•Mathematical and computational models are used to predict cases of COVID-19 in Mexico.•The data is obtained through the Daily Technical Report issued by the Mexican Ministry of Health.•Gompertz, ...Logistic and Artificial Neural Network perform the modeling of the cases confirmed by COVID-19 with an R2>0.999.•Logistic, Gompertz and inverse Artificial Neural Network predicts the maximum number of new daily cases on May 8th, June 25th and May12th, 2020, respectively.•The Gompertz, Logistic and inverse Artificial Neural Network models predict different number of cases of COVID-19 at the end of the epidemic.
This work presents the modeling and prediction of cases of COVID-19 infection in Mexico through mathematical and computational models using only the confirmed cases provided by the daily technical report COVID-19 MEXICO until May 8th. The mathematical models: Gompertz and Logistic, as well as the computational model: Artificial Neural Network were applied to carry out the modeling of the number of cases of COVID-19 infection from February 27th to May 8th. The results show a good fit between the observed data and those obtained by the Gompertz, Logistic and Artificial Neural Networks models with an R2 of 0.9998, 0.9996, 0.9999, respectively. The same mathematical models and inverse Artificial Neural Network were applied to predict the number of cases of COVID-19 infection from May 9th to 16th in order to analyze tendencies and extrapolate the projection until the end of the epidemic. The Gompertz model predicts a total of 47,576 cases, the Logistic model a total of 42,131 cases, and the inverse artificial neural network model a total of 44,245 as of May 16th. Finally, to predict the total number of COVID-19 infected until the end of the epidemic, the Gompertz, Logistic and inverse Artificial Neural Network model were used, predicting 469,917, 59,470 and 70,714 cases, respectively.
The tools available for the diagnosis and control of brucellosis and their applicability in the context of SSA are presented and gaps identified.
Display omitted
•Bacteriological and serological ...evidence of brucellosis in Sub-Saharan Africa are reviewed.•Strategies for implementation of animal vaccination are discussed.•There is a need for simpler and more affordable treatments for human brucellosis.•Development of a B. melitensis vaccine to circumvent the drawbacks of Rev 1 is overdue.•Performance of serological tests for camel and wildlife brucellosis should be investigated.
Brucellosis is a highly contagious zoonosis caused by bacteria of the genus Brucella and affecting domestic and wild mammals. In this paper, the bacteriological and serological evidence of brucellosis in Sub-Saharan Africa (SSA) and its epidemiological characteristics are discussed. The tools available for the diagnosis and treatment of human brucellosis and for the diagnosis and control of animal brucellosis and their applicability in the context of SSA are presented and gaps identified. These gaps concern mostly the need for simpler and more affordable antimicrobial treatments against human brucellosis, the development of a B. melitensis vaccine that could circumvent the drawbacks of the currently available Rev 1 vaccine, and the investigation of serological diagnostic tests for camel brucellosis and wildlife. Strategies for the implementation of animal vaccination are also discussed.
In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. ...Similarly, the minimum-mean-maximum temperatures recorded annually were below the previous year. In the literature, it is possible to find studies focused on the spread of dengue only for some specific regions of Mexico. However, given the increase in the number of cases during 2022 in regions not considered by previously published works, this study covers cases reported in all states of the country. On the other hand, determining a relationship between the dynamics of dengue cases and climatic factors through a computational model can provide relevant information on the transmission of the virus. A multiple-learning computational approach was developed to simulate the number of the different risks of dengue cases according to the classification reported per epidemiological week by considering climatic factors in Mexico. For the development of the model, the data were obtained from the reports published in the Epidemiological Panorama of Dengue in Mexico and in the National Meteorological Service. The classification of non-severe dengue, dengue with warning signs, and severe dengue were modeled in parallel through an artificial neural network model. Five variables were considered to train the model: the monthly average of the minimum, mean, and maximum temperatures, the precipitation, and the number of the epidemiological week. The selection of variables in this work is focused on the spread of the different risks of dengue once the mosquito begins transmitting the virus. Therefore, temperature and precipitation were chosen as climatic factors due to the close relationship between the density of adult mosquitoes and the incidence of the disease. The Levenberg–Marquardt algorithm was applied to fit the coefficients during the learning process. In the results, the ANN model simulated the classification of the different risks of dengue with the following precisions (R
2
): 0.9684, 0.9721, and 0.8001 for non-severe dengue, with alarm signs and severe, respectively. Applying a correlation matrix and a sensitivity analysis of the ANN model coefficients, both the average minimum temperature and precipitation were relevant to predict the number of dengue cases. Finally, the information discovered in this work can support the decision-making of the Ministry of Health to avoid a syndemic between the increase in dengue cases and other seasonal diseases.
The dehydration of air, for air conditioning purposes, either for human comfort or for industrial processes, is done most of the times by making it contact a surface at a temperature below its dew ...point. In this process not only is it necessary to cool that surface continuously, but also the air is cooled beyond the temperature necessary to the process, thus requiring reheating after dehumidification. Although the equipment for this purpose is standard and mostly low-cost, the running costs are high and high grade energy is dissipated at very low efficiency. Alternative sorption-based processes require only low grade energy for regeneration of the sorbent materials, thus incurring lower running costs. On the other hand, sorption technology equipment is usually more expensive than standard mechanical refrigeration equipment, which is essentially due to their too small market share. This paper reports the development of calculation models for the thermophysical properties of aqueous solutions of the chlorides of lithium and calcium, particularly suited for use as desiccants in sorption-based air conditioning equipment. This development has been undertaken in order to create consistent methods suitable for use in the industrial design of liquid desiccant-based air conditioning equipment. We have reviewed sources of measured data from 1850 onwards, and propose calculation models for the following properties of those aqueous solutions: Solubility boundary, vapour pressure, density, surface tension, dynamic viscosity, thermal conductivity, specific thermal capacity and differential enthalpy of dilution.
The design of metallo‐miniproteins advances our understanding of the structural and functional roles of metals in proteins. We recently designed a metal‐binding WW domain, WW‐CA‐Nle, which displays ...three histidine residues on its surface for coordination of divalent metals Ni(II), Zn(II) and Cu(II). However, WW‐CA‐Nle is a molten globule in the apo state and thus showed only moderate binding affinities with Kd values in the μM regime. In this report, we hypothesize that improved thermal stability of the apo state of the metal binding WW‐domain scaffold should lead to improved preorganization of the metal‐binding site and consequently to higher metal‐binding affinities. By redesigning WW‐CA‐Nle, we obtained WW‐CA variants, WW‐CA‐min and WW‐CA‐ANG, which were fully folded in the apo states and displayed moderate to excellent thermostabilities in the apo and holo states. We were able to show that the improved thermal stabilities led to improved metal binding, which was reflected in Kd values that were at least one order of magnitude lower compared to WW‐CA‐Nle. EPR spectroscopy and ITC measurements revealed a better defined and predisposed metal binding site in WW‐CA‐ANG.
Hold on tight. Three metal‐binding WW domains with significantly different thermal stabilities, were designed to investigate the relationship between thermostability and metal binding affinity. We show that the metal‐binding affinities for Ni(II), Zn(II) and Cu(II) correlate with the thermostabilities of the WW domains and attribute this to enhanced preorganization of the metal‐binding site in the more thermally stable scaffolds.
Cattle brucellosis is a severe zoonosis of worldwide distribution caused by Brucella abortus and B. melitensis. In some countries with appropriate infrastructure, animal tagging and movement control, ...eradication was possible through efficient diagnosis and vaccination with B. abortus S19, usually combined with test-and-slaughter (T/S). Although S19 elicits anti-smooth lipopolysaccharide antibodies that may interfere in the differentiation of infected and vaccinated animals (DIVA), this issue is minimized using appropriate S19 vaccination protocols and irrelevant when high-prevalence makes mass vaccination necessary or when eradication requisites are not met. However, S19 has been broadly replaced by vaccine RB51 (a rifampin-resistant rough mutant) as it is widely accepted that is DIVA, safe and as protective as S19. These RB51 properties are critically reviewed here using the evidence accumulated in the last 35 years. Controlled experiments and field evidence shows that RB51 interferes in immunosorbent assays (iELISA, cELISA and others) and in complement fixation, issues accentuated by revaccinating animals previously immunized with RB51 or S19. Moreover, contacts with virulent brucellae elicit anti-smooth lipopolysaccharide antibodies in RB51 vaccinated animals. Thus, accepting that RB51 is truly DIVA results in extended diagnostic confusions and, when combined with T/S, unnecessary over-culling. Studies supporting the safety of RB51 are flawed and, on the contrary, there is solid evidence that RB51 is excreted in milk and abortifacient in pregnant animals, thus being released in abortions and vaginal fluids. These problems are accentuated by the RB51 virulence in humans, lack diagnostic serological tests detecting these infections and RB51 rifampicin resistance. In controlled experiments, protection by RB51 compares unfavorably with S19 and lasts less than four years with no evidence that RB51-revaccination bolsters immunity, and field studies reporting its usefulness are flawed. There is no evidence that RB51 protects cattle against B. melitensis, infection common when raised together with small ruminants. Finally, data acumulated during cattle brucellosis eradication in Spain shows that S19-T/S is far more efficacious than RB51-T/S, which does not differ from T/S alone. We conclude that the assumption that RB51 is DIVA, safe, and efficaceous results from the uncritical repetition of imperfectly examined evidence, and advise against its use.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The present work is focused on modeling and predicting the cumulative number of deaths from COVID-19 in México by comparing an artificial neural network (ANN) with a Gompertz model applying multiple ...optimization algorithms for the estimation of coefficients and parameters, respectively. For the modeling process, the data published by the daily technical report COVID-19 in Mexico from March 19th to September 30th were used. The data published in the month of October were included to carry out the prediction. The results show a satisfactory comparison between the real data and those obtained by both models with a R
2
> 0.999. The Levenberg–Marquardt and BFGS quasi-Newton optimization algorithm were favorable for fitting the coefficients during learning in the ANN model due to their fast and precision, respectively. On the other hand, the Nelder–Mead simplex algorithm fitted the parameters of the Gompertz model faster by minimizing the sum of squares. Therefore, the ANN model better fits the real data using ten coefficients. However, the Gompertz model using three parameters converges in less computational time. In the prediction, the inverse ANN model was solved by a genetic algorithm obtaining the best precision with a maximum error of 2.22% per day, as opposed to the 5.48% of the Gompertz model with respect to the real data reported from November 1st to 15th. Finally, according to the coefficients and parameters obtained from both models with recent data, a total of 109,724 cumulative deaths for the inverse ANN model and 100,482 cumulative deaths for the Gompertz model were predicted for the end of 2020.
Drosophila suzukii (Matsumura) (Diptera: Drosophilidae) is a damaging pest of fruit. Reproductively diapausing adults overwinter in woodlands and remain active on warmer winter days. It is unknown if ...this adult phase of the lifecycle feeds during the winter period, and what the food source may be. This study characterized the flora in the digestive tract of D. suzukii using a metagenomics approach. Live D. suzukii were trapped in four woodlands in the south of England and their guts dissected for DNA extraction and amplicon‐based metagenomics sequencing (internal transcribed spacer and 16S rRNA). Analysis at genus and family taxonomic levels showed high levels of diversity with no differences in digestive tract bacterial or fungal biota between woodland sites of winter‐form D. suzukii. Female D. suzukii at one site appeared to have higher bacterial diversity in the alimentary canal than males, but there was a site, sex interaction. Many of the biota were associated with cold, wet climatic conditions and decomposition. This study provides the first evidence that winter‐form D. suzukii may be opportunistic feeders during the winter period and are probably exploiting food sources associated with moisture on decomposing vegetation during this time. A core gut microbiome has been identified for winter‐form D. suzukii.
Microglia in the Aging Brain Conde, Jessica R; Streit, Wolfgang J
Journal of neuropathology and experimental neurology,
2006-March, Letnik:
65, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The aging brain is characterized by a demonstrable decrease in weight and volume, particularly after the age of 50. This atrophy, which affects both grey and white matter, is presumed to result from ...a loss of neurons and myelinated axons. Glial cells, on the other hand, appear to increase in the aging brain, which exhibits greater immunoreactivity with both astrocytic and microglial markers. This review is focused on the morphologic and phenotypic changes that occur in microglial cells with normal aging. Although there is a consistent aging-related upregulation of microglial activation markers in experimental animals and humans that could be interpreted as aging-related neuroinflammation, it is generally difficult to show a direct correlation between ostensible microglial activation and neurodegeneration. This raises questions about whether aging-related microglial activation indeed represents reactive gliosis in the conventional sense. As an alternative, we discuss the possibility that structural and phenotypic changes that occur in microglia are a direct reflection of the aging process on microglia. Thus, microglia cells themselves may be subject to cellular senescence in the sense that they no longer function efficiently. The concept of microglial senescence offers a novel perspective on aging-related neurodegeneration, namely that neurodegeneration could also occur secondary to microglial degeneration.
•ANNim-PSO and ANNim-GA methodology were implemented to improve the PTC performance.•Short computation time is required to optimize PTC's six input variables.•Rim angle, inlet temperature, and water ...flow were optimized simultaneously.•The rim angle was the most significant influence on PTC performance.•An optimal PTC thermal efficiency increase was achieved.
This work focused on presenting a multivariate inverse artificial neural network (ANNim) by developing two functions coupled to metaheuristic algorithms to increase a parabolic trough collector (PTC). This work aims to provide a new method capable of improving the thermal efficiency of a PTC by determining multiple optimal input variables. At first, two ANN models carried out to predict the PTC thermal efficiency (ηt), validated, and compared in detail. For that, six input parameters rim-angle (φr), inlet-temperature (Tin), ambient-temperature (Tamb), water volumetric flow rate (Fw), direct-solar-radiation (Gb) and wind-speed (Vv) considered as variables in the input layer. Two non-linear transfer functions (TANSIG and LOGSIG) in the hidden layer, a linear function (PURELIN) in the output layer, and the Levenberg-Marquardt training algorithm were applied. The results showed that both ANN models achieved satisfactory results with a coefficient of determination of 0.9511 and a root mean square error of 0.0193. Then, to get the variable's optimal values: rim-angle, inlet-temperature, and water volumetric flow rate, both ANN models inverted to acquire the multivariable objective function that could be resolved with genetic-algorithms (GA) and particle-swarm-optimization (PSO). The TANSIG function demonstrated better adaptation to the ANNim model by finding all the input variables in a random test with an error of 3.96% with a computational time of 14.39 s applying PSO. The results showed that by using the ANNim methodology, it is feasible to improve the performance of the PTC by optimizing from one, two, and three variables at the same time. In optimizing one variable at a time, it was possible to increase a random test's performance up to 54.78%, 27.62%, and 51.92% by finding the rim-angle inlet-temperature and water volumetric flow rate, respectively. In optimizing two variables simultaneously, it was possible to increase a random test's performance up to 36.73% by finding the appropriate inlet-temperature and water volumetric flow rate. In optimizing three variables simultaneously, it was possible to increase a random experimental test of up to 67.12%. Finally, the new ANNim method proposed may increase the thermal efficiency of a PTC in real-time because of the coupling of metaheuristic algorithms that allow obtaining optimal variables in the shortest possible time. Therefore, it can be a promising and widely used method for optimizing and controlling thermal processes.
PDF
