The discovery in the 1980's that DNA could be extracted and sequenced from extinct animals opened-up a whole new area of research in paleobiology. The oldest authenticated sequence ever recovered is ...between 1.65 and 1.1 million years (My) old and extrapolation models on DNA degradation suggest that this is close to the temporal limit of DNA survival. However, recent data from cell nuclei in 30 to 80 My-old fossil cells from plants and dinosaurs show positive staining with standard DNA stains and fluorochromes. We heavily discuss and scrutinize the results of these studies and argue that the intracellular stainings seen in these Cenozoic and Cretaceous fossils are consistent with the presence of endogenous DNA and inconsistent with contamination. Properties of the stains suggest that the preserved molecules are made of at least the sugar-phosphate backbone of DNA, and in some instances they may be double stranded with preserved base pairs. Previous works on DNA damage also suggest that the material is crosslinked and filled with chemically modified nucleotides, which may explain why it is apparently not in a PCR-amplifiable nor in a sequenceable form.
However, even though many questions remain, it cannot yet be ruled out that retrieving ancient sequences in fossils older than the Pleistocene will be possible in the future. Here, we summarize and reassess all current evidence and propose new methods and ideas on how to further understand DNA preservation in deep time. Notably, microscopy-based DNA sequencing may offer the most promising results. The main goal of this review is to show the need for new collaborations between the fields of Ancient DNA, Molecular Paleontology and Paleohistology to better seek the truth about DNA fossilization.
The long short-term memory (LSTM) model is one of the most commonly used vehicle trajectory predicting models. In this paper, we study two problems of the existing LSTM models for long-term ...trajectory prediction in dense traffic. First, the existing LSTM models cannot simultaneously describe the spatial interactions between different vehicles and the temporal relations between the trajectory time series. Thus, the existing models cannot accurately estimate the influence of the interactions in dense traffic. Second, the basic LSTM models often suffer from vanishing gradient problem and are, thus, hard to train for long time series. These two problems sometimes lead to large prediction errors in vehicle trajectory predicting. In this paper, we proposed a spatio-temporal LSTM-based trajectory prediction model (ST-LSTM) which includes two modifications. We embed spatial interactions into LSTM models to implicitly measure the interactions between neighboring vehicles. We also introduce shortcut connections between the inputs and the outputs of two consecutive LSTM layers to handle gradient vanishment. The proposed new model is evaluated on the I-80 and US-101 datasets. Results show that our new model has a higher trajectory predicting accuracy than one state-of-the-art model maneuver-LSTM (M-LSTM).
Seawater electrolysis is an attractive technique for massive green hydrogen production owing to the dominant advantages of seawater resources, namely low‐cost and limitlessness. However, the oxygen ...evolution reaction (OER) catalysts will be easily deactivated for severe seawater Cl− permeation and corrosion. Herein, a structural buffer engineering strategy is reported to endow the Co2(OH)3Cl with long‐term operation stability and a high OER selectivity of ≈99.6% in seawater splitting. The lattice Cl− of Co2(OH)3Cl can act as the structural buffer, whose continuous leaching during OER can leave vacancies for seawater Cl− invasion, so as to avoid catalyst deactivation. Accordingly, Co2(OH)3Cl can maintain 99.9% of its initial current density after 60 000 s operation, while that of Co(OH)2 decays by 52.7% in 7 000 s. Meanwhile, the lattice Cl− of Co2(OH)3Cl can optimize the binding energy of reaction intermediates on the neighboring OCoO site. Thus, Co2(OH)3Cl exhibits a current density of 330.5 mA cm–2 at the potential of 1.63 V versus RHE, 45.9 times higher than that of Co(OH)2. The structural buffer strategy may be applied to incorporate other metal oxides with suitable anions, and effectively boost their OER activity and stability in alkaline seawater.
The Co2(OH)3Cl nanoplatelets are developed into highly efficient and robust seawater oxidation electrocatalyst via structural buffer engineering to strike a balance between lattice Cl− leaching and electrolyte Cl− incorporation during reaction. Co2(OH)3Cl could maintain 99.9% of its initial current density after 60 000 s operation with the OER selectivity of 99.6%.
In this paper, we study the difference between two major strategies of cooperative driving at nonsignalized intersections: namely the "ad hoc negotiation-based" strategy and the "planning-based" ...strategy. The fundamental divide of these two strategies lies in how to determine the passing order of vehicles at intersections. The "ad hoc negotiation-based" strategy makes vehicles roughly follow first-come-first-served order but allows some adjustments. This leads to a local optimal solution in many situations. The "planning-based" strategy aims to find a global optimal passing order of vehicles. However, constrained by the planning complexity and time requirement, we often stop at a local optimal solution, too. We carry out a series of simulations to compare the solutions found by two strategies, under different traffic scenarios. Results indicate the performance of a strategy mainly depends on the passing order of vehicles that it finds. Although there exist several trajectory planning algorithms associating with the solving process of passing orders, their differences are negligible. Moreover, if the traffic demand is very low, the performance difference between two strategies is small. When the traffic demand becomes high, the "planning-based" strategy yields significantly better performance since it finds better passing orders. These findings are important to cooperative driving study.
•We enhance PPCA method by considering multiple sensors’ measurements.•We show imputing errors can be reduced by using temporal–spatial dependence.•We suggest a simple yet effective way to embed ...temporal–spatial dependence.
The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.
Luminescent nanomaterials have attracted great attention in luminescence‐based bioanalysis due to their abundant optical and tunable surface physicochemical properties. However, luminescent ...nanomaterials often suffer from serious autofluorescence and light scattering interference when applied to complex biological samples. Time‐resolved luminescence methodology can efficiently eliminate autofluorescence and light scattering interference by collecting the luminescence signal of a long‐lived probe after the background signals decays completely. Lanthanides have a unique Xe4fN electronic configuration and ladder‐like energy states, which endow lanthanide‐doped nanoparticles with many desirable optical properties, such as long luminescence lifetimes, large Stokes/anti‐Stokes shifts, and sharp emission bands. Due to their long luminescence lifetimes, lanthanide‐doped nanoparticles are widely used for high‐sensitive biosensing and high‐contrast bioimaging via time‐resolved luminescence methodology. In this review, recent progress in the development of lanthanide‐doped nanoparticles and their application in time‐resolved biosensing and bioimaging are summarized. At the end of this review, the current challenges and perspectives of lanthanide‐doped nanoparticles for time‐resolved bioapplications are discussed.
Lanthanide‐doped nanoparticles are ideal luminescent probes for eliminating background fluorescence and light scattering interference via time‐resolved luminescence methodology. In this review, recent progress in the development of lanthanide‐doped nanoparticles and their application for time‐resolved biosensing and bioimaging are summarized.
Metal–organic framework (MOFs) two‐dimensional (2D) nanosheets have many coordinatively unsaturated metal sites that act as active centres for catalysis. To date, limited numbers of 2D MOFs ...nanosheets can be obtained through top‐down or bottom‐up synthesis strategies. Herein, we report a 2D oxide sacrifice approach (2dOSA) to facilely synthesize ultrathin MOF‐74 and BTC MOF nanosheets with a flexible combination of metal sites, which cannot be obtained through the delamination of their bulk counterparts (top‐down) or the conventional solvothermal method (bottom‐up). The ultrathin iron–cobalt MOF‐74 nanosheets prepared are only 2.6 nm thick. The sample enriched with surface coordinatively unsaturated metal sites, exhibits a significantly higher oxygen evolution reaction reactivity than bulk FeCo MOF‐74 particles and the state‐of‐the‐art MOF catalyst. It is believed that this 2dOSA could provide a new and simple way to synthesize various ultrathin MOF nanosheets for wide applications.
Think thin: The so‐called 2D oxide sacrifice approach is developed to coordinate the metal atoms of amorphous metal oxide nanosheets with ligands to synthesize metal–organic framework (MOF) nanosheets. The resulting ultrathin FeCo MOF‐74 nanosheets (2.6 nm) show an excellent oxygen evolution reaction (OER) activity owing to abundant coordinatively unsaturated metal sites and heteroatom synergy.
This paper discusses the trend modeling for traffic time series. First, we recount two types of definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic ...differences. We show that, by assuming an implicit temporal connection among the time series observed at different days/locations, the PCA trend brings several advantages to traffic time series analysis. We also describe and define the so-called short-term trend that cannot be characterized by existing definitions. Second, we sequentially review the role that trend modeling plays in four major problems in traffic time series analysis: abnormal data detection, data compression, missing data imputation, and traffic prediction. The relations between these problems are revealed, and the benefit of detrending is explained. For the first three problems, we summarize our findings in the last ten years and try to provide an integrated framework for future study. For traffic prediction problem, we present a new explanation on why prediction accuracy can be improved at data points representing the short-term trends if the traffic information from multiple sensors can be appropriately used. This finding indicates that the trend modeling is not only a technique to specify the temporal pattern but is also related to the spatial relation of traffic time series.
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies ...make this problem challenging. Most existing methods suffer from two major shortcomings: (1) They ignore the local context semantics when modeling temporal dependencies. (2) They lack the ability to capture the spatial dependencies of multiple patterns. To tackle such issues, we propose a novel Transformer-based model for multivariate time series forecasting, called the spatial-temporal convolutional Transformer network (STCTN). STCTN mainly consists of two novel attention mechanisms to respectively model temporal and spatial dependencies. Local-range convolutional attention mechanism is proposed in STCTN to simultaneously focus on both global and local context temporal dependencies at the sequence level, which addresses the first shortcoming. Group-range convolutional attention mechanism is designed to model multiple spatial dependency patterns at graph level, as well as reduce the computation and memory complexity, which addresses the second shortcoming. Continuous positional encoding is proposed to link the historical observations and predicted future values in positional encoding, which also improves the forecasting performance. Extensive experiments on six real-world datasets show that the proposed STCTN outperforms the start-of-the-art methods and is more robust to nonsmooth time series data.
The autonomous vehicle is regarded as a promising technology with the potential to reshape mobility and solve many traffic issues, such as accessibility, efficiency, convenience, and especially ...safety. Many previous studies on driving strategies mainly focused on the low-level detailed driving behaviors or specific traffic scenarios but lacked the high-level driving strategy studies. Though researchers showed increasing interest in driving strategies, there still has no comprehensive answer on how to proactively implement safe driving. After analyzing several representative driving strategies, we propose three characteristic dimensions that are important to measure driving strategies: preferred objective, risk appetite, and collaborative manner. According to these three characteristic dimensions, we categorize existing driving strategies of autonomous vehicles into four kinds: defensive driving strategies, competitive driving strategies, negotiated driving strategies, and cooperative driving strategies. This paper provides a timely comparative review of these four strategies and highlights the possible directions for improving the high-level driving strategy design.
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