Towards safer tower crane operations: An innovative knowledge-based decision support system for automated safety risk assessment.

INTRODUCTION: Tower cranes are commonly employed in construction projects, despite presenting significant hazards to the workforce involved. METHOD: To address these safety concerns, a Knowledge-Based Decision-Support System for Safety Risk Assessment (KBDSS-SRA) has been developed. The system's capacity to thoroughly evaluate associated risks is illustrated through its utilization in various construction endeavors. RESULTS: The system accomplishes the following goals: (1) compiles essential risk factors specific to tower crane operations, (2) identifies critical safety risks that jeopardize worker well-being, (3) examines and assesses the identified safety risks, and (4) automates the labor-intensive and error-prone processes of safety risk assessment. The KBDSS-SRA assists safety management personnel in formulating well-grounded decisions and implementing effective measures to enhance the safety of tower crane operations. PRACTICAL APPLICATIONS: This is facilitated by an advanced computerized tool that underscores the paramount significance of safety risks and suggests strategies for their future mitigation.

Development of Automated Tool for Electrode Array Insertion and its Study on Intracochlear Pressure.

Cochlear implantation is the most successful approach for people with profound sensorineural hearing loss. Manual insertion of the electrode array may result in damaging the soft tissue structures and basilar membrane. An automated electrode array insertion device is reported to be less traumatic in cochlear implant surgery. OBJECTIVES: The present work develops a simple, reliable, and compact device for automatically inserting the electrode array during cochlear implantation and test the device to observe intracochlear pressure during simulated electrode insertion. METHODS: The device actuates the electrode array by a roller mechanism. For testing the automated device, a straight cochlea having the dimension of the scala tympani and a model electrode is developed using a 3D printer. A pressure sensor is utilized to observe the pressure change at different insertional conditions. RESULTS: The electrode is inserted into a prototype cochlea at different speeds without any pause, and it is noticed that the pressure is increased with the depth of insertion of the electrode irrespective of the speed of electrode insertion. The rate of pressure change is observed to be increased exponentially with the speed of insertion. CONCLUSION: At an insertion speed of 0.15 mm/s, the peak pressure is observed to be 133 Pa, which can be further evaluated in anatomical models for clinical scenarios. LEVEL OF EVIDENCE: N/A Laryngoscope, 134:1388-1395, 2024.

RJAfinder: An automated tool for quantification of responding to joint attention behaviors in autism spectrum disorder using eye tracking data.

Deficits in responding to joint attention (RJA) are early symptoms of autism spectrum disorder (ASD). Currently, no automated tools exist for identifying and quantifying RJA behaviors. A few eye tracking studies have investigated RJA in ASD children but have produced conflicting results. In addition, little is known about the trajectory of RJA development through developmental age. Here, a new video was designed including 12 clips of an actor pointing to or looking at an object. Eye tracking technology was used to monitor RJA in three groups: 143 ASD children assessed with the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) (4-7 years old), 113 age- and gender-matched typically developing children (TDC), and 43 typically developing adults (TDA) (19-32 years old). RJAfinder was developed in R and MATLAB to quantify RJA events from the eye tracking data. RJA events were compared among the three groups. Spearman correlation coefficients between total number of RJA events in ASD and the Social Responsiveness Scale (SRS) scores were calculated. A logistic regression model was built using the average valid sampling rate and the total number of RJA events as two predictive variables to classify ASD and TDC groups. ASD children displayed statistically significantly less RJA events than the TDC and TDA groups with medium-to-large-sized effects. ASD and TDC children both displayed more RJA events in response to pointing stimuli than to looking stimuli. Our logistic regression model predicted ASD tendency with 0.76 accuracy in the testing set. RJA ability improved more slowly between the ages of 4-7 years old in the ASD group than in the TDC group. In ASD children, RJA ability showed negative correlation with SRS total T-score as well as the scores of five subdomains. Our study provides an automated tool for quantifying RJA and insights for the study of RJA in ASD children, which may help improve ASD screening, subtyping, and behavior interventions.

Ab.ai - A Novel Automated AI Tool for Reporting Antibiograms.

Anti-Microbial Resistance is one of the greatest threats that mankind faces right now due to the inappropriate use of antibiotics. Institution of appropriate antibiotics in right dose for the right patient at right time is the "gamechanger" in fighting AMR. Antibiotic Sensitivity Testing (AST) or antibiogram is done to ascertain the sensitivity profile of the organism. The most widely used method in laboratory practice in India is the Kirby-Bauer's disk diffusion test. There are few shortcomings in the manual interpretation of antibiograms in the form of high inter-operator variability, mandatory requirement of trained microbiologists - which is difficult in low-resource settings and high degree of interpersonal bias due to various factors like stress, workload, and visual acuity. We propose the Ab.ai tool for automating the AST procedures in laboratory. The Ab.ai tool comprises of 3 phases: first for data collection, second for data processing and the third for generation of antibiotic sensitivity reports. Various software packages like OpenCV and EasyOCR are used for the development of the Ab.ai tool. A total of 50 antibiograms of both GPC and GNB are interpreted both by manual and automated method. The manual method is considered the "gold-standard" and the performance of Ab.ai tool was compared against the manual method. The Ab.ai tool achieved an agreement of 98.4% on susceptibility categorization of GPC antibiotics and 97.6% on that of GNB antibiotics against the gold standard manual method. The proposed Ab.ai tool serves as a perfect candidate for automating AST procedures and would prove to be a "game-changer" in battling AMR.

A bit-vector differential model for the modular addition by a constant and its applications to differential and impossible-differential cryptanalysis.

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR. To evaluate the resistance of an ARX cipher against differential and impossible-differential cryptanalysis, the recent automated methods employ constraint satisfaction solvers to search for optimal characteristics or impossible differentials. The main difficulty in formulating this search is finding the differential models of the non-linear operations. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this operation from being evaluated with automated methods. In this paper, we present the first bit-vector differential model for the n-bit modular addition by a constant input. Our model contains O ( log 2 ( n ) ) basic bit-vector constraints and describes the binary logarithm of the differential probability. We describe an SMT-based automated method that includes our model to search for differential characteristics of ARX ciphers including constant additions. We also introduce a new automated method for obtaining impossible differentials where we do not search over a small pre-defined set of differences, such as low-weight differences, but let the SMT solver search through the space of differences. Moreover, we implement both methods in our open-source tool ArxPy to find characteristics and impossible differentials of ARX ciphers with constant additions in a fully automated way. As some examples, we provide related-key impossible differentials and differential characteristics of TEA, XTEA, HIGHT, LEA, SHACAL-1, and SHACAL-2, which achieve better results compared to previous works.

AutoVEM2: A flexible automated tool to analyze candidate key mutations and epidemic trends for virus.

In our previous work, we developed an automated tool, AutoVEM, for real-time monitoring the candidate key mutations and epidemic trends of SARS-CoV-2. In this research, we further developed AutoVEM into AutoVEM2. AutoVEM2 is composed of three modules, including call module, analysis module, and plot module, which can be used modularly or as a whole for any virus, as long as the corresponding reference genome is provided. Therefore, it's much more flexible than AutoVEM. Here, we analyzed three existing viruses by AutoVEM2, including SARS-CoV-2, HBV and HPV-16, to show the functions, effectiveness and flexibility of AutoVEM2. We found that the N501Y locus was almost completely linked to the other 16 loci in SARS-CoV-2 genomes from the UK and Europe. Among the 17 loci, 5 loci were on the S protein and all of the five mutations cause amino acid changes, which may influence the epidemic traits of SARS-CoV-2. And some candidate key mutations of HBV and HPV-16, including T350G of HPV-16 and C659T of HBV, were detected. In brief, we developed a flexible automated tool to analyze candidate key mutations and epidemic trends for any virus, which would become a standard process for virus analysis based on genome sequences in the future.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling.

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

AutoVEM: An automated tool to real-time monitor epidemic trends and key mutations in SARS-CoV-2 evolution.

With the global epidemic of SARS-CoV-2, it is important to effectively monitor the variation, haplotype subgroup epidemic trends and key mutations of SARS-CoV-2 over time. This is of great significance to the development of new vaccines, the update of therapeutic drugs, and the improvement of detection methods. The AutoVEM tool developed in the present study could complete all mutations detections, haplotypes classification, haplotype subgroup epidemic trends and candidate key mutations analysis for 131,576 SARS-CoV-2 genome sequences in 18 h on a 1 core CPU and 2 GB RAM computer. Through haplotype subgroup epidemic trends analysis of 131,576 genome sequences, the great significance of the previous 4 specific sites (C241T, C3037T, C14408T and A23403G) was further revealed, and 6 new mutation sites of highly linked (T445C, C6286T, C22227T, G25563T, C26801G and G29645T) were discovered for the first time that might be related to the infectivity, pathogenicity or host adaptability of SARS-CoV-2. In brief, we proposed an integrative method and developed an efficient automated tool to monitor haplotype subgroup epidemic trends and screen for the candidate key mutations in the evolution of SARS-CoV-2 over time for the first time, and all data could be updated quickly to track the prevalence of previous key mutations and new candidate key mutations because of high efficiency of the tool. In addition, the idea of combinatorial analysis in the present study can also provide a reference for the mutation monitoring of other viruses.

Evaluation of a New Digital Automated Glycemic Pattern Detection Tool.

BACKGROUND: Blood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks. METHODS: Multicenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns. RESULTS: eDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification. CONCLUSION: eDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study.

A toolbox for multiple sclerosis lesion segmentation.

INTRODUCTION: Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images. METHODS: Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image. RESULTS: The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches. CONCLUSION: Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.

PeptideManager: a peptide selection tool for targeted proteomic studies involving mixed samples from different species.

The search for clinically useful protein biomarkers using advanced mass spectrometry approaches represents a major focus in cancer research. However, the direct analysis of human samples may be challenging due to limited availability, the absence of appropriate control samples, or the large background variability observed in patient material. As an alternative approach, human tumors orthotopically implanted into a different species (xenografts) are clinically relevant models that have proven their utility in pre-clinical research. Patient derived xenografts for glioblastoma have been extensively characterized in our laboratory and have been shown to retain the characteristics of the parental tumor at the phenotypic and genetic level. Such models were also found to adequately mimic the behavior and treatment response of human tumors. The reproducibility of such xenograft models, the possibility to identify their host background and perform tumor-host interaction studies, are major advantages over the direct analysis of human samples. At the proteome level, the analysis of xenograft samples is challenged by the presence of proteins from two different species which, depending on tumor size, type or location, often appear at variable ratios. Any proteomics approach aimed at quantifying proteins within such samples must consider the identification of species specific peptides in order to avoid biases introduced by the host proteome. Here, we present an in-house methodology and tool developed to select peptides used as surrogates for protein candidates from a defined proteome (e.g., human) in a host proteome background (e.g., mouse, rat) suited for a mass spectrometry analysis. The tools presented here are applicable to any species specific proteome, provided a protein database is available. By linking the information from both proteomes, PeptideManager significantly facilitates and expedites the selection of peptides used as surrogates to analyze proteins of interest.