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Polychlorinated biphenyls (PCBs), dichlorodiphenyltrichloroethane (DDT), and dichlorodiphenyldichloroethylene (DDE) are suspected to be associated with breast cancer risk, but the results are controversial. This study was performed to evaluate the associations between adipose tissue PCB, DDT, and DDE concentrations and breast cancer risk. Two hundred and nine pathologically diagnosed breast cancer cases and 165 controls were recruited from three local hospitals in Shantou city, China, from 2014 to 2016. Concentrations of 7 PCB congeners, p,p′-DDT, and p,p′-DDE were measured in adipose tissues obtained from the breast for cases and the breast/abdomen for controls during surgery. Clinicopathologic information and demographic characteristics were collected from medical records. PCBs, p,p′-DDT, and p,p′-DDE concentrations in adipose tissues were compared between cases and controls. Multivariate logistic regression model was used to analyze the risk of breast cancer by PCBs, p,p′-DDT, and p,p′-DDE concentrations in adipose tissues. Breast cancer cases have relatively higher menarche age, higher breastfeeding and postmenopausal proportion than controls. Levels of PCB-52, PCB-101, PCB-118, PCB-138, PCB-153, PCB-180, total PCBs (∑PCBs), and p,p′-DDE were relatively higher in breast cancer cases than controls. Breast cancer risk was increased in the third tertile of PCB-101, PCB-118, PCB-138, PCB-153, PCB-180, ∑PCBs, and p,p′-DDE as compared with the first tertile in both adjusted and unadjusted logistic regression models (odds ratios [ORs] were from 1.58 to 7.88); and increased linearly across categories of PCB-118 and p,p′-DDE in unadjusted model, and PCB-118 and PCB-153 in the adjusted model with trend (all P  < 0.01). While breast cancer risk was declined in the second tertile of PCB-28, PCB-52, and PCB-101 in both unadjusted and adjusted models, also second tertile of p,p′-DDT and third tertile of PCB-28 in the adjusted models. This study suggests associations between the exposure of PCBs, p,p′-DDT, and p,p′-DDE and breast cancer risk. Based on adjusted models, PCB-118, PCB-138, PCB-153, PCB-180, ∑PCBs, and p,p′-DDE exposures increase breast cancer risk at current exposure levels, despite existing inconsistent even inverse results in PCB-28, PCB-52, PCB-101, and p,p′-DDT. More epidemiological studies are still needed to verify these findings in different populations.

Recent baryon acoustic oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI) collaboration, combined with the cosmic microwave background (CMB) and type Ia supernovae observations, suggest a preference for dynamical dark energy (DDE) with w0 > −1 and wa < 0. Given the cosmological origin of fast radio bursts (FRBs), the combination of their dispersion measures (DMs) and host galaxy redshifts makes localized FRBs a valuable tool for probing cosmology. Using an updated sample of 92 localized FRBs, along with DESI BAO, PlantheonPlus, and CMB data, we constrain the dark energy (DE) equation of state (EoS) under the Chevallier–Polarski–Linder parameterization. We find that even without incorporating CMB data, DDE remains preferred with w0=−0.855−0.084+0.084 and wa=−1.174−0.491+0.462 at a confidence level of ∼2.5σ. A joint analysis constrains these to be w0=−0.784−0.064+0.064 and wa=−0.872−0.278+0.269 , showing a discrepancy with ΛCDM at a ∼3.1σ level. Furthermore, using localized FRBs alone, we estimate the Hubble constant H0 to be 69.04−2.07+2.30 and 75.61−2.07+2.23kms−1Mpc−1 , assuming the Galactic electron density models to be NE2001 and YMW16, respectively. Thus, accurate accounting of the Galactic DM is crucial for resolving the Hubble tension with FRBs. Future BAO measurements, next-generation CMB experiments, and more localized FRBs will further constrain the DE EoS and the cosmological parameters.

Abstract Accurate identification of diagenetic facies is crucial for reservoir characterization, as it directly determines the evaluation of petrophysical properties, pore structure types, and reservoir quality, thereby playing a pivotal role in predicting high-quality hydrocarbon-bearing zones. However, conventional identification approaches are often limited by subjective interpretation, lack of standardized criteria, and low operational efficiency. Meanwhile, existing intelligent classification techniques frequently fail to adequately discriminate subtle but critical data variations, leading to suboptimal classification accuracy. To address these challenges, this paper develops a novel method for diagenetic facies identification based on data difference enhancement (DDE). The proposed methodology consists of three key steps: first, high-dimensional well-logging data are projected into a lower-dimensional feature space using the t-SNE algorithm to improve computational efficiency while preserving nonlinear relationships. Subsequently, the k-means clustering algorithm partitions the processed dataset into distinct groups, thereby amplifying intra-cluster data homogeneity and inter-cluster separability. Next, an ensemble learning architecture is constructed using the stacking algorithm, where cluster-specific meta-classifiers are individually optimized to enhance model robustness. During application, unclassified samples are assigned to their nearest cluster based on Euclidean distance metrics, followed by targeted prediction using the corresponding meta-classifier. The data from the second member of the Upper Triassic Xujiahe Formation are employed for model evaluation, and the results show that the DDE machine learning method significantly outperforms conventional machine learning algorithms, including k-nearest neighbours, support vector machines, and random forests, achieving an accuracy rate of 86.4%. This workflow enables efficient and reliable diagenetic facies classification using standard well-logging curves, offering both theoretical insights and practical tools for reservoir quality prediction in hydrocarbon exploration.

Early observations with JWST have led to the discovery of an unexpectedly large density (stellar-mass density ρ * ≈ 106 M ⊙ Mpc−3) of massive galaxies (stellar masses M * ≥ 1010.5 M ⊙) at extremely high redshifts z ≈ 10. While such a result is based on early measurements that are still affected by uncertainties currently under consideration by several observational groups, its confirmation would have a strong impact on cosmology. Here we show that—under the most conservative assumptions and independently of the baryon physics involved in galaxy formation—such galaxy abundance is not only in tension with the standard ΛCDM cosmology but provides extremely tight constraints on the expansion history of the universe and on the growth factors corresponding to a wide class of Dynamical Dark Energy (DDE) models. Adopting a parameterization w = w 0 + w a (1 − a) for the evolution of the DDE equation of the state parameter w with the expansion factor a, we derive constraints on combinations of (w 0, w a ) that rule out with confidence level >2σ a major portion of the parameter space (w 0, w a ) allowed (or even favored) by existing cosmological probes.

Chilika Lake is the largest coastal lagoon in Asia and the second largest in the world covering an area of 1100 km 2 and spread over three districts of Odisha state of India. It is the first Indian wetland designated as a wetland of international importance under the Ramsar Convention in 1981. The lake ecosystem sustains large and diversified resources of plants and animals including fisheries. Pollution of the ecosystem caused by residues of pesticides originating from different sources was assessed through multiple sampling from 2012 to 2016 from three potential sites of contamination, viz., Palur Bridge, Daya River Estuary, and Makara River. Incidence of organochlorinated (OC) pesticide residues was noticed in about 25% water samples. HCH (α, γ&δ), DDD (op | ), DDE (op | &pp. | ) and heptachlor were the OCs detected in concentration varying from 0.025 to 23.4 μg/l. None of the eight targeted synthetic pyrethroid (SP) pesticides was found in water, but among the organophosphates (OP), chlorpyrifos (0.019–2.73 μg/l), and dichlorvos (0.647 μg/l) were recorded. In sediment samples, residues of OC or OP pesticides were not present, but one SP pesticide was recorded. Fish samples were contaminated to the extent of 55%, mostly with residues of OCs and OPs and less with SPs. However, their concentrations were below the permissible limit, so there was no direct threat of health hazards to humans.

Many of South Africa’s freshwater impoundments are compromised by pesticide pollution, and the Roodeplaat Dam, near Pretoria, is no exception. This paper is, to the best of our knowledge, the first report of the herbicides Dacthal, metribuzin, simazine, tebuthiuron, terbuthylazine, and the fungicides azoxystrobin, carbendazim, epoxiconazole, metalaxyl (Ridomil), propiconazole, pyrimethanil and thiabendazole in a South African freshwater impoundment. This short note reports on the screening results of water and muscle tissue samples against a comprehensive library of pesticides, herbicides and fungicides in the polluted Roodeplaat Dam. Muscle samples of Oreochromis mossambicus screened positive for p,p' -DDE and p,p' -DDD and for DCPA (chlorthal-methyl). The muscle tissue of Clarias gariepinus screened positive for p,p'- DDE and p,p'- DDD, chlorpyrifos, trans -chlordane, DCPA and terbuthylazine. The presence of these pesticides, herbicides and fungicides in this impoundment is of great concern as there is substantial evidence of adverse health effects in fish exposed to these chemicals.

Biological regulatory systems, such as cell signaling networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into overall behavior. However, such models often overlook time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equation (DDE) models, both analytically and numerically. We find many broadly applicable results, including parameter reduction versus canonical ordinary differential equation (ODE) models, analytical relations for converting between ODE and DDE models, criteria for when delays may be ignored, a complete phase space for autoregulation, universal behaviors of feedforward loops, a unified Hill-function logic framework, and conditions for oscillations and chaos. We conclude that explicit-delay modeling simplifies the phenomenology of many biological networks and may aid in discovering new functional motifs. Network motif models focus on small sub-networks in biological systems to quantitatively describe overall behavior but they often overlook time delays. Here, the authors systematically examine the most common network motifs via delay differential equations (DDE), often leading to more concise descriptions.

The insertion sequence IS26 plays a major role in the mobilization, expression and dissemination of antibiotic resistance genes in Gram-negative bacteria. Though IS26 is abundant in sequenced genomes and in plasmids that harbour antibiotic resistance genes, only a few minor variations in the IS26 sequence have been recorded. The most common variant, IS26* (also known as IS15Δ1), encodes a Tnp26 transposase with a single amino acid substitution, G184N in the catalytic domain. Using computational modelling, this substitution was predicted to increase the length of the helix that includes the E173 residue of the catalytic DDE triad, and its effect on activity was tested. An IS26 mutant generated in vitro producing Tnp26-G184N formed cointegrates in a standard untargeted reaction at 5-fold higher frequency than IS26 producing Tnp26. When the target included a single copy of IS26, the G184N substitution increased the cointegration frequency 10-fold and the reaction was targeted and conservative. Hence, the substitution increased Tnp26 activity. The longer helix may stabilise the position of the E173 of the DDE for the catalysis reaction and the specific G184N substitution may also enhance activity by increasing binding to the terminal inverted repeats.

The purpose of this study was to review the epidemiological and experimental evidence linking background exposure to a selection of environmental endocrine-disrupting chemicals (EDCs) with diabetes and impaired glucose metabolism. The review summarises the literature on both cross-sectional and prospective studies in humans, as well as experimental in vivo and in vitro studies. The findings were subjected to evidence grading according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) classification. We found >40 cross-sectional and seven prospective studies regarding EDCs and risk of diabetes. Taken together, there is moderate evidence for a relationship between exposure to dichlorodiphenyldichloroethylene (p,p′-DDE), a metabolite of the pesticide dichlorodiphenyltrichloroethane, and diabetes development. Regarding polychlorinated biphenyls (PCBs), it is likely that the rodent models used are not appropriate, and therefore the evidence is poorer than for p,p′-DDE. For other EDCs, such as bisphenol A, phthalates and perfluorinated chemicals, the evidence is scarce, since very few prospective studies exist. Brominated flame retardants do not seem to be associated with a disturbed glucose tolerance. Thus, evidence is accumulating that EDCs might be involved in diabetes development. Best evidence exists for p,p′-DDE. For other chemicals, both prospective studies and supporting animal data are still lacking.

Background: Recent experimental evidence suggests that prenatal exposure to endocrinedisrupting chemicals (EDCs) may increase postnatal obesity risk and that these effects may be sex or diet dependent. Objectives: We explored whether prenatal organochlorine compound (OC) concentrations [polychlorinated biphenyls (PCBs), dichlorodiphenyldichloroethylene (DDE), and dichlorodiphenyltrichloroethane (DDT)] were associated with overweight at 6.5 years of age and whether child sex or fat intakes modified these associations. Methods: We studied 344 children from a Spanish birth cohort established in 1997-1998. Overweight at 6.5 years was defined as a body mass index (BMI) z-score ≥ 85th percentile of the World Health Organization reference. Cord blood OC concentrations were measured and treated as categorical variables (tertiles). Children's diet was assessed by food frequency questionnaire. Relative risks (RRs) were estimated using generalized linear models. Results: After multivariable adjustment, we found an increased RR of overweight in the third tertile of PCB exposure [RR = 1.70; 95% confidence interval (CI): 1.09, 2.64] and the second tertile of DDE exposure (RR = 1.67; 95% CI: 1.10, 2.55), but no association with DDT exposure in the population overall. Associations between overweight and PCB and DDE concentrations were strongest in girls (ρ-interaction between 0.01 and 0.28); DDT was associated with overweight only in boys. For DDT we observed stronger associations in children with fat intakes at or above compared with below the median, but this interaction was not significant (ρ-interaction > 0.05). Conclusions: This study suggests that prenatal OC exposures may be associated with overweight in children and that sex and high-fat intake may influence susceptibility.