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Silver-based nanomaterials have proven interesting and promising material for numerous applications such as biosensor, antimicrobial, anticancer agent, catalyst, food and water treatment, energy storage devices etc.In this study, nanoparticles of Silver and Copper were prepared by chemical reduction method, using hydrazine hydrate and Sodium borohydride as reducing agent. Fine powder of Ag-Cu nanoparticles (NPs)was obtained. The structural analysis of the sample wasdone using Powder XRD, SEM and TEM images and particle size analysis by DLS.The chemical purity and the elemental compositions of synthesized NPs were studied using SEM-EDX. Optical properties of the Ag-Cu NPs were analyzed using UV-DRS spectrum and FTIR spectrum. PXRD reveals that the NPs are highly crystalline in nature.The average crystallite size is30 nm. SEM and TEM images confirm the spherical morphology and the particle size is in nm. The DLS-particle size analyzer shows the size distribution of most of the NPs ranging from 9 nm to 100 nm. The EDX analysis reveals the percentage of elemental composition as 14.71, 9.06 and 76.23 for silver, copper and oxygen respectively. UV-DRS spectrum shows the absorption maximum occur at 371 nm. Due to the synergistic effect of silver and copper, there is blue shift in the absorption maximum. The IR spectrum discloses the metal oxide bond in the synthesized NPs.

Rich natural resources such as fertilizers, environment, groundwater, rivers, and land are abundant in many countries. Agriculture is the primary source of income for the people living in different countries. There have not been shortages of resources like river water and groundwater, in recent decades. But, the lack of knowledge on how to use those valuable resources is the main reason for resource wastage. The amount of water applied to crop fields in a variety of soil, weather, and crop growth stages can be managed and optimized using smart farming. The crop field’s soil moisture can be measured using sensors positioned at various observation points, which will show how much water has been retained. Unfortunately, the smart farming system is not capable to receive the soil moisture data provided by the irrigation management due to issues with connectivity or sensor failure. Innovative agricultural approaches can be facilitated by the Internet of Things (IoT) technologies. These IoT nodes have encountered energy limitations and challenging routing techniques as a result of their low capacity. Therefore, it is imperative to resolve the issues by implementing an effective IoT-based irrigation system in the agricultural area. The major steps of the developed model are data collection and prediction. Initially, essential image and sensor data is attained from the benchmark resources. Next, the collected images are provided to the level of the irrigation prediction phase. This phase facilitates the farmers to maximize the crop yields and minimize the production cost. Here, effective irrigation prediction is performed using an Adaptive Hybrid (1D-2D) Convolution-based ShuffleNetV2 model (AHC-ShuffleNetV2). Moreover, the parameters of the suggested AHC-ShuffleNetV2 are optimized using a Fitness-based Piranha Foraging Optimization Algorithm (FPFOA). This increases the performance rates of the proposed model. Later, several experimental analyses are executed in the developed model over classical techniques to display their effectualness rate. When considering the sigmoid activation function, the implemented smart irrigation level prediction framework’s RMSE was minimized by 73.15% of POA-ShuffleNetV2, 72.36% of RSA-ShuffleNetV2, 78.94% of MRS-ShuffleNetV2, and 79.47% of PFOA-ShuffleNetV2 respectively. Hence, it is revealed that the designed smart irrigation level prediction model attained low error rates and also achieved higher efficacy than the other baseline techniques.

The world experienced the beginning of the COVID-19 pandemic by the end of 2019 to the beginning of 2020. Governments implemented strategies to contain it, most based on lockdowns. Mexico was no exception. The lockdown was initiated in March 2020, and with it, a reduction in the seismic noise level was witnessed by the seismic stations of the national and Valley of Mexico networks. Stations located in municipalities with more than 50 000 people usually experience larger seismic noise levels at frequencies between 1 and 5 Hz, associated with human activity. The largest noise levels are recorded in Mexico City, which has the largest population in the country. The largest drop was observed in Hermosillo, Sonora; however, it was also the city with the fastest return to activities, which seems to correlate with a quick increase in confirmed COVID-19 cases. Mexico initiated a traffic-light system to modulate the re-opening of economic activities for each state. Therefore, since 1 June, noise levels have generally reflected the colour of the state traffic light. Furthermore, the reduction in the noise level at seismic stations has allowed identification of smaller earthquakes without signal processing. Also, people in cities have perceived smaller or more distant quakes.

Seismometers have detected the social response to lockdown measures implemented following the onset of the COVID-19 pandemic in cities around the world. This long-lasting pandemic has been a particular challenge in countries such as Mexico, where the informal economy constitutes most of the working population. This context motivated the monitoring of the mobility of populations throughout the various phases of lockdown measures independently of people's access to the internet and mobile technology. Here we use the variation of anthropogenic seismic noise in the city of Querétaro (central Mexico) recorded by a network of low-cost Raspberry Shake seismic stations to study the spatial and temporal variation of human activity in the city throughout the pandemic and during sporting events. The results emphasize the importance of densifying urban seismic networks and of tracking human activities without the privacy concerns associated with mobile technology.

During the COVID-19 pandemic, most countries put in place social interventions, restricting the mobility of citizens, to slow the spread of the epidemic. Italy, the first European country severely impacted by the COVID-19 outbreak, applied a sequence of progressive restrictions to reduce human mobility from the end of February to mid-March 2020. Here, we analysed the seismic signatures of these lockdown measures in densely populated eastern Sicily, characterized by the presence of a permanent seismic network used for earthquake and volcanic monitoring. We emphasize how the anthropogenic seismic noise decrease is visible even at stations located in remote areas (Etna and Aeolian Islands) and that the amount of this reduction (reaching ∼ 50 %–60 %), its temporal pattern and spectral content are strongly station-dependent. Concerning the latter, we showed that on average the frequencies above 10 Hz are the most influenced by the anthropogenic seismic noise. We found similarities between the temporal patterns of anthropogenic seismic noise and human mobility, as quantified by the mobile-phone-derived data shared by Google, Facebook and Apple, as well as by ship traffic data. These results further confirm how seismic data, routinely acquired worldwide for seismic and volcanic surveillance, can be used to monitor human mobility too.

Treatment with antiviral drugs for human immunodeficiency virus type 1 (HIV-1) infection causes a rapid reduction in plasma viral load. Viral decline occurs in several stages and provides information on important kinetic constants of virus replication in vivo and pharmacodynamic properties. We present a mathematical model that not only considers the intracellular phase of the viral life cycle, defined as the time between the infection of a cell and the production of new viral particles, but we also consider that this parameter together with the virus decay are interactive fuzzy numbers.

Every year, a dense smoke haze covers a large portion of South America originating from fires in the Amazon Basin and central parts of Brazil during the dry biomass burning season between August and October. Over a large portion of South America, the average aerosol optical depth at 550 nm exceeds 1.0 during the fire season, while the background value during the rainy season is below 0.2. Biomass burning aerosol particles increase scattering and absorption of the incident solar radiation. The regional-scale aerosol layer reduces the amount of solar energy reaching the surface, cools the near-surface air, and increases the diffuse radiation fraction over a large disturbed area of the Amazon rainforest. These factors affect the energy and CO2 fluxes at the surface. In this work, we applied a fully integrated atmospheric model to assess the impact of biomass burning aerosols in CO2 fluxes in the Amazon region during 2010. We address the effects of the attenuation of global solar radiation and the enhancement of the diffuse solar radiation flux inside the vegetation canopy. Our results indicate that biomass burning aerosols led to increases of about 27 % in the gross primary productivity of Amazonia and 10 % in plant respiration as well as a decline in soil respiration of 3 %. Consequently, in our model Amazonia became a net carbon sink; net ecosystem exchange during September 2010 dropped from +101 to −104 TgC when the aerosol effects are considered, mainly due to the aerosol diffuse radiation effect. For the forest biome, our results point to a dominance of the diffuse radiation effect on CO2 fluxes, reaching a balance of 50–50 % between the diffuse and direct aerosol effects for high aerosol loads. For C3 grasses and savanna (cerrado), as expected, the contribution of the diffuse radiation effect is much lower, tending to zero with the increase in aerosol load. Taking all biomes together, our model shows the Amazon during the dry season, in the presence of high biomass burning aerosol loads, changing from being a source to being a sink of CO2 to the atmosphere.

Polymersomes are used in many applications, ranging from drug delivery to microfluidics, but controlling their shape is difficult. Here, Christianen and co-workers have precisely controlled polymersome shape via an out-of-equilibrium process and fully understood the mechanism of this procedure.

Real-time quantitative polymerase chain reaction (qPCR) of complementary DNA is now a standard method for studies of gene expression. However, qPCR can identify genuine variation only when transcript quantities are accurately normalized to an appropriate reference. To identify the most reliable reference genes for transcript quantification by qPCR, we describe a systematic evaluation of candidate reference genes of Arabidopsis thaliana ecotype Columbia-0 (Col-0). Twelve genes were selected for transcript stability studies by qPCR of complementary DNA prepared from Arabidopsis leaf tissue infected with one of five plant viruses (Cauliflower mosaic virus, Tobacco mosaic virus, Tomato spotted wilt virus, Turnip mosaic virus, and Turnip yellow mosaic virus). The F-box family protein, elongation factor 1-α, sand family protein, and protodermal factor 2 gene transcripts showed the most stable accumulation, whereas a traditionally used reference gene, Actin8, showed the least stable accumulation as measured by the geNorm algorithm. The data furnish plant virologists with reference genes for normalization of qPCR-derived gene expression in virus-infected Arabidopsis and will be beneficial to the selection and design of primers targeting orthologous genes in other plant species.

We present a case of prenatal diagnosis of congenital rubella. After birth, in addition to traditional serologic and clinical examinations to confirm the infection, we could identify the virus in the "first fluid aspirated from the oropharynx of the newborn", using polimerase chain reaction (PCR). We propose that this first oropharynx fluid (collected routinely immediately after birth) could be used as a source for identification of various congenital infection agents, which may not always be easily identified by current methodsRelatamos um caso de diagnóstico pré-natal de rubéola congênita. Após o nascimento, além da confirmação feita através do exame físico e sorológico do recém-nascido, o vírus também pode ser demonstrado no primeiro fluido aspirado da orofaringe do recém-nascido, utilizando-se a reação em cadeia da polimerase (PCR). Sugerimos que este fluido (colhido rotineiramente no momento da reanimação neonatal) possa ser utilizado na pesquisa de outros agentes infecciosos, que não são facilmente identificados por outros métodos

Stress triggers a set of adaptative reactions that can compromise the homeostasis of the organism, such as transport from one environment to another. The aim of the present study was to determine whether there is a periodic variation in serotonin levels following transport stress. We used a databank to obtain data on plasma serotonin levels of healthy adult horses collected at regular eight-hour intervals for three consecutive days on two different occasions: before and after transport. The results were tabulated and submitted to Hurst statistical analysis procedures. Curves were created of the variation in serotonin levels as a function of time using the cosinor method. Plasma serotonin levels exhibited circadian variations independently of sex and age. The circadian variation pattern was significantly affected when the animals experienced transport stress (p < 0.05). Thus, plasma serotonin levels can be used as an indicator of physiological disorders and the promotion of animal wellbeing. [ABSTRACT FROM AUTHOR]

Background. The study hypothesized that zinc supplementation would increase or preserve lean body mass in Duchenne muscular dystrophy (DMD) patients. Therefore, we aimed to evaluate the body composition of DMD patients before and after zinc supplementation. Materials and Methods. The study is a clinical trial comprising 21 boys diagnosed with DMD. Dietary intake parameters were evaluated before zinc supplementation. Serum zinc levels, anthropometry, and body composition were measured in three moments, four months apart. The patients received 5, 10, or 15 mg of zinc bis‐glycine supplementation according to age as an oral solution daily for four months. The sample was distributed into two groups according to serum zinc status: zinc deficiency (G1) or adequate zinc (G2). Results. There was a significant difference in lean body mass between the groups: zinc deficiency (G1) or adequate zinc (G2), at three times (p = 0.041, 0.016, and 0.009, respectively). After oral zinc supplementation, serum zinc levels were not different between groups. We did not observe differences when associating the parameters between times and groups. Conclusion. Zinc supplementation was able to maintain lean body mass and fat mass in patients with DMD with previous deficiencies. Therefore, it is necessary to have a prior screening of serum zinc levels to observe changes after supplementation. [ABSTRACT FROM AUTHOR]