Modelling the colony growth dynamics of Listeria monocytogenes single cells after exposure to peracetic acid and acidic conditions.

Studies of classical microbiology rely on the average behaviour of large cell populations without considering that clonal bacterial populations may bifurcate into phenotypic distinct sub-populations by random switching mechanisms.Listeria monocytogenes exposure to sublethal stresses may induce different physiological states that co-exist (i.e., sublethal injury or dormancy) and present variable resuscitation capacity. Exposures to peracetic acid (PAA; 10-30 ppm; for 3 h), acetic acid and hydrochloric acid (AA and HCl; pH 3.0-2.5; for 5 h) at 20 °C were used to induce different physiological states in L. monocytogenes, Scott A strain. After stress exposure, colony growth of single cells was monitored, on Tryptic Soy Agar supplemented with 0.6 % Yeast Extract, using time-lapse microscopy, at 37 °C. Images were acquired every 5 min and were analyzed using BaSCA framework. Most of the obtained growth curves of the colonies were fitted to the model of Baranyi and Roberts for the estimation of lag time (λ) and maximum specific growth rate (µmax), except the ones obtained after exposure to AA pH 2.7 and 2.5 that were fitted to the Trilinear model. The data of λ and µmax that followed a multivariate normal distribution were used to predict growth variability using Monte Carlo simulations. Outgrowth kinetics after treatment with AA (pH 2.7 and 2.5; for 5 h at 20 °C), PAA (30 ppm; for 3 h at 20 °C) revealed that these stress conditions increase the skewness of the variability distributions to the right, meaning that the variability in lag times increases in favour of longer outgrowth. Exposures to AA pH 2.5 and 30 ppm PAA resulted in two distinct subpopulations per generation with different growth dynamics. This switching mechanism may have evolved as a survival strategy for L. monocytogenes cells, maximizing the chances of survival. Simulation of microbial growth showed that heterogeneity in growth dynamics is increased when cells are recovering from exposure to sublethal stresses (i.e. PAA and acidic conditions) that may induce injury or dormancy.

Optimized efficient attention-based network for facial expressions analysis in neurological health care.

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and care.The code and data used in this work are available at: https://github.com/munsif200/Neurological-Health-Care.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud.

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.

Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol.

(1) Objective: We explore the predictive power of a novel stream of patient data, combining wearable devices and patient reported outcomes (PROs), using an AI-first approach to classify the health status of Parkinson's disease (PD), multiple sclerosis (MS) and stroke patients (collectively named PMSS). (2) Background: Recent studies acknowledge the burden of neurological disorders on patients and on the healthcare systems managing them. To address this, effort is invested in the digital transformation of health provisioning for PMSS patients. (3) Methods: We introduce the data collection journey within the ALAMEDA project, which continuously collects PRO data for a year through mobile applications and supplements them with data from minimally intrusive wearable devices (accelerometer bracelet, IMU sensor belt, ground force measuring insoles, and sleep mattress) worn for 1-2 weeks at each milestone. We present the data collection schedule and its feasibility, the mapping of medical predictor variables to wearable device capabilities and mobile application functionality. (4) Results: A novel combination of wearable devices and smartphone applications required for the desired analysis of motor, sleep, emotional and quality-of-life outcomes is introduced. AI-first analysis methods are presented that aim to uncover the prediction capability of diverse longitudinal and cross-sectional setups (in terms of standard medical test targets). Mobile application development and usage schedule facilitates the retention of patient engagement and compliance with the study protocol.

FoGGAN: Generating Realistic Parkinson's Disease Freezing of Gait Data Using GANs.

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.

Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Deciphering the induction of Listeria monocytogenes into sublethal injury using fluorescence microscopy and RT-qPCR.

The adaptive response of bacterial cells to changing environmental conditions depends on the behavior of single cells within the population. Exposure of Listeria monocytogenes to sublethal acidic conditions in foods or in the gastrointestinal track of the host may induce injuries relevant to difficult physiological states within the dormancy continuum. In this study, exposure to acidic conditions (acetic-AA and hydrochloric acid-HCl adjusted to pH 3.0, 2.7, 2.5 at 20 °C for 5 h) was used to evaluate injury of L. monocytogenes, Scott A strain. To differentiate the resistant sub-population from the total, Tryptic Soy Agar with 0.6 % Yeast Extract (TSAYE) supplemented or not with 5 % NaCl were comparatively used. Sublethally injured cells were detected by comparing plate counts with fluorescence microscopy, using combinations of CFDA (viability) and Propidium-Iodide (death). Effect of acid stress on the relative transcription of clpP, mazE, mazF, relA, gadC, gadD, gadB, sigB, inlA and prfA upon transition of total population into different physiological stages was evaluated through RT-qPCR. AA treated cells showed measurable logarithmic reduction at pH 2.7 and 2.5, while there was a significant percentage of CFDA-/PI+ cells. Evaluation of the potentially culturable population on TSAYE, from the percentage of CFDA/PI-stained cells, revealed that unstained cells represented a non-culturable sub-population. Exposure to Ringer's solution pH 2.7, adjusted with AA, resulted in higher percentages of non-esterase active with membrane integrity cells (CFDA-/PI-) compared to the percentages of the enumerated culturable cells on TSAYE after 4 and 5 h. Under the same conditions, after 1 h of exposure macroscopic observation revealed size colony variations (SCVs) of the total population (CFU on TSAYE). L. monocytogenes retained its culturability after hydrochloric acid exposure, while cells remained metabolically active (CFDA+). However, a stochastic change in cell's shape, was detected after exposure to pH 3.0 and 2.5, adjusted with HCl, for 2 h at 20 °C. A pattern of gene up-regulation was observed during treatment with AA pH 2.7 and HCl pH 3.0 at the 3rd h of exposure. Deciphering L. monocytogenes sublethal injury sheds light into the physiological and molecular characteristics of this state and provides the food science community with quantitative data to improve risk assessment.

Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses.

The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers.

The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: "satisfactory": 4−7 log CFU/g, "acceptable": 7−8 log CFU/g, and "unacceptable": >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41−89.71%, and, for the MSI data, in the range of 74.63−85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables.

Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.