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Tumor metastasis is a common event in patients with gastric cancer (GC) who previously underwent curative gastrectomy. It is meaningful to employ high-volume clinical data for predicting the survival of metastatic GC patients. We aim to establish an improved machine learning (ML) classifier for predicting if a patient with metastatic GC would die within 12 months. Eligible patients were enrolled from a Chinese GC cohort, and the complete detailed information from medical records was extracted to generate a high-dimensional dataset. Appropriate feature engineering and feature filter were conducted before modeling with eight algorithms. A 10-fold cross validation (CV) nested in a holdout CV (8:2) was employed for hyperparameter tuning and model evaluation. Model selection was based on the area under the receiver operating characteristic (AUROC) curve, recall, and precision. The selected model was globally explained using interpretable surrogate models. Of the total 399 cases (median survival of 8.2 months), 242 patients survived less than 12 months. The linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) model had the highest AUROC (0.78 ± 0.021), recall (0.93 ± 0.031), and precision (0.80 ± 0.026), respectively. The LDA model created a new function that generally separated the two classes. The predicted probability of the SVM model was interpreted using a linear regression model visualized by a nomogram. The predicted class of the RF model was explained using a decision tree model. In summary, analyzing high-volume medical data by ML is helpful to produce an improved model for predicting the survival in patients with metastatic GC. The algorithm should be carefully selected in different practical scenarios.

This paper proposes a fast direction of arrival (DOA) estimation method based on positive incremental modified Cholesky decomposition atomic norm minimization (PI-CANM) for augmented coprime array sensors. The approach incorporates coprime sampling on the augmented array to generate a non-uniform, discontinuous virtual array. It then utilizes interpolation to convert this into a uniform, continuous virtual array. Based on this, the problem of DOA estimation is equivalently formulated as a gridless optimization problem, which is solved via atomic norm minimization to reconstruct a Hermitian Toeplitz covariance matrix. Furthermore, by positive incremental modified Cholesky decomposition, the covariance matrix is transformed from positive semi-definite to positive definite, which simplifies the constraint of optimization problem and reduces the complexity of the solution. Finally, the Multiple Signal Classification method is utilized to carry out statistical signal processing on the reconstructed covariance matrix, yielding initial DOA angle estimates. Experimental outcomes highlight that the PI-CANM algorithm surpasses other algorithms in estimation accuracy, demonstrating stability in difficult circumstances such as low signal-to-noise ratios and limited snapshots. Additionally, it boasts an impressive computational speed. This method enhances both the accuracy and computational efficiency of DOA estimation, showing potential for broad applicability.

High-fat diet could lead to a series of metabolic diseases, including obesity, and its mechanism is not clear. In this study, the rabbit individuals were fed with high-fat diet, the liver tissues were collected, high-throughput sequencing technology was used to reveal the expression of lncRNA and miRNA difference, and the molecular regulation mechanism of lncRNA-miRNA. A total of 24,615 DE lncRNAs and 52 DE miRNAs were identified, including 15 novel discovered DE miRNAs (5 upregulated and 10 downregulated). Furthermore, five miRNAs and three mRNAs were verified by qRT-PCR, and the results showed that the expression of the DE miRNAs and DE lncRNAs in the two groups was consistent with our sequencing results. GO and KEGG analyzed 7,57,139 target genes respectively, enriching the pathways related to lipid metabolism, including mucin O-glycan biosynthesis pathway, insulin resistance and glucagon signaling pathway. Moreover, 65 targeting relationships were obtained. Among them, LOC103348122/miR-450a-5p, LOC103350359/miR-450a-3p and LOC103350429/miR-148a-5p were proposed the first time. Significantly, LOC103348122/miR-450a-5p and LOC103350429/miR-148a-5p were related to lipid metabolism in the liver. This study is of great significance to the CeRNA regulatory network related to lipid metabolism in the liver of rabbits, and provides a basis for understanding hepatic steatosis in rabbits.

Abstract The development of bioorthogonal fluorogenic probes constitutes a vital force to advance life sciences. Tetrazine‐encoded green fluorescent proteins (GFPs) show high bioorthogonal reaction rate and genetic encodability but suffer from low fluorogenicity. Here, we unveil the real‐time fluorescence mechanisms by investigating two site‐specific tetrazine‐modified superfolder GFPs via ultrafast spectroscopy and theoretical calculations. Förster resonance energy transfer is quantitatively modeled and revealed to govern the fluorescence quenching; for GFP150‐Tet with a fluorescence turn‐on ratio of ∼9, it contains trimodal subpopulations with good (P1), random (P2), and poor (P3) alignments between the transition dipole moments of protein chromophore (donor) and tetrazine tag (Tet‐v2.0, acceptor). By rationally designing a more free/tight environment, we created new mutants Y200A/S202Y to introduce more P2/P1 populations and improve the turn‐on ratios to ∼14/31, making the fluorogenicity of GFP150‐Tet‐S202Y the highest among all up‐to‐date tetrazine‐encoded GFPs. In live eukaryotic cells, the GFP150‐Tet‐v3.0‐S202Y mutant demonstrates notably increased fluorogenicity, substantiating our generalizable design strategy. Key points Ultrafast spectroscopy reveals FRET in action and inhomogeneous populations with different transition dipole moment alignments. Rational protein design of two new superfolder GFP mutants with record‐high fluorogenicity. Bioimaging application of the designed bioorthogonal protein mutant in live eukaryotic cells.

Abstract Background Biogenic volatile organic compounds (BVOCs) play an essential role in tropospheric atmospheric chemical reactions. There are few studies conducted on BVOCs emission of dominant forest species in the Jing-Jin-Ji area of China. Based on the field survey, forest resources data and the measured standard emission factors, the Guenther model developed in 1993 (G93) was applied in this paper to estimate the emission of BVOCs from several dominant forest species (Platycladus orientalis, Quercus variabilis, Betula platyphylla, Populus tomentosa, Pinus tabuliformis, Robinia pseudoacacia, Ulmus pumila, Salix babylonica and Larix gmelinii) in the Jing-Jin-Ji area in 2017. Then the spatiotemporal emission characteristics and atmospheric chemical reactivity of these species were extensively evaluated. Results The results showed that the total annual BVOCs emission was estimated to be 70.8 Gg C·year− 1, consisting 40.5 % (28.7 Gg C·year− 1) of isoprene, 36.0 % (25.5 Gg C·year− 1) of monoterpenes and 23.4 % (16.6 Gg C·year− 1) of other VOCs. The emissions from Platycladus orientalis, Quercus variabilis, Populus tomentosa and Pinus tabulaeformis contributed 56.1 %, 41.2 %, 36.0 % and 31.1 %, respectively. The total BVOCs emission from the Jing-Jin-Ji area accounted for 61.9 % and 1.8 % in summer and winter, respectively. Up to 28.8 % of emission was detected from Chengde followed by Beijing with 24.9 %, that mainly distributed in the Taihang Mountains and the Yanshan Mountains. Additionally, the Robinia pseudoacacia, Populus tomentosa, Quercus variabilis, and Pinus tabulaeformis contributed mainly to BVOCs reaction activity. Conclusions The BVOCs emission peaked in summer (June, July, and August) and bottomed out in winter (December, January, and February). Chengde contributed the most, followed by Beijing. Platycladus orientalis, Quercus variabilis, Populus tomentosa, Pinus tabulaeformis and Robinia pseudoacacia represent the primary contributors to BVOCs emission and atmospheric reactivity, hence the planting of these species should be reduced.

Using SnCl2·2H2O and SnCl4·5H2O as tin source and polyvinylpyrrolidone (PVP) as polymer matrix, fibril was prepared by using simple and controllable electrostatic spinning technology, and SnO2 nanofibers were obtained after calcination. Scanning electron microscope (SEM), X-ray diffractometer (XRD) etc. were used to investigate the effect of PVP molecular weight and different tin sources on the morphological properties of SnO2 nanofibers, and the specific surface area and gas-sensing characteristics of SnO2 nanofibers were tested. The results show that the fiber precursor prepared by electrospinning can remove the polymer matrix and solvent after calcination, and obtain pure tetragonal rutile SnO2 nanofibers, which show the characteristics of large specific surface area, high sensitivity and fast response speed. When PVP-K30 is used as high molecular matrix and SnCl4 · 5H2O is used as tin source, the prepared SnO2 nanofibers have uniform diameter, clear fiber, good orientation and better gas sensing characteristics.

Abstract Background Atrioventricular (AV) delay could affect AV and ventricular synchrony in cardiac resynchronization therapy (CRT). Strategies to optimize AV delay according to optimal AV synchrony (AVopt-AV) or ventricular synchrony (AVopt-V) would potentially be discordant. This study aimed to explore a new AV delay optimization algorithm guided by electrograms to obtain the maximum integrative effects of AV and ventricular resynchronization (opt-AV). Methods Forty-nine patients with CRT were enrolled. AVopt-AV was measured through the Ritter method. AVopt-V was obtained by yielding the narrowest QRS. The opt-AV was considered to be AVopt-AV or AVopt-V when their difference was 20 ms. Results The results showed that sensing/pacing AVopt-AV (SAVopt-AV/PAVopt-AV) were correlated with atrial activation time (Pend-As/Pend-Ap) (P 20 ms difference between SAVopt-AV/PAVopt-AV and SAVopt-V/PAVopt-V. The opt-AV could be estimated based on electrogram parameters.

Abstract A great challenge for all aqueous batteries, including Zn‐metal batteries, is the parasitic hydrogen evolution reaction on the low‐potential anode. Herein, we report the formula of a highly concentrated aqueous electrolyte that mitigates hydrogen evolution by transforming water molecules more inert. The electrolyte comprises primarily ZnCl2 and LiCl as an additive, both of which are inexpensive salts. The O–H covalent bonds in water get strengthened in a chemical environment that has fewer hydrogen bonding interactions and a greater number of Zn–Cl superhalides, as suggested by integrated characterization and simulation. As a result, the average Coulombic efficiency of zinc‐metal anode is raised to an unprecedented >99.7% at 1 mA cm−2. In the new electrolyte, the plating/stripping processes leave the zinc‐metal anode dendrite‐free, and the zinc‐metal anode delivers stable plating/stripping cycles for 4000 hours with an areal capacity of 4 mAh cm−2 at 2 mA cm−2. Furthermore, the high Coulombic efficiency of zinc‐metal anode in the ZnCl2‐LiCl mixture electrolyte is demonstrated in full cells with a limited anode. The V2O5·H2O||Zn full cell with an N/P mass ratio of 1.2 delivers a stable life of more than 2500 cycles, and the LiMn2O4||Zn hybrid cell with an N/P mass ratio of 0.6 exhibits 1500 cycles in its stable life.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results.

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms.

Abscisic acid (ABA) plays a vital role in many developmental processes and the response to adaptive stress in plants. Under drought stress, plants enhance levels of ABA and activate ABA receptors, but under harsh environmental stress, plants usually cannot efficiently synthesize and release sufficient quantities of ABA. The response of plants to harsh environmental stress may be improved through ABA‐independent activation of ABA receptors. The molecular basis of ABA‐independent inhibition of group A protein phosphatases type 2C (PP2Cs) by pyrabactin resistance/Pyr1‐like (PYR1/PYLs) is not yet clear. Here, we used our previously reported structures of PYL3 to first obtain the monomeric PYL3 mutant and then to introduce bulky hydrophobic residue substitutions to promote the closure of the Gate/L6/CL2 loop, thereby mimicking the conformation of ABA occupancy. Through structure‐guided mutagenesis and biochemical characterization, we investigated the mechanism of ABA‐independent activation of PYL3. Two types of PYL3 mutants were obtained: (a) PYL3 V108K V107L V192F can bind to ABA and effectively inhibit HAB1 without ABA; (b) PYL3 V108K V107F V192F, PYL3 V108K V107L V192F L111F and PYL3 V108K V107F V192F L111F cannot recognize ABA but can greatly inhibit HAB1 without ABA. Intriguingly, the ability of PYL3 mutants to bind to ABA was severely compromised if any two of three variable residues (V107, V192 and L111) were mutated into a bulky hydrophobic residue. The introduction of PYL3 mutants into transgenic plants will help elucidate the functionality of PYL3 in vivo and may facilitate the future production of transgenic crops with high yield and tolerance of abiotic stresses.

Despite the rapid advances in the liver-on-a-chip platforms, it remains a daunting challenge to construct a biomimetic liver-on-a-chip for in vitro research. This study aimed to reconstruct the tissue-tissue interfaces based on bilayer microspheres and form vascularized liver tissue. Firstly, we designed a tri-vascular liver-on-a-chip (TVLOC) comprising a hepatic artery, a portal vein and a central vein, and theoretically analyzed the distribution of velocity and concentration fields in the culture area. Secondly, we designed a bilayer microsphere generating microsystem based on the coaxial confocal principle, which is primarily used to produce bilayer microspheres containing different kinds of cells. Finally, the bilayer microspheres were co-cultured with endothelial cells in the cell culture area of the TVLOC to form vascularized liver tissue, and the cell viability and vascular network growth were analyzed. The results revealed that the TVLOC designed in this study can provide a substance concentration gradient similar to that of the liver microenvironment, and the bilayer microspheres can form a three-dimensional (3D) orderly liver structure with endothelial cells. Such a liver-on-a-chip is capable of maintaining the function of hepatocytes (HCs) pretty well. This work provides full insights into further simulation of the liver-on-a-chip.

Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011–2018), this article developed a retrieval model suitable for turbidity. The determination coefficient (R2) of the model was 0.946 and the root-mean-square error was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China (1984–2018). The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a (P < 0.05) with obvious seasonal differences (i.e., decreased from May to the lowest in July, and then increased from September onward). Finally, we quantitatively examined how several typical factors affect turbidity variation at different scales. We found that water turbidity was highly correlated with NDVI (R = 0.56, P < 0.001), followed by water temperature and wind speed (0.02 < R < 0.5, P < 0.05). Water turbidity varies because of the interaction of multiple factors (e.g., area, temperature, water depth, precipitation, and land use) instead of one factor. This article highlights the potential of remote sensing in large-scale and long-term monitoring of lake water quality, and provides important information and support for water quality management in China.

Sketch face recognition refers to the process of matching sketches to photos. Recently, there has been a growing interest in using deep learning to learn discriminative features for sketch face recognition. However, the success of deep learning relies on the large-scale paired images to counteract effects such as over-fitting, since the amount of the paired training data is relatively small, the discriminative power of the deeply learned features will inevitably be reduced. This paper proposes a novel deep metric learning method termed domain alignment embedding network for sketch face recognition. Specifically, a training episode strategy is designed to alleviate the small sample problem, and a domain alignment embedding loss is proposed to guide the feature embedding network to learn discriminative features. Extensive experimental results on the UoM-SGFSv2 and PRIP-VSGC datasets are verified to show the effectiveness of the proposed method.

Housing price in New Zealand has appreciated substantially after the Global Financial Crisis, resulting in an affordability problem for first home buyers. This paper studies whether changes in immigration activity and mortgage interest rate influence housing price. Empirical findings derived using VECM confirm the impact of immigration and mortgage interest rate on housing property price. Both variables explain 11.4 percent of the variation of Housing Index. An increase of 1 percent in mortgage interest rate would reduce the housing index movement by 1.44 percent whilst a 1 percent increase in immigrants would increase the housing index by 0.30 percent. In addition, about 2 percent of the short-run deviations of housing prices are adjusted towards the long-run equilibrium each month.

The study aims to examine the frequency of high-level visits between China and Southeast Asian countries from 2008 through 2019. It argues that the Belt and Road Initiative (BRI) has encouraged diplomatic exchanges between China and Southeast Asian countries. However, the BRI is not the only reason that contributes to the high frequency of visits. The institutionalisation of bilateral and multilateral cooperation mechanism between China and Southeast Asian countries has also increased the close bond between the two sides. Other reasons that affect the frequency of visits include cordial political relations between China and individual Southeast Asian countries. Although the BRI has encouraged mutual visits between China and Southeast Asian countries, the South China Sea dispute remains a challenge to the relations between China and other claimants in the region.

Study region: A typical grassland (Medicago sativa plot) and a typical forest land (Robinia pseudoacacia plot) located in the semi-arid Loess Plateau, China. Study focus: Field observations and scenario simulation approaches were used to investigate the potential impact of future warming and drying climate on the whole water budget and to assess whether, how, and to what degree the highly recommended man–driven tree–to–grass conversion could alleviate the adverse impact of such climate change on soil water. New hydrological insights for the region: Differences of interception and transpiration between two plots resulted in their different soil water storage. Under the warming and drying future climate, interception and transpiration in two plots would experience significant reductions, especially transpiration, and M. sativa plot could maintain more water than R. pseudoacacia plot. When R. pseudoacacia was replaced by M. sativa, although interception would dramatically increase, the transpiration would significantly decrease, which made the soil water storage experience an insignificant change. Climate change and tree–to–grass conversion mainly exerted their influences in months with larger precipitation, and vegetation conversion would reduce monthly T/ET. Our study improves the understanding of the hydrological processes under climate change and vegetation conversion, and also provides valuable guidelines for the management of water resources and the restoration of ecological environment in the semi–arid Loess Plateau.

BackgroundInulin-type fructans (ITF) have been used as prebiotics to alleviate glucose and lipid metabolism disorders. However, few studies evaluated the microbial mechanism of ITF in improving maternal metabolic status during pregnancy.MethodsC57BL/6J mice were fed a high-fat/sucrose diet (HFD) for 4 weeks before and throughout pregnancy to induce a model of gestational diabetes mellitus (GDM). Body weight, glycolipid metabolic parameters, and fecal short-chain fatty acids (SCFAs) were assessed in the experimental process. The effects of ITF on the fecal microbiota were analyzed by 16S rRNA gene amplicon sequencing.ResultsPregnant HFD-fed mice displayed significant insulin resistance and dyslipidemia. ITF (3.33 g/kg/day) treatment improved glucose and lipid metabolism disorder parameters in HFD-induced GDM mice and alleviated fat accumulation and glucose intolerance. The alpha diversity of the gut microbial community was increased in ITF mice, while the beta diversity returned to the level of normal chow diet (NCD) mice. Interestingly, Verrucomicrobia, Bifidobacterium, and Akkermansia were obviously enriched, while Dubosiella was obviously lessened after inulin treatment. Further analysis indicated that Dubosiella was positively correlated with markers of glycolipid metabolism disorders, whereas the ITF-supplemented diet partially reversed the changes.ConclusionOur results suggest that the ITF treatment may alleviate glucose and lipid metabolism disorders with the mediation of gut microbiota.

Recently, deep learning methods have widely been employed for surface defect segmentation in industrial production with remarkable success. Nevertheless, accurate segmentation of various types of defects is still challenging due to their irregular appearance and low contrast with the background. In light of this challenge, we propose an attention-based network with a U-shaped structure, referred to as AFFNet. In the encoder part, we present a newly designed module, Residual-RepGhost-Dblock (RRD), which focuses on the extraction of more representative features using CA attention and dilated convolution with varying expansion rates without a concomitant increase in the parameters. In the decoder part, we introduce a novel global feature attention (GFA) module to selectively fuse low-level and high-level features, suppressing distracting information such as background. Moreover, considering the imbalance of the dataset sampled from actual industrial production and the difficulty of training samples with small defects, we use the online hard sample mining (OHEM) cross-entropy loss function to improve the learning ability of hard samples. Experimental results on the NEU-seg dataset demonstrate the superiority of our method over other state-of-the-art methods.

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9–5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.

Proton transfer processes of organic molecules are key to charge transport and photoprotection in biological systems. Among them, excited-state intramolecular proton transfer (ESIPT) reactions are characterized by quick and efficient charge transfer within a molecule, resulting in ultrafast proton motions. The ESIPT-facilitated interconversion between two tautomers (PS and PA) comprising the tree fungal pigment Draconin Red in solution was investigated using a combination of targeted femtosecond transient absorption (fs-TA) and excited-state femtosecond stimulated Raman spectroscopy (ES-FSRS) measurements. Transient intensity (population and polarizability) and frequency (structural and cooling) dynamics of –COH rocking and –C=C, –C=O stretching modes following directed stimulation of each tautomer elucidate the excitation-dependent relaxation pathways, particularly the bidirectional ESIPT progression out of the Franck–Condon region to the lower-lying excited state, of the intrinsically heterogeneous chromophore in dichloromethane solvent. A characteristic overall excited-state PS-to-PA transition on the picosecond timescale leads to a unique “W”-shaped excited-state Raman intensity pattern due to dynamic resonance enhancement with the Raman pump–probe pulse pair. The ability to utilize quantum mechanics calculations in conjunction with steady-state electronic absorption and emission spectra to induce disparate excited-state populations in an inhomogeneous mixture of similar tautomers has broad implications for the modeling of potential energy surfaces and delineation of reaction mechanisms in naturally occurring chromophores. Such fundamental insights afforded by in-depth analysis of ultrafast spectroscopic datasets are also beneficial for future development of sustainable materials and optoelectronics.

In recent years, deep learning has achieved good results in the semantic segmentation of medical images. A typical architecture for segmentation networks is an encoder–decoder structure. However, the design of the segmentation networks is fragmented and lacks a mathematical explanation. Consequently, segmentation networks are inefficient and less generalizable across different organs. To solve these problems, we reconstructed the segmentation network based on mathematical methods. We introduced the dynamical systems view into semantic segmentation and proposed a novel segmentation network based on Runge–Kutta methods, referred to hereafter as the Runge–Kutta segmentation network (RKSeg). RKSegs were evaluated on ten organ image datasets from the Medical Segmentation Decathlon. The experimental results show that RKSegs far outperform other segmentation networks. RKSegs use few parameters and short inference time, yet they can achieve competitive or even better segmentation results compared to other models. RKSegs pioneer a new architectural design pattern for segmentation networks.

Nuclei segmentation and classification are two basic and essential tasks in computer-aided diagnosis of digital pathology images, and those deep-learning-based methods have achieved significant success. Unfortunately, most of the existing studies accomplish the two tasks by splicing two related neural networks directly, resulting in repetitive computation efforts and a redundant-and-large neural network. Thus, this paper proposes a lightweight deep learning framework (GSN-HVNET) with an encoder–decoder structure for simultaneous segmentation and classification of nuclei. The decoder consists of three branches outputting the semantic segmentation of nuclei, the horizontal and vertical (HV) distances of nuclei pixels to their mass centers, and the class of each nucleus, respectively. The instance segmentation results are obtained by combing the outputs of the first and second branches. To reduce the computational cost and improve the network stability under small batch sizes, we propose two newly designed blocks, Residual-Ghost-SN (RGS) and Dense-Ghost-SN (DGS). Furthermore, considering the practical usage in pathological diagnosis, we redefine the classification principle of the CoNSeP dataset. Experimental results demonstrate that the proposed model outperforms other state-of-the-art models in terms of segmentation and classification accuracy by a significant margin while maintaining high computational efficiency.

Vulvovaginal candidiasis (VVC) is considered the second most common cause of vaginitis after bacterial vaginosis and the most common lower genital tract infection during pregnancy. Candida albicans (C. albicans), an opportunistic pathogen, is the major species causing VVC. Recently, increasing researches have shown that lower reproductive tract infection during pregnancy can lead to various adverse pregnancy outcomes. However, the underlying mechanisms are not fully understood. Hence, we successfully established a mouse model of vaginal C. albicans infection and characterized the adverse pregnancy outcomes. C. albicans infection strikingly increased abortion rate and decreased litter size. Further analysis of placental development demonstrated that placental structure was abnormal, including that the area of spongiotrophoblast (Spo) and labyrinth (Lab) was reduced, and the formation of placental vessel was decreased in Lab zone. Accordingly, the expression of marker genes during placental development was downregulated. Collectively, the above findings revealed that vaginal C. albicans infection during pregnancy can inhibit placental development and ultimately lead to adverse pregnancy outcomes. This study enhances our comprehension of the effect of VVC on pregnancy, and placental dysplasia as a feasible orientation to explore VVC during pregnancy.

A three-dimensional finite element analysis was carried out for the influence of soil-structure interaction (SSI) on the seismic response of an isolated continuous bridge with a friction pendulum system (FPS). SSI was modelled using the soil resistance vs deflection nonlinear springs (following p-y curves). The influence of SSI on the dynamic behaviour and the seismic response of isolated bridges with different friction coefficients of FPS were investigated using six measured ground motions. Results show that the effect of SSI is limited on the isolation period of the bridge, but significant on the response amplitude of the bridge structures, and particularly on the response amplitude at the pier top and bottom. The displacement and acceleration at the pier top are underestimated, while the shear force and bending moment at the pier bottom are overestimated if the SSI effects are ignored, which suggests that SSI should be considered in the seismic analyses of continuous bridges.

Fluorescent proteins (FPs) are indispensable tools for noninvasive bioimaging and sensing. Measuring the free cellular calcium (Ca2+) concentrations in vivo with genetically encodable FPs can be a relatively direct measure of neuronal activity due to the complex signaling role of these ions. REX-GECO1 is a recently developed red-green emission and excitation ratiometric FP-based biosensor that achieves a high dynamic range due to differences in the chromophore response to light excitation with and without calcium ions. Using steady-state electronic measurements (UV/Visible absorption and emission), along with time-resolved spectroscopic techniques including femtosecond transient absorption (fs-TA) and femtosecond stimulated Raman spectroscopy (FSRS), the potential energy surfaces of these unique biosensors are unveiled with vivid details. The ground-state structural characterization of the Ca2+-free biosensor via FSRS reveals a more spacious protein pocket that allows the chromophore to efficiently twist and reach a dark state. In contrast, the more compressed cavity within the Ca2+-bound biosensor results in a more heterogeneous distribution of chromophore populations that results in multi-step excited state proton transfer (ESPT) pathways on the sub-140 fs, 600 fs, and 3 ps timescales. These results enable rational design strategies to enlarge the spectral separation between the protonated/deprotonated forms and the Stokes shift leading to a larger dynamic range and potentially higher fluorescence quantum yield, which should be broadly applicable to the calcium imaging and biosensor communities.

Amine groups are common constituents of organic dyes and play important roles in tuning fluorescence properties. In particular, intensive research works have demonstrated the tendency and capabilities of amines in influencing chromophore brightness. Such properties have been explained by multiple mechanisms spanning from twisted intramolecular charge transfer (TICT) to the energy gap law and beyond, which introduce additional nonradiative energy dissipation pathways. In this review, we aim to provide a focused overview of the mechanistic insights mainly for the TICT mechanism, accompanied by a few other less common or influential fluorescence quenching mechanisms in the amine-containing fluorescent molecules. Various aspects of current scientific findings including the rational design and synthesis of organic chromophores, theoretical calculations, steady-state and time-resolved electronic and vibrational spectroscopies are reviewed. These in-depth understandings of how the amine groups with diverse chemical structures at various atomic sites affect excited-state nonradiative decay pathways will facilitate the strategic and targeted development of fluorophores with desired emission properties as versatile chemosensors for broad applications.

Nitrophenols are a group of small organic molecules with significant environmental implications from the atmosphere to waterways. In this work, we investigate a series of nitrophenols and nitrophenolates, with the contrasting ortho-, meta-, and para-substituted nitro group to the phenolic hydroxy or phenolate oxygen site (2/3/4NP or NP−), implementing a suite of steady-state and time-resolved spectroscopic techniques that include UV/Visible spectroscopy, femtosecond transient absorption (fs-TA) spectroscopy with probe-dependent and global analysis, and femtosecond stimulated Raman spectroscopy (FSRS), aided by quantum calculations. The excitation-dependent (400 and 267 nm) electronic dynamics in water and methanol, for six protonated or deprotonated nitrophenol molecules (three regioisomers in each set), enable a systematic investigation of the excited-state dynamics of these functional “nanomachines” that can undergo nitro-group twisting (as a rotor), excited-state intramolecular or intermolecular proton transfer (donor–acceptor, ESIPT, or ESPT), solvation, and cooling (chromophore) events on molecular timescales. In particular, the meta-substituted compound 3NP or 3NP− exhibits the strongest charge-transfer character with FSRS signatures (e.g., C–N peak frequency), and thus, does not favor nitroaromatic twist in the excited state, while the ortho-substituted compound 2NP can undergo ESIPT in water and likely generate nitrous acid (HONO) after 267 nm excitation. The delineated mechanistic insights into the nitro-substituent-location-, protonation-, solvent-, and excitation-wavelength-dependent effects on nitrophenols, in conjunction with the ultraviolet-light-induced degradation of 2NP in water, substantiates an appealing discovery loop to characterize and engineer functional molecules for environmental applications.

Cervical cancer is one of the most common cancers that threaten women’s lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer. However, the frequent presence of adherent or overlapping cervical cells in Pap smear images makes separating them individually a difficult task. Currently, there are few studies on the segmentation of adherent cervical cells, and the existing methods commonly suffer from low segmentation accuracy and complex design processes. To address the above problems, we propose a novel star-convex polygon-based convolutional neural network with an encoder-decoder structure, called SPCNet. The model accomplishes the segmentation of adherent cells relying on three steps: automatic feature extraction, star-convex polygon detection, and non-maximal suppression (NMS). Concretely, a new residual-based attentional embedding (RAE) block is suggested for image feature extraction. It fuses the deep features from the attention-based convolutional layers with the shallow features from the original image through the residual connection, enhancing the network’s ability to extract the abundant image features. And then, a polygon-based adaptive NMS (PA-NMS) algorithm is adopted to screen the generated polygon proposals and further achieve the accurate detection of adherent cells, thus allowing the network to completely segment the cell instances in Pap smear images. Finally, the effectiveness of our method is evaluated on three independent datasets. Extensive experimental results demonstrate that the method obtains superior segmentation performance compared to other well-established algorithms.