Conflating point of interest (POI) data: A systematic review of matching methods

Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research

Temporal Generalization Estimation in Evolving Graphs

Graph Neural Networks (GNNs) are widely deployed in vast fields, but they often struggle to maintain accurate representations as graphs evolve. We theoretically establish a lower bound, proving that under mild conditions, representation distortion inevitably occurs over time. To estimate the temporal distortion without human annotation after deployment, one naive approach is to pre-train a recurrent model (e.g., RNN) before deployment and use this model afterwards, but the estimation is far from satisfactory. In this paper, we analyze the representation distortion from an information theory perspective, and attribute it primarily to inaccurate feature extraction during evolution. Consequently, we introduce Smart, a straightforward and effective baseline enhanced by an adaptive feature extractor through self-supervised graph reconstruction. In synthetic random graphs, we further refine the former lower bound to show the inevitable distortion over time and empirically observe that Smart achieves good estimation performance. Moreover, we observe that Smart consistently shows outstanding generalization estimation on four real-world evolving graphs. The ablation studies underscore the necessity of graph reconstruction. For example, on OGB-arXiv dataset, the estimation metric MAPE deteriorates from 2.19% to 8.00% without reconstruction.Comment: Published as a conference paper at ICLR 202

Reforestation regulated soil bacterial community structure along vertical profiles in the Loess Plateau

IntroductionReforestation is a widely used strategy for ecological restoration in areas facing ecological degradation. Soil bacteria regulate many functional processes in terrestrial ecosystems; however, how they respond to reforestation processes in surface and deep soils remains unclear.MethodsArtificial Robinia pseudoacacia plantation with different stand ages (8, 22, and 32 years) in a typical fallow forest on the Loess Plateau was selected to explore the differential response of soil bacterial community to reforestation in different soil depths (surface 0–200 cm, middle 200–500 cm, and deep 500-100 cm). Soil bacterial diversity, community composition and the co-occurrence patterns, as well as the functions were analyzed.Results and discussionThe results showed that alpha diversity and the presence of biomarkers (keynote species) decreased with the increasing soil depth, with a sharp reduction in family-level biomarker numbers in 500–1,000 cm depth, while reforestation had a positive impact on bacterial alpha diversity and biomarkers. Reforestation induced a more loosely connected bacterial community, as evidenced by an increase of 9.38, 22.87, and 37.26% in the average path length of the co-occurrence network in all three soil layers, compared to farmland. In addition, reforestation reduced the hierarchy and complexity but increased the modularity of the co-occurrence network in top and deep soil layers. Reforestation also led to enrichment in the relative abundance of functional pathways in all soil layers. This study sheds light on the strategies employed by deep soil bacteria in response to reforestation and underscores the significant potential of deep soil bacteria in terrestrial ecosystems, particularly in the context of human-induced environmental changes

Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

acceptedVersio

Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers

Microbes inhabiting deep soil layers are known to be different from their counter-part in topsoil yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (>1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18-m depth profiles at 20–50-cm intervals across contrasting aridity condi-tions in semi-arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity de-clined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant-derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa–taxa and bacteria–fungi associations and more influ-ence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep-soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria–fungi associations, but increased the relative abundance of aerobic ammonia oxidation,manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, com-plexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that, even microbial communi-ties and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole-soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios

Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers

24 páginas.- 7 figuras.- referenciasMicrobes inhabiting deep soil layers are known to be different from their counterpart in topsoil yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (>1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18-m depth profiles at 20-50-cm intervals across contrasting aridity conditions in semi-arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity declined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant-derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa-taxa and bacteria-fungi associations and more influence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep-soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria-fungi associations, but increased the relative abundance of aerobic ammonia oxidation, manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, complexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that, even microbial communities and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole-soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios.This project was supported by the Joint Key Funds of the National Natural Science Foundation of China (U21A20237), the Strategic Priority Research Program of Chinese Academy of Sciences (XDB40020202). M.D.-B. acknowledges support from TED2021-130908B-C41/AEI/10.13039/501100011033/Unión Europea NextGenerationEU/PRTR and from the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. R.O.H. was funded by the Ramón y Cajal program of the MICINN (RYC-2017 22032), by the R&D Project of the Ministry of Science and Innovation PID2019-106004RA-I00 funded by MCIN/AEI/10.13039/501100011033, and by the European Agricultural Fund for Rural Development (EAFRD) through the “Aid to operational groups of the European Association of Innovation (AEI) in terms of agricultural productivity and sustainability,” Reference: GOPC-CA-20-0001Peer reviewe

RPS23RG1 modulates tau phosphorylation and axon outgrowth through regulating p35 proteasomal degradation

Tau蛋白病（Tauopathies）是由过度磷酸化的tau蛋白聚集形成神经纤维缠结为特征的一类神经退行性疾病，包括阿尔茨海默病（Alzheimer’s disease, AD）、进行性核上性麻痹（Progressive superanuclear palsy, PSP）、额颞叶痴呆（Frontotemporal dementia, FTD）等。随着全球社会结构的老龄化，tau蛋白病患者比率迅速增加，给个人和社会带来巨大的经济及精神负担。厦门大学神经科学研究所张云武教授团队最新发现RPS23RG1（RR1）的胞内羧基端区域能够与Cdk5激酶的激活蛋白p35的氨基端相互作用，介导p35的膜定位并影响其泛素化降解，从而调控在tau蛋白异常磷酸化过程中发挥重要作用的Cdk5激酶的活性。团队研究表明RPS23RG1通过其胞内羧基端与p35相互作用，介导p35膜结合和降解，从而抑制Cdk5活性，平衡tau磷酸化水平，促进轴突生长。此外，RPS23RG1的跨膜区与腺苷酸环化酶AC相互作用，抑制GSK3-β活性，同样控制tau过度磷酸化。提示RPS23RG1是改善tau过度磷酸化水平及治疗tau蛋白病的潜在靶点。 厦门大学医学院神经科学研究所博士后赵东栋为该研究第一作者，张云武教授为通讯作者。【Abstract】Tauopathies are a group of neurodegenerative diseases characterized by hyperphosphorylation of the microtubule-binding protein, tau, and typically feature axon impairment and synaptic dysfunction. Cyclin-dependent kinase5 (Cdk5) is a major tau kinase and its activity requires p35 or p25 regulatory subunits. P35 is subjected to rapid proteasomal degradation in its membrane-bound form and is cleaved by calpain under stress to a stable p25 form, leading to aberrant Cdk5 activation and tau hyperphosphorylation. The type Ib transmembrane protein RPS23RG1 has been implicated in Alzheimer’s disease (AD). However, physiological and pathological roles for RPS23RG1 in AD and other tauopathies are largely unclear. Herein, we observed retarded axon outgrowth, elevated p35 and p25 protein levels, and increased tau phosphorylation at major Cdk5 phosphorylation sites in Rps23rg1 knockout (KO) mice. Both downregulation of p35 and the Cdk5 inhibitor roscovitine attenuated tau hyperphosphorylation and axon outgrowth impairment in Rps23rg1 KO neurons. Interestingly, interactions between the RPS23RG1 carboxyl-terminus and p35 amino-terminus promoted p35 membrane distribution and proteasomal degradation. Moreover, P301L tau transgenic (Tg) mice showed increased tau hyperphosphorylation with reduced RPS23RG1 levels and impaired axon outgrowth. Overexpression of RPS23RG1 markedly attenuated tau hyperphosphorylation and axon outgrowth defects in P301L tau Tg neurons. Our results demonstrate the involvement of RPS23RG1 in tauopathy disorders, and implicate a role for RPS23RG1 in inhibiting tau hyperphosphorylation through homeostatic p35 degradation and suppression of Cdk5 activation. Reduced RPS23RG1 levels in tauopathy trigger aberrant Cdk5-p35 activation, consequent tau hyperphosphorylation, and axon outgrowth impairment, suggesting that RPS23RG1 may be a potential therapeutic target in tauopathy disorders.This work was supported by grants from National Key Research and Development Program of China (2016YFC1305903 and 2018YFC2000400 to Y-wZ), National Natural Science Foundation of China (81771377, U1705285, 91332112, and 81225008 to Y-wZ), Fundamental Research Funds for the Central Universities (20720180049 to Y-wZ), the Fujian Provincial Health Commission-Education Department Joint Tackling Plan (WKJ2016-2-18 to F-rL), and Postdoctoral Science Foundation of China (2020M671948 to DZ)

Big data analytics in production and distribution management

International audienceProduction and distribution are two key constituents of a supply chain. In view of the growing availability of data and advances in big data analytics techniques, there have been more and more applications of data analytics to deal with the problems in production and distribution management. With this in mind, we proposed a special issue on ‘Big Data Analytics in Production and Distribution Management' to report the latest development in this field. In this editorial, we first introduce the background and examine the existing review works on the applications of data analytics to operations management. We then introduce the papers accepted in the issue, and discuss how different types of big data analytics techniques are applied to production and distribution management, including demand forecasting, production scheduling, distribution management, manufacturing management, and supply chain management. Finally, we conclude the paper with a discussion of future research

A new view on bone graft in dental implantation: Autogenous bone mixed with titanium granules

Introduction : Dental implants have been widely applied in clinic for many years. However, the success rate is still challenging mainly because of bone deficiency. An ideal bone graft is traditionally thought to guide and induce new bone regeneration as well as been absorbed completely by human body. The Hypothesis: Autogenous bone mixed with titanium granules might be an ideal bone graft for dental implantation. Evaluation of the Hypothesis: First, we analyzed advantages of grafts of autogenous bone mixed with titanium granules, such as serving as a s scaffold for wound healing and tissue regeneration, creating sui microenvironment for implant-bone integration, shortening the new bone′s creeping distance, etc. Then we creatively hypothesized a novel alternative bone graft with premixed autogenous bone and non-absorbent titanium granules. Apart from repairing bone deficiency, our hypothesis could promote the integration between new bone and titanium implant from the perspective of microenvironment. We believe that the method is promising and worth extension in clinical application