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Aiming at the lack of research on the performance of deep learning models in predicting software refactoring in the current field, a deep learning-based software refactoring prediction evaluation method was proposed to assess the refactoring predictive performance of these models. Firstly, refactoring and non-refactoring labeled instances were collected from 303 Java projects using static analysis tools, and seven datasets comprising source code metrics were constructed for seven refactoring operations: extracting class, extracting subclass, extracting super class, extracting interface, moving class, renaming class, and moving and renaming class. Secondly, convolutional neural network (CNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) model, multilayer perceptron(MLP), and autoencoder(AE) were trained and tested on the datasets. Finally, each model was evaluated based on accuracy, precision, recall, and F1-measure.The results show that the average accuracy, precision, recall, and F1-measure of the five deep learning models for predicting refactoring are all above 93%, with the highest accuracy in predicting the extract subclass refactoring, and the CNN model has a higher average accuracy compared to other models. The CNN model is efficient for software refactoring prediction evaluation, which provides reference for future utilization of deep learning models in assisting with completing refactoring recommendation tasks.

Abstract Fetal growth restriction (FGR) is a common complication of pregnancy, which seriously endangers fetal health and still lacks effective therapeutic targets. Clostridium difficile (C. difficile) is associated with fetal birth weight, and its membrane vesicles (MVs) are pathogenic vectors. However, the role of C. difficile and its MVs in FGR remains unclear. Here we found that supplementation with C. difficile altered the characteristics of gut microbiota and reduced the birth weight in mice. Interestingly, C. difficile MVs entered placenta, inhibited trophoblast motility, and induced fetal weight loss in mice. Mechanistically, C. difficile MVs activated the PPAR pathway via enhancing the transcriptional activity of PPARγ promoter, consequently inhibiting trophoblast motility. Moreover, PPARγ expression was significantly elevated in FGR placenta, and negatively correlated with fetal birth weight. Together, our findings reveal the significance of C. difficile and its MVs in FGR, providing new insights into the mechanisms of FGR development.

Abstract Modern facial surgical planning and therapeutic strategies rely heavily on the precise segmentation of the nasal cavity and paranasal sinuses from computed tomography (CT) images for quantitative analysis. Nevertheless, manual segmentation is labor-intensive and prone to inconsistencies, highlighting the need for automatic segmentation methods. A significant challenge in this field is the lack of publicly available clinical datasets for research. To address this issue, we introduce NagalSeg, the first large-scale, publicly available dataset for nasal cavity and paranasal sinus segmentation. In comparison to existing nasal structure segmentation datasets, which are either private or small-scale, NagalSeg stands out as the first publicly accessible dataset. It provides an order of magnitude more labeled data, consisting of 130 3D CT scans with pixel-wise annotations of five anatomical structures: the left nasal cavity, right nasal cavity, nasopharynx, left maxillary sinus, and right maxillary sinus. The NagalSeg dataset serves as an open-access resource to facilitate the development and evaluation of segmentation algorithms and promote future in-depth research towards the clinical application of artificial intelligence methods.

Abstract Schizophrenia is a severe neuropsychiatric disease, but the initiation mechanisms are unclear. Although antipsychotics are effective against positive symptoms, therapeutic interventions for negative symptoms are limited due to the lack of pathophysiological mechanisms. Here we identify synaptotagmin-11 (Syt11) as a potential genetic risk factor and dopamine over-transmission as a mechanism in the development of schizophrenia. Syt11 expression is reduced in individuals with schizophrenia but restored following the treatment with antipsychotics. Syt11 deficiency in dopamine neurons in early adolescence, but not in adults, leads to persistent social deficits and other schizophrenia-like behaviors by mediating dopamine over-transmission in mice. Accordingly, dopamine neuron over-excitation before late adolescence induces persistent schizophrenia-associated behavioral deficits, along with the structural and functional alternations in the mPFC. Notably, local intervention of D2R with clinical drugs presynaptically or postsynaptically exhibits both acute and long-lasting therapeutic effects on social deficits in schizophrenia mice models. These findings not only define Syt11 as a risk factor and DA over-transmission as a potential risk factor initiating schizophrenia, but also propose two D2R-targeting strategies for the comprehensive and long-term recovery of schizophrenia-associated social withdrawal.

Background Interleukin-18 (IL-18), or interferon (IFN)-γ-inducing factor, potentiates T helper 1 and natural killer cell activation as well as CD8+ T-cell proliferation. Recombinant IL-18 has displayed limited clinical efficacy in part due to the expression of the decoy receptor, IL-18 binding protein (IL-18BP). A series of IL-18 variants that are devoid of IL-18BP binding, termed DR18 (decoy-resistant IL-18), was developed via directed evolution. We tested DR18 using oncolytic adenovirus (oAd) as a platform for delivery in syngeneic mouse tumor models.Methods oAd harboring wild-type IL-18 or DR18 (oAdDR18) was constructed by inserting IL-18 mutant into modified oAd backbone with Ad5/3 chimeric fiber. The delivery effect and IFN-γ induction were determined by ELISA. The antitumor efficiency of oAdDR18 was tested in CT26, B16BL6 and 4T1 tumor-bearing mice, or athymic nude mice and compared with recombinant DR18 protein (rDR18). 4T1 lung metastasis model was used to evaluate the antitumor efficiency of local and distant tumors. Antitumor memory and synergistic effect with an anti-programmed cell death protein-1 (PD-1) antibody was evaluated. The phenotypes of the immune cells in tumor microenvironment were analyzed by flow cytometry and immunohistochemistry.Results Mice received oAdDR18 maintained stable production of IL-18 and IFN-γ compared with those received rDR18. Intratumoral delivery of oAdDR18 significantly reduced tumor growth across several tumor models, but not in the athymic nude mouse model. Mice that had tumor remission showed antitumor memory. The antitumor effect was associated with intratumor infiltration of CD4+ and CD8+ T cells. DR18 delivered by oAd demonstrated long-lasting and enhanced antitumor activities against local and distant tumors compared with that received rDR18 or wild-type IL-18 delivered by oAd (oAdwtIL-18). oAdDR18 treatment also reduced 4T1 lung metastasis. In addition, combination of this virotherapy with immune checkpoint inhibitors (ICIs)like the anti-PD-1 antibody further enhanced the antitumor activity as compared with respective monotherapy.Conclusions oAdDR18 demonstrates enhanced antitumor activities through the induction of stronger local and system immunities and modulation of the tumor microenvironment compared with those of oAdwtIL-18 and rDR18. A combination of oncolytic virotherapy with cytokine engineering would lead to cytokine-based therapeutics for cancer and other diseases.

The adoption of Western planning ideas has significantly shaped China’s Central Business Districts (CBDs) and spurred economic growth. This article goes beyond existing research by exploring the “provincialization” of Western planning concepts, resulting in unique CBD landscapes in Chinese cities. Through a study of the Guangzhou CBD, it reveals how introduced ideas and local agents mutually adjust and respond to each other’s needs during the provincialization process. Under the “Reforms and Opening-up” policy, Chinese authorities drew from Hong Kong’s experiences to introduce the CBD concept, adapting it to underdeveloped land in Guangzhou and blending it with American downtown design. To meet urban space demands, industrial growth, and land economy needs, urban authorities reorganized local resources to create a discourse of the “central axis” to integrate the CBD concept into the urban layout. Consequently, both introduced ideas and local agents are intertwined to address challenges posed by each other, resulting in Guangzhou’s CBD framing a hybrid urban landscape.

Abstract Background The administration of performance assessments during the coronavirus disease of 2019 (COVID-19) pandemic posed many challenges, especially for examinations employed as part of certification and licensure. The National Assessment Collaboration (NAC) Examination, an Objective Structured Clinical Examination (OSCE), was modified during the pandemic. The purpose of this study was to gather evidence to support the reliability and validity of the modified NAC Examination. Methods The modified NAC Examination was delivered to 2,433 candidates in 2020 and 2021. Cronbach’s alpha, decision consistency, and accuracy values were calculated. Validity evidence includes comparisons of scores and sub-scores for demographic groups: gender (male vs. female), type of International Medical Graduate (IMG) (Canadians Studying Abroad (CSA) vs. non-CSA), postgraduate training (PGT) (no PGT vs. PGT), and language of examination (English vs. French). Criterion relationships were summarized using correlations within and between the NAC Examination and the Medical Council of Canada Qualifying Examination (MCCQE) Part I scores. Results Reliability estimates were consistent with other OSCEs similar in length and previous NAC Examination administrations. Both total score and sub-score differences for gender were statistically significant. Total score differences by type of IMG and PGT were not statistically significant, but sub-score differences were statistically significant. Administration language was not statistically significant for either the total scores or sub-scores. Correlations were all statistically significant with some relationships being small or moderate (0.20 to 0.40) or large (> 0.40). Conclusions The NAC Examination yields reliable total scores and pass/fail decisions. Expected differences in total scores and sub-scores for defined groups were consistent with previous literature, and internal relationships amongst NAC Examination sub-scores and their external relationships with the MCCQE Part I supported both discriminant and criterion-related validity arguments. Modifications to OSCEs to address health restrictions can be implemented without compromising the overall quality of the assessment. This study outlines some of the validity and reliability analyses for OSCEs that required modifications due to COVID.

With Polygonatum cyrtonema tender buds as subjects, Polygonatum cyrtonema tender bud tea was prepared using the technology for processing flat green tea after fixation by microwave, pan frying and steaming, respectively. The effects of different fixation methods on the quality of Polygonatum cyrtonema tender bud tea was then explored through physicochemical composition, color and gloss of tea broth, volatile aroma component analysis and organoleptic evaluation. The results showed that the Polygonatum cyrtonema tender bud tea prepared by microwave fixation not only presented good organoleptic properties, but also had the highest contents of water extract, soluble sugar, tea polyphenols and total flavonoids, 48.04%, 14.49%, 1.32% and 1.66%, respectively. A total of 84 volatile olfactory compounds were detected in Polygonatum cyrtonema tender bud tea prepared by the three different fixation methods, mainly consisting of aldehydes (12 types), ketones (11 types), olefins (10 types), alcohols (8 types) and esters (8 types). There were 10, 12 and 7 key aroma components in Polygonatum cyrtonema tender bud tea with microwave, pan frying and steaming fixation, respectively. Among them, ethyl butyrate was a unique key aroma component to microwave fixation, while heptanal, pentanal, hexanal and 1-pentanol were the key aroma components unique to pan frying fixation. Overall, there are differences in the quality of Polygonatum cyrtonema tender bud tea prepared by different fixation methods, among which microwave fixation is more suitable for the preparation of Polygonatum cyrtonema tender bud tea.

Context information is significant for semantic extraction and recovery of messages in semantic communication. However, context information is not fully utilized in the existing semantic communication systems since relationships between sentences are often ignored. In this paper, we propose an Extended Context-based Semantic Communication (ECSC) system for text transmission, in which context information within and between sentences is explored for semantic representation and recovery. At the encoder, self-attention and segment-level relative attention are used to extract context information within and between sentences, respectively. In addition, a gate mechanism is adopted at the encoder to incorporate the context information from different ranges. At the decoder, Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery. Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.

Increasing research has focused on semantic communication, the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the receiver. In this paper, we design a novel encoding and decoding semantic communication framework, which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various channels. On the sender side, the average semantic loss caused by the wrong detection is defined, and a semantic source encoding strategy is developed to minimize the average semantic loss. To further improve communication reliability, a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the receiver. Extensive simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.

Abstract The discovery of polar metal opens the door to incorporating electric polarization into electronics with the potential to invigorate next‐generation multifunctional electronic devices. Especially, electric polarization can be induced by geometric design in non‐polar perovskite oxides. Here, the epitaxial strain exerted on the deposited single‐crystalline NdNiO3 thin films is systematically varied in both sign and amplitude by choosing substrates with different lattice mismatch. The pseudocubic NdNiO3(111) film, which is non‐polar in its bulk state, is induced to be polar under both compressive and tensile strain. The fine‐tuning of epitaxial strain is realized by continuously varying the film thickness using the “thickness‐wedge” growth technique, and from the elucidated thickness dependence, the electric polarization and metallicity can be further optimized. Moreover, transitioning from isotropic to anisotropic epitaxial strain gives rise to an ideal polar metal state in the pseudocubic NdNiO3(102) film on an orthorhombic substrate, achieving a remarkably low resistivity of 173 µΩ cm at room temperature. The metal–insulator transition in NdNiO3 is completely suppressed and the polar metal state becomes the ground state at all temperatures. These results demonstrate alluring possibilities of induction and manipulation of both electric polarization and electric transport properties in functional perovskite oxides by epitaxial strain engineering.

Abstract The burgeoning need for extensive data processing has sparked enthusiasm for the development of a novel optical logic gate platform. In this study, junction field‐effect phototransistors based on molybdenum disulfide/Germanium (MoS2/Ge) heterojunctions are constructed as optical logic units. This device demonstrates a positive photoresponse that is attributed to the photoconductivity effect occurring upon irradiation with visible (Vis) light. Under the illumination of near‐infrared (NIR) optics with wavelengths within the communication band, the device shows a negative photoresponse, which is associated with the interlayer Coulomb interactions. The current state of the device can be effectively modulated as different logical states by precisely tuning the wavelength and power density of the optical. Within a 3 × 3 MoS2/Ge phototransistor array, five essentially all‐optical logic gates (“AND,” “OR,” “NAND,” “NOT,” and “NOR”) can be achieved in every signal unit. Furthermore, three complex all‐optical logical operations are demonstrated by integrating two MoS2/Ge phototransistors in series. Compared to electronic designs, these all‐optical logic devices offer a significant reduction in transistor number, with savings of 50–94% when implementing the above‐mentioned functions. These results present opportunities for the development of photonic chips with low power consumption, high fidelity, and large volumes.

IntroductionThe Ecological Systems Model of Creativity Development (ESMCD) proposes that parental involvement positively impacts student creativity. However, prior empirical studies present mixed results, including positive, negative, and no correlations between these variables.MethodsTo synthesize these inconsistent primary studies, the current study conducted a systematic meta-analysis synthesizing 30 primary studies involving 37 independent samples with 70 effect sizes and a total N = 20,906 participants.ResultsThe results demonstrated: (1) an overall significant small, positive correlation (r = 0.101) between parental involvement and student creativity; (2) significant small, positive correlations between specific involvement types (autonomy support r = 0.144; behavioral control r = 0.133; content support r = 0.131) and creativity, alongside a significant small, negative correlation between psychological control and creativity (r = −0.117); (3) no statistically significant moderating effects of student grade level, parental gender, region, or publication type.DiscussionThis systematic meta-analytic review consolidates empirical evidence indicating that parental involvement positively predicts students’ creativity, while highlighting the detrimental impact of psychological control on creative outcomes. Further research elucidating the mechanisms underlying these relations is critical for informing parenting approaches and education policies seeking to foster creativity development among students.

Abstract Background Light is a critical factor in plant growth and development, particularly in controlled environments. Light-emitting diodes (LEDs) have become a reliable alternative to conventional high pressure sodium (HSP) lamps because they are more efficient and versatile in light sources. In contrast to well-known specialized LED light spectra for vegetables, the appropriate LED lights for crops such as cotton remain unknown. Results In this growth chamber study, we selected and compared four LED lights with varying percentages (26.44%–68.68%) of red light (R, 600–700 nm), combined with other lights, for their effects on growth, leaf anatomy, and photosynthesis of cotton seedlings, using HSP lamp as a control. The total photosynthetic photon flux density (PPFD) was (215 ± 2) μmol·m−2·s−1 for all LEDs and HSP lamp. The results showed significant differences in all tested parameters among lights, and the percentage of far red (FR, 701–780 nm) within the range of 3.03%–11.86% was positively correlated with plant growth (characterized by leaf number and area, plant height, stem diameter, and total biomass), palisade layer thickness, photosynthesis rate (P n), and stomatal conductance (G s). The ratio of R/FR (4.445–11.497) negatively influenced the growth of cotton seedlings, and blue light (B) suppressed stem elongation but increased palisade cell length, chlorophyll content, and P n. Conclusion The LED 2 was superior to other LED lights and HSP lamp. It had the highest ratio of FR within the total PPFD (11.86%) and the lowest ratio of R/FR (4.445). LED 2 may therefore be used to replace HPS lamp under controlled environments for the study of cotton at the seedling stage.

Quantum key distribution offers a promising avenue for establishing secure communication networks. However, its performance is significantly hampered by the conventional two-level information carriers (i.e., qubits) due to their limited information capacity and noise resilience. A fundamental approach to overcoming these limitations involves the adoption of high-dimensional qudits. Practical qudit platforms require robust propagation, outstanding controllability, and extreme compactness, to which integrated photonics provides a promising solution. Here, we achieved, for the first time, microlaser-enabled high-dimensional quantum communication through leveraging spin-orbit photon qudits, where the dynamical generation and manipulation of these multi-degrees-of-freedom complex quantum state are realized by a non-Hermitian-physics-driven integrated microlaser quantum transmitter. Such a microlaser photon manipulation, as a novel route towards high-dimensional quantum state generation, promises high energy efficiency, along with fast, compact, and precise qudit state reconfigurability. The four spin-orbit eigenstates emitted by the microlaser possess the same spatial-temporal structures, ensuring homogeneity between all qudit states used for key distribution, which effectively eliminates propagation dephasing and walk-off problems, thereby delivering the high-dimensional spin-orbit secret key generation to construct a robust quantum link. The demonstrated long-term system stability showcases the practical potential of the microlaser quantum transmitter, providing a critical step towards compact, high-information-capacity quantum communication networks.

The application of machine learning and artificial intelligence to solve scientific challenges has significantly increased in recent years. A remarkable development is the use of Physics-Informed Neural Networks (PINNs) to solve Partial Differential Equations (PDEs) numerically. However, current PINN techniques often face problems with accuracy and slow convergence. To address these problems, we propose an importance sampling method to generate optimal interpolation points during training. Experimental results demonstrate that our method achieves a 43% reduction in root mean square error compared to state-of-the-art methods when applied to the one-dimensional Korteweg–De Vries equation.

Summary: Cancer cachexia is a prevalent and often fatal wasting condition that cannot be fully reversed with nutritional interventions. Muscle atrophy is a central component of the syndrome, but the mechanisms whereby cancer leads to skeletal muscle atrophy are not well understood. We performed single-nucleus multi-omics on skeletal muscles from a mouse model of cancer cachexia and profiled the molecular changes in cachexic muscle. Our results revealed the activation of a denervation-dependent gene program that upregulates the transcription factor myogenin. Further studies showed that a myogenin-myostatin pathway promotes muscle atrophy in response to cancer cachexia. Short hairpin RNA inhibition of myogenin or inhibition of myostatin through overexpression of its endogenous inhibitor follistatin prevented cancer cachexia-induced muscle atrophy in mice. Our findings uncover a molecular basis of muscle atrophy associated with cancer cachexia and highlight potential therapeutic targets for this disorder.

Objective: Dementia is an important disease burden among the elderly, and its occurrence may be profoundly affected by environmental factors. Evidence of the relationship between air pollution and dementia is emerging, but the extent to which this can be offset by lifestyle factors remains ambiguous. Methods: This study comprised 155,828 elder adults aged 60 years and above in the UK Biobank who were dementia-free at baseline. Cox proportional hazard models were conducted to examine the associations of annual average levels of air pollutants in 2010, including nitrogen dioxide (NO2), nitrogen oxides (NOX), particulate matter (PM2.5, PM10, and PMcoarse) and lifestyle factors recorded at baseline [physical activity (PA), sleep patterns, or smoking status] with incident risk of dementia, and their interactions on both multiplicative and additive scales. Results: During a 12-year period of follow-up, 4,389 incidents of all-cause dementia were identified. For each standard deviation increase in ambient NO2, NOX or PM2.5, all-cause dementia risk increases by 1.07-fold [hazard ratio (HR) and 95 % confidence interval (CI) = 1.07 (1.04, 1.10)], 1.05-fold (95 % CI: 1.02, 1.08) and 1.07-fold (95 % CI: 1.04, 1.10), whereas low levels of PA, poor sleep patterns, and smoking are associated with an elevated risk of dementia [HR (95 % CI) = 1.17 (1.09, 1.26), 1.13 (1.00, 1.27), and 1.14 (1.07, 1.21), respectively]. Furthermore, these air pollutants show joint effects with low PA, poor sleep patterns, and smoking on the onset of dementia. The moderate to high levels of PA could significantly or marginally significantly modify the associations between NO2, NOX or PM2.5 (P-int = 0.067, 0.036, and 0.067, respectively) and Alzheimer’s disease (AD), but no significant modification effects are found for sleep patterns or smoking status. Conclusion: The increased exposures of NO2, NOX, or PM2.5 are associated with elevated risk of dementia among elderly UK Biobank population. These air pollutants take joint effects with low PA, poor sleep patterns, and smoking on the development of dementia. In addition, moderate to high levels of PA could attenuate the incident risk of AD caused by air pollution. Further prospective researches among other cohort populations are warranted to validate these findings.

BackgroundMandibular tumor surgery necessitates precise osteotomies based on tumor boundaries; however, conventional osteotomies often lack accuracy in predicting osteotomy positions and planes, potentially leading to excessive resection of normal bone tissues or residual tumors, thus compromising postoperative quality of life and clinical outcomes. Robotic-assisted surgery (RAS) augmented with artificial intelligence (AI) offers precise localization capabilities, aiding surgeons in achieving accurate osteotomy positioning. This study aimed to evaluate the feasibility and accuracy of a robotic magnetic navigation system for positioning and osteotomy in an intraoral surgical trial of a mandibular tumor model.MethodsPatient computed tomography (CT) imaging data of mandibular chin and body tumors were utilized to create 3D printed models, serving as study subjects for mandibular tumor resection. Ten pairs of models were printed for the experimental and control groups. The experimental group (EG) underwent osteotomy using a robot-assisted surgical navigation system, performing osteotomy under robotic navigation following alignment based on preoperative design. The control group (CG) underwent traditional surgery, estimating osteotomy position empirically according to preoperative design. Postoperative CT scans were conducted on both models, and actual postoperative results were compared to preoperative design. Osteotomy accuracy was evaluated by positional and angular errors between preoperatively designed and actual osteotomy planes.ResultsFor ten randomly selected spots on the left and right sides, respectively, the EG group had mean distance errors of 0.338 mm and 0.941 mm. These values were obtained from the EG group. In the EG group, on the left side, the mean angular errors were 14.741 degrees, while on the right side, they were 13.021 degrees. For the 10 randomly selected spots on the left and right sides, respectively, the CG had mean distance errors of 1.776 mm and 2.320 mm. This is in contrast to the results obtained by the EG. It was determined that the left side had a mean angle error of 16.841 degrees, while the right side had an error of 18.416 degrees in the CG group. The above results indicated significantly lower point errors of bilateral osteotomy planes in the experimental group compared to the control group.ConclusionThis study demonstrates the feasibility of electromagnetic navigation robot-assisted intraoral osteotomy for mandibular tumors and suggests that this approach can enhance the precision of clinical surgery.

Abstract The aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs’ behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs’ V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA.

In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects’ and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to $95.41~\pm ~2.70$ %, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached $93.21~\pm ~10.18$ % for the online experiment. All the experimental results demonstrated the effectiveness of the brain-computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.

Abstract Prodrug photolysis enables spatiotemporal control of drug release at the desired lesions. For photoactivated therapy, near-infrared (NIR) light is preferable due to its deep tissue penetration and low phototoxicity. However, most of the photocleavable groups cannot be directly activated by NIR light. Here, we report a upconversion-like process via only one step of energy transfer for NIR light-triggered prodrug photolysis. We utilize a photosensitizer (PS) that can be activated via singlet-triplet (S-T) absorption and achieve photolysis of boron-dipyrromethene (BODIPY)-based prodrugs via triplet-triplet energy transfer. Using the strategy, NIR light can achieve green light-responsive photolysis with a single-photon process. A wide range of drugs and bioactive molecules are designed and demonstrated to be released under low-irradiance NIR light (100 mW/cm2, 5 min) with high yields (up to 87%). Moreover, a micellar nanosystem encapsulating both PS and prodrug is developed to demonstrate the practicality of our strategy in normoxia aqueous environment for cancer therapy. This study may advance the development of photocleavable prodrugs and photoresponsive drug delivery systems for photo-activated therapy.

As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in blood cell detection. To address blood cell detection, we developed the TW-YOLO approach, leveraging multi-scale feature fusion techniques. Firstly, traditional CNN (Convolutional Neural Network) convolution has poor recognition capabilities for certain blood cell features, so the RFAConv (Receptive Field Attention Convolution) module was incorporated into the backbone of the model to enhance its capacity to extract geometric characteristics from blood cells. At the same time, utilizing the feature pyramid architecture of YOLO (You Only Look Once), we enhanced the fusion of features at different scales by incorporating the CBAM (Convolutional Block Attention Module) in the detection head and the EMA (Efficient Multi-Scale Attention) module in the neck, thereby improving the recognition ability of blood cells. Additionally, to meet the specific needs of blood cell detection, we designed the PGI-Ghost (Programmable Gradient Information-Ghost) strategy to finely describe the gradient flow throughout the process of extracting features, further improving the model’s effectiveness. Experiments on blood cell detection datasets such as BloodCell-Detection-Dataset (BCD) reveal that TW-YOLO outperforms other models by 2%, demonstrating excellent performance in the task of blood cell detection. In addition to advancing blood cell image analysis research, this work offers strong technical support for future automated medical diagnostics.

Abstract Background Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC. Methods We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth. Results NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%). Conclusions By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace

Abstract Background Domains of unknown function (DUF) proteins are a number of uncharacterized and highly conserved protein families in eukaryotes. In plants, some DUFs have been predicted to play important roles in development and response to abiotic stress. Among them, DUF568-containing protein family is plant-specific and has not been described previously. A basic analysis and expression profiling was performed, and the co-expression and interaction networks were constructed to explore the functions of DUF568 family in rice. Results The phylogenetic tree showed that the 8, 9 and 11 DUF568 family members from rice, Arabidopsis and maize were divided into three groups. The evolutionary relationship between DUF568 members in rice and maize was close, while the genes in Arabidopsis were more distantly related. The cis-elements prediction showed that over 82% of the elements upstream of OsDUF568 genes were responsive to light and phytohormones. Gene expression profile prediction and RT-qPCR experiments revealed that OsDUF568 genes were highly expressed in leaves, stems and roots of rice seedling. The expression of some OsDUF568 genes varied in response to plant hormones (abscisic acid, 6-benzylaminopurine) and abiotic stress (drought and chilling). Further analysis of the co-expression and protein–protein interaction networks using gene ontology showed that OsDUF568 − related genes were enriched in cellular transports, metabolism and processes. Conclusions In summary, our findings suggest that the OsDUF568 family may be a vital gene family for the development of rice roots, leaves and stems. In addition, the OsDUF568 family may participate in abscisic acid and cytokinin signaling pathways, and may be related to abiotic stress resistance in these vegetative tissues of rice.

Abstract Zinc (Zn)–dysprosium (Dy) binary alloys are promising biodegradable bone fracture fixation implants owing to their attractive biodegradability and mechanical properties. However, their clinical application is a challenge for bone fracture healing, due to the lack of Zn–Dy alloys with tailored proper bio‐mechanical and osteointegration properties for bone regeneration. A Zn‐5Dy alloy with high strength and ductility and a degradation rate aligned with the bone remodeling cycle is developed. Here, mechanical stability is further confirmed, proving that Zn‐5Dy alloy can resist aging in the degradation process, thus meeting the mechanical requirements of fracture fixation. In vitro cellular experiments reveal that the Zn‐5Dy alloy enhances osteogenesis and angiogenesis by elevating SIRT4‐mediated mitochondrial function. In vivo Micro‐CT, SEM‐EDS, and immunohistochemistry analyses further indicate good biosafety, suitable biodegradation rate, and great osteointegration of Zn‐5Dy alloy during bone healing, which also depends on the upregulation of SIRT4‐mediated mitochondrial events. Overall, the study is the first to report a Zn‐5Dy alloy that exerts remarkable osteointegration properties and has a strong potential to promote bone healing. Furthermore, the results highlight the importance of mitochondrial modulation and shall guide the future development of mitochondria‐targeting materials in enhancing bone fracture healing.

Intertidal macroalgae are important research subjects in stress biology. Basic region-leucine zipper transcription factors (bZIPs) play an important regulatory role in the expression of target genes under abiotic stress. We herein identified a bZIP2 gene PhbZIP2 to regulate abiotic stress tolerance in Pyropia haitanensis, a representative intertidal macroalgal species. Cloning and sequencing of the cDNA characterized a BRLZ structure and an α coiled-coil structure between amino acids and Expression of PhbZIP2 was detected to upregulate under both high temperature and salt stresses. A DAP-seq analysis revealed the PhbZIP2-binding motifs of (T/C)TCCA(C/G) and A (A/G)AAA (G/A), which differed from the conserved motifs in plants. Overexpression of PhbZIP2 was indicative of a high temperature and salt stress tolerances in transgenic Chlamydomonas reinhardtii. It was suggested that PhbZIP2 was probably involved in regulating expression of the photosynthetic-related genes and the response to the abiotic stresses in P. haitanensis, which provide new insights for elucidating efficient adaptation strategies of intertidal macroalgae.

Summary: Adolescent idiopathic scoliosis (AIS), a sideways curvature of the spine, is sexually dimorphic, with increased incidence in females. A genome-wide association study identified a female-specific AIS susceptibility locus near the PAX1 gene. Here, we use mouse enhancer assays, three mouse enhancer knockouts, and subsequent phenotypic analyses to characterize this region. Using mouse enhancer assays, we characterize a sequence, PEC7, which overlaps the AIS-associated variant, and find it to be active in the tail tip and intervertebral disc. Removal of PEC7 or Xe1, a known sclerotome enhancer nearby, or deletion of both sequences lead to a kinky tail phenotype only in the Xe1 and combined (Xe1+PEC7) knockouts, with only the latter showing a female sex dimorphic phenotype. Extensive phenotypic characterization of these mouse lines implicates several differentially expressed genes and estrogen signaling in the sex dimorphic bias. In summary, our work functionally characterizes an AIS-associated locus and dissects the mechanism for its sexual dimorphism.

Abstract The Qinghai‐Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub‐daily precipitation observations in this region has hindered our understanding of rainstorm‐related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub‐daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub‐daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub‐daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1‐km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm‐related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub‐daily precipitation and assessing its uncertainty in ungauged regions.

Aiming at the problem that most current researches on text semantic communication mainly rely on simulation system for theoretical verification, an extensible hierarchical semantic communication system was proposed by taking advantage of the separation of hierarchical encoding and decoding architecture in semantic level and grammatical level.The system was compatible with the reliable communication technology under the framework of Shannon information through the mode of semantic and syntactic separation, and realized the nested combination of semantic communication and traditional communication.Furthermore, a universal and extensible verification system was built based on the software radio platform to verify the proposed semantic communication system architecture.The verification system took semantic communication software platform as the driving core of hardware drive and algorithm call, integrated the whole process of signal generation, information transmission, data acquisition, decoding and evaluation at the receiving end, and could be further extended for semantic and syntactic level.Finally, the text semantic communication was tested based on this verification system, which verified that it had higher validity and reliability than the traditional communication mode.

This article examines the nature of urbanism in British concessions in China from the mid-nineteenth century to the present by investigating the evolution of Tianjin’s Victoria Park, the centre of the largest British concession in China. While existing works often subsume the urban development of concessions under the hegemony of British arrivals, this research explores how residents of the British concession and locals in Tianjin interacted to frame their urbanism, contributing to our understanding of the urban formation of the British concession in China and beyond. It reveals that initially Victoria Park was primarily a place of entertainment, and then, through interaction between the local Chinese and British residents, evolved firstly into the very symbol of British pride in the concession; then into a neglected pocket of parkland – representing the dark side of British settlers; and finally, into an important part of the precious heritage of the city.

Abstract Skeletal muscle fibers express distinct gene programs during development and maturation, but the underlying gene regulatory networks that confer stage-specific myofiber properties remain unknown. To decipher these distinctive gene programs and how they respond to neural activity, we generated a combined multi-omic single-nucleus RNA-seq and ATAC-seq atlas of mouse skeletal muscle development at multiple stages of embryonic, fetal, and postnatal life. We found that Myogenin, Klf5, and Tead4 form a transcriptional complex that synergistically activates the expression of muscle genes in developing myofibers. During myofiber maturation, the transcription factor Maf acts as a transcriptional switch to activate the mature fast muscle gene program. In skeletal muscles of mutant mice lacking voltage-gated L-type Ca2+ channels (Cav1.1), Maf expression and myofiber maturation are impaired. These findings provide a transcriptional atlas of muscle development and reveal genetic links between myofiber formation, maturation, and contraction.

The World Health Organization (WHO) articulated a vision in 1986, hoping that countries around the world would actively promote the construction and development of healthy cities. In the context of China, a paradigm shift in urban development has been triggered by the deep implementation of the Healthy China Strategy and the gradual completion of the comprehensive national spatial planning. This shift emphasizes a micro-level focus, advocating a human-centered approach to urban space exploration. In this paper, the Dingwangtai Area of Changsha City is selected as a case study. A street space health evaluation index system called “5D+” is constructed from six dimensions, namely, human perception, degree of mixing, density, distance to transit, destination accessibility, and devise. This research adopts the community as the fundamental unit of analysis, employing the Entropy-weighting TOPSIS method for the computation of indicators. The results of this study show that the Dingwangtai Area exhibits relative deficiencies in the dimensions of density, devise, and destination accessibility. Specifically, at the community level, the Ma Wang Street Community and the Fanhou Street Community have scored particularly low. In response, this paper proposes targeted measures and detailed recommendations aimed at optimizing the design of the street, enhancing the human experience, enriching functional attributes, and refining the street network. The ultimate goal is to propose a Healthy Streets Evaluation Index System based on the concept of a healthy city and to explore its relationship with healthy streets so as to provide valuable insights for the development of healthy city streets.

Aging contributes significantly to cognitive decline. Aerobic exercise (AE) has been shown to induce substantial neuroplasticity changes, enhancing cognitive and brain health. Likewise, recent research underscores the cognitive benefits of foreign language learning (FLL), indicating improvements in brain structure and function across age groups. However, the lack of a comprehensive paradigm integrating language learning with exercise limits research on combined effects in older adults. In order to address this gap, we devised a novel approach using a virtual world tourism scenario for auditory-based language learning combined with aerobic cycling. Our study examines the impact of simultaneous AE and FLL integration on cognitive and language learning outcomes compared to FLL alone. A total of 20 older adults were randomly assigned to AE + FLL and FLL-only groups. The results revealed significantly improved Spanish language learning outcomes in both combined and language learning-only groups. Additionally, significant cognitive function improvement was observed in the FLL group following short-term language learning.

BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.

Algal toxins produced by microalgae, such as domoic acid (DA)11 (DA) domoic acid, have toxic effects on humans. However, toxicity tests using mice only yield lethal doses of algal toxins without providing insights into the mechanism of action on cells. In this study, a fast segmentation of microfluidic flow cytometry cell images based on the bidirectional background subtraction (BBS)22 (BBS) bidirectional background subtraction method was developed to get the visual evidence of apoptosis in both bright-field and fluorescence images. This approach enables mapping of changes in cell morphology and activity under algal toxins, allowing for fast (within 60 s) and automated biological detection. By combining microfluidics with flow cytometry, the intricate cellular-level reaction process can be observed in micro samples of 293 T cells and mouse spleen cells, offering potential for future in vitro experiments.

Adolescent idiopathic scoliosis (AIS) is a common and progressive spinal deformity in children that exhibits striking sexual dimorphism, with girls at more than fivefold greater risk of severe disease compared to boys. Despite its medical impact, the molecular mechanisms that drive AIS are largely unknown. We previously defined a female-specific AIS genetic risk locus in an enhancer near the PAX1 gene. Here, we sought to define the roles of PAX1 and newly identified AIS-associated genes in the developmental mechanism of AIS. In a genetic study of 10,519 individuals with AIS and 93,238 unaffected controls, significant association was identified with a variant in COL11A1 encoding collagen (α1) XI (rs3753841; NM_080629.2_c.4004C>T; p.(Pro1335Leu); p=7.07E–11, OR = 1.118). Using CRISPR mutagenesis we generated Pax1 knockout mice (Pax1-/-). In postnatal spines we found that PAX1 and collagen (α1) XI protein both localize within the intervertebral disc-vertebral junction region encompassing the growth plate, with less collagen (α1) XI detected in Pax1-/- spines compared to wild-type. By genetic targeting we found that wild-type Col11a1 expression in costal chondrocytes suppresses expression of Pax1 and of Mmp3, encoding the matrix metalloproteinase 3 enzyme implicated in matrix remodeling. However, the latter suppression was abrogated in the presence of the AIS-associated COL11A1P1335L mutant. Further, we found that either knockdown of the estrogen receptor gene Esr2 or tamoxifen treatment significantly altered Col11a1 and Mmp3 expression in chondrocytes. We propose a new molecular model of AIS pathogenesis wherein genetic variation and estrogen signaling increase disease susceptibility by altering a PAX1-COL11a1-MMP3 signaling axis in spinal chondrocytes.

Neoporphyra haitanensis is a traditional coastal aquaculture species in China; it is rich in essential amino acids, minerals, and vitamins. It is not only beneficial economically, but also adjusts the ecosystem. However, fine varieties of N. haitanensis are lacking even with the enlargement and extension of the N. haitanensis growing area. Since humans began breeding N. haitanensis, most produced strains have been domesticated from wild N. haitanensis collected from rocks. The germplasm collection and usage is considered the bottleneck issue that often causes N. haitanensis quality degradation. Therefore, it is important to breed new varieties. Selective breeding, cross-breeding, and mutation breeding are among the most common methods used to breed new N. haitanensis varieties. Mutation breeding is a quick, simple, and convenient genetic tools. For several decades, natural means to induce genetic diversity have been exploited to breed new varieties, as the naturally occurring mutations are insufficient. One of the most important genetic breakthroughs was the invention of artificial methods to induce mutations. Physical mutagenesis is a safer and more efficient procedure than chemical mutagenesis. To obtain artificial mutants, N. haitanensis (NSD35) gametophytes were treated with different γ-ray irradiation intensities (700 Gy, 900 Gy, 1100 Gy, 1300 Gy, and 1500 Gy). After culture recovery, the results showed that gametophytic cell death increased with the irradiation escalation, with more than 90% cell viability after low radiation dose treatment (700 Gy and 900 Gy). With doses more than 1100 Gy, the cell viability decreased, in which the NSD35 cellular space became larger and a few middle cells died, although no obvious change was observed in tip cells. After induction, the cell morphology changed with higher radiation doses (1300 Gy and 1500 Gy). Some cells exhibited microscopic features, such as enlarged size and alternation from irregular cell shape to circular for some cells. Notably, the cell death rate decreased systematically from the blade base to its tip and from that to its center. Microscopic examination revealed that the number of mutant cells increased and then decreased with the radiation dose rise. The pigmented mutant cells frequency was higher in low radiation doses, further increasing with the rise in irradiation from 700 Gy to 1300 Gy. Nonetheless, the pigmented mutant cells frequency decreased with the highest dose of 1500 Gy. The best mutagenic effect was observed in gametophytes treated with 1300 Gy as they had the largest number of pigmented mutant cells. Cells cultured in vitro were obtained by enzymatic hydrolysis and individual regeneration. Preliminary screening of 67 mutants was performed to construct the mutant library, and 45 pigmented mutant strains, 51 morphological mutants, and 11 other mutants were obtained. Most of the mutants showed two or more mutations of phenotypic character. Twenty-one F1 generation cell lines were screened, and 14 traits (length, width, fresh weight, daily length growth rate, daily width growth rate, daily weight growth rate, length-breadth ratio, base thickness, center thickness, tip thickness, color, base section form, leaf form, saw tooth size, twist, and maturity) were analyzed by correlation analysis and system analysis. The coefficients of variation results showed significant phenotypic differences between F1 and control cells. The F1 coefficients of variation ranged from 8.74% to 59.49%, revealing a moderate variability. Correlation analysis also showed that most of the traits had significant correlations. There was no significant correlation between the length and width or fresh weight, while a significant positive correlation was observed between the width and leaf fresh weight (P < 0.01). Significant positive correlations were also found between quantitative traits, while there was no correlation between qualitative and quantitative traits. These allow indirect selection to improve breeding efficiency. Cluster analysis showed that at the Euclidean distance of 20, all 21 individuals analyzed were clustered into four groups. Overall, the phenotypic traits of different groups were significantly different. Mutants are critical materials for studying the genetic regulation of mechanisms involved in the control of economically important N. haitanensis traits. Pigmented mutants are a dominant-marker trait used for breeding that have high value for theoretical research and practical applications. Our study identified many pigmented mutants that can be used to study relevant biological mechanisms. Nevertheless, the variation coefficients of four quantitative characters (length, width, fresh weight, and thickness) of the F1 N. haitanensis generation were less than those of the control group and were biased towards negative variation. For example, more thin leaf mutants were obtained after the gamma rays´ treatment. It is interesting as a thin N. haitanensis is more suitable for automatic processing, and its primary products can be reprocessed with higher added value. In conclusion, after being treated with γ-ray, the N. haitanensis blades were guided to variation enabling the selection and cultivation of new strains, from which strains with improved traits strains were selected. This study provides interesting materials for accelerating the N. haitanensis breeding research and the selection of excellent varieties.

Lattice shrinkage is a dominating factor for the strain-induced change of the electronic properties in vdW layered materials. Here, the authors discover a piezoresistivity in pressurized β′-In2Se3, which originates from the intralayer atomic motions.

Morphology, composition, and uniformity of highly entropic nanoalloys are critical to their properties and applications. Here, the authors develop a DNA origami-based metallization reaction concept for the precise synthesis of multimetallic nanopatterns.

The terrestrial water storage anomaly (TWSA) is an important parameter for assessing the land water budget, and it interacts well with terrestrial ecosystems via complex hydrological processes. Recently, the decline in central Asian terrestrial water storage (TWS) has threatened the health of local ecosystems. Therefore, it is of great significance to adopt an efficient approach to explore and identify the nonlinear relationship between two important indicators, i.e., the TWSA and normalized difference vegetation index (NDVI) in the arid central Asian endorheic basins. In this study, we analysed the long-term trends of the TWSA and NDVI, and identified the lag month (1 month) as the optimal moving window of the nonlinear Granger causality test embedded in random forest to detect the nonlinear NDVI change response of NDVI changes in vegetation to the TWSA from 2003 to 2015. There are decreasing trends in TWSAs over approximately 81.7% of the study area and the NDVI generally decreased resulting in approximately 36% vegetation browning in the study area. The nonlinear Granger unidirectional causes of the TWSA were responsible for 97.9% of the NDVI variation in the study area considering the optimal response time for moving windows. The causes of vegetation browning in the central Asian Aral Sea basin and vegetation greening in the basins of Northwest China could be mostly explained by the changes in TWS. Our findings contribute to understanding the nonlinear causal linkages between vegetation and the TWSA in endorheic basins, and these findings provide insights for obtaining terrestrial water consumption patterns and water resource management under the joint influence of climate change and human activities.

Nano-particles possess desirable attributes such as small particle size, excellent injectivity, and migration performance, making them highly compatible and adaptable for addressing the water flooding requirements of the low-permeability oil reservoir. When selecting an oil displacement agent for enhancing water flooding and improving oil recovery, factors such as injectivity and migration need to be carefully considered. In this study, through a comprehensive analysis of the mechanism and technical characteristics of nano-particle oil displacement agents, the plugging and profile control mechanisms recognized by the mainstream of nano-particles are elucidated. By examining various elements including outcrop fractures, natural micro-fractures, artificial support fractures, and dynamic monitoring data, a reevaluation of the dominant channel scale governing water drive in low permeability reservoirs is conducted, thereby defining the target entities for profile control and flooding operations. Drawing upon Darcy’s percolation law and leveraging enhanced oil recovery techniques based on the classical Kozeny equation, a profile control and flooding mechanism is proposed that focuses on increasing the specific surface area of polymer particles while simultaneously reducing reservoir permeability. This innovative approach establishes a novel matching method between nano-polymer particles and the diverse media found within the reservoir. Lastly, the application of nanoparticle flooding technology in Changqing Oilfield is presented, highlighting its practical implementation and benefits.

Nuclear power plays a crucial role in achieving the target of carbon neutrality to build a sustainable society. However, it is not “carbon-free” when considering its entire life cycle. Therefore, accurate accounting and monitoring of its generated carbon emissions are required to avoid miscalculations of nuclear energy as a clean energy source. In this study, the life-cycle carbon emissions of nuclear power plants (NPPs) with different reactor types are reviewed. In addition to the characteristic differences among different reactors, disparities in the review results originate from the varying emissions at the respective stages of the nuclear fuel cycle, technology choices at each stage and accounting methods and boundaries. The carbon emissions resulting from NPP construction and operation are underestimated due to the limited data and methods, which creates uncertainty in the evaluation of NPP carbon emissions. An integrated framework for carbon emissions accounting considering the construction and operation of NPPs (CACO-NPP) is proposed. This integrated framework aims to improve the accounting accuracy for carbon emissions originating from NPPs. An emerging Generation III NPP with the latest technology, HPR1000 (an advanced pressurized water reactor), was adopted as a case study. The results show that the total emissions resulting from vegetation loss, equipment manufacturing and labor input during construction and operation are 1232.91 Gg CO2 with a carbon intensity of 1.31 g CO2/kWh, indicating the notable mitigation capability of Generation III NPPs. By combining the maturity of HPR1000 technology with successive design improvements, the carbon emissions of such reactor types could be further reduced. This development is very important for realizing China’s carbon neutrality target.

Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods.

As the core area of human activities and economic development in the Xinjiang Autonomous Region, the hilly oasis zone of Xinjiang directly affects the regional sustainable development and stability of the ecosystem. Understanding the effects of different geomorphic types on vegetation distribution is crucial for maintaining vegetation growth and development, especially the improvement in the terrestrial ecological environment in arid areas under the background of climate change. However, there are few studies on the effect of spatial differences in detailed geomorphic types on vegetation distribution patterns. Therefore, this paper divides the Xinjiang hilly oasis zone into six geomorphologic level zones and innovatively investigates the influence of detailed geomorphologic types on the spatial distribution of vegetation and vegetation cover. Further, the area proportion of detailed landform types corresponding to different vegetation coverage in each geomorphic area was quantitatively calculated. Finally, the Geodetector method was used to detect the drivers of interactions between vegetation and the environment. The findings are shown as follows: (1) In the same climate zone, the spatial differentiation of landforms has a great influence on the vegetation distribution, manifesting as the significantly different vegetation distribution in different landform types. Grassland is the main vegetation type in the erosion and denudation of Nakayama; cultivated vegetation and meadows have a larger coverage in the alluvial flood plain and alluvial plain; and the distribution of vegetation in the Tianshan economic zone is characterized by obvious vertical zoning with the geomorphology. (2) The landform type and morphological types are the strongest driving factors for vegetation coverage with q values of 0.433 and 0.295, respectively, which effectually fill the gap caused by only using two terrain indicators, slope and elevation, to study the relationship between landforms and vegetation. (3) In addition, the improved nonlinear interaction resulting from the double factor of landform type and slope is 0.486, which has a stronger control on vegetation coverage than the single factor of landform type. These findings are conducive to enhancing the supply services of vegetation to the ecosystem in arid areas as well as providing important scientific guidance for the construction of ecological civilization and sustainable development in Xinjiang.