Interactive effects of aridity and local environmental factors on the functional trait composition and diversity of macroinvertebrate assemblages in dryland rivers.

Drought and local habitat alteration are major environmental stressors shaping the aquatic biota in dryland rivers. However, the combined effects of these factors on aquatic biodiversity remain poorly understood. We collected macroinvertebrate data from Central Asian dryland rivers in Xinjiang, China, from 2012 to 2022, to investigate the individual and interactive effects of drought (as indicated by increasing values of Aridity, AI) and local habitat conditions (fine sediments, velocity and pH) on aquatic macroinvertebrate functional trait composition and diversity. We found that interactions of the selected environmental stressors exhibited more frequent additive than synergistic or antagonistic effects, leading to shifts in macroinvertebrate functional trait composition and diversity accordingly. Interaction of AI and fine sediments showed more pronounced synergistic effects (positive or negative) compared to others and had positive influences on traits like small body size, ovoviviparity, etc. Functional diversity metrics responded differently to stressor interactions, with FRic and FDis being negatively affected, whereas FEve was positively correlated to stressor interaction, suggesting the complementary roles of functional diversity metrics to diagnose impacts of stressor interactions. Overall, our study provides new insights into macroinvertebrate assemblage-stressor relationships in dryland rivers and can help better assess, predict and manage aquatic biodiversity in these rivers under ongoing environmental change.

Data Processing for Predicting DNA Damaging Properties of Complex UV Sources.

A growing number of experimental evidence emphasizes that photobiological phenomena are not always the sum of the effect of individual wavelengths present in the emission spectrum of light sources. Unfortunately, tools are missing to identify such non-additive effects and predict effects of various exposure conditions. In the present work, we addressed these points for the formation of pyrimidine dimers in DNA upon co-exposure to UVC, UVB and UVA radiation. We first applied a combination index approach to determine whether mixtures of theses UV ranges exhibited additive, inhibitory or synergistic effects on the formation of cyclobutane pyrimidine dimers, (6-4) photoproducts and Dewar valence isomers. A predictive approach based on an experimental design strategy was then used to quantify the contribution of each wavelength range to the formation of DNA photoproducts. The obtained models allowed us to accurately predict the level of pyrimidine dimers in DNA irradiated under different conditions. The data were found to be more accurate than those obtained with the simple additive approach underlying the use of action spectra. Experimental design thus appears as an attractive concept that could be widely applied in photobiology even for cellular experiments.

Discovering non-additive heritability using additive GWAS summary statistics.

LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.

Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.

Non-parametric quantile regression-based modelling of additive effects to solar irradiation in Southern Africa.

Modelling of solar irradiation is paramount to renewable energy management. This warrants the inclusion of additive effects to predict solar irradiation. Modelling of additive effects to solar irradiation can improve the forecasting accuracy of prediction frameworks. To help develop the frameworks, this current study modelled the additive effects using non-parametric quantile regression (QR). The approach applies quantile splines to approximate non-parametric components when finding the best relationships between covariates and the response variable. However, some additive effects are perceived as linear. Thus, the study included the partial linearly additive quantile regression model (PLAQR) in the quest to find how best the additive effects can be modelled. As a result, a comparative investigation on the forecasting performances of the PLAQR, an additive quantile regression (AQR) model and the new quantile generalised additive model (QGAM) using out-of-sample and probabilistic forecasting metric evaluations was done. Forecasted density plots, Murphy diagrams and results from the Diebold-Mariano (DM) hypothesis test were also analysed. The density plot, the curves on the Murphy diagram and most metric scores computed for the QGAM were slightly better than for the PLAQR and AQR models. That is, even though the DM test indicates that the PLAQR and AQR models are less accurate than the QGAM, we could not conclude an outright greater forecasting performance of the QGAM than the PLAQR or AQR models. However, in situations of probabilistic forecasting metric preferences, each model can be prioritised to be applied to the metric where it performed slightly the best. The three models performed differently in different locations, but the location was not a significant factor in their performances. In contrast, forecasting horizon and sample size influenced model performance differently in the three additive models. The performance variations also depended on the metric being evaluated. Therefore, the study has established the best forecasting horizons and sample sizes for the different metrics. It was finally concluded that a 20% forecasting horizon and a minimum sample size of 10000 data points are ideal when modelling additive effects of solar irradiation using non-parametric QR.

Genotype diversity enhances invasion resistance of native plants via soil biotic feedbacks.

Although native species diversity is frequently reported to enhance invasion resistance, within-species diversity of native plants can also moderate invasions. While the positive diversity-invasion resistance relationship is often attributed to competition, indirect effects mediated through plant-soil feedbacks can also influence the relationship. We manipulated the genotypic diversity of an endemic species, Scirpus mariqueter, and evaluated the effects of abiotic versus biotic feedbacks on the performance of a global invader, Spartina alterniflora. We found that invader performance on live soils decreased non-additively with genotypic diversity of the native plant that trained the soils, but this reversed when soils were sterilized to eliminate feedbacks through soil biota. The influence of soil biota on the feedback was primarily associated with increased levels of microbial biomass and fungal diversity in soils trained by multiple-genotype populations. Our findings highlight the importance of plant-soil feedbacks mediating the positive relationship between genotypic diversity and invasion resistance.

Testing for synergistic effects of natural and anthropogenic disturbance on ecological communities at a landscape scale.

Context: Anthropogenic and natural disturbances may interact synergistically, magnifying their individual effects on biodiversity. However, few studies have measured responses of ecological communities to multiple stressors at landscape scales. Objectives: We use a long-term dataset to test for synergistic effects of anthropogenic and natural disturbance on plant community diversity and composition in a large protected area. Methods: We quantified changes in plant communities over two decades in 98 plots in Waterton Lakes National Park, Canada. Fifty-three plots burned in a wildfire in the interim. We modeled the effects of wildfire, proximity to trails or roads, and their interaction on changes in species richness, community composition, relative abundance of disturbance-associated species, and colonization by exotic species. Results: Interactions between wildfire and proximity to roads and trails affected all metrics except species richness. Only one interaction was synergistic: the relative abundance of disturbance-associated species following wildfire was magnified closer to recreational corridors. The other community metrics showed unexpected patterns. For example, plots with no exotic species in the baseline survey that burned in the wildfire were more likely to gain exotic species than unburned plots only when they were distant from recreational corridors. Conclusions: Our study demonstrates interactive effects of natural and anthropogenic disturbance at landscape scales within a protected area. Plant community response to wildfire was influenced by proximity to recreational corridors, sometimes in surprising ways. As the frequency and severity of anthropogenic and natural disturbances both continue to rise, documenting the prevalence and magnitude of interactions between them is key to predicting long-term effects and designing mitigation strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-024-01844-w.

Stepwise introduction of stabilizing mutations reveals nonlinear additive effects in de novo TIM barrels.

Over the past decades, the TIM-barrel fold has served as a model system for the exploration of how changes in protein sequences affect their structural, stability, and functional characteristics, and moreover, how this information can be leveraged to design proteins from the ground up. After numerous attempts to design de novo proteins with this specific fold, sTIM11 was the first validated de novo design of an idealized four-fold symmetric TIM barrel. Subsequent efforts to enhance the stability of this initial design resulted in the development of DeNovoTIMs, a family of de novo TIM barrels with various stabilizing mutations. In this study, we present an investigation into the biophysical and thermodynamic effects upon introducing a varying number of stabilizing mutations per quarter along the sequence of a four-fold symmetric TIM barrel. We compared the base design DeNovoTIM0 without any stabilizing mutations with variants containing mutations in one, two, three, and all four quarters-designated TIM1q, TIM2q, TIM3q, and DeNovoTIM6, respectively. This analysis revealed a stepwise and nonlinear change in the thermodynamic properties that correlated with the number of mutated quarters, suggesting positive nonadditive effects. To shed light on the significance of the location of stabilized quarters, we engineered two variants of TIM2q which contain the same number of mutations but positioned in different quarter locations. Characterization of these TIM2q variants revealed that the mutations exhibit varying effects on the overall protein stability, contingent upon the specific region in which they are introduced. These findings emphasize that the amount and location of stabilized interfaces among the four quarters play a crucial role in shaping the conformational stability of these four-fold symmetric TIM barrels. Analysis of de novo proteins, as described in this study, enhances our understanding of how sequence variations can finely modulate stability in both naturally occurring and computationally designed proteins.

Independent and additive effects of binge drinking and obesity on liver enzymes: a cross-sectional analysis using the Korean National Health Insurance Service data.

Background: Binge drinking (BD) has been associated with elevated liver enzymes, but the joint association of BD and adiposity with liver enzymes is understudied. We aimed to examine the combined association of BD and obesity with elevated liver enzymes. Methods: Data were obtained from 285,600 patients in the Korean National Health check-up program during 2009-2015. Level I BD (BD I) was defined as alcohol consumption of >60 g (men) or >40 g (women) on one occasion in the previous year. High-intensity BD (HIBD) corresponded to at least two times the BD I levels. General and abdominal obesity were defined by body mass index and waist circumference. Logistic regression was used to examine the independent and joint associations of BD and obesity with elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) levels. Relative excess risk (RERI), attributable proportion (AP), and synergy index (SI) were calculated to estimate the additive interaction effects. Results: The mean age was 42.1 ± 0.03 years and 50.2% were women. Elevated ALT [odds ratio (OR) 1.09, 95% confidence interval (CI) 1.02-1.16], AST (OR 1.16, 95% CI 1.11-1.23), and GGT (OR 1.84, 95% CI 1.05-1.94) were associated with HIBD. Higher odds of elevated ALT (OR 3.57, 95% CI 3.43-3.71), AST (OR 3.47, 95% CI 3.37-3.58), and GGT (OR 2.10, 95% CI 1.98-2.12) were observed in individuals with general obesity. A similar trend was observed for abdominal obesity. The RERI, AP, and SI for the interaction effect of BD and general obesity were 23%, 7%, and 13% for elevated AST levels, and 67%, 24%, and 58% for elevated GGT levels, respectively. Similar effects were observed for the interaction between BD and abdominal obesity. Conclusions: Obesity aggravated the odds of elevated liver AST and GGT levels in HIBD.

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

Combined Efficacy of Systemically Acting Diclofenac Sodium Patch and Alpha-2-Delta Calcium Channel Ligand in Chronic Low Back Pain: Subanalysis of a Phase III Study.

INTRODUCTION: Chronic low back pain often comprises mixed pain types and involves multiple factors. Therefore, we hypothesized that the systemic transdermal formulation of diclofenac sodium (DF systemic patch), which is effective for nociceptive pain, and an α2δ Ca2+ channel ligand, which is effective for neuropathic pain, would have additive effects in the treatment of chronic low back pain. METHODS: From among participants in a randomized, double-blind, placebo-controlled study of DF systemic patch (75 or 150 mg) applied once daily for 2 weeks in patients with chronic low back pain, we performed a subpopulation analysis of those who were concomitantly treated with an α2δ Ca2+ channel ligand during the study period. The efficacy endpoint was pain intensity score on a visual analog scale (VAS). RESULTS: The difference (95% confidence interval) in the least square mean pain VAS score between patients in the 150-mg combination group, who were treated with 150-mg DF systemic patch and an α2δ Ca2+ channel ligand (n = 11), and those in the non-combination group, who were treated with placebo patch and α2δ Ca2+ channel ligand (n = 22), was - 15.09 mm (- 26.45, - 3.73). Because the upper limit of the 95% confidence interval was less than zero, this result indicates that the pain VAS score improved more in the 150-mg combination group than in the non-combination group (placebo group). CONCLUSIONS: The combination of the DF systemic patch and an α2δ Ca2+ channel ligand may be more effective than α2δ Ca2+ channel ligand monotherapy for controlling chronic low back pain. TRIAL REGISTRATION NUMBERS: JPRN-JapicCTI-205134 and jRCT2080225040.

Additive effects of 10-year exposures to PM2.5 and NO2 and primary cancer incidence in American older adults.

Epidemiologic evidence on the relationships between air pollution and the risks of primary cancers other than lung cancer remained largely lacking. We aimed to examine associations of 10-year exposures to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with risks of breast, prostate, colorectal, and endometrial cancers. Methods: For each cancer, we constructed a separate cohort among the national Medicare beneficiaries during 2000 to 2016. We simultaneously examined the additive associations of six exposures, namely, moving average exposures to PM2.5 and NO2 over the year of diagnosis and previous 2 years, previous 3 to 5 years, and previous 6 to 10 years, with the risk of first cancer diagnosis after 10 years of follow-up, during which there was no cancer diagnosis. Results: The cohorts included 2.2 to 6.5 million subjects for different cancers. Exposures to PM2.5 and NO2 were associated with increased risks of colorectal and prostate cancers but were not associated with endometrial cancer risk. NO2 was associated with a decreased risk of breast cancer, while the association for PM2.5 remained inconclusive. At exposure levels below the newly updated World Health Organization Air Quality Guideline, we observed substantially larger associations between most exposures and the risks of all cancers, which were translated to hundreds to thousands new cancer cases per year within the cohort per unit increase in each exposure. Conclusions: These findings suggested substantial cancer burden was associated with exposures to PM2.5 and NO2, emphasizing the urgent need for strategies to mitigate air pollution levels.

Endothelial Cell Response to Combined Photon or Proton Irradiation with Doxorubicin.

Surgery, radiotherapy, and chemotherapy are essential treatment modalities to target cancer cells, but they frequently cause damage to the normal tissue, potentially leading to side effects. As proton beam radiotherapy (PBT) can precisely spare normal tissue, this therapeutic option is of increasing importance regarding (neo-)adjuvant and definitive anti-cancer therapies. Akin to photon-based radiotherapy, PBT is often combined with systemic treatment, such as doxorubicin (Dox). This study compares the cellular response of human microvascular endothelial cells (HMEC-1) following irradiation with photons (X) or protons (H) alone and also in combination with different sequences of Dox. The cellular survival, cell cycle, apoptosis, proliferation, viability, morphology, and migration were all investigated. Dox monotreatment had minor effects on all endpoints. Both radiation qualities alone and in combination with longer Dox schedules significantly reduced clonogenic survival and proliferation, increased the apoptotic cell fraction, induced a longer G2/M cell cycle arrest, and altered the cell morphology towards endothelial-to-mesenchymal-transition (EndoMT) processes. Radiation quality effects were seen for metabolic viability, proliferation, and motility of HMEC-1 cells. Additive effects were found for longer Dox schedules. Overall, similar effects were found for H/H-Dox and X/X-Dox. Significant alterations between the radiation qualities indicate different but not worse endothelial cell damage by H/H-Dox.

Genetic factors linked to aberrant neural activity of individuals with substance use disorder phenotypes: A systematic review and meta-analysis of EEG studies.

Background: Alterations in EEG activity have been considered valid endophenotypes of substance use disorders (SUDs). Empirical evidence has supported the association between genetic factors (e.g., genes, single nucleotide polymorphisms [SNPs]) and SUDs, considering both clinical samples and individuals with a positive family history of SUDs [F+SUD]). Nevertheless, the relationship between genetic factors and intermediate phenotypes (i.e., altered EEG activity) among individuals with SUD phenotypes remains unclear.Objective(s): The current study aims at summarizing genetic factors linked to aberrant EEG activity among individuals with SUDs and those with F+SUD.Methods: Sixteen studies (5 [N = 986] + 11 from the Collaborative Studies On Genetics of Alcoholism [COGA] sample [432 ≤ N ≤ 8810]) were included for a qualitative systematic review. Thirteen studies (5 + 8 studies from the COGA sample) were used for multi-level meta-analytic procedures.Results: Qualitative analyses highlighted a multivariate genetic architecture linked to alterations in EEG waves among individuals with SUD phenotypes (i.e., augmented resting-state beta waves; reduced resting-state alpha waves; reduced resting-state and task-dependent theta waves). The most recurrent genetic factors were involved in cellular energy homeostasis, modulation of inhibitory and excitatory neural activity together with neural cell growth. Meta-analytic results showed a moderate association between genetic factors and altered resting-state and task-dependent EEG activity. Meta-analytic results also suggested non-additive genetic effects on altered EEG activity.Conclusions: Complex genetic interactions mediating neural activity and brain development might constitute a causal pathway toward intermediate phenotypes associated with phenotypic features, which in turn are linked to SUDs.

Non-interactive effects drive multiple stressor impacts on the taxonomic and functional diversity of atlantic stream macroinvertebrates.

Freshwaters are considered among the most endangered ecosystems globally due to multiple stressors, which coincide in time and space. These local stressors typically result from land-use intensification or hydroclimatic alterations, among others. Despite recent advances on multiple stressor effects, current knowledge is still limited to manipulative approaches minimizing biological and abiotic variability. Thus, the assessment of multiple stressor effects in real-world ecosystems is required. Using an extensive survey of 50 stream reaches across North Portugal, we evaluated taxonomic and functional macroinvertebrate responses to multiple stressors, including marked gradients of nutrient enrichment, flow reduction, riparian vegetation structure, thermal stress and dissolved oxygen depletion. We analyzed multiple stressor effects on two taxonomic (taxon richness, Shannon-diversity) and two trait-based diversity indices (functional richness, functional dispersion), as well as changes in trait composition. We found that multiple stressors had additive effects on all diversity metrics, with nutrient enrichment identified as the most important stressor in three out of four metrics, followed by dissolved oxygen depletion and thermal stress. Taxon richness, Shannon-diversity and functional richness responded similarly, whereas functional dispersion was driven by changes in flow velocity and thermal stress. Functional trait composition changed along a major stress gradient determined by nutrient enrichment and oxygen depletion, which was positively correlated with organisms possessing fast-living strategies, aerial respiration, adult phases, and gathering-collector feeding habits. Overall, our results reinforce the need to consider complementary facets of biodiversity to better identify assembly processes in response to multiple stressors. Our data suggest that stressor interactions may be less frequent in real-word streams than predicted by manipulative experiments, which can facilitate mitigation strategies. By combining an extensive field survey with an integrative consideration of multiple biodiversity facets, our study provides new insights that can help to better assess and manage rivers in a global change context.

Maize/peanut intercropping has greater synergistic effects and home-field advantages than maize/soybean on straw decomposition.

Introduction: The decomposition of plant litter mass is responsible for substantial carbon fluxes and remains a key process regulating nutrient cycling in natural and managed ecosystems. Litter decomposition has been addressed in agricultural monoculture systems, but not in intercropping systems, which produce species-diverse litter mass mixtures. The aim here is to quantify how straw type, the soil environment and their combined effects may influence straw decomposition in widely practiced maize/legume intercropping systems. Methods: Three decomposition experiments were conducted over 341 days within a long-term intercropping field experiment which included two nitrogen (N) addition levels (i.e. no-N and N-addition) and five cropping systems (maize, soybean and peanut monocultures and maize/soybean and maize/peanut intercropping). Experiment I was used to quantify litter quality effects on decomposition; five types of straw (maize, soybean, peanut, maize-soybean and maize-peanut) from two N treatments decomposed in the same maize plot. Experiment II addressed soil environment effects on root decomposition; soybean straw decomposed in different plots (five cropping systems and two N levels). Experiment III addressed 'home' decomposition effects whereby litter mass (straw) was remained to decompose in the plot of origin. The contribution of litter and soil effects to the home-field advantages was compared between experiment III ('home' plot) and I-II ('away' plot). Results and discussions: Straw type affected litter mass loss in the same soil environment (experiment I) and the mass loss values of maize, soybean, peanut, maize-soybean, and maize-peanut straw were 59, 77, 87, 76, and 78%, respectively. Straw type also affected decomposition in the 'home' plot environment (experiment III), with mass loss values of maize, soybean, peanut, maize-soybean and maize-peanut straw of 66, 74, 80, 72, and 76%, respectively. Cropping system did not affect the mass loss of soybean straw (experiment II). Nitrogen-addition significantly increased straw mass loss in experiment III. Decomposition of maize-peanut straw mixtures was enhanced more by 'home-field advantage' effects than that of maize-soybean straw mixtures. There was a synergistic mixing effect of maize-peanut and maize-soybean straw mixture decomposition in both 'home' (experiment III) and 'away' plots (experiment I). Maize-peanut showed greater synergistic effects than maize-soybean in straw mixture decomposition in their 'home' plot (experiment III). These findings are discussed in terms of their important implications for the management of species-diverse straw in food-production intercropping systems.

Ebselen oxide and derivatives are new allosteric HER2 inhibitors for HER2-positive cancers.

Human epidermal growth factor receptor 2 (ErbB2/HER2) is a tyrosine kinase receptor that is overexpressed in 25% of primary human breast cancers, as well as in multiple other cancers. HER2-targeted therapies improved progression-free and overall survival in patients with HER2+ breast cancers. However, associated resistance mechanisms and toxicity highlight the need for new therapeutic approaches for these cancers. We recently established that, in normal cells, HER2 is stabilized in a catalytically repressed state by direct interaction with members of the ezrin/radixin/moesin (ERM) family. In HER2-overexpressing tumors, the low expression of moesin contributes to the aberrant activation of HER2. Through a screen designed to find moesin-mimicking compounds, we identified ebselen oxide. We show that ebselen oxide, and some derivatives, conferred an efficient allosteric inhibition of overexpressed HER2, as well as mutated and truncated oncogenic forms of HER2, which are resistant to current therapies. Ebselen oxide selectively inhibited anchorage-dependent and -independent proliferation of HER2+ cancer cells and showed a significant benefit in combination with current anti-HER2 therapeutic agents. Finally, ebselen oxide significantly blocked HER2+ breast tumor progression in vivo. Collectively, these data provide evidence that ebselen oxide is a newly identified allosteric inhibitor of HER2 to be considered for therapeutic intervention on HER2+ cancers.

Additive Effects of L-Arginine with Potassium Carbonate on the Quality Profile Promotion of Phosphate-Free Frankfurters.

The present study investigated the additive effects of L-Arginine (L-Arg) with potassium carbonate (PC) on the quality characteristics of phosphate-free frankfurters. The results showed that L-Arg combined with PC could act as a viable phosphate replacer by decreasing cooking loss and improving the textural properties of phosphate-free frankfurters (p < 0.05), mainly because of its pH-raising ability. Moreover, L-Arg could assist PC in effectively retarding lipid oxidation in phosphate-free frankfurters during storage (p < 0.05). Furthermore, 0.1% L-Arg combined with 0.15% PC was found to exhibit the best optimal phosphate-replacing effect. This combination could also overcome quality defects and promote the sensory attributes of phosphate-free frankfurters to the maximum extent. Therefore, our results suggest that L-Arg combined with PC can be considered a feasible alternative for the processing of phosphate-free frankfurters with an improved quality profile and superior health benefits.

Combination of orthokeratology lens with 0.01% atropine in slowing axial elongation in children with myopia: a randomized double-blinded clinical trial.

BACKGROUND: To evaluate the additive effects of orthokeratology (OK) lenses and 0.01% atropine on slowing axial elongation in myopic children. METHODS: A prospective, randomized, double-blinded, placebo-controlled trial was conducted over a 12-month period. Sixty children aged 8 to 12 years with spherical equivalent refraction from - 1.00 to -4.00 D who had been wearing OK lenses successfully for 2 months (as baseline) were randomly assigned in a 1:1 ratio to combination group (combination of OK lens and 0.01% atropine eye drops) and control group (combination of OK lens and placebo). The primary outcome was change in axial length, along with secondary outcomes including change in pupil diameter (PD) and accommodative amplitude (AMP) at 12 months (measured at 4-month intervals). RESULTS: After 12 months, the overall axial elongation was 0.10 ± 0.14 mm and 0.20 ± 0.15 mm (p = 0.01) in the combination and control groups, respectively. The change in axial length in the two groups showed significant differences only in the first four months (median [Q1, Q3] (95% CI), -0.01 mm [-0.07, 0.05] (-0.06, 0.04) vs. 0.04 mm [0.00, 0.10] (0.02, 0.09); p = 0.04), but no difference thereafter. Multivariate linear regression analysis showed that the axial elongation was significantly slower in the combination group than in the control group (standard ß = -0.10, p = 0.02). PD significantly increased by 0.45 mm [0.20, 0.68] at the 4th month visit (p < 0.001) and then remained stable in the combination group. The PD in the control group and AMP in the two groups remained stable from baseline to 12 months (all p > 0.05). CONCLUSION: The combination therapy was more effective than the OK lens alone in slowing axial elongation after 12 months of treatment, and mainly in the first 4 months. TRIAL REGISTRATION: The First Affiliated Hospital of Zhengzhou University, ChiCTR2000033904. Registered 16/06/2020, http://www.chictr.org.cn/login.aspx?referurl=%2flistbycreater.aspx.

Genomic prediction through machine learning and neural networks for traits with epistasis.

Genomic wide selection (GWS) is one contributions of molecular genetics to breeding. Machine learning (ML) and artificial neural networks (ANN) methods are non-parameterized and can develop more accurate and parsimonious models for GWS analysis. Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible ML methods, automatically modeling nonlinearities and interactions of the predictor variables. This study aimed to evaluate and compare methods based on ANN, ML, including MARS, and G-BLUP through GWS. An F2 population formed by 1000 individuals and genotyped for 4010 SNP markers and twelve traits from a model considering epistatic effect, with QTL numbers ranging from eight to 480 and heritability ( h 2 ) of 0.3, 0.5 or 0.8 were simulated. Variation in heritability and number of QTL impacts the performance of methods. About quantitative traits (40, 80, 120, 240, and 480 QTLs) was observed highest R2 to Radial Base Network (RBF) and G-BLUP, followed by Random Forest (RF), Bagging (BA), and Boosting (BO). RF and BA also showed better results for traits to h 2 of 0.3 with R 2 values 16.51% and 16.30%, respectively, while MARS methods showed better results for oligogenic traits with R 2 values ranging from 39,12 % to 43,20 % in h 2 of 0.5 and from 59.92% to 78,56% in h 2 of 0.8. Non-additive MARS methods also showed high R2 for traits with high heritability and 240 QTLs or more. ANN and ML methods are powerful tools to predict genetic values in traits with epistatic effect, for different degrees of heritability and QTL numbers.