Morphology, photosynthetic physiology and biochemistry of nine herbaceous plants under water stress

Global climate warming and shifts in rainfall patterns are expected to trigger increases in the frequency and magnitude of drought and/or waterlogging stress in plants. To cope with water stress, plants develop diverse tactics. However, the adoption capability and mechanism vary depending upon the plant species identity as well as stress duration and intensity. The objectives of this study were to evaluate the species-dependent responses of alpine herbaceous species to water stress. Nine herbaceous species were subjected to different water stresses (including moderate drought and moderate waterlogging) in pot culture using a randomized complete block design with three replications for each treatment. We hypothesized that water stress would negatively impact plant growth and metabolism. We found considerable interspecies differences in morphological, physiological, and biochemical responses when plants were exposed to the same water regime. In addition, we observed pronounced interactive effects of water regime and plant species identity on plant height, root length, root/shoot ratio, biomass, and contents of chlorophyll a, chlorophyll b, chlorophyll (a+b), carotenoids, malondialdehyde, soluble sugar, betaine, soluble protein and proline, implying that plants respond to water regime differently. Our findings may cast new light on the ecological restoration of grasslands and wetlands in the Qinghai-Tibetan Plateau by helping to select stress-tolerant plant species

Is transcranial direct current stimulation, alone or in combination with antidepressant medications or psychotherapies, effective in treating major depressive disorder? A systematic review and meta-analysis.

BACKGROUND: Transcranial direct current stimulation (tDCS) has shown mixed results for depression treatment. The efficacies of tDCS combination therapies have not been investigated deliberately. This review aims to evaluate the clinical efficacy of tDCS as a monotherapy and in combination with medication, psychotherapy, and ECT for treating adult patients with major depressive disorder (MDD) and identified the factors influencing treatment outcome measures (i.e. depression score, dropout, response, and remission rates). METHODS: The systematic review was performed in PubMed/Medline, EMBASE, PsycINFO, Web of Sciences, and OpenGrey. Two authors performed independent literature screening and data extraction. The primary outcomes were the standardized mean difference (SMD) for continuous depression scores after treatment and odds ratio (OR) dropout rate; secondary outcomes included ORs for response and remission rates. Random effects models with 95% confidence intervals were employed in all outcomes. The overall effect of tDCS was investigated by meta-analysis. Sources of heterogeneity were explored via subgroup analyses, meta-regression, sensitivity analyses, and assessment of publication bias. RESULTS: Twelve randomised, sham-controlled trials (active group: N = 251, sham group: N = 204) were included. Overall, the integrated depression score of the active group after treatment was significantly lower than that of the sham group (g = - 0.442, p = 0.017), and further analysis showed that only tDCS + medication achieved a significant lower score (g = - 0.855, p < 0.001). Moreover, this combination achieved a significantly higher response rate than sham intervention (OR = 2.7, p = 0.006), while the response rate remained unchanged for the other three therapies. Dropout and remission rates were similar in the active and sham groups for each therapy and also for the overall intervention. The meta-regression results showed that current intensity is the only predictor for the response rate. None of publication bias was identified. CONCLUSION: The effect size of tDCS treatment was obviously larger in depression score compared with sham stimulation. The tDCS combined selective serotonin re-uptake inhibitors is the optimized therapy that is effective on depression score and response rate. tDCS monotherapy and combined psychotherapy have no significant effects. The most important parameter for optimization in future trials is treatment strategy

Bilateral Habenula deep brain stimulation for treatment-resistant depression: clinical findings and electrophysiological features.

Deep brain stimulation (DBS) of structures in the brain's reward system is a promising therapeutic option for patients with treatment-resistant depression (TRD). Recently, DBS of the habenula (HB) in the brain's anti-reward system has also been reported to alleviate depressive symptoms in patients with TRD or bipolar disorder (BD). In this pilot open-label prospective study, we explored the safety and clinical effectiveness of HB-DBS treatment in seven patients with TRD or BD. Also, local field potentials (LFPs) were recorded from the patients' left and right HB to explore the power and asymmetry of oscillatory activities as putative biomarkers of the underlying disease state. At 1-month follow-up (FU), depression and anxiety symptoms were both reduced by 49% (n = 7) along with substantial improvements in patients' health status, functional impairment, and quality of life. Although the dropout rate was high and large variability in clinical response existed, clinical improvements were generally maintained throughout the study [56%, 46%, and 64% reduction for depression and 61%, 48%, and 70% reduction for anxiety at 3-month FU (n = 5), 6-month FU (n = 5), and 12-month FU (n = 3), respectively]. After HB-DBS surgery, sustained improvements in mania symptoms were found in two patients who presented with mild hypomania at baseline. Another patient, however, experienced an acute manic episode 2 months after surgery that required hospitalization. Additionally, weaker and more symmetrical HB LFP oscillatory activities were associated with more severe depression and anxiety symptoms at baseline, in keeping with the hypothesis that HB dysfunction contributes to MDD pathophysiology. These preliminary findings indicate that HB-DBS may offer a valuable treatment option for depressive symptoms in patients who suffer from TRD or BD. Larger and well-controlled studies are warranted to examine the safety and efficacy of HB-DBS for treatment-refractory mood disorders in a more rigorous fashion

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep brain stimulation based on neural states integrating multiple neural oscillations

Effect of Sodium Selenite Concentration and Culture Time on Extracellular and Intracellular Metabolite Profiles of Epichlo&euml; sp. Isolated from Festuca sinensis in Liquid Culture

Selenium (Se) is not only an essential trace element critical for the proper functioning of an organism, but it is also an abiotic stressor that affects an organism&rsquo;s growth and metabolite profile. In this study, Epichlo&euml; sp. from Festuca sinensis was exposed to increasing concentrations of Na2SeO3 (0, 0.1, and 0.2 mmol/L) in a liquid media for eight weeks. The mycelia and fermentation broth of Epichlo&euml; sp. were collected from four to eight weeks of cultivation. The mycelial biomass decreased in response to increased Se concentrations, and biomass accumulation peaked at week five. Using gas chromatography-mass spectrometry (GC-MS), approximately 157 and 197 metabolites were determined in the fermentation broth and mycelia, respectively. Diverse changes in extracellular and intracellular metabolites were observed in Epichlo&euml; sp. throughout the cultivation period in Se conditions. Some metabolites accumulated in the fermentation broth, while others decreased after different times of Se exposure compared to the control media. However, some metabolites were present at lower concentrations in the mycelia when cultivated with Se. The changes in metabolites under Se conditions were dynamic over the experimental period and were involved in amino acids, carbohydrates, organic acids, fatty acids, and nucleotides. Based on these results, we conclude that selenite concentrations and culture time influence the growth, extracellular and intracellular metabolite profiles of Epichlo&euml; sp. from F. sinensis

Using Multi-Angular Hyperspectral Data to Estimate the Vertical Distribution of Leaf Chlorophyll Content in Wheat

Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution of the leaf chlorophyll content (LCC). However, these studies performed LCC inversion of different vertical layers using a fixed view zenith angle (VZA), but rarely considered the contribution of the components of the non-target layers to the spectral response. The main goal of this work was to determine the LCC of different vertical layers of the canopy of winter wheat (Triticum aestivum L.), using multi-angular remote sensing and spectral vegetation indices. Different combinations of VZAs were used for obtaining the LCC of different layers. The results revealed that the responses of the transformed chlorophyll in reflectance absorption index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI)/optimized soil-adjusted vegetation index (OSAVI) to the upper-layer LCC were strongest at VZA 10°. For the middle-layer LCC, the response was strongest at 30°, but the response was significantly lower than that of the upper-layer. For the bottom-layer LCC, the responses were weak due to the obscuring effect of the upper- and middle-layer; thus, the LCC inversion of the bottom-layer data was not optimal for a single VZA. The optimal VZA or VZA combinations for LCC estimation were VZA 10° for the upper-layer LCC (TCARI with coefficient of determination (R2) = 0.69, root mean square error (RMSE) = 4.80 ug/cm2, MCARI/OSAVI with R2 = 0.73, RMSE = 4.17 ug/cm2), VZA 10° and 30° for the middle-layer LCC (TCARI with R2 = 0.17, RMSE = 4.81 ug/cm2, MCARI/OSAVI with R2 = 0.17, RMSE = 4.76 ug/cm2), and VZA 10°, 30°, and 50° for the bottom-layer LCC (TCARI with R2 = 0.40, RMSE = 6.29 ug/cm2, MCARI/OSAVI with R2 = 0.40, RMSE = 6.36 ug/cm2). The proposed observation strategy provided a significantly higher estimation accuracy of the target layer LCC than the single VZA approach, and demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the wheat canopy chlorophyll content vertical distribution

Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data

Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely on handcrafted features and theoretical formulas under idealized assumptions, which limits their accuracy. Deep neural networks have demonstrated great superiority in automatic feature extraction and complicated nonlinear approximation, but their application to LAI and biomass estimation has been hindered by the shortage of in situ data. Therefore, bridging the gap of data shortage and making it possible to leverage deep neural networks to estimate maize LAI and biomass is of great significance. Optical data cannot provide information in the lower canopy due to the limited penetrability, but synthetic aperture radar (SAR) data can do this, so the integration of optical and SAR data is necessary. In this paper, 158 samples from the jointing, trumpet, flowering, and filling stages of maize were collected for investigation. First, we propose an improved version of the mixup training method, which is termed mixup+, to augment the sample amount. We then constructed a novel gated Siamese deep neural network (GSDNN) based on a gating mechanism and a Siamese architecture to integrate optical and SAR data for the estimation of the LAI and biomass. We compared the accuracy of the GSDNN with those of other machine learning methods, i.e., multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and a multilayer perceptron (MLP). The experimental results show that without the use of mixup+, the GSDNN achieved a similar accuracy to that of the simple neural network MLP in terms of R2 and RMSE, and this was slightly lower than those of MLR, SVR, and RFR. However, with the help of mixup+, the GSDNN achieved state-of-the-art performance (R2 = 0.71, 0.78, and 0.86 and RMSE = 0.58, 871.83, and 150.76 g/m2, for LAI, Biomass_wet, and Biomass_dry, respectively), exceeding the accuracies of MLR, SVR, RFR, and MLP. In addition, through the integration of optical and SAR data, the GSDNN achieved better accuracy in LAI and biomass estimation than when optical or SAR data alone were used. We found that the most appropriate amount of synthetic data from mixup+ was five times the amount of original data. Overall, this study demonstrates that the GSDNN + mixup+ has great potential for the integration of optical and SAR data with the aim of improving the estimation accuracy of the maize LAI and biomass with limited in situ data

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep brain stimulation based on neural states integrating multiple neural oscillations

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model’s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model’s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth