An Efficient Edge Computing-Enabled Network for Used Cooking Oil Collection.

In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system's success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system's maintenance intervals.

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging.

The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.

Durability of the deltamethrin-treated polypropylene long-lasting net LifeNet® in a pyrethroid resistance area in south western Benin: A phase III trial.

BACKGROUND: Long-lasting insecticidal bed nets (LLINs) are a key measure for preventing malaria and their evaluation is coordinated by the World Health Organization Pesticide Evaluation Scheme (WHOPES). LifeNet® was granted WHOPES time-limited interim recommendation in 2011 after successful Phase I and Phase II evaluations. Here, we evaluated the durability and community acceptance of LifeNet® in a Phase III trial from June 2014 to June 2017 in Benin rural area. METHODS: A prospective longitudinal, cluster-randomized, controlled trial with households as the unit of observation was designed to assess the performance of LifeNet® over a three-year period, using a WHOPES fully recommended LLIN (PermaNet® 2.0) as a positive control. The primary outcomes were the bioassay performance using WHO cone assays and tunnel tests, the insecticide content and physical integrity. RESULTS: At baseline, 100% of LLINs were within the tolerance limits of their target deltamethrin concentrations. By 36 months only 17.3% of LifeNet® and 8.5% of PermaNet® LLINs still were within their target deltamethrin concentrations. Despite these low rates, 100% of both LLINs meet WHO efficacy criteria (≥ 80% mortality or ≥ 95% knockdown or tunnel test criteria of ≥ 80% mortality or ≥ 90% blood-feeding inhibition) after 36 months using WHO cone bio-assays and tunnel tests. The proportion of LLINs in good physical condition was 33% for LifeNet® and 29% for PermaNet® after 36 months. After 36 M the survivorship was 21% and 26% for LifeNet® and PermaNet® respectively. Although both LLINs were well accepted by the population, complaints of side effects were significantly higher among LifeNet® users than PermaNet® ones. CONCLUSION: LifeNet® LLINs did meet WHO criteria for bio-efficacy throughout the study period and were well accepted by the population. This is an important step towards getting a full WHO recommendation for use in malaria endemic countries.

Measuring entomological parameters before implementing a study on asymptomatic carriers of Plasmodium falciparum in the Zè District in southern Benin.

BACKGROUND: The objective of this study was to estimate malaria transmission and insecticide resistance status in malaria vectors in Adjrako village from Zè District in Southern Benin. The present study was carried out prior to investigations on infectivity of blood from asymptomatic carriers of Plasmodium falciparum to malaria vector mosquitoes. METHODS: Human landing collections (HLCs) were performed in Adjrako village during the rainy season (September-November 2021). In this village, host-seeking mosquitoes were collected during three nights per survey from 22:00 to 06:00 in six randomly selected houses. Malaria vectors were dissected in orders to determinate their parity. Plasmodium falciparum infection in malaria vectors was determined by qPCR and the entomological inoculation rate (EIR) was calculated. The World Health Organization (WHO) insecticide susceptibility test-kits were used to evaluate the susceptibility of Anopheles gambiae sensu lato (s.l.) to deltamethrin at 0.05% and bendiocarb at 0.1%. RESULTS: A total of 3260 females of mosquitoes belonging to 4 genera (Anopheles, Culex, Aedes and Mansonia) were collected. Most of the mosquitoes collected were An. gambiae sensu lato (s.l.). The entomological inoculation rate (EIR) for the three collection months was 8.7 infective bites per person and the parity rate was 84%. Mortality rates of An. gambiae s.l. exposed to 0.05% deltamethrin and 0.1% bendiocarb were 18% and 96%, respectively, indicating that this vector population was resistant to deltamethrin and possibly resistant to bendiocarb in the study area. CONCLUSION: This study showed that malaria transmission is effective in the study area and that An. gambiae s.l. is the main malaria vector. The entomological parameters indicate this study area is potentially favourable for investigations on P. falciparum asymptomatic carriers.

Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope.

Statistics show that an estimated 64 million people worldwide suffer from glaucoma. To aid in the detection of this disease, this paper presents a new public dataset containing eye fundus images that was developed for glaucoma pattern-recognition studies using deep learning (DL). The dataset, denoted Brazil Glaucoma, comprises 2000 images obtained from 1000 volunteers categorized into two groups: those with glaucoma (50%) and those without glaucoma (50%). All images were captured with a smartphone attached to a Welch Allyn panoptic direct ophthalmoscope. Further, a DL approach for the automatic detection of glaucoma was developed using the new dataset as input to a convolutional neural network ensemble model. The accuracy between positive and negative glaucoma detection, sensitivity, and specificity were calculated using five-fold cross-validation to train and refine the classification model. The results showed that the proposed method can identify glaucoma from eye fundus images with an accuracy of 90.0%. Thus, the combination of fundus images obtained using a smartphone attached to a portable panoptic ophthalmoscope and artificial intelligence algorithms yielded satisfactory results in the overall accuracy of glaucoma detection tests. Consequently, the proposed approach can contribute to the development of technologies aimed at massive population screening of the disease.

Human biting rhythm of Anopheles gambiae Giles, 1902 (Diptera: Culicidae) and sleeping behaviour of pregnant women in a lagoon area in Southern Benin.

OBJECTIVE: In the framework of EVALMOUS study aiming to assess the use and effectiveness of mosquito nets by pregnant women and other members of their household in a lagoon area in southern Benin, the behaviour of pregnant women relative to the time they go to bed using the net were recorded. Malaria vectors biting rhythm, Plasmodium falciparum infection and insecticide resistance genes in malaria vectors were also determined. RESULTS: Overall, 3848 females of Anopheles gambiae s. l were collected and 280 pregnant women responded to the survey. Almost all Anopheles gambiae s. l. tested were Anopheles coluzzi Coetzee and Wilkerson 2013 (Diptera: Culicidae). The CSP index in malaria vector was 1.85% and the allelic frequency of kdr gene was 74.4%. Around 90% of bites and Plasmodium falciparum Welch, 1897 (Haemosporida: Plasmodiidae) transmission occurred between 10 p.m. and 6 a.m., which coincides with the period when more than 80% of pregnant women were under bednet. Despite a slight early evening and early morning biting activity of malaria vectors in the study area, the good use of nets might remain a useful protection tool against mosquito biting and malaria transmission.

Impact of the use and efficacy of long lasting insecticidal net on malaria infection during the first trimester of pregnancy - a pre-conceptional cohort study in southern Benin.

BACKGROUND: Malaria in pregnancy is prevalent in Sub-Saharan Africa. The first trimester of pregnancy is a critical period and the best preventive measure is Long Lasting Insecticidal Nets (LLIN). Unfortunately, few studies have been conducted which focuses on the usage and efficacy of LLIN on malaria prevention during the first trimester. METHODS: We assessed the use and effectiveness of LLIN in early pregnancy in Benin and its impact on malaria infection risk. We followed-up a cohort of 240 pregnant women from pre-conception to the end of the first trimester of pregnancy in Southern Benin. Parasitological, maternal and LLIN data were actively collected before, at the beginning and end of the first trimester of pregnancy. A Cox regression model was used to determine the relationship between the time to onset of the first malaria infection and the use, physical integrity, and bio-efficacy of the LLIN, adjusted for relevant covariables. RESULTS: The good use, good physical integrity and biological efficacy of LLIN were associated with a decreased risk of occurrence of the first malaria infection in early pregnancy (HRa = 0.38; (0.18-0.80); p < 0.001; HRa = 0.59; (0.29-1.19); p < 0.07; HRa = 0.97; (0.94-1.00); p < 0.04 respectively), after adjustment for other covariates. Primi/secundigravidity and malaria infection before pregnancy were associated with a risk of earlier onset of malaria infection. CONCLUSION: The classically used LLIN's indicators of possession and use may not be sufficient to characterize the true protection of pregnant women in the first trimester of pregnancy. Indicators of physical integrity and bio-efficacy should be integrated with those indicators in evaluation studies.

Dynamics of pyrethroid resistance in malaria vectors in southern Benin following a large scale implementation of vector control interventions.

BACKGROUND: Large-scale implementation of Indoor Residual Spraying and Insecticide Treated Nets has been implemented in Plateau Department, Benin between 2011 and 2014. The purpose of this study was to monitor the frequency and mechanisms of pyrethroid resistance in malaria vectors following the implementation of vector control tools for malaria prevention. METHODS: Anopheles larvae were collected in 13 villages twice a year from 2012 to 2014. WHO tube tests were used to assess the phenotypic resistance of each population to 0.05 % deltamethrin. Sibling species within Anopheles gambiae complex were identified by PCR techniques. Taqman and biochemical assays were performed to identify the presence of kdr mutations in individual mosquitoes and to detect any increase in the activity of enzymes putatively involved in insecticide metabolism (oxidases, esterase and glutathione-S-transferases). Quantitative real time PCR was used to measure the expression of three metabolic genes involved in pyrethroid resistance (CYP6P3, CYP6M2 and GSTD3). RESULTS: Anopheles populations showed < 90 % mortality to deltamethrin in all villages and at all time points. The 1014 F kdr allele frequency was close to fixation (> 0.9) over the sampling periods in both An. gambiae and An. coluzzii. Biochemical assays showed higher activities of alpha esterase and GST in field malaria vector populations compared to susceptible mosquitoes. qPCR assays showed a significant increase of CYP6P3, CYP6M2 GSTD3 expression in An. gambiae after a three-year implementation of LLINs. CONCLUSION: The study confirmed that deltamethrin resistance is widespread in malaria vectors in Southern Benin. We suspect that the increase in deltamethrin resistance between 2012 and 2014 resulted from an increased expression of metabolic detoxification genes (CYP6M2 and CYP6P3) rather than from kdr mutations. It is urgent to evaluate further the impact of metabolic resistance on the efficacy of vector control interventions using pyrethroid insecticides.