Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.

How many human proteoforms are there

New traffic sensing devices based on wireless sensing technologies were designed and tested. Such devices encompass a cost-effective, battery-free, and energy self-sustained architecture for real-time traffic measurement over distributed points in a transportation system. A weather-resistant enclosure was designed and manufactured to protect the sensor from traffic impacts on the highway. This scalable technology can monitor traffic parameters such as flow, occupancy, point speed, and vehicle classification on road systems in real-time. The data collector device is also equipped with a memory card reader, which makes it suitable for temporary installation and short-term data collection. In addition to traditional traffic parameters, the sensors can measure and report the temperature of their surroundings because they are surface-mounted. The sensors developed as a result of this project are also capable of capturing a digital magnetic signature of vehicles within any intervals required by clients. The digital magnetic signature was processed to calculate traffic volume, vehicle speed, and vehicle length estimation for classification. With the help from Maryland State Highway Administration (SHA) and Inter County Connector (ICC) staff, five sensors were permanently deployed and thoroughly tested on a section of the ICC.

Traffic data collection and anonymous vehicle detection using wireless sensor networks : research summary.

Traffic Monitoring System is a traffic analysis program that authorizes the commuter to make better choices for the everyday commute. This system provides near real-time analysis and display of local traffic information measured by historical data and GPS data along roads and accessed by the user via a web server. There has always been the necessity of accurate and real time traffic information among the commuters and drivers of large cities. In present times, with the increased use and availability of GPS enabled device, a traffic monitoring system based on GPS data is highly practical. Vehicles equipped with a GPS device driving through the traffic of different roads can generate useful information, for example, vehicle geolocation and vehicle information regarding the road. Therefore, we have developed a system using these data, so that it can be sent back to a web service and stored in a database. Later based on this information, a map can be generated that reflects the near real time traffic condition with the vehicle marker of a city at any given time to any user. Traffic intensity and estimate waiting time can be generated also based on the collected information. Our traffic monitoring system based on these principles, requires less physical maintenance, has faster deployment capability and potentially can monitor a large section of the area.

Development of a System for Traffic Data Analysis and Recommendation

This study explores the potential contributors to online technology in academic writing and designs a policy for using online technology. Focus group discussion and the survey (n = 151) were used for variable selection, questionnaire development, and data collection. A mini-experiment was conducted at the Department of Economics, Government Edward College, Pabna, Bangladesh and the survey was conducted at seven different colleges also in Pabna district. Students’ socio-economic-demographic characteristics like age, gender, religion, students’ parent income, students’ daily expenses, household composition, and level of education are major influential determinants of effective usage of online technology. Tutorial sessions for using online technology in writing, cost-effective internet package for students, institutional and infrastructural supports, motivation, students’ residential location, and electronic gadgets provided to the students at a subsidized price are important contributors to use online technology. Online technology-aided academic writing is a joint effort of students, teachers, college administration, University authority, and the Government.

Online Technology: Effective Contributor to Academic Writing

Internet of Things (IoT) is the network of the huge number of resource-personated nodes whose main feature is the reliable communication and fast data exchange among devices in any network topology. In the upcoming world of internet, proper selection of desire or appropriate destination node and finding the optimized path from the huge number of the activated nodes is a very important task. Here many nodes are available in one or more hop distance and each node has various network parameters in the IoT world, so the selection of best path and nodes is quite complex. For this reason, we need to develop an algorithm to find out the best path towards the destination node considering significant network parameters. This paper proposes a general algorithm considering any network topology for discovering the path towards the destination node from the source node and give a solution to the contradiction which may arise if there is more than one consonant path. The proposed algorithm considers the node property of all node for calculating the behavior value, path influence behavior value and finally discover the optimized path from source node to destination node. The proposed algorithm is also evaluated by simulation to determine the impact of parameters in path determination.

Path and Partner Selection in Unstructured Resource-Constrained Environments Based on Node Psychology in the Context of IoT

Intelligent Transportation System and safety driver assistance systems are significant topics of research in the field of transport and traffic management The most challenging of moving vehicle detection, tracking and classification is in the Intelligent Transportation System (ITS) and smart vehicles sector This paper provides a method for vision based tracking and classify different classes of vehicle by controlling the video surveillance system Several verification techniques were investigated based on matching templates and classifying images This paper focused on improving the performance of a single camera vehicle detection, tracking and classification system and proposed a method based on a Histogram of Oriented Gradient (HOG) function that is one of the most discriminatory features to extract the object features and trained for classification and object on the Linear Support Vector Machine (SVM) classifier Also categorize vehicles on the shape or dimension based feature extraction with cascade based Adaboost classifier which has the high predictive accuracy and the low cost of storage affirm the efficacy for real-time vehicle classification In the final stage, for minimizing the number of missing vehicles, Kalman Filter was used to track the moving vehicles in video frame Our proposed system is checked using different videos and provided the best output with appropriate processing time The experimental results shows the efficiency of the algorithm

Tanusree Debi

Papers

Development of a System for Traffic Data Analysis and Recommendation

Online Technology: Effective Contributor to Academic Writing

Path and Partner Selection in Unstructured Resource-Constrained Environments Based on Node Psychology in the Context of IoT

Developing a Framework for Vehicle Detection, Tracking and Classification in Traffic Video Surveillance