Cytotoxic Peptaibols from Trichoderma strigosum.

Five new lipopeptaibols (1-5) and eight new 19-residue peptaibols (8-15) along with two known lipopeptaibols, lipovelutibols C (6) and D (7) were isolated from Trichoderma strigosum. The planar structures of the newly discovered peptaibols (1-5, 8-15) were elucidated using 1D and 2D NMR, and UPLC-MS/MS data. The absolute configurations for new peptaibols (1-5, 8-15) were elucidated using the advanced Marfey's method and GITC (2,3,4,6-tetra-O-acetyl-ß-d-glucopyranosyl isothiocyanate) derivatization. Through analysis of CD spectra, these peptabols were found to have right-handed helical conformations. While most of the new compounds were significantly more active than the positive control, 9, 10, 12, and 15 containing Ser and Leu at positions 10 and 11, respectively, were the most cytotoxic against MDA-MB-231, SNU449, SKOV3, DU145, and HCT116 cancer cell lines, and the 19-residue peptaibols were generally more potent than lipopeptaibols.

ANXA2 (annexin A2) is crucial to ATG7-mediated autophagy, leading to tumor aggressiveness in triple-negative breast cancer cells.

Triple-negative breast cancer (TNBC) is associated with a poor prognosis and metastatic growth. TNBC cells frequently undergo macroautophagy/autophagy, contributing to tumor progression and chemotherapeutic resistance. ANXA2 (annexin A2), a potential therapeutic target for TNBC, has been reported to stimulate autophagy. In this study, we investigated the role of ANXA2 in autophagic processes in TNBC cells. TNBC patients exhibited high levels of ANXA2, which correlated with poor outcomes. ANXA2 increased LC3B-II levels following bafilomycin A1 treatment and enhanced autophagic flux in TNBC cells. Notably, ANXA2 upregulated the phosphorylation of HSF1 (heat shock transcription factor 1), resulting in the transcriptional activation of ATG7 (autophagy related 7). The mechanistic target of rapamycin kinase complex 2 (MTORC2) played an important role in ANXA2-mediated ATG7 transcription by HSF1. MTORC2 did not affect the mRNA level of ANXA2, but it was involved in the protein stability of ANXA2. HSPA (heat shock protein family A (Hsp70)) was a potential interacting protein with ANXA2, which may protect ANXA2 from lysosomal proteolysis. ANXA2 knockdown significantly increased sensitivity to doxorubicin, the first-line chemotherapeutic regimen for TNBC treatment, suggesting that the inhibition of autophagy by ANXA2 knockdown may overcome doxorubicin resistance. In a TNBC xenograft mouse model, we demonstrated that ANXA2 knockdown combined with doxorubicin administration significantly inhibited tumor growth compared to doxorubicin treatment alone, offering a promising avenue to enhance the effectiveness of chemotherapy. In summary, our study elucidated the molecular mechanism by which ANXA2 modulates autophagy, suggesting a potential therapeutic approach for TNBC treatment.Abbreviation: ATG: autophagy related; ChIP: chromatin-immunoprecipitation; HBSS: Hanks' balanced salt solution; HSF1: heat shock transcription factor 1; MTOR: mechanistic target of rapamycin kinase; TNBC: triple-negative breast cancer; TFEB: transcription factor EB; TFE3: transcription factor binding to IGHM enhancer 3.

Host-specific growth responses of Larix kaempferi and Quercus acutissima to Asian gypsy moth defoliation in central Korea.

As the risk of gypsy moth outbreaks that have detrimental effects on forest ecosystem in the Northern Hemisphere increase due to climate change, a quantitative evaluation of the impact of gypsy moth defoliation is needed to support the adaptive forest management. To evaluate the host-specific impact of gypsy moth defoliation, radial growth and annual carbon accumulation were compared for one severely defoliated (Larix kaempferi (Lamb.) Carrière) and one moderate defoliated (Quercus acutissima Carruth.) host, in defoliated and non-defoliated site using tree-ring analysis. Finally, the resilience indices of radial growth variables were calculated to assess the ability of sampled trees to withstand defoliation. Gypsy moth defoliation mainly decreased latewood width and caused reduction in annual carbon absorption more than 40% for both tree species. However, L. kaempferi, showed the reduced growth until the year following defoliation, while Q. acutissima, showed no lagged growth depression and rapid growth recover. The findings show how each species reacts differently to gypsy moth defoliation and highlight the need of managing forests in a way that takes resilient tree species into account.

Selection of climate variables in ant species distribution models: case study in South Korea.

The selection of explanatory variables is important in modeling prediction of changes in species distribution in response to climate change. In this study, we evaluated the importance of variable selection in species distribution models. We compared two different types of models for predicting the distribution of ant species: temperature-only and both temperature and precipitation. Ants were collected at 343 forest sites across South Korea from 2006 through 2009. We used a generalized additive model (GAM) to predict the future distribution of 16 species that showed significant responses to changes in climatic factors (temperature and/or precipitation). Four types of GAMs were constructed: temperature, temperature with interaction of precipitation, temperature and precipitation without interaction, and temperature and precipitation with interaction. Most species displayed similar results between the temperatureonly and the temperature and precipitation models. The results for predicted changes in species richness were different from the temperature-only model. This indicates higher uncertainty in the prediction of species richness, which is obtained by combining the prediction results of distribution change for each species, than in the prediction of distribution change. The turnover rate of the ant assemblages was predicted to increase with decreases in temperature and increases in elevation, which was consistent with other studies. Finally, our results showed that the prediction of the distribution or diversity of organisms responding to climate change is uncertain because of the high variability of the model outputs induced by the variables used in the models.

A systematic review of and reflection on the applications of factor mixture modeling.

Factor mixture modeling (FMM) incorporates both continuous latent variables and categorical latent variables in a single analytic model clustering items and observations simultaneously. After two decades since the introduction of FMM to psychological and behavioral science research, it is an opportune time to review FMM applications to understand how these applications are utilized in real-world research. We conducted a systematic review of 76 FMM applications. We developed a comprehensive coding scheme based on the current methodological literature of FMM and evaluated common usages and practices of FMM. Based on the review, we identify challenges and issues that applied researchers encounter in the practice of FMM and provide practical suggestions to promote well-informed decision making. Lastly, we discuss future methodological directions and suggest how FMM can be expanded beyond its typical use in applied studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Are "well" teachers "better" teachers? A look into the relationship between first-year teacher emotion and use of evidence-based instructional strategies.

Teachers experience significant stress with 93% reported high levels of stress (Herman et al., 2018), and literature examining teacher stress levels during the COVID-19 pandemic reveals that 30 percent of teachers experience significantly high levels of stress (Silva et al., 2021). Low levels of teacher well-being and higher levels of stress have been linked to punitive behavior management (Jennings & Greenberg, 2009) and lower student academic achievement (Goddard et al., 2000). The aim of the present study is to explore the relationship between teacher emotion and use of evidence-based instructional strategies that promote student success. In this study, 17 first-year teachers completed a novel measurement, inclusive of single-item scales intended to assess feelings of stress, efficacy, and school connectedness, several times a day, each day of the week, for 1 month. The classroom strategies assessment system of evidence-based academic and behavioral instructional strategies was used in weekly teacher observations. Results indicated validity between these single-item scales and established measures of teacher well-being. A positive relationship between teachers' in-the-moment well-being and evidence-based instruction was found, as well as substantial variation in teacher emotional response across days and weeks. Implications and future directions are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Effects of Thinning Intensity on Litterfall Production, Soil Chemical Properties, and Fine Root Distribution in Pinus koraiensis Plantation in Republic of Korea.

It is crucial to evaluate the effects of thinning on litterfall production, soil chemical properties, and fine root dynamics when implementing thinning as a silvilcultural technique to enhance tree growth and timber yield in Pinus koraiensis plantations. Thus, we determined the 10-year effects (2007-2017) of different thinning intensities on litterfall production, soil chemical properties, and fine root biomass and necromass within a P. koraiensis plantation in South Korea. The soil chemical parameters and fine root biomass and necromass were also compared across three soil depths (0-10, 10-20, and 20-30 cm). Three thinning treatments were employed: no thinning (CON), light thinning (32% removed, LT), and heavy thinning (64% removed, HT). Results revealed that litterfall was consistent across all thinning treatments, but broadleaf species had considerably higher litterfall production at HT stands than at CON/LT stands. Soil chemical properties, except exchangeable K+, were generally lower at LT stands, particularly at a depth of 20-30 cm soil. After ten years, there was a decrease in fine root biomass and necromass with increasing soil depth. Over 80% of fine roots were found in the upper layer (0-20 cm), while very fine roots (0-1 mm) consisted mainly of 47% pine and 53% other species and were concentrated in the 0-10 cm soil depth in HT. In conclusion, different thinning intensities had diverse effects on the parameters measured within the plantation. Future studies can explore how the effects of thinning intensities on litterfall production, soil chemistry, and fine root dynamics affect species diversity, carbon storage, and understory vegetation in P. koraiensis plantations.

C-Reactive Protein Signaling Pathways in Tumor Progression.

Many cancers arise from sites of chronic inflammation, which creates an inflammatory microenvironment surrounding the tumor. Inflammatory substances secreted by cells in the inflammatory environment can induce the proliferation and survival of cancer cells, thereby promoting cancer metastasis and angiogenesis. Therefore, it is important to identify the role of inflammatory factors in cancer progression. This review summarizes the signaling pathways and roles of C-reactive protein (CRP) in various cancer types, including breast, liver, renal, and pancreatic cancer, and the tumor microenvironment. Mounting evidence suggests the role of CRP in breast cancer, particularly in triple-negative breast cancer (TNBC), which is typically associated with a worse prognosis. Increased CRP in the inflammatory environment contributes to enhanced invasiveness and tumor formation in TNBC cells. CRP promotes endothelial cell formation and angiogenesis and contributes to the initiation and progression of atherosclerosis. In pancreatic and kidney cancers, CRP contributes to tumor progression. In liver cancer, CRP regulates inflammatory responses and lipid metabolism. CRP modulates the activity of various signaling molecules in macrophages and monocytes present in the tumor microenvironment, contributing to tumor development, the immune response, and inflammation. In the present review, we overviewed the role of CRP signaling pathways and the association between inflammation and cancer in various types of cancer. Identifying the interactions between CRP signaling pathways and other inflammatory mediators in cancer progression is crucial for understanding the complex relationship between inflammation and cancer.

Development of the non-occupational post-exposure prophylaxis (NPEP) knowledge scale among Chinese men who have sex with men.

BACKGROUND: Nonoccupational post-exposure prophylaxis (NPEP) is a short course of medication taken to reduce the likelihood of acquiring human immunodeficiency virus (HIV) infection upon exposure. A review of the literature demonstrates an urgent need for an empirically validated instrument that measures detailed knowledge of NPEP among the key population of men who have sex with men (MSM). METHODS: Semi-structured interviews, focus groups, and a cross-sectional survey with a sample of 419 MSM was conducted in 2018 in China to develop and psychometrically evaluate the new instrument, the NPEP Knowledge Scale. Exploratory and confirmatory factor analyses, differential item functioning analyses, and structural equation modeling were conducted using Mplus 7.4. RESULTS: The NPEP Knowledge Scale demonstrated excellent reliability and validity. Cronbach's alpha was 0.903. The range of item R2 were 0.527-0.969, p's < 0.001. Model estimated inter-item correlations ranged between 0.534 and 0.968. In addition, HIV knowledge, NPEP use, and NPEP knowledge were all significantly correlated. CONCLUSIONS: The NPEP Knowledge Scale is suitable for research, program evaluation, and clinical and community services that require using NPEP to minimize the ever-present risk of new HIV infections.

Faculty Time Expenditure Across Research, Teaching, and Service: Do Gender Differences Persist?

Faculty members are continually confronted with a multitude of activities among which they must divide their time. Prior research suggests that while men and women academics spend the same number of weekly hours working, women tend to expend more time on teaching and service relative to men while men expend more time on research relative to women. Based on cross-sectional survey data from a sample of 783 tenured or tenure-track faculty members from multiple universities, we examine gender differences in time spent in research, teaching, and university service. Regression analyses show that gender differences in time allocation continue to persist after controlling for work and family factors. More specifically, women report more time on teaching and university service than do men, while men report more time spent on research than do women. Results provide evidence that gendered differences in faculty time allocation are robust across time. Potential implications for policy are discussed.

Efficacy and safety of enavogliflozin, a novel SGLT2 inhibitor, in Korean people with type 2 diabetes: A 24-week, multicentre, randomized, double-blind, placebo-controlled, phase III trial.

AIMS: To evaluate the efficacy and safety of a novel sodium-glucose cotransporter 2 inhibitor, enavogliflozin 0.3 mg monotherapy, in Korean people with type 2 diabetes mellitus (T2DM) inadequately controlled with diet and exercise. MATERIALS AND METHODS: This study was a randomized, double-blind, placebo-controlled trial conducted in 23 hospitals. Individuals with haemoglobin A1c (HbA1c) of 7.0%-10.0% after at least 8 weeks of diet and exercise modification were randomized to receive enavogliflozin 0.3 mg (n = 83) or placebo (n = 84) for 24 weeks. The primary outcome was a change in HbA1c at week 24 from baseline. Secondary outcomes included the proportion of participants achieving HbA1c <7.0%, change in fasting glucose, body weight and lipid levels. Adverse events were investigated throughout the study. RESULTS: At week 24, the placebo-adjusted mean change in HbA1c from baseline in the enavogliflozin group was -0.99% (95% confidence interval -1.24%, -0.74%). The proportions of patients achieving HbA1c <7.0% (71% vs. 24%) at week 24 was significantly higher in the enavogliflozin group (p < .0001). Placebo-adjusted mean changes in fasting plasma glucose (-40.1 mg/dl) and body weight (-2.5 kg) at week 24 were statistically significant (p < .0001). In addition, a significant decrease in blood pressure, low-density lipoprotein cholesterol, triglyceride, and homeostasis model assessment of insulin resistance were observed, along with a significant increase in high-density lipoprotein cholesterol. No significant increase in treatment-related adverse events was observed for enavogliflozin. CONCLUSIONS: Monotherapy with enavogliflozin 0.3 mg improved glycaemic control in people with T2DM. Enavogliflozin therapy also exerted beneficial effects on body weight, blood pressure and lipid profile.

Multi-informant Assessment of Internalizing Concerns: Rater Concordance and Implications for Decision-Making.

Rising rates of mental health challenges among youths have become a significant concern following the COVID 19 pandemic. Although strong evidence supports the implementation of universal screening as a preventative approach to address unmet mental health concerns, the research is less clear surrounding the use of such data in decision-making processes when significant discrepancies between informants (e.g., students and teachers) exist. The purpose of the study was twofold. First, the study aimed to determine the degree of rater concordance between teachers and students on students' internalizing concerns. The second objective was to determine whether concordance on internalizing behaviors differs across ages/grades and if this differentially impacts distal (i.e., academic) outcomes. Results indicated that teachers and students demonstrated limited agreement on ratings of internalizing behaviors. However, when students and teachers agreed, higher and more positive emotional behaviors were linked to higher reading/math performance. Furthermore, patterns of informant dis/agreement and relationships between internalizing concerns and academic outcomes were similar across grade levels. Implications and areas for future research are discussed.

Lipid raft protein flotillin-1 is important for the interaction between SOS1 and H-Ras/K-Ras, leading to Ras activation.

Ras mutations have been frequently observed in human cancer. Although there is a high degree of similarity between Ras isomers, they display preferential coupling in specific cancer types. The binding of Ras to the plasma membrane is essential for its activation and biological functions. The present study elucidated Ras isoform-specific interactions with the membrane and their role in Ras-mediated biological activities. We investigated the role of a lipid raft protein flotillin-1 (Flot-1) in the activations of Ras. We found that Flot-1 was co-localized with H-Ras, but not with N-Ras, in lipid rafts of MDA-MB-231 human breast cells. The amino-terminal hydrophobic domain (1-38) of Flot-1 interacted with the hypervariable region of H-Ras. The epidermal growth factor-stimulated activation of H-Ras required Flot-1 which was not necessary for that of N-Ras in breast cancer cells. Flot-1 interacted with son of sevenless (SOS)-1, which promotes the conversion of Ras-bound GDP to GTP. Notably, Flot-1 was crucial for the interaction between SOS1 and H-Ras/K-Ras in breast and pancreatic cancer cells. Stable knockdown of Flot-1 reduced the in vivo metastasis in a mouse xenograft model with human breast carcinoma cells. A tissue microarray composed of 61 human pancreatic cancer samples showed higher levels of Flot-1 expression in pancreatic tumor tissues compared to normal tissues, and a correlation between K-Ras and Flot-1. Taken together, our findings suggest that Flot-1 may serve as a membrane platform for the interaction of SOS1 with H-Ras/K-Ras in human cancer cells, presenting Flot-1 as a potential target for Ras-driven cancers.

Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting.

Factor mixture modeling (FMM) has been increasingly used in behavioral and social sciences to examine unobserved population heterogeneity. Covariates (e.g., gender, race) are often included in FMM to help understand the formation and characterization of latent subgroups or classes. This Monte Carlo simulation study evaluated the performance of one-step and three-step approaches to covariate inclusion across three scenarios, i.e., correct specification (study 1), model misspecification (study 2), and model overfitting (study 3), in terms of direct covariate effects on factors. Results showed that the performance of these two approaches was comparable when class separation was large and the specification of covariate effect was correct. However, one-step FMM had better class enumeration than the three-step approach when class separation was poor, and was more robust to the misspecification or overfitting concerning direct covariate effects. Recommendations regarding covariate inclusion approaches are provided herein depending on class separation and sample size. Large sample size (1000 or more) and the use of sample size-adjusted BIC (saBIC) in class enumeration are recommended.

A nutrition education intervention to improve eating behaviors of children with autism spectrum disorder: Study protocol for a pilot randomized controlled trial.

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and social behaviors. Children with ASD often experience mealtime behavior challenges and selective eating behaviors. They also tend to consume fewer fruits and vegetables and more high-energy dense foods, compared to neurotypical peers. A nutrition intervention was designed to prevent the development of feeding disorders and the long-term negative health impacts associated with poor dietary intake. This randomized controlled trial will evaluate the feasibility and preliminary efficacy of the nutrition education intervention for children with ASD and their parents through the Early Intervention (EI) services. We will recruit EI providers and parent-child dyads (n = 48) from EI programs, and randomly assign them into Autism Eats intervention (n = 24) or enhance usual care (EUC) comparison group (n = 24). The Autism Eats is 10 weekly sessions delivered individually as part of EI, while the EUC group will receive only 1 nutrition education session and then weekly parent handouts. The Autism Eats integrates ASD-specific feeding strategies and behaviorally-focused intervention strategies such as goal setting. Feasibility indicators include reach/participation, attrition, completion, fidelity, compatibility, and qualitative participant feedback. Outcome measures include dietary intakes and mealtime behaviors of children with ASD using 3-day food records and a validated questionnaire, the Brief Autism Mealtime Behavior Inventory (BAMBI). We will examine whether there are differences in children's food intakes, variety, diet quality, and mealtime behaviors between Autism Eats and EUC groups at post-intervention and 5-month follow-up assessment. This study will provide critical data to inform a full-scale randomized controlled trial.

Robustness of Latent Profile Analysis to Measurement Noninvariance Between Profiles.

Latent profile analysis (LPA) identifies heterogeneous subgroups based on continuous indicators that represent different dimensions. It is a common practice to measure each dimension using items, create composite or factor scores for each dimension, and use these scores as indicators of profiles in LPA. In this case, measurement models for dimensions are not included and potential noninvariance across latent profiles is not modeled in LPA. This simulation study examined the robustness of LPA in terms of class enumeration and parameter recovery when the noninvariance was unmodeled by using composite or factor scores as profile indicators. Results showed that correct class enumeration rates of LPA were relatively high with small degree of noninvariance, large class separation, large sample size, and equal proportions. Severe bias in profile indicator mean difference was observed with intercept and loading noninvariance, respectively. Implications for applied researchers are discussed.

Deep learning method for prediction of patient-specific dose distribution in breast cancer.

BACKGROUND: Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. METHODS: Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. RESULTS: The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, - 0.88 ± 1.83%, - 1.16 ± 2.58%, and - 0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, - 0.29 ± 0.98%, 1.30 ± 0.86%, - 0.32 ± 1.10%, 0.12 ± 2.13%, and - 1.74 ± 1.79, respectively. CONCLUSIONS: In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.

Combined Approach to Multi-Informant Data Using Latent Factors and Latent Classes: Trifactor Mixture Model.

Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the trifactor mixture model that combines the trifactor model and the mixture model. This combined approach allows researchers to investigate the common and unique perspectives of multiple informants on targets using latent factors and simultaneously take into account potential heterogeneity of targets using latent classes. We demonstrate this model using student self-rated and teacher-rated academic behaviors (N = 24,094). Model specification and testing procedures are explicated in detail. Methodological and practical issues in conducting the trifactor mixture analysis are discussed.

7-ketocholesterol enhances autophagy via the ROS-TFEB signaling pathway in osteoclasts.

Oxysterols play a critical role in human health and diseases associated with high cholesterol and oxidative stress. Given that a positive correlation was observed between cholesterol and collagen type 1 fragment (CTX-1) or serum reactive oxygen species (ROS) in humans, we hypothesized that oxidized cholesterol metabolites may participate in cholesterol-induced bone loss. Therefore, this study aimed to identify the metabolite responsible for cholesterol-associated bone loss and evaluate its effect on osteoclasts (OCs) leading to bone loss. An atherogenic diet in mice increased the levels of the oxysterol, 7-ketocholesterol (7-KC) in bone, as well as serum ROS. 7-KC increased the number and activity of OCs by enhancing autophagy via the ROS-transcription factor EB signaling pathway. These findings suggest that 7-KC acts as a cholesterol metabolite and is at least partially responsible for cholesterol-induced bone loss by inducing autophagy in OCs.