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Hydrogen is the lightest atom and composes approximately half of the atomic content in macromolecules, yet their location can only be inferred or predicted in most macromolecular structures. This is because hydrogen can rarely be directly observed by the most common structure determination techniques (such as X-ray crystallography and electron cryomicroscopy). However, knowledge of hydrogen atom positions, especially for enzymes, can reveal protonation states of titratable active site residues, hydrogen bonding patterns, and the orientation of water molecules. Though we know they are present, this vital layer of information, which can inform a myriad of biological processes, is frustratingly invisible to us. The good news is that, even at modest resolution, neutron crystallography (NC) can reveal this layer and has emerged this century as a powerful tool to elucidate enzyme catalytic mechanisms. Due to its strong and coherent scattering of neutrons, incorporation of deuterium into the protein crystal amplifies the power of NC. This is especially true when solvation and the specific participation of key water molecules are crucial for catalysis. Neutron data allow the modeling of all three atoms in water molecules and have even revealed previously unobserved and unique species such as hydronium (D 3 O + ) and deuteroxide (OD - ) ions as well as lone deuterons (D + ). Herein, we briefly review why neutrons are ideal probes for identifying catalytically important water molecules and these unique water-like species, limitations in interpretation, and four vignettes of enzyme success stories from disparate research groups. One of these groups was that of Dr. Chris G. Dealwis, who died unexpectedly in 2022. As a memorial appreciation of his scientific career, we will also highlight his interest and contributions to the neutron crystallography field. As both the authors were mentored by Chris, we feel we have a unique perspective on his love of molecular structure and admiration for neutrons as a tool to query those structures.

Solar-driven CO 2 conversion into high-value-added chemicals, powered by photovoltaics, is a promising technology for alleviating the global energy crisis and achieving carbon neutrality. However, most of these endeavors focus on CO 2 electroreduction to small-molecule fuels such as CO and ethanol. In this paper, inspired by the photosynthesis of green plants and artificial photosynthesis for the electroreduction of CO 2 into value-added fuel, CO 2 artificial photosynthesis for the electrocarboxylation of bromobenzene (BB) with CO 2 to generate the value-added carboxylation product methyl benzoate (MB) is demonstrated. Using two series-connected dye-sensitized photovoltaics and high-performance catalyst Ag electrodes, our artificial photosynthesis system achieves a 61.1% Faraday efficiency (FE) for carboxylation product MB and stability of the whole artificial photosynthesis for up to 4 h. In addition, this work provides a promising approach for the artificial photosynthesis of CO 2 electrocarboxylation into high-value chemicals using renewable energy sources., Competing Interests: The authors declare no conflicts of interest.

Graphitic carbon nitride ( g -C 3 N 4 ), recognized for its considerable potential as a heterogeneous photocatalyst in water splitting, has attracted extensive research interest. By using density functional theory (DFT) calculations, the regulatory role of p -block metal (PM) single atoms on the photocatalytic activity of g -C 3 N 4 in overall water splitting was systematically explored. The incorporation of PM atoms (Ge, Sn and Pb) led to a reduction in the overpotentials required for both the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER). Combined with the electronic structures analysis via hybrid functional, it was found that the introduction of Ge, Sn or Pb optimizes the positions of the valence band maximum (VBM) and the conduction band minimum (CBM), providing a robust driving force for HER and ensuring substantial driving force for OER. Meanwhile, the presence of these three PMs induces the spatial separation of VBM and CBM, inhibiting the recombination of carriers. These findings have significant implications for the design and preparation of efficient photocatalysts.

In recent years, computer vision has witnessed remarkable advancements in image classification, specifically in the domains of fully convolutional neural networks (FCNs) and self-attention mechanisms. Nevertheless, both approaches exhibit certain limitations. FCNs tend to prioritize local information, potentially overlooking crucial global contexts, whereas self-attention mechanisms are computationally intensive despite their adaptability. In order to surmount these challenges, this paper proposes cross-and-diagonal networks (CDNet), innovative network architecture that adeptly captures global information in images while preserving local details in a more computationally efficient manner. CDNet achieves this by establishing long-range relationships between pixels within an image, enabling the indirect acquisition of contextual information. This inventive indirect self-attention mechanism significantly enhances the network's capacity. In CDNet, a new attention mechanism named "cross and diagonal attention" is proposed. This mechanism adopts an indirect approach by integrating two distinct components, cross attention and diagonal attention. By computing attention in different directions, specifically vertical and diagonal, CDNet effectively establishes remote dependencies among pixels, resulting in improved performance in image classification tasks. Experimental results highlight several advantages of CDNet. Firstly, it introduces an indirect self-attention mechanism that can be effortlessly integrated as a module into any convolutional neural network (CNN). Additionally, the computational cost of the self-attention mechanism has been effectively reduced, resulting in improved overall computational efficiency. Lastly, CDNet attains state-of-the-art performance on three benchmark datasets for similar types of image classification networks. In essence, CDNet addresses the constraints of conventional approaches and provides an efficient and effective solution for capturing global context in image classification tasks.

Masu salmon, Oncorhynchus masou (Brevoort 1856), a commercially important fish species endemic to the North Pacific Ocean, attained national second-level protected animal status in China in 2021. Despite this recognition, knowledge about the trophic ecology of this fish remains limited. This study investigated the diet diversity of fluviatile Masu salmon in the Mijiang River, China, utilizing the gastrointestinal tract environmental DNA (GITeDNA) metabarcoding and morphological identification. The results revealed a diverse prey composition, ranging from terrestrial and aquatic invertebrates to small fishes. The fluviatile Masu salmon in general consumed noteworthily more aquatic prey than terrestrial prey. There were much more prey taxa and a higher diet diversity detected by GITeDNA metabarcoding than by morphological identification. GITeDNA metabarcoding showed that larger and older Masu salmon consumed significantly more terrestrial insects than aquatic prey species did, with 7366 verses 5012 sequences in the group of ≥20 cm, 9098 verses 4743 sequences in the group of ≥100 g and 11,540 verses 729 sequences in the group of age 3+. GITeDNA metabarcoding also showed size- and age-related diet diversity, indicating that the dietary niche breadth and trophic diversity of larger and older Masu salmon increased with food resources expanding to more terrestrial prey. Terrestrial invertebrates of riparian habitats play a vital role in the diet of fluviatile Masu salmon, especially larger individuals, highlighting their importance in connecting aquatic and terrestrial food webs. Conservation plans should prioritize the protection and restoration of riparian habitats. This study advocates the combined use of GITeDNA metabarcoding and morphological observation for a comprehensive understanding of fish diet diversity.

As a new type of reflective display, electrowetting display (EWD) has excellent dynamic display performance, which is based on polymer coatings. However, there are still some issues which can limit its performance, such as oil backflow and the hysteresis effect which reduces the stability and response speed of EWDs. Therefore, an effective driving waveform was proposed to overcome these drawbacks, which consisted of grayscale conversions between low gray levels and high gray levels. In the driving waveform, to stabilize the EWD at any initial grayscale (low gray levels/high gray levels), an exponential function waveform and an AC signal were used. Then, the grayscale conversion was performed by using an AC signal with a switching voltage to quickly achieve the target grayscale. Finally, another AC signal was used to stabilize the EWD at the target grayscale. A set of driving waveforms in grayscale ranging across four levels was designed using this method. According to the experimental results, oil backflow and the hysteresis effect could be effectively attenuated by the proposed driving waveforms. During conversion, the response speed of EWDs was boosted by at least 9.37% compared to traditional driving waveforms.

The balance between memory Th17 cells (mTh17) and memory Treg cells (mTreg) plays a key role in the pathogenesis of ulcerative colitis (UC), and TIGIT signaling is involved in the differentiation of mTh17/mTreg cells. Astragalus polysaccharide (APS) has good immunomodulatory and anti-inflammatory effects. Here, the regulatory effects and potential mechanisms of APS on mTh17/mTreg cells in UC are explored. A UC model was induced with dextran sulfate sodium (DSS) and treated simultaneously with APS (200 mg/kg/day) for 10 days. After APS treatment, the mice showed a significant increase in colonic length and a significant decrease in colonic weight, colonic weight index and colonic weight/colonic length, and more intact mucosa and lighter inflammatory cell infiltration. Notably, APS significantly down-regulated the percentages of Th17 (CD4 + CCR6 + ), cmTh17 (CD4 + CCR7 + CCR6 + ) and emTh17 (CD4 + CCR7 - CCR6 + ) cells and significantly up-regulated the percentages of cmTreg (CD4 + CCR7 + Foxp3 + ) and emTreg (CD4 + CCR7 - Foxp3 + ) cells in the mesenteric lymph nodes of the colitis mice. Importantly, APS reversed the expression changes in the TIGIT molecule on mTh17/mTreg cells in the colitis mice with fewer CD4 + CCR6 + TIGIT + , CD4 + CCR7 - CCR6 + TIGIT + and CD4 + CCR7 - CCR6 + TIGIT + cells and more CD4 + Foxp3 + TIGIT + , CD4 + CCR7 - Foxp3 + TIGIT + and CD4 + CCR7 - Foxp3 + TIGIT + cells. Meanwhile, APS significantly inhibited the protein expression of the TIGIT ligands CD155, CD113 and CD112 and downstream proteins PI3K and AKT in the colon tissues of the colitis mice. In conclusion, APS effectively alleviated DSS-induced UC in mice by regulating the balance between mTh17/mTreg cells, which was mainly achieved through regulation of the TIGIT/CD155 signaling pathway.

In the field of quadruped robots, the most classic motion control algorithm is based on model prediction control (MPC). However, this method poses challenges as it necessitates the precise construction of the robot's dynamics model, making it difficult to achieve agile movements similar to those of a biological dog. Due to these limitations, researchers are increasingly turning to model-free learning methods, which significantly reduce the difficulty of modeling and engineering debugging and simultaneously reduce real-time optimization computational burden. Inspired by the growth process of humans and animals, from learning to walk to fluent movements, this article proposes a hierarchical reinforcement learning framework for the motion controller to learn some higher-level tasks. First, some basic motion skills can be learned from motion data captured from a biological dog. Then, with these learned basic motion skills as a foundation, the quadruped robot can focus on learning higher-level tasks without starting from low-level kinematics, which saves redundant training time. By utilizing domain randomization techniques during the training process, the trained policy function can be directly transferred to a physical robot without modification, and the resulting controller can perform more biomimetic movements. By implementing the method proposed in this article, the agility and adaptability of the quadruped robot can be maximally utilized to achieve efficient operations in complex terrains.

Understanding the impact of irradiation and temperature on the mechanical properties of GaN single crystals holds significant relevance for rational designs and applications of GaN-based transistors, lasers, and sensors. This study systematically investigates the influence of C-ion irradiation and temperature on pop-in events, hardness, Young's modulus, and fracture behavior of GaN single crystals through nanoindentation experiments. In comparison with unirradiated GaN samples, the pop-in phenomenon for ion-irradiated GaN samples is associated with a larger critical indentation load, which decreases with increasing temperature. Both unirradiated and ion-irradiated GaN samples exhibit a decline in hardness with increasing indentation depth, while Young's moduli do not exhibit a clear size effect. In addition, intrinsic hardness displays an inverse relationship with temperature, and ion-irradiated GaN single crystals exhibit greater intrinsic hardness than their unirradiated counterparts. Our analysis further underscores the significance of Peierls stress during indentation, with this stress decreasing as temperature rises. Examinations of optical micrographs of indentation-induced fractures demonstrate an irradiation embrittlement effect. This work provides valuable insights into the mechanical behavior of GaN single crystals under varying irradiation and temperature conditions.

Schizophrenia and depression are psychiatric disorders with overlapping clinical and biological features. This study aimed to identify common and distinct neuropathological mechanisms in schizophrenia and depression patients using resting-state functional magnetic resonance imaging (fMRI). The study included 28 patients with depression (DEP), 29 patients with schizophrenia (SCH), and 30 healthy control subjects (HC). Intrinsic connectivity contrast (ICC) was used to identify functional connectivity (FC) changes at the whole-brain level, and significant ICC differences were found in the bilateral orbitofrontal cortex (OFC) across all three groups. Further seed-based FC analysis indicated that compared to the DEP and HC groups, the FC between bilateral OFC and medial prefrontal cortex (MPFC), right anterior insula, and right middle frontal gyrus were significantly lower in the SCH group. Additionally, the FC between right OFC and left thalamus was decreased in both patient groups compared to the HC group. Correlation analysis showed that the FC between OFC and MPFC was positively correlated with cognitive function in the SCH group. These findings suggest that OFC connectivity plays a critical role in the pathophysiology of schizophrenia and depression and may provide new insights into the potential neural mechanisms underlying these two disorders.

An isolate of chilli veinal mottle virus (ChiVMV; genus Potyvirus ) of Solanum nigrum L. from southwest China (ChiVMV-YunN/Yuxi) was identified and sequenced (GenBank: OP404087). Comparison with other ChiVMV isolates and recombination analyses suggested a recombinant origin. The most significant recombination event among all 21 complete ChiVMV isolates was an ending breakpoint at 1408-1488 for ChiVMV-YunN/Yuxi with ChiVMV-TaiW and ChiVMV-YunN/Ca operating as the respective major and minor parents. Interestingly, the 5' UTR of ChiVMV-YunN/Yuxi is 15 nucleotides ('AAAAATAAAACAACC') longer than other reported isolates. A full-length clone of ChiVMV-YunN/Yuxi was constructed and was shown to be infectious in Nicotiana benthamiana . The additional 15 nt of 5' UTR in ChiVMV-YunN/Yuxi was stable when transmitted through three generations. Experiments with modified clones showed that the additional 15 nt are essential for infection by this isolate.

Cardiac tissue engineering is a promising strategy for the treatment of myocardial damage. Mesenchymal stem cells (MSCs) are extensively used in tissue engineering. However, transformation of MSCs into cardiac myocytes is still a challenge. Furthermore, weak adhesion of MSCs to substrates often results in poor cell viability. Here, we designed a composite matrix based on silk fibroin (SF) and graphene oxide (GO) for improving the cell adhesion and directing the differentiation of MSCs into cardiac myocytes. Specifically, patterned SF films were first produced by soft lithographic. After being treated by air plasma, GO nanosheets could be successfully coated on the patterned SF films to construct the desired matrix (P-GSF). The resultant P-GSF films presented a nano-topographic surface characterized by linear grooves interlaced with GO ridges. The P-GSF films exhibited high protein absorption and suitable mechanical strength. Furthermore, the P-GSF films accelerated the early cell adhesion and directed the growth orientation of MSCs. RT-PCR results and immunofluorescence imaging demonstrated that the P-GSF films significantly improved the cardiomyogenic differentiation of MSCs. This work indicates that patterned SF films coated with GO are promising matrix in the field of myocardial repair tissue engineering.

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Influenced by water injection, a dominant flow channel is easily formed in the high water cut stage of a conglomerate reservoir, resulting in the inefficient or ineffective circulation of the injected water. With gel flooding as one of the effective development methods to solve the above problems, its parameter optimization determines its final development effect, which still faces great challenges. A new optimization method for gel flooding is proposed in this paper. Firstly, the gel flooding parameters were obtained through physical experiments; then, an experimental model of gel flooding was established according to the target reservoir, and parameter sensitivity analysis was carried out. Next, a history matching of the gel flooding experiment was carried out. Finally, history matching of the target reservoir was also carried out, and a gel flooding scheme was designed and optimized to determine the best parameters. The experimental results showed that the gelation time was 4 h and the gel viscosity was 6332 mPa·s; the breakthrough pressure, resistance factor (RF), and residual resistance factor (RRF) all decreased with the increase in permeability. The gel had a good profile control ability and improved oil recovery by 16.40%. The numerical simulation results illustrated that the porosity of the high permeability layer (HPL) had the greatest impact on the cumulative oil production (COP) of the HPL, and the maximum polymer adsorption value of the HPL had the largest influence on the COP of the low permeability layer (LPL) and the water cut of both layers. Benefiting from parameter sensitivity analysis, history matching of the gel flooding experiment and a conglomerate reservoir in the Xinjiang A Oilfield with less time consumed and good quality was obtained. The optimization results of gel flooding during the high water cut stage in a conglomerate reservoir of the Xinjiang A Oilfield were as follows: the gel injection volume, injection rate, and polymer concentration were 2000 m 3 , 50 m 3 /d, and 2500 mg/L, respectively. It was predicted that the water cut would decrease by 6.90% and the oil recovery would increase by 2.44% in two years. This paper not only provides a more scientific and efficient optimization method for gel flooding in conglomerate reservoirs but also has important significance for improving the oil recovery of conglomerate reservoirs.

Ultraviolet Visible (UV-Vis) spectroscopy detection technology has been widely used in quantitative analysis for its advantages of rapid and non-destructive determination. However, the difference of optical hardware severely restricts the development of spectral technology. Model transfer is one of the effective methods to establish models on different instruments. Due to the high dimension and nonlinearity of spectral data, the existing methods cannot effectively extract the hidden differences in spectra of different spectrometers. Thus, based on the necessity of spectral calibration model transfer between the traditional large spectrometer and the micro-spectrometer, a novel model transfer method based on improved deep autoencoder is proposed to realize spectral reconstruction between different spectrometers. Firstly, two autoencoders are used to train the spectral data of the master and slave instrument, respectively. Then, the hidden variable constraint is added to enhance the feature representation of the autoencoder, which makes the two hidden variables equal. Combined with a Bayesian optimization algorithm for the objective function, the transfer accuracy coefficient is proposed to characterize the model transfer performance. The experimental results show that after model transfer, the spectrum of the slave spectrometer is basically coincident with the master spectrometer and the wavelength shift is eliminated. Compared with the two commonly used direct standardization (DS) and piecewise direct standardization (PDS) algorithms, the average transfer accuracy coefficient of the proposed method is improved by 45.11% and 22.38%, respectively, when there are nonlinear differences between different spectrometers.

To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time-frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time-frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time-frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre-training and supervised inverse fine-tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN-based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.

The development of urban agglomerations dominated by the service industry is an important driving force for further sustainable economic growth of China. Spatial analysis marked by population density and regional integration is an essential perspective for studying the human development index (HDI) in China. Based on Bayesian estimation, this paper examines the influence of a spatial factor on HDI by using a spatial hierarchical factor model within the framework of Sen Capability Approach theory, overcoming the neglect of spatial factors and their equal weight in traditional measurement of HDI. On this basis, the HDI including the spatial factor was measured based on the panel data from 2000 to 2018. The results reveal that (1) provinces with high population densities and regional integration have higher rankings and low uncertainties of HDI, which can be attributed to the improvement of education weights; (2) HDI has a certain spatial spillover effect, and the spatial association increases year by year; (3) robust test by using nighttime lighting as an alternative indicator of GDP supports that the spatial correlation is positively related to HDI ranking. The policy recommendations of this paper are to remove the obstacles for cross-regional population mobility and adjust the direction and structure of public expenditure.

Herein, we present a mild strategy for deprotecting cyclic sulfamidates via the Kukhtin-Ramirez reaction to access amino sugars. The method features the removal of the sulfonic group of cyclic sulfamidates, which occurs through an N-H insertion reaction that implicates the Kukhtin-Ramirez adducts, followed by a base-promoted reductive N-S bond cleavage. The mild reaction conditions of the protocol enable the formation of amino alcohols including analogs that bear multiple functional groups.

Intelligent video surveillance based on artificial intelligence, image processing, and other advanced technologies is a hot topic of research in the upcoming era of Industry 5.0. Currently, low recognition accuracy and low location precision of devices in intelligent monitoring remain a problem in production lines. This paper proposes a production line device recognition and localization method based on an improved YOLOv5s model. The proposed method can achieve real-time detection and localization of production line equipment such as robotic arms and AGV carts by introducing CA attention module in YOLOv5s network model architecture, GSConv lightweight convolution method and Slim-Neck method in Neck layer, add Decoupled Head structure to the Detect layer. The experimental results show that the improved method achieves 93.6% Precision, 85.6% recall, and 91.8% mAP@0.5, and the Pascal VOC2007 public dataset test shows that the improved method effectively improves the recognition accuracy. The research results can substantially improve the intelligence level of production lines and provide an important reference for manufacturing industries to realize intelligent and digital transformation.

g-C 3 N 4 /Ag 3 PO 4 /TiO 2 nanocomposite materials were loaded onto a polyvinylidene fluoride (PVDF) membrane using a phase inversion method to obtain a photocatalytic flat membrane for dye removal. The morphology, structure, and photocatalytic activity of the g-C 3 N 4 /Ag 3 PO 4 /TiO 2 nanoparticles and composite membrane were evaluated. The g-C 3 N 4 /Ag 3 PO 4 /TiO 2 /PVDF membrane exhibited superior morphology, hydrophilic properties, and antifouling performance compared with the raw PVDF membrane. Four-stage filtration was performed to evaluate the self-cleaning and antifouling capacity of the g-C 3 N 4 /Ag 3 PO 4 /TiO 2 /PVDF membrane. Upon irradiating the composite membrane with visible light for 30 min, its irreversible fouling resistance (Rir) was low (9%), and its flux recovery rate (FRR) was high (71.0%) after five filtration cycles. The removal rate of rhodamine B (RhB) from the composite membrane under visible light irradiation reached 98.1% owing to the high photocatalytic activity of the membrane, which was superior to that of raw PVDF membrane (42.5%). A mechanism of photocatalytic composite membranes for RhB degradation was proposed. Therefore, this study is expected to broaden prospects in the field of membrane filtration technology., Competing Interests: The authors declare that they have no conflict of interest.

In this paper, an inductorless and gain-controllable 0.5~2.5 GHz wideband low noise amplifier (LNA) based on second generation current controlled current conveyors (CCCIIs) is presented. The proposed wideband LNA utilizes CCCIIs as building blocks to implement the amplifier stage and impedance matching stage. By varying the DC biasing current of the CCCII, the voltage gain of the proposed LNA is controllable in the range of 1~18 dB. In the frequency range of 0.5~2.5 GHz, the post-layout simulation results show that the proposed LNA has a typical voltage gain S21 of 12.6 dB with a gain ripple of ±1.5 dB, an input and output return loss (S11 and S22) of, respectively, -21.4 dB to -16.6 dB and -18.6 dB to -10.6 dB, and a high reverse isolation S12 of -65.2 dB to -39.5 dB. A noise figure of 4.05~4.35 dB is obtained across the whole band, and the input third-order intercept point (IIP3) is -2.5 dBm at 1.5 GHz. Using a 0.18 μm RF CMOS technology, the LNA occupies an active chip area of only 0.096 mm 2 with a power consumption of 12.0 mW., Competing Interests: The authors declare no conflict of interest.

The current diagnostic criteria for cancer cachexia are inconsistent, and the prognostic value of cachexia in gastric cancer (GC) is controversial. This study aimed to investigate the prognostic value of the cachexia index (CXI) in patients with GC. We calculated the CXI as skeletal muscle index (SMI) × serum albumin/neutrophil-lymphocyte ratio (NLR), and a total of 161 and 163 patients were included in the high and low CXI groups, respectively. Low CXI was significantly associated with a more advanced tumor−node−metastasis (TNM) stage, a higher level of serum C-reactive protein, serum interleukin-6, and NLR, but also a decreased level of serum prealbumin and albumin. In addition, patients in the low CXI group were more likely to have postoperative pulmonary infections (9.8% vs. 3.7%, p = 0.03). Cox proportional analyses indicated that patients with low CXI (HR 0.45, 95% CI 0.29 to 0.69; p < 0.001) or TNM stage III+IV (HR 4.38, 95% CI 2.54 to 7.55; p < 0.001) had a significantly poorer overall survival (OS). Kaplan−Meier survival curves suggested that patients with low CXI had a significantly decreased OS, which was not affected by subgroup analyses of different sex, age, cachexia, body mass index (BMI), and TNM stage. Furthermore, low CXI combined with cachexia, low BMI, or TNM stage III+IV caused the worst OS in each subgroup analysis, respectively. Our study demonstrated that CXI had a good prognostic value in GC. Greater attention should be paid to patients with low CXI, particularly those combined with cachexia, low BMI, or TNM stage III+IV.

The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is challenging, especially in the early stage. To improve the anti-noise ability and detect incipient faults, a novel signal de-noising method based on enhanced time-frequency manifold (ETFM) and kurtosis-wavelet dictionary is proposed. First, to mine the high-dimensional features, the C-C method and Cao's method are combined to determine the embedding dimension and delay time of phase space reconstruction. Second, the input parameters of the liner local tangent space arrangement (LLTSA) algorithm are determined by the grid search method based on Renyi entropy, and the dimension is reduced by manifold learning to obtain the ETFM with the highest time-frequency aggregation. Finally, a kurtosis-wavelet dictionary is constructed for selecting the best atom and eliminating the noise and reconstruct the defective signal. Actual simulations showed that the proposed method is more effective in noise suppression than traditional algorithms and that it can accurately reproduce the amplitude and phase information of the raw signal.

Frustrated Lewis pair (FLP) catalysts have attracted much recent interest because of their exceptional ability to activate small molecules in homogeneous catalysis. In the past ten years, this unique catalysis concept has been extended to heterogeneous catalysis, with much success. Herein, we review the recent theoretical advances in understanding FLP-based heterogeneous catalysis in several applications, including metal oxides, functionalized surfaces, and two-dimensional materials. A better understanding of the details of the catalytic mechanism can help in the experimental design of novel heterogeneous FLP catalysts.

Fracture during the assembly process is an important failure mode for high-lock bolts used in the aviation industry, which greatly increases the potential of unpredictable accidents during service. In the current study, the underlying reasons for fracture during the assembly of a TC4 high-lock bolt was investigated using a tensile test and finite element analysis (FEA). The microstructure of the as-received bolt consisted of a high proportion of α phase, some β phase, and a small amount of α' phase formed via martensite phase transformation during the rammer process. The experimental force-displacement curves revealed an average yield load of 55.9 kN and a breaking load of 67.65 kN. The corresponding yield strength was calculated to be 0.9 GPa, which was smaller than the standard value of TC4. This was attributed to the preload-induced stress concentration on the thread surface, leading to obvious strain hardening, which can lead to crack initiation. The effect of preload was further confirmed by the fractographies in which the initial crack was observed on the thread surface. The fractographies suggested that hybrid fracture occurred on the tensile loaded bolt. The initial failure was brittle fracture on the thread surface, transforming into ductile fracture in the screw. The results can contribute to understanding the effect of preload on the load carry capacity of high-lock bolts and provide a strategy to design its assembly specification.

Auxin response factors (ARFs) play important roles in plant growth and development; however, research in peanut ( Arachis hypogaea L.) is still lacking. Here, 63, 30, and 30 AhARF genes were identified from an allotetraploid peanut cultivar and two diploid ancestors ( A . duranensis and A . ipaensis ). Phylogenetic tree and gene structure analysis showed that most AhARF s were highly similar to those in the ancestors. By scanning the whole-genome for ARF-recognized cis -elements, we obtained a potential target gene pool of AhARFs, and the further cluster analysis and comparative analysis showed that numerous members were closely related to root development. Furthermore, we comprehensively analyzed the relationship between the root morphology and the expression levels of AhARF s in 11 peanut varieties. The results showed that the expression levels of AhARF14 / 26 / 45 were positively correlated with root length, root surface area, and root tip number, suggesting an important regulatory role of these genes in root architecture and potential application values in peanut breeding.

The development and treatment of some diseases, such as large-area cerebral infarction, cerebral hemorrhage, brain tumor, and craniocerebral trauma, which may involve the injury of the dura mater, elicit the need to repair this membrane by dural grafts. However, common dural grafts tend to result in dural adhesions and scar tissue and have no further neuroprotective effects. In order to reduce or avoid the complications of dural repair, we used PLGA, tetramethylpyrazine, and chitosan as raw materials to prepare a nanofibrous dura mater (NDM) with excellent biocompatibility and adequate mechanical characteristics, which can play a neuroprotective role and have an antifibrotic effect. We fabricated PLGA NDM by electrospinning, and then chitosan was grafted on the nanofibrous dura mater by the EDC-NHS cross-linking method to obtain PLGA/CS NDM. Then, we also prepared PLGA/TMP/CS NDM by coaxial electrospinning. Our study shows that the PLGA/TMP/CS NDM can inhibit the excessive proliferation of fibroblasts, as well as provide a sustained protective effect on the SH-SY5Y cells treated with oxygen-glucose deprivation/reperfusion (OGD/R). In conclusion, our study may provide a new alternative to dural grafts in undesirable cases of dural injuries.

Gemcitabine is a chemotherapeutic used clinically to treat a variety of cancers. However, because it lacks tumor cell specificity, gemcitabine may cause off-target cytotoxicity and adversely impact patients. To impart cancer cell specificity to gemcitabine and improve its therapeutic efficacy, we synthesized a unique aptamer-drug conjugate that carries a high gemcitabine payload (three molecules) via a dendrimer structure and enzymatically cleavable linkers for controlled intracellular drug release. First, linker-gemcitabinedendrimer-linker-gemcitabine products were produced, which had significantly lower cytotoxicity than an equimolar amount of free drug. Biochemical analysis revealed that lysosomal cathepsin B protease rapidly cleaved the dendritic linkers and released the conjugated gemcitabine as a free drug. Subsequently, the dendrimer-linker-gemcitabine was coupled with a cell-specific aptamer to form aptamer-gemcitabine conjugates. Functional assays confirmed that, under aptamer guidance, aptamer-gemcitabine conjugates were selectively bound to and then internalized by triple-negative breast cancer cells. Cellular therapy studies indicated that the aptamer-gemcitabine conjugates potentiated cytotoxic activity to targeted cancer cells but did not affect off-target control cells. Our study demonstrates a novel approach to aptamer-mediated targeted drug delivery that combines a high drug payload and an enzymatically controlled drug release switch to achieve higher therapeutic efficacy and fewer off-target effects relative to free-drug chemotherapy.

The human placenta is a rapidly developing transient organ that is key to pregnancy success. Early development of the conceptus occurs in a low oxygen environment before oxygenated maternal blood begins to flow into the placenta at ~10-12 weeks' gestation. This process is likely to substantially affect overall placental gene expression. Transcript variability underlying gene expression has yet to be profiled. In this study, accurate transcript expression profiles were identified for 84 human placental chorionic villus tissue samples collected across 6-23 weeks' gestation. Differential gene expression (DGE), differential transcript expression (DTE) and differential transcript usage (DTU) between 6-10 weeks' and 11-23 weeks' gestation groups were assessed. In total, 229 genes had significant DTE yet no significant DGE. Integration of DGE and DTE analyses found that differential expression patterns of individual transcripts were commonly masked upon aggregation to the gene-level. Of the 611 genes that exhibited DTU, 534 had no significant DGE or DTE. The four most significant DTU genes ADAM10 , VMP1 , GPR126 , and ASAH1, were associated with hypoxia-responsive pathways. Transcript usage is a likely regulatory mechanism in early placentation. Identification of functional roles will facilitate new insight in understanding the origins of pregnancy complications.

Pseudorabies virus (PRV) infection could cause severe histopathological damage via releasing multiple factors, including cytokines, peptides, etc. Here, peptidomic results showed that 129 peptides were identified in PRV-infected mouse lungs and were highly involved in the process of PRV infection. The role of one down-regulated biological peptide (designated as AGDP) during PRV infection was investigated. To verify the expression profiles of AGDP in response to PRV infection, the expression level of the precursor protein of AGDP mRNA was significantly decreased in PRV-infected mouse lungs and cells. The synthesized AGDP-treating cells were less susceptible to PRV challenges than the controls, as demonstrated by the decreased virus production and gE expression. AGDP not only inhibited the expression of TNF-α and IL-8 but also appeared to suppress the extracellular release of high-mobility group box 1 (HMGB1) by inhibiting the output of nuclear HMGB1 in cells. AGDP could also inhibit the degradation of IκBα and the phosphorylation levels of P65 after PRV infection. In total, our results revealed many meaningful peptides involved in PRV infection, thereby enhancing the current understanding of the host response to PRV infection, and how AGDP may serve as a promising candidate for developing novel anti-PRV drugs.

Although targeted cancer therapy can induce higher therapeutic efficacy and cause fewer side effects in patients, the lack of targetable biomarkers on triple-negative breast cancer (TNBC) cells limits the development of targeted therapies by antibody technology. Therefore, we investigated an alternative approach to target TNBC by using the PDGC21T aptamer, which selectively binds to poorly differentiated carcinoma cells and tumor tissues, although the cellular target is still unknown. We found that synthetic aptamer probes specifically bound cultured TNBC cells in vitro and selectively targeted TNBC xenografts in vivo. Subsequently, to identify the target molecule on TNBC cells, we performed aptamer-mediated immunoprecipitation in lysed cell membranes followed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Sequencing analysis revealed a highly conserved peptide sequence consistent with the cell surface protein CD49c (integrin α3). For target validation, we stained cultured TNBC and non-TNBC cells with an aptamer probe or a CD49c antibody and found similar cell staining patterns. Finally, competition cell-binding assays using both aptamer and anti-CD49c antibody revealed that CD49c is the biomarker targeted by the PDGC21T aptamer on TNBC cells. Our findings provide a molecular foundation for the development of targeted TNBC therapy using the PDGC21T aptamer as a targeting ligand.

Human activity recognition is becoming increasingly important. As contact with oneself and the environment accompanies almost all human activities, a Smart-Sleeve, made of soft and stretchable textile pressure sensor matrix, is proposed to sense human contact with the surroundings and identify performed activities in this work. Additionally, a dataset including 18 activities, performed by 14 subjects in 10 repetitions, is generated. The Smart-Sleeve is evaluated over six classical machine learning classifiers (support vector machine, k-nearest neighbor, logistic regression, random forest, decision tree and naive Bayes) and a convolutional neural network model. For classical machine learning, a new normalization approach is proposed to overcome signal differences caused by different body sizes and statistical, geometric, and symmetry features are used. All classification techniques are compared in terms of classification accuracy, precision, recall, and F-measure. Average accuracies of 82.02% (support vector machine) and 82.30% (convolutional neural network) can be achieved in 10-fold cross-validation, and 72.66% (support vector machine) and 74.84% (convolutional neural network) in leave-one-subject-out validation, which shows that the Smart-Sleeve and the proposed data processing method are suitable for human activity recognition.

Mechanical properties of gallium nitride (GaN) single crystals upon carbon ion irradiation are examined using nanoindentation analysis at room temperature. Pop-in events in the load-depth curves are observed for unirradiated and irradiated GaN samples. A statistical linear relationship between the critical indentation load for the occurrence of the pop-in event and the associated displacement jump is exhibited. Both the slope of linear regression and the measured hardness increase monotonically to the ion fluence, which can be described by logistic equations. Moreover, a linear relationship between the regression slope as a micromechanical characterization and the hardness as a macroscopic mechanical property is constructed. It is also found that the maximum resolved shear stress of the irradiated samples is larger than that of the unirradiated samples, as the dislocation loops are pinned by the irradiation-induced defects. Our results indicate that the nanoindentation pop-in phenomenon combined with a statistical analysis can serve as a characterization method for the mechanical properties of ion-irradiated materials.

As the value form of public access to environmental information, the impact of social capital on pro-environmental behavior cannot be ignored. Based on the data of the Chinese General Social Survey 2013 (CGSS2013), this study measures social capital from four aspects-social trust, social norms, social network, and social participation-and it empirically tests the impact of social capital on private and public pro-environmental behavior. The study finds that social capital helps promote pro-environmental behavior. Specifically, the more the public abides by social norms, the higher the degree of social participation, and the stronger the willingness to adopt private and public pro-environmental behaviors. However, the improvement of social trust only has a significant impact on the private environmental behaviors, and the expansion of the social network scale only has a significant impact on the public pro-environmental behaviors. The enhancement of social capital enriches environmental knowledge and promotes pro-environmental behaviors. The mechanism test shows that environmental knowledge plays an intermediary role in the path of social capital affecting individual pro-environmental behavior. The improvement of social capital has a significant impact on the environmental knowledge of individuals with high subjective social class. The gender heterogeneity of social capital affecting environmental knowledge mainly stems from social trust and social network. The stronger the degree of social trust, the richer the environmental knowledge of women, and the social network mainly affects the knowledge level of men. In addition, the publics in the southern region are more likely to be affected by social trust and improve environmental knowledge. Based on the above research conclusions, this paper puts forward policy suggestions on institutional aspects, such as increasing support for informal environmental organizations, carrying out differentiated sustainable development education, and improving the mechanism of environmental information communication.

The workflow for estimating the temperature in agricultural fields from multiple sensors needs to be optimized upon testing each type of sensor's actual user performance. In this sense, readily available miniaturized UAV-based thermal infrared (TIR) cameras can be combined with proximal sensors in measuring the surface temperature. Before the two types of cameras can be operationally used in the field, laboratory experiments are needed to fully understand their capabilities and all the influencing factors. We present the measurement results of laboratory experiments of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. For these uncooled sensors, it took 30 to 60 min for the measured signal to stabilize and the sensor temperature drifted continuously. The drifting sensor temperature was strongly correlated to the measured signal. Specifically for WIRIS, the automated non-uniformity correction (NUC) contributed to extra uncertainty in measurements. Another problem was the temperature measurement dependency on various ambient environmental parameters. An increase in the measuring distance resulted in the underestimation of surface temperature, though the degree of change may also come from reflected radiation from neighboring objects, water vapor absorption, and the object size in the field of view (FOV). Wind and radiation tests suggested that these factors can contribute to the uncertainty of several Celsius degrees in measured results. Based on these indoor experiment results, we provide a list of suggestions on the potential practices for deriving accurate temperature data from radiometric miniaturized TIR cameras in actual field practices for (agro-)environmental research.

Ovarian cancer has the highest mortality rate in the world. Therefore, it is urgent but still challenging to develop an efficient circulating tumor cell (CTC) detection method to sensitively detect ovarian cancer. To address such issues, herein, for the first time, we present a novel CTC detection method for ovarian cancer cells by designing sensitive and rapid graphene-based biosensors. This graphene-based sensor, consisting of a cell pool and two electrodes, can be prepared by a conventional chip fabrication process. It demonstrates high-sensitivity detection even for several ovarian cancer cells by comparing the electrical signal before and after adding cell solution. Moreover, the graphene-based biosensors can perform rapid detection with good repeatability. This suggests that this novel method is possible to use for the early detection of ovarian cancer with very low CTC cell concentration. This work provides a novel and quick strategy to detect ovarian cancer and further judge or predict the risk of the transfer of ovarian cancer.

Differences in economic development, public services, production, and lifestyle between urban and rural areas lead to significant differences in people's attitudes and abilities to cope with environmental pollution. Furthermore, environmental pollution has heterogeneous effects on the health of individuals in urban and rural areas. The article takes the health of left-behind children as an entry point to analyze the impact of haze pollution on the health of urban and rural left-behind children. Using children's survey data from the China Health and Nutrition Survey and the urban and rural raster PM2.5 data from 2000 to 2015, this study applies a logit model to analyze the heterogeneity of the impact of haze pollution on the health of left-behind children. This research finds that, first, the health effects of haze pollution on rural left-behind children are more severe than those on rural children not left behind. Moreover, the same results are not present in the sample of urban left-behind children. Second, the health of left-behind children is more vulnerable to haze pollution than the others when neither parent is at home in rural areas. Third, no evidence proves that haze pollution has more severe health effects on rural children aged 0-6 years with parents away from home. Meanwhile, haze pollution will more easily influence the health status of left-behind children aged 7 years and above in rural areas due to their parents' absence. Fourth, the finding that haze pollution significantly affects the health of left-behind children with parents away from home only applies to the central and western rural samples in China.

The Janus kinase (JAK) family are known to respond to extracellular cytokine stimuli and to phosphorylate and activate signal transducers and activators of transcription (STAT), thereby modulating gene expression profiles. Recent studies have highlighted JAK abnormality in inducing over-activation of the JAK/STAT pathway, and that the cytoplasmic JAK tyrosine kinases may also have a nuclear role. A couple of anti-JAK therapeutics have been developed, which effectively harness lymphoid cancer cells. Here we discuss mutations and fusions leading to JAK deregulations, how upstream nodes drive JAK expression, how classical JAK/STAT pathways are represented in lymphoid malignancies and the noncanonical and nuclear role of JAKs. We also summarize JAK inhibition therapeutics applied alone or synergized with other drugs in treating lymphoid malignancies.

The abrasion failure is the key factor for prolonging the service life and energy saving of furrow openers. The hardness enhancement was reported to be an effective strategy to increase the wear resistance against the soil abrasion. D517 coatings were deposited on Q235 steel by electric spark to improve the wear-resistant property with an affordable cost for farmers. The wear behavior of the coatings was characterized in a pin on disk friction equipment and a homemade soil abrasion simulation system. The soil adhesion, which is highly related to energy consumption, was also evaluated. Results showed that D517 coatings revealed dendrite structure with some randomly distributed carbides. The electric current exerted a great influence on the microstructure, hardness, friction coefficient, and soil wear rate. The wear rate of samples deposited with 80 A and 90 A reduced to 79% and 84%, respectively, as compared with the normalized heat-treated 65 Mn steel after 6 h in soil. This work provides a promising solution to increase the wear resistance of furrow openers. It needs to be noted that the coating would increase the soil adhesion of the opener, which needs to be further explored to decrease the energy consumption.

Bone tissue engineering commonly encompasses the use of three-dimensional (3D) scaffolds to provide a suitable microenvironment for the propagation of cells to regenerate damaged tissues or organs. 3D printing technology has been extensively applied to allow direct 3D scaffolds manufacturing. Polycaprolactone (PCL) has been widely used in the fabrication of 3D scaffolds in the field of bone tissue engineering due to its advantages such as good biocompatibility, slow degradation rate, the less acidic breakdown products in comparison to other polyesters, and the potential for loadbearing applications. PCL can be blended with a variety of polymers and hydrogels to improve its properties or to introduce new PCL-based composites. This paper describes the PCL used in developing state of the art of scaffolds for bone tissue engineering. In this review, we provide an overview of the 3D printing techniques for the fabrication of PCL-based composite scaffolds and recent studies on applications in different clinical situations. For instance, PCL-based composite scaffolds were used as an implant surgical guide in dental treatment. Furthermore, future trend and potential clinical translations will be discussed.

Doxorubicin (DOX) is a common anti-tumor drug that binds to DNA or RNA via non-covalent intercalation between G-C sequences. As a therapeutic agent, DOX has been used to form aptamer-drug conjugates for targeted cancer therapy in vitro and in vivo. To improve the therapeutic potential of aptamer-DOX conjugates, we synthesized trifurcated Newkome-type monomer (TNM) structures with three DOX molecules bound through pH-sensitive hydrazone bonds to formulate TNM-DOX. The aptamer-TNM-DOX conjugate (Apt-TNM-DOX) was produced through a simple self-loading process. Chemical validation revealed that Apt-TNM-DOX stably carried high drug payloads of 15 DOX molecules per aptamer sequence. Functional characterization showed that DOX payload release from Apt-TNM-DOX was pH-dependent and occurred at pH 5.0, which reflects the microenvironment of tumor cell lysosomes. Further, Apt-TNM-DOX specifically targeted lymphoma cells without affecting off-target control cells. Aptamer-mediated cell binding resulted in the uptake of Apt-TNM-DOX into targeted cells and the release of DOX payload within cell lysosomes to inhibit growth of targeted lymphoma cells. The Apt-TNM-DOX provides a simple, non-toxic approach to develop aptamer-based targeted therapeutics and may reduce the non-specific side effects associated with traditional chemotherapy.

The primary objective of this study was to investigate the energy recovery performance of the permafrost hydrate deposit in the Qilian Mountain at site DK-2 using depressurization combined with thermal injection by the approach of numerical simulation. A novel multi-well system with five horizontal wells was applied for large-scale hydrate mining. The external heat is provided by means of water injection, wellbore heating, or the combinations of them through the central horizontal well, while the fluids are extracted outside from the other four production wells under constant depressurization conditions. The injected water can carry the heat into the hydrate deposit with a faster rate by thermal convection regime, while it also raises the local pressure obviously, which results in a strong prohibition effect on hydrate decomposition in the region close to the central well. The water production rate is always controllable when using the multi-well system. No gas seepage is observed in the reservoir due to the resistance of the undissociated hydrate. Compared with hot water injection, the electric heating combined with normal temperature water flooding basically shows the same promotion effect on gas recovery. Although the hydrate regeneration is more severe in the case of pure electric heating, the external heat can be more efficiently assimilated by gas hydrate, and the efficiency of gas production is best compared with the cases involving water injection. Thus, pure wellbore heating without water injection would be more suitable for hydrate development in deposits characterized by low-permeability conditions.

Among aquatic biota, corals provide shelter with sufficient nutrition to a wide variety of underwater life. However, a severe decline in the coral resources can be noted in the last decades due to global environmental changes causing marine pollution. Hence, it is of paramount importance to develop and deploy swift coral monitoring system to alleviate the destruction of corals. Performing semantic segmentation on underwater images is one of the most efficient methods for automatic investigation of corals. Firstly, to design a coral investigation system, RGB and spectral images of various types of corals in natural and artificial aquatic sites are collected. Based on single-channel images, a convolutional neural network (CNN) model, named DeeperLabC, is employed for the semantic segmentation of corals, which is a concise and modified deeperlab model with encoder-decoder architecture. Using ResNet34 as a skeleton network, the proposed model extracts coral features in the images and performs semantic segmentation. DeeperLabC achieved state-of-the-art coral segmentation with an overall mean intersection over union (IoU) value of 93.90%, and maximum F1-score of 97.10% which surpassed other existing benchmark neural networks for semantic segmentation. The class activation map (CAM) module also proved the excellent performance of the DeeperLabC model in binary classification among coral and non-coral bodies.

N , N '-chelate organoboron compounds have been successfully applied in bioimaging, organic light-emitting diodes (OLEDs), functional polymer, photocatalyst, electroluminescent (EL) devices, and other science and technology areas. However, the concise and efficient synthetic methods become more and more significant for material science, biomedical research, or other practical science. Here, we summarized the organoboron- N , N '-chelate derivatives and showed the different routes of their syntheses. Traditional methods to synthesize N , N '-chelate organoboron compounds were mainly using bidentate ligand containing nitrogen reacting with trivalent boron reagents. In this review, we described a series of bidentate ligands, such as bipyridine, 2-(pyridin-2-yl)-1 H -indole, 2-(5-methyl-1 H -pyrrol-2-yl)quinoline, N -(quinolin-8-yl)acetamide, 1,10-phenanthroline, and diketopyrrolopyrrole (DPP).

Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0°-15°), but also has certain adaptability to the sitting posture with a medium rotation angle (15°-30°) or a large rotation angle (30°-45°). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.

Wireless fingerprinting localization (FL) systems identify locations by building radio fingerprint maps, aiming to provide satisfactory location solutions for the complex environment. However, the radio map is easy to change, and the cost of building a new one is high. One research focus is to transfer knowledge from the old radio maps to a new one. Feature-based transfer learning methods help by mapping the source fingerprint and the target fingerprint to a common hidden domain, then minimize the maximum mean difference (MMD) distance between the empirical distributions in the latent domain. In this paper, the optimal transport (OT)-based transfer learning is adopted to directly map the fingerprint from the source domain to the target domain by minimizing the Wasserstein distance so that the data distribution of the two domains can be better matched and the positioning performance in the target domain is improved. Two channel-models are used to simulate the transfer scenarios, and the public measured data test further verifies that the transfer learning based on OT has better accuracy and performance when the radio map changes in FL, indicating the importance of the method in this field.

Background: Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and its pathogenesis is associated with abnormal post-transcriptional regulation. Unbalanced competitive endogenous RNA (ceRNA) promotes tumorigenesis and progression, and greatly contributes to tumor risk classification and prognosis. However, the comprehensive analysis of the circular RNA (circRNA)-long non-coding RNA (lncRNA)-miRNA-mRNA ceRNA network in the prognosis of AML is still rarely reported., Method: We obtained transcriptome data of AML and normal samples from The Cancer Genome Atlas (TCGA), Genotype-tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases, and identified differentially expressed (DE) mRNAs, lncRNAs, and circRNAs. Then, the targeting relationships among lncRNA-miRNA, circRNA-miRNA, and miRNA-mRNA were predicted, and the survival related hub mRNAs were further screened by univariate and multivariate Cox proportional hazard regression. Finally, the AML prognostic circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network was established., Results: We identified prognostic 6 hub mRNAs (TM6SF1, ZMAT1, MANSC1, PYCARD, SLC38A1, and LRRC4) through Cox regression model, and divided the AML samples into high and low risk groups according to the risk score obtained by multivariate Cox regression. Survival analysis verified that the survival rate of the high-risk group was significantly reduced ( p < 0.0001). The prognostic ceRNA network of 6 circRNAs, 32 lncRNAs, 8 miRNAs, and 6 mRNAs was established according to the targeting relationship between 6 hub mRNAs and other RNAs., Conclusion: In this study, ceRNA network jointly participated by circRNAs and lncRNAs was established for the first time. It comprehensively elucidated the post-transcriptional regulatory mechanism of AML, and identified novel AML prognostic biomarkers, which has important guiding significance for the clinical diagnosis, treatment, and further scientific research of AML.

Although constructed wetlands (CWs) are widely used around the world with various substrates, the mechanisms of how these modified substrates affect wastewater treatment are still unknown. In this study, CW microcosms were established with and without ceramsite as a substrate, and the wastewater treatment efficiencies were evaluated during 71 days of incubation. Using the 16S rRNA high-through sequencing, the mechanisms of how CW substrate changed the microbial community was quantified. The results showed that compared to soil as substrate, the use of ceramsite as substrate material enhanced the removal of pollutants from CW systems, particularly under a short retention time (1.5-day) condition. There were more beneficial microorganism groups (nitrogen, sulfur, phosphate) in the ceramsite CW system than the non-ceramsite CW system, particularly in the bottom layers. Moreover, the CW with ceramsite substrate had more nitrification function. All of these results suggested that the ceramsite CW system enhanced the removal of pollutants because it increased the concentration of key microbes that are necessarily for nutrient cycles.

Mycoplasma contamination of cell line cultures is a common, yet often undetected problem in research laboratories. Many of the existing techniques to detect mycoplasma contamination of cultured cells are time-consuming, expensive, and have significant drawbacks. Here, we describe a mycoplasma detection system that is useful for detecting multiple species of mycoplasma in infected cell lines. The system contains three dye-labeled detection aptamers that can specifically bind to mycoplasma-infected cells and a dye-labeled control aptamer that minimally binds to cells. With this system, mycoplasma-contaminated cells can be detected within 30 min by using a flow cytometer, fluorescence microscope, or microplate reader. Further, this system may be used to detect mycoplasma-contaminated culture medium. This study presents an novel mycoplasma detection model that is simple, rapid, inexpensive, and sensitive.