Complexity organization of resting-state functional-MRI networks.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

Psychometric reliability, validity, and generalizability of 3MSE scores among American Indian adults: the Strong Heart Study.

OBJECTIVE: Modified Mini-Mental State Examination (3MSE) is often used to screen for dementia, but little is known about psychometric validity in American Indians. METHODS: We recruited 818 American Indians aged 65-95 for 3MSE examinations in 2010-2013; 403 returned for a repeat examination in 2017-2019. Analyses included standard psychometrics inferences for interpretation, generalizability, and extrapolation: factor analysis; internal consistency-reliability; test-retest score stability; multiple indicator multiple cause structural equation models. RESULTS: This cohort was mean age 73, majority female, mean 12 years education, and majority bilingual. The 4-factor and 2nd-order models fit best, with subfactors for orientation and visuo-construction (OVC), language and executive functioning (LEF), psychomotor and working memory (PMWM), verbal and episodic memory (VEM). Factor structure was supported for both research and clinical interpretation, and factor loadings were moderate to high. Scores were generally consistent over mean 7 years. Younger participants performed better in overall scores, but not in individual factors. Males performed better on OVC and LEF, females better on PMWM. Those with more education performed better on LEF and worse on OVC; the converse was true for bilinguals. All differences were significant, but small. CONCLUSION: These findings support use of 3MSE for individual interpretation in clinic and research among American Indians, with moderate consistency, stability, reliability over time. Observed extrapolations across age, sex, education, and bilingual groups suggest some important contextual differences may exist.

Response surface methodology, and artificial neural network model for removal of textile dye Reactive Yellow 105 from wastewater using Zeolitic Imidazolate-67 modified by Fe3O4 nanoparticles.

The applicability of Zeolitic Imidazolate-67, Modified by Fe3O4 Nanoparticles, was studied for removing textile dye Reactive yellow 105 from wastewater by adsorption method using response surface methodology (RSM). For the adsorption characterization of the adsorbent used in HE-4G dye adsorption, BET, FTIR, XRD, and SEM analyses were performed. The impacts of variables, including initial HE-4G dye concentration (X1), pH (X2), adsorbent dosage (X3), and sonication time (X4), the highest removal efficiency as 98%, 10 mg/L initial concentration, pH 6, 0.025 g adsorbent dosage, and 6.0 min time respectively. Adsorption equilibrium and kinetic data it, that data were for the Langmuir isotherm, pseudo-second-order kinetics, and maximum adsorption capacity (105.0 mg/g), respectively. Thermodynamic parameters indicated HE-4G dye adsorption is feasible, spontaneous and exothermic. Promising treatment capabilities of the ZIF-67-Fe3O4NPs have been during the comparative adsorption removal of HE-4G dye from DI water against spiked natural water samples and synthetic Na+, K+, Ca2+, and Mg2+ solutions. The observed outcome is the suitability of the artificial neural network model as a tool for mean square error, (MSEANN = 0.53, and R2 = 0.9926) for removing HE-4G dye. Results that ZIF-67-Fe3O4NPs, like being recyclable, and cost-efficient made it a promising absorbent for wastewater.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics.

BACKGROUND: As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile. METHODS: XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. RESULTS: The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. CONCLUSIONS: The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Identification of Fatty Acids, Amides and Cinnamic Acid Derivatives in Supercritical-CO2 Extracts of Cinnamomum tamala Leaves Using UPLC-Q-TOF-MSE Combined with Chemometrics.

Cinnamomum tamala leaf (CTL), also known as Indian bay leaf, is used all over the world for seasoning, flavoring, and medicinal purposes. These characteristics could be explained by the presence of several essential bioactive substances and lipid derivatives. In this work, rapid screening and identification of the chemical compounds in supercritical (SC)-CO2 extracts of CTL by use of UPLC-Q-TOF-MSE with a multivariate statistical analysis approach was established in both negative and positive mode. A total of 166 metabolites, including 66 monocarboxylic fatty acids, 52 dicarboxylic fatty acids, 27 fatty acid amides, and 21 cinnamic acid derivatives, were tentatively identified based on accurate mass and the mass spectrometric fragmentation pattern, out of which 142 compounds were common in all SC-CO2 extracts of CTL. Further, PCA and cluster hierarchical analysis clearly discriminated the chemical profile of analyzed extracts and allowed the selection of SC-CO2 extract rich in fatty acids, fatty acid amides, and other bioactive constituents. The result showed that the higher number of compounds was detected in CTL4 (300 bar/55 °C) extract than the other CTL extracts. The mono- and di-carboxylic fatty acids, fatty acid amides, and cinnamic acid derivatives were identified in CTL for the first time. UPLC-Q-TOF-MSE combined with chemometric analysis is a powerful method to rapidly screen the metabolite profiling to justify the quality of CTL as a flavoring agent and in functional foods.

Benefits of Zero-Phase or Linear Phase Filters to Design Multiscale Entropy: Theory and Application.

In various applications, multiscale entropy (MSE) is often used as a feature to characterize the complexity of the signals in order to classify them. It consists of estimating the sample entropies (SEs) of the signal under study and its coarse-grained (CG) versions, where the CG process amounts to (1) filtering the signal with an average filter whose order is the scale and (2) decimating the filter output by a factor equal to the scale. In this paper, we propose to derive a new variant of the MSE. Its novelty stands in the way to get the sequences at different scales by avoiding distortions during the decimation step. To this end, a linear-phase or null-phase low-pass filter whose cutoff frequency is well suited to the scale is used. Interpretations on how the MSE behaves and illustrations with a sum of sinusoids, as well as white and pink noises, are given. Then, an application to detect attentional tunneling is presented. It shows the benefit of the new approach in terms of p value when one aims at differentiating the set of MSEs obtained in the attentional tunneling state from the set of MSEs obtained in the nominal state. It should be noted that CG versions can be replaced not only for the MSE but also for other variants.

Chasing the objective upper eyelid symmetry formula; R2, RMSE, POC, MAE, and MSE.

PURPOSE: Investigate the most appropriate mathematical formula to objectively express upper eyelid contour symmetry. METHODS: 62 eyes of 31 patients were included in the study. The upper eyelid contour symmetry of the patients was classified subjectively (independent of MRD1) as poor, acceptable, and good by three oculoplastic specialists (senior, expert, and junior surgeon). Bézier curves of the upper lid contour were drawn with ImageJ software (NIH, Bethesda, MA, USA). Using the algorithms created by Author SKC in Spyder (Python 3.7.9.), the symmetry of the Bézier curves of the left eyelids were obtained according to the y-axis, and the mid-pupils of both eyes were superimposed. The lower curve moved vertically to the equal height of the other curve to equalize MRD1's. R2 (Coefficient of determination), RMSE (Root-mean-square error), MSE (Mean squared error), POC (Percentage of co-efficiency), and MAE (Mean absolute error) were calculated. We evaluated the correlation between these objective formulas and the subjective grading of three surgeons using Spearman's rho (ρ). RESULTS: The correlation coefficient of RMSE and MSE were the same for all surgeons grading. There was a strong correlation between the senior surgeon's subjective scoring (N; poor = 8, acceptable = 16, good = 8) and R2, RMSE, POC, MAE (ρ = 0.643, p < 0.001, ρ = -0.607, p < 0.001, ρ = 0.562, p < 0.001, ρ = -0.517, p < 0.001, respectively). We found a strong relationship between the expert surgeon's subjective scoring (N; poor = 9, acceptable = 13, good:10) and R2 (ρ = 0.611, p < 0.001), RMSE (ρ = -0.549, p < 0.001), POC (ρ = 0.511, p < 0.001), and MAE (ρ = -0.450, p < 0.05). We found a strong correlation between junior surgeon's subjective scoring (N; poor = 6, acceptable = 18, good = 8) and R2, RMSE, and POC (ρ: -0.517, p < 0.001; ρ: -0.470, p < 0.001; ρ: 0.521, p < 0.001; respectively) and moderate correlation between MAE (ρ:-0.394, p < 0.05). The highest correlation is observed with R2. CONCLUSIONS: RMSE, MSE, POC, MAE, and especially R2, may quantitatively express upper eyelid contour symmetry, comparable with the oculoplastic surgeon. The highest correlation was observed between the senior surgeon and R2, and decreases with the experience of the surgeon.

Physiology of trans-translation deficiency in Bacillus subtilis - a comparative proteomics study.

trans-Translation is the most effective ribosome rescue system known in bacteria. While it is essential in some bacteria, Bacillus subtilis possesses two additional alternative ribosome rescue mechanisms that require the proteins BrfA or RqcH. To investigate the physiology of trans-translation deficiency in the model organism B. subtilis, we compared the proteomes of B. subtilis 168 and a ΔssrA mutant in the mid-log phase using gel-free label-free quantitative proteomics. In chemically defined medium, the growth rate of the ssrA deletion mutant was 20% lower than that of B. subtilis 168. An 35 S-methionine incorporation assay demonstrated that protein synthesis rates were also lower in the ΔssrA strain. Alternative rescue factors were not detected. Among the 34 proteins overrepresented in the mutant strain were eight chemotaxis proteins. Indeed, both on LB agar and minimal medium the ΔssrA strain showed an altered motility and chemotaxis phenotype. Despite the lower growth rate, in the mutant proteome ribosomal proteins were more abundant while proteins related to amino acid biosynthesis were less abundant than in the parental strain. This overrepresentation of ribosomal proteins coupled with a lower protein synthesis rate and down-regulation of precursor supply reflects the slow ribosome recycling in the trans-translation-deficient mutant.

Resting-state brain signal complexity discriminates young healthy APOE e4 carriers from non-e4 carriers.

It is well established that the e4 allele of the APOE gene is associated with impaired brain functionality and cognitive decline in humans at elder age. However, it is controversial whether and how the APOE e4 allele is associated with superior brain function among young healthy individuals, thus indicates a case of antagonistic pleiotropy of APOE e4 allele. Signal complexity is a critical aspect of brain activity that has been associated with brain function. In this study, the multiscale entropy (MSE) of resting-state EEG signals among a sample of young healthy adults (N = 260) as an indicator of brain signal complexity was investigated. It was of interest whether MSE differs across APOE genotype groups while age and education level were controlled for and whether the APOE genotype effect on MSE interacts with MSE time scale, as well as EEG recording condition. Results of linear mixed models indicate overall larger MSE in APOE e4 carriers. This genotype-dependent difference is larger at high as compared with low time scales. The interaction effect between APOE genotype and recording condition indicates increased between-state MSE change in young healthy APOE e4 carriers as compared with non-carriers. Because higher complexity is commonly taken to be associated with better cognitive functioning, the present results complement previous findings and therefore point to a pleiotropic spectrum of the APOE gene polymorphism.

On interquantile smoothness of censored quantile regression with induced smoothing.

Quantile regression has emerged as a useful and effective tool in modeling survival data, especially for cases where noises demonstrate heterogeneity. Despite recent advancements, non-smooth components involved in censored quantile regression estimators may often yield numerically unstable results, which, in turn, lead to potentially self-contradicting conclusions. We propose an estimating equation-based approach to obtain consistent estimators of the regression coefficients of interest via the induced smoothing technique to circumvent the difficulty. Our proposed estimator can be shown to be asymptotically equivalent to its original unsmoothed version, whose consistency and asymptotic normality can be readily established. Extensions to handle functional covariate data and recurrent event data are also discussed. To alleviate the heavy computational burden of bootstrap-based variance estimation, we also propose an efficient resampling procedure that reduces the computational time considerably. Our numerical studies demonstrate that our proposed estimator provides substantially smoother model parameter estimates across different quantile levels and can achieve better statistical efficiency compared to a plain estimator under various finite-sample settings. The proposed method is also illustrated via four survival datasets, including the HMO (health maintenance organizations) HIV (human immunodeficiency virus) data, the primary biliary cirrhosis (PBC) data, and so forth.

Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread.

BACKGROUND: Bayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important. METHODS: In this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons. RESULTS: A particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall. DISCUSSION: The models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall 'best' model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality. CONCLUSIONS: From a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance. In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.

An efficient approach addressing the chemical complexity of Jiawei Fangji Huangqi decoction by integrating ultra-high-performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry and intelligent data processing workflows.

A challenge in the quality control of traditional Chinese medicine is the systematic multicomponent characterization of the compound formulae. Jiawei Fangji Huangqi, a modified form of Fangji Huangqi, is a prescription comprising seven herbal medicines. To address the chemical complexity of the Jiawei Fangji Huangqi decoction, we integrated ion mobility-quadrupole time-of-flight high-definition MSE coupled to ultra-high-performance liquid chromatography and intelligent data processing workflows available in the UNIFI software package. Good chromatographic separation was achieved on CORTECS UPLC T3 column within 52 min, and high-accuracy MS2 data were acquired using high-definition MSE in the negative and positive modes. A chemical library of 1250 compounds was created and incorporated into the UNIFI software to enable automatic peak annotation of the high-definition MSE data. We identified or tentatively characterize 430 compounds in the Jiawei Fangji Huangqi decoction. The potential superiority of high-definition MSE over conventional MS data acquisition approaches was revealed in its spectral quality (MS2 ), differentiation of isomers, separation of coeluting compounds, and target mass coverage. The multiple components of the Jiawei Fangji Huangqi decoction were elucidated, offering insight into its improved pharmacological action compared with that of the Fangji Huangqi formula.

An integrated analytical strategy to decipher the metabolic profile of alkaloids in Compound Kushen injection based on UHPLC-ESI-QTOF/MSE.

Compound Kushen injection (CKI) is a kind of sterilised water-soluble traditional Chinese medicine preparation that has been used for the clinical treatment of a variety of cancers (hepatocellular carcinoma, lung cancer, etc.) for 19 years. However, to date, the metabolism-related study on CKI in vivo has not been conducted.An integrated analytical strategy was established to investigate the metabolic profile of alkaloids of CKI in rat plasma, urine, and faeces based on ultra-high performance liquid chromatography-electrospray quadrupole time-of-flight mass spectrometry in MSE mode (UHPLC-ESI-QTOF/MSE).Nineteen prototype alkaloids (including 12 matrine-type alkaloids, 2 cytisine-type alkaloids, 3 lupinine-type alkaloids, and 2 aloperine-type alkaloids) of CKI were identified in vivo. Furthermore, 71 metabolites of alkaloids (including 11 of lupanine-related metabolites, 14 of sophoridine-related metabolites, 14 of lamprolobine-related metabolites, and 32 of baptifoline-related metabolites) were tentatively characterised. Metabolic pathways involved in the metabolism of phase I (include oxidation, reduction, hydrolysis, and desaturation), phase II (mainly include glucuronidation, acetylcysteine or cysteine conjugation, methylation, acetylation, and sulphation), and associated combination reactions.The integrated analytical strategy was successfully used to characterise the prototype alkaloids and their metabolites of CKI in vivo, and the results laying a foundation for further study its pharmacodynamic substances.

The Antinociceptive Effect of a Hydroalcoholic Extract of Polygala altomontana and Its Chemical Profile Using UPLC-ESI-QTOF-HR-MS.

The hydroalcoholic extract of Polygala altomontana (30, 100, and 300 mg/kg, i.g.) showed a dose-dependent antinociceptive action during the inflammatory phase of the formalin test. In addition, the preparation (30 and 300 mg/kg, i.g.) showed anti-hyperalgesic action when tested on a mechanical nociception model. UPLC-ESI-QTOF-MS data indicated the active extract contained phenylpropanoid sucrose esters, glycosylated quercetin derivatives, styrylpyrones, and coumarins. Some identified compounds, including styrylpyrones and coumarins, have previously demonstrated antinociceptive action. The results also show that P. altomontana shows potential for developing pain-relieving herbal remedies and drugs.

Advanced data post-processing method for rapid identification and classification of the major triterpenoids of Alismatis rhizoma by ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry.

INTRODUCTION: Alismatis rhizoma (AR), a distinguished diuretic traditional Chinese herbal medicine, is widely used for the treatment of diarrhea, edema, nephropathy, hyperlipidemia, and tumors in clinical settings. Most beneficial effects of AR are attributed to the major triterpenoids, whose contents are relatively high in AR. To date, only 25 triterpenoids in AR have been characterized by LC-MS because the low-mass diagnostic ions are hardly triggered in MS, impeding structural identification. Herein, we developed an advanced data post-processing method with abundant characteristic fragments (CFs) and neutral losses (NLs) for rapid identification and classification of the major triterpenoids in AR by UPLC-Q-TOF-MSE . OBJECTIVE: We aimed to establish a systematic method for rapid identification and classification of the major triterpenoids of AR. METHODS: UPLC-Q-TOF-MSE coupled with an advanced data post-processing method was established to characterize the major triterpenoids of AR. The abundant CFs and NLs of different types of triterpenoids were discovered and systematically summarized. The rapid identification and classification of the major triterpenoids of AR were realized by processing the data and comparing with information described in the literature. RESULTS: In this study, a total of 44 triterpenoids were identified from AR, including three potentially new compounds and 41 known ones, which were classified into six types. CONCLUSION: The newly established approach is suitable for the chemical profiling of the major triterpenoids in AR, which could provide useful information about chemical constituents and a basis for further exploration of its active ingredients in vivo.

A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease.

According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. However, delayed treatment of this disease may lead to several other health problems, such as fertility issues and obesity. Therefore, early treatment is essential for patient survival. The proposed technology could be used for the prediction of hypothyroid disease and its severity during its early stages. Though several classification and regression algorithms are available for the prediction of hypothyroid using clinical information, there exists a gap in knowledge as to whether predicted outcomes may reach a higher accuracy or not. Therefore, the objective of this research is to predict the existence of hypothyroidism with higher accuracy by optimizing the estimator list of the pycaret classifier model. With this overview, a blunge calibration intelligent feature classification model that supports the assessment of the presence of hypothyroidism with high accuracy is proposed. A hypothyroidism dataset containing 3163 patient details with 23 independent and one dependent feature from the University of California Irvine (UCI) machine-learning repository was used for this work. We undertook dataset preprocessing and determined its incomplete values. Exploratory data analysis was performed to analyze all the clinical parameters and the extent to which each feature supports the prediction of hypothyroidism. ANOVA was used to verify the F-statistic values of all attributes that might highly influence the target. Then, hypothyroidism was predicted using various classifier algorithms, and the performance metrics were analyzed. The original dataset was subjected to dimensionality reduction by using regressor and classifier feature-selection algorithms to determine the best subset components for predicting hypothyroidism. The feature-selected subset of the clinical parameters was subjected to various classifier algorithms, and its performance was analyzed. The system was implemented with python in the Spyder editor of Anaconda Navigator IDE. Investigational results show that the Gaussian naive Bayes, AdaBoost classifier, and Ridge classifier maintained the accuracy of 89.5% for the regressor feature-selection methods. The blunge calibration regression model (BCRM) was designed with naive Bayes, AdaBoost, and Ridge as the estimators with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCRM showed 99.5% accuracy in predicting hypothyroidism. The implementation results show that the Kernel SVM, KNeighbor, and Ridge classifier maintained an accuracy of 87.5% for the classifier feature-selection methods. The blunge calibration classifier model (BCCM) was developed with Kernel SVM, KNeighbor, and Ridge as the estimators, with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCCM showed 99.7% accuracy in predicting hypothyroidism. The main contribution of this research is the design of BCCM and BCRM models that were built with accuracy optimization with soft blending based on the sum of predicted probabilities of classifiers. The BCRM and BCCM models uniqueness's are achieved by updating the estimators list with the effective classifiers and regressors that suit the application at runtime.

Digital workflow for mini-implant-assisted rapid palatal expander fabrication-a case report.

BACKGROUND: Non-surgical mini-implant assisted rapid palatal expansion, or midfacial skeletal expansion, is a paradigm-shifting concept that in recent years has expanded the envelope of orthopedic movement in the transverse direction for adult patients. Although adding mini-screws to a rapid palatal expander is not complicated, accurate and successful expansion strongly depends on the device's position and its relation to the resisting structures of the maxillofacial complex. CASE PRESENTATION: This article presents a digital workflow to locate the optimal position of the Midfacial Skeletal Expander (MSE) device in a CBCT-combined intraoral scan file and describes how to transfer the MSE position intra-orally with properly sized bands during the device fabrication. The complete digital workflow of MSE fabrication and its application for a Class III orthognathic surgical case is presented in detail. CONCLUSIONS: This report describes a completely digital process that can accurately position the MSE device according to the orientation and morphology of maxillary basal bone, which is crucial in adult cases demand maxillary expansion.

Evaluation and management of enuresis in the general paediatric setting.

Assessing enuresis involves distinguishing monosymptomatic from non-monosymptomatic for this common paediatric problem, and identifying concomitant comorbidities. Addressing co-occurring factors concurrently ensures the best opportunity for a satisfactory outcome. Treatment begins with patient and family education on the natural history of enuresis and practical behavioural guidance. Evidence to support particular interventions is limited, and children and families should be involved when choosing appropriate therapy. Enuresis alarms and desmopressin are treatment options when more active intervention is desired. Clinical refinements and combined treatment modalities are emerging.

Yeast Secretes High Amounts of Human Calreticulin without Cellular Stress.

The ER chaperone calreticulin (CALR) also has extracellular functions and can exit the mammalian cell in response to various factors, although the mechanism by which this takes place is unknown. The yeast Saccharomyces cerevisiae efficiently secretes human CALR, and the analysis of this process in yeast could help to clarify how it gets out of eukaryotic cells. We have achieved a secretion titer of about 140 mg/L CALR in our S. cerevisiae system. Here, we present a comparative quantitative whole proteome study in CALR-secreting yeast using non-equilibrium pH gradient electrophoresis (NEPHGE)-based two-dimensional gel electrophoresis (2DE) as well as liquid chromatography mass spectrometry in data-independent analysis mode (LC-MSE). A reconstructed carrier ampholyte (CA) composition of NEPHGE-based first-dimension separation for 2DE could be used instead of formerly commercially available gels. Using LC-MSE, we identified 1574 proteins, 20 of which exhibited differential expression. The largest group of differentially expressed proteins were structural ribosomal proteins involved in translation. Interestingly, we did not find any signs of cellular stress which is usually observed in recombinant protein-producing yeast, and we did not identify any secretory pathway proteins that exhibited changes in expression. Taken together, high-level secretion of human recombinant CALR protein in S. cerevisiae does not induce cellular stress and does not burden the cellular secretory machinery. There are only small changes in the cellular proteome of yeast secreting CALR at a high level.

A multidimensional strategy to rapidly identify the chemical constituents in Shengxian Decoction by using ultra-performance liquid chromatography coupled with ion mobility spectrometry quadrupole time-of-flight mass spectrometry.

As a well-known traditional Chinese medicine formula, the chemical constituents of Shengxian Decoction still remain unclear due to its complexity. In this study, a multidimensional strategy based on ultra-performance liquid chromatography coupled with ion mobility spectrometry quadrupole time-of-flight mass spectrometry and informatics UNIFI platform was applied to achieve rapid and comprehensive identification of the complex composition of Shengxian Decoction. Data-independent acquisition, fast data-directed analysis, and high-definition MSE were used to obtain more and cleaner mass spectrum information. As a result, a total of 120 compounds including 74 saponins, 17 flavonoids, 7 cinnamic acid derivatives, 8 triterpenoids, and 14 others were identified or tentatively characterized by high-resolution molecular mass, fragment ions, and collision cross-section values. Furthermore, high-definition MSE was used to identify six pairs of co-eluting isomers that could not be detected from conventional data-independent acquisition and fast data-directed analysis. This research strategy has a certain potential for the analysis of other compound formulae and lays the foundation for the study of traditional Chinese medicine efficacy.