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Gait recognition based on millimeter waves (mmWave) has a wide range of applications, such as smart homes and health monitoring, and attracted extensive attention due to its non-contact, privacy protection, and light-independent characteristics. Existing gait recognition based on mmWave performs well on the datasets collected at a single time or environment. However, the recognition accuracy declines significantly with the time and environment domains shifting, which affects its practical applications. In this paper, we propose a novel mmWave gait recognition CRNet that is robust to both time and environment, which is realized through two-stage training. Specifically, the first stage designs a contrastive learning strategy to pre-train the encoder module, which aims at learning the general gait features across different seen time and environment domains. The second stage further trains the classification module based on specific recognition tasks. After the two-stage training, CRNet experiments on test sets with unseen time or environment domains. We collect a mmWave-based multi-person gait recognition dataset with multiple time and environment domains. Experiments show that CRNet still performs well in unfamiliar domains which increases the accuracy from 75.7% to 91.2% compared to the baseline. © 2024 Z. Meng, A. Zhou, H. Ma and Q. Zhang.

Synthetic messenger RNA (mRNA)-based therapeutics have emerged as promising candidates for the prevention and treatment of a wide range of diseases and disorders. mRNA possesses notable advantages, including exceptional safety, the capacity to replace various functional proteins produced by the human body, and the flexibility and industrial scalability of mRNA design and synthesis. In this review, we aim to summarize the latest advancements in mRNA nanotherapy and explore recent breakthroughs in the development of mRNA delivery systems, such as lipid-, polymer-, and polymer-lipid hybrid-based platforms. Notably, we highlight recent innovations in mRNA-based nanotherapy for cancer treatment, encompassing protein replacement, vaccines, cellbased therapies, and gene editing. Furthermore, we present our insights on the future prospects and remaining challenges in harnessing the potential of mRNA-based nanotherapy in the battle against cancer.

Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios. While considerable success has been achieved, there still exist urgent limitations. Existing works ignore the locality relationships of representations and attributes, which have effective transferability between seeable classes and unseeable classes. Also, the continuous-value attributes are not fully harnessed. In response, we conduct a COMprehensive Attribute Exploration for ZSH, named COMAE, which depicts the relationships from seen classes to unseen ones through three meticulously designed explorations, i.e., point-wise, pair-wise and class-wise consistency constraints. By regressing attributes from the proposed attribute prototype network, COMAE learns the local features that are relevant to the visual attributes. Then COMAE utilizes contrastive learning to comprehensively depict the context of attributes, rather than instance-independent optimization. Finally, the class-wise constraint is designed to cohesively learn the hash code, image representation, and visual attributes more effectively. Experimental results on the popular ZSH datasets demonstrate that COMAE outperforms state-of-the-art hashing techniques, especially in scenarios with a larger number of unseen label classes., Comment: 13 pages, 7 figures

Public trust in medical institutions is essential for ensuring compliance with medical directives. However, the politicization of public health issues and the polarized nature of major news outlets suggest that partisanship and news consumption habits can influence medical trust. This study employed a survey with 858 participants and used regression analysis to assesses how news consumption habits and information assessment traits (IATs) influence trust in medical scientists. IATs included were conscientiousness, openness, need for cognitive closure (NFCC), and cognitive reflective thinking (CRT). News sources were classified on the basis of factuality and political bias. Initially, readership of liberally biased news was positively associated with medical trust (p < 0.05). However, this association disappeared when controlling for the news source's factuality (p = 0.28), while CRT (p < 0.05) was positively associated with medical trust. When controlling for conservatively biased news sources, factuality of the news source (p < 0.05) and NFCC (p < 0.05) were positively associated with medical trust. While partisan media bias may influence medical trust, these results suggest that those who have higher abilities to assess information and who prefer more credible news sources have a greater trust in medical scientists.

Seed size is a critical factor that can impact the regeneration process of plant populations. The objective of this study is to identify optimal seeds for the cultivation of two overexploited native forest species in Côte d'Ivoire, namely Khaya senegalensis (Desr.) A.Juss., 1830 and Parkia biglobosa (Jacq.) R.BR. ex G. Don, 1830. A total of 1080 seeds per species were evaluated for germination and seedling vigour in two distinct environments (dry and humid) in Côte d'Ivoire. The results demonstrated that, for Khaya senegalensis, larger seeds exhibited higher germination rates, whereas for Parkia biglobosa, smaller and medium-sized seeds showed better germination performance. However, regardless of species and study site, larger seeds consistently produced more vigorous seedlings. In this case, pretreatment tests on large seeds of Parkia biglobosa can potentially enhance their germination performance. Large seeds, ranging from 0.25–0.37 g in Khaya senegalensis and 0.2–0.32 g in Parkia biglobosa, appear to be the most suitable and best candidates for high-quality, high-performance seeds to optimise the cultivation of these species in Côte d'Ivoire. Distributing such seeds to farmers can contribute to the success of reforestation and agroforestry programs involving these target species in Côte d'Ivoire. These findings contribute to the sustainable management of the target species and can serve as a basis for restructuring forest regeneration policies in Côte d'Ivoire.

As an emerging concept, the Metaverse has the potential to revolutionize the social interaction in the post-pandemic era by establishing a digital world for online education, remote healthcare, immersive business, intelligent transportation, and advanced manufacturing. The goal is ambitious, yet the methodologies and technologies to achieve the full vision of the Metaverse remain unclear. In this paper, we first introduce the three infrastructure pillars that lay the foundation of the Metaverse, i.e., human-computer interfaces, sensing and communication systems, and network architectures. Then, we depict the roadmap towards the Metaverse that consists of four stages with different applications. To support diverse applications in the Metaverse, we put forward a novel design methodology: task-oriented design, and further review the challenges and the potential solutions. In the case study, we develop a prototype to illustrate how to synchronize a real-world device and its digital model in the Metaverse by task-oriented design, where a deep reinforcement learning algorithm is adopted to minimize the required communication throughput by optimizing the sampling and prediction systems subject to a synchronization error constraint., Comment: This paper is accepted by the IEEE Wireless Communications

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization(C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization(PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk(CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error., Comment: This paper is accepted by IEEE Journal on Selected Areas in Communications, JSAC-SI-HCM 2024

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The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with the help of Mixed Reality (MR) technologies; still, to provide a satisfactory experience for users, the synchronization between the physical world and its digital models is crucial. This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on tracking the Mean Squared Error (MSE) between a real-world device and its digital model in the metaverse. To optimize the sampling rate and the prediction horizon, we exploit expert knowledge and develop a constrained Deep Reinforcement Learning (DRL) algorithm, named Knowledge-assisted Constrained Twin-Delayed Deep Deterministic (KC-TD3) policy gradient algorithm. We validate our framework on a prototype composed of a real-world robotic arm and its digital model. Compared with existing approaches: (1) When the tracking error constraint is stringent (MSE=0.002 degrees), our policy degenerates into the policy in the sampling-communication co-design framework. (2) When the tracking error constraint is mild (MSE=0.007 degrees), our policy degenerates into the policy in the prediction-communication co-design framework. (3) Our framework achieves a better trade-off between the average MSE and the average communication load compared with a communication system without sampling and prediction. For example, the average communication load can be reduced up to 87% when the track error constraint is 0.002 degrees. (4) Our policy outperforms the benchmark with the static sampling rate and prediction horizon optimized by exhaustive search, in terms of the tail probability of the tracking error. Furthermore, with the assistance of expert knowledge, the proposed algorithm KC-TD3 achieves better convergence time, stability, and final policy performance.

Plants exhibit plasticity in response to various external conditions, characterized by changes in physiological and morphological features. Although being non-negligible, compared to the other environmental factors, the effect of wind on plant growth is less extensively studied, either experimentally or computationally. This study aims to propose a modeling approach that can simulate the impact of wind on plant growth, which brings a biomechanical feedback to growth and biomass distribution into a functional–structural plant model (FSPM). Tree reaction to the wind is simulated based on the hypothesis that plants tend to fit in the environment best. This is interpreted as an optimization problem of finding the best growth-regulation sink parameter giving the maximal plant fitness (usually seed weight, but expressed as plant biomass and size). To test this hypothesis in silico, a functional–structural plant model, which simulates both the primary and secondary growth of stems, is coupled with a biomechanical model which computes forces, moments of forces, and breakage location in stems caused by both wind and self-weight increment during plant growth. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to maximize the multi-objective function (stem biomass and tree height) by determining the key parameter value controlling the biomass allocation to the secondary growth. The digital trees show considerable phenotypic plasticity under different wind speeds, whose behavior, as an emergent property, is in accordance with experimental results from works of literature: the height and leaf area of individual trees decreased with wind speed, and the diameter at the breast height (DBH) increased at low-speed wind but declined at higher-speed wind. Stronger wind results in a smaller tree. Such response of trees to the wind is realistically simulated, giving a deeper understanding of tree behavior. The result shows that the challenging task of modeling plant plas

Magnesium–Sulfur batteries are one of most appealing options among the post-lithium battery systems due to its potentially high energy density, safe and sustainable electrode materials. The major practical challenges are originated from the soluble magnesium polysulfide intermediates and their shuttling between the electrodes, which cause high overpotentials, low sulfur utilization, and poor Coulombic efficiency. Herein, a functional Mo6S8 modified separator is designed to effectively address these issues. Both the experimental results and density functional theory calculations show that the electrochemically active Mo6S8 layer has a superior adsorption capability of polysulfides and simultaneously acts as a mediator to accelerate the polysulfide conversion kinetics. Remarkably, the magnesium–sulfur cell assembled with the functional separator delivers a high specific energy density (942.9 mA h g−1 in the 1st cycle) and can be cycled at 0.2 C for 200 cycles with a Coulombic efficiency of 96%. This work demonstrates a new design concept toward high-performance metal–sulfur batteries.

There is a persistent $H_0$-tension, now at more than $\gtrsim 4\sigma$ level, between the local distance ladder value and the \emph{Planck} cosmic microwave background measurement, in the context of flat $\Lambda$CDM model. We reconstruct $H(z)$ in a cosmological-model-independent way using three low-redshift distance probes including the latest data from baryon acoustic oscillation, Type Ia supernova and four gravitational lensing Time-Delay observations. We adopt general parametric models of $H(z)$ and assume a Gaussian prior on the sound horizon at drag epoch, $r_{\mathrm s}$, from \emph{Planck} measurement. The reconstructed $H_0$ using Pantheon SN Ia and BAO data are consistent with the \emph{Planck} flat $\Lambda$CDM value. When including the GLTD data, $H_0$ increases mildly, yet remaining discrepant with the local measurement at $\sim 2.5\sigma$ level. Our reconstructions being blind to the dark sectors at low redshift, we reaffirm the earlier claims that the Hubble tension is not likely to be solved by modifying the energy budget of the low-redshift universe. We further forecast the constraining ability of future realistic mock BAO data from DESI and GLTD data from LSST, combining which, we anticipate that the uncertainty of the inferred $H_0$ would be improved by $\sim 38\%$, reaching $\sigma_{H_0} \approx 0.56$ uncertainty level., Comment: 12 pages, 5 figures, new reference added

Background: With up to 200 published contributions, the GreenLab mathematical model of plant growth, developed since 2000 under Sino-French co-operation for agronomic applications, is descended from the structural models developed in the AMAP unit that characterize the development of plants and encompass them in a conceptual mathematical framework. The model also incorporates widely recognized crop model concepts (thermal time, light use efficiency and light interception), adapting them to the level o:f the individual plant. Scope Such long-term research work calls for an overview at some point. That is the objective of this review paper, which retraces the main history of the model's development and its current status, highlighting three aspects. (1) What are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols? (3) What kind of applications can such a model address? This last question is answered using case studies as illustrations, and through the Discussion. Conclusions: The results obtained over several decades illustrate a key feature of the GreenLab model: owing to its concise mathematical formulation based on the factorization of plant structure, it comes along with dedicated methods and experimental protocols for its parameter estimation, in the deterministic or stochastic cases, at single-plant or population levels. Besides providing a reliable statistical framework, this intense and long-term research effort has provided new insights into the internal trophic regulations of many plant species and new guidelines for genetic improvement or optimization of crop systems.

There is a persistent $H_0$-tension, now at more than $\gtrsim 4\sigma$ level, between the local distance ladder value and the \emph{Planck} cosmic microwave background measurement, in the context of flat $\Lambda$CDM model. We reconstruct $H(z)$ in a cosmological-model-independent way using three low-redshift distance probes including the latest data from baryon acoustic oscillation, Type Ia supernova and four gravitational lensing Time-Delay observations. We adopt general parametric models of $H(z)$ and assume a Gaussian prior on the sound horizon at drag epoch, $r_{\mathrm s}$, from \emph{Planck} measurement. The reconstructed $H_0$ using Pantheon SN Ia and BAO data are consistent with the \emph{Planck} flat $\Lambda$CDM value. When including the GLTD data, $H_0$ increases mildly, yet remaining discrepant with the local measurement at $\sim 2.5\sigma$ level. Our reconstructions being blind to the dark sectors at low redshift, we reaffirm the earlier claims that the Hubble tension is not likely to be solved by modifying the energy budget of the low-redshift universe. We further forecast the constraining ability of future realistic mock BAO data from DESI and GLTD data from LSST, combining which, we anticipate that the uncertainty of the inferred $H_0$ would be improved by $\sim 38\%$, reaching $\sigma_{H_0} \approx 0.56$ uncertainty level., Comment: 12 pages, 5 figures, new reference added

The piston skirt is one of the main contributors to the total mechanical loss in internal combustion engines. Usually, the skirt friction experiences a rapid change during the break-in period largely due to the wear of the machine marks or roughness against soft coatings. It is thus important to consider the effect of the change of the roughness for a realistic prediction of the piston skirt friction and system optimization. In this work, an existing model of piston skirt lubrication was improved with the consideration of a breaking in process for the most commonly used triangle machine marks. A new set of flow factors in the averaged Reynolds equation were analytically derived for the trapezoid shape formed after wear of the original triangle shape. A new asperity contact model was developed for the trapezoid shape. The calculation results reflect the trend of friction mean effective pressure (FMEP) during break-in in an engine test and showed quantitative agreement under the same amount of wear.

This paper presents a smart sliding Chinese pinyin Input Method Editor (IME) for touchscreen devices which allows user finger sliding from one key to another on the touchscreen instead of tapping keys one by one, while the target Chinese character sequence will be predicted during the sliding process to help user input Chinese characters efficiently. Moreover, the layout of the virtual keyboard of our IME adapts to user sliding for more efficient inputting. The layout adaption process is utilized with Recurrent Neural Networks (RNN) and deep reinforcement learning. The pinyin-to-character converter is implemented with a sequence-to-sequence (Seq2Seq) model to predict the target Chinese sequence. A sliding simulator is built to automatically produce sliding samples for model training and virtual keyboard test. The key advantage of our proposed IME is that nearly all its built-in tactics can be optimized automatically with deep learning algorithms only following user behavior. Empirical studies verify the effectiveness of the proposed model and show a better user input efficiency., Comment: There are some insufficient explanations that may confuse readers. We will continue the research, but it will take a lot of time. After discussing with co-authors, we decide to withdraw this version from ArXiv, instead of replacement. We may re-upload a new version of this work in the future

Background and aims: Cannabis is one of the most commonly used substances among adolescents and young adults. Earlier age at cannabis initiation is linked to adverse life outcomes, including multi-substance use and dependence. This study estimated the heritability of age at first cannabis use and identified associations with genetic variants. Methods: A twin-based heritability analysis using 8055 twins from three cohorts was performed. We then carried out a genome-wide association meta-analysis of age at first cannabis use in a discovery sample of 24 953 individuals from nine European, North American and Australian cohorts, and a replication sample of 3735 individuals. Results: The twin-based heritability for age at first cannabis use was 38% [95% confidence interval (CI) = 19–60%]. Shared and unique environmental factors explained 39% (95% CI = 20–56%) and 22% (95% CI = 16–29%). The genome-wide association meta-analysis identified five single nucleotide polymorphisms (SNPs) on chromosome 16 within the calcium-transporting ATPase gene (ATP2C2) at P < 5E-08. All five SNPs are in high linkage disequilibrium (LD) (r2 > 0.8), with the strongest association at the intronic variant rs1574587 (P = 4.09E-09). Gene-based tests of association identified the ATP2C2 gene on 16q24.1 (P = 1.33e-06). Although the five SNPs and ATP2C2 did not replicate, ATP2C2 has been associated with cocaine dependence in a previous study. ATP2B2, which is a member of the same calcium signalling pathway, has been associated previously with opioid dependence. SNP-based heritability for age at first cannabis use was non-significant. Conclusion: Age at cannabis initiation appears to be moderately heritable in western countries, and individual differences in onset can be explained by separate but correlated genetic liabilities. The significant association between age of initiation and ATP2C2 is consistent with the role of calcium signalling mechanisms in substance use disorders.

Obtaining a high-performance nonprecious electrocatalyst for oxygen and hydrogen evolution reactions (OER and HER) under mild conditions is highly desirable yet challenging. Transition metal nitrides are emerging as cost-effective substitutes for state-of-the-art noble metal based catalysts, but large OER overpotentials plague these nitrides. Herein, we developed the heterostructure of CoO-domain-anchored Co4N nanowhiskers as an effective bifunctional electrocatalyst for both OER and HER in neutral media. The exterior CoO targets OER, and the CO4N matrix is active for HER. The periodically-aligned {111} crystalline planes between Co4N and CoO provide good electron transport through their interface. The heterostructure performs dual functions of elevating activity and diminishing interior resistance of the catalyst. Moreover, the porous metallic Co4N nanowhiskers were integrated on a nickel foam, and this configuration contributes an arterial channel for collecting current as well as a spacious surface area for increasing the reaction cross section for heterocatalysis. These advantages actuate our bifunctional 3D catalytic electrode to achieve low OER and HER overpotentials in neutral systems. It finally provides an unprecedentedly low cell voltage (1.79 V for 10 mA cm−2) for catalyzing overall water splitting in neutral media.

Obtaining a high-performance nonprecious electrocatalyst for oxygen and hydrogen evolution reactions (OER and HER) under mild conditions is highly desirable yet challenging. Transition metal nitrides are emerging as cost-effective substitutes for state-of-the-art noble metal based catalysts, but large OER overpotentials plague these nitrides. Herein, we developed the heterostructure of CoO-domain-anchored Co4N nanowhiskers as an effective bifunctional electrocatalyst for both OER and HER in neutral media. The exterior CoO targets OER, and the CO4N matrix is active for HER. The periodically-aligned {111} crystalline planes between Co4N and CoO provide good electron transport through their interface. The heterostructure performs dual functions of elevating activity and diminishing interior resistance of the catalyst. Moreover, the porous metallic Co4N nanowhiskers were integrated on a nickel foam, and this configuration contributes an arterial channel for collecting current as well as a spacious surface area for increasing the reaction cross section for heterocatalysis. These advantages actuate our bifunctional 3D catalytic electrode to achieve low OER and HER overpotentials in neutral systems. It finally provides an unprecedentedly low cell voltage (1.79 V for 10 mA cm−2) for catalyzing overall water splitting in neutral media.

BACKGROUND AND AIMS: Cannabis is one of the most commonly used substances among adolescents and young adults. Earlier age at cannabis initiation is linked to adverse life outcomes including multi-substance use and dependence. This study estimated the heritability of age at first cannabis use and identify associations with genetic variants.METHODS: A twin-based heritability analysis using 8,055 twins from three cohorts was performed. We then carried-out a genome wide survival meta-analysis of age at first cannabis use in a discovery sample of 24,953 individuals from nine European, North American, and Australian cohorts, and a replication sample of 3,735 individuals.RESULTS: The twin-based heritability for age at first cannabis use was 38% (95% confidence interval [CI] 19-60%). Shared and unique environmental factors explained 39% (95% CI 20-56%) and 22% (95% CI 16-29%). The genome wide survival meta-analysis identified five SNPs on chromosome 16 within the Calcium-transporting ATPase gene (ATP2C2) at P < 5E-08. All five SNPs are in high LD (r2 >0.8) with the strongest association at the intronic variant rs1574587 (P=4.09E-09). Gene-based tests of association identified the ATP2C2 gene on 16q24.1 (P=1.33e-06). Although the five SNPs and ATP2C2 did not replicate, ATP2C2 has been associated with cocaine dependence in a previous study. ATP2B2, which is a member of the same calcium signalling pathway, has been previously associated with opioid dependence. SNP-based heritability for age at first cannabis use was non-significant.CONCLUSION: Age at cannabis initiation appears to be moderately heritable in Western countries, and individual differences in onset can be explained by separate but correlated genetic liabilities. The significant association between age of initiation and ATP2C2 is consistent with the role of calcium signalling mechanisms in substance use disorders.

Background and aimsCannabis is one of the most commonly used substances among adolescents and young adults. Earlier age at cannabis initiation is linked to adverse life outcomes, including multi-substance use and dependence. This study estimated the heritability of age at first cannabis use and identified associations with genetic variants.MethodsA twin-based heritability analysis using 8055 twins from three cohorts was performed. We then carried out a genome-wide association meta-analysis of age at first cannabis use in a discovery sample of 24 953 individuals from nine European, North American and Australian cohorts, and a replication sample of 3735 individuals.ResultsThe twin-based heritability for age at first cannabis use was 38% [95% confidence interval (CI) = 19-60%]. Shared and unique environmental factors explained 39% (95% CI = 20-56%) and 22% (95% CI = 16-29%). The genome-wide association meta-analysis identified five single nucleotide polymorphisms (SNPs) on chromosome 16 within the calcium-transporting ATPase gene (ATP2C2) at P < 5E-08. All five SNPs are in high linkage disequilibrium (LD) (r2  > 0.8), with the strongest association at the intronic variant rs1574587 (P = 4.09E-09). Gene-based tests of association identified the ATP2C2 gene on 16q24.1 (P = 1.33e-06). Although the five SNPs and ATP2C2 did not replicate, ATP2C2 has been associated with cocaine dependence in a previous study. ATP2B2, which is a member of the same calcium signalling pathway, has been associated previously with opioid dependence. SNP-based heritability for age at first cannabis use was non-significant.ConclusionAge at cannabis initiation appears to be moderately heritable in western countries, and individual differences in onset can be explained by separate but correlated genetic liabilities. The significant association between age of initiation and ATP2C2 is consistent with the role of calcium signal

BACKGROUND AND AIMS: Cannabis is one of the most commonly used substances among adolescents and young adults. Earlier age at cannabis initiation is linked to adverse life outcomes including multi-substance use and dependence. This study estimated the heritability of age at first cannabis use and identify associations with genetic variants.METHODS: A twin-based heritability analysis using 8,055 twins from three cohorts was performed. We then carried-out a genome wide survival meta-analysis of age at first cannabis use in a discovery sample of 24,953 individuals from nine European, North American, and Australian cohorts, and a replication sample of 3,735 individuals.RESULTS: The twin-based heritability for age at first cannabis use was 38% (95% confidence interval [CI] 19-60%). Shared and unique environmental factors explained 39% (95% CI 20-56%) and 22% (95% CI 16-29%). The genome wide survival meta-analysis identified five SNPs on chromosome 16 within the Calcium-transporting ATPase gene (ATP2C2) at P < 5E-08. All five SNPs are in high LD (r2 >0.8) with the strongest association at the intronic variant rs1574587 (P=4.09E-09). Gene-based tests of association identified the ATP2C2 gene on 16q24.1 (P=1.33e-06). Although the five SNPs and ATP2C2 did not replicate, ATP2C2 has been associated with cocaine dependence in a previous study. ATP2B2, which is a member of the same calcium signalling pathway, has been previously associated with opioid dependence. SNP-based heritability for age at first cannabis use was non-significant.CONCLUSION: Age at cannabis initiation appears to be moderately heritable in Western countries, and individual differences in onset can be explained by separate but correlated genetic liabilities. The significant association between age of initiation and ATP2C2 is consistent with the role of calcium signalling mechanisms in substance use disorders.

Obtaining a high-performance nonprecious electrocatalyst for oxygen and hydrogen evolution reactions (OER and HER) under mild conditions is highly desirable yet challenging. Transition metal nitrides are emerging as cost-effective substitutes for state-of-the-art noble metal based catalysts, but large OER overpotentials plague these nitrides. Herein, we developed the heterostructure of CoO-domain-anchored Co4N nanowhiskers as an effective bifunctional electrocatalyst for both OER and HER in neutral media. The exterior CoO targets OER, and the CO4N matrix is active for HER. The periodically-aligned {111} crystalline planes between Co4N and CoO provide good electron transport through their interface. The heterostructure performs dual functions of elevating activity and diminishing interior resistance of the catalyst. Moreover, the porous metallic Co4N nanowhiskers were integrated on a nickel foam, and this configuration contributes an arterial channel for collecting current as well as a spacious surface area for increasing the reaction cross section for heterocatalysis. These advantages actuate our bifunctional 3D catalytic electrode to achieve low OER and HER overpotentials in neutral systems. It finally provides an unprecedentedly low cell voltage (1.79 V for 10 mA cm−2) for catalyzing overall water splitting in neutral media.

Obtaining a high-performance nonprecious electrocatalyst for oxygen and hydrogen evolution reactions (OER and HER) under mild conditions is highly desirable yet challenging. Transition metal nitrides are emerging as cost-effective substitutes for state-of-the-art noble metal based catalysts, but large OER overpotentials plague these nitrides. Herein, we developed the heterostructure of CoO-domain-anchored Co4N nanowhiskers as an effective bifunctional electrocatalyst for both OER and HER in neutral media. The exterior CoO targets OER, and the CO4N matrix is active for HER. The periodically-aligned {111} crystalline planes between Co4N and CoO provide good electron transport through their interface. The heterostructure performs dual functions of elevating activity and diminishing interior resistance of the catalyst. Moreover, the porous metallic Co4N nanowhiskers were integrated on a nickel foam, and this configuration contributes an arterial channel for collecting current as well as a spacious surface area for increasing the reaction cross section for heterocatalysis. These advantages actuate our bifunctional 3D catalytic electrode to achieve low OER and HER overpotentials in neutral systems. It finally provides an unprecedentedly low cell voltage (1.79 V for 10 mA cm−2) for catalyzing overall water splitting in neutral media.

In this paper, we use the model dependent method to revisit the constraint on the well-known cosmic distance duality relation (CDDR). By using the latest SNIa samples, such as Union2.1, JLA and SNLS, we find that the SNIa data alone can not constrain the cosmic opacity parameter $\varepsilon$, which denotes the deviation from the CDDR, $d_{\rm L} = d_{\rm A}(1+z)^{2+\varepsilon}$, very well. The constraining power on $\varepsilon$ from the luminosity distance indicator provided by SNIa and GRB is hardly to be improved at present. When we include other cosmological observations, such as the measurements of Hubble parameter, the baryon acoustic oscillations and the distance information from cosmic microwave background, we obtain the tightest constraint on the cosmic opacity parameter $\varepsilon$, namely the 68\% C.L. limit: $\varepsilon=0.023\pm0.018$. Furthermore, we also consider the evolution of $\varepsilon$ as a function of $z$ using two methods, the parametrization and the principle component analysis, and do not find the evidence for the deviation from zero. Finally, we simulate the future SNIa and Hubble measurements and find the mock data could give very tight constraint on the cosmic opacity $\varepsilon$ and verify the CDDR at high significance., Comment: 9 pages, 5 figures, 2 tables, Accepted for publication in Physics of Dark Universe

In this paper, we use the model dependent method to revisit the constraint on the well-known cosmic distance duality relation (CDDR). By using the latest SNIa samples, such as Union2.1, JLA and SNLS, we find that the SNIa data alone can not constrain the cosmic opacity parameter $\varepsilon$, which denotes the deviation from the CDDR, $d_{\rm L} = d_{\rm A}(1+z)^{2+\varepsilon}$, very well. The constraining power on $\varepsilon$ from the luminosity distance indicator provided by SNIa and GRB is hardly to be improved at present. When we include other cosmological observations, such as the measurements of Hubble parameter, the baryon acoustic oscillations and the distance information from cosmic microwave background, we obtain the tightest constraint on the cosmic opacity parameter $\varepsilon$, namely the 68\% C.L. limit: $\varepsilon=0.023\pm0.018$. Furthermore, we also consider the evolution of $\varepsilon$ as a function of $z$ using two methods, the parametrization and the principle component analysis, and do not find the evidence for the deviation from zero. Finally, we simulate the future SNIa and Hubble measurements and find the mock data could give very tight constraint on the cosmic opacity $\varepsilon$ and verify the CDDR at high significance., Comment: 9 pages, 5 figures, 2 tables, Accepted for publication in Physics of Dark Universe

In this study, the main goal is to investigate the displacement gain characteristics of novel dual-valve Electro-Hydraulic Excitation system (EHES). Relative to conventional system, the dual-valve system can improve the amplitude range, but the range is always affected by some nonlinear factors. In order to analysis on the issue, a nonlinear mathematical model of dual-valve EHES is established based on Bernoulli and classical electro-hydraulic servo equation. Then, the results of numerical simulations by using of the MATLAB/Simulink software are compared with experimental results. Simulation and experimental results show that the sink flow mainly influences on the flow dynamic characteristics of dual-valve EHES. Because of that, dual-valve EHES only can improve 50 %-70 % of displacement within 5 to 50 Hz of excitation frequency and 45 %-80 % of displacement amplitude within 20 % to 100 % of command value.

The GreenLab model was used to study the interaction between source-sink dynamics at the whole tree level, wood production and distribution within the stem, and tree mechanical stability through simulation and optimization. In this first promising numerical attempt, two GreenLab parameters were considered in order to maximize wood production: the sink strength for cambial growth and a coefficient that determines the way the biomass assigned to cambial growth is allocated to each metamer, through optimization and simulation respectively. The optimization procedure that has been used is based on a heuristic optimization algorithm called Particle Swarm Optimization (PSO). In the first part of the paper, wood production was maximized without considering the effect of wood distribution on tree mechanical stability. Contrary to common idea that increasing sink strength for cambial growth leads to increasing wood production, an optimal value can be found. The optimization results implied that an optimal source and sink balance should be considered to optimize wood production. In a further step, the mechanical stability of trees submitted to their self weight was taken into account based on simplified mechanical assumptions. Simulation results revealed that the allocation of wood at the stem base strongly influenced its global deformation. Such basic mechanical criterion can be an indicator of wood quality if we consider further the active biomechanical processes involved in tree gravitropic responses, e.g. formation of reaction wood.