Reliable Approach Research Articles

First ATR-FTIR Characterization of Black, Green and White Teas (Camellia sinensis) from European Tea Gardens: A PCA Analysis to Differentiate Leaves from the In-Cup Infusion.

ATR-FTIR (Attenuated Total Reflectance Fourier Transform InfraRed) spectroscopy, combined with chemometric, represents a rapid and reliable approach to obtain information about the macromolecular composition of food and plant materials. With a single measurement, the chemical fingerprint of the analyzed sample is rapidly obtained. Hence, this technique was used for investigating 13 differently processed tea leaves (green, black and white) all grown and processed in European tea gardens, and their vacuum-dried tea brews, prepared using both hot and cold water, to observe how the components differ from tea leaf to the in-cup infusion. Spectra were collected in the 1800-600 cm-1 region and were submitted to Principal Component Analysis (PCA). The comparison of the spectral profiles of leaves and hot and cold infusions of tea from the same country, emphasizes how they differ in relation to the different spectral regions. Differences were also noted among the different countries. Furthermore, the changes observed (e.g., at ~1340 cm-1) due to catechin content, confirm the antioxidant properties of these teas. Overall, this experimental approach could be relevant for rapid analysis of various tea types and could pave the way for the industrial discrimination of teas and of their health properties without the need of time-consuming, lab chemical assays.

Product improvement in a big data environment: A novel method based on text mining and large group decision making

Product improvement has become a multifaceted and uncertain endeavour for manufacturers in an increasingly competitive business environment. Online platforms have emerged to solicit consumer opinions and product feedback. However, product improvement requires a critical yet complex decision-making approach for manufacturers. Managers face the challenge of identifying the most effective decision-making methodology for product improvement, especially in a big data environment. In this research, we comprehensively evaluate different product improvement decision-making methodologies through a series of experimental investigations. Specifically, three different experiments are conducted, including: i) an initial selection guided by intuitive perception, ii) expert decision-making, and iii) a hybrid method that incorporates consumer big data and large-scale group decision-making. Product criteria sets are categorized using the Latent Dirichlet Allocation (LDA) method, while the importance of these criteria is determined by applying the TextRank and Word2Vec algorithms. Our empirical results show that the mixed method, which utilizes text-mining techniques in conjunction with large-group decision-making, provides a more reliable and effective approach to facilitating product improvement.

Plunger gate effects on magneto transport in double-top gate spin-orbit devices

A propagation matrix method is used to investigate the quantum conductance of an n-type double-top gate device under a strong Rashba-Zeeman coupling interaction. The effects of a plunger gate potential and the total top gate length on the magneto conduction properties are investigated. Transcendental equations and analytical formulas are derived to accurately predict the resonance state and hole-like bound state structures. Furthermore, the effect of the distance of the two top gates from the plunger gate is examined for both symmetric and asymmetric structures. The analytical technique employed in this study provides an accurate and reliable approach for calculating the conductance of the quantum transport and is therefore beneficial for the design of n-type semiconductor devices.

Genetic Programming Guided Mapping of Forest Canopy Height by Combining LiDAR Satellites with Sentinel-1/2, Terrain, and Climate Data

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency (R2 = 0.85, RMSE = 2.2m, pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R2, RMSE, and pRMSE of 0.64, 3.38m, and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models.

Limited Incision Rhinoplasty: An Approach for All Primary and Revision Cases.

Rhinoplasty was one of the most frequently performed aesthetic surgeries in the USA in 2022. Traditionally, the open approach has been preferred by the majority of surgeons often due to familiarity and ease of visualization. However, patient interest in closed and preservation rhinoplasty is driving a resurgence in the popularity of endonasal techniques. We present a series of 100 consecutive endonasal primary and revision rhinoplasty cases using bilateral isolated modified rim incisions. One-hundred consecutive patients underwent closed rhinoplasty via isolated modified rim incisions at a single-surgeon private practice. The senior author performs 100% endonasal rhinoplasty. A retrospective chart review was performed from 06/25/20 to 09/21/22. Information on demographics, complications, and need for revision was collected. Eighty-four patients underwent primary rhinoplasty, 11 underwent secondary rhinoplasty, 4 underwent tertiary rhinoplasty, and 1 underwent quaternary rhinoplasty. Isolated modified rim incisions were used in all cases except in cases of septoplasty when a unilateral Killian incision was added, or in cases of lateral osteotomy when vestibular stab incisions were added. Post-operatively, six (6.0%) patients required revision, all of which were performed under local anesthesia. Limited incision rhinoplasty is a reliable surgical approach that produces predictable results with a low revision rate. This technique is highly effective in minimizing soft tissue disruption to ensure safe, reliable, and effective outcomes in primary and revision rhinoplasty. It is an easier technique to learn compared to traditional endonasal and even arguably open rhinoplasty, thus lending itself to widespread adoption especially among novice rhinoplasty surgeons. Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Efficient machine learning model to detect early stage Parkinson’s disease

<p class="root-block-node">Parkinson’s disease (PD) often manifests itself in memory loss and cognitive decline. The decline is inexorable, and damage to the brain’s cortex has already occurred. Numerous studies have shown that by detecting dementia early and beginning treatment, the disease’s course can be slowed, and any further atrophy can be prevented. Brain imaging data, such as from an MRI, is frequently used in the diagnosis of Parkinson’s disease (PD). In recent years, utilizing deep convolutional neural networks has greatly improved Parkinson’s disease diagnosis. However, getting to the level of quality needed for clinical use is still challenging. In this study, we introduce a machine learning-based approach for more accurately diagnosing Parkinson’s disease. This research makes use of information gleaned from single-photon emission computerized tomography (SPECT) scan and positron emission tomography (PET) scans performed on patients with Parkinson’s disease (PD) and healthy controls. The most crucial characteristics of these datasets are isolated with the aid of the Fisher discriminate ratio (FDR) and non-negative matrix factorization (NMF). The K-nearest neighbor, Decision Tree, Support vector machine (SVM), and Deep Convolution neural network (DCCN) classifiers with confidence bounds classify the NMF-transformed data sets with a decreased number of features. The proposed DCCN technique has a classification accuracy of up to 93.7 percent when compared to decision trees, K-Nearest Neighbor (KNN)s, and SVMs. The DCCN is now a reliable approach for classifying SPECT and PET, PD images.</p>

Investigation of antimicrobial and anti-cancer activity of thermally sensitive SnO2 nanostructures with green-synthesized cauliflower morphology at ambient weather conditions

A tin oxide (SnO2) nanostructure was prepared using Matricaria recutita leaf extract to investigate its anticancer activity against SK-MEL-28 cells. The tetragonal crystal structure of tin oxide nanoparticles with an average crystal size of 27 nm was confirmed by X-ray diffraction (XRD) analysis. The tetragonal crystal structure of the tin oxide nanoparticles, with an average crystallite size of 27 nm, was confirmed by XRD an absorbance peak at 365 nm was identified by UV–visible spectroscopy analysis as belonging to the bio-mediated synthesis of SnO2 nanoparticles. The SnO2 NPs are capped and stabilized with diverse functional groups derived from bioactive molecules, including aldehydes, benzene rings, amines, alcohols, and carbonyl stretch protein molecules. Fourier transform infrared spectroscopy (FTIR) analysis validated the presence of these capping and stabilizing chemical bonds. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) studies revealed the cauliflower-shaped morphology of the SnO2 nanoparticles with an average particle size of 28 nm. The antimicrobial activity of both prepared and encapsulated samples confirmed their biological activities. Furthermore, both prepared and encapsulated tin oxide samples exhibited excellent anticancer activity against SK-MEL-28 human cancer cells. The present study introduces a reliable and uncomplicated approach to produce SnO2 nanoparticles and demonstrates their effectiveness in various applications, including cancer therapy, drug administration, and disinfectant.

An integrated hesitant 2-tuple linguistic Pythagorean fuzzy decision-making method for single-pilot operations mechanism evaluation

Single-pilot operations (SPO), i.e., one pilot on board the flight in charge of all the operations, has received extensive attention in recent years. SPO involves several participants including the pilot, the autopilot, and the ground operator, and it is of great significance to determine the roles of these participants under the SPO mechanism. However, the evaluation of SPO mechanism has received little attention. Aiming at providing a reliable approach for SPO mechanism evaluation under uncertainty, this paper proposes a novel hesitant 2-tuple linguistic Pythagorean fuzzy decision-making method based on hesitant fuzzy linguistic term sets (HFLTS), 2-tuple linguistic information, Pythagorean fuzzy sets (PFS), and VIKOR method to handle the SPO mechanism evaluation problem. Firstly, based on the analysis of SPO, the evaluation criteria system for SPO mechanism is established. Then, considering the uncertain information in the SPO mechanism evaluation process, the HFLTS is used to represent the linguistic judgments of experts under uncertainty. Next, the HFLTSs of different experts are converted into 2-tuple linguistic information and PFSs to model the overall judgments of different criteria. Finally, the extended VIKOR method is adopted to evaluate and rank different SPO mechanisms. A case study of SPO mechanism evaluation for the ground proximity warning system (GPWS) is used to demonstrate the effectiveness and feasibility of the proposed method, and the SPO mechanism that the autopilot being responsible for GPWS is evaluated by the proposed method to be optimal. Sensitivity and comparison analyses further confirm that the proposed method could provide reliable and reasonable SPO mechanism evaluation results. In conclusion, the proposed method presents a novel and effective way to evaluate and select appropriate SPO mechanisms.

Response of leaf day respiration in C4 plants to irradiance and vapour pressure deficit

Leaf day respiration rate (RL) plays a crucial role in the global carbon cycle. However, RL of C4 species has not been sufficiently studied and its response to environmental factors is largely unknown. This work studied the response of RL of three C4 species, Setaria viridis, Sorghum sudanense, and Zea mays, to alterations in the vapour pressure deficit (VPD) and irradiance of the growth environment. RL was estimated using the Kok method (RL Kok) and an improved method that combined gas exchange and chlorophyll fluorescence measurements (RL Yin). On average, shade treatment led to a 24% reduction in RL Yin and a 20% reduction in respiration in the dark (RDk), while a consistent VPD effect on RL was not observed. RL and RDk were positively correlated with nitrogen content per leaf area and net CO2 assimilation rate but were not correlated with the capacity of carboxylation enzymes. We found a non-significant light inhibition of respiration (1 ​± ​2%), contradicting the assumption that respiration is inhibited by light and affected by light intensity. Our findings indicate that assuming RL to be equal to RDk at the same temperature is a straightforward but reliable approach to model respiration of the examined C4 species.

Comparative Analysis of Surface Characterization Techniques for Atomic Layer Etching

The growing demand for precise atomic level control in device fabrication has led to the rising popularity of Atomic Layer Etching (ALE).1-3 As the name implies, the process facilitates layer-by-layer material removal in an atomic scale, making low surface damage one of the key features of this process. The low-power ion bombardment and the self-limited nature of different process steps help to reduce the damage induced by ALE. However, the damage is not easy to characterize. Atomic Force Microscopy (AFM) is one of the methods that is widely used for surface characterization. However, roughness measurements of the surface using AFM may be misleading as it primarily reflects the top surface, which is typically composed of native oxide rather than the actual Si surface.In this work, we discuss how we used X-Ray reflectometry (XRR) together with ellipsometry to measure the damage induced to the Si device layer of a Silicon on Insulator (SOI) wafer.The experiments were done on an SOI wafer with a 55 nm thick device layer and 145 nm box layer. In order to remove native oxide, all samples were treated with Buffered oxide etch (BOE) 10:1 right before the etch process. The cyclic process was performed in a commercial Inductively Coupled Plasma Reactive Ion Etcher (ICP-RIE) Takachi™ tool from Plasma-Therm LLC, USA. The process gases were Chlorine to activate the Si surface and Ar to remove the activated surface. There was no plasma during activation and purging, it was only ignited during the removal step. XRR, AFM, and ellipsometry characterizations were used to monitor the damage induced by the process. These methods were used to measure the surface roughness before and after the process. In the ellipsometry model, a damage layer is incorporated to evaluate the Si surface that has undergone plasma bombardment resulting in its transformation into amorphous Si. This layer is typically referred to as the surface damage layer. The XRR can measure the roughness on Si layer right beneath the native oxide which again demonstrates the surface damage. On the other hand, AFM measures the roughness at the surface of native oxide.Our results show that while in XRR and ellipsometry, the roughness change follows the same trend, the AFM measurement is not. Results have revealed that in the case of a non-ALE process, both XRR and ellipsometry techniques exhibit a substantial level of surface damage, whereas AFM indicates lower damage. This provides evidence that AFM alone may not be a reliable approach for assessing surface damage. In addition, both XRR and ellipsometry confirm a thick native oxide on top which is very much correlated to the damage. Typically, the thickness of the native oxide is in the ranges between 15-25 Å, and our results support that the thickness of the measured native oxide layer increases with the level of surface damage.

Study on performance enhancement of electro-fueled solar flow battery system by nickel-doped titanium dioxide photoanode

Integrated design of energy systems is a practical and economical solution to the low-carbon transition of international energy. In this work, a solar flow battery with electro-fuel is designed and assembled. The system directly converts solar energy into electro-fuel without biased electronic components, which is a reliable renewable energy utilization approach with low cost, low weight, and easy transmission. In the system, nickel-doped titanium dioxide photoanodes with different contents and annealing temperatures are prepared by the sol–gel method. The morphology, crystal structure and photo properties of the photoanodes are explored by various characterization methods. The performance of photoanode and the electro-fueled solar flow battery system is evaluated by the current–time curve, linear sweep voltammetry, Mott-Schottky plot, and other tests. The results show that 6% nickel doping annealing at 400 °C exhibits a higher photospontaneous charging current density. Only relying on the self-built electric field at the interface between the photoanode and the electrolyte, the photocurrent reaches 2.5 m∙cm−2, indicating the charge transfer rate at the photoanode-electrolyte interface is improved. The solar-chemical energy output efficiency achieves 2.51% without additional potential, which is higher than the current aqueous solar flow batteries. The photoanode-electrolyte interface was also further explored, and the energy level of nickel-doped titanium dioxide was quantified.

Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method

Heart disease has emerged as a serious health concern for many individuals due to its high death rate around the world. The routine clinical data analysis has a significant difficulty in the early diagnosis of cardiac disease. The identification of cardiac disease may benefit from the use of machine learning. To improve machine learning models, several studies have previously been conducted. The suggested study uses the maximum voting ensemble technique of classification to effectively identify heart disease. The suggested classifier is a more reliable and accurate approach. To identify and eliminate outliers, conduct Inter quartile range outlier removal and min-max normalization during preprocessing. Accuracy, Precision, Recall, and F1 Score are calculated and evaluated against various models. For the heart disease dataset collected from the Kaggle, the suggested max voting ensemble classifier has an accuracy of 99.22%.

Can the electronic medical record provide reliable indicators of primary care behavioral health fidelity? Comparison of accessibility and productivity indicators assessed through observational coding.

The primary care behavioral health (PCBH) model is one of the most widely implemented integrated care approaches. However, research on the model has been limited by inconsistent measurement and reporting of model fidelity. One way of making measurement of PCBH model fidelity more routine is to incorporate fidelity indicators into the electronic medical record (EMR), though research regarding the accuracy of EMR data is mixed. In this study, we aimed to assess the reliability of EMR data as a PCBH fidelity measurement tool by comparing key EMR indicators of PCBH fidelity to those recorded by an observational coder. Over an 8-month period (October 2021-May 2022), 12 behavioral health consultants (BHCs; 92% White, 75% female) across five primary care clinics recorded indicators of PCBH fidelity in the EMR as part of their routine charting of behavioral health visits. During that same period, one observational coder completed seven 4-hr visits per clinic to obtain multiple samples of data from each over time and recorded the same variables (i.e., percentage of visits prompted by warm handoffs, number of warm handoffs, and number of patient visits). We used bivariate correlations to test the associations between the EMR variables and the observer-coded variables. Correlations between EMR and observer-coded variables were moderate to strong, ranging from r = .46 to r = .97. Leveraging EMR data appears to be a fairly reliable approach to capturing indicators of PCBH model fidelity in the key domains of accessibility and high productivity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Study toward a More Reliable Approach to Elucidate the Lignin Structure-Property-Performance Correlation.

The correlation between lignin structure, its properties, and performance is crucial for lignin engineering in high-value products. Currently, a widespread approach is to compare lignins which differ by more than one parameter (i.e., Kraft vs organosolv vs lignosulfonates) in various applications by attributing the changes in their properties/performance specifically to a certain variable (i.e., phenolic -OH groups). Herein, we suggest a novel approach to overcome this issue by changing only one variable at a time while keeping all others constant before investigating the lignin properties/performance. Indulin AT (Ind-AT), a softwood Kraft lignin, was chosen as the model substrate for this study. Selective (analytical) lignin modifications were used to mask/convert specific functionalities, such as aliphatic (AliphOH) including benzylic -OH (BenzOH) and phenolic -OH (PhOH) groups, carboxyl groups (-COOH) and carbonyl groups (CO) via methylation, acetylation, and reduction. The selectivity and completeness of the reactions were verified by comprehensive NMR analysis (31P and 2D HSQC) of the modified preparations together with state-of-the-art molar mass (MM) characterization. Methylene blue (MB) adsorption, antioxidant activity, and glass transition temperature (Tg) were used to demonstrate and compare the properties/performance of the obtained modified lignins. We found that the contribution of different functionalities in the adsorption of MB follows the trend BenzOH > -COOH > AlipOH > PhOH. Noteworthy, benzylic -OH contributes ca. 3 and 2.3 times more than phenolic and aliphatic -OH, respectively. An 11% and 17% increase of Tg was observed with respect to the unmodified Indulin by methylating benzylic -OH groups and through reduction, respectively, while full acetylation/methylation of aliphatic and phenolic -OH groups resulted in lower Tg. nRSI experiments revealed that phenolic -OH play a crucial role in increasing the antioxidant activity of lignin, while both aliphatic -OH groups and -COOHs possess a detrimental effect, most likely due to H-bonding. Overall, for the first time, we provide here a reliable approach for the engineering of lignin-based products in high value applications by disclosing the role of specific lignin functionalities.

Genome-wide association study of leaf photosynthesis using a high-throughput gas exchange system in rice.

Enhancing leaf photosynthetic capacity is essential for improving the yield of rice (Oryza sativa L.). Although the exploitation of natural genetic resources is considered a promising approach to enhance photosynthetic capacity, genomic factors related to the genetic diversity of leaf photosynthetic capacity have yet to be fully elucidated due to the limitation of measurement efficiency. In this study, we aimed to identify novel genomic regions for the net CO2 assimilation rate (A) by combining genome-wide association study (GWAS) and the newly developed rapid closed gas exchange system MIC-100. Using three MIC-100 systems in the field at the vegetative stage, we measured A of 168 temperate japonica rice varieties with six replicates for three years. We found that the modern varieties exhibited higher A than the landraces, while there was no significant relationship between the release year and A among the modern varieties. Our GWAS scan revealed two major peaks located on chromosomes 4 and 8, which were repeatedly detected in the different experiments and in the generalized linear modelling approach. We suggest that high-throughput gas exchange measurements combined with GWAS is a reliable approach for understanding the genetic mechanisms underlying photosynthetic diversities in crop species.

Russian Short-Term Mortality Fluctuations Data Series

The COVID-19 pandemic has revealed significant gaps in the coverage and quality of the existing international and national statistical surveillance systems. The most reliable approach to estimating mortality associated with short-term risk factors or factors fluctuating within calendar years is based on estimating weekly excess mortality. Although this approach is obvious, its application turned out to be problematic due to the lack of reliable data. In response to this challenge, a new Short-Term Mortality Fluctuations (STMF) data series was created in 2020. The Russian Short-Term Mortality Fluctuations Data Series (RuSTMF) is a further development of the STMF idea at the regional level. It contains weekly crude and standardized death rates for the Russian regions and Russia as a whole. The main source for calculating death rates is depersonalized individual data on the deceased provided by the Rosstat. This database provides for the analysis of short- and medium-term changes in mortality in males, females and total population in Russia as a whole and its regions, as well as the estimation of «excess» deaths during short-term sharp increases in mortality due to, for example, heat or cold waves, seasonal influenza epidemics, the Covid-19 pandemic or technogenic catastrophes.

Pre-implantation Genetic Testing in Inherited Metabolic Diseases? Stateof- the-art and current challenges.

Inborn errors of metabolism (IEM) are genetic diseases involving congenital disorders of enzyme activities. Most follow Mendelian autosomal recessive inheritance and few follow mitochondrial inheritance. In many cases, after the birth of an affected child parents discover that have been the carriers for the condition and worry about the risk of recurrence in future offspring. Preimplantation genetic testing (PGT) can analyze embryos before their transfer to the uterus and prevent the transmission of hereditary conditions to descendants, however this procedure is of limited value in mtDNA conditions. The list of diseases currently approved for PGT were reviewed. The process for eligibility, was as for the Comissão Nacional Procriação Medicamente Assistida (CNPMA), of Portugal (PT). Review of international practices for Assisted Reproductive Techniques (ART) in IEM was carried out. As of 07.2022, 23 IEM diseases associated with deleterious variants in nDNA were approved for PGT in PT. Couples at risk for conditions not included in the list can solicit an evaluation from an expert committee, after a medical genetics consultation. To qualify for approval, diseases must cause significant suffering and/or premature death. Due to a greater number of solicitations many more IEM conditions have been approved for PGT across the world. ART for mtDNA is not available in PT. International expert centers include PGT for specific well documented variants and mitochondrial donation. PGT is a reliable approach to reduce the risk of transmission of a genetic condition to the offspring. The list of IEM disorders currently accepted for this technique in Portugal are small, but it is expanding, as many more diseases fit the necessary criteria. While appealing in theory, low success rates coupled with limited availability can be discouraging for patients. Genetic counselling is of paramount importance after the diagnosis of IEM diseases. It is important for both clinicians and patients to be made aware of the available reproductive options and their limitations.

MACHINE LEARNING MODEL FOR THE PREDICTION OF CONDITION OF MUSEUM OBJECTS

An accurate prediction of the future condition of museum objects is crucial for developing appropriate proactive maintenance and preservation strategies. Despite this, there are very few such damage models that can be used in practice. The main reasons, for this lack of deterioration models, include complexity of deterioration problem and lack of understanding of the degradation mechanisms affecting various materials and objects, and lack of reliable quantitative approaches. In the article, we discuss the machine learning model, which predicts the future condition of museum objects. For this purpose, the model uses the data of MuIS (Estonian Museum Information System). To predict deterioration, we experimented primarily with various tree-based machine learning algorithms, such as the decision tree, the random forest, and XGBoost. The best results were obtained using the decision forest algorithm, which was able to identify 92% of deteriorating museum objects with 50% accuracy. The machine learning model provides a way to model ageing processes of museum objects over the course of time and thus better plan the preservation work of museums.

Molecular diversity assessed by MALDI mass spectrometry of two scorpion species venom from two different locations in Morocco

Scorpion venom is a cocktail of molecules whose composition is remarkably plastic, controlled by several factors. The Moroccan scorpion fauna is characterized by its richness and high rate of endemism and the venom molecular variability of many species is not yet well characterized. The aim of the present study was to highlight the molecular variability of the venom composition of Androctonus amoreuxi and Buthacus stockmanni (endemic species), both belonging to the Buthidae family, collected from two Moroccan regions, Zagora and Tan-tan. Characterization of the molecular mass fingerprints (MFPs) of each specimen was performed by Matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) using a sandwich (Sand) and a dried-droplet (DD) sample preparation and dilutions. Considering these two methods, a total of 828 ion signals were detected, and Sand method produced more adducts (56%) than DD (44%). We observed interspecific variations in the venom composition between these two species showing they share 235 ion signals, while 226 and 367 are specific for these two species, respectively. Moreover, B. stockmanni specimens showed a clear difference in their MFPs between the two geographical areas studied, suggesting intraspecific variations. Moreover, specimens from each population also show an intraspecific variability. In addition, for the same individual, a variation in the venom composition was also recorded depending on the milking frequency. Our results confirmed the presence of characteristic components in each extracted venom sample. In conclusion, MFPs assessed by MALDI-MS represent a fast, non-supervised, sensitive, reliable and cost-efficient approach for taxonomic identification and molecular variability characterization. This study undoubtedly represents a step forward for understanding the scorpion venom plasticity, intra/inter variations, and their temporal and geographical variability.

Few-shot learning convolutional neural network for primitive indian paddy grain identification using 2D-DWT injection and grey wolf optimizer algorithm

Rice (Oryza sativa) is extensively taken and sold throughout the world. The most common primitive paddy are Goswami, Jamuna, Jamut, Mamata, Pooja, Rambanbas, Sanghmitra, Sanjeevani, Sadhana, and Sannasriya. The primary objective of this study is to enhance the classification accuracy of few-shot learning datasets via the development of a reliable feature extraction approach and the Optimization of convolutional neural network (CNN) hyperparameters. Here, the 2D-DWT features were injected before the fully connected layer and added to the deep features of the CNN. Furthermore, the hyperparameters of the CNN layer were optimized using the Grey Wolf Optimizer (GWO). This optimization strategy enhances the recognition rate on a limited training dataset, and the injection of 2D-DWT increases the dimension of the feature vector, which further elevates the performance of the model. The performance of proposed model is examined with our own dataset, which is shared in mendely (sethy, PRABIRA (2023), “PRIMITIVE PADDY VARITIES OF ODISHA, INDIA”, Mendeley Data, V1, https://doi.org/10.17632/67zv95bzt8.1) and achieved accuracy of 98.50 %, sensitivity of 98.50 %, specificity of 99.83 %, precosion of 98.56 %, FPR of 0.17 %, F1score of 98.50 %, MCC of 98.35 % and kappa of 91.67 %.