Relationship between longitudinal changes in lipid composition and ischemic stroke among hypertensive patients

BACKGROUND Dyslipidemia was strongly linked to stroke,however the relationship between dyslipidemia and its components and ischemic stroke remained unexplained.AIM To investigate the link between longitudinal changes in lipid profiles and dyslipidemia and ischemic stroke in a hypertensive population.METHODS Between 2013 and 2014,6094 hypertension individuals were included in this,and ischemic stroke cases were documented to the end of 2018.Longitudinal changes of lipid were stratified into four groups:(1)Normal was transformed into normal group;(2)Abnormal was transformed into normal group;(3)Normal was transformed into abnormal group;and(4)Abnormal was transformed into abnormal group.To examine the link between longitudinal changes in dyslipidemia along with its components and the risk of ischemic stroke,we utilized multivariate Cox proportional hazards models with hazard ratio(HR)and 95%CI.RESULTS The average age of the participants was 62.32 years±13.00 years,with 329 women making up 54.0%of the sample.Over the course of a mean follow-up of 4.8 years,143 ischemic strokes happened.When normal was transformed into normal group was used as a reference,after full adjustments,the HR for dyslipidemia and ischemic stroke among abnormal was transformed into normal group,normal was transformed into abnormal group and abnormal was transformed into abnormal Wei CC et al.Dyslipidemia changed and ischemic stroke WJCC https://www.wjgnet.com 2 February 6,2025 Volume 13 Issue 4 group were 1.089(95%CI:0.598-1.982;P=0.779),2.369(95%CI:1.424-3.941;P<0.001)and 1.448(95%CI:1.002-2.298;P=0.047)(P for trend was 0.233),respectively.CONCLUSION In individuals with hypertension,longitudinal shifts from normal to abnormal in dyslipidemia-particularly in total and low-density lipoprotein cholesterol-were significantly associated with the risk of ischemic stroke.

Macular microvascular and structural changes on optical coherence tomography angiography in atypical optic neuritis

BACKGROUND Atypical optic neuritis,consisting of neuromyelitis optica spectrum disorders(NMOSD)or myelin oligodendrocyte glycoprotein antibody disease(MOGAD),has a very similar presentation but different prognostic implications and longterm management strategies.Vascular and metabolic factors are being thought to play a role in such autoimmune neuro-inflammatory disorders,apart from the obvious immune mediated damage.With the advent of optical coherence tomography angiography(OCTA),it is easy to pick up on these subclinical macular microvascular and structural changes.AIM To study the macular microvascular and structural changes on OCTA in atypical optic neuritis.METHODS This observational cross-sectional study involved 8 NMOSD and 17 MOGAD patients,diagnosed serologically,as well as 10 healthy controls.Macular vascular density(MVD)and ganglion cell+inner plexiform layer thickness(GCIPL)were studied using OCTA.RESULTS There was a significant reduction in MVD in NMOSD and MOGAD affected as well as unaffected eyes when compared with healthy controls.NMOSD and MOGAD affected eyes had significant GCIPL thinning compared with healthy controls.NMOSD unaffected eyes did not show significant GCIPL thinning compared to healthy controls in contrast to MOGAD unaffected eyes.On comparing NMOSD with MOGAD,there was no significant difference in terms of MVD or GCIPL in the affected or unaffected eyes.CONCLUSION Although significant microvascular and structural changes are present on OCTA between atypical optic neuritis and normal patients,they could not help in differentiating between NMOSD and MOGAD cases.

Spatio-temporal changes of underground coal fires during 2008-2016 in Khanh Hoa coal field(North-east of Viet Nam) using Landsat time-series data

Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.

Vegetation Change of Ecotone in West of Northeast China Plain Using Time-series Remote Sensing Data

Multi-temporal series of satellite SPOT-VEGETATION normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data from 1998 to 2007 were used for analyzing vegetation change of the ecotone in the west of the Northeast China Plain. The yearly and monthly maximal values,anomalies and change rates of NDVI and NDWI were calculated to reveal the interannual and seasonal changes in vegetation cover and vegetation water content. Linear regression method was adopted to characterize the trends in vegetation change. The yearly maximal NDVI decreased from 0.41 in 1998 to 0.37 in 2007,implying the decreasing trend of vegetation activity. There was a significant decrease of maximal NDVI in spring and summer over the study period,while an increase trend was observed in autumn. The vegetation-improved regions and vegetation-degraded regions occupied 17.03% and 20.30% of the study area,respectively. The maximal NDWI over growing season dropped by 0.027 in 1998–2007,and about 15.15% of the study area showed a decreasing trend of water content. Vegetation water stress in autumn was better than that in spring. Vegetation cover and water content variations were sensitive to annual precipitation,autumn precipitation and summer temperature. The vegetation degradation trend in this ecotone might be induced by the warm-drying climate especially continuous spring and summer drought in the recent ten years.

Recent advances in graphene-based phase change composites for thermal energy storage and management

Energy storage and conservation are receiving increased attention due to rising global energy demands.Therefore,the development of energy storage materials is crucial.Thermal energy storage(TES)systems based on phase change materials(PCMs)have increased in prominence over the past two decades,not only because of their outstanding heat storage capacities but also their superior thermal energy regulation capability.However,issues such as leakage and low thermal conductivity limit their applicability in a variety of settings.Carbon-based materials such as graphene and its derivatives can be utilized to surmount these obstacles.This study examines the recent advancements in graphene-based phase change composites(PCCs),where graphene-based nanostructures such as graphene,graphene oxide(GO),functionalized graphene/GO,and graphene aerogel(GA)are incorporated into PCMs to substantially enhance their shape stability and thermal conductivity that could be translated to better storage capacity,durability,and temperature response,thus boosting their attractiveness for TES systems.In addition,the applications of these graphene-based PCCs in various TES disciplines,such as energy conservation in buildings,solar utilization,and battery thermal management,are discussed and summarized.

Global Change in Agricultural Flash Drought over the 21st Century

Agricultural flash droughts are high-impact phenomena, characterized by rapid soil moisture dry down. The ensuing dry conditions can persist for weeks to months, with detrimental effects on natural ecosystems and crop cultivation. Increases in the frequency of these rare events in a future warmer climate would have significant societal impact. This study uses an ensemble of 10 Coupled Model Intercomparison Project(CMIP) models to investigate the projected change in agricultural flash drought during the 21st century. Comparison across geographical regions and climatic zones indicates that individual events are preceded by anomalously low relative humidity and precipitation, with long-term trends governed by changes in temperature, relative humidity, and soil moisture. As a result of these processes, the frequency of both upperlevel and root-zone flash drought is projected to more than double in the mid-and high latitudes over the 21st century, with hot spots developing in the temperate regions of Europe, and humid regions of South America, Europe, and southern Africa.

Shikimic acid accelerates phase change and flowering in Chinese jujube

The juvenile-to-adult phase change with first flowering as the indicator plays a crucial role in the lifecycle of fruit trees. However, the molecular mechanisms underlying phase change in fruit trees remain largely unknown. Shikimic acid (ShA) pathway is a main metabolic pathway closely related to the synthesis of hormones and many important secondary metabolites participating in plant phase change. So,whether ShA regulates phase change in plants is worth clarifying. Here, the distinct morphological characteristics and the underlying mechanisms of phase change in jujube (Ziziphus jujuba Mill.), an important fruit tree native to China with nutritious fruit and outstanding tolerance abiotic stresses, were clarified. A combined transcriptome and metabolome analysis found that ShA is positively involved in jujube(Yuhong’×Xing 16’) phase change. The genes in the upstream of ShA synthesis pathway (ZjDAHPS, ZjDHQS and ZjSDH), the contents of ShA and the downstream secondary metabolites like phenols were significantly upregulated in the phase change period. Further, the treatment of spraying exogenous ShA verified that ShA at a very low concentration (60 mg·L^(-1)) can substantially speed up the phase change and flowering of jujube and other tested plants including Arabidopsis, tomato and wheat. The exogenous ShA (60 mg·L^(-1)) treatment in jujube seedlings could increase the accumulation of endogenous ShA, enhance leaf photosynthesis and the synthesis of phenols especially flavonoids and phenolic acids, and promote the expression of genes (ZjCOs, ZjNFYs and ZjPHYs) involved in flowering pathway. Basing on above results, we put forward a propose for the underlying mechanism of ShA regulating phase change, and a hypothesis that ShA could be considered a phytohormone-like substance because it is endogenous, ubiquitous, movable and highly efficient at very low concentrations. This study highlights the critical role of ShA in plant phase change and its phytohormone-like properties.

Projected changes in extreme snowfall events over the Tibetan Plateau based on a set of RCM simulations

Extreme snowfall events over the Tibetan Plateau(TP)cause considerable damage to local society and natural ecosystems.In this study,the authors investigate the projected changes in such events over the TP and its surrounding areas based on an ensemble of a set of 21st century climate change projections using a regional climate model,RegCM4.The model is driven by five CMIP5 global climate models at a grid spacing of 25 km,under the RCP4.5 and RCP8.5 pathways.Four modified ETCCDI extreme indices-namely,SNOWTOT,S1mm,S10mm,and Sx5day-are employed to characterize the extreme snowfall events.RegCM4 generally reproduces the spatial distribution of the indices over the region,although with a tendency of overestimation.For the projected changes,a general decrease in SNOWTOT is found over most of the TP,with greater magnitude and better cross-simulation agreement over the eastern part.All the simulations project an overall decrease in S1mm,ranging from a 25%decrease in the west and to a 50%decrease in the east of the TP.Both S10mm and Sx5day are projected to decrease over the eastern part and increase over the central and western parts of the TP.Notably,S10mm shows a marked increase(more than double)with high cross-simulation agreement over the central TP.Significant increases in all four indices are found over the Tarim and Qaidam basins,and northwestern China north of the TP.The projected changes show topographic dependence over the TP in the latitudinal direction,and tend to decrease/increase in low-/high-altitude areas.

Climate change drives flooding risk increases in the Yellow River Basin

The Yellow River Basin(YRB)has experienced severe floods and continuous riverbed elevation throughout history.Global climate change has been suggested to be driving a worldwide increase in flooding risk.However,owing to insufficient evidence,the quantitative correlation between flooding and climate change remains illdefined.We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements.Variations in yearly maximum flood discharge show distinct periods:a dramatic decreasing period from 1843 to 1950,and an oscillating gentle decreasing from 1950 to 2021,with the latter period also showing increasing more extreme floods.A Mann-Kendall test analysis suggests that the latter period can be further split into two distinct sub-periods:an oscillating gentle decreasing period from 1950 to 2000,and a clear recent increasing period from 2000 to 2021.We further predict that climate change will cause an ongoing remarkable increase in future flooding risk and an∼44.4 billion US dollars loss of floods in the YRB in 2100.

Self‑Assembly of Binderless MXene Aerogel for Multiple‑Scenario and Responsive Phase Change Composites with Ultrahigh Thermal Energy Storage Density and Exceptional Electromagnetic Interference Shielding

The severe dependence of traditional phase change materials(PCMs)on the temperature-response and lattice deficiencies in versatility cannot satisfy demand for using such materials in complex application scenarios.Here,we introduced metal ions to induce the self-assembly of MXene nanosheets and achieve their ordered arrangement by combining suction filtration and rapid freezing.Subsequently,a series of MXene/K^(+)/paraffin wax(PW)phase change composites(PCCs)were obtained via vacuum impregnation in molten PW.The prepared MXene-based PCCs showed versatile applications from macroscale technologies,successfully transforming solar,electric,and magnetic energy into thermal energy stored as latent heat in the PCCs.Moreover,due to the absence of binder in the MXene-based aerogel,MK3@PW exhibits a prime solar-thermal conversion efficiency(98.4%).Notably,MK3@PW can further convert the collected heat energy into electric energy through thermoelectric equipment and realize favorable solar-thermal-electric conversion(producing 206 mV of voltage with light radiation intensity of 200 mw cm^(−2)).An excellent Joule heat performance(reaching 105℃with an input voltage of 2.5 V)and responsive magnetic-thermal conversion behavior(a charging time of 11.8 s can achieve a thermal insulation effect of 285 s)for contactless thermotherapy were also demonstrated by the MK3@PW.Specifically,as a result of the ordered arrangement of MXene nanosheet self-assembly induced by potassium ions,MK3@PW PCC exhibits a higher electromagnetic shielding efficiency value(57.7 dB)than pure MXene aerogel/PW PCC(29.8 dB)with the same MXene mass.This work presents an opportunity for the multi-scene response and practical application of PCMs that satisfy demand of next-generation multifunctional PCCs.

Timing theory integrated nursing combined behavior change integrated theory of nursing on primiparous influence

BACKGROUND The comprehension and utilization of timing theory and behavior change can offer a more extensive and individualized provision of support and treatment alternatives for primipara.This has the potential to enhance the psychological well-being and overall quality of life for primipara,while also furnishing healthcare providers with efficacious interventions to tackle the psychological and physiological obstacles encountered during the stages of pregnancy and postpartum.AIM To explore the effect of timing theory combined with behavior change on selfefficacy,negative emotions and quality of life in patients with primipara.METHODS A total of 80 primipara cases were selected and admitted to our hospital between August 2020 and May 2022.These cases were divided into two groups,namely the observation group and the control group,with 40 cases in each group.The nursing interventions differed between the two groups,with the control group receiving routine nursing and the observation group receiving integrated nursing based on the timing theory and behavior change.The study aimed to compare the pre-and post-nursing scores of Chinese Perceived Stress Scale(CPSS),Edinburgh Postpartum Depression Scale(EPDS),Self-rating Anxiety Scale(SAS),breast milk knowledge,self-efficacy,and SF-36 quality of life in both groups.RESULTS After nursing,the CPSS,EPDS,and SAS scores of the two groups was significantly lower than that before nursing,and the CPSS,EPDS,and SAS scores of the observation group was significantly lower than that of the control group(P=0.002,P=0.011,and P=0.001 respectively).After nursing,the breastfeeding knowledge mastery,selfefficacy,and SF-36 quality of life scores was significantly higher than that before nursing,and the breastfeeding knowledge mastery(P=0.013),self-efficacy(P=0.008),and SF-36 quality of life(P=0.011)scores of the observation group was significantly higher than that of the control group.CONCLUSION The integration of timing theory and behavior change integrated theory has been found to be an effective approach in alleviating negative mood and stress experienced by primipara individuals,while also enhancing their selfefficacy and overall quality of life.This study focuses on the key concepts of timing theory,behavior change,primipara individuals,negative mood,and quality of life.

A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

The alpine meadow ecosystem accounts for 27%of the total area of the Tibetan Plateau and is also one of the most important vegetation types.The Dangxiong alpine meadow ecosystem,located in the south-central part of the Tibetan Plateau,is a typical example.To understand the carbon and water fluxes,water use efficiency(WUE),and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area,two parameter estimation methods,the Model-independent Parameter Estimation(PEST)and the Dynamic Dimensions Search(DDS),were used to optimize the Biome-BGC model.Then,the gross primary productivity(GPP)and evapotranspiration(ET)were simulated.The results show that the DDS parameter calibration method has a better performance.The annual GPP and ET show an increasing trend,while the WUE shows a decreasing trend.Meanwhile,ET and GPP reach their peaks in July and August,respectively,and WUE shows a“dual-peak”pattern,reaching peaks in May and November.Furthermore,according to the simulation results for the next nearly 100 years,the ensemble average GPP and ET exhibit a significant increasing trend,and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario.WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario.This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.

Prevalence of vegetation browning in China’s drylands under climate change

Vegetation greening has long been acknowledged,but recent studies have pointed out that vegetation greening is possibly stalled or even reversed.However,detailed analyses about greening reversal or increased browning of vegetation remain scarce.In this study,we utilized the normalized difference vegetation index(NDVI)as an indicator of vegetation to investigate the trends of vegetation greening and browning(monotonic,interruption,and reversal)through the breaks for the additive season and trend(BFAST)method across China’s drylands from 1982 to 2022.It also reveals the impacts of ecological restoration programs(ERPs)and climate change on these vegetation trends.We find that the vegetation displays an obvious pattern of east-greening and west-browning in China’s drylands.Greening trends mainly exhibits monotonic greening(29.8%)and greening with setback(36.8%),whereas browning shows a greening to browning reversal(19.2%).The increase rate of greening to browning reversal is 0.0342/yr,which is apparently greater than that of greening with setback,0.0078/yr.This research highlights that,under the background of widespread vegetation greening,vegetation browning is pro-gressively increasing due to the effects of climate change.Furthermore,the ERPs have significantly increased vegetation coverage,with the increase rate in 2000-2022 being twice as much as that of 1982-1999 in reveg-etation regions.Vegetation browning in southwestern Qingzang Plateau is primarily driven by adverse climatic factors and anthropogenic disturbances,which offset the efforts of ERPs.

Heat transfer enhanced inorganic phase change material compositing carbon nanotubes for battery thermal management and thermal runaway propagation mitigation

Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase change material(PCM)with nonflammability has the potential to achieve this dual function.This study proposed an encapsulated inorganic phase change material(EPCM)with a heat transfer enhancement for battery systems,where Na_(2)HPO_(4)·12H_(2)O was used as the core PCM encapsulated by silica and the additive of carbon nanotube(CNT)was applied to enhance the thermal conductivity.The microstructure and thermal properties of the EPCM/CNT were analyzed by a series of characterization tests.Two different incorporating methods of CNT were compared and the proper CNT adding amount was also studied.After preparation,the battery thermal management performance and TR propagation mitigation effects of EPCM/CNT were further investigated on the battery modules.The experimental results of thermal management tests showed that EPCM/CNT not only slowed down the temperature rising of the module but also improved the temperature uniformity during normal operation.The peak battery temperature decreased from 76℃to 61.2℃at 2 C discharge rate and the temperature difference was controlled below 3℃.Moreover,the results of TR propagation tests demonstrated that nonflammable EPCM/CNT with good heat absorption could work as a TR barrier,which exhibited effective mitigation on TR and TR propagation.The trigger time of three cells was successfully delayed by 129,474 and 551 s,respectively and the propagation intervals were greatly extended as well.

Analysis of piezoelectric semiconductor fibers under gradient temperature changes

Piezoelectric semiconductors(PSs)possess both semiconducting properties and piezoelectric coupling effects,making them optimal building blocks for semiconductor devices.PS fiber-like structures have wide applications in multi-functional semiconductor devices.In this paper,a one-dimensional(1D)theoretical model is established to describe the piezotronic responses of a PS fiber under gradient temperature changes.The theoretical model aims to explain the mechanism behind the resistance change caused by such gradient temperature changes.Numerical results demonstrate that a gradient temperature change significantly affects the physical fields within the PS fiber,and can induce changes in its surface resistance.It provides important theoretical guidance on the development of piezotronic devices that are sensitive to temperature effects.

Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Identification and effective regulation of scarb1 gene involved in pigmentation change in autotetraploid Carassius auratus

The autotetraploid Carassius auratus(4nRR,4n=200,RRRR)is derived from whole-genome duplication of Carassius auratus red var.(RCC,2n=100,RR).In the current study,we demonstrated that chromatophores and pigment changes directly caused the coloration and variation of 4nRR skin(red in RCC,brownish-yellow in4nRR).To further explore the molecular mechanisms underlying coloration formation and variation in 4nRR,we performed transcriptome profiling and molecular functional verification in RCC and 4nRR.Results revealed that scarb1,associated with carotenoid metabolism,underwent significant down-regulation in 4nRR.Efficient editing of this candidate pigment gene provided clear evidence of its significant role in RCC coloration.Subsequently,we identified four divergent scarb1 homeologs in 4nRR:two original scarb1 homeologs from RCC and two duplicated ones.Notably,three of these homeologs possessed two highly conserved alleles,exhibiting biased and allelespecific expression in the skin.Remarkably,after precise editing of both the original and duplicated scarb1homeologs and/or alleles,4nRR individuals,whether singly or multiply mutated,displayed a transition from brownishyellow skin to a cyan-gray phenotype.Concurrently,the proportional areas of the cyan-gray regions displayed a gene-dose correlation.These findings illustrate the subfunctionalization of duplicated scarb1,with all scarb1genes synergistically and equally contributing to the pigmentation of 4nRR.This is the first report concerning the functional differentiation of duplicated homeologs in an autopolyploidfish,substantiallyenrichingour understanding of coloration formation and change within this group of organisms.