A Survey on Current-Mode Interfaces for Bio Signals and Sensors.

In this study, a review of second-generation voltage conveyor (VCII) and current conveyor (CCII) circuits for the conditioning of bio signals and sensors is presented. The CCII is the most known current-mode active block, able to overcome some of the limitations of the classical operational amplifier, which provides an output current instead of a voltage. The VCII is nothing more than the dual of the CCII, and for this reason it enjoys almost all the properties of the CCII but also provides an easy-to-read voltage as an output signal. A broad set of solutions for relevant sensors and biosensors employed in biomedical applications is considered. This ranges from the widespread resistive and capacitive electrochemical biosensors now used in glucose and cholesterol meters and in oximetry to more specific sensors such as ISFETs, SiPMs, and ultrasonic sensors, which are finding increasing applications. This paper also discusses the main benefits of this current-mode approach over the classical voltage-mode approach in the realization of readout circuits that can be used as electronic interfaces for different types of biosensors, including higher circuit simplicity, better low-noise and/or high-speed performance, and lower signal distortion and power consumption.

Evaluating the effectiveness of publishers' features in fake news detection on social media.

With the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of control and verification mechanism has led to the spread of fake news as one of the most critical threats to democracy, economy, journalism, health, and freedom of expression. So, designing and using efficient automated methods to detect fake news on social media has become a significant challenge. One of the most relevant entities in determining the authenticity of a news statement on social media is its publishers. This paper examines the publishers' features in detecting fake news on social media, including Credibility, Influence, Sociality, Validity, and Lifetime. In this regard, we propose an algorithm, namely CreditRank, for evaluating publishers' credibility on social networks. We also suggest a high accurate multi-modal framework, namely FR-Detect, for fake news detection using user-related and content-related features. Furthermore, a sentence-level convolutional neural network is provided to properly combine publishers' features with latent textual content features. Experimental results show that the publishers' features can improve the performance of content-based models by up to 16% and 31% in accuracy and F1, respectively. Also, the behavior of publishers in different news domains has been statistically studied and analyzed.

A Review on VCII Applications in Signal Conditioning for Sensors and Bioelectrical Signals: New Opportunities.

This study reviews second-generation voltage conveyor (VCII)-based read-out circuits for sensors and bioelectrical signal conditioning from existing literature. VCII is the dual circuit of a second-generation current conveyor (CCII), which provides the possibility of processing signals in the current domain while providing output signals in the voltage form. The scope of this paper is to discuss the benefits and opportunities of new VCII-based read-out circuits over traditional ones and bioelectrical signals. The achieved main benefits compared to conventional circuits are the simpler read-out circuits, producing an output signal in a voltage form that can be directly used, improved accuracy, possibility of gain adjustment using a single grounded resistor, and the possibility of connecting several SiPM sensors to the readout circuit. The circuits studied in this paper include VCII- based read-out circuits suitable for all types of sensors configured in the current-mode Wheatstone bridge (CMWB) topology, the VCII-based read-out circuits solutions reported for silicon photomultiplier, spiral-shaped ultrasonic PVDF and differential capacitive sensors, and, finally, a simple readout circuitry for sensing bioelectrical signals. There are still not many VCII-based readout circuits, and we hope that the outcome of this study will enhance this area of research and inspire new ideas.

CRISPR genome editing using computational approaches: A survey.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one's-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible.

A VCII-Based Stray Insensitive Analog Interface for Differential Capacitance Sensors.

In this paper, a novel approach to implement a stray insensitive CMOS interface for differential capacitive sensors is presented. The proposed circuit employs, for the first time, second-generation voltage conveyors (VCIIs) and produces an output voltage proportional to differential capacitor changes. Using VCIIs as active devices inherently allows the circuit to process the signal in the current domain, and hence, to benefit from its intrinsic advantages, such as high speed and simple implementation, while still being able to natively interface with voltage mode signal processing stages at necessity. The insensitiveness to the effects of parasitic capacitances is achieved through a simple feedback loop. In addition, the proposed circuit shows a very simple and switch-free structure (which can be used for both linear and hyperbolic sensors), improving its accuracy. The readout circuit was designed in a standard 0.35 µm CMOS technology under a supply voltage of ±1.65 V. Before the integrated circuit fabrication, to produce tangible proof of the effectiveness of the proposed architecture, a discrete version of the circuit was also prototyped using AD844 and LF411 to implement a discrete VCII. The achieved measurement results are in good agreement with theory and simulations, showing a constant sensitivity up to 412 mV/pF, a maximum linearity error of 1.9%FS, and acknowledging a good behavior with low baseline capacitive sensors (10 pF in the proposed measurements). A final table is also given to summarize the key specs of the proposed work comparing them to the available literature.

Complex analyses on clinical information systems using restricted natural language querying to resolve time-event dependencies.

PURPOSE: This paper reports on a generic framework to provide clinicians with the ability to conduct complex analyses on elaborate research topics using cascaded queries to resolve internal time-event dependencies in the research questions, as an extension to the proposed Clinical Data Analytics Language (CliniDAL). METHODS: A cascaded query model is proposed to resolve internal time-event dependencies in the queries which can have up to five levels of criteria starting with a query to define subjects to be admitted into a study, followed by a query to define the time span of the experiment. Three more cascaded queries can be required to define control groups, control variables and output variables which all together simulate a real scientific experiment. According to the complexity of the research questions, the cascaded query model has the flexibility of merging some lower level queries for simple research questions or adding a nested query to each level to compose more complex queries. Three different scenarios (one of them contains two studies) are described and used for evaluation of the proposed solution. RESULTS: CliniDAL's complex analyses solution enables answering complex queries with time-event dependencies at most in a few hours which manually would take many days. CONCLUSION: An evaluation of results of the research studies based on the comparison between CliniDAL and SQL solutions reveals high usability and efficiency of CliniDAL's solution.

Restricted natural language based querying of clinical databases.

PURPOSE: To elevate the level of care to the community it is essential to provide usable tools for healthcare professionals to extract knowledge from clinical data. In this paper a generic translation algorithm is proposed to translate a restricted natural language query (RNLQ) to a standard query language like SQL (Structured Query Language). METHODS: A special purpose clinical data analytics language (CliniDAL) has been introduced which provides scheme of six classes of clinical questioning templates. A translation algorithm is proposed to translate the RNLQ of users to SQL queries based on a similarity-based Top-k algorithm which is used in the mapping process of CliniDAL. Also a two layer rule-based method is used to interpret the temporal expressions of the query, based on the proposed temporal model. The mapping and translation algorithms are generic and thus able to work with clinical databases in three data design models, including Entity-Relationship (ER), Entity-Attribute-Value (EAV) and XML, however it is only implemented for ER and EAV design models in the current work. RESULTS: It is easy to compose a RNLQ via CliniDAL's interface in which query terms are automatically mapped to the underlying data models of a Clinical Information System (CIS) with an accuracy of more than 84% and the temporal expressions of the query comprising absolute times, relative times or relative events can be automatically mapped to time entities of the underlying CIS and to normalized temporal comparative values. CONCLUSION: The proposed solution of CliniDAL using the generic mapping and translation algorithms which is enhanced by a temporal analyzer component provides a simple mechanism for composing RNLQ for extracting knowledge from CISs with different data design models for analytics purposes.

Mapping query terms to data and schema using content based similarity search in clinical information systems.

This paper reports on the issues in mapping the terms of a query to the field names of the schema of an Entity Relationship (ER) model or to the data part of the Entity Attribute Value (EAV) model using similarity based Top-K algorithm in clinical information system together with an extension of EAV mapping for medication names. In addition, the details of the mapping algorithm and the required pre-processing including NLP (Natural Language Processing) tasks to prepare resources for mapping are explained. The experimental results on an example clinical information system demonstrate more than 84 per cent of accuracy in mapping. The results will be integrated into our proposed Clinical Data Analytics Language (CliniDAL) to automate mapping process in CliniDAL.

A temporal model for Clinical Data Analytics language.

The proposal of a special purpose language for Clinical Data Analytics (CliniDAL) is presented along with a general model for expressing temporal events in the language. The temporal dimension of clinical data needs to be addressed from at least five different points of view. Firstly, how to attach the knowledge of time based constraints to queries; secondly, how to mine temporal data in different CISs with various data models; thirdly, how to deal with both relative time and absolute time in the query language; fourthly, how to tackle internal time-event dependencies in queries, and finally, how to manage historical time events preserved in the patient's narrative. The temporal elements of the language are defined in Bachus Naur Form (BNF) along with a UML schema. Its use in a designed taxonomy of a five class hierarchy of data analytics tasks shows the solution to problems of time event dependencies in a highly complex cascade of queries needed to evaluate scientific experiments. The issues in using the model in a practical way are discussed as well.