Functionalized Single Crystal Perovskite Materials for SERS and Their Potential Detection Applications.

Recent advances in detection and diagnostic tools have improved understanding and identification of plant physiological and biochemical processes. Effective and safe Surface Enhanced Raman Spectroscopy (SERS) can find objects quickly and accurately. Raman enhancement amplifies the signal by 1014-1015 to accurately quantify plant metabolites at the molecular level. This paper shows how to use functionalized perovskite substrates for SERS. These perovskite substrates have lots of surface area, intense Raman scattering, and high sensitivity and specificity. These properties eliminate sample matrix component interference. This study identified research gaps on perovskite substrates' effectiveness, precision, and efficiency in biological metabolite detection compared to conventional substrates. This article details the synthesis and use of functionalized perovskites for plant metabolites measurement. It analyzes their pros and cons in this context. The manuscript analyzes perovskite-based SERS substrates, including single-crystalline perovskites with enhanced optoelectronic properties. This manuscript aims to identify this study gap by comprehensively reviewing the literature and using it to investigate plant metabolite detection in future studies.

Integrated assessment of groundwater quality dynamics and Land use/land cover changes in rapidly urbanizing semi-arid region.

Rapid urbanization worldwide, poses numerous environmental challenges between escalating land use land cover (LULC) changes and groundwater quality dynamics. The main objective of this study was to investigate the dynamics of groundwater quality and LULC changes in Sargodha district, Punjab, Pakistan. Groundwater hydrochemistry reveals acceptable pH levels (<8) but total dissolved solids (TDS), electrical conductivity (EC) and HCO3- showed dynamic fluctuations by exceeding WHO limits. Piper diagrams, indicated dominance by magnesium and bicarbonate types, underscoring the influence of natural processes and anthropogenic activities. Major ion relationships in 2010, 2015, and 2021 showed a high correlation (R2 > 0.85) between Na+ and Cl-, suggesting salinization. whereas, the poor correlation (<0.17) between Ca2+ and HCO3- does not support calcite dissolution as the primary process affecting groundwater composition. The examination of nitrate contamination in groundwater across the years 2010, 2015, and 2021 was found to be high in the municipal sewage zone, suggesting a prevailing issue of nitrate contamination attributed to urban activities. The Nitrate Pollution Index (NPI) reveals a concerning trend, with a higher proportion of samples classified under moderate to high pollution categories in 2015 and 2021 compared to 2010. The qualitative assessment of nitrate concentration on spatiotemporal scale showed lower values in 2010 while a consistent rise from 2015 to 2021 in north-east and western parts of district. Likewise, NPI was high in the north-eastern and south-western regions in 2010, then reduced in subsequent years, which may be attributed to effective waste management practices and alterations in agricultural practices. The health risk assessment of 2010 indicated Total Health Hazard Quotient (THQ) within the standard limit, while in 2015 and 2021, elevated health risk was observed. This study emphasizes the need to use multiple approaches to groundwater management for sustainable land use planning and regulations that prioritize groundwater quality conservation.

Pectoral Nerve Block II for Cardiac Implantable Electronic Devices.

AIM: This study aimed to assess the feasibility and effectiveness of the pectoral nerves (PECS) II block in facilitating cardiac implantable electronic device (CIED) insertion in a sample of 120 patients, with a focus on the percentage of cases completed without additional intraoperative local anesthesia. METHODS: PECS II blocks were performed on the left side using ultrasound guidance in all 120 patients. Feasibility was assessed by the proportion of cases completed without the need for extra intraoperative local anesthetic. Secondary outcomes included the amount of additional local anesthetic used, intraoperative opioid requirements, postoperative pain scores, time to first postoperative analgesia, analgesic consumption, patient satisfaction, and block-related complications. RESULTS: Of the 120 patients, 78 (65%) required additional intraoperative local anesthetic, with a median volume of 8.2 mL (range 3-13 mL). Fifteen patients (12.5%) needed intraoperative opioid supplementation. Nine patients (7.5%) required postoperative tramadol for pain relief. In total, 98 patients (81.7%) reported high satisfaction levels with the procedure. CONCLUSIONS: The PECS II block, when combined with supplementary local anesthetic, provided effective postoperative analgesia for at least 24 h in 120 patients undergoing CIED insertion. While it did not completely replace surgical anesthesia in most cases, the PECS II block significantly contributed to a smoother intraoperative experience for patients.

Molecular genotyping analysis suggested high clonality and simple marker loci in Puccinia striiformis f. sp. tritici population in Uzbekistan, Central Asian region.

Puccinia striiformis f. sp. tritici (Pst) is a destructive pathogen that causes wheat stripe rust worldwide. Understanding the population structure and dynamic of pathogen spread is critical to fight against this disease. Limited information is available for the population genetic structure of Pst in Uzbekistan, Central Asia. In this study, we carried out surveillance from 9 different regions (Andijan, Fergana, Jizzakh, Kashkadarya, Namangan, Samarkand, Sirdaryo, Surkhandarya and Tashkent) of Uzbekistan to fill this gap. A total of 255 isolates were collected, which were genotyped using 17 polymorphic simple sequence repeats (SSR) markers. The DAPC analysis results showed no population subdivision in these sample-collected regions except Surkhandarya. Multilocus genotype (MLG) analysis, FST, and Nei's genetic distance results indicated a clonal population (rBarD ≤ 0.12) and merely three MLGs accounting for 70% of the overall population. MLG-34 was predominant in all Uzbekistan regions, followed by MLG-36 and MLG-42. Low genotypic diversity was observed in Andijan, Fergana, Jizzakh, Kashkadarya, Namangan, Sirdaryo, and Tashkent (0.56 to 0.76), compared with Samarkand (0.82) and Surkhandarya (0.97). No virulence against Yr5, Yr15, YrSp, and Yr26 was found, while resistant was overcome against Yr1, Yr2, Yr6, Yr9, Yr17, and Yr44 genes (Virulence frequency =≥75%). Comparative study results of Uzbekistan with previous Himalayan population were showed divergence from China and Pakistan populations. Further studies need to be conducted in a worldwide context to understand migration patterns; for that purpose, collaborative work is essential due to the Pst long-distance migration capability.

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model.

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Korean Black Goat Extract Exerts Estrogen-like Osteoprotective Effects by Stimulating Osteoblast Differentiation in MC3T3-E1 Cells and Suppressing Osteoclastogenesis in RAW 264.7 Cells.

Postmenopausal osteoporosis, characterized by an imbalance between osteoclast-mediated bone resorption and osteoblast-driven bone formation, presents substantial health implications. In this study, we investigated the role of black goat extract (BGE), derived from a domesticated native Korean goat, estrogen-like activity, and osteoprotective effects in vitro. BGE's mineral and fatty acid compositions were analyzed via the ICP-AES method and gas chromatography-mass spectrometry, respectively. In vitro experiments were conducted using MCF-7 breast cancer cells, MC3T3-E1 osteoblasts, and RAW264.7 osteoclasts. BGE exhibits a favorable amount of mineral and fatty acid content. It displayed antimenopausal activity by stimulating MCF-7 cell proliferation and augmenting estrogen-related gene expression (ERα, ERß, and pS2). Moreover, BGE positively impacted osteogenesis and mineralization in MC3T3-E1 cells through Wnt/ß-catenin pathway modulation, leading to heightened expression of Runt-related transcription factor 2, osteoprotegerin, and collagen type 1. Significantly, BGE effectively suppressed osteoclastogenesis by curtailing osteoclast formation and activity in RAW264.7 cells, concurrently downregulating pivotal signaling molecules, including receptor activator of nuclear factor κ B and tumor necrosis factor receptor-associated factor 6. This study offers a shred of preliminary evidence for the prospective use of BGE as an effective postmenopausal osteoporosis treatment.

Assessment of historical and projected changes in extreme temperatures of Balochistan, Pakistan using extreme value theory.

The fundamental consequences of global warming include an upsurge in the intensity and frequency of temperature extremes. This study provides an insight into historical trends and projected changes in extreme temperatures on annual and seasonal scales across "Balochistan, Pakistan". Historical trends are analyzed through the Mann Kendal test, and extreme temperatures (Tmax and Tmin) are evaluated using generalized extreme value (GEV) distribution for historical period (1991-2020) from the observational data and the two projected periods as near-future (2041-2070) and far-future (2071-2100) using a six-member bias-corrected ensemble of regional climate models (RCMs) projections from the coordinate regional downscaling experiment (CORDEX) based on the worst emission scenario (RCP8.5). The evaluation of historical temperature trends suggests that Tmax generally increase on yearly scale and give mixed signals on seasonal scale (winter, spring, summer, and autumn); however, Tmin trends gave mixed signals at both yearly and seasonal scale. Compared to the historical period, the return levels are generally expected to be higher for Tmax and Tmin during the both projection periods in the order as far-future > near-future > historical on yearly and seasonal basis; however, the changes in Tmin are more evident. Station-averaged anomalies of + 1.9 °C and + 3.6 °C were estimated in 100-year return levels for yearly Tmax for near-future and far-future, respectively, while the anomalies in Tmin were found to be + 3.5 °C and + 4.8 °C which suggest the intensified heatwaves but milder colder extreme in future. The findings provide guidance on improved quantification of changing frequencies and severity in temperature extremes and the associated impacts.

Genome-Wide Identification and Expression Analysis Reveals the B3 Superfamily Involved in Embryogenesis and Hormone Responses in Dimocarpus longan Lour.

B3 family transcription factors play an essential regulatory role in plant growth and development processes. This study performed a comprehensive analysis of the B3 family transcription factor in longan (Dimocarpus longan Lour.), and a total of 75 DlB3 genes were identified. DlB3 genes were unevenly distributed on the 15 chromosomes of longan. Based on the protein domain similarities and functional diversities, the DlB3 family was further clustered into four subgroups (ARF, RAV, LAV, and REM). Bioinformatics and comparative analyses of B3 superfamily expression were conducted in different light and with different temperatures and tissues, and early somatic embryogenesis (SE) revealed its specific expression profile and potential biological functions during longan early SE. The qRT-PCR results indicated that DlB3 family members played a crucial role in longan SE and zygotic embryo development. Exogenous treatments of 2,4-D (2,4-dichlorophenoxyacetic acid), NPA (N-1-naphthylphthalamic acid), and PP333 (paclobutrazol) could significantly inhibit the expression of the DlB3 family. Supplementary ABA (abscisic acid), IAA (indole-3-acetic acid), and GA3 (gibberellin) suppressed the expressions of DlLEC2, DlARF16, DlTEM1, DlVAL2, and DlREM40, but DlFUS3, DlARF5, and DlREM9 showed an opposite trend. Furthermore, subcellular localization indicated that DlLEC2 and DlFUS3 were located in the nucleus, suggesting that they played a role in the nucleus. Therefore, DlB3s might be involved in complex plant hormone signal transduction pathways during longan SE and zygotic embryo development.

Thermal and solutal transport by Cattaneo-Christov model for the magnetohydrodynamic Williamson fluid with joule heating and heat source/sink.

This article scrutinizes the 2-dimensional and boundary layer flow of magnetohydrodynamic Williamson fluid flowing on a stretchable surface with variable viscosity. The thermal and solutal rates are examined through the Cattaneo-Christov model with Joule heating, heat source/sink, and chemical reaction. The authors are motivated to conduct this study because of its practical and scientific significance in various processes, including polymer processing, textile industries, food industries, solar energy, biomedical science, wind turbine blades, oil spill clean-up, metal rolling, and forging. With the mentioned assumptions, the partial differential equations are achieved by using the basic governing laws, including momentum law, energy law, and concentration law. This non-linear system of equations is transmuted into ordinary differential equations by taking similarity transformations. The main novelty behind the conduction of this work is the numerical technique, namely the 'Adams-Milne (Predictor-Corrector)' method along with the Runge-Kutta technique on Matlab software, which has not previously been studied by any researcher in the literature. The analytical solution of the determined equations is not possible due to their highly non-linear nature; therefore the multistep numerical method namely the 'Adams-Milne (Predictor-Corrector)' method, along with the Runge-Kutta technique is used to determine the numerical results. The outcomes are noted due to numerous parameters for velocity, temperature, and concentration profiles. The explanation of graphical and numerical results is discussed here. The graphical impression of the Williamson parameter reveals that the velocity and temperature curves diminish for higher inputs of this parameter. The movement of fluid shows the declining behavior for the Hartmann number and viscosity parameter. The solutal and thermal findings due to Cattaneo-Christov heat and mass relaxation coefficients mark the reducing behaviour in respective field. The rise in reaction coefficient decreases the mass distribution. The analyses of comparison of results are also presented here.

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

Graphical Insight: Revolutionizing Seizure Detection with EEG Representation.

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.

Demand-side load forecasting in smart grids using machine learning techniques.

Electrical load forecasting remains an ongoing challenge due to various factors, such as temperature and weather, which change day by day. In this age of Big Data, efficient handling of data and obtaining valuable information from raw data is crucial. Through the use of IoT devices and smart meters, we can capture data efficiently, whereas traditional methods may struggle with data management. The proposed solution consists of two levels for forecasting. The selected subsets of data are first fed into the "Daily Consumption Electrical Networks" (DCEN) network, which provides valid input to the "Intra Load Forecasting Networks" (ILFN) network. To address overfitting issues, we use classic or conventional neural networks. This research employs a three-tier architecture, which includes the cloud layer, fog layer, and edge servers. The classical state-of-the-art prediction schemes usually employ a two-tier architecture with classical models, which can result in low learning precision and overfitting issues. The proposed approach uses more weather features that were not previously utilized to predict the load. In this study, numerous experiments were conducted and found that support vector regression outperformed other methods. The results obtained were 5.055 for mean absolute percentage error (MAPE), 0.69 for root mean square error (RMSE), 0.37 for normalized mean square error (NRMSE), 0.0072 for mean squared logarithmic error (MSLE), and 0.86 for R2 score values. The experimental findings demonstrate the effectiveness of the proposed method.

Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine.

Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the spectra-based classification of 11,408 B-type and 2422 hot subdwarf stars. The study employs baseline correction using Asymmetric Least Squares (ALS) to enhance classification accuracy. It applies the Pan-Core concept to identify 500 unique patterns or ranges for both types of stars. These patterns are the foundation for creating Support Vector Machine (SVM) models, including the linear (L-SVM), polynomial (P-SVM), and radial basis (R-SVM) kernels. Parameter tuning for the SVM models is achieved through cross-validation. Evaluation of the SVM models on test data reveals that the linear kernel SVM achieves the highest accuracy (87.0%), surpassing the polynomial kernel SVM (84.1%) and radial kernel SVM (80.1%). The average calibrated accuracy falls within the range of 90-95%. These results demonstrate the potential of using spectrum-based classification to aid astronomers in improving and expanding their understanding of stars, with a specific focus on the identification of hot subdwarf stars. This study presents a valuable investigation for astronomers, as it enables the classification of stars based on their spectra, leveraging machine learning techniques to enhance their knowledge and insights in astronomy.

A novel immunoinformatics approach for developing a poly-epitope vaccine targeting foot and mouth disease virus, exploiting structural VP proteins.

Foot and mouth Disease virus (FMDV) belongs to Picornaviridae family and Aphthovirus genus causing Foot and mouth disease (FMD) in cloven-hoofed animals. FMDV, a prevalent virus induces both acute and chronic infections with high mutation rates resulting in seven primary serotypes, making vaccine development indispensable. Due to time and cost effectiveness of the immunoinformatic approach, we designed in-silico polyepitope vaccine (PEV) for the curtailment of FMDV. Structural and immunogenic parts of FMDV (Viral Protein 1 (VP1), Viral Protein 2 (VP2), Viral Protein 3 (VP3), and Viral Protein 4 (VP4)) were used to design the cytotoxic T Lymphocyte (CTL), Helper T Lymphocyte (HTL), and B-cell epitopes, followed by screening for antigenic, non-allergenic, Interferon (IFN) simulator, and non-toxicity, which narrowed down to 7 CTL, 3 HTL, and 12 B-cell epitopes. These selected epitopes were linked using appropriate linkers and Cholera Toxin B (CTB) adjuvant for immunological modulation. The physiochemical analyses followed by the structure prediction demonstrated the stability, hydrophilicity and solubility of the PEV. The interactions and stability between the vaccine, Toll like Receptor 3 (TLR3) and Toll like receptor 7 (TLR7) were revealed by molecular docking and Molecular Mechanics/Poisson Boltzmann Surface Area (MMPBSA) with high stability and compactness verified by MD simulation. In-silico immune simulation demonstrated a strong immunological response. FMDV-PEV (Poly epitope vaccine) will be effectively produced in an E. coli system, as codon optimization and cloning in an expression vector was performed. The effectiveness, safety, and immunogenicity profile of FMDV-PEV may be confirmed by further experimental validations.Communicated by Ramaswamy H. Sarma.

The structural and immunogenic parts of FMDV were targeted for developing VaccineCTB-adjuvant and appropriate linkers, enhancing the immunogenicity of the PEVMinimal deformability and high stability of Vaccine using immunoinformaticsStrong antigen-specific humoral and cellular immune response of potential vaccineResults indicating the effectiveness, safety, and immunogenicity of the PEV.

An efficient deepfake video detection using robust deep learning.

The creation and manipulation of synthetic images have evolved rapidly, causing serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from spreading on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake videos and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these frames to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++). We achieved 96.23% accuracy when using the FF++ digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF++ and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.

Repurposing of US-FDA-approved drugs as negative modulators of ubiquitin specific protease-7 (USP7).

Ubiquitin-specific protease7 (USP7) regulates the stability of the p53 tumor suppressor protein and several other proteins critical for tumor cell survival. Aberrant expression of USP7 facilitates human malignancies by altering the activity of proto-oncogenes/proteins, and tumor suppressor genes. Therefore, USP7 is a validated anti-cancer drug target. In this study, a drug repurposing approach was used to identify new hits against the USP7 enzyme. It is one of the most strategic approaches to find new uses for drugs in a cost- and time-effective way. Nuclear Magnetic Resonance-based screening of 172 drugs identified 11 compounds that bind to the catalytic domain of USP7 with dissociation constant (Kd) values in the range of 0.6-1.49 mM. These 11 compounds could thermally destabilize the USP7 enzyme by decreasing its melting temperature up to 9 °C. Molecular docking and simulation studies provided structural insights into the ligand-protein complexes, suggesting that these compounds bind to the putative substrate binding pocket of USP7, and interact with its catalytically important residues. Among the identified 11 hits, compound 6 (oxybutynin), 7 (ketotifen), 10 (pantoprazole sodium), and 11 (escitalopram) also showed anti-cancer activity with an effect on the expression of proto-oncogenes and tumor-suppressor gene at mRNA level in HCT116 cells. The compounds identified in this study can serve as potential leads for further studies.

ALL classification using neural ensemble and memetic deep feature optimization.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.

In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.

Estimation of population median using bivariate auxiliary information in simple random sampling.

To estimate the unknown population median, several researchers have developed efficient estimators but these estimators are unable to provide efficient results in the existence of outliers. Keeping this point in view, the present work suggests enhanced class of robust estimators to estimate population median under simple random sampling in case of outliers/extreme observations. The suggested estimators are a mixture of bivariate auxiliary information and robust measures with the linear combination of deciles mean, tri-mean and Hodges Lehmann estimator. Mathematical properties associated with the improved class of robust estimators are evaluated in terms of bias and mean squared error. Moreover, the potentiality of our suggested estimators as compared to already available estimators is checked by considering two real-life data sets with outlier(s). In addition, a simulation study is also added in this regard. From theoretical and numerical findings, it is observed that our newly suggested estimators outperforms as compared to its competitors.

Outcome of modified fleur-de-lis latissimus dorsi myocutaneous flap for breast reconstruction.

BACKGROUND: Reconstruction of breast following mastectomy is important in terms of rehabilitating patients of breast cancer. Numerous approaches have been used in the reconstruction process. A procedure that has gained interest of the patients is reconstruction of breast using the autologous form. Main objective of this study is to determine the outcomes of modified fleur-de-lis latissimus dorsi flap in patients undergoing breast reconstruction. METHODOLOGY: This is a retrospective case series which was conducted at the Department of Plastic Surgery, SIMS/Services Hospital, Lahore, from January 2020 till December 2022. 184 patients age 25-60 years and Females with a history of mastectomy, who had to undergo creation of breast shape using a tissue flap from another part of the body at the site of breast following mastectomy were included. All patients were subjected to standard procedure of breast reconstruction with latissimus dorsi flap using modified fleur-de-lis technique and postoperatively weekly assessment in the first month and then monthly until 3 months was carried out and outcome of the study was analysed. RESULTS: The mean age and VAS score of the patients was 49.7 ± 9.17 and 6 ± 2.21, respectively. 57.1 % patients have DCIS, benign in 38 % patients and other tumours were present in 4.9 % patients. Immediate versus delayed reconstruction was done in 63.6 % versus 36.4 % patients respectively. Good aesthetic outcome was achieved in 80.3 % patients CONCLUSION: Modified fleur-de-lis latissimus dorsi flap in patients undergoing breast reconstruction yielded a good aesthetic outcome in the majority of the patients.