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While organic fertilizers enhance tobacco quality and economic value, their low maturity levels often limit their effectiveness when applied within the same growing season, discouraging farmer adoption. Conventional composting methods typically require over 90 days to achieve full maturity. This study aimed to optimize composting methods and material formulations for large-scale factory production to shorten the fermentation period, enabling same-season application and benefits. Two large-scale experiments (each exceeding 100 tons) compared molecular membrane forced aeration combined with static and windrow composting against traditional windrow composting. Different material ratios of cow dung, mushroom residue, and distillery waste were also evaluated. Results showed that the optimized method, utilizing molecular membrane technology and adjusted material ratios, reduced the composting time from over 90 days to 60 days. The optimal formula consisted of cow dung, mushroom residue, and distillery waste at a 4:3:3 dry weight ratio with a moisture content ≤ 63%. After 60 days, this optimized process yielded organic fertilizer meeting all quality standards, including humic acid content, nutrient levels, and absence of harmful organisms. This accelerated composting method facilitates same-season fertilizer application, benefiting farmers and enhancing the efficiency and cost-effectiveness of organic fertilizer production.

Abstract Sea cucumbers are predominant deposit feeders in benthic ecosystems, providing protective benefits to coral reefs by reducing disease prevalence. However, how they receive sufficient nutrition from seabed sediments remains poorly understood. Here, we investigate Holothuria leucospilota, an ecologically significant tropical sea cucumber, to elucidate digestive mechanisms underlying marine deposit-feeding. Genomic analysis reveals intriguing evolutionary adaptation characterized by an expansion of digestive carbohydrase genes and a contraction of digestive protease genes, suggesting specialization in digesting microalgae. Developmentally, two pivotal dietary shifts, namely, from endogenous nutrition to planktonic feeding, and from planktonic feeding to deposit feeding, induce changes in digestive tract enzyme profiles, with adults mainly expressing carbohydrases and lipases. A nuanced symbiotic relationship exists between gut microbiota and the host, namely, specific resident bacteria supply crucial enzymes for food digestion, while other bacteria are digested and provide assimilable nutrients. Our study further identifies Holothuroidea lineage-specific lysozymes that are restrictedly expressed in the intestines to support bacterial digestion. Overall, this work advances our knowledge of the evolutionary innovations in the sea cucumber digestive system which enable them to efficiently utilize nutrients from seabed sediments and promote food recycling within marine ecosystems.

Abstract The use of nanomaterials in gene editing and synthetic biology has emerged as a pivotal strategy in the pursuit of refined treatment methodologies for pulmonary disorders. This review discusses the utilization of nanomaterial-assisted gene editing tools and synthetic biology techniques to promote the development of more precise and efficient treatments for pulmonary diseases. First, we briefly outline the characterization of the respiratory system and succinctly describe the principal applications of diverse nanomaterials in lung ailment treatment. Second, we elaborate on gene-editing tools, their configurations, and assorted delivery methods, while delving into the present state of nanomaterial-facilitated gene-editing interventions for a spectrum of pulmonary diseases. Subsequently, we briefly expound on synthetic biology and its deployment in biomedicine, focusing on research advances in the diagnosis and treatment of pulmonary conditions against the backdrop of the coronavirus disease 2019 pandemic. Finally, we summarize the extant lacunae in current research and delineate prospects for advancement in this domain. This holistic approach augments the development of pioneering solutions in lung disease treatment, thereby endowing patients with more efficacious and personalized therapeutic alternatives.

The development of electric vehicles (EVs) reduces dependence on fossil fuels, promotes energy conservation and emissions reduction, and facilitates the transition to clean energy sources in the power grid. However, subjective charging behavior among EV owners can lead to blind charging practices, compromising the reliability of the distribution network by widening the peak-to-valley difference. To address safety concerns during the charging process, this paper proposes hardware and software systems for an experimental verification system. The network architecture, focused on charging safety, is examined. Analysis of the system’s operation data reveals that it enables bidirectional interaction between electric vehicles and the power grid. This solution proves ef-fective for integrating a large number of EVs in peak-shaving and valley-filling efforts, laying a technical foundation for their inclusion in the power grid for peak shaving, valley filling, as well as providing standby and frequency regulation services.

Metal magnetic memory (MMM) is an innovative, nondestructive testing method. It can detect both stress concentrations and macroscopic defects. The three-dimensional force-magnetic coupling model was established by the ANSYS simulation software, the evolution process of different defect depths was studied in detail, and the change of the signal characteristic was analyzed. The results showed that the variation trend and amplitude characteristic of MMM signals resulted in obvious differences among different defect types. Meanwhile, the impacts caused by the defect parameters and the type are complex, which cannot be decoupled or calculated by a certain formula. The accuracy of the simulation data was verified by experiments. To solve the classification prediction problem in MMM detection, the signal peak and valley Hp-v, the signal width W, the gradient Ky, and the peak energy Hy were selected as characteristic parameters to evaluate different defect types according to the change in the signal waveform. Finally, using these vectors as the input variables, the radial basis function neural network (RBFNN) pre-classification test model was established to realize the classification recognition of pit defects, crack defects, and porosity defects. The results show that the accuracy of the training and test sets, and it is feasible to use this model to complete the intelligent classification of defects.

Abstract Hydrogels are a class of highly absorbent and easily modified polymer materials suitable for use as slow-release carriers for drugs. Gene therapy is highly specific and can overcome the limitations of traditional tissue engineering techniques and has significant advantages in tissue repair. However, therapeutic genes are often affected by cellular barriers and enzyme sensitivity, and carrier loading of therapeutic genes is essential. Therapeutic gene hydrogels can well overcome these difficulties. Moreover, gene-therapeutic hydrogels have made considerable progress. This review summarizes the recent research on carrier gene hydrogels for the treatment of tissue damage through a summary of the most current research frontiers. We initially introduce the classification of hydrogels and their cross-linking methods, followed by a detailed overview of the types and modifications of therapeutic genes, a detailed discussion on the loading of therapeutic genes in hydrogels and their characterization features, a summary of the design of hydrogels for therapeutic gene release, and an overview of their applications in tissue engineering. Finally, we provide comments and look forward to the shortcomings and future directions of hydrogels for gene therapy. We hope that this article will provide researchers in related fields with more comprehensive and systematic strategies for tissue engineering repair and further promote the development of the field of hydrogels for gene therapy. Graphical abstract

Abstract The sterile insect technique is based on the overflooding of a target population with released sterile males inducing sterility in the wild female population. It has proven to be effective against several insect pest species of agricultural and veterinary importance and is under development for Aedes mosquitoes. Here, we show that the release of sterile males at high sterile male to wild female ratios may also impact the target female population through mating harassment. Under laboratory conditions, male to female ratios above 50 to 1 reduce the longevity of female Aedes mosquitoes by reducing their feeding success. Under controlled conditions, blood uptake of females from an artificial host or from a mouse and biting rates on humans are also reduced. Finally, in a field trial conducted in a 1.17 ha area in China, the female biting rate is reduced by 80%, concurrent to a reduction of female mosquito density of 40% due to the swarming of males around humans attempting to mate with the female mosquitoes. This suggests that the sterile insect technique does not only suppress mosquito vector populations through the induction of sterility, but may also reduce disease transmission due to increased female mortality and lower host contact.

Digestive system diseases, such as gastritis, gastric ulcers, chronic liver disease, inflammatory bowel disease (IBD), and colorectal cancer, represent a major group of diseases that have high morbidity and death rates worldwide. Their incidence continues to rise owing to factors such as dietary structure changes, accelerated lifestyles, increased environmental pollution, and population aging. Despite the rapid development of the medical technology, the treatment of digestive diseases still faces many challenges, such as addressing drug-resistant Helicobacter pylori infections, treating IBD, and improving the efficacy of advanced gastrointestinal tumor therapies. Fortunately, the emergence of drug-releasing materials has provided new insights that can be used in the treatment of digestive disorders. Drug-releasing materials are a category of specially designed carriers or systems capable of carrying drugs and controlling their release at specific time intervals on demand to achieve the desired therapeutic effect. This article reviews recent research progress of drug-releasing materials used to diagnose and treat digestive disorders. First, the limitations of traditional oral drug delivery methods, such as low bioavailability and nonspecific distribution, are discussed. Second, different types of drug-releasing materials, such as liposomes, dendritic polymers, micelles, nanogels, inorganic nanoparticles, and extracellular vesicles, along with their advantages in terms of improved drug stability, biocompatibility, targeting, and controlled release, are outlined. In addition, the application strategies and preclinical findings of various drug release materials for different digestive disorders are discussed in detail. This Review could help researchers explore more advanced nanomaterials for personalized treatment of drug delivery for digestive disorders.

Abstract Background Oxidized regenerated cellulose (ORC) is a type of biodegradable hemostatic material, which has been widely used in the field of surgery. However, its hemostatic effect in patients undergoing total knee arthroplasty (TKA) is uncertain. Accordingly, this study investigated the effectiveness and safety of ORC in patients receiving TKA. Methods Seventy patients undergoing unilateral TKA were randomized into blank control group and ORC (2 pieces of ORC placed in the joint cavity) groups. Then, the two groups were compared for primary (perioperative blood loss [total blood loss, intraoperative blood loss, and hidden blood loss] and hemoglobin drop values) and secondary (coagulation indicators, inflammatory indicators,operation time, and complication rates) outcomes. Results The total blood loss in the ORC group was 902.32 ± 307.82 mL, which was statistically significantly lower than that in the control group (1052.25 ± 308.44 mL) (P

Soil nutrients are an important component of soil fertility, and having accurate and timely soil nutrients information is conducive to scientifically guided agricultural fertilization and improved crop yields. Traditional soil nutrient measurement methods are accurate but time-consuming and costly. Visible near-infrared (vis-NIR) and mid-infrared (MIR) as techniques of spectroscopy measurements are cheap, fast, and non-destructive, and, after training, have been used separately to predict soil nutrients. They can give complementary information, so using them separately may not be optimal. This study investigated whether the fusion of vis-NIR and MIR spectra would improve the prediction of six soil nutrients: total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP) and available potassium (AK). The sample set was 501 tillage (0 ∼ 20 cm) soil samples from Guizhou Province (China). Three different sensor fusion techniques were compared: (1) direct fusion of spectra (low-level fusion; SF1); (2) fusion of spectral features selected by the least absolute shrinkage and selection operator (LASSO) algorithm (middle-level fusion; SF2); and (3) fusion using the Granger-Ramanathan averaging (GRA) method of the separate vis-NIR and MIR results (high-level fusion; SF3). Prediction models were built using partial least squares regression (PLSR) and support vector machine (SVM), and evaluated using leave-one-out cross-validation (LOOCV), coefficient of determination (R2), and ratio of performance to interquartile (RPIQ), defined as Rcv2 and RPIQCV. All three sensor fusion methods were more accurate than single-sensor methods in predicting TN, TK and AN (0.60 ≤ Rcv2 ≤ 0.92, 1.86 ≤ RPIQCV ≤ 5.08), but were less accurate for TP, AP and AK (Rcv2 ≤ 0.34, RPIQCV ≤ 1.55). Compared to the best prediction using each single sensor model, low-level fusion improved the prediction of TN and AN; middle-level fusion improved the prediction of TN, TP, AN, AP and AK; and high-level fusion improved the prediction of all soil nutrients. Moreover, the SF2-PLSR model provided the best prediction for TN (Rcv2 = 0.83, RPIQCV = 3.18), the SF2-SVM model for AN (Rcv2 = 0.67, RPIQCV = 2.06), and the SF3-PLSR model for TK (Rcv2 = 0.92, RPIQCV = 5.08). The SF2 and SF3 methods are recommended to predict TN and AN, and MIR spectra are recommended to predict TK (Rcv2 = 0.92, RPIQCV = 5.02) since using only a single sensor is cost-effective. The SF2 and SF3 methods improved the prediction accuracy of AP and AK, but the prediction accuracy was still low. This study only covered one area with its set of soil landscapes, and only used well-established modelling methods. The results can motivate research on new spectra techniques and advanced modelling methods, as well as transferability to other soil landscapes.

During the seedlings culture of sea cucumbers including Holthuria leucospilota, natural microalgae are generally used as ideal food for the pelagic larvae of sea cucumbers, which lead to a strong demand for microalgae production. However, microalgae production is time-consuming and laborious and susceptible to pests and variable whether, and unstable supply of microalgae often results in culture failure of sea cucumber larvae. In this study, we tested the effect of three different diets on the growth, survival rate, and internal microbiota composition of sea cucumbers aiming to develop an artificial diet for H. leucospilota larvae. The results showed that the sea cucumber larvae fed with the mixed artificial diet (composed of Sargassum powder, chilled macroalgae solution, wall-breaking Chlorella powder, and yeast powder) had the most balanced growth rate, highest survival rate, and a higher α-diversity and abundance of Plesiocystis, Haloferula, and Firmicutes in body when they were compared to the larvae fed with the natural bait, Chaetoceros muelleri. However, the abundance of potential pathogenic bacteria, Francisella and Acinetobacter, was higher in the larvae fed with the natural bait. In conclusion, the mixed artificial diet was developed for H. leucospilota larval culture, which can be used as a good alternative to live microalga with cumbersome processes.

2, 6-diisopropylaniline (2, 6-DIPA) is a crucial non-intentionally organic additive that allows the assessment of the production processes, formulation qualities, and performance variations in biodegradable mulching film. Moreover, its release into the environment may have certain effects on human health. Hence, this study developed simultaneous heating hydrolysis–extraction and amine switchable hydrophilic solvent vortex-assisted homogeneous liquid–liquid microextraction for the gas chromatography–mass spectrometry analysis of the 2, 6-DIPA additive and its corresponding isocyanates in poly(butylene adipate-co-terephthalate) (PBAT) biodegradable agricultural mulching films. The heating hydrolysis–extraction conditions and factors influencing the efficiency of homogeneous liquid–liquid microextraction, such as the type and volume of amine, homogeneous-phase and phase separation transition pH, and extraction time were investigated and optimized. The optimum heating hydrolysis–extraction conditions were found to be a H2SO4 concentration of 2.5 M, heating temperature of 87.8 °C, and hydrolysis–extraction time of 3.0 h. As a switchable hydrophilic solvent, dipropylamine does not require a dispersant. Vortex assistance is helpful to speed up the extraction. Under the optimum experimental conditions, this method exhibits a better linearity (0.0144~7.200 μg mL−1 with R = 0.9986), low limit of detection and quantification (0.0033 μg g−1 and 0.0103 μg g−1), high extraction recovery (92.5~105.4%), desirable intra- and inter-day precision (relative standard deviation less than 4.1% and 4.7%), and high enrichment factor (90.9). Finally, this method was successfully applied to detect the content of the additive 2, 6-DIPA in PBAT biodegradable agricultural mulching films, thus facilitating production process monitoring or safety assessments.

To date, studies on apoptosis in echinoderms mainly focus on the caspase-dependent extrinsic apoptotic pathway, while knowledges regarding the intrinsic apoptotic pathway is still limited. In this study, 55 genes belong to 7 families of the intrinsic apoptotic pathway, namely, Bcl-2, caspase, AIF, Cyt c, Apaf-1, Smac, HTRA2 and Endo G, were identified by screening of the genomic database of the tropical sea cucumber Holothuria leucospilota. The Bcl-2 family showed significant gene expansion in H. leucospilota but not in other echinoderms, while the pro-apoptotic factor Endo G is generally lost in echinoderms. Transcriptomic data showed that the genes of the intrinsic apoptotic pathway were ubiquitously expressed in all tissues, and all embryonic and larval developmental stages of H. leucospilota. The gene numbers and functional domains of the caspase family and AIF family were further clarified and showed conserved with their counterparts in vertebrates. In primary coelomocytes, the transcript expression of different caspases showed different responses to pathogenic challenges of vibrio and pathogen-associated molecular patterns, and environmental challenges of heave metal and reactive oxygen species. Overall, this study indicates that the intrinsic apoptotic pathway is conserved between vertebrates and echinoderms by using the sea cucumber H. leucospilota as a model. This study may contribute in increasing the knowledge of development and innate immunity in ancestral deuterostomia species.

Background: The impact of COVID-19 on public health has mandated an ‘all hands on deck’ scientific response. The current clinical study and basic research on COVID-19 are mainly based on existing publications or our knowledge of coronavirus. However, efficiently retrieval of accurate, relevant knowledge on COVID-19 can pose significant challenges for researchers. Methods: To improve quality in accessing important literature findings, we developed a novel natural language processing (NLP) method to automatically recognize the associations among potential targeted host organ systems, associated clinical manifestations, and pathways. We further validated these associations through clinician experts' evaluations and prioritize candidate drug targets through bioinformatics network analysis. Results: We found that the angiotensin-converting enzyme 2 (ACE2), a receptor that SARS-CoV-2 required for cell entry, is associated with cardiovascular and endocrine organ system and diseases. Furthermore, we found SARS-CoV-2 is associated with some important pathways such as IL-6, TNF-alpha, and IL-1 beta-induced dyslipidemia, which are related to inflammation, lipogenesis, and oxidative stress mechanisms, suggesting potential drug candidates. Conclusion: We prioritized the list of therapeutic targets involved in antiviral and immune modulating drugs for experimental validation, rendering it valuable during public health crises marked by stresses on clinical and research capacity. Our automatic intelligence pipeline also contributes to other novel and emerging disease management and treatments in the future.

BackgroundDengue fever has been responsible for around 12 countrywide large outbreaks in Pakistan, resulting in 286,262 morbidities and 1,108 deaths. Khyber Pakhtunkhwa (KP) is the most recently impacted province. This study aimed to investigate the molecular, epidemiological, and potential elements that contribute to increasing dengue transmission patterns, and knowledge, attitude, and practice (KAP) toward dengue in KP province.MethodThis cross-sectional community-based study was conducted (June-December, 2021) in two phases. Phase I involved the epidemiological (n = 5,242) and molecular analysis of DENV in 500 randomly collected blood samples of the 2021 dengue outbreak in KP. Phase II focused on assessing dengue-KAP levels in healthy communities (n = 14,745, aged >18 years), adopting a cross-sectional clustered multistage sampling in eight districts (dengue-hotspot vs. non-hotspot) of KP. Chi-square tests and logistic regression analysis were applied.ResultsPeshawar district had the highest dengue cases (60.0%) associated with the predominant co-circulation of DENV-2 (45.8%) and DENV-3 (50.4%) serotypes. A rise in cases was reported in October (41.8%) followed by September (27.9%) and August (14.4%; p < 0.001). Males (63.7%, p < 0.001) and individuals aged 16–30 years (37.0%, p < 0.001) were highly affected. General workers (18.0%), families with a monthly income of 10,000–20,000 Pak rupees (50.5%), unmarried (71.0%), uneducated (31%), families with higher human density (>10 individuals per household), and those (29.0%) who faced power outages for more than 7/24 h were the most affected. Moreover, co-morbidities like renal failure and bronchial asthma were associated with disease severity. A community survey on KAP revealed that an average of 74, 60, and 43% of the participants demonstrated good knowledge, attitudes, and dengue preventive practices, respectively.ConclusionMultiple poor socioeconomic elements are influencing dengue fever transmission in the province. Higher KAP levels may explain the low frequency of dengue in non-hotspot districts. Our study emphasizes the need for effective and long-term public health education, strengthened vector surveillance, and expanded laboratory capacity for better diagnosis and management of dengue cases to better predict the burden and seasonality of disease in the country.

Multi-modal (i.e., visible, near-infrared, and thermal-infrared) vehicle re-identification has good potential to search vehicles of interest in low illumination. However, due to the fact that different modalities have varying imaging characteristics, a proper multi-modal complementary information fusion is crucial to multi-modal vehicle re-identification. For that, this paper proposes a progressively hybrid transformer (PHT). The PHT method consists of two aspects: random hybrid augmentation (RHA) and a feature hybrid mechanism (FHM). Regarding RHA, an image random cropper and a local region hybrider are designed. The image random cropper simultaneously crops multi-modal images of random positions, random numbers, random sizes, and random aspect ratios to generate local regions. The local region hybrider fuses the cropped regions to let regions of each modal bring local structural characteristics of all modalities, mitigating modal differences at the beginning of feature learning. Regarding the FHM, a modal-specific controller and a modal information embedding are designed to effectively fuse multi-modal information at the feature level. Experimental results show the proposed method wins the state-of-the-art method by a larger 2.7% mAP on RGBNT100 and a larger 6.6% mAP on RGBN300, demonstrating that the proposed method can learn multi-modal complementary information effectively.

Ever-increasing consumer demand for sea cucumbers mainly leads to huge damage to wild sea cucumber resources, including Stichopus monotuberculatus, which in turn exerts negative impacts on marine environments due to the lack of ecological functions performed by sea cucumbers. Aquaculture of sea cucumbers is an effective way to meet consumer demand and restore their resources. Unsynchronous growth is a prominent problem in the aquaculture of sea cucumbers which has concealed unelucidated molecular mechanisms until now. In this study, we carried out an integrative analysis of transcriptomics and metabolomics on fast-growing (SMF) and slow-growing (SMS) groups of S. monotuberculatus cultured in the same environmental conditions. The results revealed that a total of 2054 significantly differentially expressed genes (DEGs) were identified, which are mainly involved in fat digestion and absorption, histidine metabolism, arachidonic acid metabolism, and glutathione metabolism. 368 differential metabolites (DMs) were screened out between the SMF group and the SMS group; these metabolites are mainly involved in glycerophospholipid metabolism, purine metabolism, biosynthesis of unsaturated fatty acids, pyrimidine metabolism, arachidonic acid metabolism, and other metabolic pathways. The integrative analysis of transcriptomics and metabolomics of S. monotuberculatus suggested that the SMF group had a higher capacity for lipid metabolism and protein synthesis, and had a more frequent occurrence of apoptosis events, which are likely to be related to coping with environmental stresses. The results of this study provide potential values for the aquaculture of sea cucumbers which may promote their resource enhancement.

Poly(butylene adipate-co-terephthalate) biodegradable mulch film (PBAT-BMF) is gradually applied in agricultural production, but its potential ecological risks have not been studied so far. In this paper, methyl tert-butyl ether (MTBE) was used to extract organic compounds from PBAT-BMF to evaluate its effects on tobacco seed germination and seedling metabolism. The results showed that six organic compounds were found in the extract of PBAT-BMF, among which the content of diisopropylphenyl isocyanate was the highest at 557.27 μg g−1. A germination test showed that the extract scan inhibited the germination of tobacco seeds. The germination time was 1 d later than the control(CK), and the germination percentage and germination energy were non-significantly decreased by 2.50% and 1.00%. In contrast, the extracts significantly affected the root length, and seedlings height (p < 0.01), decreased by 0.24 cm and 0.28 cm, respectively. A metabolic analysis revealed that the extracts have a certain stress effect on tobacco seedlings and showed an up-regulating effect on soluble sugar, critical organic acid, biogenic amine, and down-regulating alkaloid, which indicated that the carbon and nitrogen metabolism pathway of the tricarboxylic acid cycle and alkaloid synthesis were disturbed. These results indicated that organic compounds extracted from PBAT-BMF had stress effects on germination and the growth of tobacco seeds, which significantly changed the metabolism pathway. This study can provide a theoretical basis for the development and application of PBAT-BMF.

Fertilization decisions depend on the measurement of a large set of soil fertility indicators, usually through laboratory determination, which is costly and time-consuming. Visible and near-infrared (vis-NIR) spectroscopy combined with machine learning can simultaneously predict various soil fertility indicators. Spectroscopy is inherently less accurate than direct laboratory determination. However, in many fertilization recommendation contexts, farmers mainly fertilize according to classified fertility indicators, rather than by continuous soil property values. These classes have defined limits of property values. We hypothesized that the additional inaccuracy from spectroscopy may not be important for properties grouped into classes. This study compared the indirect and direct prediction of soil fertility classes. Indirectly, by (1) using vis-NIR spectra with machine learning to predict 20 soil fertility indicators (pH, soil organic matter (SOM), cation exchange capacity (CEC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), calcium (Ca), magnesium (Mg), silicon (Si), sulfur (S), boron (B), iron (Fe), manganese (Mn), copper (Cu), Zinc (Zn), molybdenum (Mo) and chlorine (Cl)) and (2) allocating the indicators to soil fertility classes. Directly, by predicting soil fertility classes directly from vis-NIR spectra using machine learning. The prediction accuracy of these two methods were compared and the accuracies needed for the acceptable class allocation of the fertility indicators were determined. The example dataset is a soil spectral library from the Guizhou Province, southwest China. The model performance was evaluated by the overall allocation accuracy and tau index, which accounts for class imbalance. For direct allocation based on three fertility classes (low, medium and high), the overall allocation accuracy of eight properties (CEC, Cu, Si, Zn, S, Mn, Ca and Mg), nine properties (B, AN, TK, AK, SOM, TN, TP, Fe and Mo) and three properties (Cl, AP and pH) were within the range of 0.80–1.0, 0.60–0.80 and 0.40–0.60, respectively. For indirect allocation based on the same classes, the allocation accuracy of nine properties (TN, CEC, Cu, S, Zn, Si, Mn, Ca and Mg), nine properties (B, TK, pH, TP, AK, AN, Fe, Mo and SOM) and two properties (Cl and AP) were within the range of 0.80–1.0, 0.60–0.80 and 0.40–0.60, respectively. We conclude that vis-NIR spectroscopy was fairly successful for soil fertility class allocation for most of the soil properties, using either direct or indirect models. The advantage of indirect models is that both specific property values and soil fertility classes can be obtained at no increase in cost, while direct models are suggested when only soil fertility class information are available.

Mobile edge computing (MEC) is an effective assistant technology that can overcome some defects of cloud computing. For the sake of alleviating the clashes between the capability constraint of cloudlets and the needs of mobile devices (MDs) for reducing executing latency as well as decreasing the power consumption of MDs, a user-oriented use case in the MEC named computation offloading is taken into consideration. Computation offloading is capable of effectively making the MEC adapt to the resources of cloudlets and MDs in different environments, and it is very beneficial to the development of the internet of things. Owing to the finite computation capabilities of the MDs and the resources of cloudlets are heterogeneous and limited; a three-objective model is established to optimise the time consumption, and the energy consumption of MDs as well as the load balancing of cloudlets jointly. Technically, the authors propose an effective multi-user multi-application computation offloading method in the multi-cloudlet environment on the basis of improved non-dominated sorting genetic algorithm III. Finally, comprehensive experiments and analysis were conducted to validate the effectiveness and efficiency of the proposed method.

Abstract Background The trehalose (Tre) pathway has strong effects on growth and development in plants through regulation of carbon metabolism. Altering either Tre or trehalose 6-phosphate (T6P) can improve growth and productivity of plants as observed under different water availability. As yet, there are no reports of the effects of modification of Tre orT6P on plant performance under limiting nutrition. Results Here we report that nitrogen (N) metabolism is positively affected by exogenous application of Tre in nitrogen-deficient growing conditions. Spraying foliage of tobacco (Nicotiana tabacum) with trehalose partially alleviated symptoms of nitrogen deficiency through upregulation of nitrate and ammonia assimilation and increasing activities of nitrate reductase (NR), glycolate oxidase (GO), glutamine synthetase (GS) and glutamine oxoglutarate aminotransferase (GOGAT) with concomitant changes in ammonium (NH4 +) and nitrate (NO3 −) concentrations, glutamine and amino acids. Chlorophyll and total nitrogen content of leaves and rates of photosynthesis were increased compared to nitrogen-deficient plants without applied Tre. Total plant biomass accumulation was also higher in Tre -fed nitrogen-deficient plants, with a smaller proportion of dry weight partitioned to roots, compared to nitrogen-deficient plants without applied Tre. Consistent with higher nitrogen assimilation and growth, Tre application reduced foliar starch. Minimal effects of Tre feeding were observed on nitrogen-sufficient plants. Conclusions The data show, for the first time, significant stimulatory effects of exogenous Tre on nitrogen metabolism and growth in plants growing under deficient nitrogen. Under such adverse conditions metabolism is regulated for survival rather than productivity. Application of Tre can alter this regulation towards maintenance of productive functions under low nitrogen. This has implications for considering approaches to modifying the Tre pathway for to improve crop nitrogen-use efficiency and production.

The growth and development of plants are sensitive to their surroundings. Although numerous studies have analyzed plant transcriptomic variation, few have quantified the effect of combinations of factors or identified factor-specific effects. In this study, we performed RNA sequencing (RNA-seq) analysis on tobacco leaves derived from 10 treatment combinations of three groups of ecological factors, i.e., climate factors (CFs), soil factors (SFs), and tillage factors (TFs). We detected 4980, 2916, and 1605 differentially expressed genes (DEGs) that were affected by CFs, SFs, and TFs, which included 2703, 768, and 507 specific and 703 common DEGs (simultaneously regulated by CFs, SFs, and TFs), respectively. GO and KEGG enrichment analyses showed that genes involved in abiotic stress responses and secondary metabolic pathways were overrepresented in the common and CF-specific DEGs. In addition, we noted enrichment in CF-specific DEGs related to the circadian rhythm, SF-specific DEGs involved in mineral nutrient absorption and transport, and SF- and TF-specific DEGs associated with photosynthesis. Based on these results, we propose a model that explains how plants adapt to various ecological factors at the transcriptomic level. Additionally, the identified DEGs lay the foundation for future investigations of stress resistance, circadian rhythm and photosynthesis in tobacco.

In markets for beef and sheep meat, an appropriate level of intramuscular fat (IMF) is highly desirable for meat-eating quality, but strategies to improve it usually lead to an undesirable excess in carcase fat, presenting a major challenge to livestock producers. To solve this problem, we need to understand the partitioning of fat among the major fat depots: IMF, subcutaneous fat (SCF) and visceral fat (VF). In most genotypes of cattle and sheep, the rate of accretion is lower for IMF than for SCF and VF, so genetic selection for a high level of IMF, or the use of an increased dietary energy supply to promote IMF deposition, will increase overall fatness and feed costs. On the other hand, feeding postnatal calves with excessive concentrates promotes IMF deposition, so a nutritional strategy is feasible. With genetic strategies, several problems arise: 1) positive genetic correlations between IMF, SCF and VF differ among genotypes in both cattle and sheep; 2) genotypes appear to have specific, characteristic rates of accretion of IMF during periods of growth and fattening; 3) most breeds of cattle and sheep naturally produce meat with relatively low levels of IMF, but IMF does vary substantially among individuals and breeds so progress is possible through accurate measurement of IMF. Therefore, an essential prerequisite for selection will be knowledge of the genetic correlations and fat accretion rates for each genotype. Currently, selection for IMF is based on existing technology that directly measures IMF in the progeny or siblings, or estimates IMF in live animals. New technology is needed to permit the simultaneous measurement of SCF and IMF in the field, thus opening up the possibility of accurate selection, particularly for fat partitioning in live animals. Specifically, there would be great value in detecting individuals with an IMF advantage at an early age so the generation interval could be shortened and genetic gain accelerated. Genetic gain would also be greatly aided if we could select for genes that control adipogenesis and lipogenesis and are also differentially expressed in the various depots.