Bio-Based Polyurethane Networks Containing Sunflower Oil Based Polyols.

This work focused on the preparation and investigation of polyurethane (SO-PU)-containing sunflower oil glycerides. By transesterification of sunflower oil with glycerol, we synthesized a glyceride mixture with an equilibrium composition, which was used as a new diol component in polyurethanes in addition to poly(ε-caprolactone)diol (PCLD2000). The structure of the glyceride mixture was characterized by physicochemical methods, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), nuclear magnetic resonance spectroscopy (NMR), and size exclusion chromatography (SEC) measurements. The synthesis of polyurethanes was performed in two steps: first the prepolymer with the isocyanate end was synthesized, followed by crosslinking with an additional amount of diisocyanate. For the synthesis of the prepolymer, 4,4'-methylene diphenyl diisocyanate (MDI) or 1,6-hexamethylene diisocyanate (HDI) were used as isocyanate components, while the crosslinking was carried out using an additional amount of MDI or HDI. The obtained SO-PU flexible polymer films were characterized by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). The so-obtained flexible SO-PU films were proved to be suitable for the preparation of potentially biocompatible and/or biodegradable scaffolds. In addition, the stress versus strain curves for the SO-PU polymers were interpreted in terms of a mechanical model, taking into account the yield and the strain hardening.

Application of a newly constructed NGS panel with 45 X-linked microhaplotypes demonstrates the unique value of X-MH for kinship testing and mixture analysis.

X-linked microhaplotypes (X-MHs) have the potential to be a valuable supplementary tool in complex kinship identification or the resolution of DNA mixtures, because they bring together the distinctive genetic pattern of X chromosomal markers and the benefits of microhaplotypes (MHs). In this study, we used the 1000 Genome database to screen and select 63 X-MHs; 18 MHs were filtered out though a batch sequencing assessment of the DNA samples collected from 112 unrelated Chinese Han individuals. The resulting 45-plex panel performed well in comprehensive assessments including repeatability, sensitivity, species specificity, resistance to PCR inhibitors or degradation, mutation rate, and accuracy in detecting DNA mixture samples. The minimum amount of DNA template that can be tested with this panel is 0.5 ng. Additionally, the alleles of the minor contributor can be accurately detected when the mixture rate is larger than 1:9 in female-male mixture or 1:19 in male-male mixture. Then, we calculated population parameters on each MH based on the allele frequency data obtained from the sequence results of the aforementioned 112 unrelated samples. Combining these parameters on each MH, it can be calculated that TDPm, TDPf, CPET, CPEDFM, CPEDFF and CNCEP3 of the 45-plex system were 1-8.99×10-13, 1-1.62×10-19, 0.9999999995, 0.9999981, 0.9955, 0.9999971 and 0.99940, respectively, indicating that the panel is capable in personal identification and parentage testing. To reveal the unique advantage of X-MHs in the analyses of complex kinship and male DNA mixture, further assessments were made. For complex kinship identification, 22 types of individual pairs with different second-degree kinship were simulated and different types of likelihood ratios (LR) were calculated for each. The results revealed that the panel can achieve accuracy of approximately 70 %∼80 % when dividing each of the three types of second-degree kinships into three or four groups. Theoretically, such sub-division cannot be done by using independent autosomal markers. For male DNA mixture analysis without suspects, the maximum likelihood ratio strategy was derived and employed in the estimation of the number of male contributors (NOMC). Simulations were conducted to verify the efficacy of the 45-plex panel in the field and to compare it with autosomal markers by assuming the 45 MHs as autosomal ones. The results showed that X-MHs can achieve higher accuracy in the estimation of NOMC than autosomal ones when the mixed males were unrelated. The results highlighted the unique value of X-linked MHs in complex kinship and male mixture analyses.

Source-free domain adaptation for semantic image segmentation using internal representations.

Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance. Classic unsupervised domain adaptation (UDA) attempts to address a similar problem when there is target domain with no annotated data points through transferring knowledge from a source domain with annotated data. We develop an online UDA algorithm for semantic segmentation of images that improves model generalization on unannotated domains in scenarios where source data access is restricted during adaptation. We perform model adaptation by minimizing the distributional distance between the source latent features and the target features in a shared embedding space. Our solution promotes a shared domain-agnostic latent feature space between the two domains, which allows for classifier generalization on the target dataset. To alleviate the need of access to source samples during adaptation, we approximate the source latent feature distribution via an appropriate surrogate distribution, in this case a Gaussian mixture model (GMM).

Sweet-bitter taste interactions in binary mixtures of sweeteners: Relationship between taste receptor activities and sensory perception.

This study investigated the effects of various binary sweetener mixtures on sweetness enhancement and their interactions with sweet or bitter taste receptors, focusing on sensory perception and receptor activity. Acesulfame K or saccharin was mixed with allulose, aspartame, erythritol, fructose, glucose, or sucrose to match a target sucrose sweetness. The effects of the mixtures on sweet and bitter taste receptors (in the human embryonic kidney -293 cells) and sensory taste intensities were evaluated. Sweetness enhancement at the sweet taste receptor level was observed in some cases, with several monosaccharides reducing the acesulfame K- or saccharin-induced bitter taste receptor activity. Combining acesulfame K or saccharin with any of the six sweeteners perceptually enhanced sweetness (60% âˆ¼ 100% in 50:50 ratio), correlating with a reduction in inherent bitterness (-35% âˆ¼ -63% in 50:50 ratio). This finding suggests that sweetness perception likely increased because the monosaccharides mitigate the activation of bitter receptors caused by high-potency sweeteners.

Behind the heterogeneity in the long-term course of first-episode psychosis: Different psychotic symptom trajectories are associated with different patterns of cannabis and stimulant use.

BACKGROUND: Data-driven classification of long-term psychotic symptom trajectories and identification of associated risk factors could assist treatment planning and improve long-term outcomes in psychosis. However, few studies have used this approach, and knowledge about underlying mechanisms is limited. Here, we identify long-term psychotic symptom trajectories and investigate the role of illness-concurrent cannabis and stimulant use. METHODS: 192 participants with first-episode psychosis were followed up after 10 years. Psychotic symptom trajectories were estimated using growth mixture modeling and tested for associations with baseline characteristics and cannabis and stimulant use during the follow-up (FU) period. RESULTS: Four trajectories emerged: (1) Stable Psychotic Remission (54.2 %), (2) Delayed Psychotic Remission (15.6 %), (3) Psychotic Relapse (7.8 %), (4) Persistent Psychotic Symptoms (22.4 %). At baseline, all unfavorable trajectories (2-4) were characterized by more schizophrenia diagnoses, higher symptom severity, and longer duration of untreated psychosis. Compared to the Stable Psychotic Remission trajectory, unstable trajectories (2,3) showed distinct associations with cannabis/stimulant use during the FU-period, with dose-dependent effects for cannabis but not stimulants (Delayed Psychotic Remission: higher rates of frequent cannabis and stimulant use during the first 5 FU-years; Psychotic Relapse: higher rates of sporadic stimulant use throughout the entire FU-period). The Persistent Psychosis trajectory was less clearly linked to substance use during the FU-period. CONCLUSIONS: The risk for an adverse long-term course could be mitigated by treatment of substance use, where particular attention should be devoted to preventing the use of stimulants while the use reduction of cannabis may already yield positive effects.

Empirical model Variability: Developing a new global optimisation approach to populate compression and compaction mixture rules.

This study systematically evaluated the predictive accuracy of common empirical models for pharmaceutical powder compaction. A dataset of nine placebo and twelve active pharmaceutical ingredient (API) loaded blend formulations (four APIs at three drug loadings) was fitted to the widely used empirical tablet compression (Gurnham, Heckel, and Kawakita) and compaction (Ryshkewitch-Duckworth and Leuenberger) models. At low API loadings (<20w/w%), all models achieved R2 above 90 % and RRMSE (relative root mean squared error) below 0.1. However, as API loads increased, overall model performance decreased, notably in the Heckel model. A parameter variability analysis identified multiple parameter pairs achieving acceptable fits. Consequently, a novel global optimization approach was developed populating arithmetic, geometric, and harmonic mixture rules for empirical tuning parameters. This method outperformed the traditional line of best fit approach. A cross validation study revealed that this method is capable of predicting tuning parameters which achieve an acceptable Goodness of Fit for new blends. Finally, with the restriction of maintaining consistent parameters for the placebo blend, the proposed method could substantially reduce the experimental requirements and API consumption for the exploration of new blends.

Exploring novel type of lipid-bases drug delivery systems that contain one lipid and one water-soluble surfactant only.

None of transitional lipid-based drug delivery systems (LBDDS) includes compositions containing one lipid and one water-soluble surfactant that form stable microemulsions. The conversion of liquid LBDDS to solid LBDDS has been limited by low drug loading. Previously, we have developed drug solid microemulsions containing one lipid and TPGS (a water-soluble surfactant) that achieved high drug loading and remarkably increased oral bioavailability. This study aimed to test if binary lipid systems (BLS), composed of one lipid and one water-soluble surfactant that form stable self-emulsifying microemulsions, is not an exclusive but widely applicable type of LBDDS for other lipids and surfactants and evaluate the influences of chemical structures of lipids and surfactants on microemulsions and solid microemulsions. We systemically identified new BLS by using a library of lipids and surfactants. Propylene glycol diesters and glycerol triesters were favorable for forming stable microemulsions with Tween 80, Cremophor EL, or TPGS. To the best of our knowledge, this is the first report exploring and confirming that the BLS is a new addition to traditional LBDDS, provides a promising option for researchers, and has the potential to increase drug loading to facilitate the development of solid microemulsions.

Combined toxicity of abamectin and carbendazim on enzymatic and transcriptional levels in the soil-earthworm microcosm.

To reveal the toxicological mechanisms of pesticide mixtures on soil organisms, this study concentrated on evaluating enzymatic activity and gene expression changes in the earthworm Eisenia fetida (Savigny 1826). Despite being frequently exposed to multiple pesticides, including the common combination of abamectin (ABA) and carbendazim (CAR), environmental organisms have primarily been studied for the effects of individual pesticides. Acute toxicity results exhibited that the combination of ABA and CAR caused a synergistic impact on E. fetida. The levels of MDA, ROS, T-SOD, and caspase3 demonstrated a significant increase across most individual and combined groups, indicating the induction of oxidative stress and cell death. Additionally, the expression of three genes (hsp70, gst, and crt) exhibited a significant decrease following exposure to individual pesticides and their combinations, pointing toward cellular damage and impaired detoxification function. In contrast, a noteworthy increase in ann expression was observed after exposure to both individual pesticides and their mixtures, suggesting the stimulation of reproductive capacity in E. fetida. The present findings contributed to a more comprehensive understanding of the potential toxicity mechanisms of the ABA and CAR mixture, specifically on oxidative stress, cell death, detoxification dysfunction, and reproductive capacity in earthworms. Collectively, these data offered valuable toxicological insights into the combined effects of pesticides on soil organisms, enhancing our understanding of the underlying risks associated with the coexistence of different pesticides in natural soil environments.

Towards unbiased interpretations of interactive effects in ecotoxicological studies.

Ecotoxicological research has increasingly focused on the interactive effects of chemical mixtures on biological models, emphasising additive, synergistic, or antagonistic interactions. However, these combination studies often test chemicals at unique concentrations (e.g. x:y), limiting our understanding of the effects across the full spectrum of possible combinations. Evidence from human toxicology suggests that interactive effects among chemicals can vary significantly with total concentration (e.g. x:y vs. 2x:2y), their ratio (e.g. x:2y vs. 2x:y), and the magnitude of the tested effect (e.g. LC10vs. LC50). Our non-exhaustive review of studies on binary mixtures in bee ecotoxicology reveals that such parameters are frequently neglected. Of the 60 studies we examined, only two utilised multiple total concentrations and ratios, thus exploring a broad range of possible combinations. In contrast, 26 studies tested only a single concentration of each chemical, resulting in incomplete interpretations of the potential interactive effects. Other studies utilised various concentrations and/or ratios but failed to capture a broad spectrum of possible combinations. We also discuss potential discrepancies in interactive effects based on different metrics and exposure designs. We advocate for future ecotoxicological studies to investigate a wider spectrum of chemical combinations, including various concentrations and ratios, and to address different levels of effects.

Upgrade of Acetone-Butanol-Ethanol Mixture to High-Value Biofuels over Ca-Doped Ni-CaO-SiO2 catalyst.

The selective conversion of biomass fermentation derived from an acetone-butanol-ethanol (ABE) mixture into high-value biofuels is of paramount importance for industrial applications. However, challenges persist in effectively controlling the selectivity of long carbon chain ketones in elevated ABE conversion. In this research, a Ca-doped Ni-CaO-SiO2 catalyst was designed and employed to achieve a remarkable conversion of 89.9% into ketone products from the extracted ABE mixture. The selectivity for C8+ ketones reaches 41.8%, demonstrating exceptional performance. The reversible phase transition between Ca2SiO4 and CaCO3 enhances the recyclability, thereby improving the sustainability of the process. Additionally, the trace intermediate 3-hepten-2-one was successfully detected using two-dimensional GC×GC-MS, elucidating the conversion pathway in the catalytic upgrading of the ABE mixture. This finding offers a potential route for the efficient utilization of biomass and the highly selective production of value-added chemicals.

Broad Learning System under Label Noise: A Novel Reweighting Framework with Logarithm Kernel and Mixture Autoencoder.

The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS.

MIECF: Multi-faceted information extraction and cross-mixture fusion for multimodal aspect-based sentiment analysis.

Aspect-level sentiment analysis within multimodal contexts, focusing on the precise identification and interpretation of sentiment attitudes linked to the target aspect across diverse data modalities, remains a focal research area that perpetuates the advancement of discourse and innovation in artificial intelligence. However, most existing methods tend to focus on extracting visual features from only one facet, such as face expression, which ignores the value of information from other key facets, such as the textual information presented by the image modality, resulting in information loss. To overcome the aforementioned constraint, we put forth a novel approach designated as Multi-faceted Information Extraction and Cross-mixture Fusion (MIECF) for Multimodal Aspect-based Sentiment Analysis. Our approach captures more comprehensive visual information in the image and integrates these local and global key features from multiple facets. Local features, such as facial expressions and textual features, provide direct and rich emotional cues. By contrast, the global feature often reflects the overall emotional atmosphere and context. To enhance the visual representation, we designed a Cross-mixture Fusion method to integrate this local and global multimodal information. In particular, the method establishes semantic relationships between local and global features to eliminate ambiguity brought by single-facet information and achieve more accurate contextual understanding, providing a richer and more precise manner for sentiment analysis. The experimental findings indicate that our proposed approach achieves a leading level of performance, resulting in an Accuracy of 79.65 % on the Twitter-2015 dataset, and Macro-F1 scores of 75.90 % and 73.11 % for the Twitter-2015 and Twitter-2017 datasets, respectively.

Integrating network pharmacology and experimental models to investigate the efficacy and mechanism of Tiansha mixture on xerosis.

Epidermal Growth Factor Receptor Inhibitors (EGFRIs) is a common cancer therapy, but they occasionally cause severe side effects such as xerosis. Tiansha mixture (TM), a traditional Chinese medicines formulation, is develpoed to treat xerosis. This study aims to understand mechanisms of TM on xerosis. Bio-active compounds were selected from databases (TCMSP, TCM-ID, HERB, ETCM) and removed for poor oral bioavailability and low drug likeness. Then a network-based approach filtered out potential active compounds against xerosis. KEGG enrichment analysis identified PI3K/AKT and ERK/MAPK pathways, which were further verified by molecular docking. Afterwards, the effect of TM on activation of PI3K/AKT and ERK/MAPK pathways was validated in gefitinib-induced xerosis rats, where AKT-activator SC79 and MAPK-activator CrPic were also applied. Skin damage was assessed by dorsal score and HE and Tunel stainings. the levels of inflammation factors IL-6 and TNF-α in serum and skin tissue were measured by ELISA. Western blot was used to detect protein levels in the pathways. Network pharmacology identified 111 bio-active compounds from TM and 14 potential targets. Docking simulation showed apigenin, luteolin, and quercetin bio-active compounds in TM bound to IKBKG, INSR, and RAF-1 proteins. In xerosis model rats, TM mitigated xerosis damage, decreased inflammation factors, and phosphorylation of PI3K/AKT and ERK/MAPK proteins. SC79 or CrPic or their combination reversed TM's effect. The current study identified potential targets and PI3K/AKT and ERK/MAPK pathways involved in the effect of TM on xerosis, thus providing a foundation for TM clinical application.

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

Scaling variation in the pinch-off of colloid-polymer mixtures.

HYPOTHESIS: The scaling laws of drop pinch-off are known to be affected by drop compositions including dissolved polymers and non-Brownian particles. When the size of the particles is comparable to the characteristic length scale of the polymer network, these particles may interact strongly with the polymer environment, leading to new types of scaling behaviors not reported before. EXPERIMENTS: Using high-speed imaging, we experimentally studied the time evolution of the neck diameter hmin of drops composed of silica nanoparticles dispersed in PEO solution when extruded from a nozzle. FINDINGS: After initial Newtonian necking with hmin âˆ¼ t2/3, the subsequent stage may exhibit scaling variation, characterized by either exponential or power-law decay, depending on the nanoparticle volume fraction ϕ. The exponential decay hmin âˆ¼ e-t/τ signifies the coil-stretch transition in typical viscoelastic suspensions. We conducted an analysis of the power-law scenario hmin âˆ¼ tα at high ϕ, categorizing the entire process into three distinct regimes based on the exponents α. The dependences of critical thicknesses at transition points and exponents on polymer concentration offer initial insights into the potential transition from heterogeneous to homogeneous thinning in the mixture. This novel scaling variation bears implications for accurately predicting and controlling droplet fragmentation in industrial applications.

AQbD-Guided Development and Validation of an Innovative Extraction Procedure and Stability-Indicating RP-HPLC Method for Quantification of Posaconazole in Tablet Formulation.

OBJECTIVE: The objective of this work is to develop a stability-indicating HPLC method for the quantification of Posaconazole (PCZ) in tablet formulation using an Analytical Quality by Design (AQbD) approach. MATERIALS AND METHODS: The development process involved the Design of Experiments (DOE) utilizing distinctive constraints mixture design for mobile phase ratio optimization and a 2-level factorial design for selection of extraction diluent compositions. Key responses measured included % assay and system suitability parameters. Method operable design regions (MODR) were determined, and final optimum conditions were selected. Forced degradation studies were conducted to assess method stability. RESULTS: The optimized HPLC method employed a Zorbax C18 column with a mobile phase consisting of pH 3.5 10mM phosphate buffer, acetonitrile, and methanol in a ratio of 30:53:17 % v/v/v. The method demonstrated stability-indicating capabilities, with PCZ degradation observed in acidic and oxidative environments, while remaining stable in alkali. Peak purity analysis from Empower software confirmed the absence of interaction with degradants. Validation according to ICH Q2 (R2) guidelines showed precision, linearity over the range of 0.25 µg/mL to 376 µg/mL, and accuracy demonstrated through recovery studies from 50 to 150%. CONCLUSION: The developed HPLC method utilizing AQbD approach is specific, robust, precise, and accurate for the quantification of PCZ in tablet formulations, thus suitable for routine analysis.

Microwave De-Icing Efficiency Improvement of Asphalt Mixture with Structural Layer Optimization and Heat-Resistance Design.

The application of microwave de-icing technology in road engineering is constrained by its low energy utilization rate, which can be attributed to low heat production rates and ineffective heat dissipation to the underlying pavement. In this work, asphalt mixtures are designed as an upper layer (heating layer) and a lower layer (thermal-resistance layer). Magnetite slag was selected as a microwave-sensitive source for generating heat, and expanded perlite powder was incorporated into the lower layer as a thermal resistance material. Structural layer optimization and thermal-resistance layer design of the asphalt mixture were carried out by changing the thickness of the upper and lower layers to further improve the heat production rates. The design effectiveness is comprehensively evaluated by factors such as the changing law of the average surface temperature of mixtures, ice-melting time, and cost-effectiveness analyses. The results show that EP possesses better thermal stability, lower microwave energy conversion ability and more excellent heat-resistance potential compared with mineral powder. The heat-resistance layer with EP can prevent heat from being conducted to the lower layer and promote it to concentrate on the specimen surface, which can endow the microwave heating efficiency of specimens to be further improved by up to 26.97% and the de-icing time reduced by 10%, ascribed to the heat-resistance design. Furthermore, the collaborative design of the structural layer optimization and heat-resistance layer can increase energy utilization efficiency and save microwave-absorbing materials while ensuring excellent microwave de-icing efficiency.

Impaired memory in Sprague-Dawley rats exposed to complex groundwater mixtures of contaminants is associated with reduced cranial blood flow and hippocampal neurotoxicity.

Exposure to industrial contaminants has been implicated in neurobehavioral toxicity in humans. To explore this potential risk, we investigated the neurotoxic effects of oral exposure to a complex groundwater mixture containing petroleum hydrocarbons, pesticides, heavy metals, and unknown parent and breakdown products using male and female Sprague Dawley rats. Rats were randomly divided into six groups and orally exposed daily via drinking water to: (i) tap water, (ii) 10 % v/v low impact groundwater, and (iii) 0.01 %, 0.1 %, 1 %, and 10 % high-impact groundwater for 60 days. Medium- and long-term memory (measured using the novel object recognition task) were impaired. However, no gross motor or coordination deficits were observed by the end of the study period (rotarod test). Doppler ultrasound of the middle cerebral and common carotid arteries was performed to examine the hemodynamic changes. The common carotid blood flow decreased in the groundwater-exposed rats compared to that in the control. However, no significant differences in cerebral blood velocity were observed between the exposed and control groups. A significant reduction in hippocampal serotonin levels was observed in groundwater-exposed rats relative to that in the control group. Collectively, these results indicate that impaired recognition memory in rats exposed to groundwater is accompanied by reduced cranial blood flow and hippocampal neurotoxicity, characterized by altered serotonergic signalling. The levels of detected contaminants known to cause neural or vascular damage were of magnitudes lower than the concentrations of contaminants found in the groundwater mixture, meaning the culprit chemical identity remains unknown. This study emphasizes the need to use whole mixture in exposures when dealing with complex contaminated sites rather than the use of individual compounds.

Breast cancer immunophenotypes and serum organochlorine pesticides in Mexican women: Mixture exposure approach.

Epidemiological studies on associations between breast cancer (BC) and organochlorine pesticides (OCP) are inconclusive. The majority of studies have evaluated the effect of single compounds without considering multiple OCP exposures and immunophenotypes of BC. We aimed to evaluate the association between BC immunophenotypes and serum OCP mixtures, and identify the main contributors within mixtures. We included 767 histopathologically confirmed incident BC cases and 908 controls from a population-based case-control study conducted from 2007 to 2011 in Northern Mexico. We obtained direct information about sociodemographic, lifestyle and reproductive characteristics. We collected data from clinical records about hormonal receptors (HR) and epidermal growth factor receptor 2 (HER2) expressions. Immunophenotypes were determined as HR+/HER2-, HER2+ or HR-/HER2-. We quantified OCP and metabolites by gas chromatography using an electron capture micro detector. We used Weighted Quantile Sum regression to assess the association of BC and exposure to multiple OCP, and their contribution within the mixture. We found a positive adjusted association between BC and an OCP mixture (OR: 3.48, 95%CI: 2.58, 4.69), whose primary contribution arose from the isomers of hexachlorocyclohexane and endosulfan, as well as endosulfan sulfate. We also identified a mixture negatively associated (OR: 0.13, 95%CI: 0.08, 0.20), characterized by p,p'-DDT and chlordane metabolites. All these associations remained regardless BC immunophenotypes. This is the first epidemiological report that identified serum OCP mixtures associated with BC immunophenotypes. Due to OCP ubiquity, biomagnification, and continuous exposure, they constitute a global problem of persistent exposure that might be related to BC risk.

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals.

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.