Class Of Pairs Research Articles

Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection

The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45 % and 96.53–98.10 % respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

Relative tilting theory in abelian categories I: Auslander–Buchweitz–Reiten approximations theory in subcategories and cotorsion-like pairs

In this paper, we introduce a special kind of relative (co)resolutions associated with a pair of classes of objects in an abelian category C.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {C}.$$\\end{document} We will see that, by studying this relative (co)resolutions, we get a possible generalization of a part of the Auslander–Buchweitz approximation theory that is useful for developing n-X\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {X}$$\\end{document}-tilting theory in Argudin Monroy and Mendoza Hernández (relative tilting theory in abelian categories II: n-X\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {X}$$\\end{document} tilting theory. arXiv:2112.14873, 2021). With this goal, new concepts as X\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {X}$$\\end{document}-complete and X\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {X}$$\\end{document}-hereditary pairs are introduced as a generalization of complete and hereditary cotorsion pairs. These pairs appear in a natural way in the study of the category of representations of a quiver in an abelian category (Argudin Monroy and Mendoza Hernández in categories of quiver representations and relative cotorsion pairs. arXiv:2311.12774v1, 2023). Our main results will include an existence theorem for relative approximations, among other results related with closure properties of relative (co)resolution classes and relative homological dimensions which are essential in the development of n-X\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {X}$$\\end{document}-tilting theory in Argudin Monroy and Mendoza Hernández (2021).

ALERT: A lightweight defense mechanism for enhancing DNN robustness against T-BFA

DNNs have become pervasive in many security–critical scenarios such as autonomous vehicles and medical diagnoses. Recent studies reveal the susceptibility of DNNs to various adversarial attacks, among which weight Bit-Flip Attacks (BFA) is emerging as a significant security concern. Moreover, Targeted Bit-Flip Attacks (T-BFA), as a novel variant of BFA, can stealthily alter specific source–target classifications while preserving accurate classifications of non-target classes, posing a more severe threat. However, due to the inadequate consideration for T-BFA’s “targeted” characteristic, existing defense mechanisms tend to perform over-protection/-modification to the network, leading to significant defense overheads or non-negligible DNN accuracy reduction.In this work, we propose ALERT, ALightweight defense mechanism for Enhancing DNN Robustness against T-BFA while maintaining network accuracy. Firstly, fully understanding the key factors that dominate the misclassification among source–target class pairs, we propose a Source-Target-Aware Searching (STAS) method to accurately identify the vulnerable weights under T-BFA. Secondly, leveraging the intrinsic redundancy characteristic of DNNs, we propose a weight random switch mechanism to reduce the exposure of vulnerable weights, thereby weakening the expected impact of T-BFA. Striking a delicate balance between enhancing robustness and preserving network accuracy, we develop a metric to meticulously select candidate weights. Finally, to further enhance the DNN robustness, we present a lightweight runtime monitoring mechanism for detecting T-BFA through weight signature verification, and dynamically optimize the weight random switch strategy accordingly. Evaluation results demonstrate that our proposed method effectively enhances the robustness of DNNs against T-BFA while maintaining network accuracy. Compared with the baseline, our method can tolerate 6.7× more flipped bits with negligible accuracy loss (<0.1% in ResNet-50).

ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open-Vocabulary Object Detection

Open-vocabulary object detection (OVOD) aims to recognize novel objects whose categories are not included in the training set. In order to classify these unseen classes during training, many OVOD frameworks leverage the zero-shot capability of largely pretrained vision and language models, such as CLIP. To further improve generalization on the unseen novel classes, several approaches proposed to additionally train with pseudo region labeling on the external data sources that contain a substantial number of novel category labels beyond the existing training data. Albeit its simplicity, these pseudo-labeling methods still exhibit limited improvement with regard to the truly unseen novel classes that were not pseudo-labeled. In this paper, we present a novel, yet simple technique that helps generalization on the overall distribution of novel classes. Inspired by our observation that numerous novel classes reside within the convex hull constructed by the base (seen) classes in the CLIP embedding space, we propose to synthesize proxy-novel classes approximating novel classes via linear mixup between a pair of base classes. By training our detector with these synthetic proxy-novel classes, we effectively explore the embedding space of novel classes. The experimental results on various OVOD benchmarks such as LVIS and COCO demonstrate superior performance on novel classes compared to the other state-of-the-art methods. Code is available at https://github.com/clovaai/ProxyDet.

Confusing Pair Correction Based on Category Prototype for Domain Adaptation under Noisy Environments

In this paper, we address unsupervised domain adaptation under noisy environments, which is more challenging and practical than traditional domain adaptation. In this scenario, the model is prone to overfitting noisy labels, resulting in a more pronounced domain shift and a notable decline in the overall model performance. Previous methods employed prototype methods for domain adaptation on robust feature spaces. However, these approaches struggle to effectively classify classes with similar features under noisy environments. To address this issue, we propose a new method to detect and correct confusing class pair. We first divide classes into easy and hard classes based on the small loss criterion. We then leverage the top-2 predictions for each sample after aligning the source and target domain to find the confusing pair in the hard classes. We apply label correction to the noisy samples within the confusing pair. With the proposed label correction method, we can train our model with more accurate labels. Extensive experiments confirm the effectiveness of our method and demonstrate its favorable performance compared with existing state-of-the-art methods. Our codes are publicly available at https://github.com/Hehxcf/CPC/.

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer’s Disease

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%.

Synthesized Image Training Techniques: On Improving Model Performance using Confusion.

The performance of supervised deep learning image classifiers has significantly improved with large, labeled datasets and increased computing power. However, obtaining large, labeled image datasets in areas like medicine is expensive. This study seeks to improve model performance on limited labeled datasets by reducing confusion. We observed that misclassification (or confusion) between classes is usually more prevalent between specific classes. Thus, we developed synthesized image training techniques (SIT2), a novel confusion-based training framework that identifies pairs of classes with high confusion and synthesizes not-sure images from these pairs. The not-sure images are utilized in three new training strategies as follows. (1) The not-sure training strategy pretrains a model using not-sure images and the original training images. (2) The sure-or-not strategy pretrains with synthesized sure or not-sure images. (3) The multi-label strategy pretrains with synthesized images but predicts the original class(es) of the synthesized images. Finally, the pretrained model is finetuned on the original dataset. An extensive evaluation was conducted on five medical and non-medical datasets. Several improvements are statistically significant, which shows the promising future of our confusion-based training framework.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BackgroundPathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.MethodsH&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.ResultsThe tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.ConclusionOur machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

A Hierarchical Classification Method for High-accuracy Instruction Disassembly with Near-field EM Measurements

Electromagnetic (EM) fields have been extensively studied as potent side-channel tools for testing the security of hardware implementations. In this work, a low-cost side-channel disassembler that uses fine-grained EM signals to predict a program's execution trace with high accuracy is proposed. Unlike conventional side-channel disassemblers, the proposed disassembler does not require extensive randomized instantiations of instructions to profile them, instead relying on leakage-model-informed sub-sampling of potential architectural states resulting from instruction execution, which is further augmented by using a structured hierarchical approach. The proposed disassembler consists of two phases: (i) In the feature-selection phase, signals are collected with a relatively small EM probe, performing high-resolution scans near the chip surface, as profiling codes are executed. The measured signals from the numerous probe configurations are compiled into a hierarchical database by storing the min-max envelopes of the probed EM fields and differential signals derived from them, a novel dimension that increases the potency of the analysis. The envelope-to-envelope distances are evaluated throughout the hierarchy to identify optimal measurement configurations that maximize the distance between each pair of instruction classes. (ii) In the classification phase, signals measured for unknown instructions using optimal measurement configurations identified in the first phase are compared to the envelopes stored in the database to perform binary classification with majority voting, identifying candidate instruction classes at each hierarchical stage. Both phases of the disassembler rely on a four-stage hierarchical grouping of instructions by their length, size, operands, and functions. The proposed disassembler is shown to recover ∼97–99% of instructions from several test and application benchmark programs executed on the AT89S51 microcontroller.

Worst-Case Discriminative Feature Learning via Max-Min Ratio Analysis.

We propose a novel discriminative feature learning method via Max-Min Ratio Analysis (MMRA) for exclusively dealing with the long-standing "worst-case class separation" problem. Existing technologies simply consider maximizing the minimal pairwise distance on all class pairs in the low-dimensional subspace, which is unable to separate overlapped classes entirely especially when the distribution of samples within same class is diverging. We propose a new criterion, i.e., Max-Min Ratio Analysis (MMRA) that focuses on maximizing the minimal ratio value of between-class and within-class scatter to extremely enlarge the separability on the overlapped pairwise classes. Furthermore, we develop two novel discriminative feature learning models for dimensionality reduction and metric learning based on our MMRA criterion. However, solving such a non-smooth non-convex max-min ratio problem is challenging. As an important theoretical contribution in this paper, we systematically derive an alternative iterative algorithm based on a general max-min ratio optimization framework to solve a general max-min ratio problem with rigorous proofs of convergence. More importantly, we also present another solver based on bisection search strategy to solve the SDP problem efficiently. To evaluate the effectiveness of proposed methods, we conduct extensive pattern classification and image retrieval experiments on several artificial datasets and real-world ScRNA-seq datasets, and experimental results demonstrate the effectiveness of proposed methods.

On the oriented coloring of the disjoint union of graphs

Let G⃗ = (V, A) be an oriented graph and G the underlying graph of G⃗. An oriented k-coloring of G⃗ is a partition of V into k color classes, such that there is no pair of adjacent vertices belonging to the same class and all the arcs between a pair of color classes have the same orientation. The smallest k such that G⃗ admits an oriented k-coloring is the oriented chromatic number χo(G⃗) = k of G⃗. The oriented chromatic number χo(G) of the undirected graph G is the maximum of χo(G⃗) for all orientations G⃗ of G. Oriented chromatic number of the product of two graphs G1, G2 was widely studied, but the disjoint union G1 ∪ G2 has not yet been considered. In this article we proved bounds for the oriented chromatic number of any two oriented graphs and we also proved that given two complete graphs Kn and Km with n ≥ m, there is a real number α ∈ (1, 3) such that χo(Kn ∪ Km) = n + m − α log2(m). Additionally, we established exact values of the union of one complete graph with one cycle and of one complete graph with a forest.

Решетка $E$-замкнутых классов мультифункций ранга 2

Multifunctions are discrete functions defined on a finite set and returning as their values all subsets of the considered set. The paper considers the classification of multifunctions defined on a two-element set with respect to the E-closure operator. E-closed sets of multifunctions are sets that are closed under superposition, the closure operator with branching by the equality predicate, the identification of variables, and the addition of dummy variables. The concept of separating sets was introduced using a greedy algorithm for the set covering problem, and 22 classes of separating sets were obtained. It is shown that the classification under consideration leads to a finite set of closed classes. The work describes all 359 E-closed classes of multifunctions, among which there are 138 pairs of dual classes and 83 self-dual classes. For each class consisting only of multifunctions, its generating system is indicated.

Feature Selection Based on Intrusive Outliers Rather Than All Instances.

Feature selection (FS) has recently attracted considerable attention in many fields. Highly-overlapping classes and skewed distributions of data within classes have been found in various classification tasks. Most existing FS methods are all instance-based, which ignores the significant differences in characteristics between the particular outliers and the main body of the class, causing confusion for classifiers. In this paper, we propose a novel supervised FS method, Intrusive Outliers-based Feature Selection (IOFS), to find out what kind of outliers lead to misclassification and exploit the characteristics of such outliers. In order to accurately identify the intrusive outliers (IOs), we provide a density-mean center algorithm to obtain the appropriate representative of a class. A special distance threshold is given to obtain the candidate for IOs. Combining with several metrics, mathematical formulations are provided to evaluate the overlapping degree of the intrusive class pairs. Features with high overlapping degrees are assigned to low rankings in IOFS method. An extension of IOFS based on a small number of extreme IOs, called E-IOFS, is also proposed. Three theoretical proofs are provided for the essential theoretical basis of IOFS. Experiments comparing against various state-of-the-art methods on eleven benchmark datasets show that IOFS is rational and effective, especially on the datasets with higher overlapping classes. And E-IOFS almost always outperforms IOFS.

The impact of Bis-GMA free and Bis-GMA containing resin composite as posterior restoration on marginal integrity: a randomized controlled clinical trial

BackgroundThere have been concerns surrounding the utilization of Bis-GMA, a type of bisphenol A (BPA) derivative, within the dental industry. The aim of this study was to compare the performance of bulk fill Bis-GMA-free resin composite class II restorations in respect of its marginal integrity in comparison to bulk fill Bis-GMA-containing resin composite class II restorations over a 12-month period in a parallel clinical trial utilizing a split-mouth, double-blind, randomized strategy.Methods20 patients participated in this study. Each patient has received one pair of class II posterior restorations, Bis-GMA-free (Admira fusion x-tra), and Bis-GMA containing (x-tra fil) on each side of the mouth (split-mouth strategy), (n = 40). The restorations’ marginal integrity was evaluated based on Ryge’s criteria (modified USPHS) at baseline (after 1 week), as well as 1 month, 3 months, 6 months, 9 months, and after 12 months of follow-up by two calibrated examiners. The statistical analyses utilizing the Friedman and Wilcoxon tests, the significance level was adjusted to 0.05.ResultsFollowing the 12-month period, all patients attended the recall visits to evaluate the restorations. The Wilcoxon signed-rank and Friedman tests, revealed that both types of bulk fill had 100% of Alpha (A) scores at baseline and after 1 month with no significant statistical differences. After 3, 6, 9, and 12 months, both tested bulk fill restorations showed Bravo (B) score with Bis-GMA free 10% and 5% for Bis-GMA containing with no statistically significant difference (p ≤ 0.05) for clinical marginal integrity parameter in USPHS criteria.ConclusionsBis-GMA-free resin composites demonstrated satisfactory, marginal integrity compared with Bis-GMA-containing resin composites within 12 months.Trial registrationThe protocol of the current study was registered at www.clinicaltrials.gov, with the identification number NCT05480852 on 29/07/2022. All procedures involving human participants were performed in accordance with the ethical standards of the Research Ethics Committee of the Faculty of Dentistry, Minia University, Egypt, under the approval number 419 on 27/06/2020.

Can mergers and acquisitions internalize positive externalities in funding innovation?

Fundamental innovation usually involves huge upfront costs, but the benefits are spread across various sectors of the economy. Given the large costs and limited appropriability of the benefits associated with fundamental innovations, individual firms underinvest in these innovations relative to the socially optimal level. We find that mergers and acquisitions (M&As) can internalize the positive externalities by merging firms from both the user industries and the producer industries of an innovation. Using the US patent citation dataset, we define the user and producer relationship between each pair of industries and between each pair of industry and technological class. We then show that after a merger between an innovation user and an innovation producer (related M&As), the quantity and the quality of innovation output increase, and the increase is driven by targeted technological classes. In contrast, innovation output drops after unrelated mergers. Firms' internal resource allocation and financial flexibility play an important role in determining the effect of M&As on innovation.

An adverse outcome pathway-based approach to assess the neurotoxicity by combined exposure to current-use pesticides

Exposure to multiple pesticides in daily life has become an important public health concern. However, the combined effects of pesticide mixtures have not been fully elucidated by the conventional toxicological testing used for individual chemicals. Grouping of chemicals by mode of action using common key events (KEs) in the adverse outcome pathway (AOP) as endpoints could be applied for efficient risk assessment of combined exposure to multiple chemicals. The purpose of this study was to investigate whether exposure to multiple pesticides has synergistic neurotoxic effects on mammalian nervous systems. According to the AOP-based approach, we evaluated the effects of 10 current-use pesticides (4 neonicotinoids, 4 pyrethroids and 2 phenylpyrazoles) on the common KEs in AOPs for neurotoxicity, such as KEs involving mitochondrial and proteolytic functions, in a mammalian neuronal cell model. Our data showed that several pyrethroids and phenylpyrazoles partly shared the effects on several common KEs, including decreases in mitochondrial membrane potential and proteasome activity and increases in autophagy activity. Furthermore, we also found that combined exposure to a type-I pyrethroid permethrin or a type-II pyrethroid deltamethrin and the phenylpyrazole fipronil decreased the cell viability and the benchmark doses much more than either single exposure, indicating that the pair exhibited synergistic effects, since the combination indexes were less than 1. These findings revealed that novel pairs of different classes of pesticides with similar effects on common KEs exhibited synergistic neurotoxicity and provide new insights into the risk assessment of combined exposure to multiple chemicals.

Response- and probability-based evaluation of spectrally matched ground motion selection strategies for bi-directional dynamic analysis of low- to mid-rise RC buildings

In this study, the effect of different strategies which consider various levels of constraints for spectrally matched ground motion (GM) selection on seismic responses of the buildings was evaluated. For this, six different three-dimensional buildings and three different local soil classes were considered. Various GM record sets were used for each strategy and local soil class pair to perform bi-directional nonlinear dynamic analyses of the buildings and maximum global and maximum inter-story drift ratio demands were obtained. To evaluate the efficiency of proposed strategies, the mean and dispersion of the demands were investigated. Then, the differences among the means of the seismic demands obtained for the strategies were evaluated using the analysis of variance method. Moreover, probability curves of the demands were constructed and compared. The results indicate that i) the mean of the demands generally used for code-based performance assessment can be estimated more consistently by the proposed strategies; ii) the supplemental constraints about spectral matching allow to control the dispersion of the spectral acceleration values of selected GMs and hence the dispersion of the demands; iii) using the appropriate one of the proposed strategies, GMs with desired dispersion of spectral accelerations can be acquired for probability-based performance assessment.

Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks.

Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection of objects of interest in images has gained more and more interest due to the increased performance of the results. In this sense, this paper proposes two ensembles of neural networks, based on the fusion of the decisions of the component neural networks for the detection of four skin lesions (basal cancer cell, melanoma, benign keratosis, and melanocytic nevi). The first system is based on separate learning of three neural networks (MobileNet V2, DenseNet 169, and EfficientNet B2), with multiple weights for the four classes of lesions and weighted overall prediction. The second system is made up of six binary models (one for each pair of classes) for each network; the fusion and prediction are conducted by weighted summation per class and per model. In total, 18 such binary models will be considered. The 91.04% global accuracy of this set of binary models is superior to the first system (89.62%). Separately, only for the binary classifications within the system was the individual accuracy better. The individual F1 score for each class and the global system varied from 81.36% to 94.17%. Finally, a critical comparison is made with similar works from the literature.

A Randomized Controlled Clinical Trial Evaluating the Performance of Bis-GMA Free Resin Composite Posterior Restorations

Objective: This study aims to assess the clinical performance of Bis-GMA free resin composites (RCs) in comparison with Bis-GMA based resin composites as posterior restorations during 1,3,6,9, and 12 months, using a split-mouth, double-blinded randomized design. Methods and Materials: The study included 20 participants who received a pair of class I or II bulk-fill composite restorations. One side of the mouth was filled with Bis-GMA free RC (Admira Fusion x-tra), while the other side received Bis-GMA based RC (X-tra fil). Restoration placement was done by a single operator following the manufacturer's guidelines and was finished and polished immediately following placement. Modified United States Public Health Services (USPHS) criteria have been adopted for restoration assessment at baseline (1 week), 1, 3, 6, 9, and 12 months. The statistical analysis was accomplished utilizing Wilcoxon tests, with a 0.05 significance level. Results: After 12 months, all patients attended the recall visits with a 100% recall rate. The Wilcoxon signed rank tests revealed insignificant differences between both groups (p?0.05) for all USPHS parameters. The two studied materials showed a decline from 100% clinically excellent scores, with a few recordings of clinically good scores at 12 months. However, most restorations maintained clinical Alpha and Bravo score throughout the 12-month period. Conclusions: The Bis-GMA-free RCs' clinical performance was comparable to that of Bis-GMA-based RCs in posterior permanent teeth restorations after 12 months. These findings suggest that Bis-GMA-free RCs can be considered a viable alternative to Bis-GMA-based RCs in clinical practice.