PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence

Peroxidase mimicking Fe₃O₄@Chitosan (Fe₃O₄@Chi) nanozyme was synthesized and used for high-sensitive enzyme-free colorimetric detection of H₂O₂. The nanozyme was characterized in comparison with  Fe₃O₄ nanoparticles (NPs) using X-ray diffraction, Fourier-transform infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The catalytic performance of Fe₃O₄@Chi nanozyme was first evaluated by UV-Vis spectroscopy using 3,3',5,5'-tetramethylbenzidine. Unlike Fe₃O₄NPs, Fe₃O₄@Chi nanozyme exhibited an intrinsic peroxidase activity with a detection limit of 69 nM. Next, the nanozyme was applied to a microfluidic paper-based analytical device (µPAD) and colorimetric analysis was performed at varying concentrations of H₂O₂ using a machine learning-based smartphone app called "Hi-perox Sens++ ." The app with machine learning classifiers made the system user-friendly as well as more robust and adaptive against variation in illumination and camera optics. In order to train various machine learning classifiers, the images of the µPADs were taken at 30 s and 10 min by four smartphone brands under seven different illuminations. According to the results, linear discriminant analysis exhibited the highest classification accuracy (98.7%) with phone-independent repeatability at t = 30 s and the accuracy was preserved for 10 min. The proposed system also showed excellent selectivity in the presence of various interfering molecules and good detection performance in tap water.

A Free App for Diagnosing Burnout (BurnOut App): Development Study

Background

Health specialists take care of us, but who takes care of them? These professionals are the most vulnerable to the increasingly common syndrome known as burnout. Burnout is a syndrome conceptualized as a result of chronic workplace stress that has not been successfully managed.

Objective

This study aims to develop a useful app providing burnout self-diagnosis and tracking of burnout through a simple, intuitive, and user-friendly interface.

Methods

We present the BurnOut app, an Android app developed using the Xamarin and MVVMCross platforms, which allows users to detect critical cases of psychological discomfort by implementing the Goldberg and Copenhagen Burnout Inventory tests.

Results

The BurnOut app is robust, user-friendly, and efficient. The good performance of the app was demonstrated by comparing its features with those of similar apps in the literature.

Conclusions

The BurnOut app is very useful for health specialists or users, in general, to detect burnout early and track its evolution.

An Innovative App (ExoDont) for Postoperative Care of Patients After Tooth Extraction: Prototype Development and Testing Study

BACKGROUND

The postoperative period is crucial for the initiation of healing and prevention of complications after any surgical procedure. Due to factors such as poor compliance, comprehension, and retention of instructions, and other unaccounted factors, the objectives of postoperative care are not always achieved. Therefore, an Android-based mobile health app (ExoDont) was developed to ensure a smooth postoperative period for patients after a dental extraction. The ExoDont app delivers reminders for postoperative instructions and drug intake at defined intervals, thus fostering self-reliance among patients in taking their prescribed dose of medication.

OBJECTIVE

The aim of this study is to design, develop, and validate ExoDont, an innovative app for improved adherence to postoperative instructions after tooth extraction.

METHODS

A postoperative treatment protocol was developed by a team of oral and maxillofacial surgeons and general dentists, following which the clinical and technological requirements of the app were determined along with the software engineers, graphic designers, and applications architect in the team. ExoDont was developed to provide timely reminders for medication and postoperative care. The app was field tested and validated using the User Version of the Mobile Application Rating Scale.

RESULTS

The ExoDont software design was divided into a 3-level architecture comprising a user interface application, logical layer, and database layer. The software architecture consists of an Android-based ExoDont app for patients and a web version of the admin panel. The testing and validation of the ExoDont app revealed that Perceived Impact received the highest mean score of all rated components (mean 4.6, SD 0.5), while Engagement received the lowest mean score (mean 3.5, SD 0.8).

CONCLUSIONS

The testing and validation of the app support its usability and functionality, as well as its impact on users. The ExoDont app has been designed, keeping the welfare of patients in view, in a user-friendly manner that will help patients adhere to the prescribed drug regimen and ensure easy and efficient dissemination of postoperative instructions. It could play an instrumental role in fostering compliance among patients and significantly decrease the complication rate following dental extractions.

Increasing engagement in pharmacology and pharmacogenetics education using games and online resources: The PharmacoloGenius mobile app

Mobile applications represent useful instruments to convey information and engage the users even during traveling, thanks to the wide diffusion of smartphones, tablets, smartwatches, and similar devices. As such, they have high potential as learning tools that can act complementary to traditional teaching approaches. In the field of pharmacology, mobile applications are increasingly being used to improve adherence of patients or to help them report suspect adverse drug reactions. However, they have been scarcely applied to pharmacology education. In this article, we present PharmacoloGenius, a free Android mobile application integrating resources useful for students as well as healthcare professionals or researchers to expand knowledge on pharmacological topics. We gave particular emphasis to pharmacogenetics, as it is a fundamental tool to achieve personalized treatment. The application offers original games such as pharmacological trivia based on textbooks or special "journal club" trivia based on research articles conveying the state of the art on specific topics. Additionally, the app offers a curated list of online resources to study pharmacology and pharmacogenetics (e.g., free online courses, videos, and databases) as well as updated news on conferences, grants, and opportunities for pharmacologists. In conclusion, PharmacoloGenius aims to be a useful instrument for people interested in expanding their knowledge on pharmacology in an engaging way.

"Canine tracker"; an app based on android platform for localisation of impacted maxillary canines using digital panoramic radigraphs

The panoramic radiographs for localisation of impacted canines are now mostly available in the digital form. It is difficult to apply the localisation techniques in the digital format especially when it is viewed and diagnosed using smart phones. So in this paper we are describing about an app we created based on the android platform. Using this app three localisation methods can be applied by using multiple tools.

MADFU: An Improved Malicious Application Detection Method Based on Features Uncertainty

Millions of Android applications (apps) are widely used today. Meanwhile, the number of malicious apps has increased exponentially. Currently, there are many security detection technologies for Android apps, such as static detection and dynamic detection. However, the uncertainty of the features in detection is not considered sufficiently in these technologies. Permissions play an important role in the security detection of Android apps. In this paper, a malicious application detection model based on features uncertainty (MADFU) is proposed. MADFU uses logistic regression function to describe the input (permissions) and output (labels) relationship. Moreover, it uses the Markov chain Monte Carlo (MCMC) algorithm to solve features' uncertainty. After experimenting with 2037 samples, for malware detection, MADFU achieves an accuracy of up to 95.5%, and the false positive rate (FPR) is 1.2%. MADFU's Android app detection accuracy is higher than the accuracy of directly using 24 dangerous permission. The results also indicate that the method for an unknown/new sample's detection accuracy is 92.7%. Compared to other state-of-the-art approaches, the proposed method is more effective and efficient, by detecting malware.

GPS Tracking App for Police to Track Ambulances Carrying COVID-19 Patients for Ensuring Safe Distancing

The outbreak of the SARS-CoV-2 virus is causing loss of lives and property all over the world. There have been more than 2.1 million cases of COVID-19 with a death of more than 1.2 lakh patients worldwide and the numbers are still rising. The virus spreads rapidly by the droplets coming out from the nose and mouth of an infected person (Sandoiu in Why does SARS-CoV-2 spread so easily? Medical news today, 2020 https://www.medicalnewstoday.com/articles/why-does-sars-cov-2-spread-so-easily). In this situation, proper quarantining and monitoring of the already infected patients are very essential. In cases where patients need to be transferred to different locations by ambulances, monitoring of these ambulances by the traffic police can help to ensure distancing and faster movement of the vehicle inside the city. This paper presents the development of a Real-time Global Positioning System-based tracking app for the ambulances carrying COVID-19 patients which would help traffic police to ensure distancing the patients from the public.

Near Field Communication-based Agricultural Management Service Systems for Family Farms

This paper presents an agricultural management service system that aims to meet the needs of Internet of Things (IoT) information upgrades in China’s family farms. The proposed agricultural management service system consists of Near Field Communication (NFC) tags, in-field service nodes, and smartphones. NFC tags are used as the core identifier of various agricultural management elements. The in-field service node, which is based on a programmable system-on-chip with intellectual property cores (IP core), supports distributed agriculture device management and smartphone operations. Smartphones in the proposed system include the management assistant application (app) and management service app, which are designed for agricultural management support functions and agricultural management application requirements. Through this system, the needs of diverse agricultural management practices can be effectively satisfied by a unified system structure. The practical results show that the design can be used to construct diversified agricultural IoT information application service systems simply and effectively, and it is especially suitable for Chinese family farm operators who are implementing IoT information upgrades for smart agriculture.

From source code to test cases: A comprehensive benchmark for resource leak detection in Android apps

Android apps share resources, such as sensors, cameras, and Global Positioning System, that are subject to specific usage policies whose correct implementation is left to programmers. Failing to satisfy these policies may cause resource leaks, that is, apps may acquire but never release resources. This might have different kinds of consequences, such as apps that are unable to use resources or resources that are unnecessarily active wasting battery. Researchers have proposed several techniques to detect and fix resource leaks. However, the unavailability of public benchmarks of faulty apps makes comparison between techniques difficult, if not impossible, and forces researchers to build their own data set to verify the effectiveness of their techniques (thus, making their work burdensome). The aim of our work is to define a public benchmark of Android apps affected by resource leaks. The resulting benchmark, called AppLeak, is publicly available on GitLab and includes faulty apps, versions with bug fixes (when available), test cases to automatically reproduce the leaks, and additional information that may help researchers in their tasks. Overall, the benchmark includes a body of 40 faults that can be exploited to evaluate and compare both static and dynamic analysis techniques for resource leak detection.

OligoCOOL: A mobile application for nucleotide sequence analysis

Today smartphones are inseparable parts of modern life and are capable of performing many desktop computers' tasks such as scientific analysis with greater convenience. Here, we present OligoCOOL, which is an Android application for analyzing nucleic sequences. This application enables users to perform several common biomedical analyses for a given nucleotide sequence. OligoCOOL is a freely accessible Android app at http://bioinf.modares.ac.ir/software/OligoCOOL, which can be a suitable tool for the experimental design in the laboratories. This application also can be used to learn the basics of nucleotide sequence analysis. © 2019 International Union of Biochemistry and Molecular Biology, 47(2): 201-206, 2019.

The InterMine Android app: Cross-organism genomic data in your pocket

InterMine is a data integration and analysis software system that has been used to create both inter-connected and stand-alone biological databases for the analysis of large and complex biological data sets. Together, the InterMine databases provide access to extensive data across multiple organisms. To provide more convenient access to these data from Android mobile devices, we have developed the InterMine app, an application that can be run on any Android mobile phone or tablet. The InterMine app provides a single interface for data access, search and exploration of the InterMine databases. It can be used to retrieve information on genes and gene lists, and their relatives across species. Simple searches can be used to access a range of data about a specific gene, while links to the InterMine databases provide access to more detailed report pages and gene list analysis tools. The InterMine app thus facilitates rapid exploration of genes across multiple organisms and kinds of data.

The InterMine Android app: Cross-organism genomic data in your pocket

InterMine is a data integration and analysis software system that has been used to create both inter-connected and stand-alone biological databases for the analysis of large and complex biological data sets. Together, the InterMine databases provide access to extensive data across multiple organisms. To provide more convenient access to these data from Android mobile devices, we have developed the InterMine app, an application that can be run on any Android mobile phone or tablet. The InterMine app provides a single interface for data access, search and exploration of the InterMine databases. It can be used to retrieve information on genes and gene lists, and their relatives across species. Simple searches can be used to access a range of data about a specific gene, while links to the InterMine databases provide access to more detailed report pages and gene list analysis tools. The InterMine app thus facilitates rapid exploration of genes across multiple organisms and kinds of data.