Characteristics of catalytic destruction of dichloromethane and ethyl acetate mixture over HxPO4-RuOx/CeO2 catalyst.

Catalytic destruction is an ascendant technology for the abatement of volatile organic compounds (VOCs) originating from solvent-based industrial processes. The varied composition tends to influence each VOC's catalytic behavior in the reaction mixture. We investigated the catalytic destruction of multi-component VOCs including dichloromethane (DCM) and ethyl acetate (EA), as representatives from pharmaceutical waste gases, over co-supported HxPO4-RuOx/CeO2 catalyst. A mutual inhibitory effect relating to concentrations because of competitive adsorption was verified in the binary VOCs oxidation and EA posed a more negative effect on DCM oxidation owing to EA's superior adsorption capacity. Preferential adsorption of EA on acidic sites (HxPO4/CeO2) promoted DCM activation on basic sites (O2-) and the dominating EA oxidation blocked DCM's access to oxidation centers (RuOx/CeO2), resulting in boosted monochloromethane yield and increased chlorine deposition for DCM oxidation. The impaired redox ability of Ru species owing to chlorine deposition in turn jeopardized deep oxidation of EA and its by-products, leading to increased gaseous by-products such as acetic acid originating from EA pyrolysis. Notably, DCM at low concentration slightly promoted EA conversion at low temperatures with or without water, consistent with the enhanced EA adsorption in co-adsorption analyses. This was mainly due to that DCM impeded the shielding effect of hydrolysate deposition from rapid EA hydrolysis depending on the decreased acidity. Moreover, water benefited EA hydrolysis but decreased CO2 selectivity while the generated water derived from EA was likely to affect DCM transformation. This work may provide theoretical guidance for the promotion of applied catalysts toward industrial applications.

Distinct trajectories of neuropsychiatric symptoms in the 12 months following traumatic brain injury (TBI): a TRACK-TBI study.

BACKGROUND: Neuropsychiatric symptoms are common after traumatic brain injury (TBI) and often resolve within 3 months post-injury. However, the degree to which individual patients follow this course is unknown. We characterized trajectories of neuropsychiatric symptoms over 12 months post-TBI. We hypothesized that a substantial proportion of individuals would display trajectories distinct from the group-average course, with some exhibiting less favorable courses. METHODS: Participants were level 1 trauma center patients with TBI (n = 1943), orthopedic trauma controls (n = 257), and non-injured friend controls (n = 300). Trajectories of six symptom dimensions (Depression, Anxiety, Fear, Sleep, Physical, and Pain) were identified using growth mixture modeling from 2 weeks to 12 months post-injury. RESULTS: Depression, Anxiety, Fear, and Physical symptoms displayed three trajectories: Stable-Low (86.2-88.6%), Worsening (5.6-10.9%), and Improving (2.6-6.4%). Among symptomatic trajectories (Worsening, Improving), lower-severity TBI was associated with higher prevalence of elevated symptoms at 2 weeks that steadily resolved over 12 months compared to all other groups, whereas higher-severity TBI was associated with higher prevalence of symptoms that gradually worsened from 3-12 months. Sleep and Pain displayed more variable recovery courses, and the most common trajectory entailed an average level of problems that remained stable over time (Stable-Average; 46.7-82.6%). Symptomatic Sleep and Pain trajectories (Stable-Average, Improving) were more common in traumatically injured groups. CONCLUSIONS: Findings illustrate the nature and rates of distinct neuropsychiatric symptom trajectories and their relationship to traumatic injuries. Providers may use these results as a referent for gauging typical v. atypical recovery in the first 12 months post-injury.

Changes in Skeletal Muscle Mass in the First 3 Months Following Gastrointestinal Cancer Surgery: A Prospective Study.

BACKGROUND: Surgical resection is the primary treatment for gastrointestinal (GI) cancers, but postoperative skeletal muscle loss (SML) is common and linked to poor prognosis. This study aims to identify patterns of muscle change, examine its association with quality of life (QoL), and explore predictors of SML in the first 3 months. PATIENTS AND METHODS: A prospective cohort study was conducted on patients newly diagnosed with GI cancer and undergoing surgery in China between September 2021 and May 2022. Skeletal muscle mass (SMM) and QoL were assessed at admission, 7 days, 1 month, and 3 months post-surgery. Demographic, clinical data, and biomarkers were collected. Missing data were imputed using multiple imputation. Data were analyzed using growth mixture modelling, bivariate analyses, and logistic regression. RESULTS: A total of 483 patients completed baseline assessment. Of the 242 patients with complete muscle assessments, 92% experienced SML. Three distinct patterns of muscle change were identified: 57% had normal preoperative SMM with mild postoperative SML, 16% had low preoperative SMM with moderate SML, and 27% had normal preoperative mass but severe postoperative SML. Moderate/severe SML was associated with more postoperative complications, poorer health, and higher symptom burden. Independent predictors included advanced age, preoperative sarcopenia, advanced cancer stage, and low prognostic nutrition index (PNI ≤ 45). The results did not change when using imputed values. CONCLUSIONS: Although SML is prevalent, patterns of muscle change are heterogeneous among patients. Advanced age, preoperative sarcopenia, advanced cancer stage, and cancer-related inflammation are predictors for moderate/severe SML, highlighting the need for early detection and management.

Sweetness interaction between a novel glycosylated rebaudioside A and various sweeteners in a binary mixture system.

The present study investigated the sweetness interaction and the sensory characteristics of a novel glycosylated rebaudioside A (g-reb_A) when mixed with other sweeteners. Binary sweetener mixtures were formulated by mixing g-reb_A with four types of sweeteners (sucrose, aspartame, acesulfame-K, saccharin). The sweetness potencies of sweeteners were measured at various concentrations. G-reb_A was mixed with each of the four other sweeteners at the concentration ratio of 35:65 or 50:50 to match the sweetness of a 10% sucrose solution. A generic descriptive analysis was conducted to evaluate the binary samples compared to the 10% sucrose solution. Most binary mixtures exhibited an additive effect on sweetness. A marginal sweetness synergistic effect was observed when g-reb_A was mixed with sucrose at the 50:50 ratio. The sensory characteristics of the binary mixture depended on the type of sweetener mixed with g-reb_A. Mixtures of g-reb_Aacesulfame-K or g-reb_Asaccharin elicited significantly higher bitter taste than the other binary mixtures.

Identifying latent subtypes of symptom trajectories in major depressive disorder patients and their predictors.

This study aimed to identify different symptom trajectories based on the severity of depression symptoms within a 2-month follow-up, and to explore predictive factors for different symptom trajectories. Three hundred and ninety-two adults diagnosed with major depressive disorder (MDD) were recruited from two longitudinal cohorts. Patients received antidepressant treatment as usual, and the depression symptoms were evaluated by the 17-item Hamilton depression rating scale (HAMD-17) at baseline, two weeks, and eight weeks. Based on the HAMD-17 scores, different trajectories of symptom change were distinguished by applying Growth Mixture Modeling (GMM). Furthermore, the baseline sociodemographic, clinical, and cognitive characteristics were compared to identify potential predictors for different trajectories. Through GMM, three unique depressive symptom trajectories of MDD patients were identified: (1) mild-severity class with significant improvement (Mild, n = 255); (2) high-severity class with significant improvement (High, n = 39); (3) moderate-severity class with limited improvement (Limited, n = 98). Among the three trajectories, the Mild class had a relatively low level of anxiety symptoms at baseline, whereas the High class had the lowest education level and the worst cognitive performance. Additionally, participants in the Limited class exhibited an early age of onset and experienced a higher level of emotional abuse. MDD patients could be categorised into three distinct latent subtypes through different symptom trajectories in this study, and the characteristics of these subtype patients may inform identifications for trajectory-specific intervention targets.

A Comparison of Statistical Methods for Studying Interactions of Chemical Mixtures.

Properly assessing the effects of environmental chemical exposures on disease risk remains a challenging problem in environmental epidemiology. Various analytic approaches have been proposed, but there are few papers that have compared the performance of different statistical methods on a single dataset. In this paper, we compare different regression-based approaches for estimating interactions between chemical mixture components using data from a case-control study on non-Hodgkin's lymphoma. An analytic challenge is the high percentage of exposures that are below the limit of detection (LOD). Using imputation for LOD, we compare different Bayesian shrinkage prior approaches including an approach that incorporates the hierarchical principle where interactions are only included when main effects exist. Further, we develop an approach where main and interactive effects are represented by a series of distinct latent functions. We also fit the Bayesian kernel machine regression to these data. All of these approaches show little evidence of an interaction among the chemical mixtures when measurements below the LOD were imputed. The imputation approach makes very strong assumptions about the relationship between exposure and disease risk for measurements below the LOD. As an alternative, we show the results of an analysis where we model the exposure relationship with two parameters per mixture component; one characterizing the effect of being below the LOD and the other being a linear effect above the LOD. In this later analysis, we identify numerous strong interactions that were not identified in the analyses with imputation. This case study demonstrated the importance of developing new approaches for mixtures when the proportions of exposure measurements below the LOD are high.

The silent threat and countermeasures: Navigating the mixture risk of endocrine-disrupting chemicals on pregnancy loss in China.

Currently, many countries and regions worldwide face the challenge of declining population growth due to persistently low rates of female reproduction. Since 2017, China's birth rate has hit historic lows and continued to decline, with the death rate now equaling the birth rate. Concerns have emerged regarding the potential impact of environmental contaminants on reproductive health, including pregnancy loss. Endocrine-disrupting chemicals (EDCs) like phthalate esters (PAEs), bisphenol A (BPA), triclosan (TCS), and perfluoroalkyl substances (PFASs) have raised attention due to their adverse effects on biological systems. While China's 14th Five-Year Plan (2021-2025) for national economic and social development included the treatment of emerging pollutants, including EDCs, there are currently no national appraisal standards or regulatory frameworks for EDCs and their mixtures. Addressing the risk of EDC mixtures is an urgent matter that needs consideration from China's perspective in the near future. In this Perspective, we delve into the link between EDC mixture exposure and pregnancy loss in China. Our focus areas include establishing a comprehensive national plan targeting reproductive-aged women across diverse urban and rural areas, understanding common EDC combinations in women and their surrounding environment, exploring the relationship between EDCs and pregnancy loss via epidemiology, and reconsidering the safety of EDCs, particularly in mixtures and low-dose scenarios. We envision that this study could aid in creating preventive strategies and interventions to alleviate potential risks induced by EDC exposure during pregnancy in China.

Developmental trajectories of loneliness in Chinese children: Environmental and personality predictors.

BACKGROUND: This study aimed to identify the developmental trajectories of loneliness in Chinese children and examine the predictive roles of domain-specific environmental factors (i.e., family dysfunction and satisfaction of relatedness needs at school), personality factors (i.e., neuroticism and extraversion), and their interactions in these developmental trajectories. METHODS: A total of 702 Chinese children (Mage = 8.95, SD = 0.76; 54.1 % boys) participated in assessments at six time points over three years at six-month intervals. Growth mixture modeling (GMM) was used to estimate trajectory classes for loneliness, followed by multivariate logistic regression analyses exploring associations between these classes and predictors. RESULTS: GMM analyses identified three distinct trajectories of loneliness: "low-stable" (81.5 %), "moderate-increasing" (9.4 %), and "high-decreasing" (9.1 %). Multivariate logistic regression analyses revealed that family dysfunction and neuroticism served as risk factors for adverse loneliness trajectories, while satisfaction of relatedness needs at school and extraversion acted as protective factors. Furthermore, the interaction between family dysfunction and extraversion indicated that extraversion did not mitigate the adverse effects of high family dysfunction on children's loneliness, emphasizing the vital need to support positive family functioning among all children. LIMITATIONS: This study did not incorporate biological variables (e.g., genetics), which are crucial in the evolutionary theory of loneliness. CONCLUSIONS: The identification of three distinct trajectory groups of children's loneliness, along with key environmental and personality predictors, suggests that interventions should be tailored to each group's unique characteristics.

Ecological Risk of Synthetic Phenolic Antioxidants: A study Based on Their Spatial Distribution in Water, Sediment, and Soil from the Yangtze River Delta, China.

Environmental occurrence and risks of novel synthetic phenolic antioxidants (SPAs) remain largely unclear. By using a typical algae (Chlorella pyrenoidosa) as model organism, we evaluated the ecological risks of both traditional and novel SPAs, based on their concentrations in water, sediment, and soil collected from the Yangtze River Delta, China. Detection frequencies (DFs) of 10 novel SPAs were 25-100% in water, 3-100% in sediment, and 0-100% in soil, with geometric means (GMs) of 2700 ng/L, 1270 ng/g, and 2440 ng/g, respectively. For 8 traditional SPAs, DFs were 50-100% (GM: 680 ng/L), 3-100% (534 ng/g), and 47-100% (2240 ng/g) in water, sediment, and soil, respectively. AO3114 was the main pollutant in water, while AO1010 dominated in sediment and soil. Notably, low-molecular-weight SPAs showed migration behavior from sediment to water. Four SPAs (AO626, AO1035, AO1098, and AO1076) showed dose- and time-dependent toxicity on Chlorella pyrenoidosa. As time progressed, sediment-released SPAs became more toxic than those in water. Two SPAs (AO1135 and BHT-Q) posed high risks (RQW > 1) to green algae, daphnia, and fish. The SPA mixture exhibited high risks (RQmix > 1) to these organisms, increasing with the trophic level. This research holds valuable guidance for further SPA risk assessments.

Yangzheng mixture reversed EMT against hepatocellular carcinoma metastasis via NF-κB/NLRP3/ß-catenin pathway.

Yangzheng mixture has been used as adjuvant tumor therapy as a traditional Chinese medicine in clinical. However, less is known about the toxicity of Yangzheng mixture. In our study, we explored the anti-tumor activity of Yangzheng mixture for HCC in vitro and in vivo.The effects of Yangzheng mixture on HCC biological behaviors were assessed using colony formation assay, EdU staining, cell cycle assay, Annexin V/PI staining, and wound healing assay. Migration and invasion of HCC cells were further evaluated via transwell assays, while molecular mechanisms were investigated through western blotting and immunofluorescence staining. Additionally, the anticancer effect of Yangzheng mixture in vivo were examined using H22 xenograft and H22 metastatic hepatocellular carcinoma models.Our results revealed that Yangzheng mixture inhibited colony formation, EdU incorporation, cell migration, and invasion, while arresting cell cycle at the G2-M phase in Bel-7402 and SMMC-7721 cells. Mechanistic studies demonstrated that Yangzheng mixture showed a markedly inhibition on Bel-7402 and SMMC-7721 cells with higher NLRP3 expression. We further confirmed that Yangzheng mixture could activated NLRP3 inflammasome through NF-κB by western blotting and immunofluorescence staining. Additionally, Yangzheng mixture inhibited ß-catenin nucleus translocation and reversed EMT process. In vivo, the H22 xenograft model depicted that Yangzheng mixture significantly reduced tumor size and weight compared with control. Moreover, H22 lung metastasis model showed that Yangzheng mixture significantly inhibited liver cancer cell spreading to lungs in mice.Overall, our finding revealed that Yangzheng mixture inhibited HCC proliferation and migration in vitro and in vivo by reversing EMT via NF-κB/NLRP3/ß-catenin pathway. These results may serve new therapeutic evidences for Yangzheng mixture application in clinical.

Template-based synergy extrapolation analysis for prediction of muscle excitations.

OBJECTIVE: Accurate prediction of unmearsured muscle excitations can reduce the required wearable surface electromyography (sEMG) sensors, which is a critical factor in the study of physiological measurement. Synergy extrapolation uses synergy excitations as building blocks to reconstruct muscle excitations. However, the practical application of synergy extrapolation is still limited as the extrapolation process utilizes unmeasured muscle excitations it seeks to reconstruct. This paper aims to propose and derive methods to provide an avenue for the practical application of synergy extrapolation with non-negative matrix factorization (NMF) methods. APPROACH: Specifically, a tunable Gaussian-Laplacian mixture distribution NMF (GLD-NMF) method and related multiplicative update rules are derived to yield appropriate synergy excitations for extrapolation. Furthermore, a template-based extrapolation structure (TBES) is proposed to extrapolate unmeasured muscle excitations based on synergy weighting matrix templates totally extracted from measured sEMG datasets, improving the extrapolation performance. Moreover, we applied the proposed GLD-NMF method and TBES to selected muscle excitations acquired from a series of single-leg stance (SLS) tests, walking tests and upper limb reaching tests. MAIN RESULTS: Experimental results show that the proposed GLD-NMF and TBES could extrapolate unmeasured muscle excitations accurately. Moreover, introducing synergy weighting matrix templates could decrease the number of sEMG sensors in a series of experiments. In addition, verification results demonstrate the feasibility of applying synergy extrapolation with NMF methods. SIGNIFICANCE: With the TBES method, synergy extrapolation could play a significant role in reducing data dimensions of sEMG sensors, which will improve the portability of sEMG sensors-based systems and promotes applications of sEMG signals in human-machine interfaces scenarios.

Compositional Optimization for Miticidal Activity, Ecotoxicity, and Phytotoxicity of Rosmarinus officinalis Essential Oils as Biorational Pesticides.

Recognizing the challenges in using botanicals as sustainable pest control agents due to compositional variation, this study addresses the limitations of traditional component-based approaches such as Hewlett and Plackett or Wadley's models. Based on the assumption of noninteractivity among constituents, these models often fail to predict outcomes accurately due to dynamic intermolecular interactions. We introduce a whole mixture-based approach, employing a combination of experimental design and polynomial modeling. This technique accurately predicts miticidal activity on Tetranychus urticae, ecotoxicity on Daphnia magna, and phytotoxic activities on Phaseolus vulgaris of Rosemarinus officinalis essential oils with varying composition. The RMSE values from the polynomial model are 66.9 and 5.0 for miticidal activity and ecotoxicity, respectively, while they are much higher in component-based models, up to 1097.7 and 41.3, respectively. Additionally, we utilize multiobjective optimization algorithms to identify the optimal supplementary blending of oils and compounds. This strategy aims to maximize miticidal effectiveness while minimizing ecotoxicity and phytotoxicity. Our approach for predicting multicomponent mixture effects is likely to bridge the knowledge gap between research and commercialization.

PI gain tuning for pressure-based MFCs with Gaussian mixture model.

A vast number of mass flow controllers (MFCs) are used in semiconductor industry. For the stable supply, an efficient production method of MFC is required. The gain tuning of the proportional-integral (PI) control to realize a setting flow rate is essential for efficient mass production. The gains are tuned to meet the specifications required for evaluation indices of response time and overshoot amount in a step response waveform. The tuning is complicated especially for the case of pressure-based MFCs. In this paper, we propose a simple method for the PI gain tuning using the Gaussian mixture model (GMM) and the direct inverse analysis applicable to the pressure-based MFCs' production. The relationship between the gains and evaluation indices for a standard unit of the MFC is modeled as the GMM. The direct inverse analysis calculates the difference between the standard and a test unit. Under the assumption that the difference can be compensated by a simple shift, gains likely to meet the specifications for the test unit are searched. We applied the method to seven test units. The result showed that the gains of all the test units were tuned within only a few iterations whose numbers were much less than the conventional manual tuning method, and there was no untunable unit.

Formulation of novel low-sodium salts using potassium salts and dietary polysaccharide.

Potassium citrate (KC) and potassium lactate (KL) are considered as salt replacers due to their saltiness, processing advantages, and health benefits. However, the obvious bitter taste associated with these compounds has limited their use in salt substitutes. Despite this challenge, little attention has been paid to improving their sensory properties. This study provided evidence that dietary polysaccharide carrageenan can effectively mask the bitterness of KC and KL by specifically binding K+ and forming double helix chains. A highly accurate prediction model was then established for the saltiness and bitterness of low-sodium salts using mixture design principles. Three low-sodium salt formulas containing different potassium salts (KC, KL, KCl), NaCl, and carrageenan were created based on the prediction model. These formulas exhibited favorable saltiness potencies (>0.85) without any noticeable odor, preserving the sensory characteristics of high-sodium food products like seasoning powder while significantly reducing their sodium content. This research provides a promising approach for the food industry to formulate alternative low-sodium products with substantially reduced sodium content, potentially contributing to decreased salt intake.

Synergistic C2H2 Binding Sites in Hydrogen-Bonded Supramolecular Framework for One-step C2H4 Purification from Ternary C2 Mixture.

Purification of C2H4 from the ternary C2 hydrocarbon mixture in one step is of critical significance but still extremely challenging according to its intermediate physical properties between C2H6 and C2H2. Hydrogen-bonded organic frameworks (HOFs) stabilized by supramolecular interactions are emerging as a new kind of adsorbents that facilitate green separation. However, it remains a problem to efficiently realize the one-step C2H4 purification from C2H6/C2H4/C2H2 mixture because of the low C2H2/C2H4 selectivity. We herein report a robust microporous HOF (termed as HOF-TDCPB) with dense O atoms and aromatic rings distributed on the pore surface which provide C2H6 and C2H2 preferred environment simultaneously. Dynamic breakthrough experiments indicate that HOF-TDCPB can not only obtain high-purity C2H4 from binary C2 mixture, but also firstly realize one-step C2H4 purification from ternary C2H6/C2H4/C2H2 mixture, with the C2H4 productivity of 3.2 L/kg (>99.999%) for one breakthrough cycle. Furthermore, HOF-TDCPB displays outstanding stability in air, organic solvents and water, which endow it excellent cycle performance even under high-humidity conditions. Theoretical calculations indicate that multiple O sites on pore channels can create synergistic binding sites for C2H2, thus affording overall stronger multipoint interactions.

Mixture Experimentation in Pharmaceutical Formulations: A Tutorial.

Mixture experimentation is commonly seen in pharmaceutical formulation studies, where the relative proportions of the individual components are modeled for effects on product attributes. The requirement that the sum of the component proportions equals 1 has given rise to the class of designs, known as mixture designs. The first mixture designs were published by Quenouille in 1953 but it took nearly 40 years for the earliest mixture design applications to be published in the pharmaceutical sciences literature by Kettaneh-Wold in 1991 and Waaler in 1992. Since then, the advent of efficient computer algorithms to generate designs has made this class of designs easily accessible to pharmaceutical statisticians, although the use of these designs appears to be an underutilized experimental strategy even today. One goal of this tutorial is to draw the attention of experimental statisticians to this class of designs and their advantages in pursuing formulation studies such as excipient compatibility studies. We present sufficient materials to introduce the novice practitioner to this class of design, associated models, and analysis strategies. An example of a mixture-process variable design is given as a case study.

Quantitative Computed Tomography Angiography for the Evaluation of Valvular Fibrocalcific Volume in Aortic Stenosis.

BACKGROUND: Aortic stenosis (AS) is characterized by calcification and fibrosis. The ability to quantify these processes simultaneously has been limited with previous imaging methods. OBJECTIVES: The purpose of this study was to evaluate the aortic valve fibrocalcific volume by computed tomography (CT) angiography in patients with AS, in particular, to assess its reproducibility, association with histology and disease severity, and ability to predict/track progression. METHODS: In 136 patients with AS, fibrocalcific volume was calculated on CT angiograms at baseline and after 1 year. CT attenuation distributions were analyzed using Gaussian-mixture-modeling to derive thresholds for tissue types enabling the quantification of calcific, noncalcific, and fibrocalcific volumes. Scan-rescan reproducibility was assessed and validation provided against histology and in an external cohort. RESULTS: Fibrocalcific volume measurements took 5.8 ± 1.0 min/scan, demonstrating good correlation with ex vivo valve weight (r = 0.51; P < 0.001) and excellent scan-rescan reproducibility (mean difference -1%, limits of agreement -4.5% to 2.8%). Baseline fibrocalcific volumes correlated with mean gradient on echocardiography in both male and female participants (rho = 0.64 and 0.69, respectively; both P < 0.001) and in the external validation cohort (n = 66, rho = 0.58; P < 0.001). The relationship was driven principally by calcific volume in men and fibrotic volume in women. After 1 year, fibrocalcific volume increased by 17% and correlated with progression in mean gradient (rho = 0.32; P = 0.003). Baseline fibrocalcific volume was the strongest predictor of subsequent mean gradient progression, with a particularly strong association in female patients (rho = 0.75; P < 0.001). CONCLUSIONS: The aortic valve fibrocalcific volume provides an anatomic assessment of AS severity that can track disease progression precisely. It correlates with disease severity and hemodynamic progression in both male and female patients.

Sample size calculation for mixture cure model with restricted mean survival time as a primary endpoint.

It is not uncommon for a substantial proportion of patients to be cured (or survive long-term) in clinical trials with time-to-event endpoints, such as the endometrial cancer trial. When designing a clinical trial, a mixture cure model should be used to fully consider the cure fraction. Previously, mixture cure model sample size calculations were based on the proportional hazards assumption of latency distribution between groups, and the log-rank test was used for deriving sample size formulas. In real studies, the latency distributions of the two groups often do not satisfy the proportional hazards assumptions. This article has derived a sample size calculation formula for a mixture cure model with restricted mean survival time as the primary endpoint, and did simulation and example studies. The restricted mean survival time test is not subject to proportional hazards assumptions, and the difference in treatment effect obtained can be quantified as the number of years (or months) increased or decreased in survival time, making it very convenient for clinical patient-physician communication. The simulation results showed that the sample sizes estimated by the restricted mean survival time test for the mixture cure model were accurate regardless of whether the proportional hazards assumptions were satisfied and were smaller than the sample sizes estimated by the log-rank test in most cases for the scenarios in which the proportional hazards assumptions were violated.

Optimizing Lactic Acid Bacteria Proportions in Sourdough to Enhance Antifungal Activity and Quality of Partially and Fully Baked Bread.

The organic acids produced by lactic acid bacteria (LAB) during the fermentation of sourdoughs have the ability to reduce the growth of different molds. However, this ability depends on the LAB used. For this reason, in this study, the proportions of different LAB were optimized to obtain aqueous extracts (AEs) from sourdough to reduce fungal growth in vitro, control the acetic acid concentration, and obtain a specific lactic to acetic acid ratio. In addition, the optimized mixtures were used to formulate partially baked bread (PBB) and evaluate the mold growth and bread quality during refrigerated storage. Using a simplex-lattice mixture design, various combinations of Lactiplantibacillus plantarum, Lacticaseibacillus casei, and Lactobacillus acidophilus were evaluated for their ability to produce organic acids and inhibit mold growth. The mixture containing only Lpb. plantarum significantly reduced the growth rates and extended the lag time of Penicillium chrysogenum and P. corylophilum compared with the control. The AEs' pH values ranged from 3.50 to 3.04. Organic acid analysis revealed that using Lpb. plantarum yielded higher acetic acid concentrations than when using mixed LAB. This suggests that LAB-specific interactions significantly influence organic acid production during fermentation. The reduced radial growth rates and extended lag times for both molds compared to the control confirmed the antifungal properties of the AEs from the sourdoughs. Statistical analyses of the mixture design using polynomial models demonstrated a good fit for the analyzed responses. Two optimized LAB mixtures were identified that maximized mold lag time, targeted the desired acetic acid concentration, and balanced the lactic to acetic acid ratio. The addition of sourdough with optimized LAB mixtures to PBB resulted in a longer shelf life (21 days) and adequately maintained product quality characteristics during storage. PBB was subjected to complete baking and sensory evaluation. The overall acceptability was slightly higher in the control without sourdough (7.50), followed by bread formulated with the optimized sourdoughs (ranging from 6.78 to 7.10), but the difference was not statistically significant (p > 0.05). The sensory analysis results indicated that the optimization was used to successfully formulate a sourdough bread with a sensory profile closely resembling that of a nonsupplemented one. The designed LAB mixtures can effectively enhance sourdough bread's antifungal properties and quality, providing a promising approach for extending bread shelf life while maintaining desirable sensory attributes.

Research on Student's T-Distribution Point Cloud Registration Algorithm Based on Local Features.

LiDAR offers a wide range of uses in autonomous driving, remote sensing, urban planning, and other areas. The laser 3D point cloud acquired by LiDAR typically encounters issues during registration, including laser speckle noise, Gaussian noise, data loss, and data disorder. This work suggests a novel Student's t-distribution point cloud registration algorithm based on the local features of point clouds to address these issues. The approach uses Student's t-distribution mixture model (SMM) to generate the probability distribution of point cloud registration, which can accurately describe the data distribution, in order to tackle the problem of the missing laser 3D point cloud data and data disorder. Owing to the disparity in the point cloud registration task, a full-rank covariance matrix is built based on the local features of the point cloud during the objective function design process. The combined penalty of point-to-point and point-to-plane distance is then added to the objective function adaptively. Simultaneously, by analyzing the imaging characteristics of LiDAR, according to the influence of the laser waveform and detector on the LiDAR imaging, the composite weight coefficient is added to improve the pertinence of the algorithm. Based on the public dataset and the laser 3D point cloud dataset acquired in the laboratory, the experimental findings demonstrate that the proposed algorithm has high practicability and dependability and outperforms the five comparison algorithms in terms of accuracy and robustness.