Accurate assessment of material and energy exchange between land and atmosphere is essential for water resources management and sustainable development of agriculture. To understand the characteristics of energy distribution and the dynamic change process of water and heat fluxes within the maize farmland ecosystem in the old course of Yellow River and their response to meteorological factors, we utilized the eddy covariance measurements and the full-element automatic weather station to continuously observe energy fluxes and conventional meteorological elements of summer maize farmland in the old course of Yellow River during 2019-2020. We analyzed the variation of energy fluxes and the effects of environmental factors, such as temperature, precipitation, and wind speed. Additionally, we calculated the energy closure rate and the proportion of energy distribution during the growth stage. The results showed that the peaks of net radiation, sensible heat flux, and latent heat flux occurred between 11:00 and 14:00, and the peak of soil heat flux occurred between 14:00 and 15:00. In terms of energy distribution, energy consumption of summer maize farmland during the whole growth period was dominated by latent heat flux and sensible heat flux. Energy was mainly consumed by sensible heat flux at sowing-emergence stage, accounting for 37.1% of net radiation, respectively. Energy in the rest of growth stages was dominated by latent heat flux. The energy closure rate during the whole growth period was better, with a coefficient of determination of 0.83, and the closure rate was higher in day and lower at night. Precipitation affected latent heat flux and sensible heat flux, and latent heat flux was more sensitive to precipitation. The increase of latent heat flux after rainfall was lower in late growth stage than in early growth stage. During the whole growth period of summer maize, solar radiation was the most significant meteorological factor affecting both sensible heat flux and latent heat flux, followed by vapor pressure deficit. The contribution of temperature and vapor pressure deficit to latent heat flux was significantly higher than sensible heat flux, while the relative contribution of wind speed, relative humidity, and solar radiation to latent heat flux was lower than sensible heat flux. Leaf area index and fractional vegetation cover had a significant positive correlation with latent heat flux and a significant negative correlation with sensible heat flux. Our results could deepen the understanding of water and heat transfer law of summer maize farmland in the old course of Yellow River, providing a theoretical basis for efficient water use of crops.
精确评估地-气之间物质和能量交换对于有效管理水资源和促进农业可持续发展至关重要。为揭示黄河故道区玉米农田生态系统的能量分配特征和水热分量动态变化过程及其对气象因子的变化响应,本研究基于涡度相关系统和全要素自动气象站,对2019—2020年黄河故道区典型夏玉米农田生态系统的能量通量以及常规气象要素进行不间断观测,分析夏玉米农田各能量通量的变化特征以及与气温、降水、风速等环境因子的相关关系,并计算夏玉米生育时期的能量闭合率和能量分配比率。结果表明: 研究区夏玉米净辐射、显热通量和潜热通量的峰值出现在 11:00—14:00,土壤热通量的峰值发生在 14:00—15:00。在能量分配上,夏玉米农田全生育期的能量分配以潜热通量和显热通量为主,播种-出苗期能量主要被显热通量消耗,占净辐射的37.1%,其余生育周期的能量均以潜热通量消耗为主。全生育期能量闭合率较好,决定系数为0.83,其中,白天闭合率较高,晚上较低。降水对潜热通量和显热通量都有明显影响,潜热通量对降水的反应更敏感,生育后期潜热通量在降雨后与降雨前的增幅比生育前期低。在夏玉米全生育期内,太阳辐射是水热通量最主要的气象因子,其次为饱和水汽压差。温度和饱和水汽压差对潜热通量的贡献度明显高于显热通量,风速、相对湿度和太阳辐射对潜热通量的解释率低于显热通量,叶面积指数和植被覆盖度与潜热通量呈显著正相关、与显热通量呈显著负相关。本研究的定量化结果可以深化对黄河故道区夏玉米农田水热传输规律的认识,为作物高效用水提供理论依据。.

Promising hyperspectral remote sensing exhibits substantial potential in monitoring soil heavy metal (SHM) contamination. Nevertheless, the local spatial perturbation effects induced by environmental factors introduce considerable variability in SHM distribution. This engenders non-stationary relationship between SHM concentrations and spectral reflectance, posing challenges for accurate inversion of SHM globally. Addressing this gap, a novel Hierarchical Residual Correction-based Hyperspectral Inversion Method (HRCHIM) is proposed for SHM, considering their spatial heterogeneity. Initially, a global model is constructed using ground hyperspectral data to predict SHM concentration, capturing overarching contamination trends. Subsequently, four hierarchical levels, segmented by residual standard deviation (SD) intervals, identify critical environmental factors via Geodetector. These factors inform local residual correction models, refining global model predictions. HRCHIM aims to synergize global trends and local stochasticity to enhance prediction accuracy and interpretation of SHM spatial heterogeneity. Validated through a case study of a Cadmium(Cd)-contaminated mine area, six critical environmental factors were identified, exhibiting significant differences across hierarchical levels. By incorporating hierarchical correction models, HRCHIM demonstrated superior inversion performance compared to other conventional methods, achieving optimal prediction accuracies (Rv2 = 0.94, RMSEv = 0.21, and RPDv = 4.11). This innovative method can facilitate more precise and targeted strategies for preventing and controlling SHM contamination.

In the environment, the class of chemicals known as polycyclic aromatic hydrocarbons (PAHs) behave somewhat differently. This review covers situations where PAHs can be \'labile\' and where they can be persistent. The in-silico prediction of toxicity and the properties of selected 29 PAHs were estimated using programs developed by the U.S. Environmental Protection Agency (EPA), such as the Estimation Programs Interface (E.P.I.) and the Toxicity Estimation Software Tool (version 5.1.2) (TEST), with online software such as SwissADME and SwissDock. TEST was used to estimate the LC50 of the fathead minnow (with a range of 14.53 mg/L for 1-indanone and 2.14 × 10-2 mg/L for cyclopenta[c,d]pyrene), the LC50 of Daphnia magna (with a range of 14.95 mg/L for 1-indanone and 7.53 × 10-2 mg/L for coronene), the IGC50 of Tetrahymena pyriformis (with a range of 66.14 mg/L for 1-indanone and 0.36 mg/L for coronene), the bioconcentration factor (8.36 for 1,2-acenaphthylenedione and 910.1 for coronene), the developmental toxicity (0.30 (-) for 1,2-acenaphthylenedione and 0.82 (+) for 4-hydroxy-9-fluorenone), and the mutagenicity (0.25 (-) for 2-methyl-9-fluorenone and 1.09 (+) for coronene). The carbon chain and molecular weight have a significant effect on the properties of PAHs. Overall, it was found that PAHs with a lower molecular weight (LMW) have a higher water solubility and LC50 value and a smaller LogKow value, whereas the opposite is true for heavier PAHs, with TEST predicting that PAHs with an MW of over 168.2 g/mol, with a few exceptions, are mutagenic. Hence, LMW PAHs have a higher potential to be in the environment but are less toxic.

This study focuses on understanding the behavior and activity patterns of the critically endangered Protobothrops mangshanensis in China in order to better provide scientific data for upcoming artificial breeding and propagation efforts. We conducted a long-term observation of 15 Mangshan pit vipers at different sites in Hunan Province during the summer and autumn of 2021. Our methods involved analyzing the influence of environmental factors such as temperature, relative humidity, and light condition on the snakes\' day and night activity and behaviors. The results revealed that the wild behaviors of Protobothrops mangshanensis include resting, sunbathing, crawling, and exploring, with distinct rhythms in their diel behavior. The snakes\' diel activity exhibits three peak periods which may be related to food activity and sunbathing. This study also highlights the complex interplay of environmental factors on the activity of Protobothrops mangshanensis. Relative humidity was identified as a critical factor accounting for the difference in activity between observation groups. There was little inter-individual variation among the 15 Protobothrops mangshanensis, even though these snakes used terrestrial and arboreal habitats under different environmental conditions. These findings enhance our understanding of Protobothrops mangshanensis behavior and provide a basis for effective conservation measures for this rare and critically endangered species.

Schizophrenia is one of the most disabling psychiatric disorders characterized by positive (hallucinations, delusions, formal thinking disorder) and negative symptoms (anhedonia, lack of speech and motivation). The present study aimed to identify the predictive factors of schizophrenia in adults, and potential differences in the environment of origin, sex, levels of occupational stress, intellectual level, marital status and age of onset of the disease depending on the severity of symptoms using analysis of data collected from 120 patients with a diagnosis of schizophrenia. The study was conducted at the \'Prof. Dr. Alexandru Obregia\' Clinical Psychiatric Hospital in Bucharest and included adult patients hospitalized between March 2018 and January 2021 diagnosed with schizophrenia and evaluated by general clinical examination, psychiatric, neurological and psychological evaluation. Results revealed that robust predictors of mild and moderate symptoms were affective symptoms, heredo-collateral history of schizophrenia, late onset, the presence of positive and negative symptoms, substance abuse, stress and marital status, unmarried, lower IQ and mental deficiency. For moderate-severe and severe symptoms, predictors were affective symptoms, heredo-collateral history of schizophrenia and affective disorders, substance abuse, stress, borderline IQ and mild mental deficiency. The present results can be used for further development of psychopharmacological management of schizophrenia.

Ecosystem stability arises from the interplay of species diversity, environmental conditions, and external disturbances. Understanding the structure of plant communities, interspecific relationships, and community stability in urban ecosystems is fundamental to ecological restoration and community development. This study utilized the karst city of Guiyang as a case study and employed the α diversity index, variance ratio method (VR), χ 2-test, Pearson correlation test, Spearman rank correlation test, M. Godron stability, and canonical correspondence analysis (CCA). The research focused on analyzing the species diversity, interspecific associations, community stability, and environmental factors of lithophytic moss at various elevations (989-1398 m). The findings revealed the presence of 58 species belonging to 27 genera and 13 families of lithophytic moss in the study area. Notably, the Brachytheciaceae and Pottiaceae emerged as dominant, exhibiting a broad ecological range and adaptation mechanisms, thereby playing a crucial role in the ecological environment of rocky desertification. The study observed that the highest species richness and dominance values of lithophytic moss were recorded at the N4 (1296-1398 m) elevation gradient, while the highest species diversity and uniformity values were observed at the N3 (1194-1295 m) elevation gradient, indicating a significant impact of altitude on lithobryophyte species diversity, particularly at middle and high altitudes. The analysis of interspecific associations and stability indicated a predominantly negative overall association within the lithophytic moss community, suggesting an early stage of succession, with weak interspecific associations and correlations among dominant pairs, tending towards relative independence. Only the communities at N2 (1092-1193 m) elevation exhibited stability, while the other communities were in an unstable stage, showing no significant correlation with species diversity. Furthermore, light intensity (182-129300 lux) exerted the greatest influence on community stability. Additionally, air humidity (36.5-52.3%) and altitude (998-1327 m) emerged as the primary environmental factors influencing community distribution, with a close and positive correlation between the two. These results hold significant reference value for promoting the succession and steady development of vegetation in rocky desertification areas and enhancing the conservation and restoration of vegetation community diversity in karst urban ecosystems.

Fine roots and absorptive roots play key roles in acquiring resources throughout soil profiles and determining plant functions along environmental gradients. Yet, the geographical pattern of carbon allocation in fine roots, particularly in absorptive roots, and their relations with plant sizes and evironment are less understood. We sampled 243 xerophytic shrubs from 63 species distributed along the latitudinal gradient (23°N to 32°N) in dry valleys of southwest China and synthetically measured biomass fractions of plant organs, especially fine roots and absorptive roots (1st to 3rd root order). We identified latitudinal patterns of biomass allocation fractions of organs and their relationships with plant sizes and environmental factors. The latitudinal patterns of both absorptive root and fine-root fractions followed weak unimodal distributions; stem biomass fraction increased with the latitude, while the leaf biomass fraction decreased. The fraction of fine-root biomass had negative relationships with plant height and root depth. The fractions of root, fine root, and absorptive root biomass were largely explained by soil moisture. Furthermore, fraction of fine-root biomass increased in a relatively humid environment. Overall, soil moisture was the most important factor in driving latitudinal patterns of biomass fraction. Our study highlighted that functional redistribution of root system biomass was the critical adaptive strategy along a latitudinal gradient.

Predicting soil heavy metal (SHM) content is crucial for understanding SHM pollution levels in urban residential areas and guide efforts to reduce pollution. However, current research indicates low SHM prediction accuracy in urban areas. Therefore, we employed a deep learning method (fully connected deep neural network) alongside four other methods (muti-layer perceptron, radial basis function neural network, multiple stepwise linear regression, and Kriging interpolation) to predict SHM content in the urban residential areas of Beijing and demonstrated the strength of deep learning in improving prediction accuracy. We found the contents of the evaluated heavy metals (Cd, Cu, Pb, and Zn) exhibited significant correlations with numerous other soil physicochemical properties and environmental factors. The prediction accuracy for Cu, Pb, and Zn contents was relatively high across different methods. Notably, deep learning showed considerable strength in predicting the contents of the four heavy metals, with the R2 for the test set of the model ranging from 0.75 to 0.91. Compared to other methods, deep learning achieved markedly higher prediction accuracy according to different accuracy evaluation indicators (e.g., deep learning showed increases in the cumulative R2 of the four heavy metals ranging from 53.16 % to 187.36 % compared to other methods). Our study indicates that deep learning can significantly improve SHM content prediction accuracy in urban areas and is highly applicable in urban residential areas with complex environmental influences.

OBJECTIVE: Celiac disease (CD) is a chronic autoimmune disorder caused by a complex interplay between genetic and environmental factors. The main goal of our case-control study was to analyse the association of environmental factors with the odds of CD development in a sample of the Slovak population.
METHODS: Data were collected from 1,226 respondents (534 CD patients and 692 controls) by a questionnaire. The impact of analysed parameters on the chance of disease development was assessed by multiple regression analysis and expressed as odds ratios (OR). Values of p < 0.05 were considered statistically significant.
RESULTS: In the patient group, celiac disease was significantly more prevalent in women than in men (OR = 1.52, p = 0.010). Respondents with a positive family history of CD showed 2.9-fold higher odds of CD compared to others (p < 0.001), and respondents with coexisting autoimmune diseases had 2.6-fold higher odds of CD (p < 0.001). Subjects who had taken antibiotics at least three times a year during childhood had 1.95-fold higher odds of developing CD compared to those who took them less frequently or not at all (p = 0.022). Conversely, individuals who were breastfed in infancy had lower odds of CD compared to non-breastfed respondents (OR = 0.53, p < 0.001). The mode of delivery (vaginal vs. caesarean section), overcoming severe infections, and the timing of gluten introduction in childhood did not show a statistically significant effect on the odds of developing CD.
CONCLUSIONS: Based on our data, being female, having a positive family history of CD, suffering from another autoimmune disease, and frequent use of antibiotics are factors associated with an increased chance of developing CD. On the other hand, breastfeeding in infancy seems to have a protective effect. Our findings highlight the importance of further research in understanding the complexities of this autoimmune condition and providing a foundation for prevention strategies.

To illuminate the temporal variations in the structure and functional groups of the root-associated fungal community associated with Mongolian pine Pinus sylvestris var. mongholica plantations in the Mu Us Sandy Land, P. sylvestris var. mongholica plantations with different stand ages （23, 33, and 44 a） were targeted. The community compositions and main drivers of root-associated fungi at different months and stand ages were identified using the Illumina high-throughput sequencing method. The results indicated that： ① There was a distinct temporal distribution in the root-associated fungal community, the sampling month had a significant effect on the diversity of root-associated fungi （P<0.05）, and the values were higher in May and July. The stand age had no significant effect on the diversity of root-associated fungi （P>0.05） and decreased gradually with increasing stand age. ② The dominant phylum of the root-associated fungal community was Ascomycota. The relative abundance of fungal function groups was different within each month and stand age, and the dominant groups were saprotroph-symbiotroph, undefined saprotroph, and ectomycorrhizal fungi. The indicator genera of ectomycorrhizal fungi in May, July, and September were Melanoleuca, Amphinema, and Tricholoma, respectively. ③ The temporal distribution of the root-associated fungal community was significantly affected by annual relative humidity, annual precipitation, soil porosity, ammonia nitrogen, annual sunshine duration, annual temperature, and soil water content （P<0.05）. Soil organic carbon content, soil porosity, annual precipitation, and annual relative humidity were the main factors that significantly affected the indicator genus of the root-associated fungal community. Our results demonstrated that the temporal distribution of the root-associated fungal community was shaped by climate and soil properties, whereas stand age contributed less. This improved information will provide a theoretical basis for the sustainable management of P. sylvestris var mongholica plantations.