Drought or/and Heat-Stress Effects on Seed Filling in Food Crops: Impacts on Functional Biochemistry, Seed Yields, and Nutritional Quality

Drought (water deficits) and heat (high temperatures) stress are the prime abiotic constraints, under the current and climate change scenario in future. Any further increase in the occurrence, and extremity of these stresses, either individually or in combination, would severely reduce the crop productivity and food security, globally. Although, they obstruct productivity at all crop growth stages, the extent of damage at reproductive phase of crop growth, mainly the seed filling phase, is critical and causes considerable yield losses. Drought and heat stress substantially affect the seed yields by reducing seed size and number, eventually affecting the commercial trait ‘100 seed weight’ and seed quality. Seed filling is influenced by various metabolic processes occurring in the leaves, especially production and translocation of photoassimilates, importing precursors for biosynthesis of seed reserves, minerals and other functional constituents. These processes are highly sensitive to drought and heat, due to involvement of array of diverse enzymes and transporters, located in the leaves and seeds. We highlight here the findings in various food crops showing how their seed composition is drastically impacted at various cellular levels due to drought and heat stresses, applied separately, or in combination. The combined stresses are extremely detrimental for seed yield and its quality, and thus need more attention. Understanding the precise target sites regulating seed filling events in leaves and seeds, and how they are affected by abiotic stresses, is imperative to enhance the seed quality. It is vital to know the physiological, biochemical and genetic mechanisms, which govern the various seed filling events under stress environments, to devise strategies to improve stress tolerance. Converging modern advances in physiology, biochemistry and biotechnology, especially the “omics” technologies might provide a strong impetus to research on this aspect. Such application, along with effective agronomic management system would pave the way in developing crop genotypes/varieties with improved productivity under drought and/or heat stresses

Drought and heat stress-related proteins: an update about their functional relevance in imparting stress tolerance in agricultural crops

Key message We describe here the recent developments about the involvement of diverse stress-related proteins in sensing, signaling, and defending the cells in plants in response to drought or/and heat stress. Abstract In the current era of global climate drift, plant growth and productivity are often limited by various environmental stresses, especially drought and heat. Adaptation to abiotic stress is a multigenic process involving maintenance of homeostasis for proper survival under adverse environment. It has been widely observed that a series of proteins respond to heat and drought conditions at both transcriptional and translational levels. The proteins are involved in various signaling events, act as key transcriptional activators and saviors of plants under extreme environments. A detailed insight about the functional aspects of diverse stress-responsive proteins may assist in unraveling various stress resilience mechanisms in plants. Furthermore, by identifying the metabolic proteins associated with drought and heat tolerance, tolerant varieties can be produced through transgenic/recombinant technologies. A large number of regulatory and functional stress-associated proteins are reported to participate in response to heat and drought stresses, such as protein kinases, phosphatases, transcription factors, and late embryogenesis abundant proteins, dehydrins, osmotins, and heat shock proteins, which may be similar or unique to stress treatments. Few studies have revealed that cellular response to combined drought and heat stresses is distinctive, compared to their individual treatments. In this review, we would mainly focus on the new developments about various stress sensors and receptors, transcription factors, chaperones, and stress-associated proteins involved in drought or/and heat stresses, and their possible role in augmenting stress tolerance in crops

Biotic and Abiotic Constraints in Mungbean Production—Progress in Genetic Improvement

Mungbean [Vigna radiata (L.) R. Wilczek var. radiata] is an important food and cash legume crop in Asia. Development of short duration varieties has paved the way for the expansion of mungbean into other regions such as Sub-Saharan Africa and South America. Mungbean productivity is constrained by biotic and abiotic factors. Bruchids, whitefly, thrips, stem fly, aphids, and pod borers are the major insect-pests. The major diseases of mungbean are yellow mosaic, anthracnose, powdery mildew, Cercospora leaf spot, halo blight, bacterial leaf spot, and tan spot. Key abiotic stresses affecting mungbean production are drought, waterlogging, salinity, and heat stress. Mungbean breeding has been critical in developing varieties with resistance to biotic and abiotic factors, but there are many constraints still to address that include the precise and accurate identification of resistance source(s) for some of the traits and the traits conferred by multi genes. Latest technologies in phenotyping, genomics, proteomics, and metabolomics could be of great help to understand insect/pathogen-plant, plant-environment interactions and the key components responsible for resistance to biotic and abiotic stresses. This review discusses current biotic and abiotic constraints in mungbean production and the challenges in genetic improvement

Development of Talus Implant based on Artificial Neural Network prediction of Talus Morphological Parameters

The design philosophies of current talus implant focus too much on mechanical simplicity and usually based on certain population which tends to ignore the bony geometry difference between populations. Thus, the talus implant for particular population was developed based on artificial neural network (ANN) prediction of talus morphometrics. By using Finite Element Method (FEM), numerical models that include mainly the talus bone and the talus implants are created to compare the performance of newly develop talus implant with the three different kind of current talus implant designs. The study demonstrates that not all current talus implant are perfect match for this particular population. While, the ANN method showed a greater capacity of prediction regarding on the low percentage of error and high correlative values with the measurements obtained through Computer Tomographic (CT) scan. ANN is highly accurate predictive methods and has the potential to be used as assisting tools in designing talus implant. For FEM results, only BOX and newly develop talus implant exceeded the contact stress recommended for the superior articular surface compared to the others. The results also showed that the stress increased near the resected surface. Thus, it is agreed that excessive bone resection may not support the force at the ankle which consequently may contribute to early loosening and subsidence of the talus implant. It is concluded that the excessive bone resection can be avoided by perfectly match talus implant which only can be achieved by designing talus implant for a particular population

Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters

Current ankle morphometric measurement tools involve the use of radiographic techniques which maybe rmacceptable to many ethical committees due to the radiation exposure to subjects. In the present study, we propose an alternative method of ankle morphometric measurement using neural network computational model based solely on existing data measurements and demographic information. The reliability and prediction power of this technique were examined and compared with the morphometric measurements of normal subjects using Computed Tomography (CT) scan measurements and Multiple Linear Regression (1.1LR) method of prediction. The Artificial Nemal Network (ANN) used in the present study was based on two-layer feed forward network. The network system included a hidden layer sigmoid transfer fllllction and a linear transfer fllllction in the output layer. For network training, standard levenberg-marquardt algorithm was used. The input used consisted of a set of demographic data (age, height and weight) while the output obtained from the analyses consisted of ankle morphometric measurements (Trochlea Tali Length (TTL) Talar Anterior Width (TaA W) Sagittal Radius of talar (SRTa) Tibia Length (TiL) Tibia Width (TiW) Widtli!LengthRatio of Talar (WLR Ta) and Widtli!Length Ratio of Tibia(WLRTi)). The applicability and accuracy of these alternative methods were evaluated by comparing the predicted values from our computational analysis with the normal CT values of 15 randomly selected volrmteers. Furthermore, our prediction values were also compared with the values predicted using the 1.1LR method. The ANN method showed a greater capacity of prediction and was folllld to estimate the ankle joint morphometric measurements with a low percentage of error and high correlative values with the measurements obtained through the use of CT scan. In addition, the ANN method was also noted to be better in predicting ankle measurements than the 1.1LR method as demonstrated by the lower average of standard deviations: SANN~ 1.35, SMLR ~ 2.20 for females and SANN~ 1.81, SMLR ~ 4.07 for males. The ANN method is potentially better alternative to predict ankle morphometric measurements than CT scan and 1.1LR methods

Quantitative analysis of the impact of disorder on the structural and electrical properties of polymer fibers

Quantifying disorder in physical systems can provide unique opportunities to engineer-specific properties. Here, we apply a methodology based on the approach pioneered by Bragg and Williams for metal alloys to quantify the disorder characterizing polymer fibers including polyaniline (PANI), polyaniline-polycaprolactone (PANI-PCL), and polyvinylidene difluoride (PVDF). Both PANI and PVDF possess electrical properties such as conductivity and piezoelectric response that find a wide range of applications in energy storage and tissue engineering. On the other hand, the mechanical properties of polymer fibers can be tuned by varying the concentration of PANI and PCL during synthesis. Here, we demonstrate that it is possible to control the amount of disorder characterizing a fiber, which provides a route to engineering desired values for specific material properties. The resulting measure of disorder is shown to have a direct relationship to Young’s modulus, band gap, and specific capacitance values. Graphical abstract: [Figure not available: see fulltext.]

Deveploment of Novel Talus Implant Based on Artificial Neural Network Prediction of Talus Morphological Parameters

The design philosophies of current talus implant focus too much on mechanical simplicity and usually based on certain population which tends to ignore the bony geometry difference between populations. Thus, the novel talus implant (NTI) for particular population was developed based on artificial neural network (ANN) prediction of talus morphometrics. By using Finite Element Method (FEM), numerical models that include mainly the talus bone and the talus implants are created to compare the performance of the NTI with the three different kind of current talus implant designs. The study demonstrates that not all current talus implant are perfect match for this particular population. While, the ANN method showed a greater capacity of prediction regarding on the low percentage of error and high correlative values with the measurements obtained through Computer Tomographic (CT) scan. ANN is highly accurate predictive methods and has the potential to be used as assisting tools in designing talus implant. For FEM results, only BOX and NTI exceeded the contact stress recommended for the superior articular surface compared to the others. The results also showed that the stress increased near the resected surface. Thus, it is agreed that excessive bone resection may not support the force at the ankle which consequently may contribute to early loosening and subsidence of the talus implant. It is concluded that the excessive bone resection can be avoided by perfectly match talus implant which only can be achieved by designing talus implant for a particular population