Diversity and selection analyses identify transmission-blocking antigens as the optimal vaccine candidates in Plasmodium falciparum.

BACKGROUND: A highly effective vaccine for malaria remains an elusive target, at least in part due to the under-appreciated natural parasite variation. This study aimed to investigate genetic and structural variation, and immune selection of leading malaria vaccine candidates across the Plasmodium falciparum's life cycle. METHODS: We analysed 325 P. falciparum whole genome sequences from Zambia, in addition to 791 genomes from five other African countries available in the MalariaGEN Pf3k Database. Ten vaccine antigens spanning three life-history stages were examined for genetic and structural variations, using population genetics measures, haplotype network analysis, and 3D structure selection analysis. FINDINGS: Among the ten antigens analysed, only three in the transmission-blocking vaccine category display P. falciparum 3D7 as the dominant haplotype. The antigens AMA1, CSP, MSP119 and CelTOS, are much more diverse than the other antigens, and their epitope regions are under moderate to strong balancing selection. In contrast, Rh5, a blood stage antigen, displays low diversity yet slightly stronger immune selection in the merozoite-blocking epitope region. Except for CelTOS, the transmission-blocking antigens Pfs25, Pfs48/45, Pfs230, Pfs47, and Pfs28 exhibit minimal diversity and no immune selection in epitopes that induce strain-transcending antibodies, suggesting potential effectiveness of 3D7-based vaccines in blocking transmission. INTERPRETATION: These findings offer valuable insights into the selection of optimal vaccine candidates against P. falciparum. Based on our results, we recommend prioritising conserved merozoite antigens and transmission-blocking antigens. Combining these antigens in multi-stage approaches may be particularly promising for malaria vaccine development initiatives. FUNDING: Purdue Department of Biological Sciences; Puskas Memorial Fellowship; National Institute of Allergy and Infectious Diseases (U19AI089680).

Genomic and metagenomic analyses of the domestic mite Tyrophagus putrescentiae identify it as a widespread environmental contaminant and a host of a basal, mite-specific Wolbachia lineage (supergroup Q).

Tyrophagus putrescentiae (mould mite) is a global, microscopic trophic generalist that commonly occurs in various human-created habitats, causing allergies and damaging stored food. Its ubiquity and extraordinary ability to penetrate research samples or cultures through air currents or by active walking through tights spaces (such as treads of screw caps) may lead to sample contamination and introduction of its DNA to research materials in the laboratory. This prompts a thorough investigation into potential sequence contamination in public genomic databases. The trophic success of T. putrescentiae is primarily attributed to the symbiotic bacteria housed in specialized internal mite structures, facilitating adaptation to varied nutritional niches. However, recent work suggests that horizontal transfer of bacterial/fungal genes related to nutritional functionality may also contribute to the mite's trophic versatility. This aspect requires independent confirmation. Additionally, T. putrescentiae harbors an uncharacterized and genetically divergent bacterium, Wolbachia, displaying blocking and microbiome-modifying effects. The phylogenomic position and supergroup assignment of this bacterium are unknown. Here, we sequenced and assembled the T. putrescentiae genome, analyzed its microbiome, and performed detailed phylogenomic analyses of the mite-specific Wolbachia. We show that T. putrescentiae DNA is a substantial source of contamination of research samples. Its DNA may inadvertently be co-extracted with the DNA of the target organism, eventually leading to sequence contamination in public databases. We identified a diversity of bacterial species associated with T. putrescentiae, including those capable of rapidly developing antibiotic resistance, such as Escherichia coli. Despite the presence of diverse bacterial communities in T. putrescentiae, we did not detect any recent horizontal gene transfers in this mite species and/or in astigmatid (domestic) mites in general. Our phylogenomic analysis of Wolbachia recovered a basal, mite-specific lineage (supergroup Q) represented by two Wolbachia spp. from the mould mite and a gall-inducing plant mite. Fluorescence in situ hybridization confirmed the presence of Wolbachia inside the mould mite. The discovery of an early derivative Wolbachia lineage (supergroup Q) in two phylogenetically unrelated and ecologically dissimilar mites suggests that this endosymbiotic bacterial lineage formed a long-term association with mites. This finding provides a unique insight into the early evolution and host associations of Wolbachia. Further discoveries of Wolbachia diversity in acariform mites are anticipated.

SNP-slice resolves mixed infections: simultaneously unveiling strain haplotypes and linking them to hosts.

MOTIVATION: Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information sometimes have to discard mixed infection samples as many downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A scalable tool to learn and resolve the SNP-haplotypes from polygenomic data is an urgent need in molecular epidemiology. RESULTS: We develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP-haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP-haplotypes and individual heterozygosities accurately without reference panels and outperforms the state-of-the-art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for using our method on empirical datasets. AVAILABILITY AND IMPLEMENTATION: The implementation of the SNP-Slice algorithm, as well as scripts to analyze SNP-Slice outputs, are available at https://github.com/nianqiaoju/snp-slice.

Predicting host range expansion in parasitic mites using a global mammalian-acarine dataset.

Multi-host parasites pose greater health risks to wildlife, livestock, and humans than single-host parasites, yet our understanding of how ecological and biological factors influence a parasite's host range remains limited. Here, we assemble the largest and most complete dataset on permanently parasitic mammalian mites and build a predictive model assessing the probability of single-host parasites to become multi-hosts, while accounting for potentially unobserved host-parasite links and class imbalance. This model identifies statistically significant predictors related to parasites, hosts, climate, and habitat disturbance. The most important predictors include the parasite's contact level with the host immune system and two variables characterizing host phylogenetic similarity and spatial co-distribution. Our model reveals an overrepresentation of mites associated with Rodentia (rodents), Chiroptera (bats), and Carnivora in the multi-host risk group. This highlights both the potential vulnerability of these hosts to parasitic infestations and the risk of serving as reservoirs of parasites for new hosts. In addition, we find independent macroevolutionary evidence that supports our prediction of several single-host species of Notoedres, the bat skin parasites, to be in the multi-host risk group, demonstrating the forecasting potential of our model.

Diversity and selection analyses identify transmission-blocking antigens as the optimal vaccine candidates in Plasmodium falciparum.

Background: A highly effective vaccine for malaria remains an elusive target, at least in part due to the under-appreciated natural parasite variation. This study aimed to investigate genetic and structural variation, and immune selection of leading malaria vaccine candidates across the Plasmodium falciparum's life cycle. Methods: We analyzed 325 P. falciparum whole genome sequences from Zambia, in addition to 791 genomes from five other African countries available in the MalariaGEN Pf3k Rdatabase. Ten vaccine antigens spanning three life-history stages were examined for genetic and structural variations, using population genetics measures, haplotype network analysis, and 3D structure selection analysis. Findings: Among the ten antigens analyzed, only three in the transmission-blocking vaccine category display P. falciparum 3D7 as the dominant haplotype. The antigens AMA1, CSP, MSP119 and CelTOS, are much more diverse than the other antigens, and their epitope regions are under moderate to strong balancing selection. In contrast, Rh5, a blood stage antigen, displays low diversity yet slightly stronger immune selection in the merozoite-blocking epitope region. Except for CelTOS, the transmission-blocking antigens Pfs25, Pfs48/45, Pfs230, Pfs47, and Pfs28 exhibit minimal diversity and no immune selection in epitopes that induce strain-transcending antibodies, suggesting potential effectiveness of 3D7-based vaccines in blocking transmission. Interpretations: These findings offer valuable insights into the selection of optimal vaccine candidates against P. falciparum. Based on our results, we recommend prioritizing conserved merozoite antigens and transmission-blocking antigens. Combining these antigens in multi-stage approaches may be particularly promising for malaria vaccine development initiatives. Funding: Purdue Department of Biological Sciences; Puskas Memorial Fellowship; National Institute of Allergy and Infectious Diseases (U19AI089680).

Genomics reveals heterogeneous Plasmodium falciparum transmission and selection signals in Zambia.

BACKGROUND: Genomic surveillance is crucial for monitoring malaria transmission and understanding parasite adaptation to interventions. Zambia lacks prior nationwide efforts in malaria genomic surveillance among African countries. METHODS: We conducted genomic surveillance of Plasmodium falciparum parasites from the 2018 Malaria Indicator Survey in Zambia, a nationally representative household survey of children under five years of age. We whole-genome sequenced and analyzed 241 P. falciparum genomes from regions with varying levels of malaria transmission across Zambia and estimated genetic metrics that are informative about transmission intensity, genetic relatedness between parasites, and selection. RESULTS: We provide genomic evidence of widespread within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in Zambia. Our analysis reveals country-level clustering of parasites from Zambia and neighboring regions, with distinct separation in West Africa. Within Zambia, identity by descent (IBD) relatedness analysis uncovers local spatial clustering and rare cases of long-distance sharing of closely related parasite pairs. Genomic regions with large shared IBD segments and strong positive selection signatures implicate genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Furthermore, differences in selection signatures, including drug resistance loci, are observed between eastern and western Zambian parasite populations, suggesting variable transmission intensity and ongoing drug pressure. CONCLUSIONS: Our findings enhance our understanding of nationwide P. falciparum transmission in Zambia, establishing a baseline for analyzing parasite genetic metrics as they vary over time and space. These insights highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.

Malaria is caused by a parasite that is spread to humans via mosquito bites. It is a leading cause of death in children under five years old in sub-Saharan Africa. Analysis of the malaria parasite's complete set of DNA (its genome) can help us to understand transmission of the disease and how this changes in response to different strategies to control the disease. We analyzed the genomes of malaria parasites from children across Zambia. Our study revealed that 77% of children harbored multiple parasite strains, which suggests that local transmission (transmission between people within the same local area) is high. Genetic evidence for long-distance transmission was rarer. Furthermore, our findings suggest parasites are evolving in response to antimalarial drugs. Our study enhances our understanding of malaria dynamics in Zambia and may help to inform strategies for improved surveillance and control.

Investigation of the miRNA-mRNA Regulatory Circuits and Immune Signatures Associated with Bronchopulmonary Dysplasia.

Background: Bronchopulmonary dysplasia (BPD) has become a major cause of morbidity and mortality in preterm infants worldwide, yet its pathogenesis and underlying mechanisms remain poorly understood. The present study sought to explore microRNA-mRNA regulatory networks and immune cells involvement in BPD through a combination of bioinformatic analysis and experimental validation. Methods: MicroRNA and mRNA microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed microRNAs (DEMs) were identified in BPD patients compared to control subjects, and their target genes were predicted using miRWalk, miRNet, miRDB, and TargetScan databases. Subsequently, protein-protein interaction (PPI) and functional enrichment analyses were conducted on the target genes. 30 hub genes were screened using the Cytohubba plugin of the Cytoscape software. Additionally, mRNA microarray data was utilized to validate the expression of hub genes and to perform immune infiltration analysis. Finally, real-time PCR (RT-PCR), immunohistochemistry (IHC), and flow cytometry were conducted using a mouse model of BPD to confirm the bioinformatics findings. Results: Two DEMs (miR-15b-5p and miR-20a-5p) targeting genes primarily involved in the regulation of cell cycle phase transition, ubiquitin ligase complex, protein serine/threonine kinase activity, and MAPK signaling pathway were identified. APP and four autophagy-related genes (DLC1, PARP1, NLRC4, and NRG1) were differentially expressed in the mRNA microarray dataset. Analysis of immune infiltration revealed significant differences in levels of neutrophils and naive B cells between BPD patients and control subjects. RT-PCR and IHC confirmed reduced expression of APP in a mouse model of BPD. Although the proportion of total neutrophils did not change appreciably, the activation of neutrophils, marked by loss of CD62L, was significantly increased in BPD mice. Conclusion: Downregulation of APP mediated by miR-15b-5p and miR-20a-5p may be associated with the development of BPD. Additionally, increased CD62L- neutrophil subset might be important for the immune-mediated injury in BPD.

Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria.

Intervention against falciparum malaria in high transmission regions remains challenging, with relaxation of control efforts typically followed by rapid resurgence. Resilience to intervention co-occurs with incomplete immunity, whereby children eventually become protected from severe disease but not infection and a large transmission reservoir results from high asymptomatic prevalence across all ages. Incomplete immunity relates to the vast antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene family known as var. Recent deep sampling of var sequences from individual isolates in northern Ghana showed that parasite population structure exhibited persistent features of high-transmission regions despite the considerable decrease in prevalence during transient intervention with indoor residual spraying (IRS). We ask whether despite such apparent limited impact, the transmission system had been brought close to a transition in both prevalence and resurgence ability. With a stochastic agent-based model, we investigate the existence of such a transition to pre-elimination with intervention intensity, and of molecular indicators informative of its approach. We show that resurgence ability decreases sharply and nonlinearly across a narrow region of intervention intensities in model simulations, and identify informative molecular indicators based on var gene sequences. Their application to the survey data indicates that the transmission system in northern Ghana was brought close to transition by IRS. These results suggest that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction.

Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of antimalarial drug resistance.

The establishment and spread of antimalarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.

Drug resistance among strains of the parasites that cause malaria is a growing problem for people relying on antimalarial drugs to protect them from the disease. This phenomenon is global yet exactly how resistance emerges, spreads and persists in a population often differs greatly between regions, which can complicate malaria control projects. For example, discontinuing the use of antimalarials can lead to the frequency of resistant strains declining in an area, such as Africa, but persisting at high levels in others, including Asia and South America. Gaining resistance often leads to parasites becoming less transmissible than other strains. When antimalarials are not used, sensitive strains usually outcompete their resistant counterparts. However, prolonged use of antimalarial drugs tends to eliminate susceptible strains, allowing the previously outcompeted resistant strains to dominate. The local dynamics of antimalarial resistance are also shaped by multiple other factors such as transmission levels (how common the disease is in the region), the type of antimalarial measures used (such as drugs and mosquito nets), or previous immunity the population may have developed to specific strains. While many computational models have been developed to capture these dynamics, they usually fail to include strain diversity ­ a parameter reflecting the number of malaria strains the immune system is exposed to. This parameter is important as parasites need to escape both host immunity and drugs in order to be successful. To address this gap, He, Chaillet, and Labbé created a computational model to investigate how strain diversity, transmission levels and other related factors influence antimalarial resistance. The model was used to explore how the frequency of resistant and susceptible strains changes over time once antimalarial drugs are rolled out and then halted. These analyses show that in areas with both low strain diversity and low transmission levels, susceptible parasites are more likely to be wiped out from the population, leading to a high frequency of resistant strains that persist after drugs are discontinued. However, in high diversity and high transmission regions, susceptible strains can remain in the population. Therefore, when drug treatments are stopped, resistance levels are more likely to drop due to these parasites outcompeting the drug-resistant ones. Overall, this work demonstrates how modelling approaches that include strain diversity can help inform public health decisions aimed at reducing antimalarial resistance. In particular, they can provide important insights into the control strategies that are best suited for a specific region, suggesting that in low transmission areas intensive drug treatment may contribute to resistance. Instead, preventative strategies such as eliminating mosquitos and preventing bites with bed nets may prove more beneficial at reducing transmission rates in such areas.

Genomics reveals heterogeneous Plasmodium falciparum transmission and population differentiation in Zambia and bordering countries.

Genomic surveillance plays a critical role in monitoring malaria transmission and understanding how the parasite adapts in response to interventions. We conducted genomic surveillance of malaria by sequencing 241 Plasmodium falciparum genomes from regions with varying levels of malaria transmission across Zambia. We found genomic evidence of high levels of within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in the country. We identified country-level clustering of parasites from Zambia and neighboring countries, and distinct clustering of parasites from West Africa. Within Zambia, our identity by descent (IBD) relatedness analysis uncovered spatial clustering of closely related parasite pairs at the local level and rare cases of long-distance sharing. Genomic regions with large shared IBD segments and strong positive selection signatures identified genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Together, our findings enhance our understanding of P. falciparum transmission nationwide in Zambia and highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.

Horizontal transmission maintains host specificity and codiversification of symbionts in a brood parasitic host.

In host-symbiont systems, interspecific transmissions create opportunities for host switches, potentially leading to cophylogenetic incongruence. In contrast, conspecific transmissions often result in high host specificity and congruent cophylogenies. In most bird-feather mite systems, conspecific transmission is considered dominant, while interspecific transmission is supposedly rare. However, while mites typically maintain high host specificity, incongruent cophylogenies are common. To explain this conundrum, we quantify the magnitude of conspecific vs. interspecific transmission in the brood parasitic shiny cowbird (Molothrus bonariensis). M. bonariensis lacks parental care, allowing the assessment of the role of horizontal transmission alone in maintaining host specificity. We found that despite frequent interspecific interactions via foster parental care, mite species dispersing via conspecific horizontal contacts are three times more likely to colonize M. bonariensis than mites transmitted vertically via foster parents. The results highlight the previously underappreciated rate of transmission via horizontal contacts in maintaining host specificity on a microevolutionary scale. On a macroevolutionary scale, however, host switches were estimated to have occurred as frequently as codivergences. This suggests that macroevolutionary patterns resulting from rare events cannot be easily generalized from short-term evolutionary trends.

An immune memory-structured SIS epidemiological model for hyperdiverse pathogens.

A hyperdiverse class of pathogens of humans and wildlife, including the malaria parasite Plasmodium falciparum, relies on multigene families to encode antigenic variation. As a result, high (asymptomatic) prevalence is observed despite high immunity in local populations under high-transmission settings. The vast diversity of "strains" and genes encoding this variation challenges the application of established models for the population dynamics of such infectious diseases. Agent-based models have been formulated to address theory on strain coexistence and structure, but their complexity can limit application to gain insights into population dynamics. Motivated by P. falciparum malaria, we develop an alternative formulation in the form of a structured susceptible-infected-susceptible population model in continuous time, where individuals are classified not only by age, as is standard, but also by the diversity of parasites they have been exposed to and retain in their specific immune memory. We analyze the population dynamics and bifurcation structure of this system of partial-differential equations, showing the existence of alternative steady states and an associated tipping point with transmission intensity. We attribute the critical transition to the positive feedback between parasite genetic diversity and force of infection. Basins of attraction show that intervention must drastically reduce diversity to prevent a rebound to high infection levels. Results emphasize the importance of explicitly considering pathogen diversity and associated specific immune memory in the population dynamics of hyperdiverse epidemiological systems. This statement is discussed in a more general context for ecological competition systems with hyperdiverse trait spaces.

Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of anti-malarial drug resistance.

The establishment and spread of anti-malarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in Sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.

Predicting Blomia tropicalis allergens using a multiomics approach.

BACKGROUND: The domestic mite Blomia tropicalis is a major source of allergens in tropical and subtropical regions. Despite its great medical importance, the allergome of this mite has not been sufficiently studied. Only 14 allergen groups have been identified in B. tropicalis thus far, even though early radioimmunoelectrophoresis techniques (27 uncharacterized allergen complexes) and comparative data based on 40 allergen groups officially recognized by the World Health Organization (WHO)/IUIS in domestic astigmatid mites suggest the presence of a large set of additional allergens. METHODS: Here, we employ a multiomics approach to assess the allergome of B. tropicalis using genomic and transcriptomic sequence data and perform highly sensitive protein abundance quantification. FINDINGS: Among the 14 known allergen groups, we confirmed 13 (one WHO/IUIS allergen, Blo t 19, was not found) and identified 16 potentially novel allergens based on sequence similarity. These data indicate that B. tropicalis shares 27 known/deduced allergen groups with pyroglyphid house dust mites (genus Dermatophagoides). Among these groups, five allergen-encoding genes are highly expressed at the transcript level: Blo t 1, Blo t 5, Blo t 21 (known), Blo t 15, and Blo t 18 (predicted). However, at the protein level, a different set of most abundant allergens was found: Blo t 2, 10, 11, 20 and 21 (mite bodies) or Blo t 3, 4, 6 and predicted Blo t 13, 14 and 36 (mite feces). INTERPRETATION: We report the use of an integrated omics method to identify and predict an array of mite allergens and advanced, label-free proteomics to determine allergen protein abundance. Our research identifies a large set of novel putative allergens and shows that the expression levels of allergen-encoding genes may not be strictly correlated with the actual allergenic protein abundance in mite bodies.

Photoelectric Measurement and Sensing: New Technology and Applications.

Laser-based measurement and sensing technology has been paid more and more attention by academia and industry because of its incomparable advantages, such as high sensitivity, fast response, and no contact [...].

SNP-Slice Resolves Mixed Infections: Simultaneously Unveiling Strain Haplotypes and Linking Them to Hosts.

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information have to discard mixed infection samples, because existing downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A reliable tool to learn and resolve the SNP haplotypes from polygenomic data is an urgent need in molecular epidemiology. In this work, we develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP haplotypes and individual heterozygosities accurately without reference panels and outperforms the state of art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for the practical use of the method.

Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions.

Here we introduce a new endpoint "census population size" to evaluate the epidemiology and control of Plasmodium falciparum infections, where the parasite, rather than the infected human host, is the unit of measurement. To calculate census population size, we rely on a definition of parasite variation known as multiplicity of infection (MOIvar), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOIvar from sequencing and counting the number of unique DBLα tags (or DBLα types) of var genes, and derive from it census population size by summation of MOIvar in the human population. We track changes in this parasite population size and structure through sequential malaria interventions by indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) from 2012 to 2017 in an area of high-seasonal malaria transmission in northern Ghana. Following IRS, which reduced transmission intensity by > 90% and decreased parasite prevalence by ~40-50%, significant reductions in var diversity, MOIvar, and population size were observed in ~2,000 humans across all ages. These changes, consistent with the loss of diverse parasite genomes, were short lived and 32-months after IRS was discontinued and SMC was introduced, var diversity and population size rebounded in all age groups except for the younger children (1-5 years) targeted by SMC. Despite major perturbations from IRS and SMC interventions, the parasite population remained very large and retained the var population genetic characteristics of a high-transmission system (high var diversity; low var repertoire similarity) demonstrating the resilience of P. falciparum to short-term interventions in high-burden countries of sub-Saharan Africa.

Development of a Stable Oxygen Sensor Using a 761 nm DFB Laser and Multi-Pass Absorption Spectroscopy for Field Measurements.

An optical sensor system based on wavelength modulation spectroscopy (WMS) was developed for atmospheric oxygen (O2) detection. A distributed feedback (DFB) laser with butterfly packaging was used to target the O2 absorption line at 760.89 nm. A compact multi-pass gas cell was employed to increase the effective absorption length to 3.3 m. To ensure the stability and anti-interference capability of the sensor in field measurements, the optical module was fabricated with isolation of ambient light and vibration design. A 1f normalized 2f WMS (WMS-2f/1f) technique was adopted to reduce the effect of laser power drift. In addition, a LabVIEW-based dual-channel lock-in amplifier was developed for harmonic detection, which significantly reduced the sensor volume and cost. The detailed detection principle was described, and a theoretical model was established to verify the effectiveness of the technique. Experiments were carried out to obtain the device's sensing performances. An Allan deviation analysis yielded a minimum detection limit of 0.054% for 1 s integration time that can be further improved to 0.009% at ~60 s. Finally, the reliability and anti-interference capability of the sensor system were verified by the atmospheric O2 monitoring.

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review.

Wheel flats are amongst the most common local surface defect in railway wheels, which can result in repetitive high wheel-rail contact forces and thus lead to rapid deterioration and possible failure of wheels and rails if not detected at an early stage. The timely and accurate detection of wheel flats is of great significance to ensure the safety of train operation and reduce maintenance costs. In recent years, with the increase of train speed and load capacity, wheel flat detection is facing greater challenges. This paper focuses on the review of wheel flat detection techniques and flat signal processing methods based on wayside deployment in recent years. Commonly used wheel flat detection methods, including sound-based methods, image-based methods, and stress-based methods are introduced and summarized. The advantages and disadvantages of these methods are discussed and concluded. In addition, the flat signal processing methods corresponding to different wheel flat detection techniques are also summarized and discussed. According to the review, we believe that the development direction of the wheel flat detection system is gradually moving towards device simplification, multi-sensor fusion, high algorithm accuracy, and operational intelligence. With continuous development of machine learning algorithms and constant perfection of railway databases, wheel flat detection based on machine learning algorithms will be the development trend in the future.

Error Analysis of Heterodyne Interferometry Based on One Single-Mode Polarization-Maintaining Fiber.

Using polarization-maintaining fiber (PMF) in dual-frequency heterodyne interferometry has the advantages of reducing the laser's own drift, obtaining high-quality light spots, and improving thermal stability. Using only one single-mode PMF to achieve the transmission of dual-frequency orthogonal, linearly polarized beam requires angular alignment only once to realize the transmission of dual-frequency orthogonal, linearly polarized light, avoiding coupling inconsistency errors, so that it has the advantages of high efficiency and low cost. However, there are still many nonlinear influencing factors in this method, such as the ellipticity and non-orthogonality of the dual-frequency laser, the angular misalignment error of the PMF, and the influence of temperature on the output beam of the PMF. This paper uses the Jones matrix to innovatively construct an error analysis model for the heterodyne interferometry using one single-mode PMF, to realize the quantitative analysis of various nonlinear error influencing factors, and clarify that the main error source is the angular misalignment error of the PMF. For the first time, the simulation provides a goal for the optimization of the alignment scheme of the PMF and the improvement of the accuracy to the sub-nanometer level. In actual measurement, the angular misalignment error of the PMF needs to be smaller than 2.87° to achieve sub-nanometer interference accuracy, and smaller than 0.25° to make the influence smaller than ten picometers. It provides theoretical guidance and an effective means for improving the design of heterodyne interferometry instruments based on PMF and further reducing measurement errors.