Multiple Information Research Articles

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph

BackgroundIdentification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery.ResultsWe proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs.ConclusionsGSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo’s dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

A method based on Vision Transformer and multiple image information for vehicle lane-changing recognition in mixed traffic and connected environment

ABSTRACT To enhance the safety of autonomous vehicles in mixed traffic and connected environment, it is crucial to recognize the lane-changing intentions (LCIs) of human-driven vehicles for autonomous vehicles. This paper presents a novel method for LCI recognition, which extracts features from the driving state and relative motion of the target vehicle and its neighbors. The method applies short-time Fourier transform, Gramian angular summation field, and Gramian angular difference field to the time-series data, and generates three grayscale images, which are merged into one information fusion image (IFI) by image processing techniques. The IFIs are then classified into three categories: lane keeping, lane-changing left, and lane-changing right, using the Vision Transformer model with transfer learning to speed up convergence and reduce training cost. The experimental results demonstrate that the proposed method outperforms the traditional methods, achieving an accuracy of 95.65% for recognizing LCI 3s before the lane change point.

An Improved Dempster–Shafer Evidence Theory with Symmetric Compression and Application in Ship Probability

Auxiliary information sources, a subset of target recognition data sources, play a significant role in target recognition. The reliability and importance of these sources can vary, thereby affecting the effectiveness of the data provided. Consequently, it is essential to integrate these auxiliary information sources prior to their utilization for identification. The Dempster-Shafer (DS) evidence theory, a well-established data-fusion method, offers distinct advantages in handling and combining uncertain information. In cases where conflicting evidence sources and minimal disparities in fundamental probability allocation are present, the implementation of DS evidence theory may demonstrate deficiencies. To address these concerns, this study refined DS evidence theory by introducing the notion of invalid evidence sources and determining the similarity weight of evidence sources through the Pearson correlation coefficient, reflecting the credibility of the evidence. The significance of evidence is characterized by entropy weights, taking into account the uncertainty of the evidence source. The proposed asymptotic adjustment compression function adjusts the basic probability allocation of evidence sources using comprehensive weights, leading to symmetric compression and control of the influence of evidence sources in data fusion. The simulation results and their application in ship target recognition demonstrate that the proposed method successfully incorporates basic probability allocation calculations for ship targets in various environments. In addition, the method effectively integrates data from multiple auxiliary information sources to produce accurate fusion results within an acceptable margin of error, thus validating its efficacy. The superiority of the proposed method is proved by comparing it with other methods that use the calculated weights to weight the basic probability allocation of the evidence sources.

Research on a Three-Way Decision-Making Approach, Based on Non-Additive Measurement and Prospect Theory, and Its Application in Aviation Equipment Risk Analysis.

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.

Dose-response relationship between arsenic in drinking water and mortality of urinary cancers in Taiwan.

Ingested arsenic is carcinogenic to the human urinary tract, but uncertainties remain regarding the dose-response relationship. To assess dose-response relationships between arsenic ingestion and urinary cancers, we evaluated the associations between the arsenic level in drinking water and mortality of cancers of the bladder, kidney, and prostate in Taiwan. We utilized the 1971-2000 Taiwan death registry data and calculated the age-standardized mortality rates (ASMRs) using the 1976 world standard population as the reference group. We used the data from a 1974-1976 census survey of wells on the arsenic levels in drinking water conducted by the government to assess exposure levels, which had been divided into three categories: below 0.05ppm, 0.05-0.35ppm, and above 0.35ppm. The data were analyzed using multiple linear regression models and geographical information system. We found no increase in ASMR for all, or any, of the urinary cancers at exposure levels of 0.05-0.35ppm arsenic, but at exposure levels > 0.35ppm arsenic was associated with increased ASMR in both males and females for bladder cancer, kidney cancer, and all urinary cancers combined. There was no increased ASMR associated with prostate cancer observed for either exposure category.

Methodological Overlap in Early Childhood Education Self-Regulation Research: An Interdisciplinary Systematic Review

AbstractYoung children’s self-regulation (SR) skills are linked to many important outcomes across the lifespan and school stakeholders widely agree that these skills should be prioritized in schools. Despite broad agreement about the importance of these skills, the diverse field of SR research is rife with a lack of clarity in both conceptual definitions and measurement with many different terms and definitions used interchangeably. With a systematic review of early childhood SR research in schools, we aimed to add to these efforts by cataloguing which SR variables researchers measure and the specific methodological choices made to capture such variables. Coders identified 319 peer-reviewed articles published between 2010–2020 that included a school-based measurement of self-regulation related constructs among children between 3- and 8-years. This revealed 11 distinct variable terms used widely in the literature to refer to SR related skills or characteristics and behavior problems was overwhelmingly the most frequent. Studies of many of these variables relied heavily on adult reports, usually teachers, and the use of multiple informants to measure variables was uncommon. Findings further highlight the conceptual clutter in SR research, point to possible points of empirical weaknesses in the measurement of these skills, and identify some methodological overlap in how variables are being operationalized in the literature.

Multi-physics information-integrated neural network for fatigue life prediction of additively manufactured Hastelloy X superalloy

ABSTRACT The inherent ‘black-box’ characteristic of neural networks renders the physical interpretability of fatigue life prediction challenging, resulting in physically inconsistent prediction results. In response to this challenge, a multi-physics information-integrated neural network framework is proposed for fatigue life prediction. Rooted in the continuous damage mechanics with embedded additive manufacturing process parameters (AM-CDM), coupled with activation functions and loss functions that amalgamate physical information, a multi-tiered and multi-physical information source network framework emerges. The framework incorporates the outcomes of the AM-CDM model as input features for domain knowledge enhancement, embeds the fatigue limit within the activation function, and establishes a loss function integrating the physical model. Through the integration of multiple physical information, the model can be restricted by different physical constraints during training, thereby improving its prediction accuracy and physical consistency. Validation of the proposed framework is conducted using laser powder bed fusion (LPBF)-fabricated Hastelloy X subjected to varying orientations, post-treatments and test temperatures. The findings demonstrate that the prediction accuracy of an integrating-physics-informed neural network (IPINN) is improved by 27.51%, and its life prediction results are capped at the fatigue limit life, yielding a better physical consistency. The IPINN framework provides a novel perspective for fatigue life prediction.

A comparative analysis of financial and nonfinancial rewards on work motivation and knowledge sharing in a postpandemic era

PurposeThis paper aims to examine the impacts of financial and nonfinancial incentives on the development of employee work motivation and knowledge sharing in the postpandemic environment. The paper further investigates the role of transformational leadership as a moderator in enhancing the relationship between work motivation and knowledge sharing.Design/methodology/approachAdopting a quantitative approach, the study uses data collected from multiple informants, specifically senior managers, in Nepalese manufacturing and service business-to-business (B2B) firms.FindingsContrary to prior research, the results reveal that nonfinancial incentives have a stronger impact on work motivation in the postpandemic era. This enhanced work motivation, in turn, contributes to knowledge sharing, with transformational leadership further strengthening the relationship.Practical implicationsThe findings suggest that B2B firms should consider moving toward leveraging nonfinancial incentives to motivate employees to develop knowledge sharing initiatives, especially in challenging circumstances such as those experienced in the postpandemic era. In addition, it is recommended that chief executive officers adopt a transformational leadership style to facilitate effective knowledge sharing within their firms.Originality/valueIn a developing economy and amid the challenges of the global pandemic, there has been limited research exploring the possible effects that financial and nonfinancial incentives could have on work motivation and knowledge sharing. This research bridges this gap by providing a fresh perspective on work motivation and knowledge management in B2B firms, contributing novel insights to the literature.

Multi‐graph representation spatio‐temporal attention networks for traffic forecasting in the cinematic metaverse

AbstractThe cinematic metaverse aims to create a virtual space with the context of a film. Users can enter this space in the form of avatars, experiencing the cinematic plot firsthand in an immersive manner. This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi‐objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks‐based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances‐based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi‐Graph Representation Spatio‐Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi‐Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio‐Temporal Attention (STAtt) module is then proposed to extract spatio‐temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state‐of‐the‐art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.

Enhance carbon emission prediction using bidirectional long short-term memory model based on text-based and data-driven multimodal information fusion

Accurate projections of carbon emissions are essential for climate change responses and environmental policy development. However, the complexity of carbon emission projections has increased due to various factors like technology and policy, posing challenges for accuracy. Many studies rely on time-series data alone, overlooking the combined impact of multiple information sources on carbon emissions. To overcome these bottlenecks, a hybrid model is proposed, integrating multimodal data, convolutional neural network inceptionv2 (CNN-inceptionv2), text convolutional neural network (TextCNN) optimized by attention mechanism, and bi-directional long and short-term memory (BiLSTM). Firstly, the time series feature set and text feature set are extracted using the CNN-inceptionv2 and the TextCNN, respectively. Subsequently, the feature sets of the two modal information are fused and outputted into a multimodal feature set, which are ultimately predicted using BiLSTM. The validity of the study model was also verified by different methods such as ablation experiments, single model and combined model. The coefficient of determination (R2) values were 0.986 for the validation set and 0.933 for the prediction set. In comparison to other models, the present model showed an average improvement of 16.6% in R2 values, fully indicating its superior predictive performance. The high point and interval predictions of carbon emissions further verified the superior prediction accuracy of the present model. The present model can accurately predict carbon emissions, providing data references for carbon emission decision makers. Additionally, it further enriches the methodological system of carbon emission prediction and aids in strategic planning for future carbon emission and energy management.

Multi-information based cloud–edge–end collaborative computational tasks offloading for industrial IoT systems

Cloud–edge–end collaborative computational task offloading (CEETO) is a promising method in industrial Internet-of-things (IIoT) to support massive computational tasks generated by equipment that has low energy/computation ability. In this work, we propose a new CEETO scheme by invoking the deep learning method (DL) with the aid of a multi-information analysis approach. Firstly, considering the delay constraints of the real-time tasks and the processing ability constraints of the cloud/edge/end servers, we formulate the CEETO problem to achieve the lowest system delay by establishing contact between CEETO problem and multiple information, such as the time-related locations and tasks requirements/features. Then, we tailor a long-short term memory network (LSTMN) to analyze the relation among time, locations and task requirements/features for predicting multiple information. Finally, the predicted multiple information is utilized for the final offloading strategy generation by invoking the simulated annealing algorithm (SAA). As the proposed CEETO process is invoked based on the predictions of multiple information, it is particularly suitable for the planning, scheduling and deployment of cloud–edge–end resources in massive equipment IIoT scenarios. Simulation results show that our proposed scheme can achieve effective computational task offloading.

DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information

Abstract One of the crucial tasks in Remaining Useful Life (RUL) prediction is to effectively extract key sensor information from numerous sensor signals. In this article, a novel dual-attention enhanced deep residual LSTM (DRLSTM-DA) is developed to deeply optimize multidimensional sensor signals. First, two LSTM layers are designed to compress and reconstruct representative degradation information from input multidimensional time series data, to generate a new feature space. Second, a novel channel adaptive soft threshold module is designed to assign different weights according to the importance of different sensor information, and simultaneously eliminate the noise information in the signal. Thirdly, a temporal attention mechanism is designed to automatically highlight moments containing important decay information while suppressing unimportant moments. Finally, compared with other SOTA methods, our architecture achieves RMSE of 11.55, 13.74, 11.25, and 14.19 on four sub-datasets of the C-MAPSS dataset, with scores of 234.24, 465.49, 202.23, and 537.66, respectively. Meanwhile, in real aeroengine operation dataset, our architecture achieved the smallest RMSE (8.62). These results all validate the good predictive performance of our model.

The effects of informational and energetic/modulation masking on the efficiency and ease of speech communication across the lifespan

Children and older adults have greater difficulty understanding speech when there are other voices in the background (informational masking, IM) than when the interference is a steady-state noise with a similar spectral profile but is not speech (due to modulation and energetic masking; EM/MM). We evaluated whether this IM vs. EM/MM difference for certain age ranges was found for broader measures of communication efficiency and ease in 114 participants aged between 8 and 80. Participants carried out interactive diapix problem-solving tasks in age-band- and sex-matched pairs, in quiet and with different maskers in the background affecting both participants. Three measures were taken: (a) task transaction time (communication efficiency), (b) performance on a secondary auditory task simultaneously carried out during diapix, and (c) post-test subjective ratings of effort, concentration, difficulty and noisiness (communication ease). Although participants did not take longer to complete the task when in challenging conditions, effects of IM vs. EM/MM were clearly seen on the other measures. Relative to the EM/MM and quiet conditions, participants in IM conditions were less able to attend to the secondary task and reported greater effects of the masker type on their perceived degree of effort, concentration, difficulty and noisiness. However, we found no evidence of decreased communication efficiency and ease in IM relative to EM/MM for children and older adults in any of our measures. The clearest effects of age were observed in transaction time and secondary task measures. Overall, communication efficiency gradually improved between the ages 8–18 years and performance on the secondary task improved over younger ages (until 30 years) and gradually decreased after 50 years of age. Finally, we also found an impact of communicative role on performance. In adults, the participant asked to take the lead in the task and who spoke the most, performed worse on the secondary task than the person who was mainly in a ‘listening’ role and responding to queries. These results suggest that when a broader evaluation of speech communication is carried out that more closely resembles typical communicative situations, the more acute effects of IM typically seen in populations at the extremes of the lifespan are minimised potentially due to the presence of multiple information sources, which allow the use of varying communication strategies. Such a finding is relevant for clinical evaluations of speech communication.

Visible-to-Near-Infrared Mechanoluminescence in Bi-Activated Spinel Compounds for Multiple Information Anticounterfeiting.

Mechanoluminescence (ML) is the nonthermal luminescence generated in the process of force-to-light conversion, which has broad prospects in stress sensing, wearable devices, biomechanics, and multiple information anticounterfeiting. Multivalence emitter ions utilize their own self-reduction process to realize multiband ML without introducing another dopant, such as Eu3+/Eu2+, Sm3+/Sm2+, and Mn4+/Mn2+. However, self-reduction-induced ML in bismuth-activated materials has rarely been reported so far. In this work, a novel visible-to-near-infrared (vis-NIR) ML induced by the self-reduction of Bi3+ to Bi2+ in the spinel-type compound (MgGa2O4) is reported. The photoluminescence (PL) spectra, PL excitation (PLE) spectra, and PL lifetime curves demonstrate that Bi3+/Bi2+ ions are the main luminescence centers. Notably, the possible self-reduction model is proposed, where a magnesium vacancy (VMg″) is considered as the driving force for the self-reduction of Bi3+ to Bi2+. Furthermore, an oxygen vacancy (VO••) is confirmed by electron paramagnetic resonance (EPR) spectroscopy. Combined with thermoluminescence (TL) glow curves and ML spectra, a plausible trap-controlled ML mechanism is illustrated, where electron-hole (VO••/VMg″) pairs play a significant role in capturing electrons and holes. It is worth noting that the proof-of-concept dual-mode electronic signature application is implemented based on the flexible ML film, which improves the capabilities of signature anticounterfeiting for high-level security applications. Besides, multistimulus-responsive luminescence behaviors of the ML film are realized under the excitation of a 254 nm UV lamp, thermal disturbance, 980 nm laser, and mechanical stimuli. In general, this study provides new insights into designing vis-NIR ML materials toward wider application possibilities.

Hypermedia-driven RESTful API for digital twins of the built environment

Core domains to establish digital twins of the built environment are Building Information Modeling (BIM), Geographic Information System (GIS) and Internet of Things (IoT). RESTful Application Programming Interfaces (APIs) of these domains allow easy access to the digital twin. While multiple information models are reasonable since the focus of these domains is distinct, interoperability becomes a challenge. Therefore, in this paper we propose a hypermedia-driven RESTful API for digital twins of the built environment. While the GIS and IoT interfaces are covered by established standards, we define a RESTful interface for Industry Foundation Classes (IFC) models of the BIM domain. The APIs are interconnected with a federated approach though hypermedia navigation links. An evaluation by two use cases – a planning model and a structural health monitoring application – shows that the API can be used to navigate through the models crossing seamlessly the scope of the domains and overcoming barriers of interoperability.

High-speed two-color scanning volumetric laser-induced fluorescence

Many problems in fluid mechanics require single-shot 3D measurements of fluid flows, but are limited by available techniques. Here, we design and build a novel flexible high-speed two-color scanning volumetric laser-induced fluorescence (H2C-SVLIF) technique. The technique is readily adaptable to a range of temporal and spatial resolutions, rendering it easily applicable to a wide spectrum of experiments. The core equipment consists of a single monochrome high-speed camera and a pair of ND: YAG lasers pulsing at different wavelengths. The use of a single camera for direct 3D imaging eliminates the need for complex volume reconstruction algorithms and easily allows for the correction of distortion defects. Motivated by the large data loads that result from high-speed imaging techniques, we develop a custom, open-source, software package, which allows for real time playback with correction of perspective defects while simultaneously overlaying arbitrary 3D data. The technique is capable of simultaneous measurement of 3D velocity fields and a secondary tracer in the flow. To showcase the flexibility and adaptability of our technique, we present a set of experiments: (1) the flow past a sphere, and (2) vortices embedded in laminar pipe flow. In the first experiment, two channel measurements are taken at a resolution of 512 × 512 × 512 with volume rates of 65.1 Hz. In the second experiment, a single-color SVLIF system is integrated on a moving stage, providing imaging at 1280 × 304 × 256 with volume rates of 34.8 Hz. Although this second experiment is only single channel, it uses identical software and much of the same hardware to demonstrate the extraction of multiple information channels from single channel volumetric images.

Young adults with SLD and/or ADHD conscripted for military service: risk, resources, and resilience

ABSTRACT This study explored multisystem protective/risk factors for explaining resilience/adjustment in emerging adults with/without neurodevelopmental disorders facing the transition to a stressful non-academic context, mandatory military service. Participants were 904 conscripts (498 males, 55%) ages 18–25 years (M = 18.70, SD = .77) in four groups, with ADHD or SLD or comorbid ADHD+SLD or typical development (TD). Data collection tapped multiple information sources. Youngsters’ multidimensional protective/risk variables spanned three levels: (a) individual level – formally diagnosed disorders (SLD, ADHD, or ADHD+SLD), ego-resiliency, sense of coherence, attachment patterns; (b) family level – family cohesion/adaptability; and (c) community/system level – youngsters’ appraisal of their commander as a secure extrafamilial attachment figure. Youngsters’ five resilience/adjustment measures comprised: positive/negative affect, vocational-institutional satisfaction, social adaptation, and commander-rated overall functioning. MANOVAs yielded significant group differences on youngsters’ protective/risk factors and resilience measures. Regression analyses revealed significant risk posed by ADHD and/or SLD and significant protection offered by ego-resiliency, sense of coherence, attachment patterns, family cohesion, and commander as a ‘secure base’ – for explaining youngsters’ resilience. Discussion focused on factors’ unique protective/risk value for explaining resilient functioning in ADHD, SLD, comorbid, and TD emerging adults, while facing highly demanding environment.

The Need to Look at Transgender and Gender Diverse People's Health: A Preliminary Descriptive Report on Pain, Sexual Distress, and Health Profile of Five Transmasculine People and One Non-Binary Person with Endometriosis.

The sexual health of transgender and gender diverse (TGD) people with endometriosis has been overlooked, and important emotional experiences, such as sexual distress and its correlates, have been ignored. This has prevented a more comprehensive look at the health experiences of TGD individuals. This descriptive online survey study preliminarily explored the experiences of pain symptoms, sexual distress, and mental health of N = 6 TGD individuals diagnosed with endometriosis. Descriptive results showed a mean delay of 10 years from the onset of symptoms to the diagnosis. Endometriosis-related pain was a common symptom, although with low to moderate intensity. Results also showed higher mean levels of pain impact, powerlessness and lack of control, somatization, depression, anxiety, and sexual distress, and lower mean levels of emotional well-being, social support, and worse self-image compared to reports on cisgender women with endometriosis in the literature. These results suggested that sexual and mental health in the context of TGD people with endometriosis has specificities and may be associated with factors that need to be accounted for to provide comprehensive and socially just healthcare, such as the recognition of the impact of endometriosis treatment on symptoms of gender dysphoria. To achieve sexual health equity for TGD people, continuous and updated professional training and inclusive research with multiple informants are necessary.

Skin lesion segmentation via Neural Cellular Automata

Skin melanoma is one of the most dangerous tumor lesions. In recent years, the number of cases and deaths caused by melanoma has been increasing. The discovery and segmentation of the lesion area are crucial for the timely diagnosis and treatment of melanoma. However, the lesion area is often similar to the healthy area, and the size scale changes greatly, which makes the segmentation of the skin lesion area a very challenging task. Neural Cellular Automata (NCA) is a model that can be described as a recurrent convolutional network. It achieves global consistency through multiple local information exchanges, thereby completing the processing of information. Recent research on NCA shows that such a local interactive model can segment low-resolution images well. However, for high-resolution images, direct use of NCA for processing is limited by high memory requirements and difficulty in model convergence. In this paper, in order to overcome these limitations, we propose a new NCA-based segmentation model, named UNCA. UNCA is a model with a U-shaped structure. The high-resolution image is down-sampled to obtain high-dimensional low-resolution features, which are then input into the NCA for information processing. Finally, the image size is restored by upsampling. The experimental results show that the UNCA proposed in this paper has achieved good results on the ISIC2017 dataset, surpassing most of the current methods.

Sex-specific effects of maternal blood pressure on newborn telomere length: A prospective study.

To investigate the relationship between maternal blood pressure (BP) and neonatal cord blood telomere length (TL) during pregnancy, and to clarify the sensitive period. We conducted a prospective cohort study with 621 mother-newborn pairs from the Guangxi Zhuang Birth Cohort (GZBC) in China. Multiple informant models, restricted cubic spline regression (RCS) models, and quantile regression models were conducted to analyze the correlation between maternal BP and neonatal TL. Maternal diastolic blood pressure (DBP) was inversely related to neonatal cord blood TL in the second trimester (P = 0.015) and the third trimester (P = 0.011). There was a male-specific relationship between maternal BP and neonatal TL. A 1 mmHg increment in maternal systolic blood pressure (SBP) and DBP during the second trimester was related with 0.42% (95% CI: -0.80%, -0.04%) and 0.61% (95% CI: -1.13%, -0.09%) shorter TL in male newborns, respectively. Per unit increase of maternal DBP during the third trimester was related with 0.54% (95% CI: -1.03%, -0.05%) shorter TL in male newborns. Pregnant women with hypertensive disease of pregnancy (HDP) had male offspring with shorter TL (P = 0.003). However, no significant relationships were found in female newborns (P = 0.570). Maternal BP during pregnancy is inversely correlated with male neonatal TL and the second and third trimesters are sensitive windows.