Soil data recency: The foundation for harmonizing soil data across time.

Sustainable soil resource management depends on reliable soil information, often derived from 'legacy soil data' or a combination of old and new soil data. However, the task of harmonizing soil data collected at different times remains a largely unexplored in the literature. Addressing this challenge requires incorporating the temporal dimension into mathematical and statistical models for spatio-temporal soil studies. This study aimed to create a comprehensive framework for harmonizing soil data across various time. We assessed the integration of historical and recent soil data, ranging from 4 to 48 years old data, using soil data recency analysis. To achieve this, we introduced an 'age of data' attribute, calculating the time difference between soil survey years and the present (e.g., 2022). We applied three machine learning models - Decision Trees (DT), Random Forest (RF), Gradient Boosting (GBM) - to a dataset containing 6339 sites and 28,149 depth-harmonized layers. The results consistently demonstrated robust performance across models, RF outperforming with an R-squared value of 0.99, RMSE of 1.41, and a concordance of 0.97. Similarly, DT and GBM also showed strong predictive power. Terrain-derived environmental covariates played a more important role than land use and land cover (LULC) change in predicting soil data recency. While LULC change showed soil organic carbon concentration variability across the different depths, it was a less important factor. Anthropogenic factors, such as LULC change and normalized difference vegetation index (NDVI), were not primary determinants of soil data recency. Variations in soil depth had no impact on predicting soil data recency. This study validated that terrain-derived covariates, especially elevation factors, effectively explain the quality of older soil data when predicting current soil attributes using the soil data recency concept. This approach has the potential to enhance real-time estimates, such as carbon budgets, and we emphasize its importance in global earth system models.

Evaluating the Soil Quality Index Using Three Methods to Assess Soil Fertility.

Soil health plays a crucial role in crop production, both in terms of quality and quantity, highlighting the importance of effective methods for preserving soil quality to ensure global food security. Soil quality indices (SQIs) have been widely utilized as comprehensive measures of soil function by integrating multiple physical, chemical, and biological soil properties. Traditional SQI analysis involves laborious and costly laboratory analyses, which limits its practicality. To overcome this limitation, our study explores the use of visible near-infrared (vis-NIR) spectroscopy as a rapid and non-destructive alternative for predicting soil properties and SQIs. This study specifically focused on seven soil indicators that contribute to soil fertility, including pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN). These properties play key roles in nutrient availability, pH regulation, and soil structure, influencing soil fertility and overall soil health. By utilizing vis-NIR spectroscopy, we were able to accurately predict the soil indicators with good accuracy using the Cubist model (R2 = 0.35-0.93), offering a cost-effective and environmentally friendly alternative to traditional laboratory analyses. Using the seven soil indicators, we looked at three different approaches for calculating and predicting the SQI, including: (1) measured SQI (SQI_m), which is derived from laboratory-measured soil properties; (2) predicted SQI (SQI_p), which is calculated using predicted soil properties from spectral data; and (3) direct prediction of SQI (SQI_dp), The findings demonstrated that SQI_dp exhibited a higher accuracy (R2 = 0.90) in predicting soil quality compared to SQI_p (R2 = 0.23).

AMFR promotes innate immunity activation and proteasomal degradation of HMGCR in response to influenza virus infection in A549 cells.

Differential regulation of the 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), which is considered the rate-limiting enzyme of the cholesterol biosynthesis pathway, has been reported in case of infection with many viruses. In our study, we have found that influenza virus infection decreases total cellular cholesterol level which is directly related to the downregulation of HMGCR protein. We found that HMGCR is degraded through ubiquitination and proteasomal-mediated pathway upon viral infection. Upregulation of Autocrine Motility Factor Receptor (AMFR), which is an E3-ubiquitin ligase of HMGCR, was also observed. Furthermore, knockdown of AMFR inhibits ubiquitination of HMGCR and also leads to inactivation of the innate immunity components TANK-binding kinase 1 (TBK1) and Interferon regulatory factor 3 (IRF3). Our study is the first to show the role of HMGCR and AMFR in influenza virus infection and reveals that AMFR plays a crucial role in the downregulation of HMGCR and the activation of innate immunity following influenza virus infection.

Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy.

Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.

Quantifying changes in soil organic carbon density from 1982 to 2020 in Chinese grasslands using a random forest model.

China has the second-largest grassland area in the world. Soil organic carbon storage (SOCS) in grasslands plays a critical role in maintaining carbon balance and mitigating climate change, both nationally and globally. Soil organic carbon density (SOCD) is an important indicator of SOCS. Exploring the spatiotemporal dynamics of SOCD enables policymakers to develop strategies to reduce carbon emissions, thus meeting the goals of "emission peak" in 2030 and "carbon neutrality" in 2060 proposed by the Chinese government. The objective of this study was to quantify the dynamics of SOCD (0-100 cm) in Chinese grasslands from 1982 to 2020 and identify the dominant drivers of SOCD change using a random forest model. The results showed that the mean SOCD in Chinese grasslands was 7.791 kg C m-2 in 1982 and 8.525 kg C m-2 in 2020, with a net increase of 0.734 kg C m-2 across China. The areas with increased SOCD were mainly distributed in the southern (0.411 kg C m-2), northwestern (1.439 kg C m-2), and Qinghai-Tibetan (0.915 kg C m-2) regions, while those with decreased SOCD were mainly found in the northern (0.172 kg C m-2) region. Temperature, normalized difference vegetation index, elevation, and wind speed were the dominant factors driving grassland SOCD change, explaining 73.23% of total variation in SOCD. During the study period, grassland SOCS increased in the northwestern region but decreased in the other three regions. Overall, SOCS of Chinese grasslands in 2020 was 22.623 Pg, with a net decrease of 1.158 Pg since 1982. Over the past few decades, the reduction in SOCS caused by grassland degradation may have contributed to soil organic carbon loss and created a negative impact on climate. The results highlight the urgency of strengthening soil carbon management in these grasslands and improving SOCS towards a positive climate impact.

Modeling the spatial variation of calcium carbonate equivalent to depth using machine learning techniques.

Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this study was to model and map calcium carbonate equivalent (CCE) presenting inorganic carbon in soil using machine learning and digital soil mapping techniques. Chahardowli Plain in foothills of the Zagros Mountains in the southeast of Kurdistan Province in Iran was taken as a case study area. CCE was measured at 0-5, 5-15, 15-30, 30-60, and 60-100 cm depths following GloalSoilMap.net project specifications. A total of 145 samples were collected from 30 soil profiles using the conditional Latin hypercube (cLHS) method of sampling. Relationships between CCE and environmental predictors were modeled using random forest (RF) and decision tree (DT) models. In general, the RF model performed slightly superior than the DT model. The mean value of CCE increased with soil depth, from 3.5% (0-5 cm) to 63.8% (30-60 cm). Remote sensing (RS) variables and terrestrial variables were equally important. The importance of RS variables was higher at the surface than terrestrial variables, and vice versa. The most significant variables were Channel Network Base Level (CNBL) variable and Difference Vegetation Index (DVI) with the same variable importance value (21.1%). In areas affected by river activities, the use of the CNBL and vertical distance to channel networks (VDCN) as variables in digital soil mapping (DSM) could increase the accuracy of soil property prediction maps. The VDCN played a principal role in soil distribution in the study area by affecting the rate of discharge and, thus, erosion and sedimentation. A high percentage of carbonate in parts of the region could exacerbate nutrient deficiencies for most crops and provide information for sustainably managing agricultural activity.

An experimental investigation of the erosion process and morphological characteristics of the tile-back type slope with non-uniform slopes based on a 3D reconstruction method.

Tile-back type slopes comprise ephemeral gullies (EGs) and hillslopes; they are a unique and widely distributed micro-landform in the Loess Plateau region of China. Gully erosion from these landforms is a serious issue, but the micro-landform makes the erosion process and its estimation complex. Quantifying soil erosion processes and their distribution characteristics at different positions on tile-back type slopes will provide a clearer picture for ecological restoration to control further soil degradation. This study investigated the erosion process of tile-back type slope with non-uniform slopes using a 3D photo-reconstruction method during eight successive simulated rainfall events. The results showed that EG erosion began with a chain of intermittent headcuts. When the accumulated rainfall reached 76 mm, serious collapses dramatically increased the amount of sediment by 216% after the first rainfall (cumulative rainfall was about 15 mm). We quantified the sediment contribution of EG erosion (46.20%), rill erosion (35.62%), and inter-rill erosion (18.18%) to total soil loss. The erosion area of the steep slope section and extremely steep slope section accounted for 33.26% and 66.74% of the total erosion area, respectively. Moreover, sediment amounts significantly correlated with morphological parameters, particularly the amount of EG erosion and maximum gully depth, with a correlation coefficient of 0.98. Cumulative gully length and erosion area had the greatest effect on rill erosion, with a correlation coefficient of 0.97. These results provide insight into the qualitative and quantitative understanding of EG erosion process on Loess Plateau of China and an important reference for the rational arrangement of EG control measures.

Phage Therapy as a Protective Tool Against Pathogenic Bacteria: How Far We Are?

Bacterial infections continue to jeopardize human and animal health, impacting millions of lives by causing significant deaths every year. The use of antibiotics remains the primary choice of therapy and has only been partly successful in reducing the disease burden due to the evolving nature of resistant microbes. Widespread and inappropriate use of antibiotics resulted in the development of antibiotic-resistant microbial species provoking substantial economic burdens. The most promising way to resolve the issue of antibiotic resistance is the use of bacterial viruses called bacteriophages to treat microbial infections. Earlier reports on experimental bacteriophage therapy showed successful patient outcomes, and many clinical trials of such clinical bacteriophages have already been investigated in many western countries. In this review, we are focusing on the advantages as well as drawbacks of bacteriophage therapy to use it as an alternative to antibiotics for microbial infections, together with its current success status. There is also a need to extensively study the past, present, and future outlook of phage therapy in comparison to presently available antimicrobial agents and especially immunological response by the host after phage administration. Our aim is to highlight the fast-promoting field of bacteriophage therapy and provocations that lie ahead as the world is gradually moving aside from complete dependence on antimicrobial agents.

A Novel Paper-Based Reagentless Dual Functional Soil Test to Instantly Detect Phosphate Infield.

Soil tests for plant-available phosphorus (P) are suggested to provide offsite P analysis required to monitor P fertilizer application and reduce P losses to downstream water. However, procedural and cost limitations of current soil phosphate tests have restricted their widespread use and have made them accessible only in laboratories. This study proposes a novel paper-based reagentless electrochemical soil phosphate sensor to extract and detect soil phosphate using an inexpensive and simple approach. In this test, concentrated Mehlich-3 and molybdate ions were impregnated in filter paper, which served as the phosphate extraction and reaction zone, and was followed by electrochemical detection using cyclic voltammetry signals. Soil samples from 22 sampling sites were used to validate this method against inductively coupled plasma optical emission spectroscopy (ICP) soil phosphate tests. Regression and correlation analyses showed a significant relationship between phosphate determinations by ICP and the proposed method, delivering a correlation coefficient, r, of 0.98 and a correlation slope of 1.02. The proposed approach provided a fast, portable, low-cost, accessible, reliable, and single-step test to extract and detect phosphate simultaneously with minimum waste (0.5 mL per sample), which made phosphate characterization possible in the field.

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy.

The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features' capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.

Response of leaf stoichiometry of Potentilla anserina to elevation in China's Qilian Mountains.

Plants adapt to changes in elevation by regulating their leaf ecological stoichiometry. Potentilla anserina L. that grows rapidly under poor or even bare soil conditions has become an important ground cover plant for ecological restoration. However, its leaf ecological stoichiometry has been given little attention, resulting in an insufficient understanding of its environmental adaptability and growth strategies. The objective of this study was to compare the leaf stoichiometry of P. anserina at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,500, and 3,800 m) in the middle eastern part of Qilian Mountains. With an increase in elevation, leaf carbon concentration [(C)leaf] significantly decreased, with the maximum value of 446.04 g·kg-1 (2,400 m) and the minimum value of 396.78 g·kg-1 (3,500 m). Leaf nitrogen concentration [(N)leaf] also increased with an increase in elevation, and its maximum and minimum values were 37.57 g·kg-1 (3,500 m) and 23.71 g·kg-1 (2,800 m), respectively. Leaf phosphorus concentration [(P)leaf] was the highest (2.79 g·kg-1) at 2,400 m and the lowest (0.91 g·kg-1) at 2,800 m. The [C]leaf/[N]leaf decreased with an increase in elevation, while [N]leaf/[P]leaf showed an opposite trend. The mean annual temperature, mean annual precipitation, soil pH, organic carbon, nitrogen, and phosphorus at different elevations mainly affected [C]leaf, [N]leaf, and [P]leaf. The growth of P. anserina in the study area was mainly limited by P, and this limitation was stronger with increased elevation. Progressively reducing P loss at high elevation is of great significance to the survival of P. anserina in this specific region.

Surface biophysical features fusion in remote sensing for improving land crop/cover classification accuracy.

Preparing up-to-date land crop/cover maps is important to study in order to achieve food security. Therefore, the aim of this study was to evaluate the impact of surface biophysical features in the land crop/cover classification accuracy and introduce a new fusion-based method with more accurate results for land crop/cover classification. For this purpose, multi-temporal images from Sentinel 1 and 2, and an actual land crop map prepared by Agriculture and Agri-Food Canada (AAFC) in 2019 were used for 3 test sites in Ontario, Canada. Firstly, surface biophysical features maps were prepared based on spectral indices from Sentinel 2 including Normalized Difference Vegetation Index (NDVI), Index-based Built-up Index (IBI), Wetness, Albedo, and Brightness and co-polarization (VV) and cross-polarization (VH) from Sentinel 1 for different dates. Then, different scenarios were generated; these included single surface biophysical features as well as a combination of several surface biophysical features. Secondly, land crop/cover maps were prepared for each scenario based on the Random Forest (RF). In the third step, based on the voting strategy, classification maps from different scenarios were combined. Finally, the accuracy of the land crop/cover maps obtained from each of the scenario was evaluated. The results showed that the average overall accuracy of land crop/cover maps obtained from individual scenario (one feature) including NDVI, IBI, Wetness, Albedo, Brightness, VV and VH were 66%, 68%, 63%, 60%, 57%, 62% and 58%, respectively, which by the surface biophysical features fusion, the overall accuracy of land crop/cover maps increased to 83%. Also, by combining the classification results obtained from different scenarios based on voting strategy, the overall accuracy increased to 89%. The results of this study indicate that the feature level-based fusion of surface biophysical features and decision level based fusion of land crop/cover maps obtained from various scenarios increases the accuracy of land crop/cover classification.

The effects of grazer exclosure duration on soil microbial communities on the Qinghai-Tibetan Plateau.

While determining the response of soil microbes to grazer exclosure duration is critical to understanding ecosystem restoration processes, few studies have focused on this issue. With seasonal grazing as a control, microbes of alpine grassland soils under 5, 13, 22, and 39 years of grazer exclosure situated in the eastern part of the Qinghai-Tibetan Plateau, were examined. Microbial diversity was determined through Illumina high-throughput sequencing of the 16S rRNA gene and an internal transcription spacer (ITS). We found that soil bacterial α-diversity showed insignificant differences between seasonal grazing and grazer exclosure and among the grazer exclosures of different durations, while fungal α-diversity under the 5-year grazer exclosure was significantly different from those under the other treatments. Soil microbial community profiles under the 13-, 22-, and 39-year grazer exclosures were significantly different compared to those under the seasonal grazing or 5-year grazer exclosure. Briefly, longer exclosure durations led to a higher relative abundance of multiple copiotrophic microbial lineages (e.g., ß-Proteobacteria, Rhizobiales, and Frankiales), whereas several oligotrophic microbial lineages (e.g., Chloroflexi, Leotiomycetes, and Xylariales) gradually and significantly decreased. Functional predictions suggest that as grazer exclosure duration was extended, the relative abundance of nitrogen fixers increased, while the proportions of plant pathogenic fungi decreased. This indicates that long-term grazer exclosure duration may contribute to enhanced soil nitrogen fixation and grassland health by maintaining plant growth and decreasing the risk of plant disease. However, this may have a resource cost as plant productivity and soil organic carbon both decreased with the extension of grazer exclosure duration. Therefore, the agroecology effect of grazer exclosure duration on the diversity and abundance of soil nitrogen fixing bacteria and plant pathogen fungi, should be given more attention in the cold and humid portion of the Qinghai-Tibetan Plateau.

Leaf Stoichiometry of Potentilla fruticosa Across Elevations in China's Qilian Mountains.

As an individual plant species can develop its own leaf stoichiometry to adapt to environmental changes, this stoichiometry can provide critical information about a plant species' growth and its potential management in the ecosystem housing it. However, leaf stoichiometry is largely undocumented in regions with large environmental changes arising from differences in elevation. The leaf stoichiometry of Potentilla fruticosa L., a major alpine shrub playing an important role in supporting ecosystem functions and services in China's Qilian Mountains (Northeast Qinghai-Tibetan Plateau), was investigated at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,500, and 3,800 m). At each elevation, leaf elemental (C, N, and P) concentrations were measured in P. fruticosa leaves sampled from three plots (10 × 10 m), and edaphic properties were assessed in nine quadrats (1 × 1 m, three quadrats per plot). Temperature and precipitation were calculated using an empirical formula. Maximum and minimum leaf carbon (C) concentrations ([C] leaf ) of 524 ± 5.88 and 403 ± 3.01 g kg-1 were measured at 2,600 and 3,500 m, respectively. Leaf nitrogen (N) concentration ([N] leaf ) showed a generally increasing trend with elevation and peaked at 3,500 m (27.33 ± 0.26 g kg-1). Leaf phosphorus (P) concentration ([P] leaf ) varied slightly from 2,400 to 3,200 m and then dropped to a minimum (0.60 ± 0.10 g kg-1) at 3800 m. The [C] leaf :[N] leaf , [C] leaf :[P] leaf , and [N] leaf :[P] leaf varied little from 2,400 to 3,000 m but fluctuated somewhat at higher elevations. The main factors affecting P. fruticosa leaf stoichiometry were soil organic C, pH, and soil total P, and the main limiting element for the growth of P. fruticosa in the study area was P. In conclusion, changes in elevation affected leaf stoichiometry of P. fruticosa mainly due to altered soil properties, and addressing phosphorus limitation, especially at higher elevations mainly due to losses caused by high precipitation and sparse vegetation, is a key measure to promote P. fruticosa growth in this region.

Modeling potential habitats and predicting habitat connectivity for Leucanthemum vulgare Lam. in northwestern rangelands of Iran.

Invasive plants can alter the function and structure of ecosystems resulting in social, economic, and ecological damage. Effective methods to reduce the dominance of invasive plants are needed. The present study was aimed at modeling the invasive species Leucanthemum vulgare Lam. in the rangelands of the Namin region in northwest Iran, as well as predicting the habitat connectivity of this species to detect areas with high habitat connectivity. Modeling of potential habitats was performed using logistic regression (LR) and maximum entropy (MaxEnt); the ensemble map which resulted from these was used to predict habitat connectivity using the electrical circuit method. Topography (elevation, slope, and aspect), climate (precipitation and temperature), and soil (acidity, electrical conductivity, soil texture, calcium, magnesium, sodium, phosphorus, potassium, organic carbon, organic matter, saturation percentage, and total neutralizing value) were used in this study. The presence and absence points of the L. vulgare were recorded using a stratified-random sampling method by means of a global positioning system. Soil samples were collected at a depth of 0 to 30 cm where L. vulgare was present and also where it was absent. According to the results, in LR, the variables of temperature, phosphorus, organic matter, and sand and in the MaxEnt, the variables of sand, total neutralizing value (TNV), and silt were the most influential factors on the distribution of L. vulgare. The appraisal of the MaxEnt performance and the precision of the model prediction were 0.97. The Kappa indices for the predicted map obtained from the LR and MaxEnt models were 0.80 and 0.73, respectively. The models' evaluation indicated that both models were able to predict the distribution of L. vulgare habitats with a high level of accuracy; however, LR was more reliable. According to the LR prediction, 9.91% (10,556.25 ha) of the Namin region was attacked by L. vulgare. Connectivity assessment showed that the current density spread of L. vulgare continued from the northeast of the Namin region toward the southeast. On the other hand, the higher current density spread was demonstrated in the eastern region (rangelands adjacent to Fandoghlu forests), and other rangelands which are more threatened by the invasion of L. vulgare. Identifying sites exposed to invasive species can help implement programs to prevent invasive species from invading areas where management and prevention should be implanted to prevent and/or reduce the spread.

Tapping the immunological imprints to design chimeric SARS-CoV-2 vaccine for elderly population.

The impact of SARS-CoV-2 and COVID-19 disease susceptibility varies depending on the age and health status of an individual. Currently, there are more than 140 COVID-19 vaccines under development. However, the challenge will be to induce an effective immune response in the elderly population. Analysis of B cell epitopes indicates the minor role of the stalk domain of spike protein in viral neutralization due to low surface accessibility. Nevertheless, the accumulation of mutations in the receptor-binding domain (RBD) might reduce the vaccine efficacy in all age groups. We also propose the concept of chimeric vaccines based on the co-expression of SARS-CoV-2 spike and influenza hemagglutinin (HA) and matrix protein 1 (M1) proteins to generate chimeric virus-like particles (VLP). This review discusses the possible approaches by which influenza-specific memory repertoire developed during the lifetime of the elderly populations can converge to mount an effective immune response against the SARS-CoV-2 spike protein with the possibilities of designing single vaccines for COVID-19 and influenza. HighlightsImmunosenescence aggravates COVID-19 symptoms in elderly individuals.Low immunogenicity of SARS-CoV-2 vaccines in elderly population.Tapping the memory T and B cell repertoire in elderly can enhance vaccine efficiency.Chimeric vaccines can mount effective immune response against COVID-19 in elderly.Chimeric vaccines co-express SARS-CoV-2 spike and influenza HA and M1 proteins.

Factors dominating the horizontal and vertical variability of soil water vary with climate and plant type in loess deposits.

Identifying the variability and predominant factors affecting soil water (SW) is essential in regions with thick vadose zones and deep-rooted plants. This information is needed to clarify the balance between water availability and plant water demand. We collected 9263 soil samples from 128 profiles of 7-25 m deep soil under different climates (arid, semiarid and subhumid), soil textures and plant types (shallow or deep roots) in China's Loess Plateau. The factors dominating the horizontal and vertical variability of SW were identified using a multimodel inference approach and stepwise regression analysis. Horizontally, the mean water content and storage increased while the water deficits decreased from the northwest to the southeast. Vertically, mean water content and storage are highest in the relatively stable layer, followed by rapidly changing layers and active layers. Plant age and soil clay content dominate the horizontally varied SW, while plant age and normalized difference vegetation index (NDVI) dominate the vertical variability of SW. However, the dominant factors appeared to differ with climate and plant type. It was determined that for climate, soil clay content and plant age in arid regions, precipitation and plant age in semiarid regions, NDVI and plant age in subhumid regions were important factors. For plants, the dominant factors are NDVI and precipitation under shallow-rooted plants; however, NDVI and plant age were dominant under deep-rooted plants. The dominance of plant age highlighted the impact of vegetation patterns on SW, especially for deep-rooted plants, which should be taken into account when managing water resources and ecosystem rehabilitation in degraded regions.

Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy.

The absorbance spectra for air-dried and ground soil samples from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) method included in 'prospectr' R package along with four modeling approaches: partial least square regression (PLSR), cubist, random forest (RF), and extreme learning machine (ELM) for prediction of the soil organic matter (SOM). The 1st derivative + gap, 2nd derivative + gap and standard normal variance (SNV) were the best preprocessing algorithms. Thus, only these three preprocessing algorithms along with four modeling approaches were used for prediction of soil pH, electrical conductively (EC), %sand, %silt, %clay, %very coarse sand (VCS), %coarse sand (CS), %medium sand (ms) and %fine sand (fs). The results showed that OM, pH, %sand, %silt and %CS were all predicted with confidence (R2 > 0.60) and the combination of 1st derivative + gap and RF gained the best performance. A detailed comparison of the preprocessing and modeling algorithms for various soil properties in this study demonstrate that for better prediction of soil properties using VIS-NIR spectroscopy requires different preprocessing and modeling algorithms. However, in general RF and 1st derivative + gap can be labeled at the best combination of preprocessing and modelling algorithms.