Local Vector Research Articles

Malaria surveillance, outbreak investigation, response and its determinant factors in Waghemra Zone, Northeast Ethiopia: unmatched case–control study

Malaria is a major global public health concern, with around half of the world's population at risk of infection. It is one of the most common epidemic-prone diseases, resulting in on-going epidemics and significant public health problems. On September 12, 2022, Waghemra Zone malaria monitoring data revealed that the district was suffering an unusually high number of malaria cases. Therefore, the aim of this study was to assess the occurrence of malaria outbreaks and investigate contracting factors in Waghemra Zone, Northeast Ethiopia. A community-based case–control study with a 1:1 ratio was employed at Waghemra Zone from September 14 to November 27, 2022. A total of 260 individuals (130 cases and 130 controls) were included in the study. A structured questionnaire was used to collect the data. Malaria cases were confirmed by either microscopy or malaria rapid diagnostic tests. The magnitude of the outbreak was described by place, person, and time. A multivariable logistic regression analysis was conducted to identify malaria risk factors. A total of 13,136 confirmed cases of malaria were detected in the Waghemra zone, with an overall attack rate of 26.5 per 1000 and slide positivity rate was 43.0%. The predominant species was Plasmodium falciparum accounting for 66.1%. Children under five years old (AOR = 5.1; 95% CI 2.6–23.0), the presence of artificial water-holding bodies (AOR: 2.7; 95% CI 1.340–5.420), intermittent rivers closer to the living house (AOR = 4.9; 95% CI 2.51–9.62), sleeping outside a home (AOR = 4.9; 95% CI 2.51–9.62), and a lack of knowledge about malaria transmission and prevention (AOR: 9.7; 95% CI 4.459–20.930) were factors associated with malaria contraction. The overall attack rate for malaria during this outbreak was high. Children less than five years, the presence of mosquito breeding sites, staying outdoors overnight, and a lack of knowledge on malaria transmission and prevention were predictors of malaria. Early management of local vector breeding places, as well as adequate health education on malaria transmission and prevention methods, should be provided to the community to prevent such outbreaks in the future.

Unique Huygens-Fresnel electromagnetic transportation of chiral Dirac wavelet in topological photonic crystal

Light propagates in various ways depending on environment, including uniform medium, surface/interface and photonic crystals, which appears ubiquitously in daily life and has been exploited for advanced optics technology. We unveiled that a topological photonic crystal exhibits unique electromagnetic (EM) transport properties originating from the Dirac frequency dispersion and multicomponent spinor eigenmodes. Measuring precisely local Poynting vectors in microstrips of honeycomb structure where optics topology emerges upon a band gap opening in the Dirac dispersion and a p-d band inversion induced by a Kekulé-type distortion respecting C6v symmetry, we showed that a chiral wavelet induces a global EM transportation circulating in the direction counter to the source, which is intimately related to the topological band gap specified by a negative Dirac mass. This brand-new Huygens-Fresnel phenomenon can be considered as the counterpart of negative refraction of EM plane waves associated with upwardly convex dispersions of photonic crystals, and our present finding is expected to open a new window for photonic innovations.

Exclusion of Horizontal and Vertical Transmission as Major Sources of Trypanosoma Cruzi Infections in a Breeding Colony of Rhesus Macaques (Macaca Mulatta).

The vector-borne protozoal parasite Trypanosoma cruzi causes Chagas disease in humans and animals. This parasite is endemic to the southern United States where outdoor-housed NHP at biomedical facilities are at risk of infection. In addi- tion to the direct morbidity caused by T. cruzi, infected animals are of limited biomedical research use because infections can produce confounding pathophysiologic changes even in animals with no clinical disease. In part due to concerns for direct T. cruzi transmission between animals, infected NHP at some institutions have been culled, removed, or otherwise isolated from uninfected animal populations. However, data that document horizontal or vertical transmission in captive NHP in the United States are not available. To evaluate the potential for inter-animal transmission and to identify environmental factors that affect the distribution of new infections in NHPs, we conducted a retrospective epidemiologic study of a rhesus macaque ( Macaca mulatta ) breeding colony in south Texas. We used archived biologic samples and husbandry records to identify the time and location of macaque seroconversion. These data were used to perform a spatial analysis of how geographic location and animal associations affected the spread of disease and to infer the importance of horizontal or vertical routes of transmission. The majority of T. cruzi infections were spatially clustered, suggesting that environmental factors promoted vector exposure in various areas of the facility. Although we cannot not rule out horizontal transmission, our data suggest that horizontal transmission was not a critical route for spread for the disease. Vertical transmission was not a contributing factor in this colony. In conclusion, our findings suggest that local triatome vectors were the major source of T. cruzi infections in captive macaques in our colony. Therefore, limiting contact with vectors, rather than segregation of infected macaques, is a key strategy for disease prevention at institutions that house macaques outdoors in the southern United States.

Point Cloud Target Registration Algorithm Based on Local Feature Description

Point cloud data has received extensive attention in the field of target recognition and target tracking due to its rich target information, and point cloud registration is one of the basic tasks in point cloud applications. Aiming at the problem that the feature descriptor in the traditional registration algorithm is complicated to calculate and cannot sufficiently describe the features of the point cloud, this paper proposes a local feature descriptor that integrates the local normal vector angle features, projection distance features, and distribution density features of the point cloud. The feature similarity is introduced to determine the corresponding point pairs between the template point cloud and the target point cloud, and the point cloud registration task is completed based on local feature matching. Experiments show that the feature descriptor in this paper is simple to calculate and can sufficiently describe the local features of the point cloud, a good correspondence between points can be established based on the feature descriptor, and the algorithm has good robustness. The registration algorithm proposed in this paper can provide a reference for the fields of target recognition and target pose estimation, etc.

Assessing the risk of West Nile Virus seasonal outbreaks and its vector control in an urbanizing bird community: An integrative R0-modelling study in the city of Merida, Mexico.

Urbanization is a global trend associated with key socio-economic issues, one of them being to control the transmission of infectious diseases to a urban fraction of the world's population that shall reach 68% in 2050. While urban growth has been shown to favor mosquito species responsible for the transmission of the West Nile Virus (WNV), a major human arbovirosis, the effects of concomitant changes in the host bird communities remain hard to anticipate albeit essential to quantify disease risk and to plan control initiatives. We developed a R0 modelling of WNV transmission in a urban bird community to assess the risk of outbreak in Merida, one of the cities with the highest growth rate in Mexico. The model was parameterized using ecological and epidemiological data collected over the past 15-years on the local vector, Culex quinquefasciatus, and avian community. We identified a 3-weeks summer period during which the vector population strongly amplifies the WNV enzootic transmission and lead to a significant risk of outbreaks in humans. Extensive sensitivity analyses showed that urbanization induced changes in the bird community could lead to an up-to 6-fold increase in the duration of the risk period, while the daily risk could rise by 40%. Interestingly, the increase in Quiscalus mexicanus abundance had 4-5 times larger impact than any other change in the bird community. In such a context, annihilating the current and future risk of WNV outbreaks in Merida requires reducing the mosquito population by 13% and up to 56%, respectively. This study provides an integrative assessment of the current and future risks of WNV outbreak in the fast urbanizing city of Merida, and points toward the implementation of epidemiological monitoring combined with preemptive measures targeting both C. quinquefasciatus and Q. mexicanus populations, as they are expected to have synergistic effects.

Complete transport of intensity equation for phase retrieval of optical vortex beams

We derive the complete form of the transport of intensity equation (TIE) for an arbitrary wave field, in which additional terms describing the effect of optical vortices are included. The Poynting vector of optical vortices goes in a spiral around the vortex core through space. Consequently, the intensity variation is not observable in the manner of wave propagation, thereby precluding the possibility of phase retrieval via near-field intensity measurements. This difficulty is elegantly overcome by introducing a hard-edged aperture, as a common procedure to produce the required Neumann boundary signal, to break the azimuthal symmetry of the propagation, and visualize the rotation of the beam’s local Poynting vector around the aperture boundary. The basic principle, numerical solution, and practical implementation of how to use this new version of TIE for phase retrieval of arbitrary wave fields including optical vortices are developed. Based on the new approach, we present the first experimental demonstration of non-interferometric phase retrieval of Laguerre–Gaussian vortex beams of different orders using only two near-field intensity measurements. Our results consummate the basic theoretical framework of TIE and represent an important step towards phase retrieval of arbitrary wave fields carrying phase singularities.

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

AbstractNon‐destructive detection of wire bonding defects in integrated circuits (IC) is critical for ensuring product quality after packaging. Image‐processing‐based methods do not provide a detailed evaluation of the three‐dimensional defects of the bonding wire. Therefore, a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision, which can achieve non‐destructive detection of bonding wire defects is proposed. The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires. Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface, a point cloud segmentation method based on spatial surface feature detection (SFD) was proposed. SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process. Furthermore, in the defect detection process, a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires. The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires. The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires, and the average accuracy of defect recognition is 96.47%, which meets the production requirements of bonding wire defect detection.

Measuring the threat from a distance: insight into the complexity and perspectives for implementing sentinel plantation to test the host range of Xylella fastidiosa

The sentinel plantation concept consists of assessing the impact of exotic factors, such as pests and pathogens, on plants of interest by planting them out of their native range. This tool is a way to enhance knowledge for pest risk analysis (PRA) by guiding decisions on how quarantine organisms should be regulated and where to focus prevention and surveillance efforts for an early detection. In this study, the sentinel method was used in the case of research on Xylella fastidiosa, a plant pathogenic bacterium that has recently been found established in southern Europe, but whose potential impact and possible host range are still poorly documented in northern areas where the bacterium is not known to occur. To improve knowledge on the susceptibility of potential hosts of X. fastidiosa in northern Europe, a sentinel plantation of Prunus domestica cv. Opal, Quercus petraea and Salix alba was established in the X. fastidiosa-infected area of Majorca. In order to assess the circulation of the bacterium in the sentinel plot and around it, surveys of the local flora and insect vectors were carried out, as well as the planting of a network of rosemary “spy plants”. Symptomatic monitoring and molecular analyses were performed on the sentinel plants for four years. During these years, X. fastidiosa was never detected in our sentinel plants most likely because of the low infectivity pressure recorded in the surroundings. This study underlines the complexity of conducting sentinel plantation assays combined with X. fastidiosa research, highlighting the need for long-term investigation and questioning the efficiency of the sentinel tool. However, this study is placed in perspective with other valuable sentinel plantations. It also highlights the complementarity of the tool and proposes elements to improve or re-orientate the implementation of future sentinel projects.

Preoperative assessment of pleural adhesions in patients with lung cancer based on quantitative motion analysis with dynamic chest radiography: A retrospective study.

Preoperative assessment of pleural adhesion is crucial for appropriate surgical planning. This study aimed to quantitatively evaluate the usefulness of motion analysis using dynamic chest radiography (DCR) for assessing pleural adhesions. Sequential chest radiographs of 146 lung cancer patients with or without pleural adhesions (n=25/121) were obtained using a DCR system during respiration (registration number: 1729). The local motion vector was measured, and the percentage of poor motion area to the maximum expiration lung area (%lung area with poor motion) was calculated. Subsequently, percentage values ≥49.0% were considered to indicate pleural adhesions. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the prediction performance. The percentage of lung area with poor motion was compared between patients with and without pleural adhesions (p<0.05). DCR-based motion analysis correctly predicted pleural adhesions in 21 out of 25 patients, with 47 false-positive results (sensitivity, 84.0%; specificity, 61.2%; PPV, 30.9%; NPV, 94.9%). The lung with pleural adhesions showed a significantly greater %lung area with poor motion than the opposite lung in the same patient, similar to the cancerous lung in patients without pleural adhesions. On DCR-based motion analysis, pleural adhesions could be indicated by an increase in the percentage of lung area with poor motion. Although the proposed method cannot identify the exact location of pleural adhesions, information regarding the presence or absence of pleural adhesions provided by DCR would help surgeons prepare for challenging surgeries and obtain informed consent from patients.

Multi-classification approach for lung nodule detection and classification with proposed texture feature in X-ray images.

Lung cancer is a widespread type of cancer around the world. It is, moreover, a lethal type of tumor. Nevertheless, analysis signifies that earlier recognition of lung cancer considerably develops the possibilities of survival. By deploying X-rays and Computed Tomography (CT) scans, radiologists could identify hazardous nodules at an earlier period. However, when more citizens adopt these diagnoses, the workload rises for radiologists. Computer Assisted Diagnosis (CAD)-based detection systems can identify these nodules automatically and could assist radiologists in reducing their workloads. However, they result in lower sensitivity and a higher count of false positives. The proposed work introduces a new approach for Lung Nodule (LN) detection. At first, Histogram Equalization (HE) is done during pre-processing. As the next step, improved Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) based segmentation is done. Then, the characteristics, including "Gray Level Run-Length Matrix (GLRM), Gray Level Co-Occurrence Matrix (GLCM), and the proposed Local Vector Pattern (LVP)," are retrieved. These features are then categorized utilizing an optimized Convolutional Neural Network (CNN) and itdetectsnodule or non-nodule images. Subsequently, Long Short-Term Memory (LSTM) is deployed to categorize nodule types (benign, malignant, or normal). The CNN weights are fine-tuned by the Chaotic Population-based Beetle Swarm Algorithm (CP-BSA). Finally, the superiority of the proposed approach is confirmed across various measures. The developed approach has exhibited a high precision value of 0.9575 for the best case scenario, and high sensitivity value of 0.9646 for the mean case scenario. The superiority of the proposed approach is confirmed across various measures.

Character and Distribution of Geometrically Necessary Dislocations in Polycrystalline Tantalum.

Geometrically necessary dislocations (GNDs) play a key role in accommodating strain incompatibility between neighboring grains in polycrystalline materials. One critical step toward accurately capturing GNDs in deformation models involves studying the microstructural features that promote GND accumulation and the resulting character of GND fields. This study utilizes high-resolution electron backscatter diffraction to map GND populations in a large polycrystalline sample of pure tantalum, under simple tension. A total of 1,989 grains, 3,518 grain boundaries (GBs), and 3,207 triple junctions (TJs) were examined in a subsurface region of the sample. Correlations between GND density and GB character, and to some extent, TJ character, are investigated. Statistical geometrical relationships between these entities are quantified, and also visualized, using a novel application of two-point statistics. The nature of GNDs across the sample is also visualized and assessed using a recently developed method of mapping the local net Burgers vectors. The different approaches to characterizing GND distribution are compared in terms of how they quantify the size of near boundary gradient zones.

Speech Segmentation Based on the Computation of Local Signal Manifold Dimension

A new computational method of unvoiced and voiced speech segmentation is proposed from the perspective of local linear manifold analysis of speech signals. It is based on the estimation of the dimension of short-time linear subspace. The subspace dimensional characteristics of the single phoneme signal are studied. The local signal vector set is analyzed by using the PCA algorithm to estimate the dimension of the data matrix formed by framing. The local PCA is used to analyze the speech signal to achieve the segmentation of unvoiced and voiced pronunciation. Simulation experiments prove the effectiveness of the proposed method.

Bed-nets Hour Density after a Usage of Three Years of Long-lasting Insecticide-treated Nets in Malaria re-elimination Areas in Ngadirejo Village, Purworejo District, Indonesia

Background: Malaria, caused by the genus Plasmodium, is still a global health problem. Its transmission and spread are strongly influenced by the presence of the Anopheles vector. One of the efforts to reduce cases is the distribution of long-lasting insecticide-treated nets to the residents of malaria-endemic areas. This study aims to measure the density of mosquitoes perched on insecticide-treated bed nets after three years of usage. Method: A cross-sectional study was conducted to observe mosquitoes perching on insecticide-treated bed nets at night and calculation of mosquito density data in units of mosquitoes/LLINs/hour. Results: The number of mosquito bites before bedtime in respondents reached 72.5%, while the number of bed nets infested with mosquitoes reached 74.4%. The density of mosquitoes perched varied between 1 and 10 mosquitoes/LLINs/hour with the highest density proportion of 3 mosquitoes/LLINs/hour. Conclusion: LLINs that have been used for three years were found to be less effective at killing and preventing mosquitoes. It is advisable to re-dip with the insecticide doses to match the local vector susceptibility level by officer assistance so that the mosquito nets still function properly

Determination of Spatial Scale in Martian Landscape Images Acquired by the Curiosity Rover, and Viewing Image Scale and Target Chemistry Using the ASIC Website

AbstractIn this paper we describe a method to compute spatial scales for images acquired by NASA's Mars Curiosity rover (Mars Science Laboratory, MSL). The method is based on the assumption that the rover stands on an infinite plane that may have any orientation with respect to the local gravity vector. While not new, it is the first time that this method is systematically applied to Martian images acquired by a lander. A continuously run software pipeline processes the images acquired by the rover within a 20 m radius, adds approximate scalebars to the raw images, and generates, whenever possible, rectified (warped) versions of those images. The products of this software pipeline and the chemical compositions of relevant rover science targets from NASA's Planetary Data System archive, are made available to the public via the Approximate Scale for Images and Chemistry website, which has been developed in collaboration with the Planetary Data System Analyst's Notebook for the MSL mission. Hyperlinks connect the two resources.

Treatment of dogs with Bravecto® (fluralaner) reduces mosquito survival and fecundity

BackgroundMosquitoes serve as the vector of canine heartworm (Dirofilaria immitis), which represents a significant and persistent threat to canine health. A reduction in the longevity and/or reproductive success of mosquitoes that take a blood meal from fluralaner-treated dogs may consequently reduce the local transmission of heartworm and prevent new infections. A novel secondary effect of an oral formulation of the ectoparasiticide fluralaner (Bravecto®) against a laboratory strain of the mosquito Aedes aegypti, a potential major vector of canine heartworm, was investigated in this study.MethodsSix dogs were administered a single dose of fluralaner orally in the form of Bravecto® Chews (at the labeled fluralaner dose of 25 mg/kg body weight), while six control dogs received no treatment. Mosquitoes were fed on blood that was collected from each dog prior to treatment and weekly for 15 weeks post-treatment to assess the continued effects of fluralaner as its serum level decreased. Mosquito fitness was assessed by three parameters: rate of successful blood-feeding, survival, and egg laying.ResultsSuccessful blood-feeding rate was similar between control and treatment groups. In the fluralaner treatment, mosquito survival was significantly reduced within the first 24 h after blood-feeding, for the first 12 weeks post-treatment of the dogs (efficacy range = 33.2–73.3%). Survival of mosquitoes up until a potentially heartworm-infective timepoint (14 days post-blood-feeding) was significantly reduced in the fluralaner-treated group at several timepoints (1, 2, 5, 11, 12, 13, 14, and 15 weeks post-treatment; efficacy range = 49.4–91.4%), but was less consistently reduced at the other timepoints. Egg laying by mosquitoes was almost completely suppressed for the first 13 weeks following treatment of the dogs with fluralaner (treatment efficacy ≥ 99.8%).ConclusionsMosquitoes fed blood from fluralaner-treated dogs experienced a significant reduction in survival and fecundity. These findings support the potential for a reduction in heartworm transmission directly by lethal effects on the vector and indirectly through a reduction of the local vector population when mosquitoes are exposed to animals treated with fluralaner.Graphical

Global-local least-squares support vector machine (GLocal-LS-SVM).

This study introduces the global-local least-squares support vector machine (GLocal-LS-SVM), a novel machine learning algorithm that combines the strengths of localised and global learning. GLocal-LS-SVM addresses the challenges associated with decentralised data sources, large datasets, and input-space-related issues. The algorithm is a double-layer learning approach that employs multiple local LS-SVM models in the first layer and one global LS-SVM model in the second layer. The key idea behind GLocal-LS-SVM is to extract the most informative data points, known as support vectors, from each local region in the input space. Local LS-SVM models are developed for each region to identify the most contributing data points with the highest support values. The local support vectors are then merged at the final layer to form a reduced training set used to train the global model. We evaluated the performance of GLocal-LS-SVM using both synthetic and real-world datasets. Our results demonstrate that GLocal-LS-SVM achieves comparable or superior classification performance compared to standard LS-SVM and state-of-the-art models. In addition, our experiments show that GLocal-LS-SVM outperforms standard LS-SVM in terms of computational efficiency. For instance, on a training dataset of 9, 000 instances, the average training time for GLocal-LS-SVM was only 2% of the time required to train the LS-SVM model while maintaining classification performance. In summary, the GLocal-LS-SVM algorithm offers a promising solution to address the challenges associated with decentralised data sources and large datasets while maintaining high classification performance. Furthermore, its computational efficiency makes it a valuable tool for practical applications in various domains.

Local linear independence of bilinear (and higher degree) B-splines on hierarchical T-meshes

We generate hierarchical T-meshes by repeatedly inserting new line segments, in order to adapt both the size and the shape of the cells to the specific requirements of the underlying application. The associated spaces of bilinear spline functions are spanned by the locally refined (LR) B-splines of Dokken et al. (2013), which are products of univariate B-splines defined over local knot vectors. Our method guarantees that these functions are linearly independent for the obtained class of hierarchical T-meshes. Furthermore, they even possess the property of local linear independence, since exactly 4 functions take non-zero values on each cell of the mesh, and they form a partition of unity without the need to perform additional scaling or truncation.The correctness of our refinement algorithm is verified by enumerating the (newly introduced) local configurations, which represent the possible topologies of the mesh in the vicinity of a cell. It is then shown that the method works well in all the resulting situations. The fairly large number (385) of cases sheds some light on the challenges associated with linear independence in the context of LR B-spline refinement.Additionally we apply our method to meshes that possess the additional property local semi-regularity (first studied by Weller and Hagen, 1995), where only 49 topologically different situations may arise. However, we observe that the resulting class of hierarchical T-meshes is less flexible, which leads to a substantial amount of excess refinement in applications.Finally we note that the same construction can be applied to generate basis functions for Cs–smooth spline spaces of degree p=2s+1, again with the property of local linear independence.

Adaptive Local Cross-Channel Vector Pooling Attention Module for Semantic Segmentation of Remote Sensing Imagery

Adding an attention module to the deep convolution semantic segmentation network has significantly enhanced the network performance. However, the existing channel attention module focusing on the channel dimension neglects the spatial relationship, causing location noise to transmit to the decoder. In addition, the spatial attention module exemplified by self-attention has a high training cost and challenges in execution efficiency, making it unsuitable to handle large-scale remote sensing data. We propose an efficient vector pooling attention (VPA) module for building the channel and spatial location relationship. The module can locate spatial information better by performing a unique vector average pooling in the vertical and horizontal dimensions of the feature maps. Furthermore, it can also learn the weights directly by using the adaptive local cross-channel interaction. Multiple weight learning ablation studies and comparison experiments with the classical attention modules were conducted by connecting the VPA module to a modified DeepLabV3 network using ResNet50 as the encoder. The results show that the mIoU of our network with the addition of an adaptive local cross-channel interaction VPA module increases by 3% compared to the standard network on the MO-CSSSD. The VPA-based semantic segmentation network can significantly improve precision efficiency compared with other conventional attention networks. Furthermore, the results on the WHU Building dataset present an improvement in IoU and F1-score by 1.69% and 0.97%, respectively. Our network raises the mIoU by 1.24% on the ISPRS Vaihingen dataset. The VPA module can also significantly improve the network’s performance on small target segmentation.

An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction

X-ray Computed Tomography (CT) techniques play a vitally important role in clinical diagnosis, but radioactivity exposure can also induce the risk of cancer for patients. Sparse-view CT reduces the impact of radioactivity on the human body through sparsely sampled projections. However, images reconstructed from sparse-view sinograms often suffer from serious streaking artifacts. To overcome this issue, we propose an end-to-end attention-based mechanism deep network for image correction in this paper. Firstly, the process is to reconstruct the sparse projection by the filtered back-projection algorithm. Next, the reconstructed results are fed into the deep network for artifact correction. More specifically, we integrate the attention-gating module into U-Net pipelines, whose function is implicitly learning to emphasize relevant features beneficial for a given assignment while restraining background regions. Attention is used to combine the local feature vectors extracted at intermediate stages in the convolutional neural network and the global feature vector extracted from the coarse scale activation map. To improve the performance of our network, we fused a pre-trained ResNet50 model into our architecture. The model was trained and tested using the dataset from The Cancer Imaging Archive (TCIA), which consists of images of various human organs obtained from multiple views. This experience demonstrates that the developed functions are highly effective in removing streaking artifacts while preserving structural details. Additionally, quantitative evaluation of our proposed model shows significant improvement in peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean squared error (RMSE) metrics compared to other methods, with an average PSNR of 33.9538, SSIM of 0.9435, and RMSE of 45.1208 at 20 views. Finally, the transferability of the network was verified using the 2016 AAPM dataset. Therefore, this approach holds great promise in achieving high-quality sparse-view CT images.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BackgroundAlthough malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.MethodsA workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.ResultsThe identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.ConclusionsThis study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.