Explore the vast range of titles available.

Biochemical markers for cold-induced sweetening (CIS) resistance were tested for their stability over years and their use in selection of parents for crossing to achieve high selection efficiency in potato breeding programs. Two regulatory enzymes directly associated with reducing sugar (RS) accumulation during potato tubers cold storage were tested as a predictor for CIS resistance. These enzymes were studied in 33 potato clones from various breeding programs over four years. Clones with the presence of A-II isozymes of UDP-glucose pyrophosphorylase (UGPase) and low activity of vacuolar acid invertase (VAcInv) enzyme had increased resistance to cold-induced sweetening (CIS). Depending on the levels of these enzymes, clones were divided into class A, class B and class C. Clones categorized as class A had average RS of 0.73 mg per g FW after six months at 5.5°C storage. Class B and C had average RS of 1.15 and 3.80 mg per g FW respectively. The enzyme activity was closely associated with RS accumulation over long-term cold storage. The biochemical markers were found to be stable over the years. Repeated-measure analysis showed 75% chance of maintaining class from one year to the next and a 25% chance of switching, No clone switched between class A and class C, even across all four years. Application of these biochemical markers can identify clones with CIS resistance early in the selection process. Biochemical markers were used to select parents for crossing and six families were established. Results showed that with both parents from class A, 95% of their offspring had desirable glucose levels and chip color, which dropped to 52% when one parent was from class A and other from class B. These results suggest that two regulatory enzymes, i.e., UGPase and VAcInv, can be used as stable prognostic biochemical markers for CIS resistance for precise parent selection resulting in progenies with significantly higher percentage of clones with acceptable processing quality.

Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.

This study presents the concentration of submicron aerosol (PM 1.0 ) collected during November, 2009 to March, 2010 at two road sites near the Indian Institute of Technology Delhi campus. In winter, PM 1.0 composed 83% of PM 2.5 indicating the dominance of combustion activity-generated particles. Principal component analysis (PCA) proved secondary aerosol formation as a dominant process in enhancing aerosol concentration at a receptor site along with biomass burning, vehicle exhaust, road dust, engine and tire tear wear, and secondary ammonia. The non-carcinogenic and excess cancer risk for adults and children were estimated for trace element data set available for road site and at elevated site from another parallel work. The decrease in average hazard quotient (HQ) for children and adults was estimated in following order: Mn > Cr > Ni > Pb > Zn > Cu both at road and elevated site. For children, the mean HQs were observed in safe level for Cu, Ni, Zn, and Pb; however, values exceeded safe limit for Cr and Mn at road site. The average highest hazard index values for children and adults were estimated as 22 and 10, respectively, for road site and 7 and 3 for elevated site. The road site average excess cancer risk (ECR) risk of Cr and Ni was close to tolerable limit (10 −4 ) for adults and it was 13–16 times higher than the safe limit (10 −6 ) for children. The ECR of Ni for adults and children was 102 and 14 times higher at road site compared to elevated site. Overall, the observed ECR values far exceed the acceptable level.

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R2 of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications.

Delta-like 1homolog (Dlk1) is an imprinted gene encoding a transmembrane protein whose increased expression has been associated with muscle hypertrophy in animal models. However, the mechanisms by which Dlk1 regulates skeletal muscle plasticity remain unknown. Here we combine conditional gene knockout and over-expression analyses to investigate the role of Dlk1 in mouse muscle development, regeneration and myogenic stem cells (satellite cells). Genetic ablation of Dlk1 in the myogenic lineage resulted in reduced body weight and skeletal muscle mass due to reductions in myofiber numbers and myosin heavy chain IIB gene expression. In addition, muscle-specific Dlk1 ablation led to postnatal growth retardation and impaired muscle regeneration, associated with augmented myogenic inhibitory signaling mediated by NF-κB and inflammatory cytokines. To examine the role of Dlk1 in satellite cells, we analyzed the proliferation, self-renewal and differentiation of satellite cells cultured on their native host myofibers. We showed that ablation of Dlk1 inhibits the expression of the myogenic regulatory transcription factor MyoD, and facilitated the self-renewal of activated satellite cells. Conversely, Dlk1 over-expression inhibited the proliferation and enhanced differentiation of cultured myoblasts. As Dlk1 is expressed at low levels in satellite cells but its expression rapidly increases upon myogenic differentiation in vitro and in regenerating muscles in vivo, our results suggest a model in which Dlk1 expressed by nascent or regenerating myofibers non-cell autonomously promotes the differentiation of their neighbor satellite cells and therefore leads to muscle hypertrophy.

The high degree of conservation of toll-like receptors (TLRs), and yet their subtle variations for better adaptation of species in the host–pathogen arms race make them worthy candidates for understanding evolution. We have attempted to track the trend of TLR evolution in the most diverse vertebrate group—teleosts, where Clarias batrachus was given emphasis, considering its traits for terrestrial adaptation. Eleven C. batrachus TLRs (TLR1, 2, 3, 5, 7, 8 9, 13, 22, 25, 26) were identified in this study which clustered in proximity to its Siluriformes relative orthologues in the phylogenetic analysis of 228 TLRs from 25 teleosts. Ten TLRs (TLR1, 2, 3, 5, 7, 8 9, 13, 21, 22) with at least 15 member orthologues for each alignment were processed for selection pressure and coevolutionary analysis. TLR1, 7, 8 and 9 were found to be under positive selection in the alignment-wide test. TLR1 also showed maximum episodic diversification in its clades while the teleost group Eupercaria showed the maximum divergence in their TLR repertoire. Episodic diversification was evident in C. batrachus TLR1 and 7 alignments. These results present a strong evidence of a divergent TLR repertoire in teleosts which may be contributing towards species-specific variation in TLR functions.

Skeletal muscle wasting is a debilitating consequence of large number of disease states and conditions. Tumor necrosis factor-α (TNF-α) is one of the most important muscle-wasting cytokine, elevated levels of which cause significant muscular abnormalities. However, the underpinning molecular mechanisms by which TNF-α causes skeletal muscle wasting are less well-understood. We have used microarray, quantitative real-time PCR (QRT-PCR), Western blot, and bioinformatics tools to study the effects of TNF-α on various molecular pathways and gene networks in C2C12 cells (a mouse myoblastic cell line). Microarray analyses of C2C12 myotubes treated with TNF-α (10 ng/ml) for 18h showed differential expression of a number of genes involved in distinct molecular pathways. The genes involved in nuclear factor-kappa B (NF-kappaB) signaling, 26s proteasome pathway, Notch1 signaling, and chemokine networks are the most important ones affected by TNF-α. The expression of some of the genes in microarray dataset showed good correlation in independent QRT-PCR and Western blot assays. Analysis of TNF-treated myotubes showed that TNF-α augments the activity of both canonical and alternative NF-κB signaling pathways in myotubes. Bioinformatics analyses of microarray dataset revealed that TNF-α affects the activity of several important pathways including those involved in oxidative stress, hepatic fibrosis, mitochondrial dysfunction, cholesterol biosynthesis, and TGF-β signaling. Furthermore, TNF-α was found to affect the gene networks related to drug metabolism, cell cycle, cancer, neurological disease, organismal injury, and abnormalities in myotubes. TNF-α regulates the expression of multiple genes involved in various toxic pathways which may be responsible for TNF-induced muscle loss in catabolic conditions. Our study suggests that TNF-α activates both canonical and alternative NF-κB signaling pathways in a time-dependent manner in skeletal muscle cells. The study provides novel insight into the mechanisms of action of TNF-α in skeletal muscle cells.

Recently released potato cultivars Dakota Russet and Easton were bred for low reducing sugars, and low acrylamide-forming potential in French fries. The objectives of this study were to determine: (1) the effects of nitrogen rate and storage time on tuber glucose concentrations in different cultivars; (2) the relationships between acrylamide, glucose, and asparagine for the new cultivars and Russet Burbank. The study was conducted at Becker, Minnesota over a period of two years on a loamy sand soil under irrigated conditions. All cultivars were subjected to five N rates from 135 to 404 kg ha−1 in a randomized complete block design. Following harvest, tubers were stored at 7.8 °C and sampled at 0, 16, and 32 weeks. Dakota Russet and Easton had significantly lower concentrations of stem- and bud-end glucose, asparagine, and acrylamide than those of Russet Burbank in both years. The effect of storage time on glucose concentration was significant but differed with cultivar and year. N rate effects on stem- and bud-end glucose concentrations were cultivar and storage time dependent. After 16 weeks of storage, both asparagine and acrylamide concentrations linearly increased with increasing N rate. Glucose concentration was positively correlated with acrylamide concentration (r2 = 0.61). Asparagine concentration was also positively correlated with acrylamide concentration (r2 = 0.45) when the asparagine:glucose ratio was <1.306. The correlation between fry color and stem-end glucose concentration was significant over three cultivars in both years, but stronger in a growing season with minimal environmental stress. Taken together, these results suggest that while acrylamide formation during potato processing is a complex process affected by agronomic practices, environmental conditions during the growing season, and storage conditions, cultivar selection may be the most reliable method to minimize acrylamide in fried products.

Time-series data for climatic variables of Jharkhand state, India were analyzed to assess the spatio-temporal variation and fluctuation over the study period of 118 years (1901–2018). Mann–Kendall (MK), Sequential Mann–Kendall (SQMK), and Sen’s slope tests were applied to determine the trend of precipitation, temperature maxima–minima, and Potential Evapotranspiration (PET) time-series data acquired after removal of serial autocorrelation called prewhitening. Minimum and maximum temperature revealed insignificant variation during pre-monsoon and monsoon season, while a remarkable increasing trend was observed for post-monsoon and winter season; however, increasing trend was obtained for annual maxima–minima. The average minimum temperature fluctuates with an increase of 0.59–0.41 ºC to a decrease of 0.79–0.39 ºC, whereas average maximum temperature fluctuates with an increase of 1–1.5 ºC to decrease of 0.82–0.14 ºC. Notably, decreasing trend of 1.09–2.3 mm/year was observed for precipitation during monsoon season, whereas decreasing trend of 1.2–2.4 mm/year was found for annual precipitation, and similarly for PET, significant decreasing trend of 0.0003–0.0012 mm/year was found for monsoon season and annually. However, the underlying persistence effect observed in all seasons and throughout the year for all climatic parameters resulted in the time-series with low-frequency fluctuations. SQMK method exhibits the periodic fluctuation of trends, which are more noticeable in pre-monsoon and monsoon season. An understanding of these alterations in pattern of climatic variables is important for planning and management of water resources and sustainable agriculture.

Robust detection of glottal instants is essential for various speech and biomedical applications. Glottal closing and glottal opening are two crucial instants/epochs of a glottal cycle. The first-order derivative of the Electroglottographic (EGG) signal demonstrates important peaks at those locations for standard voicing, but the detection of glottal instants becomes erroneous when the peak to peak amplitude of the EGG signal is very low, irregular and unpredictable. In this work, a new efficient method is proposed for identification of glottal instants from the EGG signals including the segments of the signals where the signals are feeble with irregular periodicity. The overall accuracy of detection will be enhanced by identifying the glottal instants for the whole part of the signal including the vulnerable segments of signal. As the phase of a signal is uniform in nature, the phase information of the EGG signal has been explored to detect glottal instants accurately. Under low strength of the EGG signal, the proposed method remarkably has better performance compared to the existing instants detection methods and for pathological EGG signal, the detection accuracy of glottal instants is better than other existing methods.