Positive Number Research Articles

Malicious Code Detection Using Machine Learning

Abstract: Malware, derived from "Malicious software," is a comprehensive term encompassing any software intentionally crafted to disrupt, damage, or illicitly access computer systems. It's critical to determine whether a file includes malware. The increase in malware is causing a lot of problems for businesses, including data loss and other problems. Malware can swiftly inflict significant damage to a system by slowing it down and encrypting a sizable amount of data on a personal computer. This suggests that lowering the number of false positives is important. A comprehensive description of the adaptable framework for machine learning algorithms may be found in this study. It is possible to detect malware with these methods. The justification for this is that these algorithms simplify the process of distinguishing between files that are infected with malware and those that are not. Modern antivirus and anti-malware tools offer effective protection against various malware attacks. Nevertheless, due to the constantly changing landscape of malicious activities, it is imperative to curate an up-to-date database of previous malware instances. This repository serves as a valuable resource for anticipating the characteristics of future attacks and facilitating swift responses. Different machine learning methods, including decision trees and random forests, are used in our malware detection process. The method with the highest accuracy is chosen, giving the system an excellent detection ratio. Additionally, the confusion matrix is used to calculate the false positive and false negative rates, which is how the system's performance is determined.

Analyzing Breakthrough Infections in a Tertiary Care Hospital: A Retrospective Study Utilizing RAT-Based Screening

Prompt diagnosis of coronavirus disease 2019 (COVID-19) is requisite due to the upsurge of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) cases globally. Real-time reverse-transcription polymerase chain reaction (rRT-PCR) remains as the gold standard for the diagnosis of SARS-CoV-2 due to its higher sensitivity and specificity. Regardless, due to its longer turnaround time and expertise, bed side testing rapid antigen based tests were developed in order for portable, rapid diagnosis of the disease. The present study delineates the utility of Rapid Antigen Testing (RAT) screening among symptomatic and asymptomatic patients in a tertiary care hospital. This is a retrospective data collection study from Indian Council of Medical Research (ICMR) portal by exporting the excel sheet from March 2021 to March 2022 who undergone COVID testing in a tertiary care diagnostics facility. The demographic and vaccination details of the individuals were extracted using ICMR Sample referral form (SRF) from all patients who are tested for RAT. Of the total 9898 patients tested for RAT, 392 and 9506 were vaccinated and unvaccinated, respectively. Among them, number of positives was 3% and 15% respectively. The odds ratio showed that the vaccinated individuals had 81.4% reduced risk of getting infection compared to unvaccinated individuals (OR-0.186; p=0.001). A significant difference was observed between the type of vaccine used and COVID infection, majority of them had taken Covishield (83.5%) followed by Covaxin (16%) [p=0.0005]. Males showed a marginally significant p trend with COVID infectivity (OR=1.114, p=0.073) too. RAT is useful as a screening test for rapid diagnosis of infection in patients who are more prone to spread the disease. As RAT is cost effective and had short-turnaround time, it can be recommended in areas with upsurge in SARS-CoV-2 cases to administer isolation and infection control measures.

Information criteria for structured parameter selection in high-dimensional tree and graph models

Parameter selection in high-dimensional models is typically fine-tuned in a way that keeps the (relative) number of false positives under control. This is because otherwise the few true positives may be dominated by the many possible false positives. This happens, for instance, when the selection follows from a naive optimisation of an information criterion, such as AIC or Mallows's Cp. It can be argued that the overestimation of the model comes from the optimisation process itself changing the statistics of the selected variables, in a way that the information criterion no longer reflects the true divergence between the selected model and the data generating process. Using lasso, the overestimation can also be linked to the shrinkage estimator, which makes the selection too tolerant of false positive selections. For these reasons, this paper works on refined information criteria, carefully balancing false positives and false negatives, for use with estimators without shrinkage. In particular, the paper develops corrected Mallows's Cp criteria for structured selection in trees and graphical models.

Improving the second-tier classification of methylmalonic acidemia patients using a machinelearning ensemble method.

Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive (TP)]. In this work, our goal was to refine a classification model that can minimize the number of false positives, currently an unmet need in the upstream diagnostics of MMA. We developed machinelearning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction. We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients, followed by additional ratio feature construction. Feature selection strategies (selection by filter, recursive feature elimination, and learned vector quantization) were used to determine the input set for evaluating the performance of 14 classification models to identify a candidate model set for an ensemble model development. Our work identified computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity. The best results [area under the receiver operating characteristic curve (AUROC) of 97%, sensitivity of 92%, and specificity of 95%] were obtained utilizing an ensemble of the algorithms random forest, C5.0, sparse linear discriminant analysis, and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor. The model achieved a good performance trade-off for a screening application with 6% false-positive rate (FPR) at 95% sensitivity, 35% FPR at 99% sensitivity, and 39% FPR at 100% sensitivity. The classification results and approach of this research can be utilized by clinicians globally, to improve the overall discovery of MMA in pediatric patients. The improved method, when adjusted to 100% precision, can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.

Monitoring of Remotely Reprogrammable Implantable Loop Recorders With Algorithms to Reduce False-Positive Alerts.

Implantable loop recorders (ILRs) are increasingly placed for arrhythmia detection. However, historically, ≈75% of ILR alerts are false positives, requiring significant time and effort for adjudication. The LINQII and LUX-Dx are remotely reprogrammable ILRs with dual-stage algorithms using artificial intelligence to reduce false positives, but their utility in routine clinical practice has not been studied. We identified patients with the LINQII and LUX-Dx who were monitored by the Veterans Affairs National Cardiac Device Surveillance Program between March and June 2022. ILR programming was customized on the basis of implant indication. All alerts and every 90-day scheduled transmissions were manually reviewed. ILRs were remotely reprogrammed, as appropriate, after false-positive alerts or 2 consecutive same-type alerts, unless there was ongoing clinical need for that alert. Outcomes were total number of transmissions and false positives. We performed medical record review to determine if patients experienced any adverse clinical events, including hospitalization and mortality. Among 117 LINQII patients, there were 239 total alerts, 43 (18.0%) of which were false positives. Among 105 LUX-Dx patients, there were 300 total alerts, 115 (38.3%) of which were false positives. LINQIIs were reprogrammed 22 times, resulting in a decrease in median alerts/day from 0.13 to 0.03. LUX-Dx ILRs were reprogrammed 52 times, resulting in a decrease from 0.15 to 0.01 median alerts/day. There were no adverse clinical events that could have been identified by superior or earlier arrhythmia detection. ILRs with artificial intelligence algorithms and remote reprogramming ability are associated with reduced alert burden because of higher true-positive rates than prior ILRs, without missing potentially consequential arrhythmias.

Analysis of biopsy pathology and risk factors of lymph node metastasis in prostate cancer.

To explore the relationship between biopsy pathology and lymph node metastasis in patients with prostate cancer (PCa), and to identify risk factors of lymph node metastasis (LNM). Patients diagnosed with prostate cancer were respective screened between Jan 2015 and May 2022. Patients diagnosed PCa via 13-core ultrasound-guided biopsies and underwent radical prostatectomy and lymph node dissection were identified. The clinicopathological characteristics of the patients were recorded. Relationships between LNM and non-LNM were analyzed using chi-square and independent samples t-test. Logistic regression model was fitted to analyze the risk factors of lymph node metastases. Two hundreds and fifteen patients were included, sixty-seven patients had lymph node metastasis. Gleason scores in LNM group were higher than that in non-LNM group (8.5 ± 0.9 VS 7.5 ± 1.5, p < 0.001), positive biopsy in non-LNM group was significantly lower than that in LNM group (p < 0.001), Binary logistic regression analysis indicated number of positive biopsy and number of removed lymph nodes increased the risks of LNM (odds ratio, OR = 1.28, 95% confidence interval, CI = 1.16-1.42, p < 0.001; OR = 1.11, 95% CI = 1.06-1.17, p < 0.001; respectively). Number of positive biopsy in internal gland but not external gland was significant associated with LNM (OR = 1.66, 95% CI = 1.34-2.06, p < 0.001; OR = 1.19, 95% CI = 0.88-1.61, p = 0.262; respectively). The patients with lymph nodes dissection more than 13 were about four times more likely to detect lymph node metastasis than those fewer than 13 (OR = 3.92, 95% CI = 2.10-7.33, p < 0.001). The risk of lymph node metastasis increased with the number of positive prostate biopsy cores, and tumors in the internal gland were more likely to cause lymph node metastasis. In addition, lymph node metastasis was more likely to be found when the number of lymph nodes dissection was greater than 13.

Students’ Epistemological Framings When Solving an Area Problem of a Degenerate Triangle: The Influence of Presence and Absence of a Drawing

This study explores the epistemological framings of undergraduate students when solving an area problem of a degenerate triangle, without or with a triangle drawing. Through mixed research with a triangulation design, the resolution processes and responses of students were analyzed. The aim was to analyze how students’ epistemological framing changes during the problem-solving process depending on whether the task contains the drawing of the triangle or not. Quantitative results show significant differences between students who solve the problem without a triangle drawing and those who do. Qualitative results evidence that students who solved the problem with the drawing established an initial epistemological framing that contained an “obvious fact”: the non-zero area of the triangle. They hardly modified this epistemological framing during the solving process, forcing the response to be a positive number. In contrast, students who solved the problem without the drawing easily modified their initial epistemological framing by observing that the area of the triangle was zero. Students’ perceptions of the level of difficulty of the problem are discussed, too.

On Compact Packings of Euclidean Space with Spheres of Finitely Many Sizes

AbstractFor $$d\in {\mathbb {N}}$$ d ∈ N , a compact sphere packing of Euclidean space $${\mathbb {R}}^{d}$$ R d is a set of spheres in $${\mathbb {R}}^{d}$$ R d with disjoint interiors so that the contact hypergraph of the packing is the vertex scheme of a homogeneous simplicial d-complex that covers all of $${\mathbb {R}}^{d}$$ R d . We are motivated by the question: For $$d,n\in {\mathbb {N}}$$ d , n ∈ N with $$d,n\ge 2$$ d , n ≥ 2 , how many configurations of numbers $$0&lt;r_{0}&lt;r_{1}&lt;\cdots &lt;r_{n-1}=1$$ 0 &lt; r 0 &lt; r 1 &lt; ⋯ &lt; r n - 1 = 1 can occur as the radii of spheres in a compact sphere packing of $${\mathbb {R}}^{d}$$ R d wherein there occur exactly n sizes of sphere? We introduce what we call ‘heteroperturbative sets’ of labeled triangulations of unit spheres and we discuss the existence of non-trivial examples of heteroperturbative sets. For a fixed heteroperturbative set, we discuss how a compact sphere packing may be associated to the heteroperturbative set or not. We proceed to show, for $$d,n\in {\mathbb {N}}$$ d , n ∈ N with $$d,n\ge 2$$ d , n ≥ 2 and for a fixed heteroperturbative set, that the collection of all configurations of n distinct positive numbers that can occur as the radii of spheres in a compact packing is finite, when taken over all compact sphere packings of $${\mathbb {R}}^{d}$$ R d which have exactly n sizes of sphere and which are associated to the fixed heteroperturbative set.

Volume of Seifert representations for graph manifolds and their finite covers

AbstractFor any closed orientable 3‐manifold, there is a volume function defined on the space of all Seifert representations of the fundamental group. The maximum absolute value of this function agrees with the Seifert volume of the manifold due to Brooks and Goldman. For any Seifert representation of a graph manifold, the authors establish an effective formula for computing its volume, and obtain restrictions to the representation as analogous to the Milnor–Wood inequality (about transversely projective foliations on Seifert fiber spaces). It is shown that the Seifert volume of any graph manifold is a rational multiple of . Among all finite covers of a given nongeometric graph manifold, the supremum ratio of the Seifert volume over the covering degree can be a positive number, and can be infinite. Examples of both possibilities are discovered, and confirmed with the explicit values determined for the finite ones.

Associations between Verbal Fluency and Asymmetry of White Matter Integrity in the Superior Longitudinal Fasciculus in At-Risk Mental States for Psychosis.

Verbal fluency is one of the most severely impaired components of cognitive function in schizophrenia and is also impaired in at-risk mental states (ARMSs) for psychosis. The aim of this study was to explore the markers of disease progression in subjects with ARMSs by comparing the association between the white matter integrity of the superior longitudinal fasciculus (SLF) and verbal fluency in subjects with ARMSs and healthy control (HC) subjects. The correlations of the fractional anisotropy (FA) values on diffusion tensor imaging (DTI) and the laterality index (LI) values of SLF branches I, II, and III with the verbal fluency performance were analyzed in right-handed subjects with ARMSs (ARMS group; n = 18) and HC subjects (HC group; n = 34) aged 18 to 40 years old. In the HC group compared with the ARMS group, the LI values suggested right lateralization of the SLF II and III. Letter fluency was significantly correlated with the LI of the SLF III in both the ARMS and HC groups. The regression coefficient (β) of this correlation was calculated using the least squares method and yielded a positive number (73.857) in the ARMS group and a negative number (-125.304) in the HC group. The association of the rightward asymmetry of the SLF III with the verbal fluency performance observed in the HC group appeared to be lost in the ARMS group, and this could serve as one of the markers of the pathological progression to psychosis in patients with schizophrenia.

The impact of early childhood education and medical care on children

Causal inference is a statistical approach that aims to understand and quantify the causal relationship between variables, allowing us to determine the impact of one variable on another while accounting for potential confounding factors. In this study, we chose the Infant Health and Development Program (IHDP) dataset to test whether high-quality early childhood education and medical care will enhance their cognitive and academic ability. We use some common and classic algorithms to achieve this study by calculating the CATE (Conditional Average Treatment Effect) of the dataset; if the result is a negative number, that means early treatment doesn’t improve children’s further ability; if the result is a positive number, that means treatment improves it. After fitting the targeted models, BART(Bayesian Additive Regression Trees) and Metalearners, into this dataset, we found out that all the models will get positive CATE, which means that these early treatments positively affect children’s future development. Also, we can judge which model gives us a more accurate result according to the standard variance of TE(treatment effect). The result of this study will provide us with an insightful idea about whether we should give children some treatments regarding education and medical care and which model is more suitable for this casual inference test.

Improvement of crack detectivity for noisy concrete surface by machine learning methods and infrared images

In order to maintain serviceability and reliability of concrete structures, it is essential to assess their condition as concrete structures deteriorate in time. Cracks develop in concrete due to several reasons such as severe loading, environmental effects, chemical effects etc. and cause durability loss in the structure which may also lead to loss of stability. In this research, crack detection is realized by machine learning and an infrared image. The effects of infrared images on crack detection are confirmed by random forest algorithm to select useful explanatory features. Selected features are applied to random forest algorithm and neural network algorithm. Effective filters are selected as a feature selection technique to improve the accuracy. Crack detection is also conducted by U-Net with RGB and infrared images, and the detection characteristics are compared to conventional methods. The performance of two conventional machine learning methods, random forest and neural network, are evaluated based on F1 score and false positives. Applying selected features improves the accuracy of the crack detection from an infrared image. False positives decreased due to monitoring conditions and camera specifications in the infrared image. The most effective image processing filter is the blur filter for each algorithm. Comparing algorithms for crack detection using selected features, different accuracy values are obtained. U-Net enables more accurate crack detection compared to conventional methods. The number of false positives is reduced compare to conventional method. In the detection results by three algorithms, infrared image affects the balance of false negatives and false positives.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease

BackgroundNon-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada.MethodsThe Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians.ResultsA total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters.ConclusionDiabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Self-reporting of hearing loss and tinnitus in the diagnosis of ototoxicity by meglumine antimoniate in patients treated for American tegumentary Leishmaniasis.

American Tegumentary Leishmaniasis (ATL) treatment is based on pentavalent antimonials (Sb5+), but these drugs have been associated to several adverse effects. Hearing loss and tinnitus during treatment with meglumine antimoniate (MA) have already been reported. This study aimed to describe the usefulness of self-reporting of hearing loss and tinnitus in diagnosing MA-induced ototoxicity. A prospective longitudinal study was conducted with 102 patients with parasitological diagnosis of ATL, treated with different MA schemes. The presence of clinical auditory toxicity was defined as the emergence or worsening of self-reporting hearing loss and/or tinnitus during monitoring. Measures of sensitivity, specificity, and the positive and negative predictive value of the patient's self-reporting of hearing loss and tinnitus in relation to the result of the audiometric test (considered the gold standard) were calculated. The age of the evaluated patients ranged from 15 to 81 years, with a median of 41 years, and most were male (73.5%). Seventy-five patients (73.5%) had cutaneous leishmaniasis and 27 (26.5%) mucosal leishmaniasis. Eighty-six patients (84.3%) received intramuscular (IM) treatment and 16 (15.7%) were treated with intralesional MA. During treatment, 18 (17,6%) had tinnitus and 7 (6,9%) had complaint of hearing loss. 53 (52%) patients had cochlear toxicity confirmed by tone threshold audiometry and high frequency audiometry, from which 60% received a dose of 20 mg Sb5+/kg/day (p = 0.015) and 96.2% were treated with IM MA (p = 0.001). Tinnitus has greater specificity and positive predictive value than hearing loss, with a low number of false positives, but with a high false negative value. Although the large number of false negatives suggests that self-report of hearing loss or tinnitus cannot be considered a good screening test for referring the patient to an audiometry, the low number of false positives suggests the need to value the patient's complaint for referral. Otherwise, this study reinforces the importance of audiological monitoring during treatment with MA, especially in those patients with self-reporting of hearing loss or tinnitus when treated with 20 mg Sb5+/kg/day via IM.

Metagenomic profiling of viral and microbial communities from the pox lesions of lumpy skin disease virus and sheeppox virus-infected hosts.

It has been recognized that capripoxvirus infections have a strong cutaneous tropism with the manifestation of skin lesions in the form of nodules and scabs in the respective hosts, followed by necrosis and sloughing off. Considering that the skin microbiota is a complex community of commensal bacteria, fungi and viruses that are influenced by infections leading to pathological states, there is no evidence on how the skin microbiome is affected during capripoxvirus pathogenesis. In this study, shotgun metagenomic sequencing was used to investigate the microbiome in pox lesions from hosts infected with lumpy skin disease virus and sheep pox virus. The analysis revealed a high degree of variability in bacterial community structures across affected skin samples, indicating the importance of specific commensal microorganisms colonizing individual hosts. The most common and abundant bacteria found in scab samples were Fusobacterium necrophorum, Streptococcus dysgalactiae, Helcococcus ovis and Trueperella pyogenes, irrespective of host. Bacterial reads belonging to the genera Moraxella, Mannheimia, Corynebacterium, Staphylococcus and Micrococcus were identified. This study is the first to investigate capripox virus-associated changes in the skin microbiome using whole-genome metagenomic profiling. The findings will provide a basis for further investigation into capripoxvirus pathogenesis. In addition, this study highlights the challenge of selecting an optimal bioinformatics approach for the analysis of metagenomic data in clinical and veterinary practice. For example, direct classification of reads using a kmer-based algorithm resulted in a significant number of systematic false positives, which may be attributed to the peculiarities of the algorithm and database selection. On the contrary, the process of de novo assembly requires a large number of target reads from the symbiotic microbial community. In this work, the obtained sequencing data were processed by three different approaches, including direct classification of reads based on k-mers, mapping of reads to a marker gene database, and de novo assembly and binning of metagenomic contigs. The advantages and disadvantages of these techniques and their practicality in veterinary settings are discussed in relation to the results obtained.

Inertia of Kwong matrices

Let r be any real number and for any n let p_{1},\ldots,p_{n} be distinct positive numbers. A Kwong matrix is the n\times n matrix whose (i,j) entry is (p_{i}^{r}+p_{j}^{r})/(p_{i}+p_{j}). We determine the signatures of eigenvalues of all such matrices. The corresponding problem for the family of Loewner matrices [(p_{i}^{r}-p_{j}^{r})/(p_{i}-p_{j})] has been solved earlier.

A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network

Metabolism results from enzymatic- and non-enzymatic interactions of several molecules, is easily parameterized with the dissociation constant and occurs via biochemical networks. The dissociation constant is an empirically determined parameter and cannot be used directly to investigate in silico models of biochemical networks. Here, we develop and present an algorithm to define, compute and assess the relevance of the probable dissociation constant for every reaction of a biochemical network. The reactants and reactions of this network are modelled by a stoichiometry number matrix. The algorithm computes the null space and then serially generates subspaces by combinatorially summing the spanning vectors that are non-trivial and unique. This is done until the terms of each row either monotonically diverge or form an alternating sequence whose terms can be partitioned into subsets with almost the same number of oppositely signed terms. For a selected null space-generated subspace the algorithm utilizes several statistical and mathematical descriptors to select and bin terms from each row into distinct outcome-specific subsets. The terms of each subset are summed, mapped to the real-valued open interval 0,∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left(0,\\infty \\right)$$\\end{document} and used to populate a reaction-specific outcome vector. The p1-norm for this vector is then the probable dissociation constant for this reaction. These steps are continued until every reaction of a modelled network is unambiguously annotated. The assertions presented are complemented by computational studies of a biochemical network for aerobic glycolysis. The fundamental premise of this work is that every row of a null space-generated subspace is a valid reaction and can therefore, be modelled as a reaction-specific sequence vector with a dimension that corresponds to the cardinality of the subspace after excluding all trivial- and redundant-vectors. A major finding of this study is that the row-wise sum or the sum of the terms contained in each reaction-specific sequence vector is mapped unambiguously to a positive real number. This means that the probable dissociation constants, for all reactions, can be directly computed from the stoichiometry number matrix and are suitable indicators of outcome for every reaction of the modelled biochemical network. Additionally, we find that the unambiguous annotation for a biochemical network will require a minimum number of iterations and will determine computational complexity.

Investigating epidemiological distribution (temporality and intensity) of respiratory pathogens following COVID-19 de-escalation process in Catalonia, September 2016-June 2021: Analysis of regional surveillance data.

Following the low incidence rates of non-SARS-CoV-2 respiratory viruses registered during the strict lockdown enforced in the pandemic, a resurgence of several endemic viruses in Catalonia (Spain) was noted during the early summer of 2021. In this study, we investigated whether the circulation of non-SARS-CoV-2 respiratory viruses in Catalonia, assessed by Microbiological Reporting System of Catalonia (MRSC) and the Epidemiological Surveillance Network of Catalonia, was affected by the strict lockdown measures, as well as, the implication of the Coronavirus Disease 19 (COVID-19) de-escalation process in the late season outbreaks registered during the 2020-2021 season. A retrospective comparison of epidemic patterns in the respiratory viruses' incidence, using regional public health surveillance data from MRSC, was performed between weeks 26/2016 to week 27/2021. Data were expressed as the weekly total number of test positivity for individual viruses. A segmented negative binomial regression model was conducted, with two parameters included (level and trend) for each segment of the time series (2020 pre-lockdown, 2020 post-lockdown and 2021). Results were reported as a unit changed in the strict lockdown. A total of 51588 confirmed cases of the different respiratory viruses were included in the analysis, the majority were influenza cases (63.7%). An immediate reduction in the weekly number of cases was observed in 2020 after the COVID-19 outbreak for human adenovirus virus (HAdV) (β2 = -2.606; P <0.01), human parainfluenza virus (HPIV) (β2 = -3.023; P <0.01), influenza virus (IFV) (β2 = -1.259; P <0.01), but not for respiratory syncytial virus (RSV), where the number of cases remained unchanged. During 2020, a significant negative trend was found for RSV (β3 = -0.170, P <0.01), and a positive trend for HAdV (β3 = 0.075, P <0.01). During 2021, a significant reduction in the weekly number of cases was also observed for all respiratory viruses, and a borderline non-significant reduction for HPIV (β3 = -0.027; P = 0.086). Moreover, significant positive trends were found for each viral pathogen, except for influenza during 2020-2021 season, where cases remained close to zero. The respiratory viruses increased activity and their late season epidemic start particularly affected children under 6 years old. Our data not only provides evidence that occurrence of different respiratory virus infections was affected by the strict lockdown taken against SARS-CoV-2 but it also shows a late resurgence of seasonal respiratory viruses' cases during the 2020-2021 season following the relaxation of COVID-19-targeted non-pharmaceutical interventions.

Bounded distributions place limits on skewness and larger moments.

Distributions of strictly positive numbers are common and can be characterized by standard statistical measures such as mean, standard deviation, and skewness. We demonstrate that for these distributions the skewness D3 is bounded from below by a function of the coefficient of variation (CoV) δ as D3 > δ - 1/δ. The results are extended to any distribution that is bounded with minimum value xmin and/or bounded with maximum value xmax. We build on the results to provide bounds for kurtosis D4, and conjecture analogous bounds exists for higher statistical moments.

Detection and genetic diversity of subgroup K avian leukosis virus in local chicken breeds in Jiangxi from 2021 to 2023.

Avian leukosis virus subgroup K (ALV-K) is a new subgroup of avian leukosis virus (ALV) that was first identified in Chinese native chickens in recent years. To further understand the molecular epidemiology and evolutionary diversity of ALV-K, this study investigated the molecular epidemiology of 73,664 chicken plasma samples collected from Jiangxi native chicken flocks. The results showed that ALV-J was the most predominant ALV subtype in Jiangxi native chickens, with a high positivity rate of 4.34%. From 2021 to 2023, there was a gradual upward trend in the proportion of positive numbers of ALV-K among ALV-positive samples, and there was a trend of outbreaks. ALV-J and ALV-K were the main co-infection patterns. Genetic evolutionary analysis based on ALV-K gp85 gene showed that the isolated ALV-K in this study were distributed in various branches of the evolutionary tree with genetic diversity. The homology results showed that the amino acid homology of the isolated ALV-K gp85 gene ranged from 33.9 to 88.1% with the reference strains of subtypes A, B, C, D, E, and J, and from 91.9 to 100% with the other ALV-K reference strains. Multiple mutations were present in the ALV-K gp85, and especially significant mutations were found in the highly variable region hr2. The results of ALV-K replication efficiency showed that the replication efficiency of ALV-K was significantly lower than that of ALV-J. These results enriched the genome sequence data of ALV-K in Chinese geoducks, and laid the foundation for further research on the pathogenesis and prevention of ALV-K.