Crystallization-Driven Controlled 2D Self-Assemblies via Aqueous RAFT Emulsion Polymerization.

Aqueous emulsion polymerization is a robust technique for preparing nanoparticles of block copolymers; however, it typically yields spherical nanoassemblies. The scale preparation of nanoassemblies with nonspherical high-order morphologies is a challenge, particularly 2D core-shell nanosheets. In this study, the polymerization-induced self-assembly (PISA) and crystallization-driven self-assembly (CDSA) are combined to demonstrate the preparation of 2D nanosheets and their aggregates via aqueous reversible addition-fragmentation chain transfer (RAFT) emulsion polymerization. First, the crucial crystallizable component for CDSA, hydroxyethyl methacrylate polycaprolactone (HPCL) macromonomer is synthesized by ring opening polymerization (ROP). Subsequently, the RAFT emulsion polymerization of HPCL is conducted to generate crystallizable nanomicelles by a grafting-through approach. This PISA process simultaneously prepared spherical latices and bottlebrush block copolymers comprising poly(N',N'-dimethylacrylamide)-block-poly(hydroxyethyl methacrylate polycaprolactone) (PDMA-b-PHPCL). The latexes are now served as seeds for inducing the formation of 2D hexagonal nanosheets, bundle-shaped and flower-like aggregation via the CDSA of PHPCL segments and unreacted HPCL during cooling. Electron microscope analysis trace the morphology evolution of these 2D nanoparticles and reveal that an appropriate crystallized component of PHPCL blocks play a pivotal role in forming a hierarchical structure. This work demonstrates significant potential for large-scale production of 2D nanoassemblies through RAFT emulsion polymerization.

Long term (Minimum 10 Years) Survival and Outcomes of Pyrocarbon Interposition Shoulder Arthroplasty.

BACKGROUND: There is major controversies surrounding the use and longevity of Pyrocarbon Interposition Shoulder Arthroplasty (PISA). The objective of this study was to investigate the long-term survival and outcomes (minimum 10-year) following PISA for osteoarthritis (OA) in young /active patients. METHODS: This was a retrospective review of prospectively collected data of patients who underwent PISA (InSpyre, Tornier-Stryker, USA) for OA between 2009 and 2012. Arthroplasty survival was known for 71 patients followed longitudinally for a minimum of 10 years. The clinical and radiological outcomes were assessed in 62 patients (62 shoulders) reviewed with radiographs. The mean age at surgery was 60 years (range, 23-72 years) and 31 shoulders (50%) underwent prior surgery before PISA. The diagnosis was primary osteoarthritis (POA=29), post-traumatic osteoarthritis (PTOA=23), post-instability osterarthritis (PIOA=10). Clinical failure was defined as repeat surgical intervention involving prosthesis revision. Clinical outcomes were assessed with Constant score (CS) and Subjective shoulder Value (SSV). The mean duration of follow-up was 11 ± 0.6 years (range, 10 to 14 years). RESULTS: Overall, the survival rate was 90% (95% CI: 82.8-96.8) at 5 years and 87% (95% CI: 79-94.8) at 10 years follow-up. Survival was 100% in PTOA (type 1 fracture sequelae) and in PIOA as well as 95% in primary OA with type-A glenoid. Revision surgery was significantly higher in biconcave (type-B2) glenoid (44%) compared to concentric (type-A) glenoid (2%), respectively (p=0.002). Among the 7 patients who were revised to RSA, 5 had painful glenoid erosion and 2 had bipolar (glenoid and humeral) erosion with thinning and finally fracture of the greater tuberosity (GT). Two shoulders with glenohumeral erosion were associated with secondary rotator cuff tears (one supraspinatus and one subscapularis tear). The mean time to revision and revision was 4 ± 1.7 years. Glenoid wear was more often superior (81%) than central (19%), p<0.001. For those shoulders not revised, the mean CS and SSV significantly increased from 39 ±14 to 70 ±14 points and 34% ±15 to 75% ±17, respectively (p<0.001). CONCLUSION: PISA is an efficient and durable surgical procedure for the treatment of young/active patients with post-traumatic OA, post-instability OA, and primary OA with concentric (type A) glenoid erosion, but not for those with biconcave (type B2) glenoid. Biconcave (type B2) glenoid and subscapularis tear or insufficiency are risk factors for failure and revision.

Oral and Intestinal Bacterial Flora in Patients with Increased Periodontal Inflamed Surface Area: A Cross-Sectional Study.

Background/Objectives: Periodontitis is caused by bacterial plaque. The oral microflora may interact with the intestinal microflora and play a role in the development of periodontitis. The periodontal inflamed surface area (PISA) has been shown to be a useful indicator of periodontal disease related to systemic diseases; however, few studies have shown an association between PISA and the bacterial flora. This study aimed to determine the association between PISA and oral and intestinal bacteria. Methods: Participants were recruited between 2018 and 2021 at the Medical and Dental Collaboration Center of Kanagawa Dental University Hospital. A periodontal clinical examination was performed, and the PISA was calculated. Salivary tests were conducted, and leukocyte scores in the saliva were calculated. Moreover, 16S rRNA amplicon sequencing was performed using saliva and stool samples to analyze oral and intestinal bacteria, respectively. Results: Higher PISA levels resulted in an increased presence of Bacteroides and a decreased presence of Proteobacteria and Actinobacteria in the saliva. An increase in Bacteroides was detected in the saliva of patients with high leukocyte scores. No correlation was observed between PISA and intestinal bacteria. Conclusions: Bacteroides was highly abundant in the saliva of patients with worsened periodontal conditions, as indicated by PISA. No association was found between PISA and intestinal bacteria.

Terpyridine-Decorated Polymer Nanosphere Latex: Template Nanocarriers for the Synthesis of Cu-CeO2 Hollow Spheres.

Water-soluble polymers with the ability to complex metal ions through complexing ligands have attracted significant interest in diverse domains, such as optical or catalyst applications. In this paper, we successfully synthesized, through a one-pot process combining polymerization-induced self-assembly and reversible addition-fragmentation chain transfer polymerization, aqueous dispersions of terpyridine-decorated poly[poly(ethylene glycol)methyl ether methacrylate]-b-poly(methyl methacrylate) (tpy-PPEGMA-b-PMMA) amphiphilic block copolymers. The in-situ formation of well-defined amphiphilic block copolymers and their self-assembly led to nanosphere latex with the hydrodynamic diameters increasing from 17 to 52 nm and the length of the copolymers increasing from 21,000 to 51,000 g·mol-1. These aqueous dispersed tpy-PPEGMA-b-PMMA nanospheres effectively complex metal ions, such as Cu2+, in a stoichiometric ratio of 2:1. Subsequently, these metal-complexed nanospheres were employed as soft template nanocarriers to control, on the nanometer scale, the dispersion of metal on a nanostructured support. This is exemplified by the synthesis of copper supported on cerium oxide hollow spheres (Cu-CeO2) using Cu2+-tpy-PPEGMA-b-PMMA as template nanocarriers and CeO2 nanoparticles. This novel assembly engineering strategy for the preparation of atomically dispersed metal on a nanostructured support was highlighted through the utilization of Cu-CeO2 hollow spheres as an electrocatalyst for the nitrate reduction reaction (NO3RR) to NH3. These encouraging outcomes emphasize the potential of metal-metal oxide-nanostructured materials to treat contaminated water sources with nitrate while allowing the green production of ammonia.

Assessing the Correlation of Periodontal Inflamed Surface Area (PISA) With Systemic Inflammatory Markers.

Background Periodontitis has a vital role in eliciting a cross-reactivity or systemic inflammatory response, making periodontal inflamed surface area (PISA) a primary contributor to the inflammatory burden posed by periodontitis. PISA helps in the quantification of the amount of inflamed periodontal tissue. However, the existing literature data concerning PISA as an indicator of inflammatory burden are scarce, with limited research on the relationship between systemic inflammatory markers and PISA. Aim The present clinic-hematological cross-sectional study aimed to correlate PISA with systemic inflammatory markers. The study also aimed to assess serum concentrations of inflammatory markers such as erythrocyte sedimentation rates (ESR), C-reactive protein (CRP), and peripheral blood markers such as neutrophils and monocytes and to correlate these markers with PISA. Methods The study assessed 62 subjects, who were divided into two groups of 31 subjects, each following bleeding on probing (BOP) criteria. Group I consisted of subjects with generalized chronic gingivitis, and Group II included subjects with generalized chronic periodontitis. In two groups, BOP, probing pocket depth, clinical attachment level, and gingival recession were assessed along with PISA by a custom-made R function derived from a pre-existing, freely available MS Excel spreadsheet (Microsoft Corporation, Redmond, Washington). The results of the assessment were then compared. Results A statistically highly significant positive correlation was seen in PISA and CRP with a correlation coefficient of 0.4875 and p-value of 0.000059. A similar statistically significant positive correlation was seen in ESR and PISA with a correlation coefficient of 0.4089 and p-value of 0.000968. A statistically non-significant correlation was seen in neutrophils and PISA with p=0.576018. However, a moderate and positive statistically significant association was seen in monocyte and PISA with a correlation coefficient of 0.3258 and p-value of 0.009956. Conclusions The present study concludes that most of the common systemic inflammatory markers have a positive correlation with PISA. However, more studies are required to establish this correlation.

Periodontal Inflammation and Dysbiosis Relate to Microbial Changes in the Gut.

Periodontal disease (PerioD) is a chronic inflammatory disease of dysbiotic etiology. Animal models and few human data showed a relationship between oral bacteria and gut dysbiosis. However, the effect of periodontal inflammation and subgingival dysbiosis on the gut is unknown. We hypothesized that periodontal inflammation and its associated subgingival dysbiosis contribute to gut dysbiosis even in subjects free of known gut disorders. We evaluated and compared elderly subjects with Low and High periodontal inflammation (assessed by Periodontal Inflamed Surface Area (PISA)) for stool and subgingival derived bacteria (assayed by 16S rRNA sequencing). The associations between PISA/subgingival dysbiosis and gut dysbiosis and bacteria known to produce short-chain fatty acid (SCFA) were assessed. LEfSe analysis showed that, in Low PISA, species belonging to Lactobacillus, Roseburia, and Ruminococcus taxa and Lactobacillus zeae were enriched, while species belonging to Coprococcus, Clostridiales, and Atopobium were enriched in High PISA. Regression analyses showed that PISA associated with indicators of dysbiosis in the gut mainly reduced abundance of SCFA producing bacteria (Radj = -0.38, p = 0.03). Subgingival bacterial dysbiosis also associated with reduced levels of gut SCFA producing bacteria (Radj = -0.58, p = 0.002). These results suggest that periodontal inflammation and subgingival microbiota contribute to gut bacterial changes.

Two-minute standing endurance test for axial postural abnormalities in patients with Parkinson's disease.

BACKGROUND: Photo-based measurement methods are used to assess axial postural abnormalities (PA) in Parkinson's disease (PD). However, they capture only moments in time. We developed the 2-minute standing endurance test (2 M-SET), which specifically captures temporal changes in posture, as a novel dynamic method for measuring axial PA in patients with PD. RESEARCH QUESTION: This study aimed to verify the effectiveness and validity of the 2 M-SET for capturing temporal changes in axial PA in patients with PD. METHODS: Twenty-eight patients with PD participated. The participants attempted to maintain an upright posture for 2 minutes during three tasks: standing, stepping in place, and walking. The rate of change in postural angle was recorded at 10-second intervals. Based on the results, the 2 M-SET was developed. Therapists evaluated the 2 M-SET using the NeuroPostureApp© to measure anterior trunk flexion (ATF) angles and lateral trunk flexion (LTF) angles at 0, 10, 30, 60, and 120 seconds. To assess reliability, the congruence between the measurements obtained by the therapists and those obtained using a three-dimensional motion-analysis system was examined. For validity, we assessed whether the ATF and LTF angles measured by the therapists could accurately capture postural changes at regular intervals over time. RESULTS: The average postural changes over 2 minutes for the standing, stepping in place, and gait tasks were 59.2±83.5%, 37.6±30.7%, and 45.4±50.6%, respectively. The intraclass correlation coefficients showed high reliability, with values of 0.985 and 0.970 for the ATF and LTF angles, respectively. SIGNIFICANCE: The results of our proposed 2 M-SET method, which uses temporal photo-based measurements to assess the patient's ability to maintain an upright standing position for 2 minutes, demonstrate the potential to capture temporal changes in axial PA. DATA AVAILABILITY STATEMENT: The data supporting the findings of this study are available upon reasonable request and approval from the local ethics committee.

The Impact of Periodontal Inflammation on the Severity of Coronary Atherosclerosis.

Introduction Through plausible biological mechanisms, periodontitis causes systemic inflammatory burden and response, thus resulting in damage far beyond the oral cavity. Studies have demonstrated periodontitis to be a significant risk factor for coronary heart disease (CHD) and stroke. The larger the quantum of periodontal inflamed tissue, the greater the chances of periodontitis eliciting bacteremia and systemic inflammatory responses. Studies have reported that periodontitis and other common oral infections play an important role in the development of atherosclerosis. Therefore, the quantity of inflamed periodontal tissue assumes significance in determining the severity of atherosclerosis. Hence, this study investigates the impact of periodontal inflamed surface area (PISA) on the severity of coronary atherosclerosis. Materials and methods In this cross-sectional study, a total of 160 patients who presented at the department of periodontics of The British University in Egypt (BUE) from 1 January 2023 to 30 September 2023 were enrolled. Patients were only enrolled if they had undergone coronary angiography within the last six months, were less than 60 years of age, shared their previous medical history and coronary angiographic report, and gave informed written consent. Data on classic coronary risk factors and periodontal inflammatory status and angiographic findings were recorded and subjected to appropriate statistical analysis. Results The results revealed that the periodontal inflamed surface area (p = 0.002) apart from age (p < 0.047) and low-density lipoprotein cholesterol (LDL-C) (p < 0.001) is a significant independent predictor of the severity of coronary atherosclerosis. Conclusions The periodontal inflamed surface area is an independent predictor of the severity of coronary atherosclerosis.

Polypeptide- and Protein-Based Conjugate Nanoparticles via Aqueous Ring-Opening Polymerization-Induced Self-Assembly (ROPISA).

Protein-polymer conjugates and polymeric nanomaterials hold great promise in many applications including biomaterials, medicine, or nanoelectronics. In this work, the first polymerization-induced self-assembly (PISA) approach performed in aqueous medium enabling protein-polymer conjugates and nanoparticles entirely composed of amino acids is presented by using ring-opening polymerization (ROP). It is indeed shown that aqueous ring-opening polymerization-induced self-assembly (ROPISA) can be used with protein or peptidic macroinitiators without prior chemical modification and afford the simple preparation of nanomaterials with protein-like property, for example, to implement biomimetic thermoresponsivity in drug delivery.

The impact of reading metacognitive strategies on mathematics learning efficiency and performance: An analysis using PISA 2018 data in China.

The current study employed latent profile analysis to examine the application patterns of students' reading metacognitive strategies using PISA 2018 data in China. Subsequently, it explored the differences in students' mathematics learning efficiency and performance. The results revealed that: (1) Six types of reading metacognitive strategies application patterns were identified: "Novice - indifferent," "Veteran - average," "Novice - low evaluating," "Veteran - skilled," "Novice - mixed," and "Novice - arbitrary." (2) The primary factors that affect the classification of reading metacognitive strategies application patterns were gender, and family economic, social, and cultural statuses (ESCS). (3) Mathematics learning time could positively predict performance overall, but the mathematics learning time of "Veteran - skilled" and "Novice - mixed" students had no significant correlation with their mathematics performance. The findings suggests that educators should not blindly increase students' mathematics learning time but instead provide appropriate guidance based on their mastery patterns of reading metacognitive strategies to enhance mathematics learning efficiency and performance.

The effect of school size and class size on school preparedness.

The purpose of the present study was to understand students' school readiness as a function of student and teacher behaviors but also school size and class size using both linear and non-linear analytical approaches. Data came from 21,903 schools distributed across 80 countries as per the 2018 cohort of the PISA database. Results pointed to a preference for the Cusp model in that the relationship between school and class sizes with achievement proved to be best described by the non-linearity of the Cusp catastrophe model. The critical benchmarks were a school size of 801 students and a class size of 27 students for which increases beyond those thresholds were linked to nonlinearity and unpredictability in school readiness. For this reason, we suggest using the cusp catastrophe model from Nonlinear Dynamical Systems Theory (NDST) to understand more fully such complex phenomena.

Phenanthroline Derived N-Doped Carbon Dots as Robust Metal-Free Photocatalysts for PET-RAFT Polymerization and Polymerization-Induced Self-Assembly.

Metal-free organic photocatalysts for photo-mediated reversible deactivation radical polymerization (photo-RDRP) are witnessed to make increasing advancement in the precise synthesis of polymers. However, challenges still exist in the development of high-efficiency and environmentally sustainable carbon dots (CDs)-based organocatalysts. Herein, N-doped CDs derived from phenanthroline derivative (Aphen) are prepared as metal-free photocatalysts for photoinduced electron transfer reversible addition-fragmentation chain transfer (PET-RAFT) polymerization. The introduction of phenanthroline structure enhances the excited state lifetime of CDs and expands the conjugated length of their internal structure to enable the light-absorption to reach green light region, thereby enhancing photocatalytic activity. The as-designed CDs exhibit unprecedented photocatalytic capacity in photopolymerization even in large-volume reaction (100 mL) with high monomer conversion and narrow polymer dispersity (Mw/Mn < 1.20) under green light. The photocatalytic system is compatible with PET-RAFT polymerization of numerous monomers and the production of high molecular weight polyacrylate (Mn >250 000) with exquisite spatiotemporal control. Above results confirm the potential of CDs as photocatalyst, which has not been achieved with other CDs catalysts used in photo-RDRP. In addition, the construction of fluorescent polymer nanoparticles using CDs as both photocatalyst and phosphor through photoinitiated polymerization-induced self-assembly (Photo-PISA) technology is successfully demonstrated for the first time.

Lansoprazole interferes with fungal respiration and acts synergistically with amphotericin B against multidrug-resistant Candida auris.

Candida auris has emerged as a problematic fungal pathogen associated with high morbidity and mortality. Amphotericin B (AmB) is the most effective antifungal used to treat invasive fungal candidiasis, with resistance rarely observed among clinical isolates. However, C. auris possesses extraordinary resistant profiles against all available antifungal drugs, including AmB. In our pursuit of potential solutions, we screened a panel of 727 FDA-approved drugs. We identified the proton pump inhibitor lansoprazole (LNP) as a potent enhancer of AmB's activity against C. auris. LNP also potentiates the antifungal activity of AmB against other medically important species of Candida and Cryptococcus. Our investigations into the mechanism of action unveiled that LNP metabolite(s) interact with a crucial target in the mitochondrial respiratory chain (complex III, known as cytochrome bc1). This interaction increases oxidative stress within fungal cells. Our results demonstrated the critical role of an active respiratory function in the antifungal activity of LNP. Most importantly, LNP restored the efficacy of AmB in an immunocompromised mouse model, resulting in a 1.7-log (∼98%) CFU reduction in the burden of C. auris in the kidneys. Our findings strongly advocate for a comprehensive evaluation of LNP as a cytochrome bc1 inhibitor for combating drug-resistant C. auris infections.

Quantification of Mitral Regurgitation in Mitral Valve Prolapse by Three-Dimensional Vena Contracta Area: Derived Cutoff Values and Comparison With Two-Dimensional Multiparametric Approach.

BACKGROUND: Echocardiographic grading of mitral regurgitation (MR) in mitral valve prolapse (MVP) is challenging. Three-dimensional (3D) vena contracta area (VCA) has been proposed as a valuable method. However, data defining the cutoff values of severity and validation in the subset of patients with MVP are scarce. The aim of this study was to validate the 3D VCA by 3D color-Doppler transesophageal echocardiography (TEE) in patients with MVP and to define the cutoff values of severity grading. The secondary aim was to compare 3D VCA to the effective regurgitant orifice area estimation by proximal isovelocity surface area (EROA-PISA) method. METHODS: A total of 1,138 patients with at least moderate MR who underwent TEE were included. Three-dimensional VCA was measured, and the cutoff value and area under the curve (AUC) for the prediction of severe MR were estimated by receiver operating characteristic curve using a guideline-suggested multiparametric approach as the reference standard. In a subgroup of patients, 3D regurgitant volume (RV) and 3D fraction were calculated from mitral and left ventricular outflow tract stroke volumes to further validate 3D VCA against a 3D volumetric reference standard. RESULTS: The optimal 3D VCA cutoff value for predicting severe MR was 0.45 cm2 (specificity, 0.87; sensitivity, 0.90) with an AUC of 0.95 using a multiparametric approach as reference. Three-dimensional VCA had a good linear correlation with EROA-PISA (r = 0.62, P < .05) with larger values compared to EROA-PISA (0.63 cm2 vs 0.44 cm2, P < .05). A cutoff of 0.50 cm2 (AUC of 0.84; sensitivity, 0.78; specificity, 0.78) predicts an EROA-PISA of 0.40 cm2. Three-dimensional VCA had a good linear correlation with 3D RV (r = 0.56, P < .01), with an AUC of 0.86 to predict a 3D fraction >50%. CONCLUSIONS: The present study suggests 0.45 cm2 as the best cutoff value of 3D VCA to define severe MR in patients with MVP, showing an optimal agreement with the reference standard multiparametric approach and 3D RV.

Binding Pattern and Structural Interactome of the Anticancer Drug 5-Fluorouracil: A Critical Review.

5-Fluorouracil (5-FU) stands as one of the most widely prescribed chemotherapeutics. Despite over 60 years of study, a systematic synopsis of how 5-FU binds to proteins has been lacking. Investigating the specific binding patterns of 5-FU to proteins is essential for identifying additional interacting proteins and comprehending their medical implications. In this review, an analysis of the 5-FU binding environment was conducted based on available complex structures. From the earliest complex structure in 2001 to the present, two groups of residues emerged upon 5-FU binding, classified as P- and R-type residues. These high-frequency interactive residues with 5-FU include positively charged residues Arg and Lys (P type) and ring residues Phe, Tyr, Trp, and His (R type). Due to their high occurrence, 5-FU binding modes were simplistically classified into three types, based on interactive residues (within <4 Å) with 5-FU: Type 1 (P-R type), Type 2 (P type), and Type 3 (R type). In summary, among 14 selected complex structures, 8 conform to Type 1, 2 conform to Type 2, and 4 conform to Type 3. Residues with high interaction frequencies involving the N1, N3, O4, and F5 atoms of 5-FU were also examined. Collectively, these interaction analyses offer a structural perspective on the specific binding patterns of 5-FU within protein pockets and contribute to the construction of a structural interactome delineating the associations of the anticancer drug 5-FU.

Diagnostic Classification Models for Testlets: Methods and Theory.

Diagnostic classification models (DCMs) have seen wide applications in educational and psychological measurement, especially in formative assessment. DCMs in the presence of testlets have been studied in recent literature. A key ingredient in the statistical modeling and analysis of testlet-based DCMs is the superposition of two latent structures, the attribute profile and the testlet effect. This paper extends the standard testlet DINA (T-DINA) model to accommodate the potential correlation between the two latent structures. Model identifiability is studied and a set of sufficient conditions are proposed. As a byproduct, the identifiability of the standard T-DINA is also established. The proposed model is applied to a dataset from the 2015 Programme for International Student Assessment. Comparisons are made with DINA and T-DINA, showing that there is substantial improvement in terms of the goodness of fit. Simulations are conducted to assess the performance of the new method under various settings.

The Relationship between a Competitive School Climate and School Bullying among Secondary Vocational School Students in China: A Moderated Mediation Model.

School bullying is widespread in countries around the world and has a continuous negative impact on the physical and mental health of students. However, few studies have explored the influence mechanism of a competitive school climate on school bullying among Chinese secondary vocational school students. This study aims to explore the relationship between a competitive school climate and bullying in secondary vocational schools in the Chinese context, as well as the mediating role of school belonging and the moderating role of gender. Logit regression analysis and a moderated mediation model were used to analyze 1964 secondary vocational students from China based on PISA 2018 data from Beijing, Shanghai, Zhejiang, and Jiangsu, China. (1) The detection rate of school bullying in secondary vocational schools in China is 17.8%, lower than the world average. (2) A competitive school climate is significantly and positively correlated with secondary vocational school students' exposure to school bullying. (3) A moderated mediation model suggests that school belonging is an important mechanism by which a competitive school climate influences the occurrence of school bullying, whereas gender moderates the direct effect of a competitive school climate and the indirect effect of school belonging, which mitigates the negative effects of a competitive school climate to some extent. The research results show that creating a healthy competitive climate in schools, cultivating students' sense of belonging, and facing up to gender differences are helpful to prevent school bullying in secondary vocational schools.

Adaptive and Dissipative Hierarchical Population Crowding of Synthetic Protocells through Click-PISA under Gradient Energy Inputs.

The ability of living objects to respond rapidly en masse to various stimuli or stress is an important function in response to externally applied changes in the local environment. This occurs across many length scales, for instance, bacteria swarming in response to different stimuli or stress and macromolecular crowding within cells. Currently there are few mechanisms to induce similar autonomous behaviors within populations of synthetic protocells. Herein, we report a system in which populations of individual objects behave in a coordinated manner in response to changes in the energetic environment by the emergent self-organization of large object swarms. These swarms contain protocell populations of approximately 60 000 individuals. We demonstrate the dissipative nature of the hierarchical constructs, which persist under appropriate UV stimulation. Finally, we identify the ability of the object populations to change behaviors in an adaptive population-wide response to the local energetic environment.

The effect of reading engagement on scientific literacy - an analysis based on the XGBoost method.

Scientific literacy is a key factor of personal competitiveness, and reading is the most common activity in daily learning life, and playing the influence of reading on individuals day by day is the most convenient way to improve the level of scientific literacy of all people. Reading engagement is one of the important student characteristics related to reading literacy, which is highly malleable and is jointly reflected by behavioral, cognitive, and affective engagement, and it is of theoretical and practical significance to explore the relationship between reading engagement and scientific literacy using reading engagement as an entry point. In this study, we used PISA2018 data from China to explore the relationship between reading engagement and scientific literacy with a sample of 15-year-old students in mainland China. 36 variables related to reading engagement and background variables (gender, grade, and socioeconomic and cultural status of the family) were selected from the questionnaire as the independent variables, and the score of the Scientific Literacy Assessment (SLA) was taken as the outcome variable, and supervised machine learning method, the XGBoost algorithm, to construct the model. The dataset is randomly divided into training set and test set to optimize the model, which can verify that the obtained model has good fitting degree and generalization ability. Meanwhile, global and local personalized interpretation is done by introducing the SHAP value, a cutting-edge machine model interpretation method. It is found that among the three major components of reading engagement, cognitive engagement is the more influential factor, and students with high reading cognitive engagement level are more likely to get high scores in scientific literacy assessment, which is relatively dominant in the model of this study. On the other hand, this study verifies the feasibility of the current popular machine learning model, i.e., XGBoost, in a large-scale international education assessment program, with a better model adaptability and conditions for global and local interpretation.

Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment.

Computer-based assessments provide the opportunity to collect a new source of behavioral data related to the problem-solving process, known as log file data. To understand the behavioral patterns that can be uncovered from these process data, many studies have employed clustering methods. In contrast to one-mode clustering algorithms, this study utilized biclustering methods, enabling simultaneous classification of test takers and features extracted from log files. By applying the biclustering algorithms to the "Ticket" task in the PISA 2012 CPS assessment, we evaluated the potential of biclustering algorithms in identifying and interpreting homogeneous biclusters from the process data. Compared with one-mode clustering algorithms, the biclustering methods could uncover clusters of individuals who are homogeneous on a subset of feature variables, holding promise for gaining fine-grained insights into students' problem-solving behavior patterns. Empirical results revealed that specific subsets of features played a crucial role in identifying biclusters. Additionally, the study explored the utilization of biclustering on both the action sequence data and timing data, and the inclusion of time-based features enhanced the understanding of students' action sequences and scores in the context of the analysis.