Incidence, characteristics, and risk factors of drug-associated muscle adverse reaction: a retrospective real-world study of inpatients.

OBJECTIVE: To analyze the clinical characteristics, incidence, and distribution of drug-associated muscle adverse reactions (DAMAR) in real-world inpatients, to provide valuable references for clinical medication use. METHODS: We conducted an automatic retrospective monitoring of inpatients from May 1, 2022, to April 30, 2023, to collect information on adverse drug reactions (ADR) of patients and conducted subsequent analyses. RESULTS: Among 102,430 hospitalizations, 1106 cases of DAMARs were identified, yielding an incidence of 1.08%, including 125 cases of rhabdomyolysis at an incidence of 0.12%. Seventy-five percent of the patients experienced muscle adverse reactions within 5 days after taking medication, with a median elevated creatine kinase (CK) value of 420.4 IU/L. Risk factors of DAMAR include age ≥ 65, male sex, obesity, hypertension, hepatic and renal insufficiency, and anemia. No significant correlation was observed between the duration of surgery and CK elevation, while the surgical procedure itself had an impact. The 114 drugs associated were predominantly nervous system drugs, anti-infectives for systemic use, and cardiovascular system drugs, with levofloxacin, pregabalin, and parecoxib being the most frequently associated drugs. CONCLUSION: Healthcare professionals should be vigilant with patients exhibiting the identified risk factors. Monitoring creatine kinase and related indices when using myotoxic drugs is crucial to preventing serious adverse reactions, ultimately preserving patients' quality of life.

Complementary use of visual and olfactory cues to assess capture of Bactrocera dorsalis (Hendel): Implementation and field verification via an IoT-based automatic monitoring system.

This study examined the effect of combining visual and olfactory cues to attract oriental fruit flies (OFFs). Six different colored light-emitting diodes (LEDs) served as a visual attractant and methyl eugenol served as olfactory bait to lure male flies. An internet of things (IoT)-based pest monitoring system, consisting of sensor nodes, a gateway, and automatic counting traps, was deployed in the field to automatically collect environmental data and pest counts. The results of the calibrated experiments indicated that green, yellow, or red LEDs exhibited better performance in attracting flies than white, purple, or blue LEDs or no LEDs. With an accurate combination of visual and olfactory cues, the proposed IoT-based pest monitoring system may be an effective tool in agricultural pest management, given its advantages for efficiently capturing OFFs in a labor and time saving manner, providing accurate information regarding increases in pest populations, and enabling long-term, real-time data collection.

Automatic real-time monitoring of fungal spores: the case of Alternaria spp.

We present the first implementation of the monitoring of airborne fungal spores in real-time using digital holography. To obtain observations of Alternaria spp. spores representative of their airborne stage, we collected events measured in the air during crop harvesting in a contaminated potato field, using a Swisens Poleno device. The classification algorithm used by MeteoSwiss for operational pollen monitoring was extended by training the system using this additional dataset. The quality of the retrieved concentrations is evaluated by comparison with parallel measurements made with a manual Hirst-type trap. Correlations between the two measurements are high, especially over the main dispersion period of Alternaria spp., demonstrating the potential for automatic real-time monitoring of fungal spores.

Analysis of clinical characteristics and automatic monitoring of drug-induced arrhythmias in 167,546 inpatients.

OBJECTIVE: The purpose of this study was to analyze the occurrence characteristics, clinical manifestations, medication distribution, and incidence of drug-induced arrhythmias in a real-world inpatient population. METHODS: According to the inclusion and exclusion criteria as well as the ADR evaluation criteria, we retrospectively evaluated hospitalized patients in 2019 using the arrhythmia module of the Adverse Drug Event Active Surveillance and Assessment System-II (ADE-ASAS-II). A detailed analysis was performed on the demographic data, ADR manifestations, and medication distribution of 2097 patients with drug-induced arrhythmias and QT interval prolongation. RESULTS: Of the 167,546 hospitalized patients, there were 1809 cases of drug-induced arrhythmias, with an incidence of 1.08%. The ADRs in 45.35% of positive patients occurred within 3 days after medication administration, and 46.73% of the patients were 65 years old or older. The predominant ADRs identified in this study were extrasystole, tachycardia, and QT interval prolongation, of which the incidence was 0.20%. Levofloxacin was the most involved drug, and levofloxacin-associated rates of incidence of arrhythmia and QT interval prolongation were 1.24% and 0.44%, respectively. The risk factors for drug-induced arrhythmias were male sex, advanced age, emaciation, obesity, and underlying illnesses such as cardiovascular diseases, diabetes mellitus, cerebrovascular diseases, and hepatic and renal inadequacy (P < 0.05). CONCLUSION: The incidence of drug-induced arrhythmias was in the range of common, while QTc interval prolongation was occasional. It is necessary to pay attention to patients with risk factors.

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.

BACKGROUND: e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients. OBJECTIVE: The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic. METHODS: The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients. RESULTS: Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model). CONCLUSIONS: Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

Automatic Monitoring Alarm Method of Dammed Lake Based on Hybrid Segmentation Algorithm.

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.

Real-time automatic detection of starch particles in ambient air.

Considerable amounts of starch granules can be present in the atmosphere from both natural and anthropogenic sources. The aim of this study is to investigate the variability and potential origin of starch granules in ambient air recorded at six cities situated in a region with dominantly agricultural land use. This is achieved by using a combination of laser spectroscopy bioaerosol measurements with 1 min temporal resolution, traditional volumetric Hirst type bioaerosol sampling and atmospheric modelling. The analysis of wind roses identified potential sources of airborne starch (i.e., cereal grain storage facilities) in the vicinity of all aerobiological stations analysed in this study. The analysis of the CALPUFF dispersion model confirmed that emission of dust from the location of storage towers situated about 2.5 km north of the aerobiological station in Novi Sad is a plausible source of high airborne concentrations of starch granules. This study is important for environmental health since it contributes body of knowledge about sources, emission, and dispersion of airborne starch, known to be involved in phenomena such as thunderstorm-triggered asthma. The presented approach integrates monitoring and modelling, and provides a roadmap for examining a variety of bioaerosols previously considered to be outside the scope of traditional aerobiological measurements.

An effective pressure-flow characterization of respiratory asynchronies in mechanical ventilation.

Ineffective effort during expiration (IEE) occurs when there is a mismatch between the demand of a mechanically ventilated patient and the support delivered by a Mechanical ventilator during the expiration. This work presents a pressure-flow characterization for respiratory asynchronies and validates a machine-learning method, based on the presented characterization, to identify IEEs. 1500 breaths produced by 8 mechanically-ventilated patients were considered: 500 of them were included into the training set and the remaining 1000 into the test set. Each of them was evaluated by 3 experts and classified as either normal, artefact, or containing inspiratory, expiratory, or cycling-off asynchronies. A software implementing the proposed method was trained by using the experts' evaluations of the training set and used to identify IEEs in the test set. The outcomes were compared with a consensus of three expert evaluations. The software classified IEEs with sensitivity 0.904, specificity 0.995, accuracy 0.983, positive and negative predictive value 0.963 and 0.986, respectively. The Cohen's kappa is 0.983 (with 95% confidence interval (CI): [0.884, 0.962]). The pressure-flow characterization of respiratory cycles and the monitoring technique proposed in this work automatically identified IEEs in real-time in close agreement with the experts.

Research on the Earth Pressure and Internal Force of a High-Fill Open-Cut Tunnel Using a Bilayer Lining Design: A Field Test Using an FBG Automatic Data Acquisition System.

When there are railway tunnels on both sides of a valley, a bridge is usually built to let trains pass. However, if the valley is very close to an urban area, building an open-cut tunnel at the portal and then backfilling it to create available land resources for the city and to prevent excavation slag from polluting the environment would be a wise choice. This has led to the emergence of a new type of structure, namely, the high-fill open-cut tunnel. In this paper, by performing an automatic long-term field test on the first high-fill open-cut tunnel using a bilayer design in China, the variations of earth pressure and structural internal force during the backfilling process were obtained, and different tunnel foundation types were studied. The results showed that the earth pressure significantly exceeded the soil column weight, with a maximum earth pressure coefficient between 1.341 and 2.278. During the backfilling process, the earth pressure coefficient increased at first and then decreased slowly to a relatively stable value, and a stiffer foundation would make the structure bear higher earth pressure (1.69 times the normal one observed during monitoring). The change of internal force had two stages during backfilling: before the backfill soil reached the arch crown, the internal force of the lining changed slowly and then grew linearly as the backfill process continued. Moreover, the axial force ratio of the inner and outer linings was close to their thickness proportion, and the interaction mode between the two layers was very similar to the composite beam.

Automated Surveillance of Fruit Flies.

Insects of the Diptera order of the Tephritidae family cause costly, annual crop losses worldwide. Monitoring traps are important components of integrated pest management programs used against fruit flies. Here we report the modification of typical, low-cost plastic traps for fruit flies by adding the necessary optoelectronic sensors to monitor the entrance of the trap in order to detect, time-stamp, GPS tag, and identify the species of incoming insects from the optoacoustic spectrum analysis of their wingbeat. We propose that the incorporation of automated streaming of insect counts, environmental parameters and GPS coordinates into informative visualization of collective behavior will finally enable better decision making across spatial and temporal scales, as well as administrative levels. The device presented is at product level of maturity as it has solved many pending issues presented in a previously reported study.

Paradoxical physiological responses to propranolol in a Rett syndrome patient: a case report.

BACKGROUND: Rett Syndrome (RTT), caused by a loss-of-function in the epigenetic modulator: X-linked methyl-CpG binding protein 2 (MeCP2), is a pervasive neurological disorder characterized by compromised brain functions, anxiety, severe mental retardation, language and learning disabilities, repetitive stereotyped hand movements and developmental regression. An imbalance in the sympathetic and the parasympathetic nervous system (dysautonomia) and the resulting autonomic storms is a frequent occurrence in patients with RTT. The prototypical beta blocker propranolol has been used to manage sympathetic hyperactivity in patients with RTT. CASE PRESENTATION: A 13 year old girl with RTT was referred to the Centre for Interventional Paediatric Psychopharmacology and Rare Diseases (CIPPRD), South London and Maudsley NHS Foundation Trust. Her clinical picture included disordered breathing with concomitant hyperventilation and apnoea, epilepsy, scoliosis, no QT prolongation (QT/QTc [372/467 ms on automated electrocardiogram [ECG], but manually calculated to be 440 ms]), no cardiac abnormalities (PR interval: 104 ms, QRS duration: 78 ms), and generalised anxiety disorder (ICD-10-CM Diagnosis Code F41.1). She was also constipated and was fed via percutaneous endoscopic gastrostomy (PEG). To manage the dysautonomia, propranolol was given (5 mg and 10 mg) and in parallel her physiological parameters, including heart rate, skin temperature and skin transpiration, were monitored continuously for 24 h as she went about her activities of daily living. Whilst her skin temperature increased and skin transpiration decreased, unexpectedly there was a significant paradoxical increase in the patient's average heart rate following propranolol treatment. CONCLUSION: Here, we present a unique case of a paradoxical increase in heart rate response following propranolol treatment for managing dysautonomia in a child with RTT. Further studies are warranted to better understand the underlying dysautonomia in patients with RTT and the impact this might have on treatment strategies in rare disorders such as RTT.

Short communication: Rumination and feeding behaviors differ between healthy and sick dairy cows during the transition period.

The objectives of this study were: (1) to describe the rumination and feeding behavior of freestall-housed Holstein dairy cows in the weeks around parturition, and (2) to determine the relationship between postpartum disease and precalving rumination and feeding behavior. Eighty cows were enrolled at approximately 2wk (18±7d, mean ± standard deviation) before calving. Using automatic monitoring systems, rumination and feeding behavior were recorded continuously from 10d before until 3wk after calving. Postpartum health checks were performed each day, and metritis assessment was conducted 2 times/wk. Blood ß-hydroxybutyrate was measured 3 times/week, and cows with ≥1.2mmol/L during the first 14d postpartum were diagnosed as having subclinical ketosis. The final data set included 64 cows in 5 groups: healthy (n=20), metritis (n=18), subclinical ketosis (SCK; n=9), metritis+SCK (n=9), and >1 health problem and not included before (MULT, n=8). We compared rumination and feeding data between healthy cows and the 4 categories of ill animals in each of 4 periods relative to calving: precalving (d -7 to -2), period 1 (d 3 to 8 postpartum), period 2 (d 9 to 14 postpartum), and period 3 (d 15 to 20 postpartum). Cows with SCK spent less time ruminating during the precalving period. Compared with healthy cows, those with SCK and metritis+SCK had lower dry matter intake during the precalving period and continued to eat less until d 14 and d 20 postpartum, respectively. Cows with metritis and MULT cows had lower dry matter intake during the first 2wk postpartum. Precalving feeding time was lower for SCK, metritis+SCK, and MULT cows compared with healthy cows. The difference in feeding time between healthy and metritis+SCK cows had disappeared by period 2 and between all health categories except MULT by period 3. MULT cows visited the feed bins less often and were less often replaced at the feed bin throughout all 4 periods of the study. Automatic monitoring of intake and rumination showed promise for the detection of health problems after calving. We observed differences in precalving rumination and feeding behavior. Further research is necessary to better understand the onset of behavioral changes and the relationship between rumination and disease.

A case-control study on the clinical characteristics of granisetron-related arrhythmias and the development of a predictive nomogram.

BACKGROUND: Automatic monitoring and assessment are increasingly employed in drug safety evaluations using hospital information system data. The increasing concern about granisetron-related arrhythmias requires real-world studies to improve our understanding of its safety. AIM: This study aimed to analyze the incidence, clinical characteristics, and risk factors of granisetron-related arrhythmias in hospitalized patients using real-world data obtained from the Adverse Drug Event Active Surveillance and Assessment System-II (ADE-ASAS-II) and concurrently aimed to develop and validate a nomogram to predict the occurrence of arrhythmias. METHOD: Retrospective automatic monitoring of inpatients using granisetron was conducted in a Chinese hospital from January 1, 2017, to December 31, 2021, to determine the incidence of arrhythmias using ADE-ASAS- II. Propensity score matching was used to balance confounders and analyze clinical characteristics. Based on risk factors identified through logistic regression analysis, a prediction nomogram was established and internally validated using the Bootstrap method. RESULTS: Arrhythmias occurred in 178 of 72,508 cases taking granisetron with an incidence of 0.3%. Independent risk factors for granisetron-related arrhythmias included medication duration, comorbid cardiovascular disease, concomitant use of other 5-hydroxytryptamine 3 receptor antagonists, alanine aminotransferase > 40 U/L, and blood urea nitrogen > 7.5 mmol/L. The nomogram demonstrated good differentiation and calibration, with enhanced clinical benefit observed when the risk threshold ranged from 0.10 to 0.82. CONCLUSION: The nomogram, based on the five identified independent risk factors, may be valuable in predicting the risk of granisetron-related arrhythmias in the administered population, offering significant clinical applications.

Development of a ship-based camera monitoring system for floating marine debris.

This study developed an automatic monitoring system for Floating Marine Debris (FMD) aimed at reducing the labor-intensiveness of traditional visual surveys. It involved creating a comprehensive FMD database using 55.6 h of video footage and numerous annotated images, which facilitated the training of a deep learning model based on the YOLOv8 architecture. Additionally, the study implemented the BoT-SORT algorithm for FMD tracking, significantly enhancing detection accuracy by effectively filtering out disturbances such as sea waves and seabirds, based on the movement patterns observed in FMD trajectories. Tested across 16 voyages in various marine environments, the system demonstrated high accuracy in recognizing different types of FMD, achieving a mean Average Precision (mAP@0.5) of 0.97. In terms of detecting FMD from video footage, the system reached an F1 score of 83.63 %. It showed potential as a viable substitute for manual methods for FMD larger than 20 cm.

Towards automatic insect monitoring on witloof chicory fields using sticky plate image analysis.

Context: Sticky trap catches of agricultural pests can be employed for early hotspot detection, identification, and estimation of pest presence in greenhouses or in the field. However, manual procedures to produce and analyze catch results require substantial time and effort. As a result, much research has gone into creating efficient techniques for remotely monitoring possible infestations. A considerable number of these studies use Artificial Intelligence (AI) to analyze the acquired data and focus on performance metrics for various model architectures. Less emphasis, however, was devoted to the testing of the trained models to investigate how well they would perform under practical, in-field conditions. Objective: In this study, we showcase an automatic and reliable computational method for monitoring insects in witloof chicory fields, while shifting the focus to the challenges of compiling and using a realistic insect image dataset that contains insects with common taxonomy levels. Methods: To achieve this, we collected, imaged, and annotated 731 sticky plates - containing 74,616 bounding boxes - to train a YOLOv5 object detection model, concentrating on two pest insects (chicory leaf-miners and wooly aphids) and their two predatory counterparts (ichneumon wasps and grass flies). To better understand the object detection model's actual field performance, it was validated in a practical manner by splitting our image data on the sticky plate level. Results and conclusions: According to experimental findings, the average mAP score for all dataset classes was 0.76. For both pest species and their corresponding predators, high mAP values of 0.73 and 0.86 were obtained. Additionally, the model accurately forecasted the presence of pests when presented with unseen sticky plate images from the test set. Significance: The findings of this research clarify the feasibility of AI-powered pest monitoring in the field for real-world applications and provide opportunities for implementing pest monitoring in witloof chicory fields with minimal human intervention.

Automatic Detection of Group Recumbency in Pigs via AI-Supported Camera Systems.

The resting behavior of rearing pigs provides information about their perception of the current temperature. A pen that is too cold or too warm can impact the well-being of the animals as well as their physical development. Previous studies that have automatically recorded animal behavior often utilized body posture. However, this method is error-prone because hidden animals (so-called false positives) strongly influence the results. In the present study, a method was developed for the automated identification of time periods in which all pigs are lying down using video recordings (an AI-supported camera system). We used velocity data (measured by the camera) of pigs in the pen to identify these periods. To determine the threshold value for images with the highest probability of containing only recumbent pigs, a dataset with 9634 images and velocity values was used. The resulting velocity threshold (0.0006020622 m/s) yielded an accuracy of 94.1%. Analysis of the testing dataset revealed that recumbent pigs were correctly identified based on velocity values derived from video recordings. This represents an advance toward automated detection from the previous manual detection method.

Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps.

To benefit allergy patients and the medical practitioners, pollen information should be available in both a reliable and timely manner; the latter is only recently possible due to automatic monitoring. To evaluate the performance of all currently available automatic instruments, an international intercomparison campaign was jointly organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich, Germany (March-July 2021). The automatic systems (hardware plus identification algorithms) were compared with manual Hirst-type traps. Measurements were aggregated into 3-hourly or daily values to allow comparison across all devices. We report results for total pollen as well as for Betula, Fraxinus, Poaceae, and Quercus, for all instruments that provided these data. The results for daily averages compared better with Hirst observations than the 3-hourly values. For total pollen, there was a considerable spread among systems, with some reaching R2 > 0.6 (3 h) and R2 > 0.75 (daily) compared with Hirst-type traps, whilst other systems were not suitable to sample total pollen efficiently (R2 < 0.3). For individual pollen types, results similar to the Hirst were frequently shown by a small group of systems. For Betula, almost all systems performed well (R2 > 0.75 for 9 systems for 3-hourly data). Results for Fraxinus and Quercus were not as good for most systems, while for Poaceae (with some exceptions), the performance was weakest. For all pollen types and for most measurement systems, false positive classifications were observed outside of the main pollen season. Different algorithms applied to the same device also showed different results, highlighting the importance of this aspect of the measurement system. Overall, given the 30 % error on daily concentrations that is currently accepted for Hirst-type traps, several automatic systems are currently capable of being used operationally to provide real-time observations at high temporal resolutions. They provide distinct advantages compared to the manual Hirst-type measurements.

Machine learning methods for low-cost pollen monitoring - Model optimisation and interpretability.

Pollen is a major issue globally, causing as much as 40 % of the population to suffer from hay fever and other allergic conditions. Current techniques for monitoring pollen are either laborious and slow, or expensive, thus alternative methods are needed to provide timely and more localised information on airborne pollen concentrations. We have demonstrated previously that low-cost Optical Particle Counter (OPC) sensors can be used to estimate pollen concentrations when machine learning methods are used to process the data and learn the relationships between OPC output data and conventionally measured pollen concentrations. This study demonstrates how methodical hyperparameter tuning can be employed to significantly improve model performance. We present the results of a range of models based on tuned hyperparameter configurations trained to predict Poaceae (Barnhart), Quercus (L.), Betula (L.), Pinus (L.) and total pollen concentrations. The results achieved here are a significant improvement on results we previously reported: the average R2 scores for the total pollen models have at least doubled compared to using previous parameter settings. Furthermore, we employ the explainable Artificial Intelligence (XAI) technique, SHAP, to interpret the models and understand how each of the input features (i.e. particle sizes) affect the estimated output concentration for each pollen type. In particular, we found that Quercus pollen has a strong positive correlation with particles of optical diameter 1.7-2.3 µm, which distinguishes it from other pollen types such as Poaceae and may suggest that type-specific subpollen particles are present in this size range. There is much further work to be done, especially in training and testing models on data obtained across different environments to evaluate the extent of generalisability. Nevertheless, this work demonstrates the potential this method can offer for low-cost monitoring of pollen and the valuable insight we can gain from what the model has learned.

Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests.

Pollen allergies affect a significant proportion of the global population, and this is expected to worsen in years to come. There is demand for the development of automated pollen monitoring systems. Low-cost Optical Particle Counters (OPCs) measure particulate matter and have attractive advantages of real-time high time resolution data and affordable costs. This study asks whether low-cost OPC sensors can be used for meaningful monitoring of airborne pollen. We employ a variety of methods, including supervised machine learning techniques, to construct pollen proxies from hourly-average OPC data and evaluate their performance, holding out 40 % of observations to test the proxies. The most successful methods are supervised machine learning Neural Network (NN) and Random Forest (RF) methods, trained from pollen concentrations collected from a Hirst-type sampler. These perform significantly better than using a simple particle size-filtered proxy or a Positive Matrix Factorisation (PMF) source apportionment pollen proxy. Twelve NN and RF models were developed to construct a pollen proxy, each varying by model type, input features and target variable. The results show that such models can construct useful information on pollen from OPC data. The best metrics achieved (Spearman correlation coefficient = 0.85, coefficient of determination = 0.67) were for the NN model constructing a Poaceae (grass) pollen proxy, based on particle size information, temperature, and relative humidity. Ability to distinguish high pollen events was evaluated using F1 Scores, a score reflecting the fraction of true positives with respect to false positives and false negatives, with promising results (F1 ≤ 0.83). Model-constructed proxies demonstrated the ability to follow monthly and diurnal trends in pollen. We discuss the suitability of OPCs for monitoring pollen and offer advice for future progress. We demonstrate an attractive alternative for automated pollen monitoring that could provide valuable and timely information to the benefit of pollen allergy sufferers.

Camera-Based Respiration Monitoring of Unconstrained Rodents.

Animal research has always been crucial for various medical and scientific breakthroughs, providing information on disease mechanisms, genetic predisposition to diseases, and pharmacological treatment. However, the use of animals in medical research is a source of great controversy and ongoing debate in modern science. To ensure a high level of bioethics, new guidelines have been adopted by the EU, implementing the 3R principles to replace animal testing wherever possible, reduce the number of animals per experiment, and refine procedures to minimize stress and pain. Supporting these guidelines, this article proposes an improved approach for unobtrusive, continuous, and automated monitoring of the respiratory rate of laboratory rats. It uses the cyclical expansion and contraction of the rats' thorax/abdominal region to determine this physiological parameter. In contrast to previous work, the focus is on unconstrained animals, which requires the algorithms to be especially robust to motion artifacts. To test the feasibility of the proposed approach, video material of multiple rats was recorded and evaluated. High agreement was obtained between RGB imaging and the reference method (respiratory rate derived from electrocardiography), which was reflected in a relative error of 5.46%. The current work shows that camera-based technologies are promising and relevant alternatives for monitoring the respiratory rate of unconstrained rats, contributing to the development of new alternatives for a continuous and objective assessment of animal welfare, and hereby guiding the way to modern and bioethical research.