Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.

Mining nutri-dense accessions from rice landraces of Assam, India

The Indian subcontinent is the primary center of origin of rice where huge diversity is found in the Indian rice gene pool, including landraces. North Eastern States of India are home to thousands of rice landraces which are highly diverse and good sources of nutritional traits, but most of them remain nutritionally uncharacterized. Hence, nutritional profiling of 395 Assam landraces was done for total starch, amylose content (AC), total dietary fiber (TDF), total protein content (TPC), oil, phenol, and total phytic acid (TPA) using official AOAC and standard methods, where the mean content for the estimated traits were found to be 75.2 g/100g, 22.2 g/100g, 4.67 g/100g, 9.8 g/100g, 5.26%, 0.40 GAE g/100g, and 0.34 g/100g for respectively. The glycaemic index (GI) was estimated in 24 selected accessions, out of which 17 accessions were found to have low GI (<55). Among different traits, significant correlations were found that can facilitate the direct and indirect selection such as estimated glycemic index (EGI) and amylose content (-0.803). Multivariate analyses, including principal component analysis (PCA) and hierarchical clustering analysis (HCA), revealed the similarities/differences in the nutritional attributes. Four principal components (PC) i.e., PC1, PC2, PC3, and PC4 were identified through principal component analysis (PCA) which, contributed 81.6% of the variance, where maximum loadings were from protein, oil, starch, and phytic acid. Sixteen clusters were identified through hierarchical clustering analysis (HCA) from which the trait-specific and biochemically most distant accessions could be identified for use in cultivar development in breeding programs.

Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm

Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQ_external values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p-value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world.

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R² = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Evaluation and Multivariate Analysis of Cowpea [Vigna unguiculata (L.) Walp] Germplasm for Selected Nutrients—Mining for Nutri-Dense Accessions

A total of 120 highly diverse cowpea [Vigna unguiculata (L.) Walp] genotypes, including indigenous and exotic lines, were evaluated for different biochemical traits using AOAC official methods of analysis and other standard methods. The results exhibited wide variability in the content of proteins (ranging from 19.4 to 27.9%), starch (from 27.5 to 42.7 g 100 g−1), amylose (from 9.65 to 21.7 g 100 g−1), TDF (from 13.7 to 21.1 g 100 g−1), and TSS (from 1.30 to 8.73 g 100 g−1). The concentration of anti-nutritional compounds like phenols and phytic acid ranged from 0.026 to 0.832 g 100 g−1 and 0.690 to 1.88 g 100 g−1, respectively. The correlation coefficient between the traits was calculated to understand the inter-trait relationship. Multivariate analysis (PCA and HCA) was performed to identify the major traits contributing to variability and group accessions with a similar profile. The first three principal components, i.e., PC1, PC2, and PC3, contributed to 62.7% of the variation, where maximum loadings were from starch, followed by protein, phytic acid, and dietary fiber. HCA formed six distinct clusters at a squared Euclidean distance of 5. Accessions in cluster I had high TDF and low TSS content, while cluster II was characterized by low amylose content. Accessions in cluster III had high starch, low protein, and phytic acid, whereas accessions in cluster IV contained high TSS, phenol, and low phytic acid. Cluster V was characterized by high protein, phytic acid, TSS, and phenol content and low starch content, and cluster VI had a high amount of amylose and low phenol content. Some nutri-dense accessions were identified from the above-mentioned clusters, such as EC170579 and EC201086 with high protein (&gt;27%), TSS, amylose, and TDF content. These compositions are promising to provide practical support for developing high-value food and feed varieties using effective breeding strategies with a higher economic value.

Vectorial calibration of superconducting magnets with a quantum magnetic sensor

Cryogenic vector magnet systems make it possible to study the anisotropic magnetic properties of materials without mechanically rotating the sample but by electrically tilting and turning the magnetic field. Vector magnetic fields generated inside superconducting vector magnets are generally measured with three Hall sensors. These three probes must be calibrated over a range of temperatures, and the temperature-dependent calibrations cannot be easily carried out inside an already magnetized superconducting magnet because of remaining magnetic fields. A single magnetometer based on an ensemble of nitrogen vacancy (NV) centers in diamond is proposed to overcome these limitations. The quenching of the photoluminescence intensity emitted by NV centers can determine the field in the remanent state of the solenoids and allows an easy and fast canceling of the residual magnetic field. Once the field is reset to zero, the calibration of this magnetometer can be performed in situ by a single measurement of an optically detected magnetic resonance spectrum. Thereby, these magnetometers do not require any additional temperature-dependent calibrations outside the magnet and offer the possibility to measure vector magnetic fields in three dimensions with a single sensor. Its axial alignment is given by the crystal structure of the diamond host, which increases the accuracy of the field orientation measured with this sensor, compared to the classical arrangement of three Hall sensors. It is foreseeable that the magnetometer described here has the potential to be applied in various fields in the future, such as the characterization of ferromagnetic core solenoids or other magnetic arrangements.

Comparison of Assay Kits for Unconjugated Estriol Shows that Expressing Results as Multiples of the Median Causes Unacceptable Variation in Calculated Risk Factors for Down Syndrome

We compared the performance of two methods for assaying unconjugated estriol in serum: the modified Amerlex third-trimester RIA kit, as used in seminal papers on unconjugated estriol in Down syndrome screening, and the new optimized Amerlex-M second-trimester kit. The significant difference between the results of each assay could cause unacceptable changes in the detection rate and false-positive rate of Down syndrome screening programs, especially if previously published values for estriol are used in the risk calculation. It is not possible to define new calculation parameters for every assay kit because new parameters will need to be defined every time kit changes occur, which would require a large collection of samples from Down syndrome pregnancies for standardization. Possible solutions to this problem are discussed.

The Multiple Sclerosis Inventory of Cognition for Adolescents (MUSICADO): A brief screening instrument to assess cognitive dysfunction, fatigue and loss of health-related quality of life in pediatric-onset multiple sclerosis

OBJECTIVE

Screening for cognitive impairment (CI), fatigue and also Health-related quality of life (HRQoL) in patients with pediatric-onset multiple sclerosis (POMS) is of utmost importance in clinical practice. The aim of this study was to establish a new and validated pediatric screening tool "MUSICADO" that is easy to use and time economical.

METHODS

106 patients with POMS aged 12-18 years and 210 healthy controls (HCs) stratified for age and education underwent neuropsychological testing including a screening test "Multiple Sclerosis Inventory of Cognition" for adults and 8 standardized cognitive tests and established scales to assess fatigue and HRQoL.

RESULTS

The phonemic verbal fluency task (RWT "s-words"), the Trail Making Test A (TMT-A), and the Digit Span Forward discriminated significantly between patients and HCs (p = 0.000, respectively) and showed the highest proportion of test failure in patients (24.5%, 17.9%; 15.1%, respectively). Therefore, they were put together to form the cognitive part of the "MUSICADO". After applying a scoring algorithm with balanced weighting of the subtests and age and education correction and a cut-off score for impairment, 35.8% of patients were categorized to be cognitively impaired (specificity: 88.6%). Fatigue was detected in 37.1% of the patients (specificity: 94.0%) and loss of HRQoL in 41.8% (specificity 95.7%) with the screening version, respectively.

CONCLUSION

The MUSICADO is a newly designed brief and easy to use screening test to help to early identify CI, fatigue, and loss of HRQoL in patients with POMS as cut scores are provided for all three items. Further studies will have to show its usability in independent samples of patients with POMS.

Recurrent occipital dermatofibrosarcoma protuberans tackled with wide local excision: A case report and current management

Dermatofibrosarcoma protuberans (DFSP) is a slowgrowing, locally invasive tumour of the dermis. It commonly presents in the trunks and proximal extremities but is seen to a lesser extent in the head and neck regions. We present a case report of a recurrent DFSP in a 48-year-old Iban woman at the occipital region. The patient underwent wide local excision and removal of outer table of cranium, dressing followed by split thickness skin graft. Histopathological examination confirmed dermatofibrosarcoma protuberans with clear lateral surgical margins and a deep margin of 0.5mm. She is currently undergoing radiotherapy and is planned for 50Grey 25cycles.

Hierarchically structured carbon and silica by chemical foaming

Foamed organic/silica hybrid materials are synthesized via cationic polymerization of organic carbonates with twin monomers. They are converted into hierarchically structured carbon and silica.