Identification Capacity Research Articles

Clinical application of whole-exome sequencing analysis in childhood epilepsy

The swift updates of public databases and advancements in next-generation sequencing (NGS) technologies have enhanced the genetic identification capacities of epilepsy clinics. This study aimed to evaluate the diagnostic efficacy of NGS in pediatric epilepsy patients as a whole and to present the data obtained in the whole exome sequence analysis. We enrolled 40 children with suspected childhood epilepsy in this study. All patients underwent evaluation by a clinical geneticist or pediatric neurologist and the molecular genetic analysis of those children was performed by whole-exome sequencing (WES). Out of the 40 patients, 12 (30%) received a genetic diagnosis, involving 14 mutations across 13 genes. The cumulative positive diagnostic yield was 30%. Twelve of these patients were identified to have 5 variants previously documented as pathogenic, 9 variants classified as likely pathogenic, and 5 novel variants that have not been reported before. The outcomes indicate that whole-exome sequencing offers great benefits in clinical patient diagnosis, particularly in terms of detecting diagnostic variants. This study underscored the significance of whole exome sequencing (WES) studies, where only a broad gene set is examined in epilepsy patients. This approach has the potential to establish gene-specific phenotypic profiles, particularly by uncovering novel candidate genes in epilepsy patients with well-defined phenotypes. Additionally, conducting validation studies on variants of uncertain clinical significance could enhance the outcome yield.

ETUDE COMPARATIVE DES CARACTERISTIQUES DU SOL PAR STABILISATION AU CIMENT ET A LA CHAUX EN CONSTRUCTION ROUTIERE : CAS DU SOL DE BUGANGA A MINOVA/PROVINCE DU SUD KIVU/RDC

This work focused on the comparative study of soil stabilization with cement and lime. The purpose of this study is to carry out a comparative study of the stabilization of soil with cement and lime in order to increase the bearing capacity of the Minova soil to enhance this soil for use in road constructions (base layers and road bed) but also to contribute to the identification of this soil by studying the influence of stabilizers on the characteristics of the Minova soil. To achieve the expected results. Tests for the determination of the parameters of identification, compaction (modified Proctor) and bearing capacity (CBR) on the soil of BUGANGA in its natural state and after stabilization with cement and lime. The results obtained showed that the soil of BUGANGA is made up of 47.25% of the fines, therefore classified as fine soils. As far as the type of soil is concerned, it is a sandy clay with little plasticity characterized by a low bearing capacity (CBR=5.37%) with a medium swelling. Thus, due to its low load-bearing capacity, the BUGANGA soil has been stabilized to increase its bearing capacity. The results obtained showed that the addition of cement and lime increases the bearing capacity of the BUGANGA soil. Hence, by adding different contents of cement and lime, the CBR increased from 5.37%. In various stabilization formulations with lime and cement, BUGANGA soil is suitable for use in pavement beds.

Combining Morphological and Molecular Tools Can Enhance Tick Species Identification for Improved Tick-Borne Disease Surveillance Among Pastoral Communities in Kenya.

Background: Ticks are ecto-parasites of domestic animals, rodents, and wildlife living for periods at a time on one or more vertebrate hosts. They are important vectors of viral, bacterial, or parasitic diseases in livestock and humans. Crimean-Congo haemorrhagic fever virus and the spotted fever rickettsiae are some of the tick-borne diseases of public health importance reported in Kenya. Their distribution and public health risks among communities, especially pastoralists, remain poorly characterized due to limited surveillance, affected partly by inadequate capacity for tick identification arising from a limited number of skilled taxonomists. Materials and Methods: The aim of this survey was to identify tick species currently circulating in different livestock hosts in northern Kenya. Ticks were sampled from cattle, sheep, goats, and camels in Turkana, Isiolo, Baringo, and West Pokot counties, and differential identification was carried out using morphological identification keys followed by molecular characterization based on the cytochrome c oxidase I gene (cox1). Haplotypes were determined using the DnaSP v6 software and phylogenetic relationships inferred using the maximum likelihood algorithm. Results: A total of 12,206 ticks were collected, from Turkana (45.4%), Isiolo (23.1%), Baringo (22.7%), and West Pokot (8.8%) counties in Kenya. Ten species were confirmed by molecular analysis; H. rufipes, H. impeltatum, H. dromedarii, R. pravus, R. camicasi, R. pulchellus, R. evertsi evertsi, A. variegatum, A. gemma, and A. lepidum. There was no disparity in the morphological and molecular identification of Amblyomma species. However, molecular analysis provided insight into the complexity of morphological identification especially among Hyalomma and Rhipicephalus species. High haplotype diversities (0.857-1.000) and low nucleotide diversities (0.00719-0.06319) were observed in all the tick samples tested. Conclusion: The findings highlight the diversity of tick species in dry pastoral ecologies in Kenya and the importance of confirming morphological identification by molecular analysis thus contributing to accurate mapping of tick-borne disease distribution and risk.

Deep Learning based Road Traffic Assessment for Vehicle Rerouting: An Extensive Experimental Study of RetinaNet and YOLO Models

Currently, there is a notable prevalence of substantial traffic congestion and frequent vehicular accidents on roadways in contemporary times. Amalgamation of latest front-line technologies involving Internet of Things (IoT) and image classification has immense potential to advance the progress of a proficient traffic regulation system. To mitigate the occurrence of vehicular accidents, our research endeavors revolve around the comprehensive analysis of the prevailing road conditions. This meticulous examination allows us to effectively automate traffic routes orchestration, ensuring smooth vehicular movement across all lanes of the road network. The analysis of traffic patterns is conducted through the utilization of visual data images. The real time captured traffic images undergo processing using various object detection models named RetinaNet and the YOLO (You Only Look Once) models. A series of comparative evaluations suggests an improved traffic object identification capacity for the RetinaNet model as compared to all YOLO models.

Nomogram predicting early urinary incontinence after radical prostatectomy

PurposeOne of the most frequent side effects of radical prostatectomy (RP) is urinary incontinence. The primary cause of urine incontinence is usually thought to be impaired urethral sphincter function; nevertheless, the pathophysiology and recovery process of urine incontinence remains unclear. This study aimed to identify potential risk variables, build a risk prediction tool that considers preoperative urodynamic findings, and direct doctors to take necessary action to reduce the likelihood of developing early urinary incontinence.MethodsWe retrospectively screened patients who underwent radical prostatectomy between January 1, 2020 and December 31, 2023 at the First People ‘s Hospital of Nantong, China. According to nomogram results, patients who developed incontinence within three months were classified as having early incontinence. The training group’s general characteristics were first screened using univariate logistic analysis, and the LASSO method was applied for the best prediction. Multivariate logistic regression analysis was carried out to determine independent risk factors for early postoperative urine incontinence in the training group and to create nomograms that predict the likelihood of developing early urinary incontinence. The model was internally validated by computing the performance of the validation cohort. The nomogram discrimination, correction, and clinical usefulness were assessed using the c-index, receiver operating characteristic curve, correction plot, and clinical decision curve.ResultsThe study involved 142 patients in all. Multivariate logistic regression analysis following RP found seven independent risk variables for early urinary incontinence. A nomogram was constructed based on these independent risk factors. The training and validation groups’ c-indices showed that the model had high accuracy and stability. The calibration curve demonstrates that the corrective effect of the training and verification groups is perfect, and the area under the receiver operating characteristic curve indicates great identification capacity. Using a nomogram, the clinical net benefit was maximised within a probability threshold of 0.01–1, according to decision curve analysis (DCA).ConclusionThe nomogram model created in this study can offer a clear, personalised analysis of the risk of early urine incontinence following RP. It is highly discriminatory and accurate, and it can help create efficient preventative measures and identify high-risk populations.

Machine Learning Using Template-Based-Predicted Structure of Haemagglutinin Predicts Pathogenicity of Avian Influenza.

Deep learning presents a promising approach to complex biological classifications, contingent upon the availability of well-curated datasets. This study addresses the challenge of analyzing three-dimensional protein structures by introducing a novel pipeline that utilizes open-source tools to convert protein structures into a format amenable to computational analysis. Applying a two-dimensional convolutional neural network (CNN) to a dataset of 12,143 avian influenza virus genomes from 64 countries, encompassing 119 hemagglutinin (HA) and neuraminidase (NA) types, we achieved significant classification accuracy. The pathogenicity was determined based on the presence of H5 or H7 subtypes, and our models, ranging from zero to six mid-layers, indicated that a four-layer model most effectively identified highly pathogenic strains, with accuracies over 0.9. To enhance our approach, we incorporated Principal Component Analysis (PCA) for dimensionality reduction and one-class SVM for abnormality detection, improving model robustness through bootstrapping. Furthermore, the K-nearest neighbor (K-NN) algorithm was fine-tuned via hyperparameter optimization to corroborate the findings. The PCA identified distinct clustering for pathogenic HA, yielding an AUC of up to 0.85. The optimized K-NN model demonstrated an impressive accuracy between 0.96 and 0.97. These combined methodologies underscore our deep learning framework's capacity for rapid and precise identification of pathogenic avian influenza strains, thus providing a critical tool for managing global avian influenza threats.

Durability against dry-wet and freeze-thaw cycles of carbon sequestration foamed concrete utilizing abandoned soil and waste serpentine

Although the engineering and sustainability performance of carbon sequestration foamed concrete (CFC) has been demonstrated to be satisfactory, further investigation into the durability properties of CFC is warranted. The durability against dry-wet and freeze-thaw cycles constitutes a crucial parameter for service life design of foamed concrete. This paper investigates the durability against d-w and f-t cycles of a sustainable carbon sequestration foamed concrete (CFC) developed utilizing waste materials including abandoned soil and waste serpentine. A comprehensive array of physical, mechanical and microstructural tests was undertaken to examine both microscopic and macroscopic properties of CFC subsequent to cyclic d-w and f-t testing. These tests encompassed evaluations of unconfined compressive strength, deformation modulus, stress-strain curves, phase identification, pore structure and CO2 uptake capacity. The results revealed fluctuating mass change rates in CFC specimens with increasing d-w cycles, whereas a gradual increase in mass was noted with an increase in f-t cycles. After 10 d-w and f-t cycles, the strengths of CFC specimens exhibited reductions ranging from 10 % to 70 %. Furthermore, the iterative d-w and f-t cycles demonstrated minimal influence on the carbonation products and CO2 sequestration capacity of CFC, while causing the fragmentation of dense aggregates into loose fragments. Therefore, the remarkable durability exhibited by carbon sequestration concrete renders it suitable for meeting the requirements in engineering practice.

Innovative infrastructure to access Brazilian fungal diversity using deep learning.

In the present investigation, we employ a novel and meticulously structured database assembled by experts, encompassing macrofungi field-collected in Brazil, featuring upwards of 13,894 photographs representing 505 distinct species. The purpose of utilizing this database is twofold: firstly, to furnish training and validation for convolutional neural networks (CNNs) with the capacity for autonomous identification of macrofungal species; secondly, to develop a sophisticated mobile application replete with an advanced user interface. This interface is specifically crafted to acquire images, and, utilizing the image recognition capabilities afforded by the trained CNN, proffer potential identifications for the macrofungal species depicted therein. Such technological advancements democratize access to the Brazilian Funga, thereby enhancing public engagement and knowledge dissemination, and also facilitating contributions from the populace to the expanding body of knowledge concerning the conservation of macrofungal species of Brazil.

Quantum soft-covering lemma with applications to rate-distortion coding, resolvability and identification via quantum channels

We propose a quantum soft-covering problem for a given general quantum channel and one of its output states, which consists in finding the minimum rank of an input state needed to approximate the given channel output. We then prove a one-shot quantum covering lemma in terms of smooth min-entropies by leveraging decoupling techniques from quantum Shannon theory. This covering result is shown to be equivalent to a coding theorem for rate distortion under a posterior (reverse) channel distortion criterion by two of the present authors. Both one-shot results directly yield corollaries about the i.i.d. asymptotics, in terms of the coherent information of the channel. The power of our quantum covering lemma is demonstrated by two additional applications: first, we formulate a quantum channel resolvability problem, and provide one-shot as well as asymptotic upper and lower bounds. Second, we provide new upper bounds on the unrestricted and simultaneous identification capacities of quantum channels, in particular separating for the first time the simultaneous identification capacity from the unrestricted one, proving a long-standing conjecture of the last author.

High-Throughput Proteomics Enabled by a Fully Automated Dual-Trap and Dual-Column LC-MS.

This Technical Note describes a dual-column liquid chromatography system coupled to mass spectrometry (LC-MS) for high-throughput bottom-up proteomic analysis. This system made full use of two 2-position 10-port valves and a binary pump with an integrated loading pump of a commercial LC instrument to provide successive operation of two parallel subsystems. Each subsystem consisted of a set of trap columns and an analytical column. A T-junction union was used to split the mobile phase from the loading pump into two parts. This allowed one set of columns to be washed and equilibrated, followed by the injection of the next sample, while the previous sample was eluting and being analyzed on the other set of columns, thereby greatly increasing the analysis throughput. This approach showed high reproducibility for the analysis of HeLa tryptic digests with average relative standard deviation (RSD) values of 1.75%, 6.90%, and 5.19% for the identification number of proteins, peptides, and peptide-spectrum matches (PSMs), respectively, across 10 consecutive runs. The capacity for peptide and protein identification, as well as proteome depth, of the dual-column LC system was comparable to a conventional single-column system. Due to its simple equipment requirements and set up process, this method should be highly accessible for other laboratories.

On the capacity of artificial intelligence techniques and statistical methods to deal with low-quality data in medical supply chain environments

We illustrate the capacity of Artificial Intelligence (AI) and Machine Learning (ML) techniques to preserve consistent categorization abilities whenever the quality of the data decreases, displaying mistakes or mismatches across matrix entries, while standard statistical methods exhibit significant modifications in the value of the corresponding coefficients. We design algorithms of different complexity to generate a series of comparable profiles. These profiles are compared within environments that allow for an immediate identification of the generating algorithms and within increasingly complex settings involving almost identical profiles derived from different algorithms. AI and ML techniques outperform standard statistical methods when distinguishing the algorithms generating the profiles. Building on these results, we perform a retrospective analysis where AI and ML techniques are applied to two empirical scenarios defined by different data series of patients transplanted through the period 2006–2019. The first scenario contains the variables describing the evolution of patients inputted correctly. In the second, we modify the content of the vectors of characteristics defining the evolution of patients by exchanging the values of a subset of realizations from two categorical variables. AI and ML techniques are consistently accurate when categorizing patients correctly within both scenarios, a feature particularly relevant when the quality of the information sources composing the medical chain varies. This latter problem is exacerbated among hospitals located in developing countries, where the quality of the data gathered limits their identification and extrapolation capacities.

Functionalized-MXene-based imprinted MOFs composite membranes for the selective separation of ribavirin

The evident decrease for membrane-treated performance in response to multi-component mixture greatly limited its application. Here, we prepared blended nanomolecularly imprinted composite fiber membranes (CXPM-MIMs) with in situ growth of imprinted MOFs on carboxyl functionalized MXene. Through the carboxylate-assisted coordination pathway, a chelation site is provided by the carboxylate-modified MXene to coordinate with NH2-UiO-66, creating a tightly linked NH2-UiO-66/carboxylate-functionalized MXene heterostructure. The key role of the modified carboxyl group can be identified so that it can help to create a strong coordination bond between the two materials. Conventional MOFs typically lack selectivity and specificity. Here, we present a molecularly imprinted nanocomposite MOFs membrane material that exhibits selective adsorption and molecular separation capabilities. Imprinted UiO-66 (Ce) based on diamino-terephthalic acid was synthesized with water as the only solvent without adding any regulator or additive. By adding ribavirin molecules to multicomponent MOFs using a molecular imprinting technique and carrying out adsorption tests (isothermal and kinetic) as well as selectivity testing, we were able to demonstrate this adsorption-separation mechanism. The valuable testing outcomes shown that the as-prepared CXPM-MIMs performed excellently in terms of pemselectivity for ribavirin (RBV) (permselectivity factor β = 4.89, 10.14, 9.23) in addition to exhibiting perfect specific adsorption ability (58.3 mg g−1). The strong permselectivity and particular identification capacity of CXPM-MIMs demonstrated their tremendous potential in the treatment of RBV contamination.

Numerical analysis and early detection ofPlasmodium falciparum using a high performanceplasmonic biosensor with an external sensingapproach.

The authors propose a surface plasmon resonance (SPR) sensor based on photonic crystal fibers (PCFs) using three hexagonal ring lattices. The sensor can detect biomolecules with maximum wavelength and amplitude sensitivities of 23,000nm/RIU and 1310.93R I U -1, respectively, in the RI range of 1.32 to 1.42. It can detect infected red blood cells with Plasmodium falciparum for RIs of 1.402, 1.373, 1.395, and 1.383 in various malaria-infected red blood cell stages, including ring phase, trophozoite phase, and schizont phase. Furthermore, the sensor will be able to detect biomolecules such as viruses, proteins, DNA/RNA strands, acetone, ethanol, hexane, isopropanol, hexanol, formic acid, allyl cyanide, and others in its range. With these impressive results and identification capacity, the proposed sensor would benefit the biomaterial field and be appropriate for the early identification of malaria disease.

Dynamic identification and seismic capacity of an innovative cleanroom with walkable ceiling system

AbstractPast earthquakes highlighted the vulnerability of cleanrooms, especially in earthquake-prone countries like Italy; the post-earthquake reconnaissance showed that damage to cleanrooms led to immeasurable economic losses. This study investigates the seismic performance of an innovative cleanroom, seismically designed and provided with innovative components. Full-scale shake table tests were performed on the cleanroom according to ICC-ES AC156 protocol; the cleanroom was tested under operating conditions. Both dynamic properties and seismic behavior of the specimen were assessed. The tests showed the excellent seismic behavior of the cleanroom, confirming that simple devices can significantly improve the seismic performance of nonstructural elements.

Alternative identification of wood from natural fallen trees of the Lecythidaceae family in the Central Amazonian using FT-NIR spectroscopy

The scientific identification of natural fallen trees in tropical forests is complex due to the lack of fertile material in field collection. The study evaluated the use of near-infrared spectroscopy with Fourier-transform (FT-NIR) in the discrimination of wood from fallen trees of the Lecythidaceae family. Seven trees were collected in the Central Amazonian region (Brazil), from which 63 specimens were prepared from the wood, and NIR spectra were obtained on different wood surfaces (total 756 spectra). Chemometric models were developed with a spectral data set, and the Mahalanobis algorithm was applied. The discriminant model with 2nd derivative spectra improved the identification capacity, resulting in errors < 5% in the identification of genus Couratari (3 ssp.), Eschweilera (2 ssp.), Holopyxidium (1 sp.) and Lecythis (1 sp.). The comparison of the spectral signatures of samples of fallen trees and wood library revealed that even when wood was exposed to environmental weathering, around 50% of the original bands were preserved, favouring discrimination at the genus level. The accuracy of the chemometric models developed indicates the applicability of FT-NIR spectroscopy integrative in identifying fallen trees from the Lecythidaceae family in the tropical forests.

Deterministic K-Identification for Future Communication Networks: The Binary Symmetric Channel Results

Numerous applications of the Internet of Things (IoT) feature an event recognition behavior where the established Shannon capacity is not authorized to be the central performance measure. Instead, the identification capacity for such systems is considered to be an alternative metric, and has been developed in the literature. In this paper, we develop deterministic K-identification (DKI) for the binary symmetric channel (BSC) with and without a Hamming weight constraint imposed on the codewords. This channel may be of use for IoT in the context of smart system technologies, where sophisticated communication models can be reduced to a BSC for the aim of studying basic information theoretical properties. We derive inner and outer bounds on the DKI capacity of the BSC when the size of the goal message set K may grow in the codeword length n. As a major observation, we find that, for deterministic encoding, assuming that K grows exponentially in n, i.e., K=2nκ, where κ is the identification goal rate, then the number of messages that can be accurately identified grows exponentially in n, i.e., 2nR, where R is the DKI coding rate. Furthermore, the established inner and outer bound regions reflects impact of the input constraint (Hamming weight) and the channel statistics, i.e., the cross-over probability.

Ultrasound-Assisted Acidic Natural Deep Eutectic Solvent as a New Strategy for Extracting Seabuckthorn Leaf Phenolics: Process Optimization, Compositional Identification, and Metabolic Enzyme Inhibition Capacity

Ultrasound-Assisted Acidic Natural Deep Eutectic Solvent as a New Strategy for Extracting Seabuckthorn Leaf Phenolics: Process Optimization, Compositional Identification, and Metabolic Enzyme Inhibition Capacity

Identification of closely related species in Aspergillus through Analysis of Whole-Genome.

The challenge of discriminating closely related species persists, notably within clinical diagnostic laboratories for invasive aspergillosis (IA)-related species and food contamination microorganisms with toxin-producing potential. We employed Analysis of the whole-GEnome (AGE) to address the challenges of closely related species within the genus Aspergillus and developed a rapid detection method. First, reliable whole genome data for 77 Aspergillus species were downloaded from the database, and through bioinformatic analysis, specific targets for each species were identified. Subsequently, sequencing was employed to validate these specific targets. Additionally, we developed an on-site detection method targeting a specific target using a genome editing system. Our results indicate that AGE has successfully achieved reliable identification of all IA-related species (Aspergillus fumigatus, Aspergillus niger, Aspergillus nidulans, Aspergillus flavus, and Aspergillus terreus) and three well-known species (A. flavus, Aspergillus parasiticus, and Aspergillus oryzae) within the Aspergillus section. Flavi and AGE have provided species-level-specific targets for 77 species within the genus Aspergillus. Based on these reference targets, the sequencing results targeting specific targets substantiate the efficacy of distinguishing the focal species from its closely related species. Notably, the amalgamation of room-temperature amplification and genome editing techniques demonstrates the capacity for rapid and accurate identification of genomic DNA samples at a concentration as low as 0.1 ng/μl within a concise 30-min timeframe. Importantly, this methodology circumvents the reliance on large specialized instrumentation by presenting a singular tube operational modality and allowing for visualized result assessment. These advancements aptly meet the exigencies of on-site detection requirements for the specified species, facilitating prompt diagnosis and food quality monitoring. Moreover, as an identification method based on species-specific genomic sequences, AGE shows promising potential as an effective tool for epidemiological research and species classification.

Nanoporous Dna Field Effect Transistor with Potential for Random‐Access Memory Applications: A Selectivity Performance Evaluation

AbstractMethods to encode digital data items as strands of synthetic DNA followed by selective data retrieval have been demonstrated. However, these initially bio‐oriented processes remain slow and not optimized. DNA field‐effect transistor (DNA‐FET) is studied here as a possible random‐access memory (RAM) device for simple, selective and rapid ssDNA fragment retrieval used as data pool identifier. The DNA‐FET is based on a co‐planar Au‐gated fully organic transistor appended with short single‐stranded DNA (ssDNA) probes bearing a blocking molecule to prevent partial hybridization and achieve near perfect selectivity for short length ssDNA (up to 45 nt). Examination of transconductance of the novel active layer incorporating a DNA nanopore architecture reveals enhanced binding site accessibility. This, in turn, facilitates discriminatory hybridization, particularly in the physical retrieval of short‐length ssDNA from a competitive, concentrated ssDNA background pool consisting of nine different sequences, with at least one nucleotide difference. The DNA‐FET exhibits rapid operation (9 min) in the millivolt range, low detection limit (sub‐femtomolar), high selectivity and reusability. Considering the straightforward concept, near error‐free identification capacity and hypothetically outstanding scalability, the DNA‐FET described here has potential as a foundation for further exploration of advanced RAM technology in the DNA data storage process.

Seasonal prediction of the distribution of three major malaria vectors in China: Based on an ecological niche model.

Against the backdrop of a global malaria epidemic that remains severe, China has eradicated indigenous malaria but still has to be alert to the risk of external importation. Understanding the distribution of vectors can provide an adequate and reliable basis for the development and implementation of vector control strategies. However, with the decline of malaria prevalence in recent years, the capacity of vector monitoring and identification has been greatly weakened. Here we have used new sampling records, climatic data, and topographic data to establish ecological niche models of the three main malaria vectors in China. The model results accurately identified the current habitat suitability areas for the three species of Anopheles and revealed that in addition to precipitation and temperature as important variables affecting the distribution of Anopheles mosquitoes, topographic variables also influenced the distribution of Anopheles mosquitoes. Anopheles sinensis is the most widespread malaria vector in China, with a wide region from the northeast (Heilongjiang Province) to the southwest (Yunnan Province) suitable for its survival. Suitable habitat areas for Anopheles lesteri are concentrated in the central, eastern, and southern regions of China. The suitable habitat areas of Anopheles minimus are the smallest and are only distributed in the border provinces of southern China. On this basis, we further assessed the seasonal variation in habitat suitability areas for these three major malaria vectors in China. The results of this study provide new and more detailed evidence for vector monitoring. In this new era of imported malaria prevention in China, regular reassessment of the risk of vector transmission is recommended.