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Abstract This special issue explores the relationship between digital trade and security, emphasizing the geopolitical implications for the global economy in the digital age. The rapid growth of digital trade has introduced significant challenges and opportunities, necessitating robust data governance to balance national security interests with the free flow of goods, services, and data across borders. The papers explore the different models of data governance championed by the US, China, and the EU, highlighting the complexities of cross-border data flows and their impact on international relations. Through detailed analyses of various international agreements and frameworks, this special issue provides a comprehensive overview of the current landscape of digital trade and its security implications, with a particular focus on China's evolving approach to data governance and its global influence.

With the increasing integration levels of modern electronic products, effective thermal management has become a critical concern to ensure the operational efficiency and longevity of electronic devices. Developing thermal interface materials that possess both high thermal conductivity (κ) and reliable insulation properties has presented a significant challenge. In this study, boron nitride fibers (BNF) with a substantial length-to-diameter ratio were fabricated to serve as vertically aligned thermal conduction channels within a composite pad containing a silicone gel matrix.Under typical packing pressure conditions, the resulting BNF pad exhibits an exceptionally high through-plane thermal conductivity (40 vol. %: κ⊥ = 16.77 W/mK). Additionally, the BNF pad demonstrates favorable mechanical and dielectric properties, enhancing its versatility and applicability. The results of actual heat dissipation tests further validate the excellent thermal management capabilities of these composites in high-power electronic applications.

Significant attention has been paid to indoor localization using smartphones in both research and industry.However, the accuracy and robustness of localization remain challenging issues, particularly in complex indoor environments.In light of the prevalent incorporation of pedestrian dead reckoning (PDR) devices in contemporary smartphones, an advanced indoor localization fusion method, anchored in the twin delayed deep deterministic policy gradient (TD3) framework, was proposed.In this approach, a seamless integration of Wi-Fi information and PDR data was achieved.The localization process of PDR was modeled as a Markov process, and a comprehensive continuous action space was introduced for the agent.To evaluate the performance of the proposed method, experiments were conducted and this approach was compared with three state-of-the-art deep Q network (DQN) based indoor localization methods.The experimental results demonstrate that the proposed method significantly reduces localization errors and enhances overall localization accuracy.

High red and processed meat intake and genetic predisposition are risk factors of colorectal cancer (CRC). However, evidence of their independent and joint associations on the risk of colorectal neoplasms is limited. We assessed these associations among 4774 men and women undergoing screening colonoscopy. Polygenic risk scores (PRSs) were calculated based on 140 loci related to CRC. We used multiple logistic regression models to evaluate the associations of red and processed meat intake and PRS with the risk of colorectal neoplasms. Adjusted odds ratios (aORs) were translated to genetic risk equivalents (GREs) to compare the strength of the associations with colorectal neoplasm risk of both factors. Compared to ≤1 time/week, processed meat intake >1 time/week was associated with a significantly increased risk of colorectal neoplasm [aOR (95% CI): 1.28 (1.12–1.46)]. This risk increase was equivalent to the risk increase associated with a 19 percentile higher PRS. The association of red meat intake with colorectal neoplasm was weaker and did not reach statistical significance. High processed meat intake and PRS contribute to colorectal neoplasm risk independently. Limiting processed meat intake may offset a substantial proportion of the genetically increased risk of colorectal neoplasms.

Abstract Background The genomes of present-day non-Africans are composed of 1–3% of Neandertal-derived DNA as a consequence of admixture events between Neandertals and anatomically modern humans about 50–60 thousand years ago. Neandertal-introgressed single nucleotide polymorphisms (aSNPs) have been associated with modern human disease-related traits, which are risk factors for pancreatic ductal adenocarcinoma (PDAC), such as obesity, type 2 diabetes, and inflammation. In this study, we aimed at investigating the role of aSNPs in PDAC in three Eurasian populations. Results The high-coverage Vindija Neandertal genome was used to select aSNPs in non-African populations from 1000 Genomes project phase 3 data. Then, the association between aSNPs and PDAC risk was tested independently in Europeans and East Asians, using existing GWAS data on more than 200 000 individuals. We did not find any significant associations between aSNPs and PDAC in samples of European descent, whereas, in East Asians, we observed that the Chr10p12.1-rs117585753-T allele (MAF = 10%) increased the risk to develop PDAC (OR = 1.35, 95%CI 1.19–1.54, P = 3.59 × 10–6), with a P-value close to a threshold that takes into account multiple testing. Conclusions Our results show only a minimal contribution of Neandertal SNPs to PDAC risk.

Abstract Introduction Whether and to what extent the relationship between physical activity (PA) and colorectal cancer (CRC) differs according to CRC‐related genetic risk remains to be determined, and no studies to date have quantified how much genetically determined risk could be compensated for with active exercise. Methods Genetic risk was quantified by a polygenic risk score (PRS) summarizing the estimated effect of 140 CRC‐associated genetic variants. Associations of PA with CRC risk were estimated by multivariable logistic regression across PRS levels. We also compared the impact of PA and specific PA types to the PRS using “genetic risk equivalent (GRE)”, a novel approach to enhance effective risk communication. Results Among 5058 CRC patients and 4134 controls, we observed no significant association between overall PA level in quartiles and CRC risk. However, the highest versus lowest lifetime leisure time physical activity (LTPA) was associated with a 13% lower CRC risk [odds ratio 0.87, 95% confidence interval (CI) 0.77–1.00] independent of PRS levels (adjusted p value for interaction = 0.18). This effect was equivalent to the effect of having 11 percentiles lower PRS (GRE −10.6, 95% CI −20.7 to −0.6). The GRE (95% CI) for the highest lifetime sports tertile was −23.0 (−33.9 to −12.0). Conclusions LTPA was inversely associated with CRC risk irrespective of polygenic risk for CRC, which reinforces the importance of LTPA in CRC prevention among the general population. Adequate sports activity can compensate for a large share of polygenic risk for CRC.

Objective: We aimed to directly compare the estimated effects of adherence to a healthy lifestyle with those of risk predisposition according to known genetic variants affecting colorectal cancer (CRC) risk, to support effective risk communication for cancer prevention. Methods: A healthy lifestyle score (HLS) was derived from 5 lifestyle factors: smoking, alcohol consumption, diet, physical activity, and body adiposity. The association of lifestyle and polygenic risk score (PRS) (based on 140 CRC-associated risk loci) with CRC risk was assessed with multiple logistic regression and compared through the genetic risk equivalent (GRE), a novel approach providing an estimate of the effects of adherence to a healthy lifestyle in terms of percentile differences in PRS. Results: A higher HLS was associated with lower CRC risk (4,844 cases, 3,964 controls). Those adhering to all 5 healthy lifestyle factors had a 62% (95% CI 54%–68%) lower CRC risk than those adhering to ≤ 2 healthy lifestyle factors. The estimated effect of adherence to all 5 compared with ≤ 2 healthy lifestyle factors was as strong as the effect of having a 79 percentile (GRE 79, 95% CI 61–97) lower PRS. The association between a healthy lifestyle and CRC risk was independent of PRS level but was particularly pronounced among those with a family history of CRC in ≥ 1 first-degree relative (P-interaction = 0.0013). Conclusions: A healthy lifestyle was strongly inversely associated with CRC risk. The large GRE indicated that CRC risk determined by polygenic risk may be offset to a substantial extent by adherence to a healthy lifestyle.

Abstract The incidence of pancreatic ductal adenocarcinoma (PDAC) is different among males and females. This disparity cannot be fully explained by the difference in terms of exposure to known risk factors; therefore, the lower incidence in women could be attributed to sex-specific hormones. A two-phase association study was conducted in 12,387 female subjects (5436 PDAC cases and 6951 controls) to assess the effect on risk of developing PDAC of single nucleotide polymorphisms (SNPs) in 208 genes involved in oestrogen and pregnenolone biosynthesis and oestrogen-mediated signalling. In the discovery phase 14 polymorphisms showed a statistically significant association (P

Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due to the high noise of US images, even for experienced sonographers. Especially considering that maternal mortality remains high in many developing countries, a data-driven system with a medical diagnosis can help sonographers and obstetricians make decisions rapidly and improve their work efficiency. In this study, we propose a novel data-driven system based on deep learning for the diagnosis of CSP called CA-Unet, which consists of a channel attention network to segment the CSP and a post-processing module to measure and diagnose the anomalies of CSP. We collected the US data from three hospitals in China from 2012 to 2018 year to validate the effectiveness of our system. Experiments on a fetal US dataset demonstrated that our proposed system is able to help doctors make decisions and has achieved the highest precision of 79.5% and the largest Dice score of 77.5% in the segmentation of CSP.

The past decade has witnessed an increasing nexus between the European Union’s trade and foreign policy toward Asia. On the one hand, the EU has sought to deepen economic cooperation with its key Asian partners since this region has become central to the EU’s economic growth. On the other hand, the EU has increasingly used its trade policy as a foreign policy tool to pursue its geopolitical interests in Asia. Against this background, the paper seeks to examine the dynamics of trade relations between the EU and its Asian partners by adopting the role theory. Using this theory, this paper aims to provide a holistic understanding of how the EU’s changing role in the EU-Asia trade relations has affected its pursuit of economic as well as geopolitical interests in Asia.

With the fast development of non-volatile storage technology, NAND flash memory faces more and more challenges such as data reliability and lifetime. To overcome the issue of the reliability, low-density parity-check (LDPC) codes are considered as a main candidate of error-correction-codes (ECCs) for NAND flash storages. However, conventional soft decoding algorithms for LDPC codes suffer from the drawback of slow convergence speed, which increases the decoding latency. For achieving fast convergence speed, a variable-node-based belief-propagation with message pre-processing (VNBP-MP) decoding algorithm for binary LDPC codes and a non-binary VNBP-MP (NVNBP-MP) decoding algorithm for non-binary LDPC codes are proposed. Both of the algorithms utilize the characteristics of the NAND flash channel to perform the message pre-processing operations. In theory, the propagation of unreliable messages can be effectively prevented and the propagation of reliable messages can be speeded up. To further improve the decoding convergence, the treatment for oscillating variable nodes is considered after the message pre-processing operations. The simulation results also show that the proposed algorithms both have a noticeable improvement in convergence speed and latency, without compromising error-correction performance, compared with the existing soft decoding algorithms.

In this paper, we propose a novel distributed digital transmission framework for two jointly sparse correlated signals. First, the non-zero coefficients of each signal are quantized by a standard quantizer or a novel distributed quantizer, as appropriate. Then, these quantized values are mapped to the elements of a finite field, except 0, while the zero coefficients are mapped to 0. Subsequently, compressed sensing over finite fields is applied to obtain measurements. We name such an order first quantization then compressed sensing. The two measurement signals are then converted to bit sequences, modulated, and transmitted through separate additive white Gaussian noise (AWGN) channels. At the central receiver, which has access to both channels, following demodulation, an innovative joint belief propagation (JBP) algorithm is performed for joint recovery. In this algorithm, we introduce a new type of constraint node, i.e., correlation constraint nodes, which connect two factor graphs that separately represent the CS encoding matrix of each signal. The experimental results prove that under the same framework the proposed scheme provides significant performance improvements compared to the scheme that ignores the correlated information between jointly sparse signals, especially when the correlation coefficient is high. Then, to answer the question of which order is better, we construct the first compressed sensing, then quantization framework, for fairness, two cutting edge jointly greedy pursuit algorithms are separately adopted at the joint decoder. Through simulations, we validate that the proposed framework provides more effective and robust transmissions.

Grey wolf optimizer (GWO) is an efficient swarm intelligence algorithm for kinds of optimization problems. However, GWO tends to be trapped in local optimum when solving large-scale problems. Social hierarchy is one of the main characteristics of GWO which affect the searching efficiency. Thus, an improved algorithm called hierarchy strengthened GWO (HSGWO) is proposed in this paper. First, the pack of wolves is roughly divided into two categories: dominant wolves and omega wolves. Second, the enhanced elite learning strategy is performed for dominant wolves to prevent the misguidance of low-ranking wolves and improve the collective efficiency. Then, the hybrid GWO and differential evolution (DE) strategy is executed for omega wolves to avoid falling into local optimum. In addition, a new hybrid one-dimensional and total-dimensional selection strategy is designed for omega wolves to balance the exploration and the exploitation during optimization. Finally, a perturbed operator is used to maintain the diversity of the population and further improve the exploration. To make a complete evaluation, the proposed HSGWO is first compared with six representative GWO variants for 50-dimensional problems based on CEC2014 benchmarks. The scalability of HSGWO is further tested by comparing it with eight state-of-the-art non-GWO algorithms for large-scale optimization problems with 100 decision variables. In addition, feature selection problem is used for testing the effectiveness of HSGWO on real-world applications. The experimental results demonstrated that the proposed algorithm outperforms other algorithms in terms of solution quality and convergence rate in most of the experiments.

High red and processed meat intake (RPMI) is an established risk factor for colorectal cancer (CRC). We aimed to assess the impact of RPMI on CRC risk according to and in comparison with genetically determined risk, which was quantified by a polygenic risk score (PRS). RPMI and potential confounders (ascertained by questionnaire) and a PRS (based on 140 CRC-related loci) were obtained from 5109 CRC cases and 4134 controls in a population-based case–control study. Associations of RPMI with CRC risk across PRS levels were assessed using logistic regression models and compared to effect estimates of PRS using “genetic risk equivalent” (GRE), a novel metric for effective risk communication. RPMI multiple times/week, 1 time/day, and >1 time/day was associated with 19% (95% CI 1% to 41%), 41% (18% to 70%), and 73% (30% to 132%) increased CRC risk, respectively, when compared to RPMI ≤ 1 time/week. Associations were independent of PRS levels (pinteraction = 0.97). The effect of RPMI > 1 time/day was equivalent to the effect of having 42 percentiles higher PRS level (GRE 42, 95% CI 20–65). RPMI increases CRC risk regardless of PRS levels. Avoiding RPMI can compensate for a substantial proportion of polygenic risk for CRC.

In this work, we consider the zero-delay transmission of bivariate Gaussian sources over a Gaussian broadcast channel with one-bit analog-to-digital converter (ADC) front ends. An outer bound on the conditional distortion region is derived. Focusing on the minimization of the average distortion, two types of methods are proposed to design nonparametric mappings. The first one is based on the joint optimization between the encoder and decoder with the use of an iterative algorithm. In the second method, we derive the necessary conditions to develop the optimal encoder numerically. Using these necessary conditions, an algorithm based on gradient descent search is designed. Subsequently, the characteristics of the optimized encoding mapping structure are discussed, and inspired by which, several parametric mappings are proposed. Numerical results show that the proposed parametric mappings outperform the uncoded scheme and previous parametric mappings for broadcast channels with infinite resolution ADC front ends. The nonparametric mappings succeed in outperforming the parametric mappings. The causes for the differences between the performances of two nonparametric mappings are analyzed. The average distortions of the parametric and nonparametric mappings proposed here are close to the bound for the cases with one-bit ADC front ends in low channel signal-to-noise ratio regions.

Adropin, a secretory signal peptide, has shown beneficial effects on improving glucose homeostasis and dyslipidemia. However, whether this peptide affects nonalcoholic steatohepatitis (NASH) has remained unclear. In this study, the serum adropin levels, liver injury and oxidative stress were measured in diet-induced NASH mice. Adropin knock-out mice and palmitate treated primary hepatic cells were used to investigate the influence of adropin on liver injury. Our results show that serum adropin levels were decreased and negatively correlated with liver injury in NASH mice. Knockout of adropin significantly exacerbated hepatic steatosis, inflammatory responses and fibrosis in mice after either methionine-choline deficient diet (MCD) or western diet (WD) feeding. And the treatment with adropin bioactive peptides ameliorated NASH progression in mice. Adropin alleviated hepatocyte injury by upregulating the expression of Gclc, Gclm, and Gpx1 in a manner dependent on Nrf2 transcriptional activity and by increasing the glutathione (GSH) levels. And adropin significantly increased CBP expression and promoted its binding with Nrf2, which enhanced Nrf2 transcriptional activity. Furthermore, AAV8-mediated overexpression of hepatic Nrf2 expression functionally restored the liver injury induced by adropin-deficiency MCD-fed mice. These findings provide evidence that adropin activates Nrf2 signaling and plays a protective role in liver injury of NASH and therefore might represent a novel target for the prevention and treatment of NASH. Keywords: NASH, ROS, Lipotoxicity, Nrf2, GSH

Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute i-th effective routing path decide which positions in the i-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches.

Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a monocular camera, a 3-DOF robotic arm with a gripper and a Jetson TK1 computer. Efficient and reliable visual detectors and control laws are crucial for autonomous aerial grasping using limited onboard sensing and computational capabilities. To detect and track the target object in real time, an efficient proposal algorithm is presented to reliably estimate the region of interest (ROI), then a correlation filter-based classifier is developed to track the detected object. Moreover, a support vector regression (SVR)-based grasping position detector is proposed to improve the grasp success rate with high computational efficiency. Using the estimated grasping position and the UAV?Äôs states, novel control laws of the UAV and the robotic arm are proposed to perform aerial grasping. Extensive simulations and outdoor flight experiments have been implemented. The experimental results illustrate that the proposed vision-based aerial grasping system can autonomously and reliably grasp the target object while working entirely onboard.

The association between estimated glomerular filtration rate (eGFR) and the risk of mortality among patients with coronary heart disease (CHD) is complex and still unclear. The aim of this study was to evaluate the effect of eGFR on the risk prediction of all-cause and cardiovascular disease (CVD) mortality with a long follow-up period among patients with CHD in China.We conducted a prospective cohort study of 3276 Chinese patients with CHD. Cox proportional hazards regression models were used to estimate the association of different levels of eGFR with the risks of mortality.During a mean follow-up period of 4.9 years, 293 deaths were identified. The multivariable-adjusted hazard ratios associated with different levels of eGFR (≥90 [reference group], 60-89, 30-59, 15-29 ml/min per 1.73m2) at baseline were 1.00, 1.28 (95% confidence interval [CI], 0.87-1.88), 1.96 (95% CI, 1.31-2.94), and 3.91 (95% CI, 2.15-7.13) (P

INTRODUCTION: Excess weight is an established risk factor of colorectal cancer (CRC). However, evidence is lacking on how its impact varies by polygenic risk at different stages of colorectal carcinogenesis. METHODS: We assessed the individual and joint associations of body mass index (BMI) and polygenic risk scores (PRSs) with findings of colorectal neoplasms among 4,784 participants of screening colonoscopy. Adjusted odds ratios (aORs) for excess weight derived by multiple logistic regression were converted to genetic risk equivalents (GREs) to quantify the impact of excess weight compared with genetic predisposition. RESULTS: Overweight and obesity (BMI 25-<30 and ≥30 kg/m²) were associated with increased risk of any colorectal neoplasm (aOR [95% confidence interval, CI] 1.26 [1.09-1.45] and 1.47 [1.24-1.75]). Obesity was associated with increased risk of advanced colorectal neoplasm (aOR [95% CI] 1.46 [1.16-1.84]). Dose-response relationships were seen for the PRS (stronger for advanced neoplasms than any neoplasms), with no interaction with BMI, suggesting multiplicative effects of both factors. Obese participants with a PRS in the highest tertile had a 2.3-fold (95% CI 1.7-3.1) and 2.9-fold (95% CI 1.9-4.3) increased risk of any colorectal neoplasm and advanced colorectal neoplasm, respectively. The aOR of obesity translated into a GRE of 38, meaning that its impact was estimated to be equivalent to the risk caused by 38 percentiles higher PRS for colorectal neoplasm. DISCUSSION: Excess weight and polygenic risk are associated with increased risk of colorectal neoplasms in a multiplicative manner. Maintaining normal weight is estimated to have an equivalent effect as having 38 percentiles lower PRS. [ABSTRACT FROM AUTHOR]