Trueness of intraoral scanning for different tooth-size arch-length deficiencies.

Background/purpose: As science and technology continue to advance, the utilization of intraoral scanners (IOSs) has become increasingly popular in the orthodontic workflow. The aim of this study was to discuss whether the degree of crowded arches affects scan accuracy. Materials and methods: Three different crowding levels of dental models (model MI: mild, model MO: moderate, and model SE: severe) were scanned using both an IOS and desktop scanner. Stereolithographic files were obtained and superimposed via CAD software to calculate differences between each measuring point of a model and the farthest corresponding point. The deviations from three models were compared with statistical analysis. Results: The trueness of different crowding arches showed that the deviation value of model SE was the maximum, followed by model MI, and model MO in the maxillary arch. In the mandibular arch, the order of the deviation from greatest to least was firstly model SE, then model MO, and model MI. Significant differences were observed among the maxillary models (P < 0.001), but there was no significant difference between models in the mandible (P = 0.669). Conclusion: The trueness of the three crowded arches is in the clinically acceptable range. The degree of crowding increases, the trueness of scanning at each position decreases. In the maxillary arch, more severe crowding corresponds to higher deviations. In the mandible, the degree of crowding is not explicitly related to the maximum deviation; therefore, the clinician should notice the deviation when using IOSs for crowding cases.

1D Gradient-Weighted Class Activation Mapping, Visualizing Decision Process of Convolutional Neural Network-Based Models in Spectroscopy Analysis.

Being characterized by the self-adaption and high accuracy, the deep learning-based models have been widely applied in the 1D spectroscopy-related field. However, the "black-box" operation and "end-to-end" working style of the deep learning normally bring the low interpretability, where a reliable visualization is highly demanded. Although there are some well-developed visualization methods, such as Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), for the 2D image data, they cannot correctly reflect the weights of the model when being applied to the 1D spectral data, where the importance of position information is not considered. Here, aiming at the visualization of Convolutional Neural Network-based models toward the qualitative and quantitative analysis of 1D spectroscopy, we developed a novel visualization algorithm (1D Grad-CAM) to more accurately display the decision-making process of the CNN-based models. Different from the classical Grad-CAM, with the removal of the gradient averaging (GAP) and the ReLU operations, a significantly improved correlation between the gradient and the spectral location and a more comprehensive spectral feature capture were realized for 1D Grad-CAM. Furthermore, the introduction of difference (purity or linearity) and feature contribute in the CNN output in 1D Grad-CAM achieved a reliable evaluation of the qualitative accuracy and quantitative precision of CNN-based models. Facing the qualitative and adulteration quantitative analysis of vegetable oils by the combination of Raman spectroscopy and ResNet, the visualization by 1D Grad-CAM well reflected the origin of the high accuracy and precision brought by ResNet. In general, 1D Grad-CAM provides a clear vision about the judgment criterion of CNN and paves the way for CNN to a broad application in the field of 1D spectroscopy.

Reporting the cases of alcohol-associated hepatitis using the National Inpatient Sample data.

The letter is to respond to the recent publication "Trends in hospitalization for alcoholic hepatitis from 2011 to 2017: A USA nationwide study" (World J Gastroenterol 2022; 28: 5036-5046). We noticed a significant difference in the total numbers of reported hospitalized alcohol-associated hepatitis (AH) patients between this publication and our publication on Alcohol Clin Exp Res (2022; 46: 1472-1481). We believe the number of "AH-related hospitalizations" inflated by the inclusion of patients with non-AH forms of alcohol-associated liver disease.

Truncated Tau caused by intron retention is enriched in Alzheimer's disease cortex and exhibits altered biochemical properties.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-ß plaques and Tau tangles in brain tissues. Recent studies indicate that aberrant splicing and increased level of intron retention is linked to AD pathogenesis. Bioinformatic analysis revealed increased retention of intron 11 at the Tau gene in AD female dorsal lateral prefrontal cortex as compared to healthy controls, an observation validated by quantitative polymerase chain reaction using different brain tissues. Retention of intron 11 introduces a premature stop codon, resulting in the production of truncated Tau11i protein. Probing with customized antibodies designed against amino acids encoded by intron 11 showed that Tau11i protein is more enriched in AD hippocampus, amygdala, parietal, temporal, and frontal lobe than in healthy controls. This indicates that Tau messenger RNA with the retained intron is translated in vivo instead of being subjected to nonsense-mediated decay. Compared to full-length Tau441 isoform, ectopically expressed Tau11i forms higher molecular weight species, is enriched in Sarkosyl-insoluble fraction, and exhibits greater protein stability in cycloheximide assay. Stably expressed Tau11i also shows weaker colocalization with α-tubulin of microtubule network in human mature cortical neurons as compared to Tau441. Endogenous Tau11i is enriched in Sarkosyl-insoluble fraction in AD hippocampus and forms aggregates that colocalize weakly with Tau4R fibril-like structure in AD temporal lobe. The elevated level of Tau11i protein in AD brain tissues tested, coupled with biochemical properties resembling pathological Tau species suggest that retention of intron 11 of Tau gene might be an early biomarker of AD pathology.

Altered stability of nuclear lamin-B marks the onset of aging in male Drosophila.

Epigenetic alterations occur during aging, but it remains unclear what epigenetic features are associated with the onset of physiological decline in animals. Nuclear lamin-B forms the filamentous meshwork underneath the nuclear envelope, providing the structural scaffold necessary for genome organization and gene regulation. We found that reduced level of nuclear lamin-B protein coincides with the decline in locomotor activity and stress resistance in young adult male Drosophila. Ubiquitous lamin-B expression improves locomotor activity of the male flies at the expense of lower stress resistance and shorten lifespan. This observation suggests that tissue-specific expression of lamin-B may regulate different aspects of animal physiology during aging. To test this hypothesis, specific GAL-4 lines were used to drive the expression of lamin-B in specific neuronal populations and muscle tissues in male flies. Ectopic expression of lamin-B in the dopaminergic neurons within the protocerebral anterior medial region of the brain improves the locomotor activity of the male flies with little impact on their stress responses and lifespan. Interestingly, age-dependent decrease in the level of lamin-B protein is independent of its mRNA expression. Instead, cellular thermal shift assay showed that lamin-B and CP190 insulator protein undergo significant change in their solubility during aging. This suggests that the increased solubility of lamin-B protein may contribute to its reduced stability and degradation during aging.

NELF-A controls Drosophila healthspan by regulating heat-shock protein-mediated cellular protection and heterochromatin maintenance.

NELF-mediated pausing of RNA polymerase II (RNAPII) constitutes a crucial step in transcription regulation. However, it remains unclear how control release of RNAPII pausing can affect the epigenome and regulate important aspects of animal physiology like aging. We found that NELF-A dosage regulates Drosophila healthspan: Halving NELF-A level in the heterozygous mutants or via neuronal-specific RNAi depletion improves their locomotor activity, stress resistance, and lifespan significantly. Conversely, NELF-A overexpression shortens fly lifespan drastically. Mechanistically, lowering NELF-A level facilitates the release of paused RNAPII for productive transcription of the heat-shock protein (Hsp) genes. The elevated HSPs expression in turn attenuates the accumulation of insoluble protein aggregates, reactive oxidative species, DNA damage and systemic inflammation in the brains of aging NELF-A depleted flies as compared to their control siblings. This pro-longevity effect is unique to NELF-A due to its higher expression level and more efficient pausing of RNAPII than other NELF subunits. Importantly, enhanced resistance to oxidative stress in NELF-A heterozygous mutants is highly conserved such that knocking down its level in human SH-SY5Y cells attenuates hydrogen peroxide-induced DNA damage and apoptosis. Depleting NELF-A reconfigures the epigenome through the maintenance of H3K9me2-enriched heterochromatin during aging, leading to the repression of specific retrotransposons like Gypsy-1 in the brains of NELF-A mutants. Taken together, we showed that the dosage of neuronal NELF-A affects multiple aspects of aging in Drosophila by regulating transcription of Hsp genes in the brains, suggesting that targeting transcription elongation might be a viable therapeutic strategy against age-onset diseases like neurodegeneration.

Phosphorylation of Tet3 by cdk5 is critical for robust activation of BRN2 during neuronal differentiation.

Tet3 regulates the dynamic balance between 5-methylcyotsine (5mC) and 5-hydroxymethylcytosine (5hmC) in DNA during brain development and homeostasis. However, it remains unclear how its functions are modulated in a context-dependent manner during neuronal differentiation. Here, we show that cyclin-dependent kinase 5 (cdk5) phosphorylates Tet3 at the highly conserved serine 1310 and 1379 residues within its catalytic domain, changing its in vitro dioxygenase activity. Interestingly, when stably expressed in Tet1, 2, 3 triple-knockout mouse embryonic stem cells (ESCs), wild-type Tet3 induces higher level of 5hmC and concomitant expression of genes associated with neurogenesis whereas phosphor-mutant (S1310A/S1379A) Tet3 causes elevated 5hmC and expression of genes that are linked to metabolic processes. Consistent with this observation, Tet3-knockout mouse ESCs rescued with wild-type Tet3 have higher level of 5hmC at the promoter of neuron-specific gene BRN2 when compared to cells that expressed phosphor-mutant Tet3. Wild-type and phosphor-mutant Tet3 also exhibit differential binding affinity to histone variant H2A.Z. The differential 5hmC enrichment and H2A.Z occupancy at BRN2 promoter is correlated with higher gene expression and more efficient neuronal differentiation of ESCs that expressed wild-type Tet3. Taken together, our results suggest that cdk5-mediated phosphorylation of Tet3 is required for robust activation of neuronal differentiation program.

Increased intron retention is a post-transcriptional signature associated with progressive aging and Alzheimer's disease.

Intron retention (IR) by alternative splicing is a conserved regulatory mechanism that can affect gene expression and protein function during adult development and age-onset diseases. However, it remains unclear whether IR undergoes spatial or temporal changes during different stages of aging or neurodegeneration like Alzheimer's disease (AD). By profiling the transcriptome of Drosophila head cells at different ages, we observed a significant increase in IR events for many genes during aging. Differential IR affects distinct biological functions at different ages and occurs at several AD-associated genes in older adults. The increased nucleosome occupancy at the differentially retained introns in young animals suggests that it may regulate the level of IR during aging. Notably, an increase in the number of IR events was also observed in healthy older mouse and human brain tissues, as well as in the cerebellum and frontal cortex from independent AD cohorts. Genes with differential IR shared many common features, including shorter intron length, no perturbation in their mRNA level, and enrichment for biological functions that are associated with mRNA processing and proteostasis. The differentially retained introns identified in AD frontal cortex have higher GC content, with many of their mRNA transcripts showing an altered level of protein expression compared to control samples. Taken together, our results suggest that an increased IR is an conserved signature that is associated with aging. By affecting pathways involved in mRNA and protein homeostasis, changes of IR pattern during aging may regulate the transition from healthy to pathological state in late-onset sporadic AD.

Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.

Optimized sample-weighted partial least squares.

In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles.

Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies.

In the present study a new version of nonlinear partial least-square method based on artificial neural network transformation (ANN-NLPLS) has been proposed. This algorithm firstly transforms the training descriptors into the hidden layer outputs using the universal nonlinear mapping carried by an artificial neural network, and then utilizes PLS to relate the outputs of the hidden layer to the bioactivities. The weights between the input and hidden layers are optimized by a particle swarm optimization (PSO) method using the criterion of minimized model error via PLS modeling. An F-statistic is introduced to determine automatically the number of PLS components during the weight optimization. The performance is assessed using a simulated data set and two quantitative structure-activity relation (QSAR) data sets. Results of these three data sets demonstrate that ANN-NLPLS offers enhanced capacity in modeling nonlinearity while circumventing the overfitting frequently involved in nonlinear modeling.

Adaptive configuring of radial basis function network by hybrid particle swarm algorithm for QSAR studies of organic compounds.

The configuring of a radial basis function network (RBFN) consists of selecting the network parameters (centers and widths in RBF units and weights between the hidden and output layers) and network architecture. The issues of suboptimum and overfitting, however, often occur in RBFN configuring. This paper presented a hybrid particle swarm optimization (HPSO) algorithm to simultaneously search the optimal network structure and parameters involved in the RBFN (HPSORBFN) with an ellipsoidal Gaussian function as a basis function. The continuous version of PSO was used for parameter training, while the modified discrete PSO was employed to determine the appropriate network topology. The proposed HPSORBFN algorithm was applied to modeling the inhibitory activities of substituted bis[(acridine-4-carboxamide)propyl]methylamines to murine P388 leukemia cells and the bioactivities of COX-2 inhibitors. The results were compared with those obtained from RBFNs with the parameters optimized by continuous PSO and by conventionally RBFN training the algorithm for a fixed network topology, indicating that the HPSO was competent for RBFN configuring in that it converged quickly toward the optimal solution and avoided overfitting.

Characterization of chloramphenicol palmitate drug polymorphs by Raman mapping with multivariate image segmentation using a spatial directed agglomeration clustering method.

Chemical imaging analysis holds great potential in probing the chemical heterogeneity of samples with high spatial resolution and molecular specificity. This paper demonstrates the implementation of Raman mapping for microscopic characterization of tablets containing chloramphenicol palmitate polymorphs with the aid of a new multivariate image segmentation approach based on spatial directed agglomeration clustering. This approach performs the agglomeration clustering by stepwise merging the pixels possessing both spatial closeness and spectral similarity into clusters that define the image segmentation. The incorporation of spatial closeness into the clustering process enables the approach to improve the robustness and avoid poorly defined image segmentation arising from clusters with highly separated pixels. Additionally, the stepwise merging of clusters offers an F-statistic-based procedure to automatically ascertain the number of image segments. Raman mapping analysis of tablets containing two polymorphs of chloramphenicol palmitate followed by multivariate image segmentation reveals that the proposed technique offers the identification of each polymorph and a quantitative visualization of the spatial distribution of the polymorphs identified. This technique holds promise in rapid, noninvasive, and quantitative polymorph analysis for pharmaceutical production processes.

Boosting support vector regression in QSAR studies of bioactivities of chemical compounds.

In this paper, boosting has been coupled with SVR to develop a new method, boosting support vector regression (BSVR). BSVR is implemented by firstly constructing a series of SVR models on the various weighted versions of the original training set and then combining the predictions from the constructed SVR models to obtain integrative results by weighted median. The proposed BSVR algorithm has been used to predict toxicities of nitrobenzenes and inhibitory potency of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. As comparisons to this method, the multiple linear regression (MLR) and conventional support vector regression (SVR) have also been investigated. Experimental results have shown that the introduction of boosting drastically enhances the generalization performance of individual SVR model and BSVR is a well-performing technique in QSAR studies superior to multiple linear regression.

Piecewise hypersphere modeling by particle swarm optimization in QSAR studies of bioactivities of chemical compounds.

As the structural diversity in a quantitative structure-activity relationship (QSAR) model increases, constructing a good model becomes increasingly difficult, and simply performing variable selection might not be sufficient to improve the model quality to make it practically usable. To combat this difficulty, an approach based on piecewise hypersphere modeling by particle swarm optimization (PHMPSO) is developed in this paper. It treats the linear models describing the sought-for subsets as hyperspheres which have different radii in the data space. According to the attribute of each hypersphere, all compounds in the training set are allocated to hyperspheres to construct submodels, and particle swarm optimization (PSO) is applied to search the optimal hyperspheres for finding satisfactory piecewise linear models. A new objective function is formulated to determine the appropriate piecewise models. The performance is assessed using three QSAR data sets. Experimental results have shown the good performance of this technique in improving the QSAR modeling.

QSAR analysis of substituted bis[(acridine-4-carboxamide)propyl]methylamines using optimized block-wise variable combination by particle swarm optimization for partial least squares modeling.

In the current work, we employed optimized block-wise variable combination (OBVC) by particle swarm optimization (PSO) based on partial least squares (PLS) modeling for variable combination and compared it to the traditional methods. It has been demonstrated that the modified PSO is a useful tool for searching optimized variable combination. Quantitative structure-activity relationship (QSAR) model has been formulated for a set of DNA binding topoisomerase (topo) (substituted bis[(acridine-4-carboxamide)propyl]methylamines) on murine Lewis lung carcinoma (LL(c)) cells. The spatial descriptors especially Jurs descriptors play important roles in predicting the compound's inhibitory activity to murine LL(c) cells, and polar interactions are the principal binding strength between compounds and murine LL(c) cells. In addition, rotatable bonds in molecules and molar refractivity of the compounds will markedly affect the compounds' inhibitory activity.

Optimized block-wise variable combination by particle swarm optimization for partial least squares modeling in quantitative structure-activity relationship studies.

The use of numerous descriptors that are indicative of molecular structure is becoming common in quantitative structure-activity relationship (QSAR) studies. As all of the descriptors might carry more or less molecular information, it seems more advisable to investigate the possible variable combination rather than variable selection. In this paper, an optimized block-wise variable combination (OBVC) by particle swarm optimization based on partial least squares modeling has been proposed for variable combination. An F statistic is also introduced to determine the dimensionality of the PLS model. The performance is assessed using two QSAR data sets. Experimental results have shown the good performance of this technique compared to those obtained by stepwise regression.

Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds.

The multilayer feed-forward ANN is an important modeling technique used in QSAR studying. The training of ANN is usually carried out only to optimize the weights of the neural network and without paying attention to the network topology. Some other strategies used to train ANN are, first, to discover an optimum structure of the network, and then to find weights for an already defined structure. These methods tend to converge to local optima, and may also lead to overfitting. In this article, a hybridized particle swarm optimization (PSO) approach was applied to the neural network structure training (HPSONN). The continuous version of PSO was used for the weight training of ANN, and the modified discrete PSO was applied to find appropriate the network architecture. The network structure and connectivity are trained simultaneously. The two versions of PSO can jointly search the global optimal ANN architecture and weights. A new objective function is formulated to determine the appropriate network architecture and optimum value of the weights. The proposed HPSONN algorithm was used to predict carcinogenic potency of aromatic amines and biological activity of a series of distamycin and distamycin-like derivatives. The results were compared to those obtained by PSO and GA training in which the network architecture was kept fixed. The comparison demonstrated that the HPSONN is a useful tool for training ANN, which converges quickly towards the optimal position, and can avoid overfitting in some extent.