Genetic Characterization of the RAP-1A and SBP-4 Genes of Babesia Species Infecting Cattle from Selangor, Malaysia, and Ribah, Nigeria.

Bovine babesiosis has substantial economic implications in the cattle industry, emphasizing the need for a thorough understanding of the genetic diversity of the causative apicomplexan pathogen. Although babesiosis has been extensively studied globally, the genetic diversity of Babesia species in Malaysian and Nigerian cattle remains unreported. This study aims to bridge this gap by detecting and characterizing Babesia species in selected cattle herds. Our investigation explores the genetic diversity of Babesia species in cattle from Selangor, Malaysia, and Ribah, Nigeria. Blood samples revealed a 32.9% infection rate via PCR analysis. Further genetic analysis detected variations in Malaysian Babesia bigemina isolates but genetic similarity among Nigerian isolates. Conversely, all Babesia bovis isolates displayed genetic homogeneity. In summary, this research identifies genetic diversity in Babesia species affecting Malaysian and Nigerian cattle, highlighting regional disparities.

Unsupervised novelty detection for time series using a deep learning approach.

In the Smart Homes and IoT devices era, abundant available data offers immense potential for enhancing system intelligence. However, the need for effective anomaly detection models to identify and rectify unusual data and behaviors within Smart Home Systems (SHS) remains a critical challenge. This research delves into the relatively unexplored domain of novelty anomaly detection, particularly in the context of unlabeled datasets. Introducing the novel DeepMaly method, this approach provides a practical tool for SHS developers. Functioning seamlessly in an unsupervised manner, DeepMaly distinguishes between seasonal and actual anomalies through a unique process of training on unlabeled pristine features extracted from time series data. Leveraging a combination of Long Short-Term Memory (LSTM) and Deep Convolutional Neural Network (DCNN), the model is primed to detect anomalies in real-time. The research culminates in a comprehensive data prediction and classification process into normal and abnormal data based on specified anomaly thresholds and fraction percentages. Notably, this function operates seamlessly unsupervised, eliminating the need for labeled datasets. The study concludes with a complete data forecasting and sorting method that divides data into normal and abnormal categories based on defined anomaly thresholds and fraction percentages. Working in an unsupervised mode reduces the requirement for labeled datasets. The results highlight the model's prowess in new detection, which has been successfully applied to benchmark datasets. However, there is a restriction since deep learning algorithms can recognize noise as abnormalities. Finally, the investigation enhances SHS anomaly detection, providing a crucial tool for real-time anomaly identification in the ever-changing IoT and Smart Homes scene.

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study.

BACKGROUND: Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information. OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh. METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values. RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly. CONCLUSIONS: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

A cross-sectional study of Angiostrongylus malaysiensis in rats and gastropod hosts from recreational parks in Kuala Lumpur, Malaysia: Detection, risk factors and pathology.

Angiostrongylus malaysiensis is a potential zoonotic parasite, which reported to co-occur with A. cantonensis in human cerebrospinal fluid. It is a heteroxenous nematode that primarily develops through the early larval stages in gastropods and attains sexual maturity within rats. This study was conducted to determine the host species responsible for the reservoir of A. malaysiensis and investigate the risk factor for transmission among the hosts in Kuala Lumpur, Malaysia. Sampling was conducted in six recreational parks. The rats were trapped alive using steel wire traps with bait, while the gastropods were collected by active searching. The rats were euthanized and dissected to collect any adult worms observed. The molecular detection of A. malaysiensis was performed by PCR on gastropod tissue samples. Biotic and landscape factors were recorded for risk factor analysis. In total, 82 rats and 330 gastropods were collected throughout the study. Overall, 3.64% of gastropods and 32.9% of rats were infected with A. malaysiensis. Rattus tiomanicus (Malayan wood rat) and Parmarion martensi (Yellow-shelled semi-slug) were found as important hosts for A. malaysiensis. Host species, sampling site and macrohabitat type are risk factors associated with the prevalence of A. malaysiensis infection in rats. For gastropods, host species and sampling site are risk factors that correlate with the parasite detection. In total, 128 adult A. malaysiensis were recovered from the infected rats. The mean intensity of infection with adult A. malaysiensis was 4.65 for Rattus rattus complex and 4.90 for R. tiomanicus. Adult worms were found in the pulmonary artery or right ventricle, while eggs and first-stage larvae were found in capillaries of the caudal lung lobe. Infected lungs showed extravasated red blood cells in the alveolar spaces. The pulmonary arteries in the infected lung lobe were thickened. Kepong Metropolitan Park is the hotspot area for A. malaysiensis in Kuala Lumpur. These results provide essential information for public health officials to develop targeted interventions to reduce the transmission of A. malaysiensis in urban areas, particularly in recreational parks.

Profiling Bartonella infection and its associated risk factors in shelter cats in Malaysia.

BACKGROUND: Poor disease management and irregular vector control could predispose sheltered animals to disease such as feline Bartonella infection, a vector-borne zoonotic disease primarily caused by Bartonella henselae. OBJECTIVES: This study investigated the status of Bartonella infection in cats from eight (n = 8) shelters by molecular and serological approaches, profiling the CD4:CD8 ratio and the risk factors associated with Bartonella infection in shelter cats. METHODS: Bartonella deoxyribonucleic acid (DNA) was detected through polymerase chain reaction (PCR) targeting 16S-23S rRNA internal transcribed spacer gene, followed by DNA sequencing. Bartonella IgM and IgG antibody titre, CD4 and CD8 profiles were detected using indirect immunofluorescence assay and flow cytometric analysis, respectively. RESULTS: B. henselae was detected through PCR and sequencing in 1.0% (1/101) oral swab and 2.0% (1/50) cat fleas, while another 3/50 cat fleas carried B. clarridgeiae. Only 18/101 cats were seronegative against B. henselae, whereas 30.7% (31/101) cats were positive for both IgM and IgG, 8% (18/101) cats had IgM, and 33.7% (34/101) cats had IgG antibody only. None of the eight shelters sampled had Bartonella antibody-free cats. Although abnormal CD4:CD8 ratio was observed in 48/83 seropositive cats, flea infestation was the only significant risk factor observed in this study. CONCLUSIONS: The present study provides the first comparison on the Bartonella spp. antigen, antibody status and CD4:CD8 ratio among shelter cats. The high B. henselae seropositivity among shelter cats presumably due to significant flea infestation triggers an alarm of whether the infection could go undetectable and its potential transmission to humans.

Seroprevalence and Factors Associated with Toxoplasma gondii, Neospora caninum, and Besnoitia besnoiti Infections in Cattle and Goats in Selangor, Malaysia.

Apicomplexan parasites such as Toxoplasma gondii, Neospora caninum, and Besnoitia besnoiti are widely recognized as causes of production diseases in ruminants. This study aimed to investigate the serological occurrence of T. gondii, N. caninum, and B. besnoiti in cattle and goats from smallholder farms in Selangor, Malaysia. A cross-sectional study was conducted on 19 farms by collecting 404 bovine (n = 225) and caprine (n = 179) serum samples, which were then essayed for T. gondii, N. caninum, and B. besnoiti antibodies using commercially available ELISA test kits. Farm data and animal characteristics were documented, and the data were analyzed using descriptive statistics and logistic regression models. The seroprevalence of T. gondii at animal and farm levels in cattle was 5.3% (95% CI 1.2-7.4%) and 36.8% (95% CI 22.4-58.0%), respectively. Animal-level seropositivity for N. caninum was 2.7% (95% CI 0.4-4.2%) and 5.7% for B. besnoiti (95% CI 1.3-9.4%) with corresponding farm-level seropositivity of 21.0% and 31.5%, respectively. For the goat samples, a high animal- (69.8%; 95% CI 34.1-82.0%) and farm-level (92.3%) seropositivity was recorded for T. gondii, but was relatively lower for N. caninum antibodies, at 3.9% (95% CI 1.5-6.2%) and 38.4% (5/13). The factors associated with T. gondii seropositivity were older animals (above 12 months) (OR = 5.3; 95% CI 1.7-16.6), semi-intensive farms (OR = 2.2; 95% CI 1.3-6.2), the presence of either dogs or cats (OR = 3.6; 95% CI 1.1-12.3), a large herd size (>100 animals) (OR = 3.7; 95% CI 1.4-10.0), and a single source of replacement animals (OR = 3.9; 95% CI 1.6-9.6). These findings are vital in developing effective control measures against these parasites in ruminant farms in Selangor, Malaysia. More national epidemiological research is required to elucidate the spatial distribution of these infections and their potential impact on Malaysia's livestock industry.

Augmenting ECG Data with Multiple Filters for a Better Emotion Recognition System.

A physiological-based emotion recognition system (ERS) with a unimodal approach such as an electrocardiogram (ECG) is not as popular compared to a multimodal approach. However, a single modality has the advantage of lower development and computational cost. Therefore, this study focuses on a unimodal ECG-based ERS. The ECG-based ERS has the potential to become the next mass-adopted consumer application due to the wide availability of wearable and mobile ECG devices in the market. Currently, ECG-inclusive affective datasets are limited, and many of the existing datasets have small sample sizes. Hence, ECG-based ERS studies are stunted by the lack of quality data. A novel multi-filtering augmentation technique is proposed here to increase the sample size of the ECG data. This technique augments the ECG signals by cleaning the data in different ways. Three small ECG datasets labelled according to emotion state are used in this study. The benefit of the proposed augmentation techniques is measured using the classification accuracy of five machine learning algorithms; k-nearest neighbours (KNN), support vector machine, decision tree, random forest and multilayer perceptron. The results show that with the proposed technique, there is a significant improvement in performance for all the datasets and classifiers. KNN classifier improved the most with the augmented data and the reported classification accuracies of over 90%.

Improving Water Quality Index Prediction Using Regression Learning Models.

Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.

Prevalence, risk factors, and infection intensity of fasciolosis in dairy cattle in Boyolali, Indonesia.

Background and Aim: Fasciolosis is a significant problem in veterinary and public health, causing huge economic losses. Epidemiological studies of fasciolosis in dairy cattle in Indonesia are few and existing reports primarily focus on prevalence. This study aimed to determine the prevalence, risk factors, and infection intensity of fasciolosis in dairy cattle in Boyolali, Indonesia. Materials and Methods: This cross-sectional study included 400 dairy cattle from 72 household farms in eight subdistricts. Fecal samples (n=400) were examined using the Flukefinder® kit and the simple sedimentation technique was the gold standard for fasciolosis. In-person interviews using questionnaires collected data on farmers, farms, and animal characteristics. Chi-square and logistic regression analyses were performed to evaluate the associated risk factors for fasciolosis, and p < 0.05 was considered statistically significant. Results: The overall prevalence of fasciolosis in dairy cattle in Boyolali, Indonesia, was 16.50% (95% confidence interval [CI] 12.85-20.15) at the animal level (n = 400), whereas 40.28% at household farms (n = 72) level (95% CI 18.67-51.88). The relative sensitivity and specificity of the Flukefinder® kit compared with those of the gold standard were 79.49% and 92.52%, respectively, with a moderate agreement (kappa=0.59; p < 0.001). Fasciolosis was more likely in cattle originating from the Mojosongo subdistrict than from other subdistricts (odds ratio (OR)=5.28, 95% CI 1.22-22.94); from farms that did not process manure versus from those that did (OR = 3.03, 95% CI 1.43-4.71); and with farmers that had never attended extension programs compared with those who had (OR = 4.72, 95% CI 1.99-11.19). Studied cattle were mostly affected by light Fasciola spp. infections (92.4%, 95% CI 77.8-100%) followed by moderate (6.1%, 95% CI 0-22.2%) and heavy (1.5%, 95% CI 0-5.6%) infections. Conclusion: Fasciolosis is prevalent in dairy cattle in Boyolali, Indonesia. Control efforts should target the high-risk Mojosongo subdistrict, emphasize the importance of processing manure, and encourage farmers to attend extension programs. Flukefinder® is a practical on-site diagnostic kit for fasciolosis in Indonesian dairy farms. Parasite species identification and a malacological survey of intermediate hosts of Fasciola spp. in the farming environment are required for further research.

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review.

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

Preliminary detection of mites and coccidia with their zoonotic potential in meat-farmed rabbits in three districts in Selangor, Malaysia.

Mites infestation and gastrointestinal parasites including coccidia are common problems reported in pets, petting farms and farmed practices. Sarcoptes sp. and Cheyletiella sp. could be a potential zoonosis from rabbits to human. Detection of mites and coccidia with their zoonotic potential in meat-farmed rabbits from three (3) commercial farms in Selangor were investigated. Tape impression, fur pluck, skin scraping and ear swab tests were used for mites detection and faecal samples was used for coccidia examination by using McMaster's technique and the identification of Eimeria spp. was further analysed by sporulation technique. The overall prevalence of mites and Eimeria spp. (oocysts) in rabbits were 51.85% ± 0.38 (standard deviation; S.D.) and 76.47% ± 0.42 respectively. Sarcoptes scabiei was the most frequent mite found (25.92% ± 0.44), followed by Cheyletiella parasitovorax and Psoroptes cuniculi. Nine Eimeria spp. were identified and the oocysts of E. perforans shows the highest prevalence (64.71% ± 3.97) followed by E. exigua, E. coecicola, E. magna, E. flavescens, E. irresidua, E. intestinalis, E. media and E. stiedai. There was a significant difference (p = 0.013) where large-scale farm has a higher prevalence of coccidia than small scale farms apparently due to the excessive stocking density as coccidia are easily transmitted among rabbits through ingestion of sporulated oocysts. In conclusion, mites and coccidia are commonly present in the commercial rabbit farms, thus control and preventive measures should be executed to reduce the incidence of parasites. The zoonotic mites Sarcoptes scabiei and Cheyletiella parasitovorax detected in this study could be regarded as a public health concern especially when handling the rabbit.

Development of TaqMan-based real-time RT-PCR assay based on N gene for the quantitative detection of feline morbillivirus.

BACKGROUND: Morbilliviruses are categorized under the family of Paramyxoviridae and have been associated with severe diseases, such as Peste des petits ruminants, canine distemper and measles with evidence of high morbidity and/or could cause major economic loss in production of livestock animals, such as goats and sheep. Feline morbillivirus (FeMV) is one of the members of Morbilliviruses that has been speculated to cause chronic kidney disease in cats even though a definite relationship is still unclear. To date, FeMV has been detected in several continents, such as Asia (Japan, China, Thailand, Malaysia), Europe (Italy, German, Turkey), Africa (South Africa), and South and North America (Brazil, Unites States). This study aims to develop a TaqMan real-time RT-PCR (qRT-PCR) assay targeting the N gene of FeMV in clinical samples to detect early phase of FeMV infection. RESULTS: A specific assay was developed, since no amplification was observed in viral strains from the same family of Paramyxoviridae, such as canine distemper virus (CDV), Newcastle disease virus (NDV), and measles virus (MeV), and other feline viruses, such as feline coronavirus (FCoV) and feline leukemia virus (FeLV). The lower detection limit of the assay was 1.74 × 104 copies/µL with Cq value of 34.32 ± 0.5 based on the cRNA copy number. The coefficient of variations (CV) values calculated for both intra- and inter-assay were low, ranging from 0.34-0.53% and 1.38-2.03%, respectively. In addition, the clinical sample evaluation using this assay showed a higher detection rate, with 25 (35.2%) clinical samples being FeMV-positive compared to 11 (15.5%) using conventional RT-PCR, proving a more sensitive assay compared to the conventional RT-PCR. CONCLUSIONS: The TaqMan-based real-time RT-PCR assay targeting the N gene described in this study is more sensitive, specific, rapid, and reproducible compared to the conventional RT-PCR assay targeting the N gene, which could be used to detect early infection in cats.

Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system.

Background: The electrocardiogram (ECG) is a physiological signal used to diagnose and monitor cardiovascular disease, usually using ECG wave images. Numerous studies have proven that ECG can be used to detect human emotions using numerical data; however, ECG is typically captured as a wave image rather than as a numerical data. There is still no consensus on the effect of the ECG input format (either as an image or a numerical value) on the accuracy of the emotion recognition system (ERS). The ERS using ECG images is still inadequately studied. Therefore, this study compared ERS performance using ECG image and ECG numerical data to determine the effect of the ECG input format on the ERS. Methods: This study employed the DREAMER dataset, which contains 23 ECG recordings obtained during audio-visual emotional elicitation. Numerical data was converted to ECG images for the comparison. Numerous approaches were used to obtain ECG features. The Augsburg BioSignal Toolbox (AUBT) and the Toolbox for Emotional feature extraction from Physiological signals (TEAP) extracted features from numerical data. Meanwhile, features were extracted from image data using Oriented FAST and rotated BRIEF (ORB), Scale Invariant Feature Transform (SIFT), KAZE, Accelerated-KAZE (AKAZE), Binary Robust Invariant Scalable Keypoints (BRISK), and Histogram of Oriented Gradients (HOG). Dimension reduction was accomplished using linear discriminant analysis (LDA), and valence and arousal were classified using the Support Vector Machine (SVM). Results: The experimental results indicated that numerical data achieved arousal and valence accuracy of 69% and 79%, respectively, which was greater than those of image data. For ECG images, the highest accuracy for arousal was 58% percent; meanwhile, the valence was 63%. Conclusions: The finding showed that numerical data provided better accuracy for ERS. However, ECG image data which shows positive potential and can be considered as an input modality for the ERS.

Prevalence of Toxoplasma gondii Antibodies in Stray Dogs from Various Locations in West and East Malaysia.

Toxoplasmosis is caused by an obligate intracellular protozoan parasite; Toxoplasma gondii, which is one of the most important zoonotic parasite worldwide. In dogs, the sexual reproductive cycle of T. gondii is lacking, and the animals are not widely consumed as food, but they are vital in the mechanical transmission of the parasite. However, there is no present data on the exposure of stray dogs to T. gondii in Malaysia. The objective of this serological survey was to determine the prevalence of T. gondii antibodies (IgG) and associated factors in stray dogs in East and West Malaysia. Antibodies to T. gondii were determined in serum samples from 222 stray dogs from 6 different states in East and West Malaysia (Peninsular Malaysia) using an Indirect ELISA. The seroprevalence for T. gondii was 23.4% (Confidence interval: CI 17.8-29.2%). Stray dogs from Selangor and Kuala Lumpur had the highest seroprevalence (32.4%; CI 13.2-45.5%) and lowest in those from Penang and Kedah (12.5%; CI 1.3-23.5%). Gender and breed were not associated with T. gondii seropositivity. However, adult dogs were more likely to be seropositive for T. gondii (OR=2.89; CI 1.1-7.7) compared with younger dogs. These results revealed that T. gondii is prevalent in stray dogs in the studied areas in Malaysia, and indicative of the level of environmental contamination of this parasite especially in urban areas.

Molecular detection of tick-borne haemopathogens in shelter dogs and Rhipicephalus sanguineus (sensu lato) ticks from Peninsular Malaysia.

Ticks are important vectors in transmitting various pathogens and they could jeopardize the health and welfare of humans and animals worldwide. The present study aimed to investigate the presence of important tick-borne haemopathogens (TBH) in dogs and ticks via polymerase chain reaction (PCR) assays. A total of 220 blood samples and 140 ticks were collected from 10 animal shelters in Peninsular Malaysia. Of 220 blood samples, 77 (35 %) were positive to TBH, of which 20 % were E. canis, 12 % were A. platys, 7 % were B. gibsoni and 7 % were B. vogeli. All ticks were identified as Rhipicephalus sanguineus with five samples (3.57 %) positive with TBH. Co-infections of TBH (0.45-9.55 %) in dogs were also observed in this study.

100 Questions in Livestock Helminthology Research.

An elicitation exercise was conducted to collect and identify pressing questions concerning the study of helminths in livestock, to help guide research priorities. Questions were invited from the research community in an inclusive way. Of 385 questions submitted, 100 were chosen by online vote, with priority given to open questions in important areas that are specific enough to permit investigation within a focused project or programme of research. The final list of questions was divided into ten themes. We present the questions and set them briefly in the context of the current state of knowledge. Although subjective, the results provide a snapshot of current concerns and perceived priorities in the field of livestock helminthology, and we hope that they will stimulate ongoing or new research efforts.

Distribution of Angiostrongylus vasorum and its gastropod intermediate hosts along the rural-urban gradient in two cities in the United Kingdom, using real time PCR.

BACKGROUND: Angiostrongylus vasorum is a highly pathogenic metastrongylid nematode affecting dogs, which uses gastropod molluscs as intermediate hosts. The geographical distribution of the parasite appears to be heterogeneous or patchy and understanding of the factors underlying this heterogeneity is limited. In this study, we compared the species of gastropod present and the prevalence of A. vasorum along a rural-urban gradient in two cities in the south-west United Kingdom. METHODS: The study was conducted in Swansea in south Wales (a known endemic hotspot for A. vasorum) and Bristol in south-west England (where reported cases are rare). In each location, slugs were sampled from nine sites across three broad habitat types (urban, suburban and rural). A total of 180 slugs were collected in Swansea in autumn 2012 and 338 in Bristol in summer 2014. A 10 mg sample of foot tissue was tested for the presence of A. vasorum by amplification of the second internal transcribed spacer (ITS-2) using a previously validated real-time PCR assay. RESULTS: There was a significant difference in the prevalence of A. vasorum in slugs between cities: 29.4% in Swansea and 0.3% in Bristol. In Swansea, prevalence was higher in suburban than in rural and urban areas. Comparing the sampled slug fauna, Arion rufus was found in greater numbers in Swansea than Bristol, and was commonly infected (prevalence 41%). This, alongside the timing of slug collections in summer rather than autumn, could explain low infection prevalence in the Bristol sample. In the absence of Ar. rufus as a preferred host for A. vasorum, Ar. flagellus and Limacus maculatus appear to act as versatile hosts that are present in suburban and urban areas in Swansea (prevalence in Ar. flagellus 33%; in L. maculatus 44%) and in Bristol (prevalence in Ar. flagellus 0.9%). These slug species might provide A. vasorum with an alternative vehicle to reach the final host, when the main host Ar. rufus is scarce or absent. CONCLUSION: We conclude that the composition of the slug fauna varies spatially, and that this could help explain patchiness in the prevalence of A. vasorum. A suburban peak was found in the prevalence of infection in intermediate hosts, perhaps explained by a higher density of competent intermediate and/or definitive hosts.

A synchronous-asynchronous particle swarm optimisation algorithm.

In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well.