Notes

Ways of identify as well as produce antiviral proteins.
This paper reports on an ultrasensitive and label-free electrochemical immunosensor for monitoring the SARS-CoV-2 spike protein (SARS-CoV-2 SP). A self-supported electrode, which can simultaneously serve as an antibody immobilization matrix and electron transport channel, was initially fabricated by a controlled partial exfoliation of a flexible graphitic carbon foil (GCF). Mild acidic treatment enabled the partial oxidation and exfoliation (down to a few layers) of the flexible GCF; this also provided a high percentage of oxygen functionality and an enhanced surface roughness. The substrate electrode was further functionalized with ethylenediamine (EDA) to provide a suitable platform with even a higher surface roughness, for the covalent immobilization of an anti-SARS-CoV-2 antibody. The change in the current response for the [Fe(CN)6]3-/4- redox couple, induced by the binding of SARS-CoV-2 SP to the antibody immobilized on the electrode surface, was used to determine the SARS-CoV-2 SP concentration. The immunosensor thus prepared could detect SARS-CoV-2 SP within 30 min with high reproducibility and specificity over a wide concentration range (0.2-100 ng/mL). https://www.selleckchem.com/products/abt-199.html Detection limits of 25 pg/mL and 27 pg/mL were found in a phosphate buffer solution (pH 7.4), and diluted blood plasma, respectively. The immunosensor was also employed to detect SARS-CoV-2 SP in artificial human saliva.Thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) has demonstrated tremendous potentials as a new analytical chemistry tool to detect a wide range of substances from real-world samples. However, it still faces significant challenges of multiplex sensing from complex mixtures due to the imperfect separation by TLC and the resulting interference of SERS detection. In this article, we propose a multiplex sensing method of complex mixtures by machine vision analysis of the scanning image of the TLC-SERS results. Briefly, various pure substances in solution and the complex mixture solution are separated by TLC followed by one-dimensional SERS scanning of the entire TLC plate, which generates TLC-SERS images of all target substances along the chromatography path. After that, a machine vision method is employed to extract the template images from the TLC-SERS images of pure substance solutions. Finally, we apply a feature point matching strategy based on the Winner-take-all principle, which matches the template image of each pure substance with the mixture image to confirm the existence and derive the position of each target substance in the TLC plate, respectively. Our experimental results based on the mixture solution of five different substances show that the proposed machine vision analysis is highly selective, sensitive and does not require artificial analysis of the SERS spectra. Therefore, we envision that the proposed machine vision analysis of the TLC-SERS imaging is an objective, accurate, and efficient method for multiplex sensing of trace level of target substances from complex mixtures.
The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disease diagnosis.

Based on the semi-structured data of Chinese Electronic Medical Records (CEMRs) from the China Hospital Pharmacovigilance System, this paper proposed a bi-level artificial intelligence model for the risk classification of acute respiratory diseases. It includes two levels. The first level is a dedicated design of the "BiLSTM+Dilated Convolution+3D Attention+CRF" deep learning model that is used for Chinese Clinical Named Entity Recognition (CCNER) to extract valuable information from the unstructured data in the CEMRs. Incorporating the transfer learning and semi-supervised learning technique into the proposed deep learning model achieves higher accuracy and efficiency in the CCNER task than the popular "Bert+BiLSTM+CRF" approach. Combining the extracted entity data with other structured data in the CEMRs, the second level is a customized XGBoost to realize the risk classification of acute respiratory diseases.

The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy.

Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty.
Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty.The prediction and control of COVID-19 is critical for ending this pandemic. In this paper, a nonlocal SIHRDP (S-susceptible class, I-infective class (infected but not hospitalized), H-hospitalized class, R-recovered class, D-death class and P-isolated class) epidemic model with long memory is proposed to describe the multi-wave peaks for the spread of COVID-19. Based on the basic reproduction number R 0 , which is completely controlled by fractional order, the stability of the proposed system is studied. Furthermore, the numerical simulation is conducted to gauge the performance of the proposed model. The results on Hunan, China, reveal that R 0 less then 1 suggests that the disease-free equilibrium point is globally asymptotically stable. Likewise, the situation of the multi-peak case in China is presented, and it is clear that the nonlocal epidemic system has a superior fitting effect than the classical model. Finally an adaptive impulsive vaccination is introduced based on the proposed system. Then empl9 cannot be completely eradicated.The COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. In many countries, hospitalization and in particular ICU occupancy is the primary measure for policy makers to decide on possible non-pharmaceutical interventions. In this paper a combined methodology for the prediction of COVID-19 case numbers, case-specific hospitalization and ICU admission rates as well as hospital and ICU occupancies is proposed. To this end, we employ differential flatness to provide estimates of the states of an epidemiological compartmental model and estimates of the unknown exogenous inputs driving its nonlinear dynamics. A main advantage of this method is that it requires the reported infection cases as the only data source. As vaccination rates and case-specific ICU rates are both strongly age-dependent, specifically an age-structured compartmental model is proposed to estimate and predict the spread of the epidemic across different age groups. By utilizing these predictions, case-specific hospitalization and case-specific ICU rates are subsequently estimated using deconvolution techniques. In an analysis of various countries we demonstrate how the methodology is able to produce real-time state estimates and hospital/ICU occupancy predictions for several weeks thus providing a sound basis for policy makers.We examined the cognitive, language, and instructional factors associated with reading ability in Williams syndrome (WS). Seventy 9-year-olds with WS completed standardized measures of real-word reading, pseudoword decoding, reading comprehension, phonological skills, listening comprehension, nonverbal reasoning, visual-spatial ability, verbal working memory, rapid naming, and vocabulary. Reading instruction method was determined from school records and interviews with parents and teachers. Similar to prior findings for individuals with WS, reading ability varied widely, ranging from inability to read any words to reading comprehension at age level. Multiple regression analyses indicated that the primary concurrent predictor of word reading ability was reading instruction method, with a systematic phonics approach associated with considerably better performance than other reading instruction approaches. Phonological processing skills-as assessed by a composite of phonological awareness and verbal short-term memory-also contributed significant unique variance to word reading ability, as did visual-spatial ability. The concurrent predictors of reading comprehension were single-word reading and listening comprehension. These findings indicate that the factors that predict concurrent early word reading and reading comprehension abilities for children with WS are consistent with previous findings for typically developing children and that the Simple View of Reading applies to children with WS. Children with WS benefit strongly from systematic phonics instruction regardless of IQ. Instruction focused on improving listening comprehension is likely to improve reading comprehension, especially as word reading skills increase.
The online version contains supplementary material available at 10.1007/s11145-021-10163-4.
The online version contains supplementary material available at 10.1007/s11145-021-10163-4.
Predatory insects contribute to the natural control of agricultural pests, but also use plant pollen or nectar as supplementary food resources. Resource maps have been proposed as an alternative to land cover maps for prediction of beneficial insects.

We aimed at predicting the abundance of crop pest predating insects and the pest control service they provide with both, detailed flower resource maps and land cover maps.

We selected 19 landscapes of 500m radius and mapped them with both approaches. In the centres of the landscapes, aphid predators - hoverflies (Diptera Syrphidae), ladybeetles (Coleoptera Coccinellidae) and lacewings (Neuroptera Chrysopidae) - were surveyed in experimentally established faba bean phytometers (
L. Var. Sutton Dwarf) and their control of introduced black bean aphids (
Scop.) was recorded.

Landscapes with higher proportions of forest edge as derived from land cover maps supported higher abundance of aphid predators, and high densities of aphid predators reduced aphid infestation on faba bean. Floral resource maps did not significantly predict predator abundance or aphid control services.

Land cover maps allowed to relate landscape composition with predator abundance, showing positive effects of forest edges. Floral resource maps may have failed to better predict predators because other resources such as overwintering sites or alternative prey potentially play a more important role than floral resources. More research is needed to further improve our understanding of resource requirements beyond floral resource estimations and our understanding of their role for aphid predators at the landscape scale.

The online version contains supplementary material available at 10.1007/s10980-021-01361-0.
The online version contains supplementary material available at 10.1007/s10980-021-01361-0.Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem.
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