The role of sulfur cycle in new particle formation: Cycloaddition reaction of SO3 to H2S.

The chemistry of sulfur cycle contributes significantly to the atmospheric nucleation process, which is the first step of new particle formation (NPF). In the present study, cycloaddition reaction mechanism of sulfur trioxide (SO3) to hydrogen sulfide (H2S) which is a typical air pollutant and toxic gas detrimental to the environment were comprehensively investigate through theoretical calculations and Atmospheric Cluster Dynamic Code simulations. Gas-phase stability and nucleation potential of the product thiosulfuric acid (H2S2O3, TSA) were further analyzed to evaluate its atmospheric impact. Without any catalysts, the H2S + SO3 reaction is infeasible with a barrier of 24.2 kcal/mol. Atmospheric nucleation precursors formic acid (FA), sulfuric acid (SA), and water (H2O) could effectively lower the reaction barriers as catalysts, even to a barrierless reaction with the efficiency of cis-SA > trans-FA > trans-SA > H2O. Subsequently, the gas-phase stability of TSA was investigated. A hydrolysis reaction barrier of up to 61.4 kcal/mol alone with an endothermic isomerization reaction barrier of 5.1 kcal/mol under the catalytic effect of SA demonstrates the sufficient stability of TSA. Furthermore, topological and kinetic analysis were conducted to determine the nucleation potential of TSA. Atmospheric clusters formed by TSA and atmospheric nucleation precursors (SA, ammonia NH3, and dimethylamine DMA) were thermodynamically stable. Moreover, the gradually decreasing evaporation coefficients for TSA-base clusters, particularly for TSA-DMA, suggests that TSA may participate in NPF where the concentration of base molecules are relatively higher. The present new reaction mechanism may contributes to a better understanding of atmospheric sulfur cycle and NPF.

Theoretical study on intramolecular hydrogen bonds of flavonoid cocrystals.

This study investigates the role of intramolecular hydrogen bonds in the formation of cocrystals involving flavonoid molecules, focusing on three active pharmaceutical ingredients (APIs): chrysin (CHR), isoliquiritigenin (ISO), and kaempferol (KAE). These APIs form cocrystals with different cocrystal formers (CCFs) through intramolecular hydrogen bonding. We found that disruption of these intramolecular hydrogen bonds leads to decreased stability compared to molecules with intact bonds. The extrema of molecular electrostatic potential surfaces (MEPS) show that flavonoid molecules with disrupted intramolecular hydrogen bonds have stronger hydrogen bond donors and acceptors than those with intact bonds. Using the artificial bee colony algorithm, dimeric structures of these flavonoid molecules were explored, representing early-stage structures in cocrystal formation, including API-API, API-CCF, and CCF-CCF dimers. It was observed that the number and strength of dimeric interactions significantly increased, and the types of interactions changed when intramolecular hydrogen bonds were disrupted. These findings suggest that disrupting intramolecular hydrogen bonds generally hinders the formation of cocrystals. This theoretical study provides deeper insight into the role of intramolecular hydrogen bonds in the cocrystal formation of flavonoids.

The statistical building blocks of animal movement simulations.

Animal movement plays a key role in many ecological processes and has a direct influence on an individual's fitness at several scales of analysis (i.e., next-step, subdiel, day-by-day, seasonal). This highlights the need to dissect movement behavior at different spatio-temporal scales and develop hierarchical movement tools for generating realistic tracks to supplement existing single-temporal-scale simulators. In reality, animal movement paths are a concatenation of fundamental movement elements (FuMEs: e.g., a step or wing flap), but these are not generally extractable from a relocation time-series track (e.g., sequential GPS fixes) from which step-length (SL, aka velocity) and turning-angle (TA) time series can be extracted. For short, fixed-length segments of track, we generate their SL and TA statistics (e.g., means, standard deviations, correlations) to obtain segment-specific vectors that can be cluster into different types. We use the centroids of these clusters to obtain a set of statistical movement elements (StaMEs; e.g.,directed fast movement versus random slow movement elements) that we use as a basis for analyzing and simulating movement tracks. Our novel concept is that sequences of StaMEs provide a basis for constructing and fitting step-selection kernels at the scale of fixed-length canonical activity modes: short fixed-length sequences of interpretable activity such as dithering, ambling, directed walking, or running. Beyond this, variable length pure or characteristic mixtures of CAMs can be interpreted as behavioral activity modes (BAMs), such as gathering resources (a sequence of dithering and walking StaMEs) or beelining (a sequence of fast directed-walk StaMEs interspersed with vigilance and navigation stops). Here we formulate a multi-modal, step-selection kernel simulation framework, and construct a 2-mode movement simulator (Numerus ANIMOVER_1), using Numerus RAMP technology. These RAMPs run as stand alone applications: they require no coding but only the input of selected parameter values. They can also be used in R programming environments as virtual R packages. We illustrate our methods for extracting StaMEs from both ANIMOVER_1 simulated data and empirical data from two barn owls (Tyto alba) in the Harod Valley, Israel. Overall, our new bottom-up approach to path segmentation allows us to both dissect real movement tracks and generate realistic synthetic ones, thereby providing a general tool for testing hypothesis in movement ecology and simulating animal movement in diverse contexts such as evaluating an individual's response to landscape changes, release of an individual into a novel environment, or identifying when individuals are sick or unusually stressed.

Spectroscopic techniques and chemometrics for ink analysis: Document analysis application.

This study represents a methodology to determine the falsified documents via a combined method of spectroscopic techniques and chemometrics. The techniques of UV-Vis and FTIR-ATR spectroscopies and principal component analysis methods have been applied to black, blue, and red ballpoint pens available in the Türkiye market. The analysis results of the applied spectroscopic techniques were found to be consistent with each other for the examined eleven ink samples from three different brands, and it was observed that FTIR-ATR spectroscopy had higher discriminative power. A dried sample preparation technique developed for small amounts of samples for FTIR-ATR measurements and the increased the sensitivity of FTIR-ATR technique was associated with this sample preparation method. Statistical techniques showed that the UV-Vis technique proves useful outcomes in distinguishing inks according to their colors, whereas the FTIR-ATR technique is valuable for identifying ink brands. Additional analysis of the sub-brand of black inks and falsified old-dated check sample analysis validated that our methodology has the potential to classify and identify ballpoint inks in forensic applications. Due to the limited sample quantity available for analysis in real samples, only FTIR-ATR analysis was possible, and the results demonstrated that document forgery could be detected from small parts of samples in the document. When we look at the overall outcome of this study, a comprehensive methodology enriched with statistical calculations has been proposed that can be applied even in a laboratory with limited resources, having only UV-Vis and FTIR-ATR capabilities.

Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model.

Accurate prediction of PM2.5 concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several challenges owing to the impact of unique meteorological conditions, potential correlation between PM2.5 levels in neighboring ports, and coupling influence of background pollutants in city zones. Therefore, considering the spatiotemporal correlation among the factors influencing PM2.5 concentration variations within the harbor cluster, we developed a novel blending ensemble deep learning model. The proposed model combined the strengths of four deep learning architectures: graph convolutional networks (GCN), long short-term memory networks (LSTM), residual neural networks (ResNet), and convolutional neural networks (CNN). GCN, LSTM, and ResNet served as the base models aimed at capturing the spatial correlation of PM2.5 concentrations in neighboring ports, the potential long-term dependence of meteorological factors and PM2.5 concentrations, and the effects of urban ambient air pollutants, respectively. Following the blending ensemble technique, the prediction outcomes of three base models were used as the input data for the meta-model CNN, which employs the blending ensemble technique to produce the final prediction results. Based on actual data obtained from 18 ports in Nanjing, the proposed model was compared and analyzed for its prediction performance against six state-of-the-art models. The findings revealed that the proposed model provided more accurate predictions. It reduced mean absolute error (MAE) by 10.59 %-20.00 %, reduced root mean square error (RMSE) by 13.22 %-17.11 %, improved coefficient of determination (R2) by 10 %-35.38 %, and improved accuracy (ACC) by 3.48 %-7.08 %. Additionally, the contribution of each component to the prediction performance of the proposed model was measured using a systematic ablation study. The results demonstrated that the GCN model exerted the most substantial influence on the prediction performance of the GCN-LSTM-ResNet model, followed by the LSTM model. The influence of urban background pollutants can significantly enhance the generalizability of the complete model. Moreover, a comparison with three blended ensemble models incorporating any two base models demonstrated that the GCN-LSTM-ResNet model exhibited superior prediction performance and was particularly excellent in predicting the occurrence of high-concentration events. Specifically, the GCN-LSTM-ResNet model improved MAE and RMSE by at least 12.3% and 9.2%, respectively, but reduced R2 and ACC by 26.1% and 6.8%, respectively. The proposed model provided reliable PM2.5 concentration prediction outcomes and decision support for air quality management strategies in dry bulk port clusters.

The implication of alexithymia in personality disorders: a systematic review.

BACKGROUND: Alexithymia, characterized by difficulty identifying and expressing emotions, is often associated with various psychiatric disorders, including personality disorders (PDs). This study aimed to explore the relationship between alexithymia and PD, focusing on their common origins and implications for treatment. METHODS: A systematic review was conducted following PRISMA guidelines using databases such as MEDLINE (PubMed), Scopus, and Web of Science. The inclusion criteria were studies assessing adults with DSM-5-diagnosed personality disorders using validated alexithymia scales. The Newcastle‒Ottawa Scale was used to assess the quality of the included studies. RESULTS: From an initial yield of 2434 citations, 20 peer-reviewed articles met the inclusion criteria. The findings indicate a significant association between alexithymia and personality disorders, particularly within Clusters B and C. Patients with these disorders exhibited higher levels of alexithymia, which correlated with increased emotional dysregulation and interpersonal difficulties. The review also highlighted the comorbidity burden of conditions such as psychosomatic disorders, eating disorders, depression, anxiety, suicidal behavior, and substance use disorders. CONCLUSIONS: These findings underscore the need for integrating alexithymia-focused assessments into clinical practice to enhance therapeutic approaches, allowing for more personalized and effective interventions. Addressing the emotional processing challenges in patients with personality disorders could significantly improve patient outcomes. Future research should prioritize establishing clinical guidelines and conducting longitudinal studies to explore the relationship between alexithymia and specific personality disorder subtypes, ensuring the practical translation of these findings into clinical practice.

Predicting Outcome of Total Knee Arthroplasty by Cluster Analysis of Patient-Reported Outcome Measures.

BACKGROUND: Total knee arthroplasties (TKAs) exhibit an 8 to 30% risk of suboptimal outcomes, resulting in persistent symptoms, individual morbidity, and revision surgery, prompting a contemporary focus on risk reduction and outcome improvement. This study introduces hierarchical cluster analysis as a way of preoperatively assessing the likelihood of success/failure of TKA based on several patient-reported outcome measures, which have been analyzed both intact and with component questions as individual variables. METHODS: The study utilized data on 1,433 TKAs from The Miriam Hospital's Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement (FORCE-TJR) registry. Outcomes are expressed as Knee Injury and Osteoarthritis Outcome Score (KOOS) pain and function scores. Criteria for success/failure were developed with an integrative, anchor-based, minimum clinically important difference. Preoperative and postoperative patient-reported outcome measures (PROMs) were studied by cluster analysis. RESULTS: There were three sequential cluster analyses that revealed clusters of patients, based upon preoperative patient responses, that were predictive of surgical outcomes. Clusters varied most significantly in their responses to individual component questions of preoperative PROMs. Extracting and combining the clinically meaningful patient-reported component questions yielded a new, and clinically relevant, outcome measure that has the potential to preoperatively predict postoperative outcomes of total knee arthroplasty. CONCLUSION: In contrast to a single medical, psychological, or social variable, cluster analysis offers the opportunity to develop a whole-patient profile that reflects the contextual interactions of sociodemographic and clinical variables in predicting outcomes. In the context of determining clinical meaningfulness, cluster analysis has one of its major strengths.

Thrombospondin 2 is a tumor stemness protein marker based on the cluster analysis combined with immune infiltration in gastric cancer.

Platelet reactive protein 2 (PRP2) is closely related to the characteristics of tumor stem cells. Its role in cancer development and metastasis has received increasing attention, especially its interaction with the immune microenvironment. The study used cluster analysis to extract expression data of multiple cancer types from public databases, and combined with immune infiltration analysis, to evaluate the expression level of PRP2 and its correlation with different immune cell infiltration. Bioinformatics tools were used to analyze the correlation between PRP2 and tumor stem cell markers. The results show that PRP2 is significantly upregulated in a variety of cancers and is closely related to tumor stem cell characteristics. Immunoinfiltration analysis showed that the high expression of PRP2 was associated with a significant increase in immunosuppression-related cell infiltration. Through cluster analysis, we identified the expression pattern of PRP2 in different cancer types, indicating that it may be used as a biomarker for early diagnosis and prognosis assessment, and its expression is closely related to the immune microenvironment, indicating its important role in cancer biology and potential application value in the tumor immune microenvironment.

Subtyping of pancreatic neuroendocrine tumors by transcription factors, hormones, histology, and patient outcome.

BACKGROUND: Pancreatic neuroendocrine tumors (PanNETs) show pronounced heterogeneity in terms of hormone and transcription factor (TF) expression. TFs such as ARX and PDX1 are related to alpha- and beta-cell-type features, respectively, and partly associate with patient outcome. However, detailed studies correlating hormone expression, histology, and clinical data are lacking. OBJECTIVE: The aim of this study was to identify subtypes of PanNETs that associate with histological, hormonal, and prognostic findings. METHODS: A total of 185 resected PanNETs were divided into five subtypes (types A1, A2, B, C, and D) by cluster analysis based on expression of four TFs (ARX, PDX1, ISL1, and CDX2) and correlated to the expression of hormones and DAXX/ATRX as well as ALT activation status, histology, and progression-free survival. RESULTS: Subgroup A1 (ISL1+/ARX+/PDX-/CDX2-) was most frequent (46%), followed by type B (18%; ISL1+/ARX-/PDX+/CDX2-), A2 (15%; ISL1+/ARX+/PDX+/CDX2-), C (15%; ISL1-/ARX-/PDX-/CDX2-), and D (5%; ISL1-/ARX-/PDX+/CDX2+). Subgroups A1 and A2 showed a strong association with a trabecular growth pattern and glucagon and pancreatic polypeptide (PP) expression (p < 0.001), while A2 was in addition associated with gastrin expression. Subgroup B was associated with insulin production (p < 0.001) and included all 17 insulinomas. Subgroup C was associated with solid morphology and expression of serotonin, calcitonin, and adrenocorticotropic hormone (ACTH). Subgroup D showed solid morphology, expression of ACTH, somatostatin, or serotonin and had the shortest disease-free survival (p < 0.01). ALT positivity was associated with poorer outcome in types A1 and A2 but not in other types. CONCLUSION: PanNETs can be categorized into five subgroups based on different TF signatures, which associate strongly with histology, hormone production, functionality, and patient outcome.

Low-temperature oxidation of methane mediated by Al-doped ZnO cluster and nanowire: a first-principles investigation.

CONTEXT: First-principles calculations are performed to investigate the catalytic oxidation of methane by using N2O as an oxidizing agent over aluminum (Al)-doped Zn12O12 cluster and (Zn12O12)2 nanowire. The impact of Al impurity on the geometry, electronic structure, and surface reactivity of Zn12O12 and (Zn12O12)2 is thoroughly studied. Our study demonstrates that Al-doped ZnO systems have a better adsorption ability than the corresponding pristine counterparts. It is found that N2O molecule is initially decomposed on the Al site to provide the N2 molecule, and an Al-O intermediate which is an active species for the CH4 oxidation. The conversion of CH4 into CH3OH over AlZn11O12 and (AlZn11O12)2 requires an activation energy of 0.45 and 0.29 eV, respectively, indicating it can be easily performed at normal temperatures. Besides, the overoxidation of methanol into formaldehyde cannot take place over the AlZn11O12 and (AlZn11O12)2, due to the high energy barrier needed to dissociate C-H bond of the CH3O intermediate. METHOD: Dispersion-corrected density functional theory calculations were performed through GGA-PBE exchange-correlation functional combined with a numerical double-ζ plus polarization (DNP) basis set as implemented in DMol3. To include the relativistic effects of core electrons of Zn atoms, DFT-semicore pseudopotentials were adopted. The DFT + D scheme proposed by Grimme was used to involve weak dispersion interactions within the DFT calculations. The reaction energy paths were generated by the minimum energy path calculations using the NEB method.

Evaluation of the geochemical background of soil in a hyper-arid zone using a multivariate statistical methodology: The case of the city of Antofagasta in the Atacama Desert.

This study investigates the geochemical background and factors influencing the variability of 19 environmentally relevant elements in the soils of Antofagasta, Chile, a region known for its extensive mining activities. Employing robust multivariate statistical techniques on a dataset of 94 soil samples, we identified four main factors explaining 70% of the total variance in elemental concentrations. These four factors reflect the influence of Jurassic volcanic rocks, intrusive rocks, marine sediments, and mafic to intermediate intrusive rocks. Cluster analysis revealed three distinct geochemical populations, each reflecting a unique combination of natural and anthropogenic influences. We established background concentrations for each element within these clusters using robust statistical methods. Geostatistical analysis, employing inverse distance weighted interpolation, produced factor distribution maps that, when integrated with geological data, provided insights into lithological and anthropogenic influences on soil geochemistry. Our findings highlight the complex interplay between natural geological processes, the region's unique arid climate, and anthropogenic activities in shaping the geochemical landscape of Antofagasta. This study contributes to the understanding of geochemical backgrounds in mining-intensive, arid regions and provides a methodological framework applicable to similar environments worldwide.

Transect approach for assessing major and trace elemental contamination in agricultural soils of the palamaner division, chittoor district, andhra pradesh, india.

Three soil transects located in the granitic regions of Palamaner mandal, Andhra Pradesh, India, were examined to assess the pollution levels of both primary and secondary metals (Si, Al, Fe, Ca, Mg, Na, K, Cu, Mn, P, and Zn) and to ascertain the degree of soil pollution in agricultural areas. The soils along these transects are slightly acid to neutral, with dark brown to red rubified argillic clay-rich B horizons alongside a moderate cation exchange capacity. The A horizon soils display low organic carbon levels with a moderate variability and contain over 70% SiO2, exhibiting low variability due to limited leaching in a semiarid climate. The findings suggest that iron (Fe) and manganese (Mn) oxides play a role in reducing contamination levels through oxidation and precipitation processes. Furthermore, the soils show low to moderate cation exchange capacity, which restricts the retention of absorbed heavy metals, thus lessening their negative impacts. A two-way ANOVA revealed significant differences in CEC, organic carbon, and total zinc content across different horizons and landscape positions. Si, Al, and Cu had small increases and negative geoaccumulation indices in all soil profiles, suggesting no pollution. However, the Nemerow and mean contamination degree over 6 point to slight to moderate pollution. The analysis identified three distinct clusters with significant variations in contamination factors for SiO2 and Cu. Five principal components were determined, explaining 76% of the total variance, primarily derived from geogenic sources and remaining within acceptable limits. This research on soil transects in granitic regions contributes to a better understanding of the distribution, movement, and concentration of elemental oxides based on slope position, which is essential for pollution assessment and soil quality enhancement.

Characterization of systemic lupus erythematosus subtypes using cluster analysis: insights from lymphocyte subpopulations.

BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, in which lymphocyte subsets were dysregulated. Incorporating lymphocyte subpopulations in cluster analysis offers a pathway for personalized and precise treatment, targeting specific abnormalities for more effective management. METHODS: We conducted Gaussian clustering analysis on clinical data, serological data, urine test results, and lymphocyte subpopulations for SLE patients hospitalized from September 2008 to December 2019. RESULTS: A total of 1863 SLE patients from Xi'Jing Hospital were included. After excluding those without complete assessments, 1281 patients underwent flow cytometry for lymphocyte subsets. Five SLE clusters emerged: Cluster 1 with severe kidney involvement, high SLEDAI scores, and infection rates, often accompanied by rashes and edema; cluster 2 with high urinary protein but better renal function; cluster 3 with normal lymphocyte count and low positive antibodies; cluster 4 with frequent psychiatric symptoms and pulmonary arterial hypertension (PAH); and cluster 5 with fever, arthritis, hematologic involvement, and high IgG levels despite decreased B cells. CONCLUSION: All enrolled SLE patients were ultimately categorized into five distinct clinical phenotype groups, with lymphocyte testing being meaningful for patient stratification. This finding shed light on the intricate heterogeneity of SLE, emphasizing the need for a personalized medicine approach. Targeting specific abnormalities in lymphocyte subsets holds promise for more effective and precise management of SLE. Key Points • A comprehensive analysis of SLE patients, including lymphocyte subpopulations, revealed five distinct clusters with varying clinical characteristics, emphasizing the heterogeneity of the disease. • This heterogeneity underscores the need for a personalized medicine approach in SLE management, targeting specific lymphocyte subset abnormalities for more effective and precise treatment.

The heterogeneity of aqueous solutions: the current situation in the context of experiment and theory.

The advancement of experimental methods has provided new information about the structure and structural fluctuations of water. Despite the appearance of numerous models, which aim to describe a wide range of thermodynamic and electrical characteristics of water, there is a deficit in systemic understanding of structuring in aqueous solutions. A particular challenge is the fact that even pure water is a heterogeneous, multicomponent system composed of molecular and supramolecular structures. The possibility of the existence of such structures and their nature are of fundamental importance for various fields of science. However, great difficulties arise in modeling relatively large supramolecular structures (e.g. extended hydration shells), where the bonds between molecules are characterized by low energy. Generally, such structures may be non-equilibrium but relatively long-lived. Evidently, the short times of water microstructure exchanges do not mean short lifetimes of macrostructures, just as the instability of individual parts does not mean the instability of the entire structure. To explain this paradox, we review the data from experimental and theoretical research. Today, only some of the experimental results on the lifetime of water structures have been confirmed by modeling, so there is not a complete theoretical picture of the structure of water yet. We propose a new hierarchical water macrostructure model to resolve the issue of the stability of water structures. In this model, the structure of water is presented as consisting of many hierarchically related levels (the stratification model). The stratification mechanism is associated with symmetry breaking at the formation of the next level, even with minimal changes in the properties of the previous level. Such a hierarchical relationship can determine the unique physico-chemical properties of water systems and, in the future, provide a complete description of them.

Preparation and Catalytic Properties of Gold Single-Atom and Cluster Catalysts Utilizing Nanoparticulate Mg-Al Layered Double Hydroxides.

Au single atoms and clusters were stabilized on Mg-Al layered double hydroxide nanoparticles (LDH NPs), and the obtained Au@LDH NPs were supported on SiO2 and CeO2. After hydrogen reduction, Au single atoms were found together with Au clusters on LDH/SiO2. In contrast to Au single-atom catalysts which are deposited in metal vacancies of oxide supports, the LDH NPs stabilize small Au species despite the absence of metal vacancies. The obtained Au(0)@LDH/SiO2 catalyzed aerobic oxidation of alcohols, and Au single atoms maintained after the reaction. Given that only Au NPs were observed on bulk LDH, the abundant surface OH group of LDH NPs would contribute to stabilize Au, resulting in higher activity than Au/LDH-bulk. After calcination to transform LDH to mixed metal oxide (MMO), the obtained Au(0)@MMO/SiO2 also exhibited high catalytic activity. Moreover, Au(0)@LDH/CeO2 exhibited higher activity and excellent selectivity for hydrogenation of 4-nitrostyrene to 4-aminostyrene than conventional Au catalysts such as Au/CeO2 and Au/TiO2 even though only Au NPs were present. We demonstrated that Au size can be minimized using LDH NPs, exhibiting in high catalytic performance. The basic surface OH groups of LDH would be also beneficial for deprotonation of alcohols and heterolytic H2 dissociation in the catalytic reactions.

Thin Zinc Electrodes Stabilized with Organobromine-Partnered H2O-Zn-MeOH Cluster Ions for Practical Zinc-Metal Pouch Cells.

The utilization of thin zinc (Zn) anodes with a high depth of discharge is an effective strategy to increase the energy density of aqueous Zn metal batteries (ZMBs), but challenged by the poor reversibility of Zn electrode due to the serious Zn-consuming side reactions at the Zn||electrolyte interface. Here, we introduce 2-bromomethyl-1,3-dioxolane (BDOL) and methanol (MeOH) as electrolyte additive into aqueous ZnSO4 electrolyte. In the as-formulated electrolyte, BDOL with a strong electron-withdrawing group (-CH2Br) tends to pair with the H2O-Zn-MeOH complex, leading to the formation of organobromine-partnered H2O-Zn-MeOH cluster ions. During the Zn electrodeposition process, the formed ZnO-dominated by-products induce the polymerization of BDOL monomers, which are previously adsorbed on the electrode. As a result, a uniform dual-layer SEI with ZnO-dominated outer layer and polyether-dominated inner layer is built on the surface of Zn electrode. With such an in-situ formed dual-layer SEI, the Zn||Mg0.9Mn3O7·2.7H2O pouch cell using a 10-um Zn anode (corresponding to a low negative to positive areal capacity ratio of 3.56) successfully operated for 300 cycles with a high capacity retention of 86%, promising a high practical energy density of > 120 Wh/kg (based on the total mass of Zn and Mg0.9Mn3O7·2.7H2O).

A Service-Learning Program to Prevent Online Hate Speech Perpetration Among Secondary Education Students: A Pilot Study.

The evolution and rapid expansion of online hate speech perpetration (OHSP) behaviors among adolescents toward different groups contrast with the lack of theory-based and empirically evaluated programs targeting this phenomenon. This study, developed by the Spanish Volunteering Platform, contributes to reversing this situation by investigating the impact of a service-learning program to prevent OHSP among students in secondary education. To do so, a cluster-randomized research design was adopted with a sample of secondary school students (n = 60) from two public schools. After the random assignment of each class group to either the intervention or control condition, the service-learning program previously designed was implemented for 3 months. Regression results in model 7 with all predictors and controls yield a significant effect on the variable experimental group = 0.803, p < .05, and gender = 0.987, p < .05, explaining 45.9% of the variance. The effect size for this intervention was 0.42. From a theoretical perspective, participants are moving in the right direction to reduce and/or prevent OHSP behaviors, which demonstrates the theoretical utility of the theory of planned behavior in hate speech research. Finally, several recommendations are made to improve the impact of future replications or adaptations of this program.

Characteristics of gene expression in epicardial adipose tissue and subcutaneous adipose tissue in patients at risk for heart failure undergoing coronary artery bypass grafting.

BACKGROUND: Epicardial adipose tissue (EAT) surrounds the heart and is hypothesised to play a role in the development of heart failure (HF). In this study, we first investigated the differences in gene expression between epicardial adipose tissue (EAT) and subcutaneous adipose tissue (SAT) in patients undergoing elective coronary artery bypass graft (CABG) surgery (n = 21; 95% male). Secondly, we examined the association between EAT and SAT in patients at risk for HF stage A (n = 12) and in pre-HF patients, who show signs but not symptoms of HF, stage B (n = 9). RESULTS: The study confirmed a distinct separation between EAT and SAT. In EAT 17 clusters of genes were present, of which several novel gene modules are associated with characteristics of HF. Notably, seven gene modules showed significant correlation to measures of HF, such as end diastolic left ventricular posterior wall thickness, e'mean, deceleration time and BMI. One module was particularly distinct in EAT when compared to SAT, featuring key genes such as FLT4, SEMA3A, and PTX3, which are implicated in angiogenesis, inflammation regulation, and tissue repair, suggesting a unique role in EAT linked to left ventricular dysfunction. Genetic expression was compared in EAT across all pre-HF and normal phenotypes, revealing small genetic changes in the form of 18 differentially expressed genes in ACC/AHA Stage A vs. Stage B. CONCLUSIONS: The roles of subcutaneous and epicardial fat are clearly different. We highlight the gene expression difference in search of potential modifiers of HF progress. The true implications of our findings should be corroborated in other studies since HF ACC/AHA stage B patients are common and carry a considerable risk for progression to symptomatic HF.

Exploring coral reef communities in Puerto Rico using Bayesian networks.

Most coral reef studies focus on scleractinian (stony) corals to indicate reef condition, but there are other prominent assemblages that play a role in ecosystem structure and function. In Puerto Rico these include fish, gorgonians, and sponges. The U.S. Environmental Protection Agency conducted unique surveys of coral reef communities across the southern coast of Puerto Rico that included simultaneous measurement of all four assemblages. Evaluating the results from a community perspective demands endpoints for all four assemblages, so patterns of community structure were explored by probabilistic clustering of measured variables with Bayesian networks. Most variables were found to have stronger associations within than between taxa, but unsupervised structure learning identified three cross-taxa relationships with potential ecological significance. Clusters for each assemblage were constructed using an expectation-maximization algorithm that created a factor node jointly characterizing the density, size, and diversity of individuals in each taxon. The clusters were characterized by the measured variables, and relationships to variables for other taxa were examined, such as stony coral clusters with fish variables. Each of the factor nodes were then used to create a set of meta-factor clusters that further summarized the aggregate monitoring variables for the four taxa. Once identified, taxon-specific and meta-clusters represent patterns of community structure that can be examined on a regional or site-specific basis to better understand risk assessment, risk management and delivery of ecosystem services.

Pre-cluster symptoms in a Taiwanese cohort of cluster headache: symptom profiles and clinical predictions.

BACKGROUND: Pre-cluster symptoms (PCSs) are symptoms preceding cluster bouts and might have implications for the treatment of cluster headache (CH). This study investigated the prevalence of PCSs, and their utility in predicting upcoming bouts as well as the associations with therapeutic efficacy. METHODS: We prospectively collected data from patients with CH. Each patient received a structured interview and completed questionnaire surveys during CH bouts. In sub-study 1, we cross-sectionally analyzed the prevalence, symptomatology, and predictability of upcoming bouts. Overall, 34 PCSs, divided into seven categories, were queried, including head and neck pain, cranial autonomic symptoms, restlessness, fatigue or mood changes, sleep alterations, constitutional symptoms, and generalized pain. In sub-study 2, we recorded the weekly frequency of CH attacks after the initiation of verapamil concurrently with a 14-day transitional therapy based on the patients' headache diary. A responder to verapamil was defined as a patient who have a reduction from baseline of at least 50% in the weekly frequency of CH attacks 4 weeks after the initiation of verapamil. RESULTS: A total of 168 CH patients (women/men: 39/129) completed the study. In sub-study 1, we found 149 (88.7%) experienced PCSs, with a median of 24 (IQR 18 to 72) hours before the bouts. Up to 57.7% of patients with PCS reported that they could predict upcoming bouts. Among the seven categories of PCSs, head and neck pain was the most common (81.0%) and was associated with a higher predictability of upcoming bouts (odds ratio [OR] = 4.0; 95% confidence interval [CI] 1.7-9.6). In sub-study 2, we found two categories of PCSs were associated with the response to verapamil: sleep alteration (OR = 2.5 [95% CI = 1.3-4.8], p = 0.004) and ≥ 1 cranial autonomic symptoms (OR = 2.7 [95% CI = 1.4-5.1], p = 0.003). CONCLUSION: PCSs were very common in CH and could be used to predict upcoming bouts. Different symptom categories of PCSs may have different clinical implications.