Proline Spray Relieves the Adverse Effects of Drought on Wheat Flag Leaf Function.

Drought stress is one of the key factors restricting crop yield. The beneficial effects of exogenous proline on crop growth under drought stress have been demonstrated in maize, rice, and other crops. However, little is known about its effects on wheat under drought stress. Especially, the water-holding capacity of leaves were overlooked in most studies. Therefore, a barrel experiment was conducted with wheat at two drought levels (severe drought: 45% field capacity, mild drought: 60% field capacity), and three proline-spraying levels (0 mM, 25 mM, and 50 mM). Meanwhile, a control with no stress and no proline application was set. The anatomical features, water-holding capacity, antioxidant capacity, and proline content of flag leaves as well as grain yields were measured. The results showed that drought stress increased the activity of catalase and peroxidase and the content of proline in flag leaves, lessened the content of chlorophyll, deformed leaf veins, and decreased the grain yield. Exogenous proline could regulate the osmotic-regulation substance content, chlorophyll content, antioxidant enzyme activity, water-holding capacity, and tissue structure of wheat flag leaves under drought stress, ultimately alleviating the impact of drought stress on wheat yield. The application of proline (25 mM and 50 mM) increased the yield by 2.88% and 10.81% under mild drought and 33.90% and 52.88% under severe drought compared to wheat without proline spray, respectively.

An integrated feature selection approach to high water stress yield prediction.

The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

Soil salinity is the main factor influencing the soil bacterial community assembly process under long-term drip irrigation in Xinjiang, China.

Identifying the potential factors associated with the impact of long-term drip irrigation (DI) on soil ecosystems is essential for responding to the environmental changes induced by extensive application of DI technology in arid regions. Herein, we examined the effects of the length of time that DI lasts in years (NDI) on soil bacterial diversity as well as the soil bacterial community assembly process and the factors influencing it. The results showed that long-term DI substantially reduced soil salinity and increased soil bacterial diversity while affecting the soil bacterial community structure distinctly. Null model results showed that the soil bacterial community assembly transitioned from stochastic processes to deterministic processes, as NDI increased. Homogeneous selection, a deterministic process, emerged as the dominant process when NDI exceeded 15 years. Both random forest and structural equation models showed that soil salinity was the primary factor affecting the bacterial community assembly process. In summary, this study suggested that soil bacteria respond differently to long-term DI and depends on the NDI, influencing the soil bacterial community assembly process under long-term DI.

[Effects of Drip Irrigation Patterns and Biochar Addition on Soil Mineral Nitrogen and Microbial Regulation of Greenhouse].

Drip irrigation and biochar amendment could affect the nitrogen form and transformation. Creating a deep understanding of the interacting effects of drip irrigation patterns and biochar on soil mineral nitrogen, as well as the key functional genes and microbial community involved in nitrogen transformation is helpful for improving facility agricultural management, increasing water and nitrogen use efficiency, and reducing the nitrate accumulation and groundwater pollution caused by nitrogen leaching. Four treatments [surface drip irrigation (D), insert drip irrigation (ID, insert depth 15 cm), surface drip irrigation +10 t·hm-2 of biochar (DB), and insert drip irrigation +10 t·hm-2 of biochar (IDB)] were conducted in a solar greenhouse, and non-rhizospheric and rhizospheric soils of pepper plants were studied. There was no effect of drip irrigation patterns and biochar on ammonium-nitrogen in the non-rhizospheric and rhizospheric soils. Compared with surface drip irrigation, insert drip irrigation decreased the nitrate-nitrogen concentration in the non-rhizosphere soil (P<0.05), but biochar addition weakened the difference. Biochar addition decreased the nitrate-nitrogen concentration in the rhizosphere soil under the same drip irrigation patterns. In the D treatment, biochar significantly decreased the number of copies of AOA, AOB, and nirK genes in the non-rhizospheric soil, and AOA gene copies in the rhizospheric soil (P<0.05); however, there was an increase in the number of copies of AOB and nirK genes in the rhizospheric soil of the D and ID treatments (P<0.05). Based on the structural equation model (SEM), in the non-rhizospheric and rhizospheric soils, pH and electrical conductivity were the environmental factors with the greatest influence on the ammonium-nitrogen and nitrate concentrations, respectively, and the gene copy number of AOB was the biotic factor with the greatest influence on the nitrate-nitrogen concentration. Based on PICRUSt, the γ-Proteobacteria contributed mostly to ammonia monooxygenase gene (K10945) expression, whereas the α-Proteobacteria, especially the rhizobia members, contributed mostly to nitrite reductase gene (K00368) expression. Biochar addition regulated the bacterial community structure that participated in K10945 gene expression in the non-rhizospheric soil and K00368 gene expression in the rhizospheric soil (P<0.05). Overall, biochar addition contributed more to nitrate-nitrogen and microbial mineral nitrogen-transformation processes in the agricultural soil than did the drip irrigation patterns.

[Postmenopausal osteoporosis treated with acupoint injection of salmon calcitonin:a randomized controlled trial].

OBJECTIVE: To verify the clinical efficacy on postmenopausal osteoporosis treated with acupoint injection of salmon calcitonin. METHODS: Ninety patients of postmenopausal osteoporosis were randomized into three groups, 30 cases in each one. In the acupoint injection group, Shenshu (BL 23) and Zusanli (ST 36) were selected bilaterally. The injection 4 mL was prepared with salmon calcitonin 100 U (1 mL) and 0.9% sodium chloride injection. Each acupoint was stimulated with the injection, 1 mL. In the blank group, 0.9% sodium chloride injection was applied to bilateral Shenshu (BL 23) and Zusanli (ST 36), 1 mL at each acupoint. In the intramuscular injection group, salmon calcitonin 100 U was injected at gluteus maximus. The treatment was given once every two days in the patients of the three groups and lasted for 2 months. The levels of bone mineral density (BMD), bone alkaline phosphatase (NBAP), C-terminal telopeptides of typeⅠcollagen (CTX), urine calcium/creatinine (Ca/Cr) and the symptom score of osteoporosis were detected in the patients of the three groups before and after treatment. RESULTS: In the patients of the three groups, NBAP and BMD in lumbar vertebra after treatment were higher than those before treatment (all P<0.05); CTX, Ca/Cr and symptom score were lower than those before treatment (all P<0.05). After treatment, NBAP was (32.7±2.5) µg/L in the acupoint injection group, higher than those in the blank group and the intramuscular injection group (both P<0.05). In the acupoint injection group, CTX was reduced to (239.7±63.6) µmmol/L and Ca/Cr was reduced to 0.525±0.274, apparently lower than those in the blank group and intramuscular injection group (both P<0.05). After treatment, in the acupoint injection group, BMD of lumbar vertebra was (0.731±0.062) g/m2, higher than the level of the rest two groups (both P<0.05). After treatment, the symptom score was 5.2±0.6 in the acupoint injection group, lower than those in the blank group and intramuscular injection group (both P<0.05). CONCLUSIONS: Salmon calcitonin injec-tion at Zusanli (ST 36) and Shenshu (BL 23) achieves significant efficacy on postmenopausal osteoporosis, stimulating osteoblast activity and inhibiting bone absorption of osteoclast.