PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning.

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

Evaluation of maize varieties via multivariate analysis: Roles of ionome, antioxidants, and autophagy in salt tolerance.

Salt stress presents a major obstacle to maize (Zea mays L.) production globally, impeding its growth and development. In this study, we aimed to identify salt-tolerant maize varieties through evaluation using multivariate analysis and shed light on the role of ionome, antioxidant capacity, and autophagy in salt tolerance. We investigated multiple growth indices, including shoot fresh weight, shoot dry weight, plant height, chlorophyll content, electrolyte leakage, potassium and sodium contents, and potassium-to-sodium ratio in 20 maize varieties at the V3 stage under salt stress (200 mM NaCl). The results showed significant differences in the growth indices, accompanied by a wide range in their coefficient of variation, suggesting their suitability for screening salt tolerance. Based on D values, clustering analysis categorized the 20 varieties into four distinct groups. TG88, KN20, and LR888 (group I) emerged as the most salt-tolerant varieties, while YD9, XD903, and LH151 (group IV) were identified as the most sensitive. TG88 showcased nutrient preservation and redistribution under salt stress, surpassing YD9. It maintained nitrogen and iron levels in roots while YD9 experienced decreases. TG88 redistributed more nitrogen, zinc, and potassium to its leaves, outperforming YD9. TG88 preserved sulfur levels in both roots and leaves, unlike YD9. Additionally, TG88 demonstrated higher enzymatic antioxidant capacity (SOD, POD, APX, and GR) both at the enzyme and gene expression levels, upregulation of autophagy-related (ATG) genes (ZmATG6, ZmATG8a, and ZmATG10), and increased autophagic activity. Overall, this study offers insights into accurate maize varieties evaluation methods and the physiological mechanisms underlying salt tolerance and identifies promising materials for further research.

FireProtASR: A Web Server for Fully Automated Ancestral Sequence Reconstruction.

There is a great interest in increasing proteins' stability to widen their usability in numerous biomedical and biotechnological applications. However, native proteins cannot usually withstand the harsh industrial environment, since they are evolved to function under mild conditions. Ancestral sequence reconstruction is a well-established method for deducing the evolutionary history of genes. Besides its applicability to discover the most probable evolutionary ancestors of the modern proteins, ancestral sequence reconstruction has proven to be a useful approach for the design of highly stable proteins. Recently, several computational tools were developed, which make the ancestral reconstruction algorithms accessible to the community, while leaving the most crucial steps of the preparation of the input data on users' side. FireProtASR aims to overcome this obstacle by constructing a fully automated workflow, allowing even the unexperienced users to obtain ancestral sequences based on a sequence query as the only input. FireProtASR is complemented with an interactive, easy-to-use web interface and is freely available at https://loschmidt.chemi.muni.cz/fireprotasr/.

Leaf senescence attributes: the novel and emerging role of sugars as signaling molecules and the overlap of sugars and hormones signaling nodes.

Sugars are the primary products of photosynthesis and play multiple roles in plants. Although sugars are usually considered to be the building blocks of energy storage and carbon transport molecules, they have also gradually come to be acknowledged as signaling molecules that can initiate senescence. Senescence is an active and essential process that occurs at the last developmental stage and corresponds to programmed degradation of: cells, tissues, organs, and entire organisms. It is a complex process involving: numerous biochemical changes, transporters, genes, and transcription factors. The process is controlled by multiple developmental signals, among which sugar signals are considered to play a vital role; however, the regulatory pathways involved are not fully understood. The dynamic mechanistic framework of sugar accumulation has an inconsistent effect on senescence through the sugar signaling pathway. Key metabolizing enzymes produce different sugar signals in response to the onset of senescence. Diverse sugar signal transduction pathways and a variety of sugar sensors are involved in controlling leaf senescence. This review highlights the processes underlying initiation of sugar signaling and crosstalk between sugars and hormones signal transduction pathways affecting leaf senescence. This summary of the state of current knowledge across different plants aids in filling knowledge gaps and raises key questions that remain to be answered with respect to regulation of leaf senescence by sugar signaling pathways.

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.

Mathematical Modeling Reveals That Sucrose Regulates Leaf Senescence via Dynamic Sugar Signaling Pathways.

Sucrose (Suc) accumulation is one of the key indicators of leaf senescence onset, but little is known about its regulatory role. Here, we found that application of high (120-150 mM) and low levels (60 mM) of Suc to young leaf (YL) and fully expanded leaf (FEL) discs, respectively, decreased chlorophyll content and maximum photosynthetic efficiency. Electrolyte leakage and malondialdehyde levels increased at high Suc concentrations (90-120 mM in YL and 60 and 150 mM in FEL discs). In FEL discs, the senescence-associated gene NtSAG12 showed a gradual increase in expression with increased Suc application; in contrast, in YL discs, NtSAG12 was upregulated with low Suc treatment (60 mM) but downregulated at higher levels of Suc. In YL discs, trehalose-6-phosphate (T6P) accumulated at a low half-maximal effective concentration (EC50) of Suc (1.765 mM). However, T6P levels declined as trehalose 6 phosphate synthase (TPS) content decreased, resulting in the maximum velocity of sucrose non-fermenting-1-related protein kinase (SnRK) and hexokinase (HXK) occurring at higher level of Suc. We therefore speculated that senescence was induced by hexose accumulation. In FEL discs, the EC50 of T6P occurred at a low concentration of Suc (0.9488 mM); T6P levels progressively increased with higher TPS content, which inhibited SnRK activity with a dissociation constant (Kd) of 0.001475 U/g. This confirmed that the T6P-SnRK complex induced senescence in detached FEL discs.

Circular drought-hardening confers drought tolerance via modulation of the antioxidant defense system, osmoregulation, and gene expression in tobacco.

Drought stress hinders the growth and development of crop plants and ultimately its productivity. It is expected that drought stress will be frequent and intense in the future due to drastic changes in the global climate. It is necessary to make crop plants more resilient to drought stress through various techniques; drought-hardening is one of them. Defining various metabolic strategies used by tobacco plants to confer drought tolerance will be important for maintaining plant physiological functions, but studies addressing this topic are limited. This study was designed to elucidate the drought tolerance and adaptation strategies used by tobacco plants via the application of different circular drought-hardening cycles (control: no drought-hardening, T1: one cycle of drought hardening, T2: two cycles of drought-hardening, and T3: three cycles of drought-hardening) to two tobacco varieties namely Honghuadajinyuan (H) and Yun Yan-100 (Y). The results revealed that drought-hardening decreased the fresh and dry biomass of the tobacco plants. The decrease was more pronounced in the T3 treatment for both H (23 and 29%, respectively) and Y (26 and 31%, respectively) under drought stress. The MDA contents, especially in T1 and T2 in both varieties, were statistically similar compared with control under drought stress. Similarly, higher POD, APX, and GR activities were observed, especially in T3, and elevated amounts of AsA and GSH were also observed among the different circular drought-hardening treatments under drought stress. Thus circular drought-hardening mitigated the oxidative damage by increasing the antioxidant enzyme activities and elevated the content of antioxidant substances, a key metabolic strategy under drought stress. Similarly, another important plant metabolic strategy is the osmotic adjustment. Different circular drought-hardening treatments improved the accumulation of proline and soluble sugars contents which contributed to osmoregulation. Finally, at the molecular level, circular drought-hardening improved the transcript levels of antioxidant enzyme-related genes (CAT, APX1, and GR2), proline and polyamines biosynthesis-related genes (P5CS1 and ADC2), and ABA signaling (SnRK2), and transcription factors (AREB1 and WRKY6) in response to drought stress. As a result, circular drought-hardening (T2 and T3 treatments) promoted tolerance to water stress via affecting the anti-oxidative capacity, osmotic adjustment, and regulation of gene expression in tobacco.

Comparative transcriptome combined with biochemical and physiological analyses provide new insights toward cadmium accumulation with two contrasting Nicotiana species.

Cadmium (Cd) is known as one of the most hazardous elements in the environment and a persistent soil constraint toxic to all flora and fauna. In this study, we conducted physiological, biochemical, and transcriptomic analyses of Nicotiana rustica (N. rustica) and Nicotiana tabacum (N. tabacum) treated with CdCl2 to know the underlying molecular mechanisms of Cd accumulation. As a result, N. rustica had more dry weight than N. tabacum. Additionally, N. rustica accumulated higher Cd concentration (69.65 times), Cd2+ influx (1.32-fold), glutathione S-transferases (GST) enzyme activity (2.54 times), GSH/GSSG (oxidized form of GSH) ratio, increase of superoxide dismutase and CAT and a lower H2 O2 and superoxide (O2 •- ) accumulation in their roots than N. tabacum. Cd mainly distributed in the cytoplasm of both species and N. rustica had a significant proportion in the cell wall. Furthermore, the transcriptomic analysis revealed 173 and 710 differentially expressed genes (DEGs) between control and Cd-stressed plants in the leaves and roots of N. rustica, while 576 and 1543 DEGs were found in the leaves and roots of N. tabacum, respectively. In N. rustica, phenylpropanoid biosynthesis and phenylalanine metabolism were the most enriched pathways, while GSH metabolism, ATP-binding cassette transporters and phenylpropanoid biosynthesis were the most enriched in N. tabacum. Finally, we found that DEGs related to metal influx, sequestration, remobilization, and chelation were responsible for Cd accumulation. These results indicated that N. rustica accumulated higher Cd content than N. tabacum, suggesting that each species utilized different response mechanism under the same Cd stress conditions. The DEGs identified in this study might lead to the identification of genes or pathways related to Cd regulation. This study identifies important regulators related to Cd accumulation.

Exogenous application of brassinosteroids regulates tobacco leaf size and expansion via modulation of endogenous hormones content and gene expression.

Brassinosteroids (BR) play diverse roles in the regulation of plant growth and development. BR promotes plant growth by triggering cell division and expansion. However, the effect of exogenous BR application on the leaf size and expansion of tobacco is unknown. Tobacco seedlings are treated with different concentrations of exogenous 2,4-epibrassinolide (EBL) [control (CK, 0 mol L-1), T1 (0.5 × 10-7 mol L-1), and T2 (0.5 × 10-4 mol L-1)]. The results show that T1 has 17.29% and T2 has 25.99% more leaf area than control. The epidermal cell area is increased by 24.40% and 17.13% while the number of epidermal cells is 7.06% and 21.06% higher in T1 and T2, respectively, relative to control. So the exogenous EBL application improves the leaf area by increasing cell numbers and cell area. The endogenous BR (7.5 times and 68.4 times), auxin (IAA) (4.03% and 25.29%), and gibberellin (GA3) contents (84.42% and 91.76%) are higher in T1 and T2, respectively, in comparison with control. Additionally, NtBRI1, NtBIN2, and NtBES1 are upregulated showing that the brassinosteroid signaling pathway is activated. Furthermore, the expression of the key biosynthesis-related genes of BR (NtDWF4), IAA (NtYUCCA6), and GA3 (NtGA3ox-2) are all upregulated under EBL application. Finally, the exogenous EBL application also upregulated the expression of cell growth-related genes (NtCYCD3;1, NtARGOS, NtGRF5, NtGRF8, and NtXTH). The results reveal that the EBL application increases the leaf size and expansion by promoting the cell expansion and division through higher BR, IAA, and GA3 contents along with the upregulation of cell growth-related genes. The results of the study provide a scientific basis for the effect of EBL on tobacco leaf growth at morphological, anatomical, biochemical, and molecular levels. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-00971-x.

Drought-hardening improves drought tolerance in Nicotiana tabacum at physiological, biochemical, and molecular levels.

BACKGROUND: Drought stress is the most harmful one among other abiotic stresses with negative impacts on crop growth and development. Drought-hardening is a feasible and widely used method in tobacco seedlings cultivation. It has gained extensive interests due to its role in improving drought tolerance. This research aimed to investigate the role of drought-hardening and to unravel the multiple mechanisms underlying tobacco drought tolerance and adaptation. RESULTS: This study was designed in which various drought-hardening treatments (CK (no drought-hardening), T1 (drought-hardening for 24 h), T2 (drought-hardening for 48 h), and T3 (drought-hardening for 72 h)) were applied to two tobacco varieties namely HongHuaDaJinYuan (H) and Yun Yan-100 (Y). The findings presented a complete framework of drought-hardening effect at physiological, biochemical, and gene expression levels of the two tobacco varieties under drought stress. The results showed that T2 and T3 significantly reduced the growth of the two varieties under drought stress. Similarly, among the various drought-hardening treatments, T3 improved both the enzymatic (POD, CAT, APX) and non-enzymatic (AsA) defense systems along with the elevated levels of proline and soluble sugar to mitigate the negative effects of oxidative damage and bringing osmoregulation in tobacco plants. Finally, the various drought-hardening treatments (T1, T2, and T3) showed differential regulation of genes expressed in the two varieties, while, particularly T3 drought-hardening treatment-induced drought tolerance via the expression of various stress-responsive genes by triggering the biosynthesis pathways of proline (P5CS1), polyamines (ADC2), ABA-dependent (SnRK2, AREB1), and independent pathways (DREB2B), and antioxidant defense-related genes (CAT, APX1, GR2) in response to drought stress. CONCLUSIONS: Drought-hardening made significant contributions to drought tolerance and adaptation in two tobacco variety seedlings by reducing its growth and, on the other hand, by activating various defense mechanisms at biochemical and molecular levels. The findings of the study pointed out that drought-hardening is a fruitful strategy for conferring drought tolerance and adaptations in tobacco. It will be served as a useful method in the future to understand the drought tolerance and adaptation mechanisms of other plant species. Drought-hardening improved drought tolerance and adaptation of the two tobacco varieties. T1 indicates drought-hardening for 24 h, T2 indicates drought-hardening for 48 h, T3 indicates drought-hardening for 72 h.

Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method.

A state-of-the-art brain-computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel's correlation coefficients' maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p < 0.0167) classification accuracies of 87.2 ± 7.0%, 88.4 ± 6.2%, and 88.1 ± 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems' performance.

fNIRS-based Neurorobotic Interface for gait rehabilitation.

BACKGROUND: In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented. METHODS: fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere's primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested. RESULTS: The classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively. CONCLUSION: The control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.

A multi-modal deep neural network for multi-class liver cancer diagnosis.

Liver disease is a potentially asymptomatic clinical entity that may progress to patient death. This study proposes a multi-modal deep neural network for multi-class malignant liver diagnosis. In parallel with the portal venous computed tomography (CT) scans, pathology data is utilized to prognosticate primary liver cancer variants and metastasis. The processed CT scans are fed to the deep dilated convolution neural network to explore salient features. The residual connections are further added to address vanishing gradient problems. Correspondingly, five pathological features are learned using a wide and deep network that gives a benefit of memorization with generalization. The down-scaled hierarchical features from CT scan and pathology data are concatenated to pass through fully connected layers for classification between liver cancer variants. In addition, the transfer learning of pre-trained deep dilated convolution layers assists in handling insufficient and imbalanced dataset issues. The fine-tuned network can predict three-class liver cancer variants with an average accuracy of 96.06% and an Area Under Curve (AUC) of 0.832. To the best of our knowledge, this is the first study to classify liver cancer variants by integrating pathology and image data, hence following the medical perspective of malignant liver diagnosis. The comparative analysis on the benchmark dataset shows that the proposed multi-modal neural network outperformed most of the liver diagnostic studies and is comparable to others.

Transcriptome and anatomical studies reveal alterations in leaf thickness under long-term drought stress in tobacco.

Drought is one of the foremost environmental factors that limit the growth of plants. Leaf thickness (LT) is an important quantitative trait in plant physiology. The experiment was carried out in a growth room and the plants were divided into two groups such as well-watered and drought-stressed. This work investigated leaf growth in terms of leaf surface growth and expansion rate, leaf stomata traits, LT, anticlinal growth, and leaf cell layers. The results showed that the leaf area and leaf surface expansion rate were decreased by drought stress (DS). Similarly, LT, anticlinal expansion rate, palisade and spongy tissue thickness, and their related expansion rates were also decreased at different days' time points (DTP) of DS. However, a steady increase was observed in the aforementioned parameters after 12 DTP of DS. The stomatal density increased while stomata size decreased at 3 DTP and 12 DTP (low leaf water potential and relative leaf water content at these time points) and vice versa at 24 DTP compared with the well-watered plants indicating adaptations in these traits in response to DS, and thus the leaf water status played a role in the regulation of leaf stomata traits. The cell length decreased in the upper epidermis, palisade and spongy tissues by DS up to 12 DTP led to lower LT while an increase was observed after 12 DTP that resulted in higher LT. The increase in the LT was supported by the upregulation of starch and sucrose metabolism, glycerolipid metabolism, protein processing in endoplasmic reticulum pathways at 18 DTP along with the differentially expressed genes induced that were related to cell wall remodeling (cellulose, expansin, xyloglucans) and cell expansion (auxin response factors and aquaporin). The results explain the response of leaf thickness to drought stress and show alterations in LT and leaf stomatal traits. This study might serve as a valuable source of gene information for functional studies and provide a theoretical basis to understand leaf growth in terms of leaf anatomy and leaf stomatal traits under drought stress.

RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation.

Precise segmentation is in demand for hepatocellular carcinoma or metastasis clinical diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on scanned abdominal computed tomography (CT) images. In this study, we present an automatic unified registration-free deep-learning-based model with residual block and dilated convolution for training end-to-end liver and lesion segmentation. A multi-scale approach has also been utilized to explore novel inter-slice features with multi-channel input images. A novel objective function is introduced to deal with fore- and background pixels imbalance based on the joint metric of dice coefficient and absolute volumetric difference. Further, batch normalization is used to improve the learning without any loss of useful information. The proposed methodology is extensively validated and tested on 30% of the publicly available Dircadb, LiTS, Sliver07, and Chaos datasets. A comparative analysis is conducted based on multiple evaluation metrics frequently used in segmentation competitions. The results show substantial improvement, with mean dice scores of 97.31, 97.38, 97.39 and 95.49% for the Dircadb, LiTS, Sliver07, and Chaos liver test sets, and 91.92 and 86.70% for Dircadb and LiTS lesion segmentation. It should be noted that we achieve the best lesion segmentation performance on common datasets. The obtained qualitative and quantitative results demonstrate that our proposed model outperform other state-of-the-art methods for liver and lesion segmentation, with competitive performance on additional datasets. Henceforth, it is envisaged as being applicable to pertinent medical segmentation applications.

Application, advancement and green aspects of magnetic molecularly imprinted polymers in pesticide residue detection.

Molecularly imprinted polymers (MIPs) have added a vital contribution to food quality and safety with the effective extraction of pesticide residues due to their unique properties. Magnetic molecularly imprinted polymers (MMIPs) are a superior approach to overcome stereotypical limitations due to their unique core-shell and novel composite structure, including high chemothermal stability, rapid extraction, and high selectivity. Over the past two decades, different MMIPs have been developed for pesticide extraction in actual food samples with a complex matrix. Nevertheless, such developments are desirable, yet the synthesis and mode of application of MMIP have great potential as a green chemistry approach that can significantly reduce environmental pollution and minimize resource utilization. In this review, the MMIP application for single or multipesticide detection has been summarized by critiquing each method's uniqueness and efficiency in real sample analysis and providing a possible green chemistry exploration procedure for MMIP synthesis and application for escalated food and environmental safety.

Combined Application of Manure and Chemical Fertilizers Alters Soil Environmental Variables and Improves Soil Fungal Community Composition and Rice Grain Yield.

Soil microorganisms play vital roles in energy flow and soil nutrient cycling and, thus, are important for crop production. A detailed understanding of the complex responses of microbial communities to diverse organic manure and chemical fertilizers (CFs) is crucial for agroecosystem sustainability. However, little is known about the response of soil fungal communities and soil nutrients to manure and CFs, especially under double-rice cropping systems. In this study, we investigated the effects of the application of combined manure and CFs to various fertilization strategies, such as no N fertilizer (Neg-CF); 100% chemical fertilizer (Pos-CF); 60% cattle manure (CM) + 40% CF (high-CM); 30% CM + 70% CF (low-CM); 60% poultry manure (PM) + 40% CF (high-PM), and 30% PM + 70% CF (low-PM) on soil fungal communities' structure and diversity, soil environmental variables, and rice yield. Results showed that synthetic fertilizer plus manure addition significantly increased the soil fertility and rice grain yield compared to sole CFs' application. Moreover, the addition of manure significantly changed the soil fungal community structure and increased the relative abundance of fungi such as phyla Ascomycota, Basidiomycota, Mortierellomycota, and Rozellomycota. The relative abundances dramatically differed at each taxonomic level, especially between manured and non-manured regimes. Principal coordinates analysis (PCoA) exhibited greater impacts of the addition of manure amendments than CFs on fungal community distributions. Redundancy analysis showed that the dominant fungal phyla were positively correlated with soil pH, soil organic C (SOC), total N, and microbial biomass C, and the fungal community structure was strongly affected by SOC. Network analysis explored positive relationships between microorganisms and could increase their adaptability in relevant environments. In addition, the structural equation model (SEM) shows the relationship between microbial biomass, soil nutrients, and rice grain yield. The SEM showed that soil nutrient contents and their availability directly affect rice grain yield, while soil fungi indirectly affect grain yield through microbial biomass production and nutrient levels. Our results suggest that manure application combined with CFs altered soil biochemical traits and soil fungal community structure and counteracted some of the adverse effects of the synthetic fertilizer. Overall, the findings of this research suggest that the integrated application of CF and manure is a better approach for improving soil health and rice yield.

Investigation of sugar signaling behaviors involved in sucrose-induced senescence initiation and progression in N. tabacum.

Sugar is involved in initiating leaf senescence. However, its regulatory role, especially as a signal in the senescence process, is unclear. Therefore, this study was designed to illustrate how sugar stimulates the onset of leaf senescence and controls sugar homeostasis through the T6P-SnRK (sucrose non-fermenting (SNF)-related kinase) and HXK (hexokinase) signaling pathways. We used a leaf disc system detached from fully expanded leaves of Nicotiana tabacum cv. K326 and designed a time-course study (days 3, 5, 7, and 9) with exogenously gradient concentrations (0, 30, 60, 90, 120, and 150 mM) of sucrose (Suc) treatment to identify how Suc application affects sugar metabolism and induces senescence. Our results revealed that early decreases of Fv/Fm and increases in electrolyte leakage responded to Suc on day 3. Furthermore, a substantial increase in lipid peroxidation and up-regulated expression of senescence marker genes (NtSAG12) (except 60 mM on day 3) responded sequentially by day 5. The glucose, G6P, and HXK contents were first induced by Suc on day 3 and then repressed from day 5 to day 7. However, exogenous Suc treatment significantly improved the TPS content and the subsequent precursor T6P from day 3 to day 7. Following exogenous Suc treatments, the transcript level of NtSnRK1 was markedly down-regulated from day 3 to day 7. On the other hand, a linear regression analysis demonstrated that the T6P-NtSnRK1 signaling pathway was strongly associated with senescence initiation, and was accompanied by membrane degradation and NtCP1/NtSAG12 up-regulation by day 3. The T6P-NtSnRK1 signaling pathway experienced membrane and chloroplast degradation by day 5. HXK functioned as a metabolic enzyme promoting Glc-G6P and as a Glc sensor, accelerating the initiation of senescence through the HXK-dependent pathway by repressing PSII by day 3 and the senescence process through the Glycolytic pathway by day 7. These physiological, biochemical, and molecular analyses demonstrate that exogenous Suc regulates T6P accumulation, inducing senescence through the NtSnRK signaling pathway. These results illustrate the role of Suc and the transition of the sugar signaling pathway during the progression of senescence initiation.

Application of 2,4-Epibrassinolide Improves Drought Tolerance in Tobacco through Physiological and Biochemical Mechanisms.

Drought stress is a major abiotic stress that hinders plant growth and development. Brassinosteroids (BR), including 2,4-epibrassinolide (EBR), play important roles in plant growth, development, and responses to abiotic stresses, including drought stress. This work investigates exogenous EBR application roles in improving drought tolerance in tobacco. Tobacco plants were divided into three groups: WW (well-watered), DS (drought stress), and DSB (drought stress + 0.05 mM EBR). The results revealed that DS decreased the leaf thickness (LT), whereas EBR application upregulated genes related to cell expansion, which were induced by the BR (DWF4, HERK2, and BZR1) and IAA (ARF9, ARF6, PIN1, SAUR19, and ABP1) signaling pathway. This promoted LT by 28%, increasing plant adaptation. Furthermore, EBR application improved SOD (22%), POD (11%), and CAT (5%) enzyme activities and their related genes expression (FeSOD, POD, and CAT) along with a higher accumulation of osmoregulatory substances such as proline (29%) and soluble sugars (14%) under DS and conferred drought tolerance. Finally, EBR application augmented the auxin (IAA) (21%) and brassinolide (131%) contents and upregulated genes related to drought tolerance induced by the BR (BRL3 and BZR2) and IAA (YUCCA6, SAUR32, and IAA26) signaling pathways. These results suggest that it could play an important role in improving mechanisms of drought tolerance in tobacco.