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Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this article, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate dynamic graph, which is integrated with pre-defined static graph. As far as we know, we are first to employ a generation method to model fine topology of dynamic graph at each time step. Furthermore, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently. Source codes are available at https://github.com/tsinghua-fib-lab/Traffic-Benchmark .

Authigenic albites occur widely in clastic reservoirs with important implications for diagenesis and reservoir formation. The middle Permian Lucaogou Formation in the Jimusaer Sag (Junggar Basin, NW China), where major exploration breakthroughs in shale oil have been achieved, reveals a new phenomenon that authigenic albites are abundant in unique mixed carbonate–volcanic–clastic sequences. This has not been reported in the literatures. To fill the knowledge gap, the origin of these authigenic albites and their relationship with dissolution pores (i.e. diagenesis implications) were investigated. Results show that two types (I and II) of authigenic albite were identified within the shale oil reservoirs. Euhedral Type I authigenic albites with 3–10 μm only occur in dolarenite intraclasts and are symbiotic with amorphous dolomite minerals with a pure chemical composition of >99% albite-end-member content. Larger Type II authigenic albites with 10–50 μm are widely distributed in reservoirs, primarily in dissolution pores, and coexist with authigenic dolomite minerals or dolomite overgrowths. Their chemical composition is less pure with anorthite-end-member contents that range from undetectable to 9.77%, with an average of 1.34%. A symbiotic relationship, pure chemical composition, size, and euhedral morphology indicate that Type I authigenic albites precipitated during syngenetic hydrothermal action. However, the morphology of dissolution pores, residual symbiotic “orthoclase”, impure chemical composition and carbon–oxygen isotope indicate that Type II were the products of the dissolution and reprecipitation of “perthite” crystal pyroclasts influenced by acid organic fluids in latter diagenesis. The differential dissolution of “orthoclase” and “albite” components in “perthite” crystal pyroclasts formed enormous intergranular secondary pores in the presence of dolomite minerals in the shale oil reservoirs.

Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3- D imensional G raph C onvolution N etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.

Methanogens are methane-producing archaea (some of the most primitive organisms on Earth), which possess great phylogenetic and ecological diversity in modern ecosystems. However, cellular fossil evidence of methanogens remains extremely scarce throughout the geological record. Here, we report a new population of spheroidal microstructures composed of dolomite observed in Permian lake deposits in northwestern China. The microspheres exhibit indicators of biological affinity and are well preserved in authigenic dolomite with cellular fidelity. Based on morphological and geochemical evidence, these microspheres are interpreted as fossilized cells of methanogenic archaea, which can be divided into three size-based taxa. These microfossils are the first fossil record of spheroidal methanogens. The microfossil-bearing dolomite exhibits extremely positive δ13C values (up to +20‰ relative to Vienna Peedee belemnite) that are attributed to microbial methanogenesis. The results suggest that methanogens were a significant component of this Permian lake biosphere. As a consequence of the metabolic activity of the methanogens, a large amount of biogenic methane was produced through methanogenesis in the anoxic lake sediments. This study not only fills a gap in the fossil record of methanogenic archaea, but it also provides new insights into methane emissions from ancient lakes.

With the popularity of mobile devices and location-based social network, understanding and modelling the human mobility becomes an important topic in the field of ubiquitous computing. With the model developing from personal models with own information to the joint models with population information, the prediction performance of proposed models become better and better. Meanwhile, the privacy issues of these models come into the view of community and the public: collecting and uploading private data to the centralized server without enough regulation. In this paper, we propose PMF, a privacy-preserving mobility prediction framework via federated learning, to solve this problem without significantly sacrificing the prediction performance. In our framework, based on the deep learning mobility model, no private data is uploaded into the centralized server and the only uploaded thing is the updated model parameters which are difficult to crack and thus more secure. Furthermore, we design a group optimization method for the training on local devices to achieve better trade-off between performance and privacy. Finally, we propose a fine-tuned personal adaptor for personal modelling to further improve the prediction performance. We conduct extensive experiments on three real-life mobility datasets to demonstrate the superiority and effectiveness of our methods in privacy protection settings.

The reservoir in the Wuerhe Formation in the Mahu Sag, northwestern Junggar Basin, China, exhibits complex dissolution and cementation related to zeolite. The source and mechanism of diagenetic fluids are crucial in studying the reservoir genesis. Thus we investigated the key reservoirs fluids related to the zeolite and discussed their significance in the zeolite-rich reservoir of the Permian Wuerhe Formation in the Mahu Sag. Based on thin sections and electron microscope observations of rock samples and analyses of physical properties, C-O isotopes, and major elements, it is found that the reservoir underwent mainly two stages of fluid-related dissolution and cementation processes, in which the hydrocarbon-bearing fluid played the primary role in forming the high-quality reservoir. Dissolution pores are the most important storage space, and zeolite cement is the most important dissolution mineral. The geochemical characteristics of zeolite and calcite cement indicate the presence of two diagenetic fluids. The iron-rich calcite and orange-red heulandite is related to early diagenetic fluids with high iron content and higher carbon isotope values, whereas the calcites, with high manganese content and lower carbon isotope values, are formed by late acidic organic diagenetic fluids related to oil and gas activities. The hydrocarbon-bearing fluids form different spatial diagenetic zones, including the dissolution zone, buffer zone, and cementation zone, and the dissolution zone near the oil source fault is the main site of zeolite dissolution. The late fluid has the characteristics of multi-stage activity, which makes the spatial zoning expand gradually, resulting in multiple superpositions of dissolution and cementation and increasing the complexity and heterogeneity of the reservoir diagenesis. This study expands the understandings of the dissolution activities of different fluids in zeolite-rich reservoirs and also has reference significance for dissolution activity of hydrocarbon fluid in other types of reservoirs.

In this study, we systemically investigated the petrological features and geochemical compositions of dolostones in various sedimentary facies, to provide more understanding on the effects of facies-dependent diagenesis and hydrothermal reworking during the post-deposition stage. Microscopic observations and XRD analyses of carbonate were performed as well as analyses of C–O–Sr isotopic and elemental concentrations. Multiple evidence demonstrated that dolostones in the studied section underwent meteoric diagenesis and hydrothermal alterations. Effects of meteoric diagenesis were represented by meteoric dissolution, dedolomitization and calcite cementation, and varied along the downward migration of meteoric flow. Generally, meteoric dissolution mainly occurred in the exposure ooid shoal complex in the lower Triassic and accounted for the formation of mold pore, while in the underlying reef dolostones of the upper Permian, interparticle pores and cavities were mostly blocked by sparry calcite cement. Ooid dolostone layers in the lower Triassic exhibited low 87Sr/86Sr ratios and high Ba concentrations with saddle dolomite cement rimmed around the pore, demonstrating the relatively intense hydrothermal reworking. We inferred that high-permeability layers produced by early meteoric dissolution facilitated the migration of hydrothermal fluid and water–rock interaction in the burial stage. Petrologic observations indicated the hydrothermal reworking rearranged pore systems via regional transformation from dissolution to precipitation along the migration of fluid flows, and further enhanced the heterogeneity of the dolostone reservoir. Generally, the effects of early diagenesis and hydrothermal reworking in dolostones varied in dolostones of sedimentary facies, and high-quality dolostones reservoirs for oil and nature gas in this area formed as a result of the combined effects of early meteoric dissolution and hydrothermal reworking.

Volcanism provides a reliable record of local and global tectonic events and substantially influences both modern and ancient environments, climates, and the evolution of life. The Ordovician–Silurian (O–S) transition is a special period because intensive volcanism occurred globally, including in the Yangtze region of South China. Volcanic events during this period are a symptom of plate tectonic behaviour and are thought to be responsible for the remarkable changes in climate in the early Palaeozoic, though the relationships between these events remain unclear and controversial. Coeval igneous rocks and volcanic sediments (VS) are primarily used to resolve this issue. However, limited studies have been performed on VS from the O–S transition in South China. Recently, a typical VS-bearing section was found in the Lower Yangtze region, which contains ∼100 thin, interbedded volcanic ash layers across the O–S transition. Detailed petrographic and geochemical analyses of the volcanic ashes were conducted to determine their isotopic ages, magma sources, evolutionary processes, and tectonic settings. Our preliminary results suggest that volcanic eruptions in South China lasted for more than 22 Ma across the O–S boundary, from ∼449.3 ± 3.6 to 427.6 ± 4.1 Ma, where 445.14 Ma is the lowermost graptolite biozone for Metabolograptus extraordinarius, as well as the initiation of the Late Ordovician mass extinction (LOME) event in the Yangtze region. The evolutionary history of the parental magma was constructed from a depleted mantle source in the early stage and from a crustal source in the late stage, with several transitional features in the middle. The mantle source and arc-related geochemical indicators for the volcanic ashes support the disputed “subduction-collision orogeny” model. We propose that the strong volcanism in South China, accompanied by volcanism in numerous other regions worldwide, was an important trigger for the LOME and was likely responsible for oceanic 87Sr/ 86Sr fractionation and other climatic changes during the O–S transition.

Mg isotopes in syndepositional dolomite have been suggested to be a potential proxy for understanding seawater chemistry. However, it is argued that the δ26Mg values of dolomite could be complicated by the effects of early diagenesis and later hydrothermal activities. Further investigations into the behaviors of Mg isotopes in dolomitization systems are needed to resolve this controversy. In the present study, we investigated early Ordovician dolostones from the Tarim Basin, including diagenetically altered dolomites, hydrothermally altered dolomites and well-preserved dolostones that precipitated in the slope, margin and interior of a carbonate platform. Different types of dolomites, bulk dolostone and limestone were sampled by microdrilling for analyses of C-O-Mg isotope compositions and REE concentrations. The dolostone δ13C values match those of coeval seawater, and the REE distribution patterns in the dolostones are comparable with those in the limestones, indicating that the dolostones originated from syndiagenetic dolomitization. The δ26Mg values of the various syndiagenetic dolomites that formed in the slope, margin and interior of the carbonate platform are similar, averaging approximately −2.06‰ ± 0.20‰. No stratigraphic variability in the dolomite Mg isotopes can be discerned, which implies that the Mg isotope compositions of the porewater were homogeneous during massive dolomitization and remained in equilibrium with seawater. Additionally, the δ26Mg values of the altered dolostone do not show correlations with diagenetic and hydrothermal signals, demonstrating that dolomite Mg isotopes are insensitive to postdepositional alteration. Given these facts, we propose that Mg isotopes in dolostones have conservative behaviors during diagenesis and late stage hydrothermal reworking.

The pore size distribution (PSD) and fractal dimension are two typical parameters that are used to evaluate the heterogeneous characteristics of unconventional reservoir pores. The micro/nanoscale PSD and pore fractal-dimension characteristics of tight gas sandstones from the Upper Triassic Xujiahe formation in the Yuanba area were investigated by using nuclear magnetic resonance (NMR), nuclear magnetic resonance cryoporometry (NMRC), mercury injection capillary pressure (MICP) and scanning electronic microscopy (SEM) techniques. To date, no other attempts have been made to use NMRC with octamethylcyclotetrasiloxane (OMCTS) as a probe material to describe the pore fractal characteristics of tight gas sandstones. In this study, the fractal dimensions of tight gas sandstones were estimated by processing raw NMRC data with a fractal theoretical model, and the NMRC fractal results were compared to the calculated fractals from the MICP tube model, MICP sphere model and NMR T2 spectrum. The results consistently show that the pore fractal characteristics of tight sandstone samples from the Yuanba area exhibit multiple fractal structures at different pore-size ranges; large pores in the samples are more complex with rougher surfaces, and small pores correspond to smoother surfaces. Sandstones with greater porosity and pore space possess more heterogeneous structures. The PSD and fractal dimensions can be combined and used as an indicator for the presence of gas-bearing and dry formations in the study area. The tight sandstone samples from the dry formations have both lower porosities and smoother pore surfaces with smaller fractal dimensions; these characteristics may not be beneficial for the preservation of hydrocarbon fluids in the target area.

Traffic prediction is a crucial task in many real-world applications. The task is challenging due to the implicit and dynamic spatio-temporal dependencies among traffic data. On the one hand, the spatial dependencies among traffic flows are latent and fluctuate with environmental conditions. On the other hand, the temporal dependencies among traffic flows also vary significantly over time and locations. In this paper, we propose Adaptive Spatio-Temporal Convolutional Network (ASTCN) to tackle these challenges. First, we propose a spatial graph learning module that learns the dynamic spatial relations among traffic data based on multiple influential factors. Furthermore, we design an adaptive temporal convolution module that captures complex temporal traffic dependencies with environment-aware dynamic filters. We conduct extensive experiments on three real-world traffic datasets. The results demonstrate that the proposed ASTCN consistently outperforms state-of-the-arts.

Geological background, samples and methods, Figures S1–S7, and Tables S1–S3.

Population flow prediction is one of the most fundamental components in many applications from urban management to transportation schedule. It is challenging due to the complicated spatial-temporal correlation.While many studies have been done in recent years, they fail to simultaneously and effectively model the spatial correlation and temporal variations among population flows. In this paper, we propose Convolution based Sequential and Cross Network (CSCNet) to solve them. On the one hand, we design a CNN based sequential structure with progressively merging the flow features from different time in different CNN layers to model the spatial-temporal information simultaneously. On the other hand, we make use of the transition flow as the proxy to efficiently and explicitly capture the dynamic correlation between different types of population flows. Extensive experiments on 4 datasets demonstrate that CSCNet outperforms the state-of-the-art baselines by reducing the prediction error around 7.7%∼10.4%.

Extracts of a set of the Permian Lucaogou shales collected from the southern Junggar Basin in NW China were examined for their molecular compositions and compound-specific isotopic signatures. The distribution of n-alkanes, isoprenoids, steranes, hopanes (C27 to C34) and appearance of 25-norhopanes (C26 to C33) were found to vary between samples, indicating that different degrees of biodegradation occurred in the shale strata. The non-biodegraded sample showed a complete composition of n-alkanes and isoprenoids including pristane (Pr) and phytane (Ph). Moderately biodegraded shales were characterized by near-absence of n-alkanes, but Pr and Ph still showed noticeable contents; 25-norhopanes were not detected. Heavily biodegraded shale had imperceptible content of n-alkanes, isoprenoids and steranes, but a series of abundant 25-norhopanes appeared. Compound-specific isotopic data show negative δ13C values (−44.4‰ to −55.6‰) for the regular hopanes having the same carbon number in all the examined shales, indicating a substantial contribution of methanotrophs to the sediments. Moreover, the 25-norhopanes had consistent δ13C values to the corresponding regular hopanes, which are proposed to be their parent molecules. The distributional and isotopic signatures indicate the 25-norhopanes in the heavily biodegraded Permian Lucaogou shales were genetically formed during in situ biodegradation of the extractable bitumen when the strata were uplifted from depth and may have been linked to the incursion of meteoric water.

With the rapid process of urbanization, revealing the underlying mechanisms behind urban mobility has become a crucial research problem. The movements of urban dwellers are often constituted by their daily routines, and exhibit distinct and contextual temporal modes, i.e., the patterns of individuals allocating their time across different locations. In this paper, we investigate a novel problem of detecting popular temporal modes in population-scale unlabelled trajectory data. Our key finding is that the detected temporal modes capture the semantic feature of human's living style, and is able to unravel meaningful correlations between urban mobility and human behavior. Specifically, we represent the temporal mode of a trajectory as a partition of the time duration, where the time slices associated with same locations are partitioned into same subsets. Such abstraction decouples the temporal modes from actual physical locations, and allows individuals with similar temporal modes yet completely different physical locations to have similar representations. Based on this insight, we propose a pipeline system composed of three components: 1) noise handler that eliminates the noises in the raw mobility records, 2) representation extractor for temporal modes, and 3) popular temporal modes detector. By applying our system on three real-world mobility datasets, we demonstrate that our system effectively detects the popular temporal modes embedded in population-scale mobility datasets, which is easy to be interpreted and can be justified through the associated PoIs and mobile applications usage. More importantly, our further experiments reveal insightful correlations between the popular temporal modes and individuals' social economic status, i.e. occupation information, which sheds light on the mechanisms behind urban mobility.

Map matching is important in many trajectory based applications like route optimization and traffic schedule, etc. As the widely used methods, Hidden Markov Model and its variants are well studied to provide accurate and efficient map matching service. However, HMM based methods fail to utilize the value of enormous trajectory big data, which are useful for the map matching task. Furthermore, with many following-up works, they are still easily influenced by the noisy records, which are very common in the real system. To solve these problems, we revisit the map matching task from the data perspective, and propose to utilize the great power of data to help solve these problems. We build a deep learning based model to utilize all the trajectory data for joint training and knowledge sharing. With the help of embedding techniques and sequence learning model with attention enhancement, our system does the map matching in the latent space, which is tolerant to the noise in the physical space. Extensive experiments demonstrate that our model outperforms the widely used HMM based methods more than 10% (absolute accuracy) and works robustly in the noisy settings in the meantime.

Hidden Markov Model (HMM) is a powerful tool that has been widely adopted in sequence modeling tasks, such as mobility analysis, healthcare informatics, and online recommendation. However, using HMM for modeling personalized sequences remains a challenging problem: training a unified HMM with all the sequences often fails to uncover interesting personalized patterns; yet training one HMM for each individual inevitably suffers from data scarcity. We address this challenge by proposing a state-sharing sparse hidden Markov model (S3HMM) that can uncover personalized sequential patterns without suffering from data scarcity. This is achieved by two design principles: (1) all the HMMs in the ensemble share the same set of latent states; and (2) each HMM has its own transition matrix to model the personalized transitions. The result optimization problem for S3HMM becomes nontrivial, because of its two-layer hidden state design and the non-convexity in parameter estimation. We design a new Expectation-Maximization algorithm based, which treats the difference of convex programming as a sub-solver to optimize the non-convex function in the M-step with convergence guarantee. Our experimental results show that, S3HMM can successfully uncover personalized sequential patterns in various applications and outperforms baselines significantly in downstream prediction tasks.

Modeling people's activities in the urban space is a crucial socio-economic task but extremely challenging due to the deficiency of suitable methods. To model the temporal dynamics of human activities concisely and specifically, we present State-sharing Hidden Markov Model (SSHMM). First, it extracts the urban states from the whole city, which captures the volume of population flows as well as the frequency of each type of Point of Interests (PoIs) visited. Second, it characterizes the urban dynamics of each urban region as the state transition on the shared-states, which reveals distinct daily rhythms of urban activities. We evaluate our method via a large-scale real-life mobility dataset and results demonstrate that SSHMM learns semantics-rich urban dynamics, which are highly correlated with the functions of the region. Besides, it recovers the urban dynamics in different time slots with an error of 0.0793, which outperforms the general HMM by 54.2%.

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. To address this problem, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion - kt, l-anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24% ~ 57% while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.

Understanding human mobility bene ts numerous applications such as urban planning, tra c control and city management. Previous work mainly focuses on modeling spatial and temporal patterns of human mobility. However, the semantics of trajectory are ignored, thus failing to model people's motivation behind mobility. In this paper, we propose a novel semantics-aware mobility model that captures human mobility motivation using large-scale semantics-rich spatialtemporal data from location-based social networks. In our system, we rst develop a multimodal embedding method to project user, location, time, and activity on the same embedding space in an unsupervised way while preserving original trajectory semantics. Then, we use hidden Markov model to learn latent states and transitions between them in the embedding space, which is the location embedding vector, to jointly consider spatial, temporal, and user motivations. In order to tackle the sparsity of individual mobility data, we further propose a von Mises-Fisher mixture clustering for user grouping so as to learn a reliable and ne-grained model for groups of users sharing mobility similarity. We evaluate our proposed method on two large-scale real-world datasets, where we validate the ability of our method to produce high-quality mobility models. We also conduct extensive experiments on the speci c task of location prediction. The results show that our model outperforms state-of-the-art mobility models with higher prediction accuracy and much higher efciency.

Mineral dissolution and precipitation, particularly of carbonate minerals, are ubiquitous and significant diagenetic processes in sedimentary rocks. However, the impacts of organic matter degradation on these processes in single systems remain unclear because the timing and fluid sources during progressive burial are poorly constrained. To address this issue, in situ C–O isotope analyses were conducted for zoned dolomite–ankerite/calcite cements from the Permian Lucaogou Formation in the Junggar Basin, China, using secondary ion mass spectrometry (SIMS) combined with mineralogical and elemental analyses. Carbonate minerals can be divided into early-stage and late-stage precipitations based on distinct variations in δ18O (−18.3 to −0.7‰ V-PDB), δ13C (−3.6 to +20.8‰ V-PDB), and Fe content (Fe# ranging from 0.002 to 0.373). The chemo-isotopically zoned carbonate cements record the thermal and chemical conditions of pore fluids during the burial process. The difference in δ18O values between the early and late precipitation (i.e., Δ18O [early–late]) of 11.9‰, and the difference in δ13C values (i.e., Δ13C [early–late]) of 11.7‰, reveal significant changes in precipitation temperatures and the availability of carbon sources, respectively. Early-stage precipitation occurred in association with microbial methanogenesis at lower temperatures (~25 °C) during shallow burial (~300 m), whereas late-stage precipitation was related to thermally-induced decarboxylation at higher temperatures (~90 °C or ~110 °C) during deep burial (~2500 m or ~3200 m). These two stages of dissolved pore-filling precipitation correspond to two major periods of dissolution (i.e., eogenetic and mesogenetic dissolution). Diagenetic fluids, mainly CO2 and/or organic acids, were derived from the biological and thermal degradation of organic matter, respectively. These results constrain the isotopic responses of carbonates to organic matter degradation with increasing burial depth and provide significant insights into carbonate diagenesis in organic-rich sedimentary sequences.

The Late Ordovician to early Silurian (O/S) was an important period in which many significant geological events occurred. During the O/S transition, organic-rich black shales were widely deposited and became important unconventional energy sources. Paleoproductivity, detrital input, and redox conditions are the three major factors controlling organic matter (OM) accumulation. Although the effect of each factor is relatively clear, the complex influences of these factors remain poorly understood. Specifically, the evolution of these factors and the corresponding influence on the accumulation of OM during the O/S transition has not yet been systematically investigated. This paper investigates the constraints on OM accumulation in O/S transition black shales from the Lower Yangtze region, based on graptolite fossil identification, zircon U–Pb dating, and geochemical analyses of a well sequenced drilling profile. The results show that (i) the SY-1 core sequence was well constrained from graptolite zones WF2 (447.62 Ma) to LM5 (441.57 Ma) across the O/S boundary, with decreasing sedimentation rates (SRs) in the upper Katian (WF2 to WF3) and increasing SRs in the lower Rhuddanian; (ii) complementary volcanic inputs (Katian and Rhuddanian) and upwelling events (Hirnantian) exerted strong control over paleoproductivity; (iii) orogenic processes in South China controlled detrital inputs and redox conditions; (iv) OM accumulation in the Lower Yangtze area across the O/S transition occurred in three stages. In stage 1 (WF2–WF3), strong volcanism boosted productivity, whereas OM preservation was not favored in the oxic environment. In stage 2 (WF4–LM3), upwelling and volcanism resulted in high productivity and provided the optimal configurations for OM enrichment. In stage 3 (LM4 and later), rare OM was preserved due to terrestrial dilution and unfavorable preservation conditions. Eventually, WF4–LM3 became the prominent OM-rich biozones due to the effective coordination of the controlling factors, namely upwelling, volcanism, and redox conditions.

Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of three challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; and 3) the heterogeneity and sparsity of the collected trajectory data. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. We perform experiments on three representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10%. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.

J. Inst. Brew. 113(3), 256–262, 2007 Lactic acid bacteria (LAB) were isolated from malted cereals by means of a high-throughput screening approach and investigated for antimicrobial activity against a range of beer-spoiling bacteria. Putative bacteriocin-producing strains were identified by 16S rRNA analysis and the inhibitory compounds were partially characterized. Following determination of the inhibitory spectra of the strains, an unspeciated Lactobacillus sp. UCC128, with inhibitory activity against a range of beer-spoiling strains was subjected to further characterization. A bacteriocin was purified from this strain and analyzed by mass spectrometry to determine the weight of the protein. The result indicated that the bacteriocin was highly similar to pediocin AcH /PA-1 from Pediococcus acidilactici. The bacteriocin-producers identified in this study have the potential to be used in the brewing industry to enhance the microbiological stability of beer in conjunction with hurdles already in place in the brewing process.

In this paper, a novel prefix-based patching algorithm named DP2 (Dynamic Prefix-based Patching algorithm) is proposed for mobile IPTV service in the WiBro system. Proposed algorithm increase the resource utilization of wireless bandwidth and server. It uses the prefix caching in proxy server to batch those requests that demand the same video. This algorithm just requires the mobile terminal can receive two streams at the same time. The threshold is also introduced in the novel algorithm to control the frequency of regular streams initiated. Then the optimized patching window is derived. A mobile IPTV structure for this algorithm is also proposed, which uses the MBS technique. Finally simulation result show that DP2 algorithm can reduce the data volume and patching streams significantly compares to the DS2AM2PC algorithm.

Background Little is known regarding the pool of mobile genetic elements associated with the human gut microbiome. In this study we employed the culture independent TRACA system to isolate novel plasmids from the human gut microbiota, and a comparative metagenomic analysis to investigate the distribution and relative abundance of functions encoded by these plasmids in the human gut microbiome. Results Novel plasmids were acquired from the human gut microbiome, and homologous nucleotide sequences with high identity (>90%) to two plasmids (pTRACA10 and pTRACA22) were identified in the multiple human gut microbiomes analysed here. However, no homologous nucleotide sequences to these plasmids were identified in the murine gut or environmental metagenomes. Functions encoded by the plasmids pTRACA10 and pTRACA22 were found to be more prevalent in the human gut microbiome when compared to microbial communities from other environments. Among the most prevalent functions identified was a putative RelBE toxin-antitoxin (TA) addiction module, and subsequent analysis revealed that this was most closely related to putative TA modules from gut associated bacteria belonging to the Firmicutes. A broad phylogenetic distribution of RelE toxin genes was observed in gut associated bacterial species (Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria), but no RelE homologues were identified in gut associated archaeal species. We also provide indirect evidence for the horizontal transfer of these genes between bacterial species belonging to disparate phylogenetic divisions, namely Gram negative Proteobacteria and Gram positive species from the Firmicutes division. Conclusions The application of a culture independent system to capture novel plasmids from the human gut mobile metagenome, coupled with subsequent comparative metagenomic analysis, highlighted the unexpected prevalence of plasmid encoded functions in the gut microbial ecosystem. In particular the increased relative abundance and broad phylogenetic distribution was identified for a putative RelBE toxin/antitoxin addiction module, a putative phosphohydrolase/phosphoesterase, and an ORF of unknown function. Our analysis also indicates that some plasmids or plasmid families are present in the gut microbiomes of geographically isolated human hosts with a broad global distribution (America, Japan and Europe), and are potentially unique to the human gut microbiome. Further investigation of the plasmid population associated with the human gut is likely to provide important insights into the development, functioning and evolution of the human gut microbiota.