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1.
Braz. j. biol ; 84: e254487, 2024. tab, ilus
Article in English | LILACS, VETINDEX | ID: biblio-1364508

ABSTRACT

Biological samples obtained from a small temporary pond of northern Colombia yielded the first record Coronatella undata Sousa, Elmoor-Loureiro and Santos, 2015 and of the male of C. monacantha (Sars, 1901) for Colombia. In this study, the morphology of female of Coronatella undata and female and male of C. monacantha was described and compared to other species within the genus. C. undata was originally described from Brazil and, among the species of the Coronatella monacantha complex, seems to be closely related to C. acuticostata (Sars, 1903). C. undata shows some similarities with C. monacantha, but it can be identified by important diagnostic characters such as: 1) posterior-ventral corner of valve with two denticles, 2) seta on exopodite of trunk limb II rudimentary, 3) filter comb of trunk limb II with six setae, 4) ODL seta of trunk limb I shorter than longest seta of IDL. C. monacantha is the most reported species in the Neotropical region and the male most resemble C. paulinae Sousa, Elmoor-Loureiro & Santos, 2015 in relation to (i), length/wide of postabdomen ratio (ii) basal spine almost straight and (iii)) long basal spine reaching the mid-length of basal spine. However, they can be separated by (i) number of lateral seta on the antennule, (ii) postanal angle, (iii) position of gonopore (iv) presence of a denticle on posterior-ventral corner of valve.


Amostras biológicas obtidas de uma pequena lagoa temporária do norte da Colômbia proporcionaram o primeiro registro de Coronatella undata Sousa, Elmoor-Loureiro e Santos, 2015 e do macho de Coronatella monacantha (Sars, 1901) na Colômbia. Neste estudo, foi descrita a morfologia de fêmeas de C. undata e de fêmeas e machos de C. monacantha, comparando-a com outras espécies do gênero. Coronatella undata foi descrita originalmente no Brasil e, entre as espécies do complexo C. monacantha, parece estar intimamente relacionada com Coronatella acuticostata (Sars, 1903). Coronatella undata apresenta algumas semelhanças com C. monacantha, mas pode ser identificada por seus principais caracteres, tais como: 1) ângulo posterior ventral da valva com dois dentículos; 2) cerda rudimentar no exopodito do ramo do tronco II; 3) filtro da gnatobase do apêndice torácico II com seis cerdas; 4) cerda ODL do membro do tronco I mais curta que a cerda mais longa do IDL. Coronatella monacantha é a espécie mais relatada na região neotropical, e o macho se assemelha mais a Coronatella paulinae Sousa, Elmoor-Loureiro & Santos em relação à/ao: (i) razão comprimento / largura do pós-abdômen, (ii) espinho basal quase reto e (iii) espinho basal longo com a metade do comprimento do espinho basal. No entanto, eles podem ser separados pelo/pela: (i) número de cerdas laterais na antênula, (ii) ângulo postanal, (iii) posição do gonóporo e (iv) presença de dentículo no canto ventral posterior da valva.


Subject(s)
Animals , Ponds , Records , Crustacea , Colombia
2.
Biomed Signal Process Control ; 79: 104159, 2023 Jan.
Article in English | MEDLINE | ID: mdl-36119901

ABSTRACT

Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors.

3.
J Comput Appl Math ; 419: 114624, 2023 Feb.
Article in English | MEDLINE | ID: mdl-35966169

ABSTRACT

Within two years, the world has experienced a pandemic phenomenon that changed almost everything in the macro and micro-environment; the economy, the community's social life, education, and many other fields. Governments started to collaborate with health institutions and the WHO to control the pandemic spread, followed by many regulations such as wearing masks, maintaining social distance, and home office work. While the virus has a high transmission rate and shows many mutated forms, another discussion appeared in the community: the fear of getting infected and the side effects of the produced vaccines. The community started to face uncertain information spread through some networks keeping the discussions of side effects on-trend. However, this pollution spread confused the community more and activated multi fears related to the virus and the vaccines. This paper establishes a mathematical model of COVID-19, including the community's fear of getting infected and the possible side effects of the vaccines. These fears appeared from uncertain information spread through some social sources. Our primary target is to show the psychological effect on the community during the pandemic stage. The theoretical study contains the existence and uniqueness of the IVP and, after that, the local stability analysis of both equilibrium points, the disease-free and the positive equilibrium point. Finally, we show the global asymptotic stability holds under specific conditions using a suitable Lyapunov function. In the end, we conclude our theoretical findings with some simulations.

4.
Pers Individ Dif ; 200: 111799, 2023 Jan.
Article in English | MEDLINE | ID: mdl-35789922

ABSTRACT

What factors influence how people perceive the risk of getting COVID-19? Extending beyond features of general health conditions, media coverage, and genetic susceptibility to disease, the present research investigates whether the immediacy of experience with temperature, a subtle yet pervasive environmental factor, can affect people's estimation of contagion probability. According to the attribute substitution model, people may rely on the visceral experience of coldness, a far easier quantity to evaluate, to estimate the contagion probability of the new coronavirus disease. Study 1 found that Chinese university students who perceived the indoor temperature to be lower believed that the coronavirus was more infectious. To provide causal evidence for the effect, Study 2 randomly assigned participants to different conditions. The results showed that participants in the cold condition reported a higher likelihood of contracting the coronavirus than participants in the control condition. Overall, these findings are consistent with the attribute substitution model: people tend to recruit simpler and more accessible information (e.g., local temperature) in place of more diagnostic but less tangible information (e.g., scientific data) in assessing the risk of disease transmission. Theoretical contributions and the significance of this research for policy makers are discussed.

5.
Omega ; 114: 102727, 2023 Jan.
Article in English | MEDLINE | ID: mdl-35966621

ABSTRACT

This paper analyzes an incentive contract for new vaccine research and development (R&D) under pandemic situations such as COVID-19, considering the R&D contract's adaptability to the pandemic. We study how the public sector (government) designs the adaptive R&D contract and offers it to pharmaceutical enterprises. An agency-theoretic model is employed to explore the contract whose terms are an upfront grant as a fixed fee and a sales tax credit as an incentive tool, examining how the values of related parameters affect contract term determinations. We found that the adaptability factor derived from urgent policies such as emergency use authorization (EUA) as well as tax credits, can be utilized as practical incentive tools that lead vaccine developers to increase their effort levels for R&D success. We also found that public-private state-emergency contracts may not follow the conventional wisdom. Counterintuitively, dependency on tax credits (incentive part) decrease as the client's degree of risk averseness increases in the emergency contract.

6.
Biomed Signal Process Control ; 79: 104100, 2023 Jan.
Article in English | MEDLINE | ID: mdl-36042791

ABSTRACT

Decreasing the COVID spread of infection among patients at physical isolation hospitals during the coronavirus pandemic was the main aim of all governments in the world. It was required to increase isolation places in the hospital's rules to prevent the spread of infection. To deal with influxes of infected COVID-19 patients' quick solutions must be explored. The presented paper studies converting natural rooms in hospitals into isolation sections and constructing new isolation cabinets using prefabricated components as alternative and quick solutions. Artificial Intelligence (AI) helps in the selection and making of a decision on which type of solution will be used. A Multi-Layer Perceptron Neural Network (MLPNN) model is a type of artificial intelligence technique used to design and implement on time, cost, available facilities, area, and spaces as input parameters. The MLPNN result decided to select a prefabricated approach since it saves 43% of the time while the cost was the same for the two approaches. Forty-five hospitals have implemented a prefabricated solution which gave excellent results in a short period of time at reduced costs based on found facilities and spaces. Prefabricated solutions provide a shorter time and lower cost by 43% and 78% in average values respectively as compared to retrofitting existing natural ventilation rooms.

10.
Omega ; 114: 102750, 2023 Jan.
Article in English | MEDLINE | ID: mdl-36090537

ABSTRACT

The COVID-19 pandemic - as a massive disruption - has significantly increased the need for medical services putting an unprecedented strain on health systems. This study presents a robust location-allocation model under uncertainty to increase the resiliency of health systems by applying alternative resources, such as backup and field hospitals and student nurses. A multi-objective optimization model is developed to minimize the system's costs and maximize the satisfaction rate among medical staff and COVID-19 patients. A robust approach is provided to face the data uncertainty, and a new mathematical model is extended to linearize a nonlinear constraint. The ICU beds, ward beds, ventilators, and nurses are considered the four main capacity limitations of hospitals for admitting different types of COVID-19 patients. The sensitivity analysis is performed on a real-world case study to investigate the applicability of the proposed model. The results demonstrate the contribution of student nurses and backup and field hospitals in treating COVID-19 patients and provide more flexible decisions with lower risks in the system by managing the fluctuations in both the number of patients and available nurses. The results showed that a reduction in the number of available nurses incurs higher costs for the system and lower satisfaction among patients and nurses. Moreover, the backup and field hospitals and the medical staff elevated the system's resiliency. By allocating backup hospitals to COVID-19 patients, only 37% of severe patients were lost, and this rate fell to less than 5% after establishing field hospitals. Moreover, medical students and field hospitals curbed the costs and increased the satisfaction rate of nurses by 75%. Finally, the system was protected from failure by increasing the conservatism level. With a 2% growth in the price of robustness, the system saved 13%.

11.
Comput Human Behav ; 138: 107479, 2023 Jan.
Article in English | MEDLINE | ID: mdl-36091923

ABSTRACT

Taking advantage of 3 million English-language posts by Facebook public pages, this study answers the following questions: How did the amount of COVID-19 vaccine-related messages evolve? How did the moral expressions in the messages differ among sources? How did both the sources and the five moral foundations in posts influence the number of likes to posts, after controlling for the public page's features (e.g., age, followers)? Our research findings suggest that moral expression is prevalent in the COVID-19 vaccination posts, surpassing nonmoral content. Media sources, despite the high volume of posts, on average elicited fewer likes than all other sources. Although care and fairness were the two most used moral foundations, they were negatively related to likes. In contrast, the least used two moral values of authority and sanctity were positively related to likes. We conclude with a discussion of theoretical contributions and a recommendation of possible interventions.

12.
Arch Argent Pediatr ; 121(1): e202202595, 2023 02 01.
Article in English, Spanish | MEDLINE | ID: mdl-35984671

ABSTRACT

Introduction. In Argentina, health care workers have been the first ones to receive the COVID-19 vaccine, but there are still few data on the production of anti-S IgG antibodies. Objectives. To assess specific IgG against the SARS-CoV-2 spike protein (anti-S IgG) after the vaccination of health care workers from a children's hospital. To explore the association between the presence of these antibodies, age, and history of prior infection. Population and methods. Cross-sectional study in 193 workers who received both doses of the two- component Sputnik V vaccine. The anti-S IgG antibody titer was measured and age, history of prior SARS-CoV-2 infection, and date of vaccination were recorded. Results. Anti-S IgG antibodies were produced in 98.6% of the subjects. The titer was higher in those with prior infection (p < 0.001), but no relationship was established with subjects' age. Conclusion. We provide data on post-vaccination production of IgG anti-S antibodies among health care workers from a children's hospital and explore some predictors.


Introducción. En Argentina, el personal de salud ha sido el primero en vacunarse contra COVID-19, pero todavía existen pocos datos sobre la producción de anticuerpos IgG anti-S. Objetivos. Evaluar IgG específica contra glicoproteína spike del SARS-CoV-2 (IgG anti-S) posvacunación en personal de un hospital pediátrico. Explorar la asociación entre presencia de dichos anticuerpos, edad y antecedente de infección previa. Población y métodos. Estudio transversal que incluyó 193 trabajadores vacunados con los dos componentes de la vacuna Sputnik V. Se pesquisó el título de IgG anti-S y se registraron edad, antecedente de infección previa por SARS-CoV-2 y fecha de la vacunación. Resultados. El 98,6 % de los sujetos generó IgG anti-S. El título fue mayor en quienes habían cursado infección previamente (p <0,001), pero no hubo relación con la edad de los sujetos. Conclusión. Aportamos datos de generación de anticuerpos IgG anti-S posvacunación en personal de salud de un hospital pediátrico y exploramos algunos predictores.


Subject(s)
COVID-19 , Health Personnel , SARS-CoV-2 , Antibodies, Viral , COVID-19/immunology , COVID-19 Vaccines , Cross-Sectional Studies , Hospitals, Pediatric , Humans , Immunoglobulin G , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus
13.
Biomed Signal Process Control ; 79: 104099, 2023 Jan.
Article in English | MEDLINE | ID: mdl-35996574

ABSTRACT

At the end of 2019, a novel coronavirus, COVID-19, was ravaging the world, wreaking havoc on public health and the global economy. Today, although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for COVID-19 clinical diagnosis, it is a time-consuming and labor-intensive procedure. Simultaneously, an increasing number of individuals are seeking for better alternatives to RT-PCR. As a result, automated identification of COVID-19 lung infection in computed tomography (CT) images may help traditional diagnostic approaches in determining the severity of the disease. Unfortunately, a shortage of labeled training sets makes using AI deep learning algorithms to accurately segregate diseased regions in CT scan challenging. We design a simple and effective weakly supervised learning strategy for COVID-19 CT image segmentation to overcome the segmentation issue in the absence of adequate labeled data, namely LLC-Net. Unlike others weakly supervised work that uses a complex training procedure, our LLC-Net is relatively easy and repeatable. We propose a Local Self-Coherence Mechanism to accomplish label propagation based on lesion area labeling characteristics for weak labels that cannot offer comprehensive lesion areas, hence forecasting a more complete lesion area. Secondly, when the COVID-19 training samples are insufficient, the Scale Transform for Self-Correlation is designed to optimize the robustness of the model to ensure that the CT images are consistent in the prediction results from different angles. Finally, in order to constrain the segmentation accuracy of the lesion area, the Lesion Infection Edge Attention Module is used to improve the information expression ability of edge modeling. Experiments on public datasets demonstrate that our method is more effective than other weakly supervised methods and achieves a new state-of-the-art performance.

14.
Expert Syst Appl ; 211: 118545, 2023 Jan.
Article in English | MEDLINE | ID: mdl-35996556

ABSTRACT

The outbreak of COVID-19 has exposed the privacy of positive patients to the public, which will lead to violations of users' rights and even threaten their lives. A privacy-preserving scheme involving virus-infected positive patients is proposed by us. The traditional ciphertext policy attribute-based encryption (CP-ABE) has the features of enhanced plaintext security and fine-grained access control. However, the encryption process requires the high computational performance of the device, which puts a high strain on resource-limited devices. After semi-honest users successfully decrypt the data, they will get the real private data, which will cause serious privacy leakage problems. Traditional cloud-based data management architectures are extremely vulnerable in the face of various cyberattacks. To address the above challenges, a verifiable ABE scheme based on blockchain and local differential privacy is proposed, using LDP to perturb the original data locally to a certain extent to resist collusion attacks, outsourcing encryption and decryption to corresponding service providers to reduce the pressure on mobile terminals, and deploying smart contracts in combination with blockchain for fair execution by all parties to solve the problem of returning wrong search results in a semi-honest cloud server. Detailed security proofs are performed through the defined security goals, which shows that the proposed scheme is indeed privacy-protective. The experimental results show that the scheme is optimized in terms of data accuracy, computational overhead, storage performance, and fairness. In terms of efficiency, it greatly reduces the local load, enhances personal privacy protection, and has high practicality as well as reliability. As far as we know, it is the first case of applying the combination of LDP technology and blockchain to a tracing system, which not only mitigates poisoning attacks on user data, but also improves the accuracy of the data, thus making it easier to identify infected contacts and making a useful contribution to health prevention and control efforts.

15.
Eur J Oper Res ; 304(1): 1-8, 2023 Jan 01.
Article in English | MEDLINE | ID: mdl-35874494

ABSTRACT

In this special issue, 23 research papers are published focusing on COVID-19 and operational research solution techniques. First, we detail the process from advertising the call for papers to the point where the best papers are accepted. Then, we provide a summary of each paper focusing on applications, solution techniques and insights for practitioners and policy makers. To provide a holistic view for readers, we have clustered the papers into different groups: transmission, propagation and forecasting, non-pharmaceutical intervention, healthcare network configuration, healthcare resource allocation, hospital operations, vaccine and testing kits, and production and manufacturing. Then, we introduce other possible subjects that can be considered for future research.

16.
Fuel (Lond) ; 331: 125720, 2023 Jan 01.
Article in English | MEDLINE | ID: mdl-36033729

ABSTRACT

Globally, the demand for masks has increased due to the COVID-19 pandemic, resulting in 490,201 tons of waste masks disposed of per month. Since masks are used in places with a high risk of virus infection, waste masks retain the risk of virus contamination. In this study, a 1 kg/h lab-scale (diameter: 0.114 m, height: 1 m) bubbling fluidized bed gasifier was used for steam gasification (temperature: 800 °C, steam/carbon (S/C) ratio: 1.5) of waste masks. The use of a downstream reactor with activated carbon (AC) for tar cracking and the enhancement of hydrogen production was examined. Steam gasification with AC produces syngas with H2, CO, CH4, and CO2 content of 38.89, 6.40, 21.69, and 7.34 vol%, respectively. The lower heating value of the product gas was 29.66 MJ/Nm3 and the cold gas efficiency was 74.55 %. This study showed that steam gasification can be used for the utilization of waste masks and the production of hydrogen-rich gas for further applications.

17.
Nonlinear Anal Real World Appl ; 69: 103738, 2023 Feb.
Article in English | MEDLINE | ID: mdl-36042914

ABSTRACT

Contagious pathogens, such as influenza and COVID-19, are known to be represented by multiple genetic strains. Different genetic strains may have different characteristics, such as spreading more easily, causing more severe diseases, or even evading the immune response of the host. These facts complicate our ability to combat these diseases. There are many ways to prevent the spread of infectious diseases, and vaccination is the most effective. Thus, studying the impact of vaccines on the dynamics of a multi-strain model is crucial. Moreover, the notion of complex networks is commonly used to describe the social contacts that should be of particular concern in epidemic dynamics. In this paper, we investigate a two-strain epidemic model using a single-strain vaccine in complex networks. We first derive two threshold quantities, R 1 and R 2 , for each strain. Then, by using the basic tools for stability analysis in dynamical systems (i.e., Lyapunov function method and LaSalle's invariance principle), we prove that if R 1 < 1 and R 2 < 1 , then the disease-free equilibrium is globally asymptotically stable in the two-strain model. This means that the disease will die out. Furthermore, the global stability of each strain dominance equilibrium is established by introducing further critical values. Under these stability conditions, we can determine which strain will survive. Particularly, we find that the two strains can coexist under certain condition; thus, a coexistence equilibrium exists. Moreover, as long as the equilibrium exists, it is globally stable. Numerical simulations are conducted to validate the theoretical results.

18.
Math Comput Simul ; 204: 302-336, 2023 Feb.
Article in English | MEDLINE | ID: mdl-36060108

ABSTRACT

Several mathematical models have been developed to investigate the dynamics SARS-CoV-2 and its different variants. Most of the multi-strain SARS-CoV-2 models do not capture an important and more realistic feature of such models known as randomness. As the dynamical behavior of most epidemics, especially SARS-CoV-2, is unarguably influenced by several random factors, it is appropriate to consider a stochastic vaccination co-infection model for two strains of SARS-CoV-2. In this work, a new stochastic model for two variants of SARS-CoV-2 is presented. The conditions of existence and the uniqueness of a unique global solution of the stochastic model are derived. Constructing an appropriate Lyapunov function, the conditions for the stochastic system to fluctuate around endemic equilibrium of the deterministic system are derived. Stationary distribution and ergodicity for the new co-infection model are also studied. Numerical simulations are carried out to validate theoretical results. It is observed that when the white noise intensities are larger than certain thresholds and the associated stochastic reproduction numbers are less than unity, both strains die out and go into extinction with unit probability. More-over, it is observed that, for weak white noise intensities, the solution of the stochastic system fluctuates around the endemic equilibrium (EE) of the deterministic model. Frequency distributions are also studied to show random fluctuations due to stochastic white noise intensities. The results presented herein also reveal the impact of vaccination in reducing the co-circulation of SARS-CoV-2 variants within a given population.

19.
J Comput Appl Math ; 419: 114738, 2023 Feb.
Article in English | MEDLINE | ID: mdl-36000087

ABSTRACT

COVID-19 is a drastic air-way tract infection that set off a global pandemic recently. Most infected people with mild and moderate symptoms have recovered with naturally acquired immunity. In the interim, the defensive mechanism of vaccines helps to suppress the viral complications of the pathogenic spread. Besides effective vaccination, vaccine breakthrough infections occurred rapidly due to noxious exposure to contagions. This paper proposes a new epidemiological control model in terms of Atangana Baleanu Caputo (ABC) type fractional order differ integrals for the reported cases of COVID-19 outburst. The qualitative theoretical and numerical analysis of the aforesaid mathematical model in terms of three compartments namely susceptible, vaccinated, and infected population are exhibited through non-linear functional analysis. The hysteresis kernel involved in AB integral inherits the long-term memory of the dynamical trajectory of the epidemics. Hyer-Ulam's stability of the system is studied by the dichotomy operator. The most effective approximate solution is derived by numerical interpolation to our proposed model. An extensive analysis of the vigorous vaccination and the proportion of vaccinated individuals are explored through graphical simulations. The efficacious enforcement of this vaccination control mechanism will mitigate the contagious spread and severity.

20.
Math Comput Simul ; 203: 741-766, 2023 Jan.
Article in English | MEDLINE | ID: mdl-35911951

ABSTRACT

The study explores the dynamics of a COVID-19 epidemic in multiple susceptible populations, including the various stages of vaccination administration. In the model, there are eight human compartments: completely susceptible; susceptible with dose-1 vaccination; susceptible with dose-2 vaccination; susceptible with booster dose vaccination; exposed; infected with and without symptoms, and recovered compartments. The biological feasibility of the model is analysed. The threshold value, R 0 , is derived using the next-generation matrix. The stability analysis of the equilibrium points was performed locally and globally using the threshold parameter of the model. The conditions determining disease persistence is obtained. The model is subjected to sensitivity analysis, and the most sensitive parameters are identified. Also, MATLAB is used to verify the mathematical outcomes of the system's dynamic behaviour and suggests that necessary steps should be taken to keep the spread of the omicron variant infectious disease under control. The findings of this study could aid health officials in their efforts to combat the spread of COVID-19.

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