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Abstract Background As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved. Results Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support. Conclusions In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.

Abstract Background Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary. Results Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet. Conclusions InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign . A free web service is also provided: http://www.injection.design .

Abstract Background Liquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching. Results To comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC–MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 Conclusions In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC–MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

With the continuous improvement of biological detection technology, the scale of biological data is also increasing, which overloads the central-computing server. The use of edge computing in 5G networks can provide higher processing performance for large biological data analysis, reduce bandwidth consumption and improve data security. Appropriate data compression and reading strategy becomes the key technology to implement edge computing. We introduce the column storage strategy into mass spectrum data so that part of the analysis scenario can be completed by edge computing. Data produced by mass spectrometry is a typical biological big data based. A blood sample analysed by mass spectrometry can produce a 10 gigabytes digital file. By introducing the column storage strategy and combining the related prior knowledge of mass spectrometry, the structure of the mass spectrum data is reorganized, and the result file is effectively compressed. Data can be processed immediately near the scientific instrument, reducing the bandwidth requirements and the pressure of the central server. Here, we present Aird-Slice, a mass spectrum data format using the column storage strategy. Aird-Slice reduces volume by 48% compared to vendor files and speeds up the critical computational step of ion chromatography extraction by an average of 116 times over the test dataset. Aird-Slice provides the ability to analyze biological data using an edge computing architecture on 5G networks.

Due to its good biocompatibility and compatible hardness with human bones, magnesium (Mg) alloy is considered as a promising material for bone nails and implants. However, Mg and its alloys are characteristics of active chemical properties and high corrosion rate, which is detrimental to the recovery of patients implanted with Mg alloy bone scaffold. In the present work, to retard the corrosion of the Mg alloy, calcium glycerophosphate (C3H7CaO6P•H2O) was added in the electrolyte to form the micro-arc oxidation (MAO) coatings. Various experimental techniques such as XRD, SEM and EDS have been used to characterize the surface morphology and phase composition of MAO coatings. MAO coatings of different CaP content were studied to explore the synergetic effect between the biocompatibility and corrosive properties by means of electrochemical measurements, hemolysis and pyrogen test. The results showed that higher CaP content can improve the biocompatibility and corrosion resistance while decelerating the hemolysis rate.

Abstract As the pervasive, standardized format for interchange and deposition of raw mass spectrometry (MS) proteomics and metabolomics data, text-based mzML is inefficiently utilized on various analysis platforms due to its sheer volume of samples and limited read/write speed. Most research on compression algorithms rarely provides flexible random file reading scheme. Database-developed solution guarantees the efficiency of random file reading, but nevertheless the efforts in compression and third-party software support are insufficient. Under the premise of ensuring the efficiency of decompression, we propose an encoding scheme “Stack-ZDPD” that is optimized for storage of raw MS data, designed for the format “Aird”, a computation-oriented format with fast accessing and decoding time, where the core compression algorithm is “ZDPD”. Stack-ZDPD reduces the volume of data stored in mzML format by around 80% or more, depending on the data acquisition pattern, and the compression ratio is approximately 30% compared to ZDPD for data generated using Time of Flight technology. Our approach is available on AirdPro, for file conversion and the Java-API Aird-SDK, for data parsing.

Abstract Background With the precision of the mass spectrometry (MS) going higher, the MS file size increases rapidly. Beyond the widely-used open format mzML, near-lossless or lossless compression algorithms and formats emerged in scenarios with different precision requirements. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focus more on lossless compression rate, computation-oriented formats concentrate as much on decoding speed as the compression rate. Results Here we introduce “Aird”, an opensource and computation-oriented format with controllable precision, flexible indexing strategies, and high compression rate. Aird provides a novel compressor called Zlib-Diff-PforDelta (ZDPD) for m/z data. Compared with Zlib only, m/z data size is about 55% lower in Aird average. With the high-speed decoding and encoding performance of the single instruction multiple data technology used in the ZDPD, Aird merely takes 33% decoding time compared with Zlib. We have downloaded seven datasets from ProteomeXchange and Metabolights. They are from different SCIEX, Thermo, and Agilent instruments. Then we convert the raw data into mzML, mgf, and mz5 file formats by MSConvert and compare them with Aird format. Aird uses JavaScript Object Notation for metadata storage. Aird-SDK is written in Java, and AirdPro is a GUI client for vendor file converting written in C#. They are freely available at https://github.com/CSi-Studio/Aird-SDK and https://github.com/CSi-Studio/AirdPro . Conclusions With the innovation of MS acquisition mode, MS data characteristics are also constantly changing. New data features can bring more effective compression methods and new index modes to achieve high search performance. The MS data storage mode will also become professional and customized. ZDPD uses multiple MS digital features, and researchers also can use it in other formats like mzML. Aird is designed to become a computing-oriented data format with high scalability, compression rate, and fast decoding speed.

Using a people-centered approach to new urbanization, China has committed to building high-quality living environments through improving urban livability and promoting a stronger sense of belonging among residents. Retail stores serve as one of the most immediate and accessible destinations for residents’ consumption, and their spatial configuration has a direct impact on residents’ satisfaction and happiness in their daily lives. In this context, for the present study we selected the central urban area of Lanzhou City as the case study area. Based on POI data and using the daily life circle as the basic unit, we applied methods such as kernel density analysis, hotspot analysis, and the Shannon–Weaver index to analyze spatial distribution patterns of the retail industry. Furthermore, we applied Geodetector to analyze the impacts of four factors that are closely related to the retail industry: economic level, convenience level, market demand, and location. The conclusions are as follows: In the central urban area of Lanzhou, the retail industry exhibits a belt distribution pattern along the Yellow River. The density of distribution gradually decreases from the city center toward the outskirts, forming four prominent agglomeration centers. Overall, within the central urban area of Lanzhou, the spatial distribution of the retail industry at the scale of daily living circles shows that only a small proportion of the industry demonstrates noticeable clustering effects. In terms of spatial patterns, the retail industry at the scale of the daily living circles demonstrates similar characteristics in terms of diversity and agglomeration distribution. It exhibits a decreasing trend from the urban core toward the peripheral areas. The agglomeration distribution pattern of the retail industry in the central urban area of Lanzhou is considerably influenced by market demand, economic level, convenience, and location. The spatial distribution of the retail industry in the central urban area is primarily influenced by economic factors and convenience, while market demand plays a major role and location has a relatively minimal impact.

Abstract In this paper, a general biased min‐consensus protocol for continuous‐time multi‐agent systems is presented. In this protocol, agents considered as sources maintain static states, while the state of each non‐source agent evolves using a general min‐consensus‐like function, which takes the states of its neighbours and a biased term as inputs. The properties of the general function have been identified so that the proposed protocol can achieve global asymptotic stability. Unlike conventional consensus protocols where a network of agents reach an agreement on certain quantities of interest, agents under the general biased min‐consensus protocol will converge to different states but can generate various design results pertaining to complex combinatorial optimization problems, such as the shortest path problem and its variants.

Peanut (Arachis hypogaea L.) is one of the most important crops produced worldwide. Peanut is the dominant crop in the typical upland red soil areas of China; however, phosphorus bioavailability in red soil is very low, which severely affects peanut production. To improve the phosphorus bioavailability, which substantially promotes the green development of peanut production, a peanut–green manure rotation field experiment was conducted with six treatments (milkvetch; radish; brassica rape; mustard rape; winter fallow and no-tillage), commencing in September 2017 in the red soil area of Jiangxi province, China. The results show that compared with no-tillage (NT) treatments, different green manure returning treatments had significant effects on soil pH, soil phosphorus components and available potassium content. The particulate phosphorus and soil available phosphorus contents in the green manure treatments were significantly higher than those in the winter fallow (WF) treatment. Compared with the WF treatment, the content of particulate phosphorous in brassica rape (BR), radish (R) and milkvetch (MV) treatments was significantly increased by 6.55%, 3.66% and 2.50%, respectively; the available phosphorus content in mustard rape (MR), BR, R and MV was significantly increased by 20.93%, 25.60%, 23.76% and 18.10%, respectively. In addition, the total phosphorus content of peanut shell in the MV and R treatment was significantly higher than that in the WF treatment, increasing by 33.47% and 60.66%, respectively. Compared with the WF treatment, the peanut biomass of MR, BR and R treatments increased significantly by 19.51%, 29.83% and 19.77%, respectively. The total phosphorus accumulation in all green manure treatments was higher than that in the WF treatment, and the MV treatment reached a significant level at 18.83%. Based on these results, the particulate phosphorus (PP) and available phosphorus were significantly affected by different green manure treatments; green manure amendment improves peanut phosphorus uptake. The use of green manure (especially milkvetch and brassica rape) can be recommended to improve phosphorus bioavailability and yield of peanut in red soil areas.

Robotic grasp detection is a fundamental problem in robotic manipulation. The conventional grasp methods, using vision information only, can cause potential damage in force-sensitive tasks. In this paper, we propose a tactile–visual based method using a reproducible sensor to realize a fine-grained and haptic grasping. Although there exist several tactile-based methods, they require expensive custom sensors in coordination with their specific datasets. In order to overcome the limitations, we introduce a low-cost and reproducible tactile fingertip and build a general tactile–visual fusion grasp dataset including 5,110 grasping trials. We further propose a hierarchical encoder–decoder neural network to predict grasp points and force in an end-to-end manner. Then comparisons of our method with the state-of-the-art methods in the benchmark are shown both in vision-based and tactile–visual fusion schemes, and our method outperforms in most scenarios. Furthermore, we also compare our fusion method with the only vision-based method in the physical experiment, and the results indicate that our end-to-end method empowers the robot with a more fine-grained grasp ability, reducing force redundancy by 41%. Our project is available at https://sites.google.com/view/tvgd

Excessive use of chemical fertilizers has led to a reduction in the quality of arable land and environmental pollution. Using green manure to replace chemical fertilizers is one of the most effective solutions. To study the effect of green manure on the requirement for nitrogen fertilizer in oilseed rape, a field experiment with maize–oilseed rape rotation was conducted. Green manure was intercropped between rows of maize and returned after the maize harvest, with no green manure intercropped as control. Different nitrogen fertilizer treatments (0, 65%, 75% and 100% N rates, respectively) were applied during the oilseed rape season. The results showed that with a 35% reduction in nitrogen application rate, the rapeseed grain yield was significantly higher with the maize intercropping with green manure returned to the field than with the maize monocropping treatment at the same nitrogen level. Under conditions of intercropping and return of green manure, compared with the full standard rate of nitrogen fertilizer treatment, a reduction in nitrogen application of 25–30% in the rape season had no significant effect on rape yield. The agronomic efficiency of nitrogen fertilizer on oilseed rape increased significantly, by 47.61–121%, with green manure incorporation. In addition, green manure incorporation significantly increased the soil organic matter content and the soil-available nitrogen content when chemical nitrogen fertilization was abandoned. Benefit analysis showed that a 25–35% reduction in chemical nitrogen fertilizer applied to oilseed rape crops could be achieved by intercropping green manure in the maize season before the sowing of rapeseed in the experimental area. In the long-term, this measure would increase nitrogen utility, reduce production costs, and have concomitant environmental benefits of improving the quality of cultivated land.

This article proposes an efficient and probabilistic complete planning algorithm to address motion planning problem involving orientation constraints for decoupled dual-arm robots. The algorithm is to combine sampling-based planning method with analytical inverse kinematic calculation, which randomly samples constraint-satisfying configurations on the constraint manifold using the analytical inverse kinematic solver and incrementally connects them to the motion paths in joint space. As the analytical inverse kinematic solver is applied to calculate constraint-satisfying joint configurations, the proposed algorithm is characterized by its efficiency and accuracy. We have demonstrated the effectiveness of our approach on the Willow Garage’s PR2 simulation platform by generating trajectory across a wide range of orientation-constrained scenarios for dual-arm manipulation.

Real networks are not only multi-layered yet also dynamic. The role of coordinated network evolution regarding dynamic multi-layer networks where both network and strategy evolution simultaneously show diverse interdependence by layers remains poorly addressed. Here, we propose a general and simple coevolution framework to analyze how coordination of different dynamical processes affects strategy propagation in synergistically evolving interdependent networks. The strategic feedback constitutes the main driving force of network evolution yet the inherent cross-layer self-optimization functions as its compensation. We show that these two ingredients often catalyze a better performance of network evolution in propagating cooperation. Coordinated network evolution may be a double-edged sword to cooperation and the network-adapting rate plays a crucial role in flipping its double-sided effect. It often economizes the cost and time consumption for driving the system to the full cooperation phase. Importantly, strongly coupled slow-tuned networks can outperform weakly coupled fast-regulated networks in solving social dilemmas, highlighting the fundamental advantages of coordinated network evolution and the importance of synergistic effect of dynamical processes in upholding human cooperation in multiplex networks.

The evolution of cooperation on interdependent networks is arousing increasing concern based on the fact that more and more complex systems in the real-world have been proven to be organized in the form of multi-layer networks rather than single-layer networks. In this study, we examine the effects of self-organized interdependence on the evolution and stabilization of cooperation with social dilemmas depicted by the Prisoner’s Dilemma Game (PDG) and the Public Goods Game (PGG) in which agents with the most common strategy have the chance to be rewarded proportionally to the fitness of corresponding agents belonging to the other network. We show that such a type-free rewarding rule, independent of game strategy, establishes a time-varying interdependence between two initially independent populations whereby cooperation is highly promoted as well as stabilized both in the two-player PDG and in the multi-player PGG. Majority-pressure based interdependence at stake has proven pretty neutral in regard to game strategy because it is contingent on strategy configuration rather than strategy itself, which thus gives birth to homologous communities, including cooperative as well as non-cooperative, and thereby an enhanced spatial reciprocity between non-identical networks is triggered. Of particular interest is the double-edged sword effect of network interdependence on cooperation although in most instances the heavier the interdependence, the better the evolution of cooperation. Furthermore, interpretations of the nontrivial relationship between cooperation and benchmark threshold measuring the strategy’s local popularity highlight that rewarding the minimum majority is optimal for the evolution of cooperation in such scenario. Finally, we claim our observations are also quite robust with respect to mutation.

The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs) for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS) are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.