Normal Method Research Articles

Optimization of supercritical CO2 fluid extraction process, component analysis, and antioxidant spectral activity correlation of Huoluo Xiaoling Dan

ABSTRACT This study elucidated the optimization conditions, GC-MS analysis conditions, total antioxidant activity, and components closely related to antioxidant activity for supercritical extraction of active ingredients in Huoluo Xiaoling Dan (HLXLD). The yield, octyl acetate content, and total antioxidant activity of HLXLD under different conditions were obtained through orthogonal experiments, and the optimal supercritical extraction conditions were determined to be 45°C, 25 MPa, and 4 h. The supercritical extract was subjected to GC-MS analysis and matched with the WILEY and NIST databases, resulting in the qualitative identification of 37 chemical components with a matching degree above 60. The similarity evaluation system of traditional Chinese medicine fingerprint was used to evaluate all orthogonal experimental samples, with a similarity ranging from 0.522 to 0.989. Thirteen common characteristic peaks were selected, and the quantification of these peaks was performed using peak area normalization method. The combination of the content of common characteristic peaks in different samples and their total antioxidant activity was analyzed using multivariate statistical methods such as principal component analysis and multiple regression equation. It was found that five components, F3, F2, F5, F7 and F12, among the common characteristic peaks were closely related to antioxidant activity.

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

Antimicrobial management of dental infections: Updated review.

Dental infections, which include anything from severe periodontal illnesses and abscess forms to routine tooth caries, are a major public health risk. This review article focuses on the pathophysiology and treatment of dental infections. A narrative review was conducted based on several published articles, relevant journals, and books in Google Scholar PubMed using the keywords dental caries, periodontal disease, gingivitis, and related diseases; we excluded duplicated information. Our review illustrated the types of dental infections and the proper antimicrobial drug that is suitable for this disease. Drawing from recent research findings and clinical evidence, we explore the spectrum of bacteria commonly associated with dental infections and their susceptibility profiles to various antibiotics. Emphasis is placed on understanding the mechanisms of antibiotic action and resistance in the context of dental pathogens, shedding light on optimal treatment regimens and potential challenges in clinical management. Additionally, we go over the clinical consequences of antibiotic therapy in dentistry, taking into account factors like patient selection, dose guidelines, and side effects. The management of dental infections through antimicrobial strategies has undergone significant advancements, as evidenced by this updated review. Besides the normal methods, emerging technologies such as 3D printing for drug delivery of antibiotics and disinfectants hold promise in enhancing treatment efficacy and patient outcomes. By leveraging the precision and customization afforded by 3D printing, dentistry can tailor antimicrobial interventions to individual patient needs, optimizing therapeutic outcomes while minimizing adverse effects.

Post Covid-19 Vaccination Immune Response in Hemodialysis Patient Compared to Normal Population

Abstract Background The COVID-19 pandemic has profound adverse effects on the population on dialysis. Patients requiring dialysis are at an increased risk of SARS-CoV-2 infection and mortality, and many have experienced psychological distress as well as delayed or suboptimal care. COVID-19 survivors have prolonged viral shedding, but generally develop a robust and long-lasting humoral immune response that correlates with initial disease severity. However, protection against reinfection is incomplete. Aim of the Work This study aimed to studying the immune response to COVID-19 Viruse vaccination in hemodialysis patients compared to the normal population Patients and Methods This study is a prospective case control study which include 40 prevalent HD patients received COVID-19 vaccination in haemodialysis units, Ain Shams University Hospitals. In comparison to 20 healthy control individuals who also received COVID-19 vaccination. Results Our study showed that there was no statistically significant difference between the studied groups as regard demographic data. Where the age ranges from 24-60 years old. There was 11 female and 29 males in our participant. Regarding occupation, there were 11 participants not working and 29 were working. In our study, there was a significant negative correlation between, SARS COVID IgG Au/ml & age per year, duration of hemodialysis per year, and parathyroid hormone. Whereas, there is a significant positive correlation between SARS COVID IgG Au/ml and kt/v % of renal hemodialysis dialysis patients. Otherwise, there is no significant relation between, SARS COVID IgG. In our study, there is a lower significant value of SARS COVID igG in renal dialysis patients compared to the control group regarding the history of COVID19 infection before vaccination and occurrence of post- vaccine adverse effects. Conclusion Hemodialysis patients demonstrate a hyporesponsiveness to vaccination against COVID-19. Although most patients on maintenance hemodialysis developed a substantial humoral response following the COVID vaccine, it was significantly lower than controls.

Effects of finite Larmor radius corrections and finite electron inertia on transverse Jean’s gravitational instability of radiative plasma with Hall current for star formation in ISM

We learn the consequences of finite ion Larmor radius (FLR) corrections, finite electron inertia, Hall current and radiative heat-loss function on the Jean’s-gravitational instability of an infinite homogeneous, viscous plasma integrating the impacts of finite electrical resistivity, thermal conductivity and permeability. A general dispersion relation is derived by means of the normal mode analysis method with the assistance of linearized perturbation equations of the problem. We find that the original Jeans criterion of gravitational instability is modified into the radiative instability criterion by the inclusion of thermal conductivity and radiative heat-loss function. The finite electrical resistivity eliminates the impact of the magnetic field and the viscosity of the medium eliminates the impact of FLR from radiative instability. The Hall parameter has no impact on the transverse mode of propagation. Numerical calculations demonstrate the stabilizing impact of temperature-dependent heat-loss function, FLR corrections and the destabilizing impact of density-dependent heat-loss function, finite electron inertia on the Jean’s-gravitational instability.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

SpikeFlow: automated and flexible analysis of ChIP-Seq data with spike-in control.

ChIP with reference exogenous genome (ChIP-Rx) is widely used to study histone modification changes across different biological conditions. A key step in the bioinformatics analysis of this data is calculating the normalization factors, which vary from the standard ChIP-seq pipelines. Choosing and applying the appropriate normalization method is crucial for interpreting the biological results. However, a comprehensive pipeline for complete ChIP-Rx data analysis is lacking. To address these challenges, we introduce SpikeFlow, an integrated Snakemake workflow that combines features from various existing tools to streamline ChIP-Rx data processing and enhance usability. SpikeFlow automates spike-in data scaling and provides multiple normalization options. It also performs peak calling and differential analysis with distinct modalities, enabling the detection of enrichment regions for histone modifications and transcription factor binding. Our workflow runs in-depth quality control at all the processing steps and generates an analysis report with tables and graphs to facilitate results interpretation. We validated the pipeline by performing a comparative analysis with DiffBind and SpikChIP, demonstrating robust performances in various biological models. By combining diverse functionalities into a single platform, SpikeFlow aims to simplify ChIP-Rx data analysis for the research community.

Optimized Grid Partitioning and Scheduling in Multi-Energy Systems Using a Hybrid Decision-Making Approach

This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization.

Soft Margin Spectral Normalization for GANs

In this paper, we explore the use of Generative Adversarial Networks (GANs) to speed up the simulation process while ensuring that the generated results are consistent in terms of physics metrics. Our main focus is the application of spectral normalization for GANs to generate electromagnetic calorimeter (ECAL) response data, which is a crucial component of the LHCb. We propose an approach that allows to balance between model’s capacity and stability during training procedure, compare it with previously published ones and study the relationship between proposed method’s hyperparameters and quality of generated objects. We show that the tuning of normalization method’s hyperparameters boosts the quality of generative model.

Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization

Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods.

Weakly nonlinear analysis of rotating magnetoconvection with anisotropic thermal diffusivity effect

The influence of anisotropic thermal diffusive coefficient on the stability of the horizontal fluid planar layer rotating about its vertical axis and permeated by the horizontal homogeneous magnetic field is studied. The linear stability analysis is carried out using the normal mode method. The stationary cross/oblique and parallel modes are calculated for different ranges of control parameters arising in the system. The SA (Stratification Anisotropy) parameter, α (the ratio of horizontal and vertical thermal diffusivities), plays a key role in deciding the boundaries between these modes and their instability regions when there is a combination of high and low rotation with weak and strong magnetic fields. The obtained isotropic results coincide with those obtained by pioneers in the literature. The weakly nonlinear behavior of the stationary convective motion in the vicinity of primary instability threshold is studied using the two-dimensional Landau–Ginzburg (LG) equation with cubic nonlinearity. This equation derived using the multiple scale analysis is similar to the one obtained in the literature having different relaxation time, nonlinear coefficient, and coherence lengths. These coefficients are used to study the heat transfer rate. In the case of high rotation, Nusselt number gets decreased from atmospheric (α < 1) to oceanic (α > 1) SA types. The domain for secondary instability of Eckhaus is obtained using the spatiotemporal LG equation and it is observed that the Eckhaus instability region decreases with increasing α.

Insight into local defect resonance and its in-situ measurement based on flexible nano-composite sensors

The local defect resonance has been proven to be effective for damage detection, and the methods based on LDR for damage evaluation have demonstrated appealing capabilities of selective assessment and applicability to complex structures. To explain the generation of LDR, models based on vibration theories have been developed in which the defect boundaries are assumed to be fixed. Nevertheless, existing models can only give an accurate prediction of frequency for fundamental LDR mode owing to the deviation of their assumptions from the reality. In addition, the measurement of LDR is currently limited to noncontact technologies, hampering its application to in-situ and online monitoring of inspected structures. To overcome these deficiencies, an analytical model is proposed in this investigation to gain an insight into the generation of LDR from the perspective of wave reflection at defect boundaries and wave superposition. In this model, the interaction of guided waves with defect is scrutinized using a normal mode expansion method to obtain the phase shift of the reflected waves at the defect boundaries. On this basis, the formation of standing waves is analyzed to interpret the occurrence of the resonance of guided waves, whereby the relation of the LDR frequencies and the defect parameters can be obtained. On top of this analytical investigation, the sensing of the LDR using a novel nano-composite sensor is attempted. Flexible and lightweight, these sensors can form a dense sensing network, with which the LDR response in the inspected region can be perceived. Using the LDR-based method and nano-composite sensors, this approach can give a damage characterization in an in-situ and online manner for complex structures. Experimental validations of the proposed approach is performed in which delaminations in composite structures are identified and assessed using the LDR response perceived with the nano-composite sensors.

Boosting integral-based human pose estimation through implicit heatmap learning

Human pose estimation typically encompasses three categories: heatmap-, regression-, and integral-based methods. While integral-based methods possess advantages such as end-to-end learning, full-convolution learning, and being free from quantization errors, they have garnered comparatively less attention due to inferior performance. In this paper, we revisit integral-based approaches for human pose estimation and propose a novel implicit heatmap learning framework. The framework learns the true distribution of keypoints from the perspective of maximum likelihood estimation, aiming to mitigate inherent ambiguity in shape and variance associated with implicit heatmaps. Specifically, Simple Implicit Heatmap Normalization (SIHN) is first introduced to calculate implicit heatmaps as an efficient and effective representation for keypoint localization, which replaces the vanilla softmax normalization method. As implicit heatmaps may introduce potential challenges related to variance and shape ambiguity arising from the inherent nature of implicit heatmaps, we thus propose a Differentiable Spatial-to-Distributive Transform (DSDT) method to aptly map those implicit heatmaps onto the transformation coefficients of a deformed distribution. The deformed distribution is predicted by a likelihood-based generative model to unravel the shape ambiguity quandary effectively, and the transformation coefficients are learned by a regression model to resolve the variance ambiguity issue. Additionally, to expedite the acquisition of precise shape representations throughout the training process, we introduce a Wasserstein Distance-based Constraint (WDC) to ensure stable and reasonable supervision during the initial generation of implicit heatmaps. Experimental results on both the MSCOCO and MPII datasets demonstrate the effectiveness of our proposed method, achieving competitive performance against heatmap-based approaches while maintaining the advantages of integral-based approaches. Our source codes and pre-trained models are available at https://github.com/ducongju/IHL.

RATING ANALYSIS OF THE DYNAMICS OF FOREIGN DIRECT INVESTMENT IN UKRAINE

Objective. The objective of the article is to conduct a rating analysis of the dynamics of direct foreign investment in Ukraine and to determine the priority areas and industries for investment and the main foreign investor countries. Methods. During the research, the following methods were used: an analysis method (to study the dynamics of foreign direct investment in Ukraine), a comparison method (to compare the volume of FDI between different regions, different industries and by receiving countries), a graphic method (to display the dynamics of the normalized rating of the leading countries by the volume of investments in Ukraine) the normalization method (to normalize the indicators of FDI inflows from various countries to Ukraine during 2019-2022) and the rating method (and to build a rating of the regions and industries of Ukraine that received the most FDI during the studied period). Results. As a result of the conducted research, the rankings of the regions of Ukraine according to the level of receiving FDI were constructed and the changes in these ratings during 2020-2022 were analyzed. The main trends can be defined as: a significant increase in investments in 2021; rather similar location of regions in the 2020 rating. and 2022; the growth of investment attractiveness of the western regions of Ukraine with the beginning of a full-scale invasion. In the pre-crisis years of 2019 and 2021, the leadership in foreign investments in Ukraine belonged to the countries of the European Union, as well as Cyprus and the Netherlands. In the crisis years of 2020 (the COVID-19 pandemic) and 2022 (the war), Switzerland, England and Luxembourg lead the rating of investor countries. This dynamic is repeated in two crisis periods. In 2022 the volume of FDI from each country fell almost 10 times compared to pre-war 2021. that is, the crisis dynamics of changes in FDI, characteristic of the pandemic 2020, were repeated. In 2019 and 2021, respectively, investments from each of the top investor countries amounted to about 5 billion US dollars. While in the crisis years of 2020 and 2022, these volumes decreased significantly, amounting to about $500 million and $350 million, respectively. Ratings of Ukrainian industries by the level of receiving FDI were also constructed and changes in these ratings during 2020-2022 were analyzed. In 2021 at the top of the rating were resource-intensive branches of industry, such as mining, processing, metallurgy, etc.) In 2020. and 2022 - financial, trade and telecommunications industries.

OPTIMIZATION OF THE OPERATION OF AN ISOMERIZATION INSTALLATION FOR LIGHT PETROL FRACTIONS

The interest in this process lies in the fact that it has great value since low-octane components are used as raw materials - the n fraction. temperature – 62°С and catalytic reforming raffinates. This raw material contains mainly pentane and hexane fractions. This raw material, as well as fractions С5 and С6 obtained from a gas fractionation unit (GFU), and a central gas fractionation unit (CGFU), are isomerized in a hydrogen environment in the presence of a catalyst. Hydrocarbons with a relatively high octane number are obtained. When isomerizing the pentane fraction, a product with a higher octane number is obtained. Isomerization of n-pentane is of interest not only for the oil refining industry, but also for the petrochemical industry, since isopentane can be converted by dehydrogenation into isoprene, a raw material for rubber. The article conducted a study on the selection of the optimal catalyst for the light gasoline fraction isomerization unit located at the Atyrau Oil Refinery. The catalytic activity of the catalyst samples was assessed in a laboratory flow-type installation. Determination of the mass fraction of С2-С5 hydrocarbons in С5 hydrocarbon fractions (in raw materials and catalyst) was carried out by a method based on the separation of mixture components by gas-liquid chromatography. The components leaving the chromatographic column were detected using a thermal conductivity detector. The calculation of the mass fractions of components was carried out using the normalization method, taking into account correction factors. The minimum concentration of C2-С5 hydrocarbons, determined by the method used, is 0.01-0.05% wt.

Morse Code Recognition Based on a Flexible Tactile Sensor with Carbon Nanotube/Polyurethane Sponge Material by the Long Short-Term Memory Model.

Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.

INDIVIDUAL CONTROL CHARTS FOR NON-NORMAL DATA

Individual control charts are a suitable option to consider when the processes being studied change slowly. If obtaining standard samples for Shewhart control charts is expensive or in the case of studying statistical characteristics of the technological process, they are used. Control charts are used to control technological processes in the case of a normal distribution of the possible values of the controlled parameter. Sometimes a quasi-normal distribution of the studied parameter is allowed. In practice, it is often necessary to deal with technological processes in which samples are not distributed according to a normal distribution. In such cases, using individual control charts can lead to type I and II errors. This is because the control limits of the charts are calculated for the mean and standard deviation of the normal distribution. Control charts of individual values are most sensitive to the deviation of the distribution from the normal one since individual values are used to construct the limits, to which the central limit theorem does not apply, as in the case of control charts of mean values. Therefore, it is extremely important to develop methods of applying the individual control charts for random samples, the general population of which is not normally distributed. The study examines the application of individual control charts for a random process that is non-normally distributed. This is achieved by using the normalization method to transform samples of the studied parameter into samples that are distributed normally or quasi-normally. The normalization is performed using the Box-Cox method with further construction of control charts based on the transformed samples. The mathematical apparatus of the individual control chart application for samples with non-normal distribution is described. Numerical modeling of the application of individual control charts for the chi-square distributed samples, which have a significant distribution asymmetry for small values of the shape factor, and for the samples obtained by normalizing the chi-square one, was performed. According to the research findings, the utilization of individual control charts for samples exhibiting a significantly asymmetric distribution results in a higher likelihood of type I errors being detected. However, when the method of sample normalization is employed, this issue is almost entirely eliminated. Modeling process disruption, by changing a certain individual value, also demonstrated the emergence of false signals of process disruption.

ENHANCING INVESTMENT POTENTIAL IN THE CONTEXT OF ENSURING FINANCIAL SECURITY OF THE AGRICULTURAL SECTOR IN UKRAINE

This study aims to justify ways to enhance the investment potential in the context of strengthening and improving the financial security of the agricultural sector of Ukraine's economy. According to the authors of the article, it is important to perform a scientific task that involves presenting a modern theoretical-scientific approach to assessing the investment attractiveness and the efficiency of using the investment potential of the agricultural sector in the regions of Ukraine. Investment potential in the agricultural sector means the ability and prospects for investing capital in the sector with the aim of ensuring financial security. This potential is determined by various indicators that either increase or decrease the sector's attractiveness for investment. The research uses a trend line in forecasting investments, a method of integral rating assessment, and an auxiliary method of normalization used for indicators. This study proposes a theoretical-scientific approach that includes analyzing and compiling a ranking of regions in terms of their investment attractiveness and the efficiency of using investment opportunities in the agricultural sector in the context of ensuring financial security. An algorithm is used to establish the criteria for assessing the investment attractiveness of various regions and to compile a ranking of regions based on indicators of each sphere of investment attractiveness related to the issue of ensuring financial security. Based on the results obtained, strategic decisions regarding ensuring financial security were identified. The authors note that the study has limitations due to considering only the specifics of the agricultural sector of Ukraine's economy. Moreover, due to the state of war, not all regions of the country were considered in the article. Prospects for further research involve assessing the investment potential in the post-war period for all regions of Ukraine.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy.

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

Cut-off points for knee extension strength: identifying muscle weakness in older adults.

Generalized muscle weakness is the primary characteristic of sarcopenia. Handgrip strength (HGS) is widely employed to detect muscle weakness. However, knee extension strength (KES) declines much earlier and more pronounced than HGS, and there is a stronger correlation between KES and functional performance. Therefore, KES may be a more appropriate proxy for identifying muscle weakness compared to HGS. The purpose of this review was to clarify the KES measurement towards a standardized approach and summarize the cut-off points for KES. A literature search was conducted in Web of Science, PubMed, Elsevier, Scopus and Medline databased up to July 10th, 2023. A total of 12 articles were ultimately included in this review, which proposed various cut-off points for KES. Notably, these studies exhibited high heterogeneities, including diverse living settings for participants, KES measurement, methods for KES normalization, methodologies for determining cut-off points and study designs. No consensus on cut-off points for KES was reached due to the heterogeneities in KES measurement and normalized methods among studies. To enhance the comparability among studies and facilitate the sarcopenia screening framework, a standardized approach for KES measurement and KES normalization are needed. Regarding KES measurement, the hand-held dynamometer-based isometric KES is easy to access and ideally suited for both clinical and community settings, while isokinetic KES, representing the gold standard, is preferred for research settings. Additionally, it is suggested to normalize isometric KES to body weight (BW), while normalizing isokinetic KES to allometrically scaled BW.