Nano-sensitizer with self-amplified drug release and hypoxia normalization properties potentiates efficient chemoradiotherapy of pancreatic cancer.

The hypoxic nature of pancreatic cancer, one of the most lethal malignancies worldwide, significantly impedes the effectiveness of chemoradiotherapy. Although the development of oxygen carriers and hypoxic sensitizers has shown promise in overcoming tumor hypoxia. The heterogeneity of hypoxia-primarily caused by limited oxygen penetration-has posed challenges. In this study, we designed a hypoxia-responsive nano-sensitizer by co-loading tirapazamine (TPZ), KP372-1, and MK-2206 in a metronidazole-modified polymeric vesicle. This nano-sensitizer relies on efficient endogenous NAD(P)H quinone oxidoreductase 1-mediated redox cycling induced by KP372-1, continuously consuming periphery oxygen and achieving evenly distributed hypoxia. Consequently, the normalized tumor microenvironment facilitates the self-amplified release and activation of TPZ without requiring deep penetration. The activated TPZ and metronidazole further sensitize radiotherapy, significantly reducing the radiation dose needed for extensive cell damage. Additionally, the coloaded MK-2206 complements inhibition of therapeutic resistance caused by Akt activation, synergistically enhancing the hypoxic chemoradiotherapy. This successful hypoxia normalization strategy not only overcomes hypoxia resistance in pancreatic cancer but also provides a potential universal approach to sensitize hypoxic tumor chemoradiotherapy by reshaping the hypoxic distribution.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification.

INTRODUCTION: Spatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. METHODS: We propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). RESULTS: In total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). CONCLUSION: The automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

The Impact of the Composition on the Properties of Simulated Lunar Mare Basalt Fibers.

Lunar mare basalt is recognized as an important in situ resource on the lunar surface. However, the significant compositional variability of lunar mare basalts introduces uncertainties concerning the potential for their use in fabricating fibers and composite materials. This study investigates the impact of different components on the fiber-forming capabilities of mare basalts by simulating the compositions of basalts collected from several well-known lunar missions and then preparing simulated lunar mare basalt fibers. Raman spectroscopy is primarily employed for analysis and characterization, using "peak area normalization" to explore the impact of compositional fluctuations in the simulated lunar mare basalts on the glass network structure. The findings indicate that an increase in the Fe content raises the likelihood of basalt fibers crystallizing. Additionally, Fe3+ is shown to substitute for Si and Al in constructing bridging oxygen bonds in the network structure, albeit reducing the overall polymerization of the network. Meanwhile, Fe2+ acts as a network modifier to enhance the mechanical properties of the fibers.

Challenges and opportunities in processing NanoString nCounter data.

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.

Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.

The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures.

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

The disruptive role of LRG1 on the vasculature and perivascular microenvironment.

The establishment of new blood vessels, and their subsequent stabilization, is a critical process that facilitates tissue growth and organ development. Once established, vessels need to diversify to meet the specific needs of the local tissue and to maintain homeostasis. These processes are tightly regulated and fundamental to normal vessel and tissue function. The mechanisms that orchestrate angiogenesis and vessel maturation have been widely studied, with signaling crosstalk between endothelium and perivascular cells being identified as an essential component. In disease, however, new vessels develop abnormally, and existing vessels lose their specialization and function, which invariably contributes to disease progression. Despite considerable research into the vasculopathic mechanisms in disease, our knowledge remains incomplete. Accordingly, the identification of angiocrine and angiopathic molecules secreted by cells within the vascular microenvironment, and their effect on vessel behaviour, remains a major research objective. Over the last decade the secreted glycoprotein leucine-rich α-2 glycoprotein 1 (LRG1), has emerged as a significant vasculopathic molecule, stimulating defective angiogenesis, and destabilizing the existing vasculature mainly, but not uniquely, by altering both canonical and non-canonical TGF-ß signaling in a highly cell and context dependent manner. Whilst LRG1 does not possess any overt homeostatic role in vessel development and maintenance, growing evidence provides a compelling case for LRG1 playing a pleiotropic role in disrupting the vasculature in many disease settings. Thus, LRG1 has now been reported to damage vessels in various disorders including cancer, diabetes, chronic kidney disease, ocular disease, and lung disease and the signaling processes that drive this dysfunction are being defined. Moreover, therapeutic targeting of LRG1 has been widely proposed to re-establish a quiescent endothelium and normalized vasculature. In this review, we consider the current status of our understanding of the role of LRG1 in vascular pathology, and its potential as a therapeutic target.

Barriers and facilitators of pharmacists' integration in a multidisciplinary home care team: a qualitative interview study based on the normalization process theory.

BACKGROUND: There is a growing recognition of multidisciplinary practices as the most rational approach to providing better and more efficient healthcare services. Pharmacists are increasingly integrated into primary care teams, but there is no universal approach to implementing pharmacist services across healthcare settings. In Norway, most pharmacists work in pharmacies, with very few employed outside this traditional setting. The home care workforce is primarily made up of nurses, assistant nurses, and healthcare assistants. General practitioners (GPs) are not based in the same location as home care staff. This study utilized the Normalization Process Theory (NPT) to conduct a process evaluation of the integration of pharmacists in a Norwegian home care setting. Our aim was to identify barriers and facilitators to optimal utilization of pharmacist services within a multidisciplinary team. METHODS: Semi-structured interviews (n = 9) were conducted with home care unit leaders, ward managers, registered nurses, and pharmacists in Norway, in November 2022-February 2023. Constructs from the NPT were applied to qualitative data. RESULTS: Findings from this study pertain to the four constructs of the NPT. Healthcare professionals struggled to conceptualize the pharmacists' competencies and there were no collectively agreed-upon objectives of the intervention. Consequently, some participants questioned the necessity of pharmacist integration. Further, participants reported conflicting preferences regarding how to best utilize medication-optimizing services in everyday work. A lack of stakeholder empowerment was reported across all participants. Moreover, home care unit leaders and managers reported being uninformed of their roles and responsibilities related to the implementation process. However, the presence of pharmacists and their services were well received in the setting. Moreover, participants reported that pharmacists' contributions positively impacted the multidisciplinary practice. CONCLUSION: Introducing new work methods into clinical practice is a complex task that demands expertise in implementation. Using the NTP model helped pinpoint factors that affect how pharmacists' skills are utilized in a home care setting. Insights from this study can inform the development of tailored implementation strategies to improve pharmacist integration in a multidisciplinary team.

The relationship between cellular protein content and selenium accumulation in freshwater microalgae.

Variability in the bioconcentration of selenium (Se) by primary producers at the base of the food web results in uncertainty in predictions of bioaccumulation and ecological risk to higher trophic level organisms. Water chemistry, speciation of Se, and periphyton community composition have all been suggested as factors that contribute to variability in bioconcentration by primary producers; however, the role of physiological composition of periphyton species in influencing the bioconcentration of Se has not been previously evaluated. To determine if a relationship exists between algal protein content and Se accumulation, Parachlorella kessleri, Chlorella vulgaris, and Raphidocelis subcapitata were exposed to Se (as selenate) and analyzed for total protein and tissue Se content in the exponential and stationary growth phases. Protein content and Se accumulation in R. subcapitata in the stationary phase were also measured under two light intensities. No relationship between cellular protein content and Se accumulation was found for algae in the exponential phase; however, a strong relationship was found in the stationary phase among species and for R. subcapitata under differing light intensities. Absolute Se accumulations by P. kessleri, C. vulgaris, and R. subcapitata in the stationary phase were statistically different; however, the concentrations of Se in protein were similar across species. These results suggest that cellular protein content in microalgae influences Se bioconcentration and that algal protein content may improve Se bioaccumulation modeling in food webs. Integr Environ Assess Manag 2024;00:1-10. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Selection of stable reference genes for qPCR expression of Colletotrichum lindemuthianum, the bean anthracnose pathogen.

Phaseolus vulgaris L., commonly known as the common bean, is a highly nutritious crop often called the "poor man's meat". However, it is susceptible to various diseases throughout the cropping season, with anthracnose caused by Colletotrichum lindemuthianum being a significant threat that leads to substantial losses. There is still a lack of understanding about the molecular basis of C. lindemuthianum pathogenicity. The first step in understanding this is to identify pathogenicity genes that express more during infection of common beans. A reverse transcription quantitative real-time PCR (qPCR) method can be used for virulence gene expression. However, this approach requires selecting appropriate reference genes to normalize relative gene expression data. Currently, there is no reference gene available for C. lindemuthianum. In this study, we selected eight candidate reference genes from the available genome of C. lindemuthianum to bridge the gap. These genes were ACT (Actin), ß-tub (ß-tubulin), EF (Elongation Factor), Cyt C (Cytochrome C), His H3 (Histone H3), CHS1 (Chitin synthetase), GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) and abfA (Alpha-l-Arabinofuranosidase A). The primers for these candidate reference genes were able to amplify cDNA only from the pathogen, demonstrating their specificity. The qPCR efficiency of the primers ranged from 80% to 103%. We analyzed the stability of gene expression in C. lindemuthianum by exposing the mycelium to nine different stress conditions. We employed algorithms, such as GeNorm, NormFinder, BestKeeper, and RefFinder tools, to identify the most stable gene. The analysis using these tools revealed that EF, GAPDH, and ß-tub most stable genes, while ACT and CHS1 showed relatively low expression stability. A large number of potential effector genes have been identified through bioinformatics analysis in C. lindemuthianum. The stable genes for qPCR (EF and GAPDH) discovered in this study will aid the scientific community in determining the relative expression of C. lindemuthianum effector genes.

Targeting PKM2 signaling cascade with salvianic acid A normalizes tumor blood vessels to facilitate chemotherapeutic drug delivery.

Aberrant tumor blood vessels are prone to propel the malignant progression of tumors, and targeting abnormal metabolism of tumor endothelial cells emerges as a promising option to achieve vascular normalization and antagonize tumor progression. Herein, we demonstrated that salvianic acid A (SAA) played a pivotal role in contributing to vascular normalization in the tumor-bearing mice, thereby improving delivery and effectiveness of the chemotherapeutic agent. SAA was capable of inhibiting glycolysis and strengthening endothelial junctions in the human umbilical vein endothelial cells (HUVECs) exposed to hypoxia. Mechanistically, SAA was inclined to directly bind to the glycolytic enzyme PKM2, leading to a dramatic decrease in endothelial glycolysis. More importantly, SAA improved the endothelial integrity via activating the ß-Catenin/Claudin-5 signaling axis in a PKM2-dependent manner. Our findings suggest that SAA may serve as a potent agent for inducing tumor vascular normalization.

Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization.

Background and purpose: Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs). Materials and methods: Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance. Results: Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization. Conclusions: To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.

A Comparison of Bioelectric and Biomechanical EMG Normalization Techniques in Healthy Older and Young Adults during Walking Gait.

This study compares biomechanical and bioelectric electromyography (EMG) normalization techniques across disparate age cohorts during walking to assess the impact of normalization methods on the functional interpretation of EMG data. The biomechanical method involved scaling EMG to a target absolute torque (EMGTS) from a joint-specific task and the chosen bioelectric methods were peak and mean normalization taken from the EMG signal during gait, referred to as dynamic mean and dynamic peak normalization (EMGMean and EMGPeak). The effects of normalization on EMG amplitude, activation pattern, and inter-subject variability were compared between disparate cohorts, including OLD (76.6 yrs N = 12) and YOUNG (26.6 yrs N = 12), in five lower-limb muscles. EMGPeak normalization resulted in differences between YOUNG and OLD cohorts in Biceps Femoris (BF) and Medial Gastrocnemius (MG) that were not observed with EMGMean or EMGTS normalization. EMGPeak and EMGMean normalization also demonstrated interactions between age and the phase of gait in BF that were not seen with EMGTS. Correlations showed that activation patterns across the gait cycle were similar between all methods for both age groups and the coefficient of variation comparisons found that EMGTS produced the greatest inter-subject variability. We have shown that the normalization technique can influence the interpretation of findings when comparing disparate populations, highlighting the need to carefully interpret functional differences in EMG between disparate cohorts.

Fas Ligand enhances vessel maturation and inhibits vascular leakage associated with age-related macular degeneration.

Neovascular age-related macular degeneration (AMD), results from choroidal neovascularization (CNV), retinal edema and loss of photoreceptors. Previous studies suggested that Fas Ligand (FasL) on retinal pigment epithelial cells inhibited CNV by inducing apoptosis of infiltrating Fas+ vascular endothelial cells. However, induction of apoptosis depends on membrane-bound (mFasL) while the FasL cleavage product (sFasL) is neuroprotective. To better understand how FasL regulates the development of CNV, we used a mouse model of laser CNV to evaluate the development of CNV in mice with a FasL cleavage site mutation (ΔCS) and can only express the membrane-bound form of FasL. There was no significant difference in CNV size and area of vascular leakage in homozygous FasLΔCS/ΔCS mice when compared to wild type mice. Unexpectedly, heterozygous FasLΔCS/WT mice developed significantly less vascular leakage and showed accelerated neovessel maturation. However, CNV was not prevented in heterozygous FasLΔCS/WT mice if the Fas receptor was deleted in myeloid cells (FasLΔCS/+ Fasflox/flox CreLysM). Thus, FasL-mediated CNV inhibition depends on the extent of FasL cleavage, and on FasL engagement of Fas+ myeloid cells. Moreover, accelerated neovessel maturation prevents vascular leakage in AMD.

An Anti-VEGF-B Antibody Reduces Abnormal Tumor Vasculature and Enhances the Effects of Chemotherapy.

The vascular endothelial growth factors (VEGFs) and their receptors (VEGFRs) are key regulators of blood vessel formation, including in tumors, where their deregulated function can promote the production of aberrant, leaky blood vessels, supporting tumor development. Here we investigated the VEGFR1 ligand VEGF-B, which we demonstrate to be expressed in tumor cells and in tumor stroma and vasculature across a range of tumor types. We examined the anti-VEGF-B-specific monoclonal antibody 2H10 in preclinical xenograft models of breast and colorectal cancer, in comparison with the anti-VEGF-A antibody bevacizumab. Similar to bevacizumab, 2H10 therapy was associated with changes in tumor blood vessels and intra-tumoral diffusion consistent with normalization of the tumor vasculature. Accordingly, treatment resulted in partial inhibition of tumor growth, and significantly improved the response to chemotherapy. Our studies indicate the importance of VEGF-B in tumor growth, and the potential of specific anti-VEGF-B treatment to inhibit tumor development, alone or in combination with established chemotherapies.

"Just" a painful period: A philosophical perspective review of the dismissal of menstrual pain.

Science and society typically respond to dysmenorrhea-or painful menstrual cramps-as a normal, natural, and inevitable part of menstruation. This normalization has greatly contributed to the systemic dismissal of painful menstrual cramps. Stigma, secrecy, and the expectation to "cope" fuel the normalization of menstrual pain. In this article, I argue that the normalization of menstrual pain restricts the ability to share an excruciating menstrual pain in a way that would otherwise elicit alarm or concern. This can cause clinicians to downgrade menstrual pain, and even menstruating persons to downgrade their own pain. I refer to the dismissal of menstrual pain as an example of a pain-related motivational deficit. A pain-related motivational deficit describes instances in which an utterance fails to motivate due to societal practices and ideas that make it difficult to recognize the import of the embodied experience being shared.

"Just" a painful period: why we are not concerned by reported menstrual painIt is widely believed that painful menstrual cramps are just a normal part of the menstrual cycle; something that all menstruating persons are expected to deal with. There is also a stigma around periods and an expectation to keep the experience of periods hidden. This creates a process known as normalization. Because painful menstrual cramps are normalized, it is easier to dismiss patients who report painful menstrual cramps. In this article, I argue that the idea that painful menstrual cramps are normal makes it difficult for others to be concerned or alarmed by reports of menstrual pain. Reports of menstrual pain are downgraded or are seen as not that bad. When we are unable to see how bad a pain is because society believes the reported pain is normal, the pain report fails to elicit concern from the listener. I call this process a pain-related motivational deficit.

Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.

BACKGROUND: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. OBJECTIVES: This study develops advanced models to enhance the accuracy of diagnosis of NS. METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. CONCLUSION: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.

Impact of Normalization on Entropy-Based Weights in Hellwig's Method: A Case Study on Evaluating Sustainable Development in the Education Area.

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig's method. To compare the influence of various normalization methods in both the EWM and Hellwig's method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max-min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig's method. The results are compared with the outcome obtained by Hellwig's method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig's method results.

Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification.

This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages.