Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

"All in one" lipid-polymer nanodelivery system for gene therapy of ischemic diseases.

Gene therapy offers a promising avenue for treating ischemic diseases, yet its clinical efficacy is hindered by the limitations of single gene therapy and the high oxidative stress microenvironment characteristic of such conditions. Lipid-polymer hybrid vectors represent a novel approach to enhance the effectiveness of gene therapy by harnessing the combined advantages of lipids and polymers. In this study, we engineered lipid-polymer hybrid nanocarriers with tailored structural modifications to create a versatile membrane fusion lipid-nuclear targeted polymer nanodelivery system (FLNPs) optimized for gene delivery. Our results demonstrate that FLNPs facilitate efficient cellular uptake and gene transfection via membrane fusion, lysosome avoidance, and nuclear targeting mechanisms. Upon encapsulating Hepatocyte Growth Factor plasmid (pHGF) and Catalase plasmid (pCAT), HGF/CAT-FLNPs were prepared, which significantly enhanced the resistance of C2C12 cells to H2O2-induced injury in vitro. In vivo studies further revealed that HGF/CAT-FLNPs effectively alleviated hindlimb ischemia-induced gangrene, restored motor function, and promoted blood perfusion recovery in mice. Metabolomics analysis indicated that FLNPs didn't induce metabolic disturbances during gene transfection. In conclusion, FLNPs represent a versatile platform for multi-dimensional assisted gene delivery, significantly improving the efficiency of gene delivery and holding promise for effective synergistic treatment of lower limb ischemia using pHGF and pCAT.

An efficient multiplex approach to CRISPR/Cas9 gene editing in citrus.

CRISPR/Cas9-mediated gene editing requires high efficiency to be routinely implemented, especially in species which are laborious and slow to transform. This requirement intensifies further when targeting multiple genes simultaneously, which is required for genetic screening or more complex genome engineering. Species in the Citrus genus fall into this category. Here we describe a series of experiments with the collective aim of improving multiplex gene editing in the Carrizo citrange cultivar using tRNA-based sgRNA arrays. We evaluate a range of promoters for their efficacy in such experiments and achieve significant improvements by optimizing the expression of both the Cas9 endonuclease and the sgRNA array. In the case of the former we find the UBQ10 or RPS5a promoters from Arabidopsis driving the zCas9i endonuclease variant useful for achieving high levels of editing. The choice of promoter expressing the sgRNA array also had a large impact on gene editing efficiency across multiple targets. In this respect Pol III promoters perform especially well, but we also demonstrate that the UBQ10 and ES8Z promoters from Arabidopsis are robust alternatives. Ultimately, this study provides a quantitative insight into CRISPR/Cas9 vector design that has practical application in the simultaneous editing of multiple genes in Citrus, and potentially other eudicot plant species.

Preclinical lentiviral vector-mediated hematopoietic stem and progenitor cell gene therapy corrects Pompe disease-related muscle and neurological manifestations.

Pompe disease, a rare genetic neuromuscular disorder, is caused by a deficiency of acid alpha-glucosidase (GAA), leading to an accumulation of glycogen in lysosomes, and resulting in the progressive development of muscle weakness. The current standard treatment, enzyme replacement therapy (ERT), is not curative and has limitations such as poor penetration into skeletal muscle and both the central and peripheral nervous systems, a risk of immune responses against the recombinant enzyme, and the requirement for high doses and frequent infusions. To overcome these limitations, lentiviral vector-mediated hematopoietic stem and progenitor cell (HSPC) gene therapy has been proposed as a next-generation approach for treating Pompe disease. This study demonstrates the potential of lentiviral HSPC gene therapy to reverse the pathological effects of Pompe disease in a preclinical mouse model. It includes a comprehensive safety assessment via integration site analysis, along with single-cell RNA sequencing analysis of central nervous tissue samples to gain insights into the underlying mechanisms of phenotype correction.

Human lung cancer classification and comprehensive analysis using different machine learning techniques.

Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image-processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi-layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f-score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. RESEARCH HIGHLIGHTS: Lung cancer is a leading cause of cancer-related death. Imaging (MRI, CT, and X-ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k-nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi-layer perceptron (MLP) classify cancer types; MLP excels in accuracy.

A single dose of an ALVAC vector-based RABV virus-like particle candidate vaccine induces a potent immune response in mice, cats and dogs.

Rabies, caused by the Rabies virus (RABV), is a highly fatal zoonotic disease. Existing rabies vaccines have demonstrated good immune efficacy, but the complexity of immunization procedures and high cost has impeded the elimination of RABV, particularly in the post-COVID-19 era. There is a pressing need for safer and more effective rabies vaccines that streamline vaccination protocols and reduce expense. To meet this need, we have developed a potential rabies vaccine candidate called ALVAC-RABV-VLP, utilizing CRISPR/Cas9 gene editing technology. This vaccine employs a canarypox virus vector (ALVAC) to generate RABV virus-like particles (VLPs). In mice, a single dose of ALVAC-RABV-VLP effectively activated dendritic cells (DCs), follicular helper T cells (Tfh), and the germinal center (GC)/plasma cell axis, resulting in durable and effective humoral immune responses. The survival rate of mice challenged with lethal RABV was 100%. Similarly, in dogs and cats, a single immunization with ALVAC-RABV-VLP elicited a stronger and longer-lasting antibody response. ALVAC-RABV-VLP induced superior cellular and humoral immunity in both mice and beagles compared to the commercial inactivated rabies vaccine. In conclusion, ALVAC-RABV-VLP induced robust protective immune responses in mice, dogs and cats, offering a novel, cost-effective, efficient, and promising approach for herd prevention of rabies.

Lyme Disease Under-Ascertainment During the COVID-19 Pandemic in the United States: Retrospective Study.

Background: The COVID-19 pandemic resulted in a massive disruption in access to care and thus passive, hospital- and clinic-based surveillance programs. In 2020, the reported cases of Lyme disease were the lowest both across the United States and North Carolina in recent years. During this period, human contact patterns began to shift with higher rates of greenspace utilization and outdoor activities, putting more people into contact with potential vectors and associated vector-borne diseases. Lyme disease reporting relies on passive surveillance systems, which were likely disrupted by changes in health care-seeking behavior during the pandemic. Objective: This study aimed to quantify the likely under-ascertainment of cases of Lyme disease during the COVID-19 pandemic in the United States and North Carolina. Methods: We fitted publicly available, reported Lyme disease cases for both the United States and North Carolina prior to the year 2020 to predict the number of anticipated Lyme disease cases in the absence of the pandemic using a Bayesian modeling approach. We then compared the ratio of reported cases divided by the predicted cases to quantify the number of likely under-ascertained cases. We then fitted geospatial models to further quantify the spatial distribution of the likely under-ascertained cases and characterize spatial dynamics at local scales. Results: Reported cases of Lyme Disease were lower in 2020 in both the United States and North Carolina than prior years. Our findings suggest that roughly 14,200 cases may have gone undetected given historical trends prior to the pandemic. Furthermore, we estimate that only 40% to 80% of Lyme diseases cases were detected in North Carolina between August 2020 and February 2021, the peak months of the COVID-19 pandemic in both the United States and North Carolina, with prior ascertainment rates returning to normal levels after this period. Our models suggest both strong temporal effects with higher numbers of cases reported in the summer months as well as strong geographic effects. Conclusions: Ascertainment rates of Lyme disease were highly variable during the pandemic period both at national and subnational scales. Our findings suggest that there may have been a substantial number of unreported Lyme disease cases despite an apparent increase in greenspace utilization. The use of counterfactual modeling using spatial and historical trends can provide insight into the likely numbers of missed cases. Variable ascertainment of cases has implications for passive surveillance programs, especially in the trending of disease morbidity and outbreak detection, suggesting that other methods may be appropriate for outbreak detection during disturbances to these passive surveillance systems.

The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.

PURPOSE: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. METHODS: The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. RESULTS: Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. CONCLUSION: The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.

Development of a sensor to detect methylmercury toxicity.

Methylmercury (MeHg) is a well-known neurotoxicant that induces various cellular functions depending on cellular- and developmental-specific vulnerabilities. MeHg has a high affinity for selenol and thiol groups, thus impairing the antioxidant system. Such affinity characteristics of MeHg led us to develop sensor vectors to assess MeHg toxicity. In this study, MeHg-mediated defects in selenocysteine (Sec) incorporation were demonstrated using thioredoxin reductase 1 cDNA fused with the hemagglutinin tag sequence at the C-terminus. Taking advantage of such MeHg-mediated defects in Sec incorporation, a cDNA encoding luciferase with a Sec substituted for cysteine-491 was constructed. This construct showed MeHg-induced decreases in signaling in a dose-dependent manner. To directly detect truncated luciferase under MeHg exposure, we further constructed a new sensor vector fused with a target for proteasomal degradation. However, this construct was inadequate because of the low rate of Sec insertion, even in the absence of MeHg. Finally, a Krab transcriptional suppressor fused with Sec was constructed and assessed to demonstrate MeHg-dependent increases in signal intensity. We confirmed that the vector responded specifically and in a dose-dependent manner to MeHg in cultured cerebellar granule cells. This vector is expected to allow monitoring of MeHg-specific toxicity via spatial and temporal imaging.

An improved RRT behavioral planning method for robots based on PTM algorithm.

For multi-dimensional high-order nonlinear systems with unstable path quality in parameter and extension terms, we developed a new fast search random tree strategy. First, we established a high-order Lipschitz vector field dynamic system to adapt to high-order systems of multi-degree-of-freedom robots, with the complex obstacle function being one of its key components. Secondly, we designed a classification gap filtering network layer (Classification LSTM) to screen training data models and ensure the global stability of data in path design. Additionally, the visual sensors deployed in the unit area effectively implement the path marking backtracking strategy and dead zone path simplification. Finally, three examples are provided to verify the effectiveness of this design method.

Effects of Hurricane Irma on mosquito abundance and species composition in a metropolitan Gulf coastal city, 2016-2018.

Mosquitoes are the most common disease vectors worldwide. In coastal cities, the spread, activity, and longevity of vector mosquitoes are influenced by environmental factors such as temperature, humidity, and rainfall, which affect their geographic distribution, biting rates, and lifespan. We examined mosquito abundance and species composition before and after Hurricane Irma in Miami, Dade County, Florida, and identified which mosquito species predominated post-Hurricane Irma. Our results showed that mosquito populations increased post-Hurricane Irma: 7.3 and 8.0 times more mosquitoes were captured in 2017 than at baseline, 2016 and 2018 respectively. Warmer temperatures accelerated larval development, resulting in faster emergence of adult mosquitoes. In BG-Sentinel traps, primary species like Ae. tortills, Cx. nigripalpus, and Cx. quinquefasciatus dominated the post-Hurricane Irma period. Secondary vectors that dominated post-Hurricane Irma include An. atropos, An. crucians, An. quadrimaculatus, Cx. erraticus, and Ps. columbiae. After Hurricane Irma, the surge in mosquito populations in Miami, Florida heightened disease risk. To mitigate and prevent future risks, we must enhance surveillance, raise public awareness, and implement targeted vector control measures.

Using machine learning models to identify the risk of depression in middle-aged and older adults with frequent and infrequent nicotine use: A cross-sectional study.

BACKGROUND: Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of depression among middle-aged and older adults with frequent and infrequent nicotine use, as this is quite necessary for supporting their well-being. METHODS: This study included a total of 10,821 participants, which were derived from the China Health and Retirement Longitudinal Study Wave 5, 2020 (CHARLS-5). Five machine learning (ML) algorithms were employed. Some metrics were used to evaluate the performance of models, including area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), specificity, accuracy. RESULTS: 10,821 participants (6472 males, 4349 females) had a mean age of 60.47 ± 8.98, with a score of 8.90 ± 6.53 on depression scale. For middle-aged and older adults with frequent nicotine use, random forest (RF) achieved the highest AUC value, PPV and specificity (0.75, 0.74 and 0.88, respectively). For the other group, support vector machines (SVM) showed the highest PPV (0.74), and relatively high accuracy and specificity (0.72 and 0.87, respectively). Feature importance analysis indicated that "dissatisfaction with life" was the most important variable of identifying the risk of depression in the SVM model, while "attitude towards expected life span" was the most important one in the RF model. LIMITATIONS: CHARLS-5 was collected during the COVID-19, so our results may be influenced by the pandemic. CONCLUSIONS: This study indicated that certain ML models can ideally identify the risk of depression in middle-aged and older adults, which holds significant value for their health management.

Predicting Depression Among Chinese Patients with Narcolepsy Type 1: A Machine-Learning Approach.

Objective: Depression is a common psychiatric issue among patients with narcolepsy type 1 (NT1). Effective management requires accurate screening and prediction of depression in NT1 patients. This study aims to identify relevant factors for predicting depression in Chinese NT1 patients using machine learning (ML) approaches. Methods: A total of 203 drug-free NT1 patients (aged 5-61), diagnosed based on the ICSD-3 criteria, were consecutively recruited from the Sleep Medicine Center at Peking University People's Hospital between September 2019 and April 2023. Depression, daytime sleepiness, and impulsivity were assessed using the Center for Epidemiologic Studies Depression Scale for Children (CES-DC) or the Self-Rating Depression Scale (SDS), the Epworth Sleepiness Scale for adult or children and adolescents (ESS or ESS-CHAD), and the Barratt Impulse Scale (BIS-11). Demographic characteristics and objective sleep parameters were also analyzed. Three ML models-Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)-were used to predict depression. Model performance was evaluated using receiver operating curve (AUC), accuracy, precision, recall, F1 score, and decision curve analysis (DCA). Results: The LR model identified hallucinations (OR 2.21, 95% CI 1.01-4.90, p = 0.048) and motor impulsivity (OR 1.10, 95% CI 1.02-1.18, p = 0.015) as predictors of depression. Among the ML models, SVM showed the best performance with an AUC of 0.653, accuracy of 0.659, sensitivity of 0.727, and F1 score of 0.696, reflecting its effectiveness in integrating sleep-related and psychosocial factors. Conclusion: This study highlights the potential of ML models for predicting depression in NT1 patients. The SVM model shows promise in identifying patients at high risk of depression, offering a foundation for developing a data-driven, personalized decision-making tool. Further research should validate these findings in diverse populations and include additional psychological variables to enhance model accuracy.

Association of Liver Function Tests With the Severity and Outcome of Dengue Fever in Children.

Background Dengue is one of the most common vector-borne diseases in India, and it is transmitted by Aedes family mosquitoes. Hepatic injury is known to occur from dengue infection. Direct hepatotoxicity and deranged host immune responses to the virus are responsible for this hepatic dysfunction. Hence, the study was undertaken to understand the deranged hepatic enzymes using liver function tests (LFTs) and the severity and outcome of dengue fever in children. Methods This study is an observational-descriptive study conducted between June 2022 and May 2024. The study population includes children between the ages of one month and 16 years who have been diagnosed with dengue fever and admitted to pediatric wards and pediatric intensive care units (PICUs), with a sample size of 151. Informed consent from guardians and institutional ethical clearance were obtained. Results A total of 4.8% (N = 7) mortality was seen in this study with dengue patients. Hepatomegaly was seen in 34% (N = 49) of cases. There is a clear statistical significance that is seen among the non-survived and survived dengue patients with a 10-fold increase in serum glutamic-oxaloacetic transaminase (SGOT) and serum glutamic pyruvic transaminase (SGPT) levels, respectively, along with total bilirubin, activated partial thromboplastin time (APTT), and prothrombin time (PT). Conclusions The current study shows that deranged LFTs are associated with more severe disease with more PICU admissions and mortality of the disease. The evidence clearly indicates the inclusion of LFTs as a routine investigation to understand the severity of the disease and the prognosis of the outcome.

Quantification of circulating TCR-engineered T cells targeting a human endogenous retrovirus post-adoptive transfer using nanoplate digital PCR.

In vivo expansion of genetically modified T cells in cancer patients following adoptive transfer has been linked to both anti-tumor activity and T cell-mediated toxicities. The development of digital PCR has improved the accuracy in quantifying the in vivo status of adoptively infused T cells compared to qPCR or flow cytometry. Here, we developed and evaluated the feasibility and performance of nanoplate-based digital PCR (ndPCR) to quantify adoptively infused T cells engineered with a T cell receptor (TCR) that recognizes a human endogenous retrovirus type E (HERV-E) antigen. Analysis of blood samples collected from patients with metastatic kidney cancer following the infusion of HERV-E TCR-transduced T cells established the limit of detection of ndPCR to be 0.3 transgene copies/µL of reaction. The lower limit of quantification for ndPCR was one engineered T cell per 10,000 PBMCs, which outperformed both qPCR and flow cytometry by 1 log. High inter-test and test-retest reliability was confirmed by analyzing blood samples collected from multiple patients. In conclusion, we demonstrated the feasibility of ndPCR for detecting and monitoring the fate of TCR-engineered T cells in adoptive cell therapy.

WNV and SLEV coinfection in avian and mosquito hosts: impact on viremia, antibody responses, and vector competence.

West Nile virus (WNV) and St. Louis encephalitis virus (SLEV) are closely related flaviviruses that can cause encephalitis in humans and related diseases in animals. In nature, both are transmitted by Culex, with wild birds, including jays, sparrows, and robins, serving as vertebrate hosts. WNV and SLEV circulate in the same environments and have recently caused concurrent disease outbreaks in humans. The extent that coinfection of mosquitoes or birds may alter transmission dynamics, however, is not well characterized. We therefore sought to determine if coinfection alters infection kinetics and virus levels in birds and infection rates in mosquitoes. Accordingly, American robins (Turdus migratorius), two species of mosquitoes, and vertebrate and invertebrate cells were infected with WNV and/or SLEV to assess how simultaneous exposure may alter infection outcomes. There was variable impact of coinfection in vertebrate cells, with some evidence that SLEV can suppress WNV replication. However, robins had comparable viremia and antibody responses regardless of coinfection. Conversely, in Culex cells and mosquitoes, we saw a minimal impact of simultaneous exposure to both viruses on replication, with comparable infection, dissemination, and transmission rates in singly infected and coinfected mosquitoes. Importantly, while WNV and SLEV levels in coinfected mosquito midguts were positively correlated, we saw no correlation between them in salivary glands and saliva. These results reveal that while coinfection can occur in both avian and mosquito hosts, the viruses minimally impact one another. The potential for coinfection to alter virus population structure or the likelihood of rare genotypes emerging remains unknown.IMPORTANCEWest Nile virus (WNV) and St. Louis encephalitis virus (SLEV) are closely related viruses that are transmitted by the same mosquitoes and infect the same birds in nature. Both viruses circulate in the same regions and have caused concurrent outbreaks in humans. It is possible that mosquitoes, birds, and/or humans could be infected with both WNV and SLEV simultaneously, as has been observed with Zika, chikungunya, and dengue viruses. To study the impact of coinfection, we experimentally infected vertebrate and invertebrate cells, American robins, and two Culex species with WNV and/or SLEV. Robins were efficiently coinfected, with no impact of coinfection on virus levels or immune response. Similarly, in mosquitoes, coinfection did not impact infection rates, and mosquitoes could transmit both WNV and SLEV together. These results reveal that WNV and SLEV coinfection in birds and mosquitoes can occur in nature, which may impact public health and human disease risk.

Mapping cognitive activity from electrocorticography field potentials in humans performing NBack task.

Objective. Advancements in data science and assistive technologies have made invasive brain-computer interfaces (iBCIs) increasingly viable for enhancing the quality of life in physically disabled individuals. Intracortical microelectrode implants are a common choice for such a communication system due to their fine temporal and spatial resolution. The small size of these implants makes the implantation plan critical for the successful exfiltration of information, particularly when targeting representations of task goals that lack robust anatomical correlates.Approach. Working memory processes including encoding, retrieval, and maintenance are observed in many areas of the brain. Using human electrocorticography (ECoG) recordings during a working memory experiment, we provide proof that it is possible to localize cognitive activity associated with the task and to identify key locations involved with executive memory functions.Results.From the analysis, we could propose an optimal iBCI implant location with the desired features. The general approach is not limited to working memory but could also be used to map other goal-encoding factors such as movement intentions, decision-making, and visual-spatial attention.Significance. Deciphering the intended action of a BCI user is a complex challenge that involves the extraction and integration of cognitive factors such as movement planning, working memory, visual-spatial attention, and the decision state. Examining field potentials from ECoG electrodes while participants engaged in tailored cognitive tasks can pinpoint location with valuable information related to anticipated actions. This manuscript demonstrates the feasibility of identifying electrodes involved in cognitive activity related to working memory during user engagement in the NBack task. Devoting time in meticulous preparation to identify the optimal brain regions for BCI implant locations will increase the likelihood of rich signal outcomes, thereby improving the overall BCI user experience.

Current status of the sterile insect technique for the suppression of mosquito populations on a global scale.

BACKGROUND: The World Health Organization (WHO) has emphasized the urgent need for alternative strategies to chemical insecticides for controlling mosquito populations, particularly the invasive Aedes species, which are known vectors of arboviruses. Among these alternative approaches, the sterile insect technique (SIT) is experiencing rapid development, with numerous pilot trials being conducted worldwide. MAIN TEXT: This review aims to elucidate the principles of SIT and highlight the significant recent advancements that have facilitated its scalability. I also employ a phased conditional approach to categorize the progression of 39 projects, drawing on peer reviewed studies, press releases and direct communication with project managers. This review indicates that a substantial number of projects illustrate the efficacy of SIT in suppressing Aedes populations, with one project even demonstrating a reduction in dengue incidence. I offer several recommendations to mitigate potential failures and address the challenges of compensation and overcompensation when implementing SIT field trials. Furthermore, I examine the potential implications of male mating harassment on the effectiveness of SIT in reducing disease transmission. CONCLUSIONS: This comprehensive assessment underscores the promise of SIT as a viable strategy for mosquito control. The insights gained from these trials not only contribute to the understanding of SIT's effectiveness but also highlight the importance of careful project management and ecological considerations in the pursuit of public health objectives.

Advancements, challenges, and future perspectives in developing feline herpesvirus 1 as a vaccine vector.

As the most prevalent companion animal, cats are threatened by numerous infectious diseases and carry zoonotic pathogens such as Toxoplasma gondii and Bartonella henselae, which are the primary causes of human toxoplasmosis and cat-scratch disease. Vaccines play a crucial role in preventing and controlling the spread of diseases in both humans and animals. Currently, there are only three core vaccines available to prevent feline panleukopenia, feline herpesvirus, and feline calicivirus infections, with few vaccines available for other significant feline infectious and zoonotic diseases. Feline herpesvirus, a major component of the core vaccine, offers several advantages and a stable genetic manipulation platform, making it an ideal model for vaccine vector development to prevent and control feline infectious diseases. This paper reviews the technologies involved in the research and development of the feline herpesvirus vaccine vector, including homologous recombination, CRISPR/Cas9, and bacterial artificial chromosomes. It also examines the design and effectiveness of expressing antigens of other pathogens using the feline herpesvirus as a vaccine vector. Additionally, the paper analyzes existing technical bottlenecks and challenges, providing an outlook on its application prospects. The aim of this review is to provide a scientific basis for the research and development of feline herpesvirus as a vaccine vector and to offer new ideas for the prevention and control of significant feline infectious and zoonotic diseases.

Vector abundance and associated abiotic factors that influence the distribution of ticks in six provinces of South Africa.

Background and Aim: Climatic conditions significantly impact the life stages and distribution patterns of ticks and tick-borne diseases. South Africa's central plateau and various biomes offer a distinct landscape for studying the geography's effects. The study estimated tick species prevalence and the influential factors on their survival. Materials and Methods: Ticks were gathered from communal cattle in South African provinces including Limpopo (LP), Gauteng (GP), Mpumalanga (MP), KwaZulu-Natal (KZN), the Eastern Cape (EC), and the Free State (FS), from September 2020 to November 2022. Using data from South African weathercasts, the annual climate was assessed. Results: A total of 3,409 ticks were collected, with the highest infestation observed in KZN (45%), followed by LP (26%), EC (19%), GP (5%), MP (2%), and the FS (2%). The most prevalent tick species were Amblyomma hebraeum (55.1%), Rhipicephalus evertsi evertsi (13.9%) and Rhipicephalus (Boophilus), and decoloratus (11.9%). Other species included R. (Boophilus) microplus (10.85%), Hyalomma marginatum (4.8%), Rhipicephalus appendiculatus (1.4%), Harpalus rufipes (0.8%), Rhipicephalus exophthalmos (0.2%), Rhipicephalus glabroscutatus (0.2%), Rhipicephalus sanguineus (0.2%), Haemaphysalis silacea (0.5%), Ixodes pilosus (0.1%), and Rhipicephalus simus (0.1%). The infestations were most prevalent on farms in Pongola and KZN. The temperature fluctuated between 12°C and 35°C during data gathering, while humidity varied between 40% and 65%. Conclusion: This study showed that ticks survive optimally in warm temperatures and high humidity conditions. Livestock farms with high tick infestations may be associated with several risk factors. These practices could involve suboptimal grazing, insufficient acaricidal treatment, and detrimental effects resulting from traditional animal husbandry. Future research is needed to longitudinally evaluate the effects of climate change on tick populations, pathogen transmission, hosts, habitats, and human behavior, influencing potential exposure risks.