Microbial agents obtained from tomato straw composting effectively promote tomato straw compost maturation and improve compost quality.

Appropriate management of agricultural organic waste (AOW) presents a significant obstacle in the endeavor to attain sustainable agricultural development. The proper management of AOW is a necessity for sustainable agricultural development. This can be done skillfully by incorporating microbial agents in the composting procedure. In this study, we isolated relevant bacteria strains from tomato straw AOW, which demonstrated efficient degradation of lignocellulose without any antagonistic effects in them. These strains were then combined to create a composite microbial agent called Zyco Shield (ZS). The performance of ZS was compared with a commercially effective microorganism (EM) and a control CK. The results indicate that the ZS treatment significantly prolonged the elevated temperature phase of the tomato straw pile, showing considerable degradation of lignocellulosic material. This substantial degradation did not happen in the EM and CK treatments. Moreover, there was a temperature rise of 4-6 â„ƒ in 2 days of thermophilic phase, which was not the case in the EM and CK treatments. Furthermore, the inoculation of ZS substantially enhanced the degradation of organic waste derived from tomato straw. This method increased the nutrient content of the resulting compost and elevated the enzymatic activity of lignocellulose-degrading enzymes, while reducing the urease enzyme activity within the pile. The concentrations of NH4+-N and NO3--N showed increases of (2.13% and 47.51%), (14.81% and 32.17%) respectively, which is again very different from the results of the EM and CK treatments. To some extent, the alterations observed in the microbial community and the abundance of functional microorganisms provide indirect evidence supporting the fact that the addition of ZS microbial agent facilitates the composting process of tomato straw. Moreover, we confirmed the degradation process of tomato straw through X-ray diffraction, Fourier infrared spectroscopy, and by scanning electron microscopy to analyze the role of ZS microbial inoculum composting. Consequently, reinoculation compost strains improves agricultural waste composting efficiency and enhances product quality.

Effect of Green Light Replacing Some Red and Blue Light on Cucumis melo under Drought Stress.

Light quality not only directly affects the photosynthesis of green plants but also plays an important role in regulating the development and movement of leaf stomata, which is one of the key links for plants to be able to carry out normal growth and photosynthesis. By sensing changes in the light environment, plants actively regulate the expansion pressure of defense cells to change stomatal morphology and regulate the rate of CO2 and water vapor exchange inside and outside the leaf. In this study, Cucumis melo was used as a test material to investigate the mitigation effect of different red, blue, and green light treatments on short-term drought and to analyze its drought-resistant mechanism through transcriptome and metabolome analysis, so as to provide theoretical references for the regulation of stomata in the light environment to improve the water use efficiency. The results of the experiment showed that after 9 days of drought treatment, increasing the percentage of green light in the light quality significantly increased the plant height and fresh weight of the treatment compared to the control (no green light added). The addition of green light resulted in a decrease in leaf stomatal conductance and a decrease in reactive oxygen species (ROS) content, malondialdehyde MDA content, and electrolyte osmolality in the leaves of melon seedlings. It indicated that the addition of green light promoted drought tolerance in melon seedlings. Transcriptome and metabolome measurements of the control group (CK) and the addition of green light treatment (T3) showed that the addition of green light treatment not only effectively regulated the synthesis of abscisic acid (ABA) but also significantly regulated the hormonal pathway in the hormones such as jasmonic acid (JA) and salicylic acid (SA). This study provides a new idea to improve plant drought resistance through light quality regulation.

Brown sugar as a carbon source can make agricultural organic waste compost enter the secondary thermophilic stage and promote compost decomposition.

To enhance the efficiency of composting agricultural organic waste (AOW), this study aimed to examine the impact of inoculating tomato straw compost with two distinct microbial agents: ZymoZone (ZZ), a composite microbial agent derived from the straw compost and Effective Microorganisms (EM), a commercial microbial agent. Furthermore, in order to reactivate the microorganisms within the compost during the initial high temperature phase, 10% brown sugar was introduced as a carbon source. The objective of this addition was to assess its influence on the composting process. The findings revealed that compared to the control (CK) group, the ZZ and EM treatments extended the first high-temperature phase by 2 and 1 day, respectively. Furthermore, with the addition of 10% brown sugar, the ZZ and EM treatments remained in the second high-temperature phase for 8 and 7 days, respectively, while the CK treatment had already entered the cooling stage by then. Notably, the inoculation of microbial agents and the addition of brown sugar substantially augmented the activity of lignocellulose-related hydrolases, thereby promoting the degradation of lignocellulose in the ZZ and EM treatment groups. This was confirmed by FTIR analysis, which demonstrated that the addition of microbial agents facilitated the degradation of specific substances, leading to reduced absorbance in the corresponding spectra. XRD analysis further indicated a notable reduction in cellulose crystallinity for both the ZZ (8.00%) and EM (7.73%) treatments. Hence, the incorporation of microbial agents and brown sugar in tomato straw compost effectively enhances the composting process and improves the quality of compost products.

Identification and genetic analysis of alternative splicing of long non-coding RNAs in tomato initial flowering stage.

Alternative splicing (AS) is a key modulator of development in many eukaryotic organisms. Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs that play essential regulatory roles in various developmental processes and stress responses. However, the functions of AS lncRNAs during the initial flowering of tomato are largely unknown. This study was designed to investigate the AS pattern of lncRNAs in tomato flower, leaf, and root tissues at the initial flowering stage. Using RNA-Seq, we found that 72.55% of lncRNAs underwent AS in these tissues, yielding a total of 16,995 AS events. Among them, the main type of AS event is alternative first exon (AFE), followed by retained intron (RI). We performed candidate target genes analysis on tissue-specific AS lncRNA, and the results indicated that the candidate target genes of these lncRNAs may be involved in the regulation of circadian rhythm, plant immunity, cellulose synthesis and phosphate-containing compound metabolic process. Moreover, a total of 73,085 putative SNPs and 15,679 InDels were detected, and the potential relationship between the AS of lncRNAs and interesting SNP and InDel loci, as well as their numbers, revealed their effects on tomato genetic diversity and genomic stability. Our data provide new insights into the complexity of the transcriptome and the regulation of AS.

Optical electric field sensor sensitivity direction rerouting and enhancement using a passive integrated dipole antenna.

This work introduces a passive dipole antenna integrated into the packaging of a slab-coupled optical sensor to enhance the directional sensitivity of electro-optic electric field measurements parallel to the fiber axis. Using the passive integrated dipole antenna described in this work, a sensor that can typically only sense fields transverse to the fiber direction is able to sense a 1.25 kV/m field along the fiber direction with a gain of 17.5. This is verified through simulation and experiment.

In Situ Construction of CdS/g-C3N4 Heterojunctions in Spent Thiolation@Wood-Aerogel for Efficient Excitation Peroxymonosulfate to Degradation Tetracycline.

Pollutant treatment, hazardous solid waste conversion, and biomass resource utilization are significant topics in environmental pollution control, and simultaneously achieving them is challenging. Herein, we developed a "from waste absorbent to effective photocatalyst" upcycle strategy for nontoxic conversion of Cd(II) adsorbed on thiolation@wood-aerogel (TWA) into CdS/g-C3N4 heterojunctions through the in situ chemical deposition high-temperature carbonization combined conversion method to overcome the above problems simultaneously. We used Schiff base reaction to graft l-cysteine into dialdehyde@wood-aerogel to prepare TWA with a high Cd(II) adsorption capacity (600 mg/L, 294.66 mg/g). Subsequently, the spent Cd(II)-loaded-TWA was used as a substrate for in situ construction of Cd(II) into CdS/g-C3N4 heterojunction for activating peroxymonosulfate (PMS) under simulated sunlight [simulated solar light (SSL)], achieving efficient tetracycline (TC) degradation (20 mg/L, 95.32%). The Langmuir and pseudo-second-order models indicate single-layer chemical adsorption of Cd(II) on the TWA adsorption process. In the PMS/SSL system, CdS/g-C3N4@TWA efficiently and rapidly degraded TC via an adsorption-photocatalytic synergistic degradation mechanism. The used CdS/g-C3N4@TWA has a good biocompatibility. This study proposed design and preparation of a new type of wood aerogel absorbent and provided a novel upcycling strategy for innovative use of the spent waste adsorbent.

Prediction of patient choice tendency in medical decision-making based on machine learning algorithm.

Objective: Machine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions. Method: Patient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared. Results: The accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94. Conclusion: Among the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making.

Pretreatment and composting technology of agricultural organic waste for sustainable agricultural development.

With the continuous development of agriculture, Agricultural organic waste (AOW) has become the most abundant renewable energy on earth, and it is a hot spot of research in recent years to realize the recycling of AOW to achieve sustainable development of agricultural production. However, lignocellulose, which is difficult to degrade in AOW, greenhouse gas emissions, and pile pathogenic fungi and insect eggs are the biggest obstacles to its return to land use. In response to the above problems researchers promote organic waste recycling by pretreating AOW, controlling composting conditions and adding other substances to achieve green return of AOW to the field and promote the development of agricultural production. This review summarizes the ways of organic waste treatment, factors affecting composting and problems in composting by researchers in recent years, with a view to providing research ideas for future related studies.

Systematic Identification and Analysis of Light-Responsive Circular RNA and Co-expression Networks in Lettuce (Lactuca sativa).

Circular RNA (circRNA) is a covalently-closed single-stranded RNA molecule that plays an important role in transcriptional regulation of gene expression in a variety of species. Light intensity is a pivotal environmental factor affecting plant growth and development. However, little is known regarding photoresponsive plant circRNAs. Here, we aimed to investigate the expression and function of circRNAs in lettuce leaves in response to different light intensity treatments. We performed RNA sequencing (RNA-Seq) on leaves of lettuce (Lactuca sativa) to determine circRNA expression profiles and reverse-transcription polymerase chain reaction (PCR) to validate the candidate circRNA molecules. We then combined bioinformatics approach to explore the function of the parental genes of circRNA, including network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We identified 1650 circRNAs in lettuce, of which 1508 (86.40%) were derived from exons. Using real-time PCR, we characterized 10 validated differentially expressed circRNAs and their parental genes, all of which showed expression patterns consistent with RNA-Seq data. Interestingly, the expression of circRNA was, in some cases, inversely correlated with the expression of the parental gene. Furthermore, analysis of the circRNA-microRNA-mRNA network suggests that circRNAs may be involved in plant hormone signaling and chlorophyll metabolism during photoreactivity. These findings provide an essential reference basis for studying circRNAs' biological mechanisms in light-treated plants.

LncRNA expression profile and ceRNA analysis in tomato during flowering.

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs that play essential regulatory roles in various developmental processes and stress responses. However, the functions of lncRNAs during the flowering period of tomato are largely unknown. To explore the lncRNA profiles and functions during flowering in tomato, we performed strand-specific paired-end RNA sequencing of tomato leaves, flowers and roots, with three biological replicates. We identified 10919 lncRNAs including 248 novel lncRNAs, of which 65 novel lncRNAs were significantly differentially expressed (DE) in the flowers, leaves, and roots. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out to identify the cis target gene of DE lncRNAs. The results showed that the lncRNAs might play an important role in the growth, development, and apoptosis of flowering tomato plant by regulating the formation of intima in flower tissues, binding to various molecules, influencing metabolic pathways, and inducing apoptosis. Moreover, we identified the interaction between 32, 78, and 397 kinds of miRNAs, lncRNAs, and mRNAs. The results suggest that the lncRNAs can regulate the expression of mRNA during flowering period in tomato by forming competitive endogenous RNA, and further regulate various biological metabolism pathways in tomato.

Physiological responses and small RNAs changes in maize under nitrogen deficiency and resupply.

BACKGROUND: Maize is an important crop in the world, nitrogen stress severely reduces maize yield. Although a large number of studies have identified the expression changes of microRNAs (miRNAs) under N stress in several species, the miRNAs expression patterns of N-deficient plants under N resupply remain unclear. OBJECTIVE: The primary objective of this study was to identify miRNAs in response to nitrogen stress and understand relevant physiological changes in nitrogen-deficient maize after nitrogen resupply. METHODS: Physiological parameters were measured to study relevant physiological changes under different nitrogen conditions. Small RNA sequencing and qRT-PCR analysis were performed to understand the response of miRNAs under different nitrogen conditions. RESULTS: The content of chlorophyll, soluble protein and nitrate nitrogen decreased than CK by 0.52, 0.49 and 0.82 times after N deficiency treatment and increased than ND by 0.52, 1.36 and 0.65 times after N resupply, respectively. Conversely, the activity of superoxide dismutase (SOD) and peroxidase (POD) increased by 0.67 and 1.64 times than CK after N deficiency, respectively, and decreased by 0.09 and 0.35 times than ND after N resupply. A total of 226 known miRNAs were identified by sRNA sequencing; 106 miRNAs were differentially expressed between the control and N-deficient groups, and 103 were differentially expressed between the N-deficient and N-resupply groups (P < 0.05). Real-time quantitative PCR (qPCR) was used to further validate and analyze the expression of the identified miRNAs. A total of 1609 target genes were identified by target prediction, and some differentially expressed miRNAs were predicted to target transcription factors and functional proteins. Gene Ontology (GO) analysis was used to determine the biological function of these targets and revealed that some miRNAs, such as miR169, miR1214, miR2199, miR398, miR408 and miR827 might be involved in nitrogen metabolism regulation. CONCLUSION: Our study comprehensively provides important information on miRNA functions and molecular mechanisms in response to N stress. These findings may assist to improve nitrogen availability in plants.