Measurement of very low-molecular weight metabolites by traveling wave ion mobility and its use in human urine samples

The collision cross-sections(CCS)measurement using ion mobility spectrometry(IMS)in combination with mass spectrometry(MS)offers a great opportunity to increase confidence in metabolite identification.However,owing to the lack of sensitivity and resolution,IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites(VLMs250 Da).Here,we describe an analytical method using ultrahigh-performance liquid chromatography(UPLC)coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples.The experimental CCS values,along with mass spectral properties,were reported for the 174 metabolites.The experimental data included the mass-to-charge ratio(m/z),retention time(RT),tandem MS(MS/MS)spectra,and CCS values.Among the studied metabolites,263 traveling wave ion mobility spectrometry(TWIMS)-derived CCS values(TWCCSN2)were reported for the first time,and more than 70%of these were CCS values of VLMs.The TWCCSN2 values were highly repeatable,with inter-day variations of<1%relative standard deviation(RSD).The developed method revealed excellent TWCCSN2 accuracy with a CCS difference(DCCS)within±2%of the reported drift tube IMS(DTIMS)and TWIMS CCS values.The complexity of the urine matrix did not affect the precision of the method,as evidenced by DCCS within±1.92%.According to the Metabolomics Standards Initiative,55 urinary metabolites were identified with a confidence level of 1.Among these 55 metabolites,53(96%)were VLMs.The larger number of confirmed compounds found in this study was a result of the addition of TWCCSN2 values,which clearly increased metabolite identification confidence.

Participants Recruitment for Coverage Maximization by Mobility Predicting in Mobile Crowd Sensing

Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.

Unique case of oligoastrocytoma with recurrence and grade progression:Exhibiting differential expression of high mobility group-A1 and human telomerase reverse transcriptase

Mixed gliomas, primarily oligoastrocytomas, account for about 5%-10% of all gliomas. Distinguishing oligoastrocytoma based on histological features alone has limitations in predicting the exact biological behavior, necessitating ancillary markers for greater specificity. In this case report, human telomerase reverse transcriptase(hT ERT) and high mobility group-A1(HMGA1); markers of proliferation and stemness, have been quantitatively analyzed in formalin-fixed paraffin-embedded tissue samples of a 34 years old patient with oligoastrocytoma. Customized florescence-based immunohistochemistry protocol with enhanced sensitivity and specificity is used in the study. The patient presented with a history of generalized seizures and his magnetic resonance imaging scans revealed infiltrative ill-defined mass lesion with calcified foci within the left frontal white matter, suggestive of glioma. He was surgically treated at our center for four consecutive clinical events. Histopathologically, the tumor was identified as oligoastrocytoma-grade Ⅱ followed by two recurrence events and final progression to grade Ⅲ. Overall survival of the patient without adjuvant therapy was more than 9 years. Glial fibrillary acidic protein, p53, Ki-67, nuclear atypia index, pre-operative neutrophillymphocyte ratio, are the other parameters assessed. Findings suggest that hT ERT and HMGA1 are linked to tumor recurrence and progression. Established markers can assist in defining precise histopathological grade in conjuction with conventional markers in clinical setup.

Predicting Human Mobility via Long Short-Term Patterns

Predicting human mobility has great significance in Location based Social Network applications,while it is challenging due to the impact of historical mobility patterns and current trajectories.Among these challenges,historical patterns tend to be crucial in the prediction task.However,it is difficult to capture complex patterns from long historical trajectories.Motivated by recent success of Convolutional Neural Network(CNN)-based methods,we propose a Union ConvGRU(UCG)Net,which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories.Specifically,we first incorporate historical trajectories into hidden states by a shared-weight layer,and then utilize a 1D CNN to capture short-term pattern of hidden states.Next,an average pooling method is involved to generate separated hidden states of historical trajectories,on which we use a Fully Connected(FC)layer to capture longterm pattern subsequently.Finally,we use a Recurrent Neural Net-work(RNN)to predict future trajectories by integrating current trajectories and long short-term patterns.Experiments demonstrate that UCG Net performs best in comparison with neural network-based methods.

Parking Availability Prediction with Coarse-Grained Human Mobility Data

Nowadays,the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces.The purpose of our work is to study,design and develop a parking-availability predictor that extracts the knowledge from human mobility data,based on the anonymized human displacements of an urban area,and also from weather conditions.Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution.However,access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related restrictions.To overcome this limitation,our proposal uses urban areas that are defined at very low spatial and temporal resolution.We conducted several experiments using three Artificial Neural Networks:Multilayer Perceptron,Gated Recurrent Units and bidirectional Long Short Term Memory networks and we tested their suitability using different combinations of inputs.Several metrics are provided for the sake of comparison within our study and between other studies.The solution has been evaluated in a real-world testbed in the city of Murcia(Spain)integrating an open human-mobility dataset showing high accuracy.A MAPE between 4%and 10%was reported in horizons of 1 to 3 h.

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.

Modeling urban scale human mobility through big data analysis and machinelearning

In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,o00 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85%average accuracy and about 86%mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.

Human following of a mobile service robot with single laser range finder

The human following becomes one of the significant procedure in human-friendly navigation of mobile robots.Many potential applications of human-following technology are developed lately.This paper suggests a method for a mobile robot to detect human legs and to follow the human by using a single laser range finder(LRF).We extract four simple attributes of human legs.We employ a support vector data description(SVDD)scheme in order to define the boundary of extracted attributes mathematically.To deal with occlusion,we designed an efficient human walking model to have robust tracking.The proposed approaches were successfully confirmed by carrying out various experiments.

The Design of UWB Antenna for Mobile Phone and the Antenna Influence on Human Head Model

In order to improve the performance of the antenna at low frequency,short circuit branch and coupling feed structure are introduced into the folded broadband single antenna in this paper,then resonant frequency is merged into the antenna using method of stagger tuning,finally,a UWB antenna is designed to w ork betw een 800 MHz ~ 2700 MHz. Analysis are conducted to determine the antenna's properties. The short-circuit w ire location,feeding point location and the length of folded strip are discussed in detail. The SAR is calculated by HFSS simulation softw are w hile the antenna is close to a 3D human head model. The research results show that the antenna can cover eight commonly used commercial frequency bands at present,and electromagnetic radiation of the antenna has tiny influence on the human head model.

A Preliminary Study on Spatial Spread Risk of Epidemics by Analyzing the Urban Subway Mobility Data

The prevention and treatment of epidemic is always an urgent problem faced by the human being. Due to the special space structure, huge passenger flow and great people mobility, the subway lines have become the areas with high epidemic transmission risks. However, there is no recent study related to epidemic transmission in the subway network on urban-scale. In this article, from the perspective of big data, we study the transmission risk of epidemic in Beijing subway network by using urban subway mobility data. By reintegrating and mining the urban subway mobility data, we preliminary assess the transmission risk in the subway lines from the passenger behaviors, station features, route features and individual case on the basis of subway network structure. This study has certain practical significance for the early stage of epidemic tracking and prevention.

Human Motion Recognition Using Ultra-Wideband Radar and Cameras on Mobile Robot

Cameras can reliably detect human motions in a normal environment, but they are usually affected by sudden illumination changes and complex conditions, which are the major obstacles to the reliability and robustness of the system. To solve this problem, a novel integration method was proposed to combine hi-static ultra-wideband radar and cameras. In this recognition system, two cameras are used to localize the object＇s region, regions while a radar is used to obtain its 3D motion models on a mobile robot. The recognition results can be matched in the 3D motion library in order to recognize its motions. To confirm the effectiveness of the proposed method, the experimental results of recognition using vision sensors and those of recognition using the integration method were compared in different environments. Higher correct-recognition rate is achieved in the experiment.

Design of the Mobile Phone Charging Device based on Human Motion Energy and Light Energy

This paper designs a mechanical swing of placementing mobile phone, which is inspired by the mechanical watch automatic winding process. The use of the kinetic energy generated by human body motion drives the wheel swing and the generator, it can carry out mobile phone additional charge through the electronic components rectifier and DC/DC converter regulator, the use of human motion and light energy can extend a fixed charge mobile phone standby time. The human motion power uses electromagnetic coupling technique and collects energy by using foot swing, solar power generation uses DSP chip in TMS320F28927 control a plurality of charging circuit, inverter circuit and solar maximum power point tracking by sampling and multiple output PWM wave. Finally, charging process has the basic constant current process discovered by device testing, the design of human motion and light energy mobile phone charger can satisfy the need of mobile phone rechargeable lithium batteries.

Bio-effects of 5th generation electromagnetic waves on organs of human beings

Objectives:The uses of devices of electromagnetic waves(EMWs)are increasing day by day.Similarly,the generation of the waves is increasing.The frequency spectrum of the generation of waves is also increased.In this manuscript,an analysis of the high frequency EMWs has been made by the electric fields generated due to the exposure of 5th generation(5 G)of mobile phones.Methods:Due to the emission of waves from the towers,the electric field is generated around the transmission tower of mobile phones.The electric fields are computed by the power of the transmission tower.The electric fields across the biological tissues/cells are also computed when the EMWs penetrate inside the body.The electric fields are made across the organs of different depths inside the body.Results:The induced electric fields inside the organs of the human beings are responsible for the absorption of energy from high frequency EMWs.The absorbed amount of energy from high frequency waves may become the cause of harmful effects on the life of organs of human beings.Conclusion:In this manuscript,after analysis of the computed electric fields inside the organs of human beings,it is concluded that the EMWs of 5 G spectrum of mobile phone towers may more harmful for the life of organs as 4th generation(4 G)spectrum of mobile phone waves.The energy absorption by the 4 G spectrum is lower than 5 G spectrum due to the range of frequency of waves.The effects on the pancreas,retina,skin,intestine,spleen,stomach and uterus are more than low water content organs like nails,bone,teeth etc.

一种融合注意力机制与3D双流卷积网络的动态头势识别方法

头势能够传递丰富的情绪和意图信息,属于重要的人机交互方式之一。然而,目前基于穿戴式传感器的头势识别方法虽然具有较高的识别率,但缺乏经济性和便捷性,而基于机器视觉的方法普遍存在准确率低、泛化性差、算力成本较高的问题,因此目前的头势识别方法仍难以部署于移动机器人。针对以上问题,提出了一种融合注意力机制与3D双流卷积的动态头势识别方法。该方法从动态头势视频帧中提取RGB信号和光流特征,在注意力机制的启发下,从通道域和空间域进行动作特征提取和增强,从而对关键特征进行准确提取,然后对特征进行融合与分类。实验结果表明,所提方法能够有效提取头势中关键的通道域和空间域信息,可显著提高头势识别的准确率及泛化能力,可在有限算力下实现较高的准确率与实时性。其后,将所提方法应用于助老机器人,在实际示范应用中进行了验证,结果表明本方法适于移动机器人等算力受限的移动机载计算平台。

时空矢量场下人群活动聚散模式提取与分析

由于传统方法缺乏顾及人群在区域间的动态流动而无法反映人群在未来(下一时刻)的活动聚散趋势,因此,文中借助时空矢量场来建模人群活动的趋向性,通过矢量场理论中的散度算子来定量计算人群活动聚散强度,将人群活动聚散模式提取问题转化为时间序列聚类问题识别出主要聚散模式。在海口市滴滴出行数据集上进行实验,选取角度偏态系数证明了主体方向计算方法的有效性,提取出了4种主要人群活动聚散模式,并结合POI类型的分布情况对4种模式进行了语义解释,为探索人类移动性提供研究思路和方法支持。

劳动力流动赋能城乡融合:理论机制与实证检验——基于安徽省6县1002个农户的问卷调查

城乡融合在构建新型城乡关系中发挥着基础性作用,劳动力流动则为城乡融合目标的实现提供了新的可能。安徽省作为传统的农业大省及全国农村劳动力转移大省,城乡融合问题是制约其经济发展的重要因素。因此,基于微观调研数据,探究安徽省劳动力流动对城乡融合发展的影响,并通过机制分析检验资源配置和人力资本在劳动力流动赋能城乡融合过程中的间接作用。研究发现:劳动力流动显著促进了城乡融合发展,这一结论在经过一系列严谨检验后依然成立;劳动力流动可以通过优化资源配置、聚集人力资本等路径有效推动城乡融合;劳动力流动对城乡融合发展的促进效应存在明显的区域异质性,尤其在对政策相对不完善地区的城乡融合促进上,其效应更为显著。基于此,为进一步全面深化改革、促进城乡融合发展、推进中国式现代化建设,提出应统筹资源配置、促进城乡产业融合,激发人才活力、增强城乡一体意识,完善市场规则、健全按要素分配制度,深化土地改革、推进新型城镇化建设,增强政策导向、规范劳动力有序流动等建议。

共同富裕背景下公共服务均等化对城乡居民收入差距的作用机制与实践检验

基于2011—2020年省级面板数据,运用固定效应模型、中介效应模型检验了公共服务均等化对城乡收入差距的影响、作用机制及空间差异。结果表明:第一,公共服务均等化水平的提升能够有效降低城乡居民收入差距;第二,公共服务均等化对城乡收入差距的影响存在空间异质性,在西部地区、人口净流出较多的地区,公共服务均等化对城乡收入差距的影响更为显著;第三,公共服务均等化可以通过促进劳动力流动与人力资本积累两个渠道影响城乡收入差距。研究结论为国家和政府在新时期推动经济持续稳定发展、保障和改善民生、全面推动社会建设、促进共同富裕提供了参考。

人口流动背景下黄河流域碳减排路径研究

绿色低碳对黄河流域生态保护和高质量发展意义重大,从人口角度入手,以2006-2020年黄河流域60个地级市为研究对象,通过构建固定效应模型分析人口流动与碳排放之间的关系,采用中介效应模型与调节效应模型对人口流动背景下黄河流域的碳减排路径进行探讨。研究结果表明:人口流动对流域碳排放的影响呈先抑制后促进的“U形”关系;人力资本积累是黄河流域的重要碳减排路径;金融发展水平、交通及文化基础设施建设水平的提高具有减排调节效应。据此提出黄河流域碳减排路径相关的对策建议。

血清Th1/Th2、Hmga1、PKM2表达水平及其联合HPV诊断宫颈癌的价值

目的探讨辅助性T细胞1(Th1)/辅助性T细胞2(Th2)、高迁移率族蛋白A1(Hmga1)、丙酮酸激酶M2型(PKM2)水平及联合人乳头状瘤病毒(HPV)对宫颈癌的诊断价值。方法选取2022年3月至2023年3月在河南省中医院就诊的186例宫颈疾病患者作为研究对象,根据疾病类型分为宫颈炎组(n=62)、宫颈癌前病变组(n=58)和宫颈癌组(n=66),另选取60例同期健康体检者作为对照组,比较四组及宫颈癌前病变组和宫颈癌组合并、未合并HPV感染患者入院时Th1/Th2因子[血清干扰素γ(IFN-γ)、白细胞介素-2(IL-2)、白细胞介素-4(IL-4)、白细胞介素-10(IL-10)]、Hmga1、PKM2水平,分析血清Th1/Th2、Hmga1、PKM2水平表达与肿瘤病理特征的关系及联合HPV对宫颈癌合并HPV感染的诊断价值。结果四组受检者入院(体检)时的血清IFN-γ、IL-2、IL-4、IL-10、Hmga1、PKM2水平比较,宫颈癌组>宫颈癌前病变组>宫颈炎组>对照组,差异均有统计学意义(P<0.05);与未合并HPV感染患者比较,宫颈癌前病变组、宫颈癌组合并HPV感染患者入院时的血清IFN-γ、IL-2、IL-4、IL-10、Hmga1、PKM2水平较高,差异均有统计学意义(P<0.05);不同肿瘤分化程度、临床分期、有无淋巴结转移宫颈癌合并HPV感染患者入院时的血清IFN-γ、IL-2、IL-4、IL-10、Hmga1、PKM2水平比较差异均有统计学意义(P<0.05);HPV感染对宫颈癌诊断灵敏度为84.85%(54/66),特异度为72.41%(16/28),准确度为56.45%(70/124);血清IFN-γ、IL-2、IL-4、IL-10、Hmga1、PKM2、HPV感染对宫颈癌、宫颈癌前病变诊断AUC分别为0.761、0.730、0.745、0.806、0.819、0.707、0.786,HPV感染联合各血清指标诊断AUC为0.908,大于单一指标诊断。结论血清Th1/Th2、Hmga1、PKM2水平检测对宫颈癌具有一定的诊断价值,临床可通过其联合HPV感染情况早期辅助诊断宫颈病变,为临床针对性制定干预方案提供参考。