Principal Component Analysis Research Articles

A proposal for fall-risk detection of hospitalized patients from wireless channel state information using Internet of Things devices

Falls of hospitalized patients are a breach of safety and contribute to increasing hospitalization time and worsening recovery conditions, besides impacting other costs. This work explores changes in Channel State Information (CSI), available in local area wireless networks, to detect the risk of falling for hospitalized patients. In practical scenarios, it is impossible to know all fall-risk situations; therefore, one should employ one-class-based detectors since only the risk-free status can be assessed with confidence. Wireless propagation environment changes may indicate the risk of falls and are signaled using the distance from the collected CSI data to the representation learned for the risk-free class. Considering the changes caused by inpatients and nurses’ movement in the infirmary, the learning of the fall-risk-free situation must rely on a limited amount of data and be repeated several times quickly and on the fly. This prevents data-hungry algorithms from being used to learn about risk-free situations. Consequently, we design systems using k-Nearest Neighbors (kNN), Kernel Principal Component Analysis, and Autoencoder. Assuming that the negative class corresponds to falling risk-free, the decision threshold is settled according to a prescribed False Positive Ratio (FPR), and the Matthews Correlation coefficient is used to set the decision threshold. The kNN system accomplishes the highest accuracy value of 87.95%. Thus the performance of the one-class kNN-based fall-risk detector is assessed in two environments that resemble those encountered by hospitalized patients for different FPR values, achieving recall values from 67.47% to 99.40% with an accuracy greater than 79.10%.

Reshaping energy horizon of Iran: investigating economic sanctions, export diversification, and environmental resilience.

Employing robust methodologies, including principal component analysis, autoregressive moving average, Fourier bootstrap dynamic autoregressive distributed lag, error correction model, and the Breitung-Candelon spectral Granger causality test, this study scrutinizes the impact of export diversification (EXD) on Iran's ecological footprint (EF) from 1997 to 2020, considering economic sanctions (ESI), trade openness (TOP), energy consumption per capita (ECpc), globalization (KOF), and real GDP per capita (RGDPpc). Findings consistently affirm a positive environmental impact of EXD, revealing a nuanced temporal pattern. Notably, the short-term impact (- 0.645) is more pronounced than its long-term counterpart (- .020). Increased industrial activities due to globalization (10% rise) lead to 4.26% and 1.64% EF degradation in the long and short term. Conversely, due to Iran's heavy reliance on fossil fuels, a 10% rise in ECpc correlates with 1.63% and 3.81% long- and short-term environmental quality reduction. ESI demonstrates a dual impact, improving short-term environmental quality but contributing to long-term degradation. Frequency-domain causality analysis highlights EXD and KOF as short- and long-term causes of EF, ESI, and TOP as medium- to long-term causes and RGDPpc as a long-term cause. These findings emphasize the need for sustainable policies, stringent environmental standards, and a balanced approach to fostering economic growth while preserving the environment.

Integrated liver and serum proteomics uncover sexual dimorphism and alteration of several immune response proteins in an aging Werner syndrome mouse model.

Werner syndrome (WS) is a progeroid disorder caused by mutations in a protein containing both a DNA exonuclease and DNA helicase domains. Previous studies indicated that males lacking the helicase domain of the Wrn protein orthologue exhibited hepatic transcriptomic and metabolic alterations. In this study, we used a label-free liquid chromatography-tandem mass spectrometry approach to uncover proteins abundance associated with specific biological processes that differed depending on the age (four or ten months) and/or the genotype (wild type or Wrn mutant) in the serum and liver of mice. Principal component analysis of the proteomic data from both serum and hepatic tissue revealed a sexual dimorphism regardless of the age and the genotype of the mice. Moreover, although all Wrn mutant mice exhibited fatty liver by the age of ten months, a significant age and genotype dependent enrichment of proteins involved in lipid and fatty acid metabolic processes were uncovered only in males. Also, a genotype dependent increase in serum oxidant detoxification processes was observed in the serum of Wrn mutant males. Despite these sexual differences, several aspects of the immune system were affected in both females and males. Finally, an increase of specific immunoglobulin molecules was common in the liver and serum of both older Wrn mutant females and males. Such results suggest that specific immunoglobulin variants maybe associated with fatty liver progression in WS.

Multiple hypothesis testing in cognitive IoT sensor network

Recent Internet of Things (IoT) research aims to develop generic objects to learn, reason, and perceive their environment. Therefore, a new area has emerged known as cognitive IoT (CIoT). The cognitive Internet of Things integrates IoT with intelligence and behaves as well as humans through intelligent functionality. Several inferential tasks in CIoT require multiple hypothesis testing. The situation becomes cumbersome when the data is massive and heterogeneous. Thus, this research suggests a novel technique for multiple-hypothesis testing that uses a copula function to deal effectively with massive heterogeneous data. In addition, these data may contain missing or corrupted entries. Hence, it introduced probabilistic clustering, which reduces model inefficiency and takes control over the false discovery rate (FDR). Most of the variance from each cluster was extracted using kernel principal component analysis (KPCA) to reduce the processing burden at the fusion centre. Subsequently, it computes the p-value of each cluster's first principal component data and employs the Bonferroni method for multiple hypothesis testing. Finally, this research study evaluates the performance of the proposed algorithm on six-month environmental data, revealing that the proposed technique is efficient in terms of accuracy and computation time compared to other methods in the presence of massive heterogeneous data.

Development of unique soil organic carbon stability index under influence of integrated nutrient management in four major soil orders of India

Stability of soil organic carbon (SOC) is pre-requisite for stabilization of C leading to long-term C sequestration. However, development of a comprehensive metric of SOC stability is a major challenge. The objectives for the study were to develop novel SOC stability indices by encompassing physical, chemical, and biochemical SOC stability parameters and identifying the most important indicators from a Mollisol, an Inceptisol, a Vertisol, and an Alfisol under long-term manuring and fertilization. The treatments were control, 100%NPK, 50% NPK+ 50% N through either farmyard manure, cereal residue, or green manure. SOC stability indicators were selected, transformed and integrated into unique SOC stability indices via conceptual framework and principal component analysis. Principal component analysis identified Al-macroaggregate, humic acid C-microaggregate, microaggregate-C, particulate organic matter-C-macroaggregate and polyphenol-microaggregate as the important SOC stability indicators. The principal component analysis -based SOC stability index varied from 0.2 to 0.9, 0.1 to 0.5, 0.2 to 0.6, 0.1 to 0.5 for Mollisol, Inceptisol, Vertisol and Alfisol, respectively. The SOC-stability index derived from conceptual framework and principal component analysis significantly lined up well with one another, although NaOCl-Res-C showed a high correlation with both conceptual framework (r = 0.8) and principal component analysis-based (r = 0.7) SOC stability indexes, suggesting that both methods might be used to quickly assess SOC stability in four soil orders. Overall, 50%NPK+50%N by farmyard manure or green manure emerged as the most effective management practices for enhancing stability of SOC in Mollisol, Inceptisol, Vertisol, and Alfisol of India which might act as major C sink in rice-wheat and maize-wheat cropping systems. The other aspect of C sequestration is to enhance agricultural productivity without depending much on expensive chemical fertilizers. The model yardstick thus developed for assessing SOC stability might be useful to other systems as well.

Performance measurement of Brazilian federal university hospitals: an overview of the public health care services through principal component analysis.

This paper aimed to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis. This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3). From the principal component analysis, considering mainly component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated through the production of medical residency, by specialty area. Finally, it was observed that the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV shows that these groups reflect similarities with respect to the scores and importance of the variables for both hospitals' groups. Among the main limitations it was observed that there was incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information were dealt with for 60% of SIMEC/REHUF performance indicators. The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units represented by the hospitals. It was possible to generate a large quantity of information in order to contribute with assumptions to complement the decision-making processes in these organizations. Development of public policies with emphasis on hospitals linked to teaching centers represented by university hospitals. This also involved the projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice. The originality of this paper for the scenarios of the Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generates a particular scenario for the measurement of performance in hospital organizations.

Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.

Exploring Pijuayo (Bactris gasipaes) Pulp and Peel Flours as Fat Replacers in Burgers: A Multivariate Study on Physicochemical and Sensory Traits

Meat products are known for their lipid profile rich in saturated fats and cholesterol, and also for the formation of oxidation compounds; therefore, a reduction in animal fat may result in a product less harmful to health. Pijuayo is an Amazon fruit known for its nutritional properties, such as its fiber and lipid content. For these reasons, it is an attractive fruit to replace animal fat in meat products. The present work used pijuayo pulp and peel flours to partially replace animal fat in beef-based burgers at 25% and 50% levels, considering sensory and physicochemical outcomes evaluated by Principal Component Analysis (PCA), Correspondence Analysis (CA) and Multiple Factor Analysis (MFA). Pijuayo flour affected the physicochemical characteristics evaluated by PCA, where the samples with greater fat replacement were characterized by a high carbohydrate content and instrumental yellowness. The minimal fat replacement did not abruptly affect the PCA’s instrumental texture and color, proximal composition, yield properties, and lipid oxidation. The overall liking was greater in the 25% fat reduction treatments, even greater than the control, in which positive sensory attributes for liking were highlighted for those treatments. A small segment of consumers (11% of total consumers) preferred the treatment with greater replacement of fat with pijuayo peel flour, which these consumers tended to characterize as seasoned. However, this treatment had the lowest liking. The MFA showed that the sensory characteristics tender and tasty were strongly correlated with overall liking and were highlighted in the samples of 25% fat reduction, suggesting that the pijuayo improves the tenderness and flavor of reduced-fat burgers. Other inclusion levels between 25% and 50% of fat replacement could be explored, and optimization studies are needed. In addition, the sensory characteristics and flavor-enhancing compounds of the fruit, as well as the nutritional aspects of the inclusion of pijuayo, should be studied, such as the fatty acid profile. These characteristics will be informative to explore pijuayo as a fat replacer at a pilot scale and industrial scale.

Exploring the impacts of ecommerce activities on the spatial resilience of warehouse clubs: the role of retail agglomeration

PurposeAs ecommerce becomes more prevalent, traditional brick-and-mortar retailers such as warehouse clubs (WCs) face the challenging task of maintaining and growing their customer base. This study aims to unravel the combined impact of retail agglomeration and ecommerce activities on consumer foot traffic (also referred to as “footprint”) at WC stores, placing an emphasis on the locational strategies adopted by WCs in this evolving retail landscape.Design/methodology/approachMobile-based customer foot traffic data for Costco, a major U.S. WC chain, is sourced for our analysis. We use Principal Component Analysis (PCA) to identify dimensions of general merchandise (GM) and narrow-range merchandise (NM) retail agglomeration. Two-stage least squares (2SLS) regressions are used to explore how the intensity of ecommerce activities and WC locational choices within retail agglomerations impact WC foot traffic.FindingsOur analysis highlights a notable decline in WC store visits attributable to both GM and NM ecommerce activities, with GM ecommerce presenting a more significant competitive challenge to WCs. Regarding retail agglomerations, proximity to GM clusters that include a diverse range of supercenters, department stores, and club stores, is associated with an increase in WC customer visits within their vicinity. In contrast, the influence of NM agglomerations is mixed; clusters adjacent to grocery stores lead to higher WC customer traffic compared to those focused on other specialized stores. These findings underscore the strategic importance of location in mitigating the adverse effects of ecommerce competition. Additionally, our study uncovers intricate dynamics between GM and NM retail clusters and ecommerce activities, demonstrating varied impacts on WC customer footprint.Research limitations/implicationsAccess to customer footprint data illustrates the potential of this data source for retail decision making and researchers. Our analysis is limited to one chain, notably Costco.Practical implicationsOur findings underscore the need for retailers to adeptly navigate the evolving retail landscape, including the confluence between physical and digital retail environments, to secure future success. In particular, our results emphasize the benefits of locating stores within mixed retail agglomerations and underline the need to consider the broader retail landscape in location decisions.Social implicationsThe rise of ecommerce in the U.S. has reshaped consumer behavior and altered local shopping districts’ communal dynamics. This change may spur policy interventions to help physical stores compete with online retailers, emphasizing the importance of retail diversity and community-centric environments to sustain communal retail interactions amidst digital advancements.Originality/valueThe paper makes use of a unique dataset to provide a first assessment of the combined effects of retail agglomeration and ecommerce activities on consumer foot traffic for WC retailers. Thus, this paper provides insights into the impacts on consumer shopping behavior from the dynamic interactions between physical retail clusters and online shopping behaviors.

Integrating remote sensing and field investigation for lithological mapping of Per-Eonile to Neonile sequences west of Sohag city, Egypt: Impact on urban development

In the domains of geological study, natural resource exploitation, geological hazards, sustainable development, and environmental management, lithological mapping holds significant importance. Conventional approaches to lithological mapping sometimes entail considerable effort and difficulties, especially in geographically isolated or inaccessible regions. Incorporating geological surveys and satellite data is a powerful approach that can be effectively employed for lithological mapping. During this process, contemporary RS-enhancing methodologies demonstrate a remarkable proficiency in identifying complex patterns and attributes within the data, hence facilitating the classification of diverse lithological entities. The primary objective of this study is to ascertain the lithological units present in the western section of the Sohag region. This objective will be achieved by integrated Landsat ETM+ satellite imagery and field observations. To achieve our objectives, we employed many methodologies, including the true and false color composition (FCC&TCC), the minimal noise fraction (MNF), principal component analysis (PCA), decoration stretch (DS), and independent component analysis (ICA). Our findings from the field investigation and the data presented offer compelling evidence that the distinct lithological units can be effectively distinguished. A recently introduced geology map has been incorporated within the research area. The sequence of formations depicted in this map is as follows: Thebes, Drunka, Katkut, Abu Retag, Issawia, Armant, Qena, Abbassia, and Dandara. Implementing this integrated technique enhances our comprehension of geological units and their impacts on urban development in the area. Based on the new geologic map of the study area, geologists can improve urban development in the regions by detecting building materials “aggregates”. This underscores the significance and potential of our research in the context of urban development.

The effect of anthropometric parameters on the seat transmissibility during whole-body vibration: a hybrid modeling approach based on artificial neural networks and principal component analysis

The effect of anthropometric parameters on the seat transmissibility during whole-body vibration: a hybrid modeling approach based on artificial neural networks and principal component analysis

Quality of Aleppo Pine Seedlings Produced in Two Nurseries in Algeria; an Assessment of Morphological Characteristics and Quality Indices

Objective: This article aims to assess the quality of Aleppo pine seedlings produced in two forest nurseries in Algeria and the impact of the cultural practices adopted in each nursery on seedlings growth. Theoretical Framework: Since 1962, significant investments have been made annually in reforestation programs for agricultural and forest degraded area, but the results are far from satisfactory. The success of these programs depends mainly on nursery practices and plants morphological and physiological quality. Method: follow-up of Aleppo pine seedlings growth (height, and collar diameter) every 10 days over seven months. Seedling quality was conducted through visual observations of defects and calculation of quality indices (H/D, S/R, DQI, V). Results and Conclusion: The study reveals a highly significant difference in growth and quality between the plants from the two nurseries. Principal Component Analysis (PCA) confirms the substantial influence of cultural practices on seedling growth. However, the majority of plants from both nurseries fill to meet quality standards, primarily attributed to traditional practices Implications of the Research: The results of this study may explain the high failure rates of plantations observed in the field, not only for Aleppo pine but also for other autochthonous forest species. They also highlighted the greater importance of prioritizing the nursery phase before initiating any reforestation program. Originality/Value: This study explores the importance of adapting technical itinerary according to the specific characteristics of each plant for enhanced production. It introduces several quality indices for evaluating seedling quality in Algerian nurseries for the first time. Additionally, it is the first study to assess the quality of Aleppo pine, the first species produced in Algerian forest nurseries.

Reliable renewable energy forecasting for climate change mitigation

Accurate prediction of electricity generation from diverse renewable energy sources (RES) plays a pivotal role in optimizing power schedules within RES, contributing to the collective effort to combat climate change. While prior research often focused on individual energy sources in isolation, neglecting intricate interactions among multiple sources, this limitation frequently leads to inaccurate estimations of total power generation. In this study, we introduce a hybrid architecture designed to address these challenges, incorporating advanced artificial intelligence (AI) techniques. The hybrid model seamlessly integrates a gated recurrent unit (GRU) and a ResNext model, and it is tuned with the modified jaya algorithm (MJA) to capture localized correlations among different energy sources. Leveraging its nonlinear time-series properties, the model integrates meteorological conditions and specific energy source data. Additionally, principal component analysis (PCA) is employed to extract linear time-series data characteristics for each energy source. Application of the proposed AI-infused approach to a renewable energy system demonstrates its effectiveness and feasibility in the context of climate change mitigation. Results reveal the superior accuracy of the hybrid framework compared to more complex models such as decision trees and ResNet. Specifically, our proposed method achieved remarkable performance, boasting the lowest error rates with a normalized RMSE of 6.51 and a normalized MAPE of 4.34 for solar photovoltaic (PV), highlighting its exceptional precision in terms of mean absolute errors. A detailed sensitivity analysis is carried out to evaluate the influence of every element in the hybrid framework, emphasizing the importance of energy correlation patterns. Comparative assessments underscore the increased accuracy and stability of the suggested AI-infused framework when compared to other methods.

#1390 Differences in the detection of anti-HLA antibodies between two single antigen bead methods in kidney transplantation patients

Abstract Background and Aims Two semiquantitative single-antigen bead (SAB) methods are currently available for the detection of anti-HLA antibodies and the estimation of their activity, depending on complement binding. However, data on accordance of the two methods on specificities detection and their reactivity evaluation are scarce. The purpose of this study is to compare the two methods in terms of anti-HLA antibodies specificities detection and of their complement binding capacity Method Sera from 97 patients with a positive Panel Reactive Antibody Test (>5%) was tested by two SAB methods, Immucor (IC) and One-lambda (OL), for class I and II anti-HLA antibody specificities and their ability to bind complement. The patients were either on a waiting list for kidney transplantation or transplanted patients tested routinely or after a clinical event. Statistical analysis of the results and comparison of the two methods followed. Results OL detected more class I positive specificities per patient [25 (9-34) vs. 18 (7-28) with IC, p < 0.001], while the two methods showed no difference in class II anti-HLA antibodies. IC detected 1608 out of 8148 and 1136 out of 7275 totally examined, positive anti-HLA class I and II antibody specificities respectively, with a median mean fluorescence intensity (MFI) of 4195 (1995-11272) and 6706 (2647-13184) respectively. Similarly, OL detected 1942/8148 and 1247/7275 positive specificities of anti-HLA class I and II antibodies respectively, with MFI 6185 (2855-12099) and 9498 (3630-17702)] respectively. In the IC method, 428/1608 [MFI 13900 (9540-17999)] and 409/1136 [MFI 11832 (7128-16531)] positive class I and II specificities, respectively, bound the C3d fraction of complement. Similarly, OL detected 485/1942 [MFI 15452 (9369-23095)] and 298/1247 [MFI 18852 (14415-24707)] class I and II specificities respectively that bound the C1q fraction of complement. In the analysis of the specificities that were positive by both methods, MFI showed a strong positive linear correlation between the two methods (r = 0.61, p < 0.001 and r = 0.54, p = 0.001 for classes I and II respectively). Accordingly, data were analyzed with Principal Components Analysis, in which negative results were also included. In the projection of the plane defined by the first two principal components the vector corresponding to the IC method for class I antibodies was congruent with the vectors of the complement binding detection methods, C3d and C1q. In contrast, the OL vector was nearly perpendicular to the other vectors, indicating the existence of non-complement-binding specificities that are not detected by IC. A similar picture was found for antibodies against HLA class II (Figure). Conclusion OL was more sensitive for detecting class I and II anti-HLA antibodies despite the fact that there was no difference in the number of class II specificities per patient. However, the additional specificities detected by OL, potentially do not bind complement. The MFI of the complement binding specificities were higher in both methods. The two methods were equivalent in detecting complement-binding class I specificities, however the C3d IC method was more sensitive in detecting complement-binding class II anti-HLA antibodies.

Surface-Enhanced Raman Spectroscopy-Based Detection of EMT-Related Targets in Endometrial Cancer: Potential for Diagnosis and Prognostic Prediction.

Epithelial-mesenchymal transformation (EMT) is one of the important mechanisms of malignancy in endometrial cancer, and detection of EMT targets is a key challenge to explore the mechanism of endometrial carcinoma (EC) malignancy and discover novel therapeutic targets. This study attempts to use surface-enhanced Raman spectroscopy (SERS), a highly sensitive, ultrafast, and highly specific analytical technology, to rapidly detect microRNA-200a-3p and ZEB1 in endometrial cancer cell lines. The silver nanoparticles were decorated with iodine and calcium ions, can capture the SERS fingerprints of microRNA-200a-3p and ZEB1 protein, and effectively avoid the interference of impurity signals. At the same time, the method has high sensitivity for the detection of the above EMT targets, and the lowest detection limits for microRNA-200a-3p and ZEB1 are 4.5 pmol/mL and 10 ng/mL, respectively. At the lowest detection concentration, the method still has high stability. In addition, principal component analysis can not only identify microRNA-200a-3p and ZEB1 protein from a variety of EMT-associated microRNA and proteins but also identify them in the total RNA and total protein of endometrial cancer cell lines and normal endometrial epithelial cell lines. This study modified silver nanoparticles with iodine and calcium ions and for the first time captured the fingerprints of EMT-related targets microRNA-200a-3p and ZEB1 at the same time without label, and the method has high sensitivity and stability. This SERS-based method has immense potential for elucidating the molecular mechanisms of EMT-related EC, as well as identifying biomarkers for malignant degree and prognosis prediction.

Proteomic profiling of peanut hairy root culture unveils distinctive adaptive responses induced by elicitor treatment across diverse time intervals

Plants employ a diverse array of responses to counteract and defend against stressors. Utilizing quantitative proteomic analysis allows for the exploration of global proteomic dynamics in cells and tissues at specific time points. In order to comprehend the intricate mechanisms underlying plant responses, we conducted a proteomic investigation of peanut hairy root cultures induced with a combination of chitosan (CHT), methyl jasmonate (MeJA), and cyclodextrin (CD) at multiple time points. The study identified 292, 317, and 327 differentially expressed proteins (DEPs) in hairy root tissues at 24-, 48-, and 72 h intervals post-elicitation, respectively. Principal component analysis (PCA) distinctly clustered each timepoint of treatment and the control group. Gene ontology (GO) enrichment analysis highlighted terms such as "responding to biotic stimuli" and "defense response" during the early response, with the upregulation of Heat shock cognate 70 kDa protein, Wound-induced protein 1, and Disease resistance-like protein. The 72 h time point specifically showed an upregulation of superoxide dismutase (SOD) and auxin response factor, indicating a delayed response and illustrating how hairy roots adapt to stress in later stages. Furthermore, GO enrichment at later stages implicated alterations in protein production, involving both synthesis and degradation processes. Proteolytic enzymes, including those in the ubiquitin/proteasome pathway, were identified as facilitating protein degradation. The detection of small molecular weight (MW) peptides in the culture medium of elicited hairy roots suggested their origin from the proteolytic cleavage of precursor proteins. The GO enrichment of culture medium peptides pointed towards involvement in signaling pathways. The suggested signaling cascade acts as a potential link that connects the recognition of stress to the initiation of appropriate response to external stressors. This approach delves into dynamic changes in protein expression and secretion patterns over various intervals, providing insights into the plant's adaptive response to external stress.

Delineating the uranium anomalous zones using remote sensing and radiometric data: a case study from Gabal Umm Tinassib area, North Eastern Desert, Egypt

AbstractRemote sensing (RS) and airborne gamma-ray spectrometric (AGS) methods are utilized to delineate significant uranium zones and altered mineralization areas in Gabal Umm Tinassib and its surrounding region, situated in the northern section of the Egyptian Eastern Desert. AGS serves as a valuable tool for mapping surface geology and conducting mineral exploration. It assesses the concentrations of radioactive elements such as potassium (K), equivalent uranium (eU), and equivalent thorium (eTh). The concentration of radioelements exhibits measurable and significant variation according to lithology. On the other hand, several processing steps are employed for the RS data to generate high-quality images for geological mapping and to identify the mineralized alteration zones. The analysis of RS and AGS data in this study led to insightful conclusions. The utilization of False Color Composite (FCC) with the three best bands derived from the Optimum Index Factor (OIF), Principal Component Analysis (PCA) to extract two highly informative datasets, and the application of two band ratios contributed to accurate geological mapping. These band ratios notably identified identical alteration locations on both younger and older granite basement rocks. Additionally, the constrained energy minimization (CEM) technique effectively pinpointed alterations across these strata. The statistical analysis of AGS data revealed that radioactivity levels in the region range from 1.3 to 19.3 Ur for the total-count (TC), 0.2–3.6% for K, 0.09–11.6 ppm for eU, and 1.1–30.0 ppm for eTh. The estimated coefficient of variability (CoV) demonstrated that the three radio-elements exhibited normal distribution patterns across different rock units, with CoV values of less than 100%, except for K in the Malha Formation. High radiometric readings are observed in the outcroppings of younger and older granites. However, the lowest readings are recorded over undifferentiated Upper-Cretaceous sediments, Abu Rimth Formation, Galala Formation, and some parts of Quaternary sediments. The derived ternary radio-elements map highlights significant radiometric and related uranium anomalous zones as bright white regions. A strong correlation was found between high radiometric anomalous zones and the presumed occurrence of alteration zones in the study area.

Systematic analysis of co-expression network and gene expression profile of differentially expressed lncRNA-mRNA one week after spinal cord injury

Spinal cord injury (SCI) is a severe event that occurs in the central nervous system (CNS). Long noncoding RNAs (lncRNAs) have already been proven to play key roles in different diseases of the CNS. Nevertheless, the roles of messenger RNAs (mRNAs) and lncRNAs one week after SCI remain to be elucidated. We established a rat model of SCI and examined the differential levels of mRNAs and lncRNAs, and then, the differentially expressed mRNAs (DE mRNAs) and lncRNAs (DE lncRNAs) were investigated. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DE lncRNAs and mRNAs were subsequently carried out to explore crucial biological processes and signaling pathways. Moreover, we constructed protein–protein interaction (PPI) networks, the STRING database, and identified the 10 high degree core nodes. We also established a co-expression network by calculating the correlation between lncRNA expression profile and mRNA expression profile. The identified DE lncRNAs and DE mRNAs may potentially be critical targets for the diagnosis and treatment of SCI in the future. Abbreviations: SCI: spinal cord injury; CNS: central nervous system; YLDs: years of life lived with disability; lncRNAs: long noncoding RNAs; mRNAs: messenger RNAs; DE: differentially expressed; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: gene set enrichment analysis; RIN: Renewable Identification Number; PCA: principal component analysis; PPI: protein–protein interaction; MF, molecular function; BP: biological process; CC: cellular component; MCODE: molecular complex detection

#1043 Metabolomics and proteomics study on kidney function post-liver transplantation

Abstract Background and Aims Hepatorenal syndrome (HRS) is a serious complication characterized by kidney dysfunction in patients with advanced liver disease. The pathophysiology of HRS is poorly understood and involves a combination of hemodynamic, circulatory, inflammatory, and hormonal factors. While some patients with HRS restore kidney function after liver transplantation (LT), others continue to have impaired kidney function. Understanding the mechanisms associated with persistent kidney dysfunction is essential for developing effective treatments and stratifying patients for combined liver-kidney transplantation versus LT only. Method We analyzed serum samples from 10 patients of the multicenter prospective study (INAID CTOT14). Five of them had no HRS and maintained an eGFR > 50 ml/min post-LT. One patient with HRS recovered kidney function post-LT. Four patients with HRS failed to regain kidney function at one- and two-months post-LT (eGFR < 30 ml/min). We categorized the five patients without HRS and one patient with recovered eGFR as Normal Kidney Function (NKF) group and the other four patients as Impaired Kidney Function (IKF) group. Serum samples were collected at one (visit 1) and two months (visit 2) post-LT and stored at −80°C until analysis. Both untargeted metabolomics and proteomics analysis were conducted using quantitative liquid chromatography tandem mass spectroscopy (LC-MS/MS) on Agilent® Q-TOF 6546. Data was analyzed by Agilent® Qualitative Workflow 10, Agilent® Profinder 10, MetaboAnalyst (V6.0), and Spectrum Mill MS software. Results A total of 150 metabolites were identified by LC-MS/MS based metabolomics. Principal component analysis (PCA) did not show significant differences between visit 1 and visit 2 samples (Fig. 1A). Therefore, visit 1 and visit 2 samples were analyzed together. With 12 samples in NKF and 8 samples in IKF group, the PCA score plot showed distinct clustering of the two groups (Fig. 1B). Volcano plot analysis was conducted to analyze metabolite differences between NKF and IKF. P-values were calculated using a Student's t-test, and differential metabolites were identified with a P-value cut-off of < .05 and a fold change > 2.0. The result revealed 37 downregulated and 13 upregulated metabolites when comparing NKF and IKF (Fig. 1C). Notably, creatinine was among the downregulated metabolites in the NKF group, validating the accuracy of our analysis. Untargeted proteomics analysis was also performed on all 20 samples. Using a P-value cut-off of .05 and a fold change >1.25, 43 proteins were found to be upregulated, and four proteins to be downregulated when comparing NKF to IKF. Fig. 2A lists the top 10 regulated proteins, with beta-2-microglobulin as the most significantly changed. Reactome® pathway analysis of these 47 proteins highlighted potential impacts on various biological pathways (Fig. 2B), providing potential valuable insights into the molecular alterations associated with kidney function post-LT. Conclusion Our comprehensive metabolomics and proteomics study characterized the molecular alterations associated with kidney function post-LT. The identified metabolites and proteins may provide valuable insights for understanding persistent kidney damage. Future research is warranted to confirm and correlate these findings with clinical outcomes, enhancing the applicability and significance of our results in the broader context of HRS and LT.

Multivariate analysis of the phytochemical composition and antioxidant properties in twenty-five accessions across three Achillea species

This study explored the chemical composition, antioxidant activity, and total phenol content of aerial parts from 25 accessions of three Achillea species (Achillea wilhelmsii C. Koch, Achillea vermicularis Trin., and Achillea tenuifolia Lam.). The plants were collected from various natural habitats across Iran, encompassing regions such as Central, Western, Southern, Northern, Western, and Northwestern parts of the country. Subsequently, they were grown together under field conditions. The study revealed significant variation in essential oil yields among accessions of A. wilhelmsii, ranging from 0.01 to 0.107%, A. vermicularis with a range of 0.075 to 1.5%, and A. tenuifolia showing a variation of 0.1 to 2%. The study utilized Gas Chromatography–Mass Spectrometry (GC–MS) analysis, revealing 75, 49, and 75 compounds in the essential oils of A. wilhelmsii, A. tenuifolia, and A. vermicularis, respectively. Major components included camphor, 1,8-cineole, anethole, α-pinene, and phytol in A. wilhelmsii, 1,8-cineole, camphor, levo-carvone, and δ-terpinene in A. vermicularis, and β-cubebene, elixene, β-sesquiphellandrene, 1,8-cineole, camphor, and δ-terpinene in A. tenuifolia. The essential oil compositions of A. wilhelmsii and A. vermicularis were predominantly characterized by oxygenated monoterpenes, whereas that of A. tenuifolia was characterized by sesquiterpenes. Cluster analysis grouped accessions into three clusters, with A. tenuifolia forming a distinct group. Principal Component Analysis (PCA) triplot (62.21% of total variance) confirmed these results and provided insights into compound contributions. Furthermore, total phenolic content and antioxidant activity of the accessions of three species were assessed over 2 years. A. tenuifolia exhibited the highest levels in both categories, with statistically significant linear regression between antioxidant activity and total phenol content for A. tenuifolia and A. wilhelmsii. These findings emphasize significant phytochemical diversity within Achillea species, positioning them as promising natural sources of antioxidants. Further exploration and selection of specific accessions within each species are crucial for unlocking their medicinal potential and supporting cultivation and conservation efforts.