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Carbon-based nanodots have garnered recent interest for their simple synthesis and versatile utility, ranging from biomedical to (opto) electronic applications, evolving into a tunable and biocompatible material. Here, for the first time, a biochar (lotus leaf) derived carbon nanodots was synthesized through hydrothermal carbonization. The synthesized hollow spherical biochar was engineered via functionalization by grafting -SO 3 H active sites. The attained catalyst was broadly analyzed by XRD, FTIR, TGA, BET, SEM-EDX, TEM, and XPS analysis after which it was applied for the acetalization reaction of crude glycerol (a biodiesel by-product) to form solketal, a potential fuel additive to valorize the large waste stream generated from biodiesel industry. Employing the RSM-CCD methodology, the experimental matrix was executed, and subsequent data were scrutinized through multiple regressions to model a quadratic equation. Under specific reaction parameters-a reaction duration of 14 min, a molar ratio of 7.5:1, and a catalyst loading of 5.7 wt.%, maximum solketal yield (95.7%) was attained through the ultrasonication method. Finally, to conclude, life cycle cost analysis (LCCA) for solketal production was studied here which determined the overall cost of solketal production per kilogram to be 0.719 USD ($), indicating high commercial applicability., (© 2024. The Author(s).)

Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery., (© 2024. The Author(s).)

Gallbladder cancer (GBC) is a lethal disease with surgical resection as the only curative treatment. However, many patients are ineligible for surgery, and current adjuvant treatments exhibit limited effectiveness. Next-generation sequencing has improved our understanding of molecular pathways in cancer, sparking interest in microRNA-based gene regulation. The aim of the study is to identify dysregulated miRNAs in GBC and investigate their potential as therapeutic tools for effective and targeted treatment strategies. GBC and control tissue samples were sequenced for miRNA expression using the Illumina HiSeq platform. Biological processes and related pathways were determined using the Panther and Gene Ontology databases. 439 significantly differentially expressed miRNAs were identified; 19 of them were upregulated and 29 were downregulated. Key enriched biological processes included immune cell apoptosis, endoplasmic reticulum (ER) overload response, and negative regulation of the androgen receptor (AR) signaling pathway. Panther analysis revealed the insulin-like growth factor (IGF)-mitogen activated protein kinases (MAPK) cascade, p38 MAPK pathway, p53 pathway, and FAS (a subgroup of the tumor necrosis factor receptor) signaling pathway as highly enriched among dysregulated miRNAs. Kirsten rat sarcoma virus (KRAS), AR, and interferon gamma (IFN-γ) pathways were identified among the key pathways potentially amenable to targeted therapy. We concluded that a combination approach involving miRNA-based interventions could enhance therapeutic outcomes. Our research emphasizes the importance of precision medicine, targeting pathways using sense and anti-sense miRNAs as potential therapies in GBC., (© 2023. The Author(s).)

Maize (corn) is the dominant grain grown in the world. Total maize production in 2018 equaled 1.12 billion tons. Maize is used primarily as an animal feed in the production of eggs, dairy, pork and chicken. The US produces 32% of the world's maize followed by China at 22% and Brazil at 9% ( https://apps.fas.usda.gov/psdonline/app/index.html#/app/home ). Accurate national-scale corn yield prediction critically impacts mercantile markets through providing essential information about expected production prior to harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. We build a deep learning model to predict corn yields, specifically focusing on county-level prediction across 10 states of the Corn-Belt in the United States, and pre-harvest prediction with monthly updates from August. The results show promising predictive power relative to existing survey-based methods and set the foundation for a publicly available county yield prediction effort that complements existing public forecasts.

Gallium nitride (GaN) was epitaxially grown on nitrogen doped single layer graphene (N-SLG) substrates using chemical vapour deposition (CVD) technique. The results obtained using x-ray diffractometer (XRD) revealed the hexagonal crystal structure of GaN. Photoluminescence (PL) spectroscopy, energy dispersive x-ray (EDX) spectroscopy and x-ray photoelectron (XPS) spectroscopy revealed traces of oxygen, carbon and nitrogen occurring either as contamination or as an effect of doping during the GaN growth process. In addition, PL revealed a weak yellow luminescence peak in all the samples due to the presence of N-SLG. From the obtained results it was evident that, presence of N-SLG underneath GaN helped in improving the material properties. It was seen from the current-voltage (I-V) response that the barrier height estimated is in good agreement with the Schottky-Mott model, while the ideality factor is close to unity, emphasizing that there are no surface and interface related inhomogeneity in the samples. The photodetector fabricated with this material exhibit high device performances in terms of carrier mobility, sensitivity, responsivity and detectivity. The hall measurement values clearly portray that, the GaN thus grown possess high electron contents which was beneficial in attaining extraordinary device performance.

Drug resistance and damage caused to the normal cells are the drawbacks which have limited the use of the existing effective anticancer drugs. Attainment of a steady and extended release by encapsulating dual drugs into biocompatible and biodegradable vehicles is the key to enable the use of these drugs for effective inhibition of cancer. In this study, carboxymethyl chitosan (CMCS), a proficient water-soluble derivative of chitosan has been synthesized using chemical route and used for the delivery of 5-Fluorouracil and doxorubicin individually as well as in combination. Carboxymethylation occuring at -NH 2 and OH sites of chitosan, has been confirmed using FTIR. EDX and Fluorescence studies elucidate the encapsulation of 5-Fluorouracil and doxorubicin into CMCS. The capability of CMCS to release the drugs in a more sustained and prolonged manner is evident from the obtained release profiles. About 14.9 µg/ml is enough to cause 50% cell death by creating oxidative stress and effectuating DNA fragmentation. Amidst the existing reports, the uniqueness of this work lies in using this rare coalition of drugs for the suppression of breast cancer and in reducing the side effects of drugs by encapsulating them into CMCS, which is evidenced by the high hemocompatibilty of the samples.

The newer methodology to improve the performance of cutting tool is by the constructive method of micro-texturing and green synthesized nanoparticles into the texture gaps for self-lubrication. Cross-chevron textures were made on the rake face of the cemented carbide tool using Neodymium Doped Yttrium Aluminium Garne (Nd-YAG) laser texturing machine. The environmentally friendly, non-hazardous and rapid method of producing nanoparticles was followed to produce Al 2 O 3 nanoparticles. Various techniques used for characterizing the synthesized Al 2 O 3 nanoparticles are Potential of Hydrogen (pH), Fourier Transform Infra-Red Spectroscopy Analysis (FTIR), Dynamic light scattering (DLS) and X-Ray Diffraction (XRD). The XRD shows the presence of required functional groups and the size of nanoparticles in the range of 500-550 nm. This article discusses the effect of textures, with and without nanoparticles filled on the texture gaps of the cemented carbide tool on the main cutting force, thrust force and co-efficient of friction while machining austenitic stainless steel 304. The combined effect of surface texturing and lubrication of Al 2 O 3 nanoparticles enhanced the performance of the cutting tool compared with the conventional and textured tool.

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.

Standard tissue culture of adherent cells is known to poorly replicate physiology and often entails suspending cells in solution for analysis and sorting, which modulates protein expression and eliminates intercellular connections. To allow adherent culture and processing in flow, we present 3D-shaped hydrogel cell microcarriers, which are designed with a recessed nook in a first dimension to provide a tunable shear-stress shelter for cell growth, and a dumbbell shape in an orthogonal direction to allow for self-alignment in a confined flow, important for processing in flow and imaging flow cytometry. We designed a method to rapidly design, using the genetic algorithm, and manufacture the microcarriers at scale using a transient liquid molding optofluidic approach. The ability to precisely engineer the microcarriers solves fundamental challenges with shear-stress-induced cell damage during liquid-handling, and is poised to enable adherent cell culture, in-flow analysis, and sorting in a single format., Competing Interests: The authors declare that they have no conflict of interest.

In order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybean yield loss is negatively correlated with the density of SCN eggs that are present in the soil. While there has been progress in automating extraction of egg-filled cysts and eggs from soil samples counting SCN eggs obtained from soil samples using computer vision techniques has proven to be an extremely difficult challenge. Here we show that a deep learning architecture developed for rare object identification in clutter-filled images can identify and count the SCN eggs. The architecture is trained with expert-labeled data to effectively build a machine learning model for quantifying SCN eggs via microscopic image analysis. We show dramatic improvements in the quantification time of eggs while maintaining human-level accuracy and avoiding inter-rater and intra-rater variabilities. The nematode eggs are correctly identified even in complex, debris-filled images that are often difficult for experts to identify quickly. Our results illustrate the remarkable promise of applying deep learning approaches to phenotyping for pest assessment and management.

A new technique for shaping microfluid flow, known as flow sculpting, offers an unprecedented level of passive fluid flow control, with potential breakthrough applications in advancing manufacturing, biology, and chemistry research at the microscale. However, efficiently solving the inverse problem of designing a flow sculpting device for a desired fluid flow shape remains a challenge. Current approaches struggle with the many-to-one design space, requiring substantial user interaction and the necessity of building intuition, all of which are time and resource intensive. Deep learning has emerged as an efficient function approximation technique for high-dimensional spaces, and presents a fast solution to the inverse problem, yet the science of its implementation in similarly defined problems remains largely unexplored. We propose that deep learning methods can completely outpace current approaches for scientific inverse problems while delivering comparable designs. To this end, we show how intelligent sampling of the design space inputs can make deep learning methods more competitive in accuracy, while illustrating their generalization capability to out-of-sample predictions.

Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems.