Real-time Search Research Articles

Real-Time Search-Assisted Multiplexed Quantitative Proteomics Reveals System-Wide Translational Regulation of Non-Canonical Short Open Reading Frames

Abnormal expression of histone deacetylases (HDACs) is reported to be associated with angiogenesis, metastasis and chemotherapy resistance regarding cancer in a wide range of previous studies. Suberoylanilide hydroxamic acid (SAHA) is well known to function as a pan-inhibitor for HDACs and recognized as one of the therapeutic drug candidates to epigenetically coordinate cancer cell fate regulation on a genomic scale. Here, we established a Real-Time Search (RTS)-assisted mass spectrometric platform for system-wide quantification of translated products encoded by non-canonical short open reading frames (ORFs) as well as already annotated protein coding sequences (CDSs) on the human transciptome and applied this methodology to quantitative proteomic analyses of suberoylanilide hydroxamic acid (SAHA)-treated human HeLa cells to evaluate proteome-wide regulation in response to drug perturbation. Very intriguingly, our RTS-based in-depth proteomic analysis enabled us to identify approximately 5000 novel peptides from the ribosome profiling-based short ORFs encoded in the diversified regions on presumed ‘non-coding’ nucleotide sequences of mRNAs as well as lncRNAs and nonsense mediated decay (NMD) transcripts. Furthermore, TMT-based multiplex large-scale quantification of the whole proteome changes upon differential SAHA treatment unveiled dose-dependent selective translational regulation of a limited fraction of the non-canonical short ORFs in addition to key cell cycle/proliferation-related molecules such as UBE2C, CENPF and PRC1. Our study provided the first system-wide landscape of drug-perturbed translational modulation on both canonical and non-canonical proteome dynamics in human cancer cells.

A real-time mechanical fault diagnosis approach based on lightweight architecture search considering industrial edge deployments

Mechanical intelligence diagnostic models based on deep learning have increased diagnostic accuracy. However, the industrial application of deep-learning models is hindered by the extra memory requirement and the increasing computational overhead of deep-learning models, which limit edge deployment and reduce real-time performance, respectively. Therefore, we propose a mechanical fault diagnosis strategy based on a multi-objective automatic optimization architecture search that combines variable layer search, efficient search space, and a real-time search strategy. A lightweight diagnostic network that integrates the computing requirements of edge deployment, real-time testing, and accuracy is constructed. The proposed model considers the influence of compound faults and the strong noise of rotating machinery in engineering applications. The experiments on the defect data of two sets of rotating equipment show that our model is more lightweight and has higher accuracy than other advanced baselines.

The time trend of information seeking behavior about salt reduction using Google Trends: infodemiological study in Japan.

Numerous initiatives have been implemented to reduce salt intake to prevent hypertension and cardiovascular disease. However, salt consumption remains high. No study worldwide has evaluated the public's awareness of salt reduction by analyzing Internet research activity. This research aims to assess trends in the public's attention to salt reduction using Google Trends. We evaluated the relative search volume (RSV) of "salt reduction" in Google Trends from January 1, 2004, to December 31, 2021. Regression coefficients indicated that RSVs increased 0.0091 (95% CI, 0.0085-0.0097, p < 0.001) per year for salt reduction. Among related search terms, search for "salt component," "soy sauce," and "pickled plum" contributed to 9.9 ± 3.2%, 5.8 ± 2.0%, and 3.9 ± 5.5% of total RSVs for salt reduction. Google Trends revealed that the Japanese public's awareness of salt reduction has increased. Related searches might provide insights when people search for salt reduction, which could be helpful for future effective interventions for understanding salt reduction. The trends of Relative search volumes (RSVs) for "salt reduction" and "salt reduction filtered" have significantly increased RSV in 2021 compared to 2004. Google Trends is an effective tool for salt reduction awareness research that provides large amounts of real-time search data.

Google Trends as a digital marketing tool: methodical aspects

The aim of the article is to identify and analyze the menus and options of Google Trends for the digital marketers. Analyses results. The article is devoted to the problem of expanding the information base of marketers through the use of Google Trends, which allows to analyze the search queries of users of the Google search system and create appropriate graphic visualizations of the popularity of various search terms and topics. It was established that the researched tool expands the information base for the work of digital marketers, in particular through the use of such sections of the service as «Explore», «Compare», «Trending Searchers. Daily search trends», «Trending Searchers. Realtime Search Trends», «Year in Search», «Subscriptions». For example, data from the «Year in search» category, in particular the «purchase of the year» category, shows the most relevant user needs that were relevant in a certain year. The general and additional capabilities of Google Trends for digital marketers have been highlighted. The general capabilities of this service include: determining trends by specific search terms or topics, determining the specifics of the dynamics of the popularity of specific search terms or topics, searching and analyzing competitors, researching various digital search platforms, and operational monitoring general current trends in the country. The general capabilities will be useful for all digital marketers, regardless of the specifics of their activity. Additional features of Google Trends include: creation of relevant content, refinement of the semantic core of the site, optimization of the content plan, clustering of topics and the search for reasons for website traffic fluctuations. Additional opportunities will be useful for more highly specialized product promotion specialists (content marketers, brand marketers, SEO specialists, SMM marketers, story makers, etc.). The authors summarized that the research of the dynamics of interest of network users through Google Trends helps to plan marketing initiatives for the future, align the content of sites with current trends, and analyze the activities of competitors. Conclusions and directions for further research. The analysis of the features of the use of other digital marketing tools and the comparison between them in terms of the effectiveness of their use for the performance of specific tasks set before digital marketers are identified as promising for further research.

An Injection System for the CHIME/FRB Experiment

Dedicated surveys searching for fast radio bursts (FRBs) are subject to selection effects that bias the observed population of events. Software injection systems are one method of correcting for these biases by injecting a mock population of synthetic FRBs directly into the real-time search pipeline. The injected population may then be used to map intrinsic burst properties onto an expected signal-to-noise ratio (S/N), so long as telescope characteristics such as the beam model and calibration factors are properly accounted for. This paper presents an injection system developed for the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project (CHIME/FRB). The system was tested to ensure high detection efficiency, and the pulse calibration method was verified. Using an injection population of ∼85,000 synthetic FRBs, we found that the correlation between fluence and S/N for injected FRBs was consistent with that of CHIME/FRB detections in the first CHIME/FRB catalog. We noted that the sensitivity of the telescope varied strongly as a function of the broadened burst width, but not as a function of the dispersion measure. We conclude that some of the machine-learning based Radio Frequency Interference mitigation methods used by CHIME/FRB can be retrained using injection data to increase sensitivity to wide events, and that planned upgrades to the presented injection system will allow for determining a more accurate CHIME/FRB selection function in the near future. We also provide the full injection data sets along with usage tutorials.

Efficient and Privacy-Preserving Search Over Edge–Cloud Collaborative Entity in IoT

Limited by the storage and computing capacity of Internet of Things (IoT) devices, outsourcing encrypted entity data has become a prevalent trend. The existing IoT entity search methods lack the integration and utilization of both edge and cloud resources and the protection of user privacy. Besides, the traditional searchable encryption mode is inapplicable to state-time-varying entities in IoT. Therefore, in this article, an edge–cloud collaborative entity search method with privacy protection in IoT is proposed, fusing the advantages of edge and cloud resources to fulfill users’ needs for real-time search and privacy protection. Specifically, a secure search architecture and search method for edge–cloud collaboration is designed to support various needs of users, such as real-time search and global search. Then, an adaptive discrimination method for similar interested entities through attribute analysis and feature extraction is proposed to construct attribute-distinguished encrypted index and query vector groups, enabling efficient entity search meanwhile ensuring fast index update. Simulation results demonstrate that the proposed method with privacy protection can effectively improve the efficiency of entity search in IoT while safeguarding user privacy.

Continuous trajectory similarity search with result diversification

With the continued increasing availability of spatio-temporal data from GPS-equipped devices, online map services, and a variety of location-based social media, trajectory similarity search has become a fundamental operation in location-based data analytics. It is of great importance to enable real-time search of trajectories that satisfy users’ personalized requirements. For the purpose, we study the Diversified Continuous Trajectory Similarity Search (DCTSS) problem. The DCTSS problem aims to process a large number of Continuous Location Set (CLS) queries over a stream of trajectory data while taking result diversity of each query into consideration. To answer the DCTSS problem, we develop a Diversity-Aware Trajectory Publish/Subscribe (DAT-PS) framework, which takes both trajectory data streams and CLS queries as input and performs query-trajectory matching between CLS queries in the query collection and trajectories over the trajectory data stream. Our experimental results on two real-life datasets show that our proposed DAT-PS framework is capable of demonstrating substantial superiority regarding both efficiency and scalability compared against baselines.

Evaluating Linear Ion Trap for MS3-Based Multiplexed Single-Cell Proteomics.

There is a growing demand to develop high-throughput and high-sensitivity mass spectrometry methods for single-cell proteomics. The commonly used isobaric labeling-based multiplexed single-cell proteomics approach suffers from distorted protein quantification due to co-isolated interfering ions during MS/MS fragmentation, also known as ratio compression. We reasoned that the use of MS3-based quantification could mitigate ratio compression and provide better quantification. However, previous studies indicated reduced proteome coverages in the MS3 method, likely due to long duty cycle time and ion losses during multilevel ion selection and fragmentation. Herein, we described an improved MS acquisition method for MS3-based single-cell proteomics by employing a linear ion trap to measure reporter ions. We demonstrated that linear ion trap can increase the proteome coverages for single-cell-level peptides with even higher gain obtained via the MS3 method. The optimized real-time search MS3 method was further applied to study the immune activation of single macrophages. Among a total of 126 single cells studied, over 1200 and 1000 proteins were quantifiable when at least 50 and 75% nonmissing data were required, respectively. Our evaluation also revealed several limitations of the low-resolution ion trap detector for multiplexed single-cell proteomics and suggested experimental solutions to minimize their impacts on single-cell analysis.

ODSearch

Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose ODSearch, an On-device Search framework equipped with a natural language interface for mobile and wearable devices. To implement search, ODSearch employs compression and Bloom filter, it provides near real-time search query responses without network dependency. In particular, the Bloom filter reduces the temporal scope of the search and compression reduces the size of the data to be searched. Our experiments were conducted on a mobile phone and smartwatch. We compared ODSearch with current state-of-the-art search mechanisms, and it outperformed them on average by 53 times in execution time, 26 times in energy usage, and 2.3% in memory utilization.

A Review on Real-Time Traffic Sign Recognition with Voice Warnings

Road signs are essential for providing information to drivers. Understanding road signs are essential for ensuring traffic safety because doing so can stop 4484 accidents. The identification of traffic signs has been the focus of research in recent decades. Accurate real-time recognition is the cornerstone of a robust but underdeveloped traffic sign recognition system. This study provides drivers with real-time voice-advice traffic sign recognition technology. This system is composed of two subsystems. Using a trained convolutional neural network, the first recognizes and detects traffic signs (CNN). When the system notices a particular traffic sign, the text-to-speech engine is employed to play a voice message to the driver. An efficient- CNN model is built on the reference data set using deep learning methods for search and real-time search. This system's advantage is that it recognizes traffic signs and guides the car even if the driver overlooks, ignores, or doesn't understand them. Say. These technologies are also necessary for the development of autonomous vehicles.

Reliable Wide-Area Data Transfers for Streaming Workflows

Many large science projects rely on remote clusters for (near) real-time data processing, thus they demand reliable wide-area data transfer performance for smooth end-to-end workflow executions. However, data transfers are often exposed to performance variations due to the changing network (e.g., background traffic) and dataset (e.g., average file size) conditions, necessitating adaptive solutions to meet stringent performance requirements of delay-sensitive streaming workflows. In this article, we propose <i>FStream++</i> to provide reliable transfer performance for large streaming science applications by dynamically adjusting transfer settings to adapt to changing transfer conditions. <i>FStream++</i> combines three optimization methods as <i>dynamic tuning</i> , <i>online profiling</i> , and <i>historical analysis</i> to swiftly and accurately discover optimal transfer settings that can meet workflow requirements. Dynamic tuning uses a heuristic model to predict the values of transfer parameters based on dataset characteristics and network settings. Since heuristic models fall short to incorporate many important factors such as I/O throughput and resource interference, we complement it with online profiling to execute a real-time search for a subset of transfer settings. Finally, historical analysis takes advantage of the long-running nature of streaming workflows by storing and analyzing previous performance observations to shorten the execution time of online profiling. We evaluate the performance of <i>FStream++</i> by transferring several synthetic and real-world workloads in high-performance production networks and show that it offers up to <inline-formula><tex-math notation="LaTeX">$3.6x$</tex-math></inline-formula> performance improvement over legacy transfer applications and up to 24% over our previous work <i>FStream</i> .

A Fast Radio Burst Backend for the Tianlai Dish Pathfinder Array

The Tianlai Dish Pathfinder Array is a radio interferometer array consisting of 16 six-meter dish antennas. The original digital backend integration time is at the seconds level, designed for H i intensity mapping experiment. A new digital backend with millisecond response is added to enable it to search for fast radio burst during its observations. The design and calibration of this backend, and the real time search pipeline for it are described in this paper. It is capable of forming 16 digital beams for each linear polarization, covering an area of 19.6 square degrees. The search pipeline is capable of searching for, recording and classifying FRBs automatically in real time. In commissioning, we succeeded in capturing the signal pulses from the pulsars PSR B0329+54 and B2021+51.

Using “Google trends” for dengue surveillance and epidemiological research

Millions of people worldwide search online for health-related information and search engines have become an increasingly popular resource for accessing health-related information and provides valuable source. Key words used as well as the number and geographic location of searches can provide trend data, available by Google trends. In this study exploring this resource using dengue disease as an example. Objectives were to use Google trends data for comparison across different locations in India for the past 5 years, and to assess the specific search terms used in Google trends data and to correlate the real time dengue outbreak of Tamil Nadu with Google trend search. It was a cross sectional study. Data collection was done via Google search queries and record was included. Weekly trends were accessed from Google Trends. Data is a randomly collected sample of real time and non-real time Google search queries. Search traffic for the string “dengue fever” reflected increased likelihood of exposure and the string “dengue symptoms and treatment” had higher relative traffic during rainy season. Cities and states with the highest amount of search traffic for “dengue disease” overlapped where dengue is endemic. Found that search trend data produced by Google to approximate the seasonality, spikes at September to November every year and geographic distribution also identified in dengue disease. Web search query data were found to be capable of tracking dengue activity and predict periods of large incidence of dengue with high accuracy and may prove useful.

Increasing the Throughput of Activity-Based Proteome Profiling Studies with Real-Time Search and Sample Hyperplexing

Increasing the Throughput of Activity-Based Proteome Profiling Studies with Real-Time Search and Sample Hyperplexing

When to Commit to an Action in Online Planning and Search

In online planning, search is concurrent with execution. Under the formulation of planning as heuristic search, when a planner commits to an action, it re-roots its search tree at the node representing the outcome of that action. For the system to remain controlled, the planner must commit to a new action (perhaps a no-op) before the previously chosen action completes. This time pressure results in a real-time search. In this time-bounded setting, it can be beneficial to commit early, in order to perform more lookahead search focused below an upcoming state. In this paper, we propose a principled method for making this commitment decision. Our experimental evaluation shows that our scheme can outperform previously-proposed fixed strategies.

Real-Time Search-Driven Caching for Sensing Data in Vehicular Networks

Real-time search is essential for accessing specific sensing data (SD) in vehicular networks to support safe, efficient, and intelligent road services. Considering the tremendous data volume, the SD search process should be carefully devised to avoid excessive retrieval and transmission delay. To alleviate the communication and computational burden for sensing devices and the cloud server, roadside edges are adopted to cache the SD in advance. Given a short lifetime of SD, the caching scheme is required to be efficient in facilitating both the search process and uplink/downlink transmission, which is quite challenging due to the coupling of resource allocation decisions. To guarantee the search efficacy and enhance the caching resource utilization, we propose a real-time search-driven caching (RSC) paradigm to enable the cooperation among storage-constrained edges. A hierarchical indexing framework is first introduced for cached data, based on which we then devise a search utility model to quantify the expected data freshness and response delay. With the objective of maximizing the long-term search reward, the RSC problem is formulated by jointly considering the search requests and utility model. A deep-reinforcement-learning-based caching (DRLC) method is proposed to solve the problem. Specifically, an action transition module is introduced to lower the computational complexity via reducing the selection space of caching actions. Extensive simulations are carried out based on the real trace data in Creteil, France, and results show that the intelligent DRLC method can improve the real-time search performance significantly comparing to the benchmark methods.

A Blockchain-Enabled Privacy-Preserving Verifiable Query Framework for Securing Cloud-Assisted Industrial Internet of Things Systems

Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduces several data privacy and integrity issues related to verifying the reliability of users’ queries and aggregated outcomes. In this article, we propose a blockchain-based framework for provisioning a privacy-preserving and verifiable query facility to end-users in IIoT systems. The framework uses blockchain to store IoT data as on-chain data and the cloud to store extensive data (e.g., image) as off-chain data and provisioning search services to users by executing a query in both on-chain and off-chain data and generating an aggregated result. Besides, it introduces a new privacy-preserving query mechanism for ensuring sensitive data privacy during query execution. A data owner encrypts both on-chain and off-chain data in the privacy-preserving query mechanism before sending it to the blockchain and cloud. A CSP can perform search operations on the encrypted on-chain and off-chain data to ensure sensitive data privacy. A multisignature-powered query verification model is also built for the blockchain. The query verification model allows each blockchain node to endorse the query result individually and a user to verify the endorsement of the query result before use. The experiments revealed the high efficiency and scalability of the proposed framework.

A novel real-time energy management strategy based on Monte Carlo Tree Search for coupled powertrain platform via vehicle-to-cloud connectivity

To improve the performance and efficiency of the energy management strategy used in electric vehicles equipped with a dual-motor coupled powertrain platform, this study proposes a systematic real-time search approach via vehicle-to-cloud (V2C) connectivity to reduce the battery degradation and electrical consumption by control working mode and split torque. To be specific, the Monte Carlo Tree Search (MCTS) is employed to search for optimal control sequence in the velocity feasible range in the cloud platform, considering battery loss and electric cost. The logic of time and velocity range updating is proposed as the solution for abrupt traffic changes. To evaluate the effectiveness of the proposed method, a rule-based and an online DP (Dynamic Programming) -based strategy is developed as the baseline approach. Meanwhile, the assessment conditions include standard cycles following power noise and real-world driving cycles. Finally, actual vehicle and hardware-in-the-loop (HIL) experimental results demonstrate that the proposed method significantly outperforms other strategies, the average total cost is 0.36 USD/km, and the improvements are 12.9% and 11.4% compared to the rule-based and online DP-based approaches, respectively.

Ubiquitous on-demand Water Reservoir Tracking System for Smart Village

One of the natural resources that is vital to our existence as humans is water. Since humans use water in their daily lives, it is essential to check the water level to solve extensive water supply especially during major crisis period. It is the most significant to make it available and avoid wastage. Background study highlights some earlier work on using GSM network services to monitor our system and also specifies the creation of a prepaid water metre system that lets for remote monitoring and charging of water intake without the need for human involvement. In this paper, we proposed a model that is described along with web-based data acquisition platform integrated to retrieve data through cellular networks. Using this technology many people like farmers are aware from time to time of their water level condition especially in summer or dry time in their different urban, semi-urban, or rural areas so that they use their water as per their demand. Our Internet of Things (IoT) based system also gives an automated alarm system that can send a message to its registered user for a drop of water level of any resources at their area. Therefore, our project is to develop an architecture which can measure volume of water in different water bodies along with geographical location. A mobile based application has been developed for real-time search along with water level status. So, it will help to balance our ecosystem. Our long-term goal is to provide a more reliable source of drinking water for the inhabitants of the locality.

MP38-19 PREVALENCE OF TESTOSTERONE DEFICIENCY AND SECONDARY POLYCYTHEMIA IN MEN WITH OBSTRUCTIVE SLEEP APNEA

You have accessJournal of UrologyCME1 May 2022MP38-19 PREVALENCE OF TESTOSTERONE DEFICIENCY AND SECONDARY POLYCYTHEMIA IN MEN WITH OBSTRUCTIVE SLEEP APNEA Chase Carto, Sirpi Nackeeran, and Ranjith Ramasamy Chase CartoChase Carto More articles by this author , Sirpi NackeeranSirpi Nackeeran More articles by this author , and Ranjith RamasamyRanjith Ramasamy More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002592.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Secondary polycythemia is a well-known complication of obstructive sleep apnea (OSA), a disorder that increases in incidence with age. Similarly, increased recognition of low testosterone and its associated symptoms in aging men has led to increased administration of testosterone replacement therapy (TRT), which has also been associated with secondary polycythemia. We hypothesized that given the common association of aging with OSA and testosterone deficiency, a significant percentage of men with OSA would carry concurrent diagnoses of testosterone deficiency and secondary polycythemia. The objective of this study was to investigate the epidemiology of men with OSA, secondary polycythemia, and testosterone deficiency in men using a large electronic health record network. METHODS: We performed a real-time search for adult men (18+) with a diagnosis of obstructive sleep apnea (ICD-10-CM G47.33) in the TriNetX network, which provides health record data for more than 75 million patients from 48 healthcare organizations. This cohort was used to separately identify men with testosterone deficiency, as defined by those with a diagnosis of testicular dysfunction or hypofunction (ICD-10-CM E29, E29.1), and secondary polycythemia (ICD-10-CM D75.1). Lastly, we queried the network for men with all three diagnoses: OSA, testosterone deficiency, and secondary polycythemia. All men with diagnosis of polycythemia vera or familial erythrocytosis were removed from the analysis to account for men with concurrent primary polycythemia. RESULTS: A total of 1,166,640 men with a diagnosis of OSA were identified in the network. Mean age of men with OSA was 61 [46 – 76]. Among men with OSA, 6.0% of men had testosterone deficiency (TT <300ng/dL), and 1.3% of men had associated polycythemia (Hct >52%). Interestingly, only 3,562 (0.3%) of men in the network had all three diagnoses of obstructive sleep apnea, testosterone deficiency, and secondary polycythemia. Strengths of this study include use of a large and heterogenous study population and adjustment for other causes of polycythemia. Limitations include lack of data on chronology of diagnoses, inconsistent data recording, and missing data. CONCLUSIONS: In a large, cross-sectional, multicenter electronic health record study, we found that 6.0% of men with OSA will have testosterone deficiency. Reassuringly, only 0.3% of men carry all diagnoses of obstructive sleep apnea, testosterone deficiency, and secondary polycythemia. Nevertheless, men with OSA who receive TRT warrant close follow-up for potentially dangerous increases in hematocrit when considering treatment for testosterone deficiency. Source of Funding: This work was supported by National Institutes of Health Grant R01 DK130991 and the Clinician Scientist Development Grant from the American Cancer Society to Ranjith Ramasamy © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e625 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Chase Carto More articles by this author Sirpi Nackeeran More articles by this author Ranjith Ramasamy More articles by this author Expand All Advertisement PDF DownloadLoading ...