny is replaced with Ghostblock_body, which is stacked by Ghost modules in the CSPDarknet53-Tiny backbone network to reduce the computation complexity. The convolutional block attention module (CBAM) is integrated to the algorithm in order to find the attention region in scenarios with dense objects. Then, underwater data is effectively improved by combining the Instance-Balanced Augmentation, underwater image restoration, and Mosaic algorithm. Finally, experiments demonstrate that the YOLOV4-Tinier has a mean Average Precision (mAP) of 80.77% on the improved underwater dataset and a detection speed of 86.96 fps. Additionally, compared to the baseline model YOLOV4-Tiny, YOLOV4-Tinier reduces about model size by about 29%, which is encouraging and competitive.

Here, we experimentally demonstrate a wedged fiber optic surface plasmon resonance (SPR) sensor enabling high-sensitivity temperature detection. The sensing probe has a geometry with two asymmetrical bevels, with one inclined surface coated with an optically thin film supporting propagating plasmons and the other coated with a reflecting metal film. The angle of incident light can be readily tuned through modifying the beveled angles of the fiber tip, which has a remarkable impact on the refractive index sensitivity of SPR sensors. As a result, we measure a high refractive index sensitivity as large as 8161 nm/RIU in a wide refractive index range of 1.333–1.404 for the optimized sensor. Furthermore, we carry out a temperature-sensitivity measurement by packaging the SPR probe into a capillary filled with n-butanol. This showed a temperature sensitivity reaching up to −3.35 nm/°C in a wide temperature range of 20 °C–100 °C. These experimental results are well in agreement with those obtained from simulations, thus suggesting that our work may be of significance in designing reflective fiber optic SPR sensing probes with modified geometries.

Myeloid-derived suppressor cells (MDSCs) are a group of immature and heterogeneous myeloid cells with immunosuppressive functions. MDSCs play important roles in the pathogenesis of cancer, chronic inflammatory diseases, and many autoimmune disorders. The accumulation and activation of MDSCs can be regulated by tumor necrosis factor α (TNF-α). In this review, we summarize the roles played by TNF-α in the recruitment, immunosuppressive functions, and chemotaxis of MDSCs, and discuss the potential therapeutic effects of TNF-α upon these cells in tumor growth and some inflammatory disorders.

Cardiovascular disease is the result of complicated pathophysiological processes in the tissues that make up the blood vessels and heart. Heat shock protein 90 (HSP90) can interact with 10% of the proteome and is the most widely studied molecular chaperone in recent years. HSP90 is extensively involved in the regulation of protein folding and intracellular protein stability, making HSP90 a hopeful target for the treatment of multiple cardiovascular diseases. Numerous client proteins of HSP90 have been identified in known cardiac disease pathways, including MAPK signaling, PI3K/AKT (PKB)/mTOR, and TNF-α signaling. Therefore, these pathways can be controlled by regulating HSP90. Among them, the activity of HSP90 can be regulated via numerous inhibitors. In this review, first, we will discuss the function of HSP90 and its role in pathological pathways. In addition, HSP90 plays a significant role in most cardiovascular diseases, including hypertension, pulmonary venous hypertension, atherosclerosis, and heart failure; next we will focus on this part. Finally, we will summarize the currently known HSP90 inhibitors and their potential in the treatment of heart disease.

Ti-6Al-4V (Ti64 or TC4) alloy is widely used in the industrial field. However, there have been few studies of the TC4 alloy melted by electron beam cold hearth melting (EBCHM) technology. Aging treatment has a considerable influence on the secondary α-phase in titanium alloys. Therefore, TC4 alloy melted by EBCHM technology was investigated in this study. The effect of different aging times on the microstructural evolution and mechanical properties of titanium alloy sheets was evaluated. The results showed that, with increase in aging time, the primary α-phase enlarged and grain globularization occurred. In addition, some transformed β-phases disappeared. The strength and Vickers hardness of the heat-treated sheets decreased, while the plasticity increased with increase in aging time, indicating that the mechanical properties developed with evolution of the microstructure. After aging at 560 °C for 2 h, the properties overall were optimal. The type of fracture of the samples was ductile fracture; the dimples became larger with increase in aging time. After heat treatment, the recrystallized nucleus, substructures and HAGBs increased, while the deformed structure and LAGBs decreased. Some grains had rotated following heat treatment, indicating that anisotropy was greatly reduced.

In the present work, a filling and laser shock peening (LSP) method is put forward and applied to a thin-walled pipe. Specimens were experimentally and numerically investigated to identify the residual stress field and fatigue properties of a pipe with and without LSP treatment. The numerical simulation indicated that the residual compressive stress first increased and subsequently dropped as the laser power density increased, and the extent of influence of the stretching wave, reflected from the lower surface on the unloaded area, increased with the spot diameter, causing surface tensile stress in the unloaded area. By filling the pipe with the guided-wave material, the residual stress field of the pipe that was treated with LSP was optimized, and the influence of the stress wave reflection on the residual stress field was effectively decreased. The surface residual stress of the filled guided wave material was −326 MPa, improving it by 57.6% compared with the pipe not filled with guided wave materials. The fatigue life of the pipe with the filled waveguide material that was treated by LSP was extended by 48.9%, compared with the untreated pipe.

With the exponential growth of cyber–physical systems (CPSs), security challenges have emerged; attacks on critical infrastructure could result in catastrophic consequences. Intrusion detection is the foundation for CPS security protection, and deep-packet inspection is the primary method for signature-matched mechanisms. This method usually employs regular expression matching (REM) to detect possible threats in the packet payload. State explosion is the critical challenge for REM applications, which originates primarily from features of large character sets with unbounded (closures) or bounded (counting) repetitions. In this work, we propose Offset-FA to handle these repetitions in a uniform mechanism. Offset-FA eliminates state explosion by extracting the repetitions from the nonexplosive string fragments. Then, these fragments are compiled into a fragment-DFA, while a fragment relation table and a reset table are constructed to preserve their connection and offset relationship. To our knowledge, Offset-FA is the first automaton to handle these two kinds of repetitions together with a uniform mechanism. Experiments demonstrate that Offset-FA outperforms state-of-the-art solutions in both space cost and matching speed on the premise of matching correctness, and achieves a comparable matching speed with that of DFA on practical rule sets.

Automatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic feature information such as semantic road features, with the result that the traditional method can not effectively resist nonlinear grayscale distortion, and has low matching efficiency and poor accuracy. Motivated by this, this paper proposes a novel automatic matching method named LURF via learned unstructured road features for the multimodal images. There are four main contributions in LURF. To begin with, the semantic road features were extracted from multimodal images based on segmentation model CRESIv2. Next, based on semantic road features, a stable and reliable intersection point detector has been proposed to detect unstructured key points. Moreover, a local entropy descriptor has been designed to describe key points with the local skeleton feature. Finally, a global optimization strategy is adopted to achieve the correct matching. The extensive experimental results demonstrate that the proposed LURF outperforms other state-of-the-art methods in terms of both accuracy and efficiency on different multimodal image data sets.

With the rapid development of urban rail transit, the impact of the construction of large-scale deep and circular foundation pits (LDCFPs) on the surrounding environment has become increasingly prominent; however, there is no definite standard for the design theory and construction method of the retaining structure of LDCFPs; therefore, the such standard needs further research and exploration. The paper is based on the LDCFP engineering of a subway station in Wuhan, China. Two partitioned excavation methods (step-by-step and synchronous excavation) of LDCFP were discussed by numerical simulation; the deformation characteristics of the soil around the foundation pit, the lateral deformation of the retaining structure in each partitioned district, and the distribution of the supporting axial force were analyzed; the deformation law of LDCFP was revealed; and the safe and economic excavation method was determined. The field monitoring test was carried out to reveal the deformation characteristics and stress evolution behavior for the supporting structure of the LDCFP in the subway station. It was concluded that the construction method of synchronous partitioned excavation in the same layer is more conducive to controlling the overall deformation, with the deformation of the circular retaining structure being uniform and the “vault effect” of the circular retaining structure having a certain constraint on the deformation of the foundation pit. The research results can provide guidance for the design and construction of supporting engineering and similar engineering.

The production of goat meat is determined by the growth speed of muscle fibers, and the autophagy and apoptosis of myoblast cells is a crucial process in the growth of muscle fibers. The rapid growth of muscle fibers occurs from one month old to nine months old in goats; however, the mechanisms of myoblast cells’ autophagy and apoptosis in this process are still unknown. To identify candidate genes and signaling pathway mechanisms involved in myoblast apoptosis and autophagy, we compared the expression characteristics of longissimus dorsi tissues from Wu’an goats—a native goat breed of China—at 1 month old (mon1 group) and 9 months old (mon9 group). Herein, a total of 182 differentially expressed mRNAs (DEGs) in the mon1 vs. mon9 comparison, along with the KEGG enrichments, showed that the PI3K-Akt pathway associated with autophagy and apoptosis was significantly enriched. Among these DEGs, expression of vacuole membrane protein 1 (VMP1)—a key gene for the PI3K-Akt pathway—was significantly upregulated in the older goats relative to the 1-month-old goats. We demonstrated that VMP1 promotes the proliferation and autophagy of myoblasts, and inhibits their apoptosis. The integration analysis of miRNA–mRNA showed that miR-124a was a regulator of VMP1 in muscle tissue, and overexpression and inhibition of miR-124a suppressed the proliferation and autophagy of myoblasts. The PI3K/Akt/mTOR pathway was an important pathway for cell autophagy. Additionally, the activator of the PI3K/Akt/mTOR pathway, the expression of VMP1, and ULK1 were higher than the negative control, and the expression of mTOR was depressed. The expression of VMP1, ULK1, and mTOR was the opposite when the inhibitor was added to the myoblasts. These results show that the PI3K/Akt/mTOR pathway promoted the expression of VMP1 and ULK1. By using adenovirus-mediated apoptosis and proliferation assays, we found that that miR-124a inhibits myoblast proliferation and autophagy, and promotes their apoptosis by targeting VMP1. In conclusion, our results indicated that VMP1 was highly expressed in the LD muscle tissues of nine-month-old goats, and that it was regulated by miR-124a to inhibit myoblast cells’ apoptosis through the PI3K/Akt/mTOR pathway, and to promote proliferation and autophagy. These findings contribute to the understanding of the molecular mechanisms involved in myoblast proliferation, autophagy, and apoptosis.

Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current research methods focus only on spatial correlations in local areas, ignoring global geographic contextual information. It is challenging to capture spatial information from distant nodes using shallow graph neural networks (GNNs) to model long-range spatial correlations. To handle this problem, we design a novel spatiotemporal global semantic graph-attentive convolutional network model (STSGAN), which is a deep-level network to achieve the simultaneous modelling of spatiotemporal correlations. First, we propose a graph-attentive convolutional network (GACN) to extract the importance of different spatial features and learn the spatial correlation of local regions and the global spatial semantic information. The temporal causal convolution structure (TCN) is utilized to capture the causal relationships between long-short times, thus enabling an integrated consideration of local and overall spatiotemporal correlations. Several experiments are conducted on two real-world traffic flow datasets, and the results show that our approach outperforms several state-of-the-art baselines.

There are three main types of cancer in the female reproductive system, specifically ovarian cancer (OVCA), endometrial cancer (EC), and cervical cancer (CC). They are common malignant tumors in women worldwide, with high morbidity and mortality. In recent years, androgen receptors (ARs) have been found to be closely related to the occurrence, progression, prognosis, and drug resistance of these three types of tumors. This paper summarizes current views on the role of AR in female reproductive system cancer, the associations between female reproductive system cancers and AR expression and polymorphisms. AR regulates the downstream target genes transcriptional activity and the expression via interacting with coactivators/corepressors and upstream/downstream regulators and through the gene transcription mechanism of “classical A/AR signaling” or “non-classical AR signaling”, involving a large number of regulatory factors and signaling pathways. ARs take part in the processes of cancer cell proliferation, migration/invasion, cancer cell stemness, and chemotherapeutic drug resistance. These findings suggest that the AR and related regulators could target the treatment of female reproductive system cancer.

Angiogenesis is involved in physiological and pathological processes in the body. Tumor angiogenesis is a key factor associated with tumor growth, progression, and metastasis. Therefore, there is great interest in developing antiangiogenic strategies. Hypoxia is the basic initiating factor of tumor angiogenesis, which leads to the increase of vascular endothelial growth factor (VEGF), angiopoietin (Ang), hypoxia-inducible factor (HIF-1), etc. in hypoxic cells. The pathways of VEGF and Ang are considered to be critical steps in tumor angiogenesis. A number of antiangiogenic drugs targeting VEGF/VEGFR (VEGF receptor) or ANG/Tie2, or both, are currently being used for cancer treatment, or are still in various stages of clinical development or preclinical evaluation. This article aims to review the mechanisms of angiogenesis and tumor angiogenesis and to focus on new drugs and strategies for the treatment of antiangiogenesis. However, antitumor angiogenic drugs alone may not be sufficient to eradicate tumors. The molecular chaperone heat shock protein 90 (HSP90) is considered a promising molecular target. The VEGFR system and its downstream signaling molecules depend on the function of HSP90. This article also briefly introduces the role of HSP90 in angiogenesis and some HSP90 inhibitors.

Underwater image restoration is a challenging problem because light is attenuated by absorption and scattering in water, which can degrade the underwater image. To restore the underwater image and improve its contrast and color saturation, a novel algorithm based on the underwater dark channel prior is proposed in this paper. First of all, in order to reconstruct the transmission maps of the underwater image, the transmission maps of the blue and green channels are optimized by the proposed first-order and second-order total variational regularization. Then, an adaptive model is proposed to improve the first-order and second-order total variation. Finally, to solve the problem of the excessive attenuation of the red channel, the transmission map of the red channel is compensated by Yin–Yang pair optimization. The simulation results show that the proposed restored algorithm outperforms other approaches in terms of the visual effects, average gradient, spatial frequency, percentage of saturated pixels, underwater color image quality evaluation and evaluation metric.

Due to its unique electronic and optical properties, graphene has been used to design tunable optical absorbers. In this paper, we propose a plasmonic absorber consisting of non-concentric graphene nanodisk arrays, which is designed to operate in the mid-infrared spectral range and is capable of achieving nearly perfect absorption. Two perfect absorption peaks are produced due to the impedance of the structure, which matches that of the free space. The influences of the thicknesses of the dielectric layer, the size of graphene nanodisk, and the incident conditions on the absorption are studied. Moreover, the absorption intensity can be independently tuned by varying the Fermi levels of two graphene nanodisks. Furthermore, the polarization-independent absorbance of the absorber exceeds 95% under oblique incidence, and remains very high over a wide angle. This proposed absorber has potential applications in optical detectors, tunable sensors, and band-pass filters.

In this work, a novel Zn-0.5%Li-0.1%Ag alloy was cast and extruded into rods, which were rolled into a plate, and the effects of extrusion and rolling on the microstructure and mechanical properties of the Zn-0.5%Li-0.1%Ag alloy were evaluated. The results show that grain strengthening occurs in all of the alloys because of the presence of nano-LiZn4 precipitates. The extrusion and rolling processes promote grain size refinement and orientation order, and the microstructure and mechanical properties of the Zn-0.5%Li-0.1%Ag alloy can be significantly improved by secondary processing. The elastic modulus and tensile strength of the processed alloy increased to 83.1 GPa and 251.6 MPa, respectively, compared to 75.6 GPa and 185.8 MPa, respectively, for the as-cast Zn-0.5%Li-0.1%Ag alloy. More importantly, elongation was greatly improved, from 16.9% to 92.6%, which is an increase of up to 448%, and there were transgranular cleavage planes and intergranular cleavage planes in the fracture surfaces. The intergranular cleavage planes were dominant, and they showed ductile fracture characteristics.

Application of ultra-high-performance concrete (UHPC) in joints can improve the impact resistance, crack resistance, and durability of structures. In this paper, the direct shear performance of ultra-high-performance concrete (UHPC) adhesive joints was experimentally studied. Twenty-four direct shear loading tests of UHPC adhesive joints were carried out considering different interface types and constraint states. The failure modes and load-slip curves of different interfaces were studied. Results indicated that passive confinement could enhance the strength and ductility of the interface; the average ultimate bearing capacity of the smooth, rough, grooved, and keyway specimens with passive restraint were, respectively, increased by 11.92%, 8.91%, 11.93%, and 17.766% compared with the unrestrained ones. The passive constraint force changes with the loading and finally tends to be stable. The epoxy adhesive has high reliability as a coating for the UHPC interface. The adhesive layer is not cracked before the failure of the specimen, which is also different from the common failure mode of adhesive joints. Failure of all specimens occurred in the UHPC layer, and the convex part of the groove interface shows the UHPC matrix peeling failure; the keyway interface is the shear damage of the key-tooth root, and the rest of the keyway showed UHPC surface peeling failure. According to the failure mode, the shear capacity of UHPC keyway adhesive joints under passive restraint is mainly provided by the shear resistance of key teeth, the friction force of the joint surface, and the bonding force of the UHPC surface. The friction coefficient was determined based on the test results, and the high-precision fitting formula between the shear strength of the UHPC surface and the passive constraint force was established. According to the Mohr stress circle theory, the proposed formula for direct shear strength of UHPC bonded joints under passive constraint was established. The average ratio of the proposed UHPC adhesive joint calculation formula to the test results was 0.99, and the standard deviation was 0.027.

MSTN and FGF5 gene knockout sheep generated by the CRISPR/Cas9 system exhibit the ‘double-muscle’ phenotype, and increased density and length of hairs, providing valuable new breeding material. In a previous study, we obtained MSTN and FGF5 double-knockout sheep of significant breeding value. In this study, we carried out a 90-day feeding study in Wistar rats to assess the safety of genome-edited mutton. Seven rat groups with 10 females and 10 males per group were fed different concentrations (3.75%, 7.5%, and 15%) of double-knockout mutton or wild-type mutton in a conventional commercial diet for 90 days. At the end of the feeding, routine urine and blood tests and measurements of blood biochemical indicators were performed. Furthermore, the major organs of each group of rats were weighed and examined histopathologically. Although there were significant differences among the groups in some parameters, all values were within the normal ranges. Therefore, the 90-day rat feeding study showed that the meat from MSTN and FGF5 double-knockout sheep did not have any long-term adverse effects on rat health. This study also provides valuable reference information for assessing the safety of meat from animals with knockout of multiple genes.

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the characteristics of spatio-temporal data diversity, this paper proposes a cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for traffic flow prediction. First, introduce GCN to model the historical traffic data’s three-time attributes (current, daily, and weekly) to extract time features. Second, consider the relationship between distance and traffic flow, constructing adjacency, connectivity, and regional similarity graphs to capture dynamic spatial topology information. To make full use of global information, a cross-attention mechanism is introduced to fuse temporal and spatial features separately to reduce prediction errors. Finally, the CAFMGCN model is evaluated, and the experimental results show that the prediction of this model is more accurate and effective than the baseline of other models.

Anthocyanins in red grape skin have a positive effect on fruit color and human health. The effect of foliar potassium application on anthocyanin accumulation in grape skin is not well understood. The study aimed to better understand the mechanism of anthocyanin accumulation in grape skin in response to foliar sprays of K2SO4 and KH2PO4. In this study, we investigated the effects of foliar application of KH2PO4 (T2), K2SO4 (T1) and distilled water (CK) on the skin color of ‘Kyoho’ grapes at mid-ripe and mature stages. At 90 and 110 days after full bloom (DAFB), T2 had the greatest total soluble solids (TSS), flavonoid and total anthocyanin contents, followed by T1 and CK. At two stages, the titratable acid content decreased and the juice pH increased under T2 treatment relative to CK. T1 and T2 had lower lightness (L*) than CK, and the color index of red grapes (CIRG) under T1 and T2 increased at two stages compared to CK. KEGG metabolic pathway analysis revealed that flavonoid biosynthesis was the most significantly enriched pathway in CK vs. T2 at 90 and 110 DAFB. At 90 DAFB, T2 had higher expressions of phenylalanine ammonia-lyas (PAL), cytochrome P450 CYP73A100 (CYP73A), 4-coumarate: CoA ligase (4CL), chalcone synthase (CHS), flavanone 3-dioxygenase-like (F3H) and UDP glucose: flavonoid 3-o-glucosyl transferase (UFGT) than CK and T1. Foliar application of potassium fertilizer may accelerate anthocyanin accumulation by altering the transcript levels of PAL, CYP73A, 4CL, CHS, F3H, and UFGT of the flavonoid biosynthesis.

Ovarian cancer is a heterogeneous disease and is also the major cause of death among women from gynecologic malignancies. A combination of surgery and chemotherapy is the major therapy for ovarian cancer. Unfortunately, despite good response rates to initial surgery and chemotherapy, most patients relapse and have a generally poor survival rate. The present research sheds light on the therapeutic effects of multiple natural products in patients with ovarian cancer. Notably, these natural ingredients do not have adverse effects on healthy cells and tissues, indicating that natural products can serve as a safe alternative therapy for ovarian cancer. Trans-3,4,5′-Trihydroxystibene (resveratrol) is a natural product that is commonly found in the human diet and that has been shown to have anticancer effects on various human cancer cells. This review summarizes current knowledge regarding the progress of resveratrol against tumor cell proliferation, metastasis, apoptosis induction, autophagy, sensitization, and antioxidation as well as anti-inflammation. It also provides information regarding the role of resveratrol analogues in ovarian cancer. A better understanding of the role of resveratrol in ovarian cancer may provide a new array for the prevention and therapy of ovarian cancer.

The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target’s prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density.

La0.8Sr0.2MnO3 (LSM) catalyst is prepared via a sol-gel method and modified via a typical silver mirror reaction. Silver ammonia solution is reduced in a polyvinylpyrrolidone (PVP)-containing solution to obtain silver nanoparticles and sodium dodecyl sulfate (SDS) is added as a surfactant. The microstructure and morphology of the LSM-Ag composite powder are characterized. According to the results, the Ag particles precipitate on the LSM surface in elemental form and the grain size is about one hundred nanometers. The analysis of electrocatalytic performance of LSM-Ag cathodes with different amounts of silver loading reveals that the number of electrons transferred during the oxygen reduction reaction (ORR) of the cathode with an Ag content of 14% by weight reached 3.9, which is very close to that of commercial Pt/C catalysts. Similarly, the maximum power density of the air battery made of LSM-14%Ag is 73 mW/cm2, which exceeds that of 63 mW/cm2, found for the LSM battery. Finally, increasing the amount of silver loading allows one to improve the electrochemical performance of LSM catalysts. The best effect is achieved when the Ag loading exceeds 14%.

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods.

Groundwater evapotranspiration (ETG) estimation is an important issue in semiarid areas for groundwater resources management and environmental protection. It is widely estimated by diurnal water table fluctuations. In this study, the ETG at four sites with different plants was estimated using both diurnal water table and soil moisture fluctuations in the northeastern Mu Us sandy region, in order to identify the groundwater utilization strategy by different dominant phreatophytes. Groundwater level was monitored by ventilatory pressure transducers (Solinst LevelVent, Solinst Canada Ltd.; accuracy ±3 mm), while soil moisture was monitored using EM50 loggers (Decagon Devices Inc., Pullman, USA) in K1 and K14 and simulated by Hydrus-1D in other observation wells. A significant spatial variation of ETG was found within a limited area, indicating a poor representativeness of site ETG for regional estimation. The mean values of ETG are 4.01 mm/d, 6.03 mm/d, 8.96 mm/d, and 12.26 mm/d at the Achnatherum splendens site, Carex stenophylla site, Salix psammophila site and Populus alba site, respectively, for the whole growing season. ETG is more sensitive to depth to water table (DWT) in the Carex stenophylla site than in the Achnatherum splendens site for grass-dominated areas and more sensitive to DWT in the Populus alba site than in Salix psammophila site for tree-dominated areas. Groundwater extinction depths are estimated at 4.1 m, 2.4 m, 7.1 m, and 2.9 m in the Achnatherum splendens site, Carex stenophylla site, Salix psammophila site and Populus alba site, respectively.

Female infertility is mainly caused by ovulation disorders, which affect female reproduction and pregnancy worldwide, with polycystic ovary syndrome (PCOS) being the most prevalent of these. PCOS is a frequent endocrine disease that is associated with abnormal function of the female sex hormone estrogen and estrogen receptors (ERs). Estrogens mediate genomic effects through ERα and ERβ in target tissues. The G-protein-coupled estrogen receptor (GPER) has recently been described as mediating the non-genomic signaling of estrogen. Changes in estrogen receptor signaling pathways affect cellular activities, such as ovulation; cell cycle phase; and cell proliferation, migration, and invasion. Over the years, some selective estrogen receptor modulators (SERMs) have made substantial strides in clinical applications for subfertility with PCOS, such as tamoxifen and clomiphene, however the role of ER in PCOS still needs to be understood. This article focuses on the recent progress in PCOS caused by the abnormal expression of estrogen and ERs in the ovaries and uterus, and the clinical application of related targeted small-molecule drugs.

Incorporating green tea powder (GTP) into dried noodles enriched the functional characteristics of noodles. To achieve the maximum benefits from GTP, the water cooking stability of dried green tea noodles (DGTN) should be investigated. Indeed, antioxidant activities and phenolic compounds of DGTN after water cooking markedly decreased. The results showed that large GTP particles caused the increased cooking loss of DGTN, but the phenolic compound loss of DGTN prepared with them was low after cooking. Analysis of texture properties and microstructure showed that DGTN with a 2% concentration of large GTP particles formed some holes in the noodles’ network, and its breaking strength decreased. However, we observed that many GTP particles adhered to the surface of DGTN prepared with small GTP particles, and they were easier to lose after water cooking. Comprehensive analysis concluded that cooking loss, functional compounds retention and textural properties of DGTN were related to GTP particle size and concentration via the microstructure.

Endocrine disrupting chemicals (EDCs) are exogenous substances that interfere with the stability and regulation of the endocrine system of the body or its offspring. These substances are generally stable in chemical properties, not easy to be biodegraded, and can be enriched in organisms. In the past half century, EDCs have gradually entered the food chain, and these substances have been frequently found in maternal blood. Perinatal maternal hormone levels are unstable and vulnerable to EDCs. Some EDCs can affect embryonic development through the blood-fetal barrier and cause damage to the neuroendocrine system, liver function, and genital development. Some also effect cross-generational inheritance through epigenetic mechanisms. This article mainly elaborates the mechanism and detection methods of estrogenic endocrine disruptors, such as bisphenol A (BPA), organochlorine pesticides (OCPs), diethylstilbestrol (DES) and phthalates (PAEs), and their effects on placenta and fetal health in order to raise concerns about the proper use of products containing EDCs during pregnancy and provide a reference for human health.

Many irrigated plains in arid and semi-arid regions have groundwater quality issues due to both intensive human activity and natural processes. Comprehensive studies are urgently needed to explore hydrogeochemical evolutions, investigate possible pollution sources, and understand the controls on groundwater compositions in such regions. Here, we combine geostatistical techniques and hydrogeochemical assessments to characterize groundwater quality over time in the Yinchuan Plain (a typical irrigated plain in China), using 12 physicochemical variables derived from sampling in 600 and 602 wells in 2004 and 2014, respectively. Our results show that groundwater-rock interactions and evaporation are the key natural factors controlling groundwater compositions. Hydrogeochemical water types in both 2004 and 2014 were Ca-HCO3, Na-Cl, and mixed Ca·Mg-Cl. Along with the hydrogeochemical compositions, we used ionic ratios and the saturation index to delineate mineral solution reactions and weathering processes. Dissolution of gypsum, halite, fluorite, and mirabilite, along with silicate weathering and cation exchange, were identified in the study area. Our results indicated rising ion concentrations in groundwater, which could be the result of anthropogenic influences. Increasing total hardness and nitrates over the study period were most likely caused by agricultural activity and the discharge of waste water from human residential areas.

CaO-Al2O3-SiO2 (CAS) as a liquid phase was introduced into hydroxyapatite (HAp) to prepare bone scaffolds. The effects of the CAS content (1, 2, 3, 4 and 5 wt%) on microstructure and mechanical properties of HAp ceramics were investigated. The optimal compression strength, fracture toughness and Vickers hardness reached 22.22 MPa, 1.68 MPa·m1/2 and 4.47 GPa when 3 wt% CAS was added, which were increased by 105%, 63% and 11% compared with those of HAp ceramics without CAS, respectively. The improvement of the mechanical properties was attributed to the improved densification, which was caused by the solid particle to rearrange during liquid phase sintering. Moreover, simulated body fluid (SBF) study indicated the HAp ceramics could maintain the mechanical properties and form a bone-like apatite layer when they were immersed in SBF. Cell culture was used to evaluate biocompatibility of the HAp ceramics. The results demonstrated MG-63 cells adhered and spread well.