Index for refining soil health assessment through multivariate approach under diverse agro-climatic zones in the Indo-Gangetic basin of Bihar.

A fundamental necessity in advancing sustainable crop production lies in the establishment of a reliable technique for assessing soil health. Soil health assessment is a challenge considering multiple interactions among dynamic indicators within various management strategies and agroecological contexts. Hence a study was conducted to determine the soil health variables, quantify the soil health index (SHI), and validate them with the productivity of rice (Oryza sativa L.)-wheat (Triticum aestivum L.) system for the Indo Gangetic basin of Bihar, India, under four contrasting agro-climatic zones (ACZ-I, II, IIIA & IIIB). For this study, 100 soil samples (0-15 cm) from each ACZ with a total of 400 soil samples were obtained for analyzing 20 soil health variables (soil physical, chemical, and biological properties). To identify SHI and important soil health variables, principal component analysis (PCA) was employed. Apart from specific variables, soil pH, soil organic carbon (SOC), available Zn and available water capacity (AWC) were identified as common indicators for the four ACZs. Results revealed that under the rice-wheat cropping system, ACZ-IIIB soils had a higher SHI (0.19-0.70) than other ACZs. SHI of ACZ-IIIB was significantly influenced by SOC (19.32 %), available P (10.52 %), clay (10.43 %), pH (10.80 %), and soil respiration (9.8 %). The strong relationship between SHI and system productivity of the rice-wheat (R2 = 0.79) system indicates that the selected soil health variables are representative of good soil health. It is concluded that ACZ-specific SHIs are a promising strategy for evaluating and monitoring soil health to achieve the United Nations' Sustainable Development Goal of 'zero hunger' by 2030.

Patients' Preferences for Sphingosine-1-Phosphate Receptor Modulators in Multiple Sclerosis Based on Clinical Management Considerations: A Choice Experiment.

BACKGROUND: Several sphingosine-1-phosphate receptor (S1PR) modulators are available in the US for treating relapsing forms of multiple sclerosis (RMS). Given that these S1PR modulators have similar efficacy and safety, patients may consider the clinical management characteristics of the S1PR modulators when deciding among treatments. However, none of the S1PR modulators is clearly superior in every aspect of clinical management, and for some treatments, clinical management varies based on a patient's comorbid health conditions (e.g., heart conditions [HC]). OBJECTIVES: This study aimed to determine which S1PR modulator patients with relapsing-remitting multiple sclerosis (RRMS) would prefer based on clinical management considerations, and to estimate how different clinical management considerations might drive these preferences. Preferences were explored separately for patients with and without comorbid HC. METHODS: A multicriteria decision analysis was conducted on S1PR modulators approved to treat RMS: fingolimod, ozanimod, siponimod, and ponesimod. Clinical management preferences of patients with RRMS were elicited in a discrete choice experiment (DCE) in which participants repeatedly chose between hypothetical S1PR modulator profiles based on their clinical management attributes. Attributes included first-dose observations, genotyping, liver function tests, eye examinations, drug-drug interactions, interactions with antidepressants, interactions with foods high in tyramine, and immune system recovery time. Preferences were estimated separately for patients with HC and without HC (noHC). Marginal utilities were calculated from the DCE data for each attribute and level using a mixed logit model. In the multicriteria decision analysis, partial value scores were created by applying the marginal utilities for each attribute and level to the real-world profiles of S1PR modulators. Partial value scores were summed to determine an overall clinical management value score for each S1PR modulator. RESULTS: Four hundred patients with RRMS completed the DCE. Ponesimod had the highest overall value score for patients both without (n = 341) and with (n = 59) HC (noHC: 5.1; HC: 4.0), followed by siponimod (noHC: 4.9; HC: 3.3), fingolimod (noHC: 3.4; HC: 2.8), and ozanimod (noHC: 0.9; HC: 0.8). Overall, immune system recovery time contributed the highest partial value scores (noHC: up to 1.9 points; HC: up to 1.2 points), followed by the number of drug-drug interactions (noHC: up to 1.2 points; HC: up to 1.7 points). CONCLUSIONS: When considering the clinical management of S1PR modulators, the average patient with RRMS is expected to choose a treatment with shorter immune system recovery time and fewer interactions with other drugs. Patients both with and without heart conditions are likely to prefer the clinical management profile of ponesimod over those of siponimod, fingolimod, and ozanimod. This information can help inform recommendations for treating RRMS and facilitate shared decision making between patients and their doctors.

Harnessing weedy rice as functional food and source of novel traits for crop improvement.

A relative of cultivated rice (Oryza sativa L.), weedy or red rice (Oryza spp.) is currently recognized as the dominant weed, leading to a drastic loss of yield of cultivated rice due to its highly competitive abilities like producing more tillers, panicles, and biomass with better nutrient uptake. Due to its high nutritional value, antioxidant properties (anthocyanin and proanthocyanin), and nutrient absorption ability, weedy rice is gaining immense research attentions to understand its genetic constitution to augment future breeding strategies and to develop nutrition-rich functional foods. Consequently, this review focuses on the unique gene source of weedy rice to enhance the cultivated rice for its crucial features like water use efficiency, abiotic and biotic stress tolerance, early flowering, and the red pericarp of the seed. It explores the debating issues on the origin and evolution of weedy rice, including its high diversity, signalling aspects, quantitative trait loci (QTL) mapping under stress conditions, the intricacy of the mechanism in the expression of the gene flow, and ecological challenges of nutrient removal by weedy rice. This review may create a foundation for future researchers to understand the gene flow between cultivated crops and weedy traits and support an improved approach for the applicability of several models in predicting multiomics variables.

Electrophysiological dynamics of a triple network model of cognitive control: A multi-experiment replication.

Dynamic interactions between large-scale brain networks are thought to underpin human cognitive processes, but their underlying electrophysiological dynamics remain unknown. The triple network model, which highlights the salience, default mode, and frontoparietal networks, provides a fundamental framework for understanding these interactions. To unravel the electrophysiological mechanisms underlying these network dynamics, we utilized intracranial EEG recordings from 177 participants across four distinct memory experiments. Our findings revealed a consistent pattern of directed information flow from the anterior insula, a key node of the salience network, to both the default mode and frontoparietal networks. Notably, this pattern of information transmission was observed regardless of the nature of the tasks, whether they involved externally driven stimuli during encoding or internally governed processes during free recall. Moreover, the directed information flow from the anterior insula to the other networks was present irrespective of the activation or suppression states of individual network nodes. Furthermore, we observed a specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the default mode network during memory encoding, but not recall, suggesting a task-specific functional down-regulation of this region. Crucially, these results were reliably replicated across all four experiments, underscoring the robustness and generalizability of our findings. Our study significantly advances the understanding of how coordinated neural network interactions underpin cognitive operations and highlights the critical role of the anterior insula in orchestrating the dynamics of large-scale brain networks. These findings have important implications for elucidating the neural basis of cognitive control and its potential disruptions in various neurological and psychiatric disorders.

Hippocampal-parietal cortex causal directed connectivity during human episodic memory formation: Replication across three experiments.

Hippocampus-parietal cortex circuits are thought to play a crucial role in memory and attention, but their neural basis remains poorly understood. We employed intracranial EEG from 96 participants (51 females) to investigate the neurophysiological underpinning of these circuits across three memory tasks spanning verbal and spatial domains. We uncovered a consistent pattern of higher causal directed connectivity from the hippocampus to both lateral parietal cortex (supramarginal and angular gyrus) and medial parietal cortex (posterior cingulate cortex) in the delta-theta band during memory encoding and recall. This connectivity was independent of activation or suppression states in the hippocampus or parietal cortex. Crucially, directed connectivity from the supramarginal gyrus to the hippocampus was enhanced in participants with higher memory recall, highlighting its behavioral significance. Our findings align with the attention-to-memory model, which posits that attention directs cognitive resources toward pertinent information during memory formation. The robustness of these results was demonstrated through Bayesian replication analysis of the memory encoding and recall periods across the three tasks. Our study sheds light on the neural basis of casual signaling within hippocampus-parietal circuits, broadening our understanding of their critical roles in human cognition.

Preferences of Patients with Amyotrophic Lateral Sclerosis for Intrathecal Drug Delivery: Choosing between an Implanted Drug-Delivery Device and Therapeutic Lumbar Puncture.

BACKGROUND: Novel intrathecal treatments for amyotrophic lateral sclerosis (ALS) may require delivery using lumbar puncture (LP). Implanted drug-delivery devices (IDDDs) could be an alternative but little is known about patients' preferences for intrathecal drug-delivery methods. OBJECTIVE: We aimed to elicit preferences of patients with ALS for routine LP and IDDD use. METHODS: A discrete choice experiment (DCE) and a threshold technique (TT) exercise were conducted online among patients with ALS in the US and Europe. In the DCE, patients made trade-offs between administration attributes. Attributes were identified from qualitative interviews. The TT elicited maximum acceptable risks (MARs) of complications from device implantation surgery. DCE data were analyzed using mixed logit to quantify relative attribute importance (RAI) as the maximum contribution of each attribute to a preference, and to estimate MARs of device failure. TT data were analyzed using interval regression. Four scenarios of LP and IDDD were compared. RESULTS: Participants (N = 295) had a mean age of 57.7 years; most (74.2%) were diagnosed < 3 years ago. Preferences were affected by device failure risk (RAI 28.6%), administration frequency (26.4%), administration risk (19.7%), overall duration (17.8%), and appointment location (7.5%). Patients accepted a 5.6% device failure risk to reduce overall duration from 2 h to 30 min and a 3.6% risk for administration in a local clinic instead of a hospital. The average MAR of complications from implantation surgery was 29%. Patients preferred IDDD over LP in three of four scenarios. CONCLUSION: Patients considered an IDDD as a valuable alternative to LP in multiple clinical settings.

Screening for obstructive sleep apnea in patients with cancer - a machine learning approach.

Background: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with daytime sleepiness, fatigue, and increased all-cause mortality risk in patients with cancer. Existing screening tools for OSA do not account for the interaction of cancer-related features that may increase OSA risk. Study Design and Methods: This is a retrospective study of patients with cancer at a single tertiary cancer institution who underwent a home sleep apnea test (HSAT) to evaluate for OSA. Unsupervised machine learning (ML) was used to reduce the dimensions and extract significant features associated with OSA. ML classifiers were applied to principal components and model hyperparameters were optimized using k-fold cross-validation. Training models for OSA were subsequently tested and compared with the STOP-Bang questionnaire on a prospective unseen test set of patients who underwent an HSAT. Results: From a training dataset of 249 patients, kernel principal component analysis (PCA) extracted eight components through dimension reduction to explain the maximum variance with OSA at 98%. Predictors of OSA were smoking, asthma, chronic kidney disease, STOP-Bang score, race, diabetes, radiation to head/neck/thorax (RT-HNT), type of cancer, and cancer metastases. Of the ML models, PCA + RF had the highest sensitivity (96.8%), specificity (92.3%), negative predictive value (92%), F1 score (0.93), and ROC-AUC score (0.88). The PCA + RF screening algorithm also performed better than the STOP-Bang questionnaire alone when tested on a prospective unseen test set. Conclusions: The PCA + RF ML model had the highest accuracy in screening for OSA in patients with cancer. History of RT-HNT, cancer metastases, and type of cancer were identified as cancer-related risk factors for OSA.

Evaluation of multifrequency SAR data for estimating tropical above-ground biomass by employing radiative transfer modeling.

The retrieval of the biophysical parameters and subsequent estimation of the above-ground biomass (AGB) of vegetation stands are made possible by the simulation of the extinction and scattering components from the canopy layer using vector radiative transfer (VRT) theory-based scattering models. With the use of such a model, this study aims to evaluate and compare the potential of dual-pol, multi-frequency SAR data for estimating above-ground biomass. The data selected for this work are L-band dual polarized (HH/HV) ALOS-2 data, S-band dual polarized (HH/HV) NovaSAR data, and C-band dual polarized (VV/VH) Sentinel-1 data. The two key biophysical parameters, tree height, and trunk radius are retrieved using the proposed methodology, applying the frequencies independently. A general allometric equation with vegetation-specific coefficients is used to estimate the AGB from the retrieved biophysical parameters. The retrieval results are validated using ground truth measurements collected from the study area. The L-band, with the coefficient of determination ([Formula: see text]) of 0.73 and the root mean square error (RMSE) of 35.90 t/ha, has the best correlation between the modeled and field AGBs, followed by the S-band with an [Formula: see text] of 0.37 and an RMSE of 63.37 t/ha, and the C-band with an [Formula: see text] of 0.25 and an RMSE of 72.32 t/ha. The L-band has yielded improved estimates of AGB in regression analysis as well, with an [Formula: see text] of 0.48 and an RMSE of 50.02 t/ha, compared to the S- and C-bands, which have the [Formula: see text] of 0.12 and 0.03 and the RMSE of 70.98 t/ha and 80.84 t/ha, respectively.

Influence of conservation agriculture-based production systems on bacterial diversity and soil quality in rice-wheat-greengram cropping system in eastern Indo-Gangetic Plains of India.

Introduction: Conservation agriculture (CA) is gaining attention in the South Asia as an environmentally benign and sustainable food production system. The knowledge of the soil bacterial community composition along with other soil properties is essential for evaluating the CA-based management practices for achieving the soil environment sustainability and climate resilience in the rice-wheat-greengram system. The long-term effects of CA-based tillage-cum-crop establishment (TCE) methods on earthworm population, soil parameters as well as microbial diversity have not been well studied. Methods: Seven treatments (or scenarios) were laid down with the various tillage (wet, dry, or zero-tillage), establishment method (direct-or drill-seeding or transplantation) and residue management practices (mixed with the soil or kept on the soil surface). The soil samples were collected after 7 years of experimentation and analyzed for the soil quality and bacterial diversity to examine the effect of tillage-cum-crop establishment methods. Results and Discussion: Earthworm population (3.6 times), soil organic carbon (11.94%), macro (NPK) (14.50-23.57%) and micronutrients (Mn, and Cu) (13.25 and 29.57%) contents were appreciably higher under CA-based TCE methods than tillage-intensive farming practices. Significantly higher number of OTUs (1,192 ± 50) and Chao1 (1415.65 ± 14.34) values were observed in partial CA-based production system (p ≤ 0.05). Forty-two (42) bacterial phyla were identified across the scenarios, and Proteobacteria, Actinobacteria, and Firmicutes were the most dominant in all the scenarios. The CA-based scenarios harbor a high abundance of Proteobacteria (2-13%), whereas the conventional tillage-based scenarios were dominated by the bacterial phyla Acidobacteria and Chloroflexi and found statistically differed among the scenarios (p ≤ 0.05). Composition of the major phyla, i.e., Proteobacteria, Actinobacteria, and Firmicutes were associated differently with either CA or farmers-based tillage management practices. Overall, the present study indicates the importance of CA-based tillage-cum-crop establishment methods in shaping the bacterial diversity, earthworms population, soil organic carbon, and plant nutrient availability, which are crucial for sustainable agricultural production and resilience in agro-ecosystem.

Evaluation of millets for physio-chemical and root morphological traits suitable for resilient farming and nutritional security in Eastern Himalayas.

Introduction: Millets are nutritionally superior and climate-resilient short-duration crops and hold a prominent place in cropping sequences around the world. They have immense potential to grow in a marginal environment due to diverse adaptive mechanisms. Methods: An experiment was conducted in an organic production system in the North Eastern Himalayan foothills of India for 3 consecutive years by evaluating high-yielding varieties (HYVs) of different millets, viz., finger millet, foxtail millet, little millet, barnyard millet, proso millet, and browntop millet, along with local landraces of finger millets (Sikkim-1 and Sikkim-2; Nagaland-1 and Nagaland-2) to identify stable, high-yielding, and nutritionally superior genotypes suited for the region. Results: Among the various millets, finger millet, followed by little millet and foxtail millet, proved their superiority in terms of productivity (ranging between 1.16 and 1.43 Mg ha-1) compared to other millets. Among different varieties of finger millets, cv. VL Mandua 352 recorded the highest average grain yield (1.43 Mg ha-1) followed by local landraces, Nagaland-2 (1.31 Mg ha-1) and Sikkim-1 (1.25 Mg ha-1). Root traits such as total root length, root volume, average diameter of roots, and root surface area were significantly higher in finger millet landraces Nagaland-1, Nagaland-2, and Sikkim-1 compared to the rest of the millet genotypes. The different millets were found to be rich sources of protein as recorded in foxtail millet cv. SiA 3088 (12.3%), proso millet cv. TNAU 145 (11.5%), and finger millet landraces, Sikkim-1 and Nagaland-2 (8.7% each). Finger millet landrace Sikkim-2 recorded the highest omega-6 content (1.16%), followed by barnyard millet cv. VL 207 (1.09%). Barnyard millet cv. VL 207 recorded the highest polyunsaturated fatty acid (PUFA) content (1.23%), followed by foxtail millet cv. SiA 3088 (1.09%). The local finger millet landraces Sikkim-1 and Sikkim-2 recorded the highest levels of histidine (0.41%) and tryptophan (0.12%), respectively. Sikkim-1 and Nagaland-2 recorded the highest level of thiamine (0.32%) compared to the HYVs. Conclusion: These findings indicate that finger millet has great potential in the organic production system of the North Eastern Himalayan Region (NEHR) of India, and apart from HYVs like VL Mandua 352, local landraces, viz., Nagaland-2 and Sikkim-1, should also be promoted for ensuring food and nutritional security in this fragile ecosystem.

Bioactivity and nutritional quality of nutgall (Rhus semialata Murray), an underutilized fruit of Manipur.

Introduction: Underutilized fruits plays a significant role in socio economic, cultural, nutritional and ethnomedicinal status of tribal people. However, scientific studies on the nutritional and other pharmaceuticals/biological activities of these fruits are meagre. Hence, the present study dealt with the quantification of nutritional quality and deciphering the bioactivity of nutgall (Rhus semialata Murray syn. Rhus chinensis Mill.), an underutilized fruit crop mainly found in foothill tracks of Eastern Himalaya, India, China, Japan, Korea and other South East Asian countries. Methods: The Rhus semialata Murray fruits were collected from five different locations in Purul sub-division, Senapati district, Manipur, India. The nutritional composition of the fruit pulp was analysed. Further the fruit pulp was extracted in methanol and water. The methanol and water extracts were studied for bioactivity properties such as antioxidant, antihyperglycemic, antihypertensive, antihyperuricemia, anti-tyrosinase, and antimicrobial activity. Results and discussion: The fruit was rich in essential fatty acids. The presence of linoleic and oleic acids, along with traces of docosahexaenoic acid and eicosapantaenoic acid, revealed the potential food value of the fruit. 59.18% of the total amino acid composition of the protein present was constituted by essential amino acids. The IC50 value of methanolic extract (MExt) and Water extract (WExt) of the fruit were recorded as 4.05 ± 0.22 and 4.45 ± 0.16 µg/mL, respectively, in the DPPH assay and 5.43 ± 0.37 and 11.36 ± 2.9 µg/mL, respectively, in the ABTS assay as compared to Ascorbic acid (3 and 5.4 µg/mL in DPPH and ABTS assay, respectively). The CUPRAC assay also showed a high antioxidant potential of MExt and WExt (1143.84 ± 88.34 and 456.53 ± 30.02 mg Ascorbic Acid Equivalent/g, respectively). MExt and WExt of the fruit were more active against α-glucosidase (IC50 of 1.61 ± 0.34 and 7.74 ± 0.54 µg/ mL, respectively) than α-amylase enzyme (IC50 14.15 ± 0.57 and 123.33 ± 14.7 µg/mL, respectively). In addition, the methanolic fruit extract showed low to moderate pharmacological potential in terms of antihypertensive (Angiotensin converting enzyme-I inhibition), antihyperuricemia (xanthine oxidase inhibition), anti-tyrosinase, and antimicrobial activity. The IC50 values of angiotensin-converting enzyme I inhibition, xanthine oxidase inhibition and tyrosinase inhibition were recorded as 13.35 ± 1.21 mg/mL, 93.16 ± 4.65 mg/mL, and 862.7 ± 12.62 µg/mL, respectively. The study evidently indicates that nutgall fruit is a potential source of phytonutrients, bestowed with commercially exploitable, multifaceted health benefits.

Concurrent- and After-Effects of Medial Temporal Lobe Stimulation on Directed Information Flow to and from Prefrontal and Parietal Cortices during Memory Formation.

Electrical stimulation of the medial temporal lobe (MTL) has the potential to uncover causal circuit mechanisms underlying memory function. However, little is known about how MTL stimulation alters information flow with frontoparietal cortical regions implicated in episodic memory. We used intracranial EEG recordings from humans (14 participants, 10 females) to investigate how MTL stimulation alters directed information flow between MTL and PFC and between MTL and posterior parietal cortex (PPC). Participants performed a verbal episodic memory task during which they were presented with words and asked to recall them after a delay of ∼20 s; 50 Hz stimulation was applied to MTL electrodes on selected trials during memory encoding. Directed information flow was examined using phase transfer entropy. Behaviorally, we observed that MTL stimulation reduced memory recall. MTL stimulation decreased top-down PFC→MTL directed information flow during both memory encoding and subsequent memory recall, revealing aftereffects more than 20 s after end of stimulation. Stimulation suppressed top-down PFC→MTL influences to a greater extent than PPC→MTL. Finally, MTL→PFC information flow on stimulation trials was significantly lower for successful, compared with unsuccessful, memory recall; in contrast, MTL→ventral PPC information flow was higher for successful, compared with unsuccessful, memory recall. Together, these results demonstrate that the effects of MTL stimulation are behaviorally, regionally, and directionally specific, that MTL stimulation selectively impairs directional signaling with PFC, and that causal MTL-ventral PPC circuits support successful memory recall. Findings provide new insights into dynamic casual circuits underling episodic memory and their modulation by MTL stimulation.SIGNIFICANCE STATEMENT The medial temporal lobe (MTL) and its interactions with prefrontal and parietal cortices (PFC and PPC) play a critical role in human memory. Dysfunctional MTL-PFC and MTL-PPC circuits are prominent in psychiatric and neurologic disorders, including Alzheimer's disease and schizophrenia. Brain stimulation has emerged as a potential mechanism for enhancing memory and cognitive functions, but the underlying neurophysiological mechanisms and dynamic causal circuitry underlying bottom-up and top-down signaling involving the MTL are unknown. Here, we use intracranial EEG recordings to investigate the effects of MTL stimulation on causal signaling in key episodic memory circuits linking the MTL with PFC and PPC. Our findings have implications for translational applications aimed at realizing the promise of brain stimulation-based treatment of memory disorders.

Stain color translation of multi-domain OSCC histopathology images using attention gated cGAN.

Oral Squamous Cell Carcinoma (OSCC) is the most prevalent type of oral cancer across the globe. Histopathology examination is the gold standard for OSCC examination, where stained histopathology slides help in studying and analyzing the cell structures under a microscope to determine the stages and grading of OSCC. One of the staining methods popularly known as H&E staining is used to produce differential coloration, highlight key tissue features, and improve contrast, which makes cell analysis easier. However, the stained H&E histopathology images exhibit inter and intra-variation due to staining techniques, incubation times, and staining reagents. These variations negatively impact computer-aided diagnosis (CAD) and Machine learning algorithm's accuracy and development. A pre-processing procedure called stain normalization must be employed to reduce stain variance's negative impacts. Numerous state-of-the-art stain normalization methods are introduced. However, a robust multi-domain stain normalization approach is still required because, in a real-world situation, the OSCC histopathology images will include more than two color variations involving several domains. In this paper, a multi-domain stain translation method is proposed. The proposed method is an attention gated generator based on a Conditional Generative Adversarial Network (cGAN) with a novel objective function to enforce color distribution and the perpetual resemblance between the source and target domains. Instead of using WSI scanner images like previous techniques, the proposed method is experimented on OSCC histopathology images obtained by several conventional microscopes coupled with cameras. The proposed method receives the L* channel from the L*a*b* color space in inference mode and generates the G(a*b*) channel, which are color-adapted. The proposed technique uses mappings learned during training phases to translate the source domain to the target domain; mapping are learned using the whole color distribution of the target domain instead of one reference image. The suggested technique outperforms the four state-of-the-art methods in multi-domain OSCC histopathological translation, the claim is supported by results obtained after assessment in both quantitative and qualitative ways.

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects.

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production.

Evaluation of crop research institutes under data and resource constraints: An alternative approach.

The evaluation of crop research institutes in the developing world under limited data availability has not been assessed in the past due to resource constraints. The paper assesses the social benefits of rice research taking the case of a research institute from India following a new approach. The area coverage of the varieties was estimated to be 3.4 million ha and the gain in production was 6.2 million tonnes per year in India. The additional return obtained due to the adoption of these varieties was about ₹ 14,621 million (US$ 232 million) per year at constant 2014-5 prices. The return per rupee investment in the institute's research and extension was ₹ 17. This approach is recommended for the impact evaluation of other crop research institutes in India and the developing world under resource constraints.

Evaluation of lablab bean [Lablab purpureus (L.) sweet] genotypes: unveiling superior pod yield, nutritional quality, and collar rot resistance.

Introduction: Malnutrition continues to be a significant concern at unacceptably high levels globally. There is significant potential for addressing malnutrition of human population through the biofortification of climate-resilient vegetables using strategic breeding strategies. Lablab bean [Lablab purpureus (L.) Sweet], a underutilized nutrient-dense crop holds great potential in this aspect. Despite its advantageous nutritional profile, the production, research, and consumption of lablab bean are currently limited. Addressing these limitations and unlock the nutritional benefits of lablab beans needs to prioritized for fighting malnutrition in local inhabitants on a global scale. Materials and methods: Twenty five genotypes of lablab bean collected through exploration survey in Eastern India and were evaluated in 2020-2021. Among them, the nine highly diverse well adapted genotypes were again evaluated at the experimental farm of ICAR-Research Complex for Eastern Region, Patna, Bihar, India in 2021-2022. Horticultural important traits of lablab bean were recorded by using the minimum descriptors developed by ICAR-NBPGR in New Delhi and biochemical analysis was done by using standard protocols. Genotypic and phenotypic correlation and path coefficient analysis was done used understand relationships, interdependencies, and causal pathways between different traits. The outcome was revalidated by using principal component analysis (PCA). Results: Descriptive statistics revealed substantial heterogeneity across the traits of lablab bean evaluated. Vitamin A content showed nearly a five-fold variation, Fe ranged from 5.97 to 10.5 mg/100 g, and Vitamin C varied from 4.61 to 9.45 mg/100 g. Earliness and dwarf growth was observed in RCPD-1 (60 cm) and early flowering (41 days). RCPD-3 and RCPD-12 had high pod yield due to their high number of pods and pod weight. Pod yield was significantly correlated with number of pod per plant (NPP) (rg = 0.995) and with average pod weight (APW) (rg = 0.882). A significant positive correlation was also found between protein and Zn content (rg = 0.769). Path coefficient analysis revealed that average pod weight had the most direct positive effect on pod yield, followed by NPP and protein content. The reaction of lablab bean genotypes to collar rot disease was also evaluated and significant differences in disease intensity were observed among the genotypes, with the resistant check RCPD-15 exhibiting the lowest disease intensity. Discussion: The study highlights the substantial heterogeneity in lablab bean traits, particularly in nutritional components such as vitamin A, iron, and vitamin C concentrations. Early flowering and dwarf growth habit are desirable qualities for lablab bean, and certain genotypes were found to exhibit these traits. Positive correlations, both phenotypic and genotypic, existed among different traits, suggesting the potential for simultaneous improvement. Path coefficient and PCA revealed genotypes with high yield and nutritional traits. Finally, resistant and moderately resistant lablab bean genotypes to collar rot disease were identified. These findings contribute to the selection and breeding strategies for improving lablab bean production and nutritional value.

Assessment of mangrove cover dynamics and its health status in the Gulf of Khambhat, Western India, using high-resolution multi-temporal satellite data and Google Earth Engine.

Anthropogenic activity is a major driving factor of greenhouse gas emission, leading to climate change worldwide. So, the best natural approach to lowering the carbon from the atmosphere is mangroves which have more potential to sequestrate carbon. But mangroves are under threat due to land use land cover change. This research has been carried out on the mangroves of Gulf of Khambhat, Gujarat, India, where anthropic activity is affecting the mangrove forest cover with spatiotemporal heterogeneity. In the present study, multi-temporal high-resolution satellite data AVNIR-2 (Advanced Visible and Near Infrared Radiometer type-2) and LISS-4 (Linear Imaging Self-Scanning Sensors-4) were used for the demarcation of various land use/land cover class (LULC), and change analysis and assessment of mangroves health for the years 2009, 2014, and 2019. The impact of saltpan/aquaculture on mangroves growth and its health status has been calculated by various MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data products such as gross primary productivity (GPP), enhanced vegetation index (EVI), and leaf area index (LAI) in Google Earth Engine (GEE), and field-based method was also considered. This study suggests that there is a marginal increase (17.11 km2) in mangrove cover during the assessment period 2009-2019; on other side, 65.42 km2 was degraded also. However, increase in saltpan/aquaculture is imposing an adverse effect on mangroves' basal area, plant density, and productivity. Change analysis also suggests a reduction in healthy mangrove area (from 25.20 to 2.84 km2), which will have an impact on ecosystem services.

Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants.

Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant's body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model training and validation, and model testing and deployment. The model used is a 1-D convolutional neural network (1DCNN) architecture with one convolution layer, one pooling layer, and three fully-connected layers, achieving 97.15% classification accuracy. To address the energy limitations of wearable processing, several quantization techniques are explored, and their performance and energy consumption are analyzed for the respiratory classification task. Results demonstrate a reduction of energy footprints and model storage overhead with a considerable degradation of the classification accuracy, meaning that quantization and other model compression techniques are not the best solution for respiratory classification problem on wearable devices. To improve accuracy while reducing the energy consumption, we propose a novel spiking neural network (SNN)-based respiratory classification solution, which can be implemented on event-driven neuromorphic hardware platforms. To this end, we propose an approach to convert the analog operations of our baseline trained 1DCNN to their spiking equivalent. We perform a design-space exploration using the parameters of the converted SNN to generate inference solutions having different accuracy and energy footprints. We select a solution that achieves an accuracy of 93.33% with 18× lower energy compared to the baseline 1DCNN model. Additionally, the proposed SNN solution achieves similar accuracy as the quantized model with a 4× lower energy.

Microbes-mediated integrated nutrient management for improved rhizo-modulation, pigeonpea productivity, and soil bio-fertility in a semi-arid agro-ecology.

Excessive dependence on chemical fertilizers and ignorance to organic and microbial inputs under intensive cropping systems are the basic components of contemporary agriculture, which evolves several sustainability issues, such as degraded soil health and sub-optimal crop productivity. This scenario urges for integrated nutrient management approaches, such as microbes-mediated integrated plant nutrition for curtailing the high doses as chemical fertilizers. Rationally, experiment has been conducted in pigeonpea at ICAR-IARI, New Delhi, with the aim of identifying the appropriate nutrient management technique involving microbial and organic nutrient sources for improved rhizo-modulation, crop productivity, and soil bio-fertility. The randomized block-designed experiment consisted nine treatments viz. Control, Recommended dose of fertilizers (RDF), RDF+ Microbial inoculants (MI), Vermicompost (VC), Farm Yard Manure (FYM), Leaf Compost (LC), VC + MI, FYM + MI, and LC + MI. Rhizobium spp., Pseudomonas spp., Bacillus spp., and Frateuria aurantia were used as seed-inoculating microbes. The results indicated the significant response of integration following the trend VC + MI > FYM + MI > LC + MI > RDF + MI for various plant shoot-root growth attributes and soil microbial and enzymatic properties. FYM + MI significantly improved the water-stable aggregates (22%), mean weight diameter (1.13 mm), and geometric mean diameter (0.93 mm), soil organic carbon (SOC), SOC stock, and SOC sequestration. The chemical properties viz. available N, P, and K were significantly improved with VC + MI. The study summarizes that FYM + MI could result in better soil physico-chemical and biological properties and shoot-root development; however; VC + MI could improve available nutrients in the soil and may enhance the growth of pigeonpea more effectively. The outcomes of the study are postulated as a viable and alternative solution for excessive chemical fertilizer-based nutrient management and would also promote the microbial consortia and organic manures-based agro-industries. This would add to the goal of sustainable agricultural development by producing quality crop produce, maintaining agro-biodiversity and making the soils fertile and healthy that would be a "gift to the society."

Integration of C-band SAR and high-resolution optical images for delineating palaeo-channels in Nagaur and Barmer districts, western Rajasthan, India.

Identifying hitherto unknown palaeo-channels, especially in the arid regions of the Thar Desert, is crucial since these channels may form excellent aquifers, and are also associated with valuable ore deposits of many precious minerals. This study employed integrated C-band Synthetic Aperture Radar (SAR) of Sentinel-1A and high-resolution multispectral Sentinel-2A data of pre- and post-monsoon seasons (June and November) to delineate playas and palaeo-channels. This approach is the first of its kind for this area. The palaeo-channels were delineated through a detailed visual inspection of colour composite (CC) images of Sentinel-2A data, SAR backscatter (VH) images and fused SAR and optical images. Then, we studied the topographic profiles generated from the Shuttle Radar Topography Mission - Digital Elevation Model (SRTM-DEM) across the identified palaeo-channels, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to further confirm the existence of a palaeo-channel's course and playas. As a result, several playas and palaeo-channels in the area were successfully identified, some of which were previously unmapped and undetected. The results indicate that the post-monsoon datasets are more useful for the precise delineation of palaeo-channels due to the presence of relatively higher moisture along the palaeo-channels' courses.