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The geographical origin was one of the important factors affecting the quality of hawthorn. To discriminate the geographical origin of hawthorn rapidly and nondestructively, hawthorns from five different provincial production areas were used as samples, and visible-shortwave infrared (410~2500 nm) band hyperspectral data were obtained for the pedicel face (G), side (C), and bottom (D) of each sample by using a near-infrared hyperspectral imaging system. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and random forests (RF) classification models were built by multivariate scattering correction (MSC), first derivative (D1), SG smoothing (Savitzky-Golay, SG), and standard normal transform (SNV) four preprocessing methods. The results showed that the D-D1-SVM model discriminated optimally, with 100% accuracy in both the training and prediction sets. To simplify the model, successive projections algorithm (SPA) and competitive adaptive reweighted sampling algorithm (CARS) were applied to select feature wavelength. The multivariate data analysis revealed that the D-SPA-SVM model had the best performance, with an accuracy of 95.2% and 93% for the training and prediction sets, respectively. This study could provide technical support for rapid and non-destructive identification of hawthorn origin.

Abstract Background Gastrointestinal cancer poses a serious health threat owing to its high morbidity and mortality. Although immune checkpoint blockade (ICB) therapies have achieved meaningful success in most solid tumors, the improvement in survival in gastrointestinal cancers is modest, owing to sparse immune response and widespread resistance. Metabolic reprogramming, autophagy, and ferroptosis are key regulators of tumor progression. Methods A literature review was conducted to investigate the role of the metabolic reprogramming, autophagy, and ferroptosis in immunotherapy resistance of gastrointestinal cancer. Results Metabolic reprogramming, autophagy, and ferroptosis play pivotal roles in regulating the survival, differentiation, and function of immune cells within the tumor microenvironment. These processes redefine the nutrient allocation blueprint between cancer cells and immune cells, facilitating tumor immune evasion, which critically impacts the therapeutic efficacy of immunotherapy for gastrointestinal cancers. Additionally, there exists profound crosstalk among metabolic reprogramming, autophagy, and ferroptosis. These interactions are paramount in anti‐tumor immunity, further promoting the formation of an immunosuppressive microenvironment and resistance to immunotherapy. Conclusions Consequently, it is imperative to conduct comprehensive research on the roles of metabolic reprogramming, autophagy, and ferroptosis in the resistance of gastrointestinal tumor immunotherapy. This understanding will illuminate the clinical potential of targeting these pathways and their regulatory mechanisms to overcome immunotherapy resistance in gastrointestinal cancers.

Formaldehyde is widely utilized across various industries such as food, textiles, and leather, yet its presence raises significant health concerns due to its irritation and carcinogenicity. Numerous methods have been developed to probe it in solution and gas states. Among them, absorption spectroscopy-based methods commonly employed. However, these methods require complex preprocessing for colored solutions to avoid interference. In response to this challenge, a novel interference-resistant detection kit leveraging surface-enhanced Raman spectroscopy (SERS) coupled with reaction-induced self-amplification for rapid and direct detection of formaldehyde was developed. The kit needed only ten minutes and achieved a limit of detection lower than 10−4 μg/mL with a handheld Raman spectrometer. Thanks to the kit’s reaction with formaldehyde, colloidal nanoparticles were linked to form “hot-spots” to further enhance the SERS signals. The signal demonstrated outstanding resistance to interference, including different dyes, ensuring a practical tool for direct detection of formaldehyde in complex solutions.

In this study, non-targeted metabolomic research was conducted using gas chromatography-mass spectrometry (GC-MS) on Laba garlic at different storage times to explore the signature metabolite changes during storage. Multivariate statistical analysis was used to screen out the significant metabolites of Laba garlic at different storage times. Further, the deep learning support vector machine recursive feature elimination (SVM-RFE) algorithm was used to determine its landmark metabolites, and enrichment analysis of the pathways of its landmark metabolites was carried out. A total of 57 different metabolites were screened out (VIP≥1, P

In order to explore the feasibility of using template/hydrothermal method to produce mesoporous biochars from real biomass directly for adsorptive removal of pharmaceuticals, six mesoporous biochar samples were produced from bamboo waste through this method and characterized, and their adsorption capabilities for berberine hydrochloride and matrine from water were evaluated. Characterization results confirmed the mesoporous structures of the biochar samples, and the biochars had BET surface areas in the range of 180 to 880 m2/g. Pore properties of the biochars mainly depended on the synthesis compositions and were related to their adsorption capabilities. The bamboo derived mesoporous biochars provided effective adsorption with the highest uptake amounts of 645 and 496 mg/g for berberine hydrochloride and matrine at 0.1 mg/mL and 298 K, respectively. The adsorption equilibrium could be reached in 90 min for berberine hydrochloride and 60 min for matrine on the selected mesoporous biochar. This study provides an effective strategy to produce biomass derived mesoporous biochars as efficient adsorbents in water treatment and pharmaceutical purification.

The Tianwen-1 mission, China's first interplanetary endeavor and Mars mission, touched the surface of the Red Planet on May 15, 2021. With the successful landing of the Zhurong rover on the southern Utopian Planitia of Mars, the Mars Surface Composition Detector (MarSCoDe) on board the rover has started to analyze the material composition of the Martian surface by using the laser-induced breakdown spectroscopy (LIBS). However, changes in instrument temperature and external environment during operation will cause spectral drift in LIBS data, leading to inaccurate material composition inversion results due to shifts in elemental characteristic peak positions. To address this problem, an adaptive spectral drift correction (ASDC) approach is proposed. By considering the distribution of characteristic peaks and drift differences between different elements, the proposed ASDC method corrects the spectral drift in LIBS science data according to an adaptive spectral segmentation strategy. Experimental results obtained on 36 real LIBS science data acquired over 19 exploration activities confirmed the effectiveness of the proposed approach in terms of lower average drift values, by comparing it with two reference methods. Taking the onboard calibration data as a reference, the average spectral drift values of the LIBS science data before and after applying ASDC decreased from 0.1012 nm to 0.0179 nm, 0.0926 nm to 0.0158 nm, and 0.1697 nm to 0.0103 nm in three LIBS channels, respectively. Furthermore, the resulting decrease in the standard deviation and rms error of the average drift values also proved the robustness of the proposed method.

Fermatean fuzzy sets (FFSs) serve as a nascent yet potent approach for coping with fuzziness, with their efficacy recently being demonstrated across a spectrum of practical contexts. Nevertheless, the scholarly literature remains limited in exploring the similarity and distance measures tailored for FFSs. The limited existing measures on FFSs sometimes yield counter-intuitive outcomes, which can obfuscate the accurate quantification of similarity and difference among FFSs. This paper introduces a suite of similarity and distance measures tailored for FFSs, drawing inspiration from the Tanimoto measure. We delve into the characteristics of these novel measures and offer some comparative studies with existing FFSs measures, highlighting their superior efficacy in the processing of fuzzy data from FFSs. Our proposed measures effectively rectify the counter-intuitive situations encountered with many existing measures and demonstrate a significant enhancement in differentiating between diverse FFSs. Moreover, we showcase the real-world applicability of our proposed measures through case studies in pattern recognition, medical diagnostics, and multi-attribute decision-making.

Returning extraterrestrial samples to Earth has become essential for future deep space exploration. Achieving a comprehensive evaluation of the physical and chemical properties of samples with minimal damage is key to analyzing extraterrestrial samples in the future, as well as to the future sampling and returning of heterogeneous solid samples. This article aims to reconstruct the three-dimensional internal structure of high-contrast objects, select sections of interest through internal structure and detail features, and then analyze the physical and chemical properties of the samples based on laser spectroscopy technology. This paper proposes a strategy based on Raman mapping and X-ray phase-contrast imaging technology to reconstruct the three-dimensional internal structure of a heterogeneous solid sample and detect the substance composition of the region of interest. This study takes meteorite samples as an example and uses X-ray phase-contrast imaging technology to distinguish and reconstruct the spatial distribution of different components in the meteorite, providing a three-dimensional visualization reference with a high spatial resolution for the spatial positioning of the region of interest. Raman spectroscopy, in combination with LIBS, was used to further identify the meteorite as pallasite and to achieve the spectral image fusion of high spatial and high spectral resolutions. The experimental results show that the unknown meteorite’s three-dimensional structure and its components’ spatial distribution can be evaluated based on Raman mapping combined with X-ray phase-contrast imaging technology. This article provides a highly valuable analytical strategy by which to analyze samples returned from deep space exploration.

Meteorites are an essential reference for human exploration of the universe and its cosmic evolution and an essential research object for searching for extraterrestrial life. Ways to quickly identify and screen suspected meteorite samples have become the foundation and prerequisite for research on high-value meteorite samples. Therefore, this paper proposes a Raman mapping-assisted micro-laser induced breakdown spectroscopy (micro-LIBS) technology for field detection of suspected meteorite material composition without sample pre-processing, with a high detection speed and cost-effectiveness, to realize the detection of element composition and molecular structure. Raman mapping carries out multispectral imaging with high spectral resolution of the region of interest. The fusion of Raman mapping and optical microscopy images can provide mineral categories and spatial distribution characteristics in regions of interest. A quantitative analysis model for Fe, Mg, and Na elements was constructed based on the multidimensional scaling–back propagation neural network (MDS-BPNN) algorithm. The determination coefficient of the model test set was better than 0.997, and the root mean square error was better than 0.65. The content of Fe, Mg, and Na elements in the meteorite was preliminarily evaluated, providing a reference for further analysis of element information in spectral image fusion data. The Raman–LIBS combined technology has significant application potential in rapidly evaluating suspected meteorite samples. Without high-end precision instruments or field research, this technology can provide scientists with significant reference value atomic and molecular spectral information. At the same time, this technology can be extended to other petrology research. We offer a fast, efficient, cost-effective, and reliable analysis scheme for reference.

The robotic surgery environment represents a typical scenario of human–robot cooperation. In such a scenario, individuals, robots, and medical devices move relative to each other, leading to unforeseen mutual occlusion. Traditional methods use binocular OTS to focus on the local surgical site, without considering the integrity of the scene, and the work space is also restricted. To address this challenge, we propose the concept of a fully perception robotic surgery environment and build a global–local joint positioning framework. Furthermore, based on data characteristics, an improved Kalman filter method is proposed to improve positioning accuracy. Finally, drawing from the view margin model, we design a method to evaluate positioning accuracy in a dynamic occlusion environment. The experimental results demonstrate that our method yields better positioning results than classical filtering methods.

Unmanned vehicles frequently encounter the challenge of navigating through complex mountainous terrains, which are characterized by numerous unknown continuous curves. Drones, with their wide field of view and ability to vertically displace, offer a potential solution to compensate for the limited field of view of ground vehicles. However, the conventional approach of path extraction solely provides pixel-level positional information. Consequently, when drones guide ground unmanned vehicles using visual cues, the road fitting accuracy is compromised, resulting in reduced speed. Addressing these limitations with existing methods has proven to be a formidable task. In this study, we propose an innovative approach for guiding the visual movement of unmanned ground vehicles using an air–ground collaborative vectorized curved road representation and trajectory planning method. Our method offers several advantages over traditional road fitting techniques. Firstly, it incorporates a road star points ordering method based on the K-Means clustering algorithm, which simplifies the complex process of road fitting. Additionally, we introduce a road vectorization model based on the piecewise GA-Bézier algorithm, enabling the identification of the optimal frame from the initial frame to the current frame in the video stream. This significantly improves the road fitting effect (EV) and reduces the model running time (T—model). Furthermore, we employ smooth trajectory planning along the “route-plane” to maximize speed at turning points, thereby minimizing travel time (T—travel). To validate the efficiency and accuracy of our proposed method, we conducted extensive simulation experiments and performed actual comparison experiments. The results demonstrate the superior performance of our approach in terms of both efficiency and accuracy.

The stream sediment (SS) records evolution information of the water system structure and sedimentary environment in specific regions during different geological periods, which is of great significance for studying the ancient planetary environment and the law of water system changes. Based on the SS of different geographical environments on Earth, remote laser-induced breakdown spectroscopy (remote-LIBS) technology combined with the multidimensional scaling-back propagation neural network (MDS-BPNN) algorithm was used to conduct an in-depth analysis of remote qualitative and quantitative detection of the elemental composition and content of SS. The results show that the detection system based on remote LIBS combined with an artificial neural network algorithm can achieve an ideal quantitative analysis of major and trace elements. The coefficients of determination (R2) of the test set for major elements is greater than 0.9996, and the root mean square error (RMSE) is less than 0.7325. The coefficients of determination (R2) of the test set for trace elements is greater than 0.9837, and the root mean square error is less than 42.21. In addition, for the application scenario of exploring extraterrestrial life, biominerals represented by stromatolite phosphorite (SP) are easy to form sand and enter into SS under weathering. Therefore, this paper discusses the feasibility of using remote-LIBS technology to detect and identify such minerals under the disappearance of SPs’ macro- and micro-characteristics. From our research, we can find that remote-LIBS technology is the preferred candidate for discovering dust-covered biominerals. In geological environments rich in water system sedimentary rocks, such as Mars’ ancient riverbeds, LIBS technology is crucial for deciphering the “life signals” hidden in the Martian sand.

This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 μm × 710 μm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure.

The search for biominerals is one of the core targets in the deep space exploration mission. Stromatolite phosphorite is a typical biomineral that preserves early life on Earth. The enrichment of phosphate is closely related to microorganisms and their secretions. Laser-induced breakdown spectroscopy (LIBS) has become an essential payload in deep space exploration with the ability to analyze chemical elements remotely, rapidly, and in situ. This paper aims to evaluate the rapid identification of biological and non-biological minerals through a remote LIBS payload. LIBS is used for element analysis and mineral classification determination, and molecular laser-induced fluorescence (MLIF) is used to detect halogenated element F to support the existence of fluorapatite. This paper analyzes the LIBS-MLIF spectral characteristics of stromatolites and preliminarily evaluates the feasibility of P element quantification. The results show that LIBS technology can recognize biological and non-biological signals. This discovery is significant because it is not limited to detecting and analyzing element composition. It can also realize the detection of molecular spectrum based on selective extraction of CaF molecule. Therefore, the LIBS payload still has the potential to search for biomineral under the condition of adjusting the detection strategy.

The efficient biosynthesis of chiral amines at an industrial scale to meet the high demand from industries that require chiral amines as precursors is challenging due to the poor stability and low catalytic efficiency of ω-transaminases (ω-TAs). Herein, this study adopted a green and efficient solvent engineering method to explore the effects of various aqueous solutions of deep eutectic solvents (DESs) as cosolvents on the catalytic efficiency and stability of ω-TA. Binary- and ternary-based DESs were used as cosolvents in enhancing the catalytic activity and stability of a ω-TA variant from Aspergillus terreus (E133A). The enzyme exhibited a higher catalytic activity in a ternary-based DES that was 2.4-fold higher than in conventional buffer. Moreover, the thermal stability was enhanced by a magnitude of 2.7, with an improvement in storage stability. Molecular docking studies illustrated that the most potent DES established strong hydrogen bond interactions with the enzyme’s amino acid, which enhanced the catalytic efficiency and improved the stability of the ω-TA. Molecular docking is essential in designing DESs for a specific enzyme.

Artificial intelligence (AI) technology plays a crucial role in infectious disease outbreak prediction and control. Many human interventions can influence the spread of epidemics, including government responses, quarantine, and economic support. However, most previous AI-based models have failed to consider human interventions when predicting the trend of infectious diseases. This study selected four human intervention factors that may affect COVID-19 transmission, examined their relationship to epidemic cases, and developed a multivariate long short-term memory network model (M-LSTM) incorporating human intervention factors. Firstly, we analyzed the correlations and lagged effects between four human factors and epidemic cases in three representative countries, and found that these four factors typically delayed the epidemic case data by approximately 15 days. On this basis, a multivariate epidemic prediction model (M-LSTM) was developed. The model prediction results show that coupling human intervention factors generally improves model performance, but adding certain intervention factors also results in lower performance. Overall, a multivariate deep learning model with coupled variable correlation and lag outperformed other comparative models, and thus validated its effectiveness in predicting infectious diseases.

In this work, we exploited an efficient method to release hemicellulosic polysaccharides (BLHP) from bamboo (Phyllostachys pubescens Mazel) leaves assisted by a small amount of phosphotungstic acid. Structural unit analysis proved that BLHP-A1 and BLHP-B1 samples possessed abundant low-branch chains in →4)-β-D-Xylp-(1→ skeleton mainly consisting of Xylp, Manp, Glcp, Galp, and Araf residues. According to the results of the antioxidant activity assays in vitro, both of the two fractions demonstrated the activity for scavenging DPPH⋅ and ABTS+ radicals and exhibited relatively a high reducing ability compared to the recently reported polysaccharides. Moreover, the antioxidant activities of purified polysaccharides were evaluated against H2O2-induced oxidative stress damage in HepG2 cells. BLHP-B1 showed more activity for preventing damages from H2O2 in HepG2 cells by improving the enzyme activities of SOD, CAT, and GSH-Px and decreasing the production of MDA as well as suppressing reactive oxygen species (ROS) formation. This study implied that BLHP could demonstrate its attenuation ability for oxidative stress in HepG2 cells.

Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

While corporate charitable giving(CG) can help firms obtain external innovation resource support, it can also crowd out internal innovation resources. The purpose of this study is to clarify the mechanism of CG and government green subsidies(GS) on green innovation(GI). In this regard, we integrated signaling theory and principal-agent theory to provide a new theoretical perspective for simultaneously focus on the impact of external resource acquisition and internal resource allocation on GI. We conducted a threshold regression analysis on the balanced panel data of 863 listed companies of China from 2016 to 2019 to clarify the input boundary between the promoting and inhibiting effects of corporate CG on corporate GI. And we further explored the relationship between GS and GI under the effect of different CG thresholds. Our findings indicate that there is an inverted U-shaped threshold effect of CG on GI. The impact of GS on GI shows a decreasing marginal benefit as the intensity of CG increases. Based on the findings, we propose corresponding countermeasures for the management of enterprises and the government.

Corporate charitable donations under different motivations will have different effects on innovation investment through different action paths, which provides a new perspective to solve the inconsistency of existing research results. Based on the resource-dependent and principal-agent theories, this paper compares and discusses the relationship between charitable donations and innovation investment under different motivations. Using 2008 ~2019 relevant data of listed companies as research samples, a mixed regression model is established for the hypothesis test, and further examines the state-ownership of its moderating role. The results show that the altruistic motivation-oriented corporate donations have a significant inverted u-shaped effect on innovation investment. The tool motivation-oriented corporate donations have a significant U-shaped effect on innovation investment. Moreover, it is further found that for ST (Special Treatment) corporates with the risk of delisting in the tool motivation-oriented charitable donations type, the corporate charitable donations have a significant negative effect on innovation investment. State-ownership can enhance the inverted U-shaped relationship between altruistic motivation-oriented corporate donations and innovation investment but weaken the U-shaped relationship between tool motivation-oriented corporate donations and innovation investment.

With the rapid development of the Internet, big data has been applied in a large amount of application. However, there are often redundant or irrelevant features in high dimensional data, so feature selection is particularly important. Because the feature subset obtained by a single feature selection method may be biased, an ensemble feature selection method named SA-EFS based on sort aggregation is proposed in this paper, and this method is oriented to classification tasks. For high-dimensional data sets, the results of three feature selection methods, chi-square test, maximum information coefficient and XGBoost, are aggregated by specific strategy. The integration effects of arithmetic mean and geometric mean aggregation strategy on this model are analyzed. In order to evaluate the classification and prediction performance of feature subset, three classifiers with excellent performance, KNN, Random Forest and XGBoost, are tested respectively, and the influence of threshold on classification performance is analyzed. The experimental results show that compared with the single feature selection method, the arithmetic mean aggregation ensemble feature selection can effectively improve the classification accuracy, and the threshold interval setting of 0.1 is a better choice.

Applying a three-dimensional (3-D) reconstruction from mapping-oriented offline modeling to intelligent agent-oriented environment understanding and real-world environment construction oriented to agent autonomous behavior has important research and application value. Using a scanner to scan objects is a common way to obtain a 3-D model. However, the existing scanning methods rely heavily on manual work, fail to meet efficiency requirements, and are not sufficiently compatible with scanning objects of different sizes. In this article, we propose a creative visual coverage path planning approach for the robotic multi-model perception system (RMMP) in a 3-D environment under photogrammetric constraints. To realize the 3-D scanning of real scenes automatically, we designed a new robotic multi-model perception system. To reduce the influence of image distortion and resolution loss in 3-D reconstruction, we set scanner-to-scene projective geometric constraints. To optimize the scanning efficiency, we proposed a novel path planning method under photogrammetric and kinematics constraints. Under the RMMP system, a constraints-satisfied coverage path could be generated, and the 3-D reconstruction from the images collected along the way was carried out. In this way, the autonomous planning of the pose of the end scanner in scanning tasks was effectively solved. Experimental results show that the RMMP-based 3-D visual coverage method can improve the efficiency and quality in 3-D reconstruction.

The tilt-rotor unmanned aerial vehicle (TRUAV) is characterized by both multi-rotor vertical takeoff or landing and fixed-wing long-duration flight. Not only the structure of TRUAV is complex, but also the aerodynamic coupling is severe. Especially when the TRUAV is in the transition mode, the rotor flow field and wake are complicated. The abnormal variation of the aerodynamic force of the rotor blades will directly affect the balance and manipulation of the aircraft. The downwash flow generated by the rotor spiral propeller tip vortex interference will impact the wing from every direction, forming a blocked three-dimensional effect flow field on the wing surface, which seriously affects the stability of the UAV. In this paper, the transient CFD (Computational Fluid Dynamics) numerical simulation was applied to examine the flow fields of the fuselage and rotor under the transition modal of TRUAV as well as the aerodynamic disturbance. In addition, the dynamics model of the TRUAV was established based on the change of the tilt angle state, and considering the effect of rotor slip flow, we analyzed the three-dimensional flow field distribution of TRUAV in the transition mode under the aerodynamic disturbance of the fuselage and rotor, and we identified each aerodynamic parameter required for modeling. A cascade PID control strategy is designed for the TRUAV, and the results verify that the proposed TRUAV model can remain stable even when the maximum roll angle is 20 degrees. At last, the simulation results provide data support for the optimization of the TRUAV aerodynamic profile and the design of subsequent flight control methods.

The development and application of advanced water purification technology is crucial to guarantee a sufficient supply of clean water. However, conventional water purification technology consumes large amounts of coagulants, with the formation of intractable sludge. Herein, the applicability of high-speed air flotation technology for the purification of actual water sources was evaluated in Tianjin Binhai New Area. During a three-year survey, the raw water exhibited periodic pollution characteristics with algae cells as the main removal targets in all seasons. The raw water had both low temperatures and low turbidity in winter, another obstacle for water treatment. Based on the scientific analysis of the water’s quality, the water purification process was comprehensively optimized via regulating the dosage of agents and operating parameters and using high-speed air flotation equipment. The results showed that a dissolved air pressure of 0.40 MPa, reflux ratio of 8%, and SUEZ-1# dissolved air release head combined with pre-chlorination with PACl plus FeCl3 (PACl/FeCl3 ratio = 2:1) were suitable for attaining a good purification performance. High turbidity removal rates (80.9–86.2%) and algae cell removal rates (92.5–98.1%) were obtained even in the high algae period of summer and low turbidity period in the winter, proving the superior stability and applicability of the high-speed air flotation system.

In order to better control the quality of Selexipag products and ensure drug safety,headspace gas chromatography is established to detect the amount of solvent remaining in the synthesis of Selexipag intermediates 4-\ pyrazin -2-yl) (isopropyl) amino\]-1-butanol (S5) and N-(2-chloroacetyl) methyl sulfonamide (S7). Gas chromatography is adopted with Kromat PC-624(V) capillary column (30.0 m×0.32 mm×18 μm). The FID detector temperature and injection port temperature are 250 ℃ and 200 ℃, respectively. The headspace sampling is used at temperature 80 ℃ for 30 min. The solvents revolved are methanol, ethanol, acetone, dichloromethane, isopropylacetate, tetrahydrofuran and methylbenzene. The tested component could be completely separated under the chromatographic conditions. The correlation coefficient of each solvent standard curve is greater than 0.997 and the mean recoveries of all components are in range of 95.1%~106.2%. The RSD values of precision, stability and repeatability range from 1.78% to 4.60%. The analytical method is simple, accurate and sensitive, and it can be used for the determination of residual solvents in Selexipag, which provides scientific basis for the quality control of intermediates.

Surgical robots are increasingly important in orthopedic surgeries to assist or replace surgeons in completing operations. During joint surgeries, the patient’s joint needs to be adjusted several times by the surgeon. Therefore, the virtual model, built on the preoperative medical images, cannot match the actual variation of the patient’s joint during the surgery. Conventional virtual reality techniques cannot fully satisfy the requirements of the joint surgeries. This paper proposes a real and virtual bidirectional driving method to synchronize the manipulations in both the real operation site and the virtual scene. The dynamic digital twin of the patient’s joint is obtained by decoupling the joint and dynamically updating its pose via the intraoperative measurements. During surgery, the surgeon can intuitively monitor the real-time position of the patient and the surgical tool through the system and can also manipulate the surgical robot in the virtual scene. In addition, the system can provide visual guidance to the surgeon when the patient’s joint is adjusted. A prototype system is developed for orthopedic surgeries. Proof-of-concept joint surgery demo is carried out to verify the effectiveness of the proposed method. Experimental results show that the proposed system can synchronize the manipulations in both the real operation site and the virtual scene, thus realizing the bidirectional driving.

Surface soil moisture (SM), as a crucial ecological element, is significant to monitor in semiarid mining areas characterized by aridity and little rainfall. The passive microwave remote sensing, which is not affected by weather, provides more accurate SM information, but the resolution is too coarse for mining areas. The existing downscaling method is usually pointed to natural scenarios like agricultural fields rather than mining areas with high-intensity mining. In this paper, combined with geoinformation related to SM, we designed a convolutional neural network (SM-Residual Dense Net, SM-RDNet) to downscale SMAP/Sentinel-1 Level-2 radiometer/radar soil moisture data (SPL2SMAP_S SM) into 10 m spatial resolution. Based on the in-site measured data, the root mean square error (RMSE) was utilized to verify the downscaling accuracy of SM-RDNet. In addition, we analyzed its performance for different data combinations, vegetation cover types and the advantages compared with random forest (RF). Experimental results show that: (1) The downscaling from the 3 km product with the combination of auxiliary data NDVI + DEM + slope performs best (RMSE 0.0366 m3/m3); (2) Effective data combinations can improve the downscaling accuracy at the range of 0.0477–0.1176 m3/m3 (RMSE); (3) The SM-RDNet shows better spatial completeness, details and accuracy than RF (RMSE improved by 0.0905 m3/m3). The proposed SM-RDNet can effectively obtain the fine-grained SM in semiarid mining areas. Our method bridges the gap between coarse-resolution microwave SM products and ecological applications of small-scale mining areas, and provides data and technical support for future research to explore how the mining effect SM in semiarid mining areas.

Hydrochars were prepared using bamboo sawdust as raw material through hydrothermal carbonization with the present of acid or alkali in the medium and applied to remove Congo red and 2-naphthol from aqueous solutions. This data article provides information on FTIR and SEM profiles of the bamboo hydrochars, and the equation fitting results of the adsorption isotherms and kinetics for the two organics. The FTIR spectra show the differences of functional groups on the hydrochars with different process conditions. The SEM images show the surface morphology of selected hydrochars. Freundlich equation is slightly better than Langmuir model for the correlation of adsorption isotherms for both Congo red and 2-naphthol. Correlation coefficients from the pseudo-second order equation are greater than those of the pseudo-first order equation for both the organics on selected hydorchars.

Abstract Background Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. Results We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. Conclusion CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

Device-to-device (D2D) communications underlying cellular networks are a novel approach for providing IoT services, in which D2D communication links and cellular communication links share the same channels. Unfortunately, various kinds of devices are randomly distributed in cellular networks and potentially result in critical interferences. To solve the interference problem, we propose a novel greedy-based channel assignment algorithm in this paper. First, we construct a novel interference graph that describes the interferences between pairs of communication links when they simultaneously share the same channel. Then, a novel channel assignment algorithm is proposed to alleviate system interferences by greedily assigning the best channel to each communication link, which is simple and feasible to implement. Moreover, we convert the channel assignment problem into a robust graph coloring problem and search for a near-optimal solution to minimize the system interferences. Numerical results indicate that the proposed algorithm not only dramatically improves the network capacity but also enhances the fairness among devices.

Abstract Background Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. Results In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02–0.07 for MCC, 4.18–21.47% for ST and 0.013–0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. Conclusions We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community.

Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. We propose a novel method, referred to as DANN_TF, for TFBS prediction. DANN_TF consists of a feature extractor, a label predictor, and a domain classifier. The feature extractor and the domain classifier constitute an Adversarial Network, which ensures that learned features are common features across different cell types. DANN_TF is evaluated on five TFs in five cell types with a total of 25 cell-type TF pairs and compared to a baseline method which does not use Adversarial Network. For both data augmentation and cross-cell-type prediction, DANN_TF performs better than the baseline method on most cell-type TF pairs. DANN_TF is further evaluated by an additional 13 TFs in the five cell types with a total of 65 cell-type TF pairs. Results show that DANN_TF achieves significantly higher AUC than the baseline method on 96.9% pairs of the 65 cell-type TF pairs. This is a strong indication that DANN_TF can indeed learn common features for cross-cell-type TFBS prediction.

Kiamycin (1), a new angucyclinone derivative possessing an 1,12-epoxybenz[a]anthracene ring system, was isolated from the marine Streptomyces sp. strain M268 along with the known compounds 8-O-methyltetrangomycin (3) and 8-O-methylrabelomycin (4). Their structures were elucidated by detailed spectroscopic analysis and comparison with literature data. The new angucyclinone derivative showed inhibitory activities against the human cell lines HL-60 (leukemia), A549 (lung adenocarcinoma), and BEL-7402 (hepatoma) with inhibition rates of 68.2%, 55.9%, and 31.7%, respectively, at 100 µM. It appears to have potential as an anticancer agent with selective activity.

A new anthracene derivative, 3-hydroxy-1-keto-3-methyl-8-methoxy-1,2,3,4-tetrahydro-benz[α]anthracene, was isolated from the marine strain Streptomyces sp. W007, and its structure was established by spectroscopic analysis including mass spectra, 1D- and 2D-NMR (1H–1H COSY, HMBC, HSQC and NOESY) experiments. 3-hydroxy-1-keto-3-methyl-8-methoxy-1,2,3,4-tetrahydro-benz[α]anthracene showed cytotoxicity against human lung adenocarcinoma cell line A549.