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\"The stars of the latest book by award-winning science writer and mathematician Robyn Arianrhod are unlikely celebrities--vectors and tensors. If you took a high school physics course, the word \"vector\" might remind you of the mathematics needed to determine forces on an amusement park ride, say; or of cross products, a special kind of multiplication using a bespoke table and a right-hand rule. You might also remember the introductory definition of a vector as a quantity that has magnitude and (this is the key) direction. Velocity--for example, 25 miles per hour northwest--is a vector; speed, such as 25 miles per hour, is not. Put another way, a velocity vector in space contains not one number, but three-a measurement of speed along each of three dimensions. It sounds simple, in hindsight--yet, as Arianrhod shows in this intriguing story, the idea of a single symbol expressing several things at once is a sophisticated one, millennia in the making. Vectors are examples of an even more sophisticated idea, the tensor. And it's not just space that vectors and tensors can represent, but information, too. Which means that whenever you use a search engine, say, or AI bot, computer graphics, or a host of other digital applications, vectors and tensors are there somewhere in the software. As for physics, there's much more to it than velocities and simple forces! Arianrhod shows how the discovery of vectors and tensors enabled physicists and mathematicians to think brand new thoughts-such as Maxwell did when he ushered in the wireless electromagnetic age, and Einstein when he predicted the curving of four-dimensional space-time and the existence of gravitational waves. Quantum theory, too, makes fine use of these ideas. In other words, vectors and tensors have been critical not only to the way we see our universe, but also to the invention of Wi-Fi, GPS, micro-technology, and so much else that we take for granted today. In exploring the history and significance of vectors and tensors-and introducing the fascinating people who gave them to us--Arianrhod takes readers on an extraordinary, five-thousand-year journey through the human imagination. A celebration of an idea, Vector shows the genius required to imagine the world in new dimensions-and how a clever mathematical construct can direct the future of discovery\"-- Provided by publisher.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning to predict late-onset preeclampsia using hospital electronic medical record data. The performance of the machine learning based models and models using conventional statistical methods were also compared. A total of 11,006 pregnant women who received antenatal care at Yonsei University Hospital were included. Maternal data were retrieved from electronic medical records during the early second trimester to 34 weeks. The prediction outcome was late-onset preeclampsia occurrence after 34 weeks' gestation. Pattern recognition and cluster analysis were used to select the parameters included in the prediction models. Logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, and stochastic gradient boosting method were used to construct the prediction models. C-statistics was used to assess the performance of each model. The overall preeclampsia development rate was 4.7% (474 patients). Systolic blood pressure, serum blood urea nitrogen and creatinine levels, platelet counts, serum potassium level, white blood cell count, serum calcium level, and urinary protein were the most influential variables included in the prediction models. C-statistics for the decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, stochastic gradient boosting method, and logistic regression models were 0.857, 0.776, 0.573, 0.894, 0.924, and 0.806, respectively. The stochastic gradient boosting model had the best prediction performance with an accuracy and false positive rate of 0.973 and 0.009, respectively. The combined use of maternal factors and common antenatal laboratory data of the early second trimester through early third trimester could effectively predict late-onset preeclampsia using machine learning algorithms. Future prospective studies are needed to verify the clinical applicability algorithms.

Background There is evidence that the knockdown resistance gene ( Kdr ) L1014F and acetylcholinesterase-1 gene ( Ace-1 R ) G119S mutations involved in pyrethroid and carbamate resistance in Anopheles gambiae influence malaria transmission in sub-Saharan Africa. This is likely due to changes in the behaviour, life history and vector competence and capacity of An. gambiae . In the present study, performed as part of a two-arm cluster randomized controlled trial evaluating the impact of household screening plus a novel insecticide delivery system (In2Care Eave Tubes), we investigated the distribution of insecticide target site mutations and their association with infection status in wild An. gambiae sensu lato (s.l.) populations. Methods Mosquitoes were captured in 40 villages around Bouaké by human landing catch from May 2017 to April 2019. Randomly selected samples of An. gambiae s.l. that were infected or not infected with Plasmodium sp. were identified to species and then genotyped for Kdr L1014F and Ace-1 R G119S mutations using quantitative polymerase chain reaction assays. The frequencies of the two alleles were compared between Anopheles coluzzii and Anopheles gambiae and then between infected and uninfected groups for each species . Results The presence of An. gambiae (49%) and An. coluzzii (51%) was confirmed in Bouaké. Individuals of both species infected with Plasmodium parasites were found. Over the study period, the average frequency of the Kdr L1014F and Ace-1 R G119S mutations did not vary significantly between study arms. However, the frequencies of the Kdr L1014F and Ace-1 R G119S resistance alleles were significantly higher in An. gambiae than in An. coluzzii [odds ratio (95% confidence interval): 59.64 (30.81–131.63) for Kdr , and 2.79 (2.17–3.60) for Ace-1 R ]. For both species, there were no significant differences in Kdr L1014F or Ace-1 R G119S genotypic and allelic frequency distributions between infected and uninfected specimens ( P  > 0.05). Conclusions Either alone or in combination, Kdr L1014F and Ace-1 R G119S showed no significant association with Plasmodium infection in wild An. gambiae and An. coluzzii , demonstrating the similar competence of these species for Plasmodium transmission in Bouaké. Additional factors including behavioural and environmental ones that influence vector competence in natural populations, and those other than allele measurements (metabolic resistance factors) that contribute to resistance, should be considered when establishing the existence of a link between insecticide resistance and vector competence. Graphical Abstract

Background Mosquitoes in the genus Culex are primary vectors in the US for West Nile virus (WNV) and other arboviruses. Climatic drivers such as temperature have differential effects on species-specific changes in mosquito range, distribution, and abundance, posing challenges for population modeling, disease forecasting, and subsequent public health decisions. Understanding these differences in underlying biological dynamics is crucial in the face of climate change. Methods We collected empirical data on thermal response for immature development rate, egg viability, oviposition, survival to adulthood, and adult lifespan for Culex pipiens, Cx. quinquefasciatus, Cx. tarsalis , and Cx. restuans from existing literature according to the PRISMA scoping review guidelines. Results We observed linear relationships with temperature for development rate and lifespan, and nonlinear relationships for survival and egg viability, with underlying variation between species. Optimal ranges and critical minima and maxima also appeared varied. To illustrate how model output can change with experimental input data from individual Culex species, we applied a modified equation for temperature-dependent mosquito type reproduction number for endemic spread of WNV among mosquitoes and observed different effects. Conclusions Current models often input theoretical parameters estimated from a single vector species; we show the need to implement the real-world heterogeneity in thermal response between species and present a useful data resource for researchers working toward that goal. Graphical Abstract

Background The global rise in temperature has seen a geographical range increase in mosquito populations and disease transmission. Temperature affects life history traits of mosquitoes, and hence, population dynamics and vectorial capacity. In addition, food resource abundance, required for biomass and somatic energy during mosquito larval development is dependent on temperature. How the interaction between temperature and food resources affects the life history traits of aquatic stages, and subsequent carry-over effects to the adult stage, under simulated natural conditions, remains underexplored. Methods A comparative assessment of the interactive effect of diurnal fluctuating temperature and resource level during larval development on life history traits of the yellow fever mosquito, Aedes aegypti , and three malaria vectors, Anopheles stephensi , Anopheles coluzzii and Anopheles arabiensis was conducted. Moreover, carry-over effects on teneral adults including, metabolic reserves and propensity to feed were evaluated on the four species under similar abiotic conditions. A total of 2700 larvae of each species were reared under three fluctuating temperature regimes, and maintained on different resource levels. A mixed-effects Cox regression model was used to determine effects of the two environmental factors on the time to adult emergence, and adult survival. Generalised linear mixed-effect model with a binomial error structure was used to elucidate effects of abiotic stress on feeding, whereas linear-mixed effects analysis of variance, was used to estimate the effects of temperature and resource level on adult size and metabolic reserves. Aligned Rank Transform analysis of variance was used to determine effects of abiotic stress on level of feeding. Correlation between size and survival of starved adults was determined by multivariate analysis using Spearman’s rank correlation and linear regression. Results Time to adult emergence shortened with increasing temperature and resource level. Accelerated adult emergence was associated with reduced adult size and survival at high temperature, in a resource-dependent manner. Metabolic macronutrient reserves carried over into teneral adults were differentially regulated by temperature and larval resource level, in a species dependent manner. Teneral females engaged in feeding on honey or blood depending on the two abiotic stressors, and species. Conclusions Temperature and resource level during larval development differentially affects life history traits of disease-transmitting mosquitoes, which have ramifications on population size, as well as disease transmission dynamics. Graphical Abstract

This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12 months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R2 = 0.819) and SPI-12 (R2 = 0.968) values at Paithan station; the SPI-3 (R2 = 0.736) and SPI-6 (R2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R2 = 0.846) at Paithan station and SPI-12 (R2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is good for SPI-3 prediction at both stations. The results have been showed the ability of the SVM-SMO algorithm with various kernel functions successfully applied for the forecasting of multiscale SPI under the climate changes. It can be helpful for decision making in water resource management and tackle droughts in the semi-arid region of central India.

In this study, we investigate the origin and diversification of Trypanosoma cruzi vectors within the Rhodniini tribe (Triatominae subfamily) through phylogenetic analyses based on eight genes from 17 species and 497 specimens—the largest sampling of this tribe to date. Our results predominantly support the paraphyly of the genus Rhodnius , with the three Psammolestes species forming a well-supported monophyletic clade nested within it. In two reconstructions, however, Psammolestes and Rhodnius are recovered as reciprocally monophyletic, each with strong support. In Rhodnius , we find monophyletic pallescens and pictipes groups, but a paraphyletic prolixus group, with persistent phylogenetic discordances underscoring uncertainties in species placements. Divergence estimates suggest Rhodniini originated around 5.26 million years ago, notably more recent than previously thought. Evolution within the tribe appears shaped by geography, gene flow, and incomplete lineage sorting rather than traditional taxonomy. Only four species— P. arthuri , R. ecuadoriensis , R. neivai , and R. neglectus —are consistently supported across analyses, likely diversifying during Pleistocene climate changes. Other Rhodniini species may represent a panmictic population with minor structuring influenced by the Andes uplift. This study underscores the need for integrative research combining genetic, ecological, and biogeographical data to fully understand Rhodniini speciation and diversification.

The detection of structural change points in time series is a fundamental problem in statistical analysis, with significant implications across numerous scientific disciplines. Traditional change-point detection methods often face challenges in consistently estimating the long-run variance of time series, which can limit their practical application. This paper introduces a novel change-point detection methodology that integrates Support Vector Regression (SVR) with a self-normalization framework. By leveraging SVR's flexible modeling capabilities to obtain accurate residual estimates and employing a self-normalized test statistic, our approach circumvents the need for long-run variance estimation. Under the null hypothesis of no structural change, the test statistic converges to a non-degenerate limiting distribution, while under the alternative hypothesis, it diverges to infinity, ensuring consistent detection power. Extensive simulation studies demonstrate that our method outperforms existing SVR-based tests in finite-sample performance, offering improved size control (empirical size close to nominal 0.05 level) and higher detection power across various scenarios. Empirical applications to hydrological and financial time series (Nile River flow data and Nikkei 225 index) validate the method's practical utility in real-world settings. The proposed framework provides a robust, parameter-free tool for analyzing structural instability in time series, with particular advantages in handling complex, nonlinear data structures. The method's avoidance of tuning parameters and consistent performance across different domains suggest broad applicability in scientific research and practical applications.

Background Mosquito seasonal activity is largely driven by weather conditions, most notably temperature, precipitation, and relative humidity. The extent by which these weather variables influence activity is intertwined with the animal’s biology and may differ by species. For mosquito vectors, changes in weather can also alter host–pathogen interactions thereby increasing or decreasing the burden of disease. Methods In this study, we performed weekly mosquito surveillance throughout the active season over a 2-year period in Manitoba, Canada. We then used Generalized Linear Mixed Models (GLMMs) to explore the relationships between weather variables over the preceding 2 weeks and mosquito trap counts for four of the most prevalent vector species in this region: Oc. dorsalis , Ae. vexans , Cx. tarsalis , and Cq. perturbans . Results More than 265,000 mosquitoes were collected from 17 sampling sites throughout Manitoba in 2020 and 2021, with Ae. vexans the most commonly collected species followed by Cx. tarsalis . Aedes vexans favored high humidity, intermediate degree days, and low precipitation. Coquillettidia perturbans and Oc. dorsalis activity increased with high humidity and high rainfall, respectively. Culex tarsalis favored high degree days, with the relationship between number of mosquitoes captured and precipitation showing contrasting patterns between years. Minimum trapping temperature only impacted Ae. vexans and Cq. perturbans trap counts. Conclusions The activity of all four mosquito vectors was affected by weather conditions recorded in the 2 weeks prior to trapping, with each species favoring different conditions. Although some research has been done to explore the relationships between temperature/precipitation and Cx. tarsalis in the Canadian Prairies, to our knowledge this is the first study to investigate other commonly found vector species in this region. Overall, this study highlights how varying weather conditions can impact mosquito activity and in turn species-specific vector potential. Graphical Abstract

The choice of a modeling approach is a critical decision in the modeling process, as it determines the complexity of the model and the phenomena that the model captures. In this paper, we developed an individual-based model (IBM) and compared it to a previously published ordinary differential equation (ODE) model, both developed to describe the same biological system although with slightly different emphases given the underlying assumptions and processes of each modeling approach. We used both models to examine the effect of insect vector life history and behavior traits on the spread of a vector-borne plant virus, and determine how choice of approach affects the results and their biological interpretation. A non-random distribution of insect vectors across plant hosts emerged in the IBM version of the model and was not captured by the ODE. This distribution led simultaneously to a slower-growing vector population and a faster spread of the pathogen among hosts. The IBM model also enabled us to test the effect of potential control measures to slow down virus transmission. We found that removing virus-infected hosts was a more effective strategy for controlling infection than removing vector-infested hosts. Our findings highlight the need to carefully consider possible modeling approaches before constructing a model.