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Background: Knowledge of sexually transmitted diseases (STDs) is essential in protecting women from the devastating effects of these infections. This is the first study that attempts to assess the knowledge about one of the most embarrassing diseases in women with Islamic Turkmen culture.Purpose: This study aimed to assess women’s knowledge of STDs in Telafer City and its association with sociodemographic factors.Methods: This cross-sectional study involved 451 women over 18 years old from Telfer City in Iraq. The study was conducted using a convenient sampling technique from 1 December 2021 to 15 June 2022. The study’s data were collected using a STDs knowledge questionnaire. Descriptive statistics of mean, standard deviation, frequency, and percentage were used to describe sociodemographic characteristics and the level of knowledge of STDs among women. In addition, the inferential statistics of ANOVA and Fisher’s exact test were used to determine the association between the women’s characteristics and knowledge. Results: The study signaled that the overall mean score of knowledge of STDs was (6.67±5.85). The vast majority of participants had a low level of knowledge about STDs (73.8%; n=333). Only about (14.4%; n=64) had moderate knowledge, and approximately (11.8%; n=53) of women were highly knowledgeable about STDs. The study also proved the presence of a statistically significant positive association between women’s knowledge regarding STDs with marital status (p˂0.000) and educational level (p˂0.000).Conclusion: The majority of respondents showed low level of knowledge about STDs. Marital status and educational level were associated with this knowledge. This study necessitates the construction and employment of a women-tailored health education program about STDs in Iraq and Telafer City.

Nowadays, text classification for different purposes becomes a basic task for concerned people. Hence, much research has been done to develop automatic text classification for the majority of national and international languages. However, the need for an automated text classification system for local languages is felt. The main purpose of this study is to establish a novel automatic classification system of Pashto text. In order to follow this up, we established a collection of Pashto documents and constructed the dataset. In addition, this study includes several models containing statistical techniques and neural network neural machine learning including DistilBERT-base-multilingual-cased, Multilayer Perceptron, Support Vector Machine, K Nearest Neighbor, decision tree, Gaussian naïve Bayes, multinomial naïve Bayes, random forest, and logistic regression to discover the most effective approach. Moreover, this investigation evaluates two different feature extraction methods including bag of words, and Term Frequency Inverse Document Frequency. Subsequently, this research obtained an average testing accuracy rate of 94% using the MLP classification algorithm and TFIDF feature extraction method in single label multi-class classification. Similarly, MLP+TFIDF with F1-measure of 0.81 showed the best result. Experiments on the use of pre-trained language representation models (such as DistilBERT) for classifying Pashto texts show that we need a specific tokenizer for a particular language to obtain reasonable results.

MicroRNAs (miRNAs) are noncoding RNA molecules of 22-24 nucleotides that are estimated to regulate thousands of genes in humans, and their dysregulation has been implicated in many diseases. MicroRNA-122 (miR-122) is the most abundant miRNA in the liver and has been linked to the development of hepatocellular carcinoma and hepatitis C virus (HCV) infection. Its role in these diseases renders miR-122 a potential target for small-molecule therapeutics. Here, we report the discovery of a new sulfonamide class of small-molecule miR-122 inhibitors from a high-throughput screen using a luciferase-based reporter assay. Structure-activity relationship (SAR) studies and secondary assays led to the development of potent and selective miR-122 inhibitors. Preliminary mechanism-of-action studies suggest a role in the promoter-specific transcriptional inhibition of miR-122 expression through direct binding to the liver-enriched transcription factor hepatocyte nuclear factor 4α. Importantly, the developed inhibitors significantly reduce HCV replication in human liver cells.

Recently, many researchers have focused on building and improving speech recognition systems to facilitate and enhance human-computer interaction. Today, Automatic Speech Recognition (ASR) system has become an important and common tool from games to translation systems, robots, and so on. However, there is still a need for research on speech recognition systems for low-resource languages. This article deals with the recognition of a separate word for Dari language, using Mel-frequency cepstral coefficients (MFCCs) feature extraction method and three different deep neural networks including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), and two hybrid models of CNN and RNN. We evaluate our models on our built-in isolated Dari words corpus that consists of 1000 utterances for 20 short Dari terms. This study obtained the impressive result of 98.365% average accuracy.

Hybrid stepper motors (HSMs) are a type of stepper motor that have both a magnet and teeth on their rotors. These actuators are used in very fields including automation and robotics. Although these motors have high torque densities, improving their performance, their torque density, and their response time is very essential and useful. The most important problem in HSMs is their heavy rotor. A weightier rotor means more rotor vibrations and oscillations for each step. Therefore, in this study two motors with new rotor structures are represented. The first one has a light cup-shaped rotor which improves motor response time and decreases rotor oscillation on each step, and the second one not only has these benefits but also has increased motor torque density noticeably, which is due to one extra teeth row on its rotor that has made it two motors in one structure, indeed. Finally, the results of these improvements are compared with the conventional HSMs performance. Comparisons proved the significant superiority of these two motors over conventional HSMs.

Text classification has become a crucial task in various fields, leading to a significant amount of research on developing automated text classification systems for national and international languages. However, there is a growing need for automated text classification systems that can handle local languages. This study aims to establish an automated classification system for Pashto text. To achieve this goal, we constructed a dataset of Pashto documents and applied various models, including statistical and neural machine learning models such as DistilBERT-base-multilingual-cased, Multilayer Perceptron, Support Vector Machine, K Nearest Neighbor, decision tree, Gaussian na\"ive Bayes, multinomial na\"ive Bayes, random forest, and logistic regression, to identify the most effective approach. We also evaluated two different feature extraction methods, bag of words and Term Frequency Inverse Document Frequency. The study achieved an average testing accuracy rate of 94% using the MLP classification algorithm and TFIDF feature extraction method in single-label multiclass classification. Similarly, MLP+TFIDF yielded the best results, with an F1-measure of 0.81. Furthermore, the use of pre-trained language representation models, such as DistilBERT, showed promising results for Pashto text classification; however, the study highlights the importance of developing a specific tokenizer for a particular language to achieve reasonable results., Comment: https://nlpai2023.org/papers?fbclid=IwAR2v29d3nFUaIx9U-rnfN8pqJu1tXBS9P9OV1IJnsbJ0QHN9JZAMPhZA7Ds

Additional file 1. Supplementary Material.

This paper is an extension of our previous conference paper. In recent years, there has been a growing interest among researchers in developing and improving speech recognition systems to facilitate and enhance human-computer interaction. Today, Automatic Speech Recognition (ASR) systems have become ubiquitous, used in everything from games to translation systems, robots, and more. However, much research is still needed on speech recognition systems for low-resource languages. This article focuses on the recognition of individual words in the Dari language using the Mel-frequency cepstral coefficients (MFCCs) feature extraction method and three different deep neural network models: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multilayer Perceptron (MLP), as well as two hybrid models combining CNN and RNN. We evaluate these models using an isolated Dari word corpus that we have created, consisting of 1000 utterances for 20 short Dari terms. Our study achieved an impressive average accuracy of 98.365%., Comment: https://nlpai2023.org/papers?fbclid=IwAR2v29d3nFUaIx9U-rnfN8pqJu1tXBS9P9OV1IJnsbJ0QHN9JZAMPhZA7Ds

Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research article, we have discussed the features of GPT-4, its potential applications, and the challenges that it might face. We have also compared GPT-4 with its predecessor, GPT-3. GPT-4 has a larger model size (more than one trillion), better multilingual capabilities, improved contextual understanding, and reasoning capabilities than GPT-3. Some of the potential applications of GPT-4 include chatbots, personal assistants, language translation, text summarization, and question-answering. However, GPT-4 poses several challenges and limitations such as computational requirements, data requirements, and ethical concerns.

A deep survey of the Large Magellanic Cloud at ~0.1-100TeV photon energies with the Cherenkov Telescope Array is planned. We assess the detection prospects based on a model for the emission of the galaxy, comprising the four known TeV emitters, mock populations of sources, and interstellar emission on galactic scales. We also assess the detectability of 30 Doradus and SN 1987A, and the constraints that can be derived on the nature of dark matter. The survey will allow for fine spectral studies of N157B, N132D, LMC P3, and 30 Doradus C, and half a dozen other sources should be revealed, mainly pulsar-powered objects. The remnant from SN 1987A could be detected if it produces cosmic-ray nuclei with a flat power-law spectrum at high energies, or with a steeper index 2.3-2.4 pending a flux increase by a factor >3-4 over ~2015-2035. Large-scale interstellar emission remains mostly out of reach of the survey if its >10GeV spectrum has a soft photon index ~2.7, but degree-scale 0.1-10TeV pion-decay emission could be detected if the cosmic-ray spectrum hardens above >100GeV. The 30 Doradus star-forming region is detectable if acceleration efficiency is on the order of 1-10% of the mechanical luminosity and diffusion is suppressed by two orders of magnitude within, Accepted for publication in MNRAS. Corresponding authors: Pierrick Martin, Maria Isabel Bernardos Martin, Fabio Iocco

In this article, a new modular structure of Marx generators (MGs) based on interleaved boost converter is proposed. The converter operates in discontinuous conduction mode to generate a high-voltage pulsed power with a common low-voltage source. Unlimited stages can be added to the proposed structure to reach lower maximum voltage stress for the semiconductors or to generate several kilovolts of the output pulses, while the voltage of the semiconductors remains constant. This structure resulted in a higher pulse repetition rate, fewer semiconductor elements, and lower semiconductor voltage stress in comparison to conventional MGs. Hence, the proposed structure is applicable for and efficient in supplying plasma applications with high-voltage pulses. The theoretical foundation of the introduced converter is presented and its performance is simulated using OrCAD software. Based on the obtained results, the maximum voltage of the output pulse is 40 times the input voltage in a single-stage structure. An 80-W prototype of the proposed structure was built to validate the accuracy of the theoretical foundation and the simulation results.

In this work the effect of structural defects caused by doping and vacancies on the elastic properties and self-diffusion coefficient of Li ions in the Li3OCl superionic conductor was investigated. In contrast to previous studies where high Li-ion diffusion in Li3OCl was attributed to the Li-ion vacancies and interstitials, we found that both the type and distribution of defects in Li3OCl significantly affected the diffusion coefficient, and the highest conductivity was obtained in a sample with a regular distribution of LiCl Schottky defects. Since the defect distribution will be determined by the experimental sample preparation, this provides a guide to improve the conductivity of Li3OCl electrolyte. Moreover, the introduction of these defects only causes minor changes in the elastic properties of Li3OCl solid electrolytes, which benefits the design of all-solid-state Li-ion batteries with high performance.

Solid-state sodium ion batteries (SSSIBs) have been proposed to resolve the safety hazards of traditional liquid batteries. The sodium superionic conductor (NASICON), one of the most promising solid electrolyte candidates, has received much attention. However, the practical use of NASICON has been impeded by low ion mobility at room temperature (RT) and poor interfacial connectivity. Here, the improvement of both bulk and grain boundary conductivity has been achieved simultaneously via Sc and Ge co-doping. Bulk conductivity is increased by the Sc-doping, which increases the stability of the rhombohedral phase at RT, and the Ge-doping, which reduces the monoclinic to rhombohedral phase transformation temperature. The highest total conductivity of 4.64 × 10−3 S cm−1 was obtained for the Na3.125Zr1.75Sc0.125Ge0.125Si2PO12 structure. The improvement in conductivity due to the stabilisation of the rhombohedral phase was verified by DFT calculations. Finally, two different solid-state batteries using Na3V2(PO4)3 and Sn4P3@CNT as electrodes display impressive cycling capacities of 98 and 629 mAh g − 1 after 250 cycles, respectively. This co-doping principle also provides a framework to explore multiple-doping for other materials with similar structures.

We identified and compared patients diagnosed with Plasmodium falciparum malaria at a large hospital in London, UK, prior to the COVID-19 pandemic vs following relaxation of COVID-19-associated restrictions. We found that parasitaemias, rates of hyperparasitaemia and severe malaria were significantly higher in the period post-relaxation of COVID-19 restrictions.

Personal space is the distance that people tend to maintain from others during daily life in a largely unconscious manner. For humans, personal space-related behaviors represent one form of non-verbal social communication, similar to facial expressions and eye contact. Given that the changes in social behavior and experiences that occurred during the COVID-19 pandemic, including “social distancing” and widespread social isolation, may have altered personal space preferences, we investigated this possibility in two independent samples. First, we compared the size of personal space measured before the onset of the pandemic to its size during the pandemic in separate groups of subjects. Personal space size was significantly larger in those assessed during (compared to those assessed before) the onset of the pandemic (all d > 0.613, all p r = 0.625–0.958) and similar in magnitude. Moreover, the size of personal space, as well as levels of discomfort during personal space intrusions, increased significantly during (compared to before) the COVID-19 pandemic in response to both real and virtual humans (all d > 0.842, all p p

Quantum dots (QDs) of formamidinium lead triiodide (FAPbI

Aims We have previously demonstrated that vitamin D deficiency might be associated with worse outcomes in hospitalised Covid-19 patients. The aim of our study was to explore this relationship with dexamethasone therapy. Methods We prospectively studied two cohorts of hospitalised Covid-19 patients between March and April and between September and December 2020 (n=192). Patients were tested for serum 25-hydroxyvitamin D (25-OH-D) levels during admission. The first cohort not treated with dexamethasone (n=107) were divided into vitamin D deficient (25-OH-D ≤30nmol/L) (n=47) and replete subgroups (25-OH-D >30nmol/L) (n=60). The second cohort treated with dexamethasone (n=85) was similarly divided into deficient (25-OH-D ≤30nmol/L) (n=27) and replete subgroups (25-OH-D >30nmol/L) (n=58). Primary outcome was in-hospital mortality and secondary outcomes were elevation in markers of cytokine storm and ventilatory requirement. Results No mortality difference was identified between cohorts and subgroups. The "no dexamethasone" cohort 25-OH-D deplete subgroup recorded significantly higher peak D-Dimer levels (1874ugFEU/L vs 1233ugFEU/L) (p value = 0.0309), CRP (177 vs 107.5) (p=0.0055) and ventilatory support requirement (25.5% vs 6.67%) (p value = 0.007) compared to the replete subgroup. Among the 25-OH-D deplete subgroup higher peak neutrophil counts, peak CRP, peak LDH, peak ferritin and lower trough lymphocyte counts were observed, without statistical significance. In the "dexamethasone" cohort, there was no apparent association between 25-OH-D deficiency and markers of cytokine storm or ventilatory requirement. Conclusion Vitamin D deficiency is associated with elevated markers of cytokine storm and higher ventilatory requirements in hospitalised Covid-19 patients. Dexamethasone treatment appears to mitigate adverse effects of vitamin D deficiency. This article is protected by copyright. All rights reserved.

Carbon materials such as graphene are of potential use in the development of electronic devices because of properties such as high mechanical strength and electrical and thermal conductivity. However, technical challenges, including difficulties in generating and modulating bandgaps, have limited the application of such materials. Here we show that the bandgaps of bilayers of two-dimensional C3N can be engineered by controlling the stacking order or applying an electric field. AA′ stacked C3N bilayers are found to have a smaller bandgap (0.30 eV) than AB′ stacked bilayers (0.89 eV), and both bandgaps are lower than that of monolayer C3N (1.23 eV). The larger bandgap reduction observed in AA′ stacked bilayers, compared with AB′ stacked bilayers, is attributed to the greater pz-orbital overlap. By applying an electric field of ~1.4 V nm−1, a bandgap modulation of around 0.6 eV can be achieved in the AB′ structure. We also show that the C3N bilayers can offer controllable on/off ratios, high carrier mobilities and photoelectric detection capabilities. The bandgaps of bilayers of two-dimensional C3N can be modulated by controlling the stacking order of the layers or by applying an electric field.

Background Diagnostic methods for Covid-19 have improved, both in speed and availability. Because of atypical and asymptomatic carriage of the virus and nosocomial spread within institutions, timely diagnosis remains a challenge. Machine learning models trained on blood test results have shown promise in identifying cases of Covid-19. Aims To train and validate a machine learning model capable of differentiating Covid-19 positive from negative patients using routine blood tests and assess the model’s accuracy against atypical and asymptomatic presentations. Design and methods We conducted a retrospective analysis of medical admissions to our institution during March and April 2020. Participants were categorized into Covid-19 positive or negative groups based on clinical, radiological features or nasopharyngeal swab. A machine learning model was trained on laboratory parameters and validated for accuracy, sensitivity and specificity and externally validated at an unconnected establishment. Results An Ensemble Bagged Tree model was trained on data collected from 405 patients (212 Covid-19 positive) producing an accuracy of 81.79% (95% confidence interval (CI) 77.53–85.55%), the sensitivity of 85.85% (CI 80.42–90.24%) and specificity of 76.65% (CI 69.49–82.84%). Accuracy was preserved for atypical and asymptomatic subgroups. Using an external data set for 226 patients (141 Covid-19 positive) accuracy of 76.82% (CI 70.87–82.08%), sensitivity of 78.38% (CI 70.87–84.72%) and specificity of 74.12% (CI 63.48–83.01%) was achieved. Conclusion A machine learning model using routine laboratory parameters can detect atypical and asymptomatic presentations of Covid-19 and might be an adjunct to existing screening measures.

During the COVID-19 pandemic, several factors, such as improved hand hygiene, social distancing, and restricted hospital referral, may have had an influence on the epidemiology ofThe annual CDI incidence rate of nine hospitals participating in the Dutch sentinel CI surveillance with complete data was compared between 2020 and the previous five surveillance years. Trends in characteristics of hospitalised CDI patients in 21-24 participating hospitals were compared between the first (March 13-May 12, 2020) or second Dutch COVID-19 wave (September 17, 2020-January 1, 2021) and the same calendar periods in 2015 through 2019. All analyses were adjusted for trend changes over time.The annual CDI incidence rate in 2020 was lower compared to previous years. During the second wave, the percentage of CDI patients with severe CDI was higher compared to earlier (25·8% in 2020 vs 17·9% in 2015-2019 (RR 1·6; 95%CI 1·1-2·3)). After adjustment for delayedThis study shows that a higher percentage of severe CDI cases was observed during the second COVID-19 wave. This may partially be caused by delayed diagnostics, potentially due to decreased visits to a physician or restricted hospital referral for CO-CDI patients.Dutch ministry of Health.

In endemic settings it is known that natural malaria immunity is gradually acquired following repeated exposures. Here we sought to assess whether similar acquisition of blood-stage malaria immunity would occur following repeated parasite exposure by controlled human malaria infection (CHMI). We report the findings of repeat homologous blood-stageClinicalTrials.gov, identifier NCT03906474, NCT02927145.

There are no licensed vaccines againstWe conducted two Phase I/IIa clinical trials to assess the safety, immunogenicity and efficacy of two vaccines targeting region II ofThirty-two volunteers were enrolled and vaccinated (n=16 for each vaccine). No safety concerns were identified. PvDBPII/Matrix-M™, given in the delayed dosing regimen, elicited the highest antibody responses and reduced the mean PMR following CHMI by 51% (range 36-66%; n=6) compared to unvaccinated controls (n=13). No other vaccine or regimen impacted parasite growth.Vaccination of malaria-naïve adults with a delayed booster regimen of PvDBPII/ Matrix-M™ significantly reduces the growth of blood-stage

Cloud-based Applications are suitable tools in the E-learning system, and they bring the most facilities in E-learning due to availability and on-demand self-services. During Covid-19 lockdown, while presential learning stopped and E-learning started. The Ministry of Higher Education has begun E-learning in public and private universities. Therefore, Afghanistan’s universities used Cloud-based Apps in E-learning, but these Apps are not identifying and ranking in the E-learning of Afghanistan. This study aims to identify and rank the Cloud-based Apps in terms of usefulness and effectiveness in E-learning using state-of-the-art, and its usage in public universities of Afghanistan. To get an accurate answer to the defined questions, mixed research was used. Therefore, an online questionnaire was distributed randomly to six public universities in six different parts of Afghanistan. The respondents were divided into two categories (lecturers and students) and asked to respond to Cloud-based Apps usage and effectiveness in E-learning. The responses were analySed using Chi-Square Tests. The result showed the most, low, and lowest useful Cloud-based Apps in E-learning. In conclusion, the research finding showed that Google Classroom was the most LMS, WhatsApp was the most mobile instant messaging, Gmail was the most E-mail server and Skype was the most video conferencing Cloud-based Apps in E-learning in most public universities of Afghanistan.

In this paper, an adaptive design methodology for synthesizing a harmonic free N-path filter with reduced frequency folding is presented. System level analysis of proposed architecture shows that by adding a few extra paths with proper weights to a conventional N-path filter, several characteristics such as harmonic rejection, power reduction, foldback elimination and spectrum shaping can be achieved. The designed filter is reconfigurable to be a band-pass filter (BPF) or a band-reject filter (notch), based on the requirements. By using the nth harmonic of Local Oscillator (LO) signal, instead of the fundamental harmonic, the required input clock frequency in N-phase clock generator is reduced by a factor of ${n}$ . As a proof of concept, a 0.1-5 GHz RF filter with 75 dB and 82 dB harmonic rejection at 3rd and 5th order harmonics respectively is analyzed and simulated using MATLAB and Cadence Spectre-RF. Post-layout simulations are performed using CMOS 180 nm technology with 1.8 V supply voltage. The total power consumption of the chip is less than 8.5 mW while occupying a silicon area of 0.2 mm2. Furthermore, Noise Figure (NF) of the circuit is shown to be between 3.5 and 4.7 dB and its out-of-band IIP3 is +6 dBm.

The development of cost-effective and long-life rechargeable aluminium ion batteries (AIBs) shows promising prospects for sustainable energy storage applications. Here, we report a heteroatom π-conjugated polymer featuring synergistic C=O and C=N active centres as a new cathode material in AIBs using a low-cost AlCl

Mycosporin-like amino acids (MAAs) are a group of UV-absorbing compounds, which can be produced by various organisms such as algae and cyanobacteria, particularly if they survive in highly irradiated environments. In this study, the production of MAAs by two species of

Controlled human malaria infection (CHMI) provides a highly informative means to investigate host-pathogen interactions and enable in vivo proof-of-concept efficacy testing of new drugs and vaccines. However, unlike Plasmodium falciparum, well-characterized P. vivax parasites that are safe and suitable for use in modern CHMI models are limited. Here, two healthy malaria-naïve UK adults with universal donor blood group were safely infected with a clone of P. vivax from Thailand by mosquito-bite CHMI. Parasitemia developed in both volunteers and, prior to treatment, each volunteer donated blood to produce a cryopreserved stabilate of infected red blood cells. Following stringent safety screening, the parasite stabilate from one of these donors ("PvW1") was thawed and used to inoculate six healthy malaria-naïve UK adults by blood-stage CHMI, at three different dilutions. Parasitemia developed in all volunteers, who were then successfully drug treated. PvW1 parasite DNA was isolated and sequenced to produce a high quality genome assembly by using a hybrid assembly method. We analysed leading vaccine candidate antigens and multigene families, including the Vivax interspersed repeat (VIR) genes of which we identified 1145 in the PvW1 genome. Our genomic analysis will guide future assessment of candidate vaccines and drugs, as well as experimental medicine studies.

Clostridioides difficileis the most common cause of antibiotic-associated gastrointestinal infections. Capillary-electrophoresis (CE)-PCR ribotyping is currently the gold standard forC. difficiletyping but lacks discriminatory power to study transmission and outbreaks in detail. New molecular methods have the capacity to differentiate better, but backward compatibility with CE-PCR ribotyping must be assessed. Using a well-characterized collection of diverse strains (N=630; 100 unique ribotypes [RTs]), we aimed to investigate PCR ribotyping prediction from core genome multilocus sequence typing (cgMLST). Additionally, we compared the discriminatory power of cgMLST (SeqSphere & EnteroBase) and whole genome MLST (wgMLST) (EnteroBase) with single nucleotide polymorphism (SNP) analysis). A unique cgMLST profile (>6 allele differences) was observed in 82/100 ribotypes, indicating sufficient backward compatibility. Intra-RT allele difference varied per ribotype and MLST clade. Application of cg/wgMLST and SNP analysis in two outbreak settings with ribotypes RT078 and RT181 (known with a low intra-ribotype allele difference) showed no distinction between outbreak- and non-outbreak strains, in contrast to wgMLST and SNP analysis. We conclude that cgMLST has the potential to be an alternative to CE-PCR ribotyping. The method is reproducible, easy to standardize and offers higher discrimination. However, in some ribotype complexes adjusted cut-off thresholds and epidemiological data are necessary to recognize outbreaks. We propose to decrease the current threshold of 6 to 3 alleles to better identify outbreaks.