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Causal inference unveils how forest coverage mitigates excess snakebite cases during rainfall seasons in Colombia

The effect of face masks on confusion of emotional expressions

While the use of personal protective equipment protects healthcare workers against transmissible disease, it also obscures the lower facial regions that are vital for transmitting emotion signals. Previous studies have found that face coverings can impair recognition of emotional expressions, particularly those that rely on signals from the lower regions of the face, such as disgust. Recent research on the individual differences that may influence expression recognition, such as emotional intelligence, has shown mixed results. In the current investigation, we conducted two experiments to build on previous work to further investigate the role of emotional intelligence in emotion recognition. Participants viewed a set of masked and unmasked models displaying six different emotional expressions for a limited time (0.5 s, 1.0 s, or 1.5 s in Experiment 1 and 1.0 s in Experiment 2). Participants then indicated which emotion options they felt each model expressed. Participants’ emotional intelligence was measured using the Schutte Self Report Emotional Test. Emotion recognition accuracy was lower for images with masks than images without masks in both experiments. Confusion rates were not uniform: masked disgust and sadness had the greatest increase of confusion from their unmasked conditions, happiness and surprise had the least confusion in all conditions, and both fear and anger had generally low recognition in masked and unmasked conditions. Emotional intelligence only impacted accuracy when view time was particularly restricted and parts of the face were covered. These results replicate the finding that face masks impair the transmission of emotional signals, and the impairments are a function of the occluded facial regions. Emotional intelligence may moderate this effect in dynamic social interactions where emotional expressions are brief and ambiguous.

An analysis of behavioral characteristics and enrollment year variability in 47,444 dogs entering the Dog Aging Project from 2020 to 2023

Understanding dog behavior, especially in the context of the human social environment, is critical to maintaining positive human-dog interactions and relationships. Furthermore, behavior can be an important indicator of health and welfare in companion dogs. Behavioral change can signal transitions in life stages, alert caretakers to potential illnesses or injuries, and is an important factor in understanding and measuring stress. In order to take advantage of behavioral change as a biomarker, however, we must first have a behavioral baseline to assess. Thus, using owner-reported data from dogs enrolled in the Dog Aging Project (DAP) from 2020–2023, our aim was to establish baseline behavioral measures for 47,444 dogs, with the goal of using these measures in future research investigating behavioral change in dogs and short- and long-term health outcomes. Given that the data collection period spanned the 2019 coronavirus disease (COVID-19) lockdown period and its immediate aftermath, a secondary aim of this study was to evaluate whether year of project entry impacted average reported behavior scores in dogs and to investigate additional variables that may influence observed differences. In our analyses of cohort baseline and year-over-year changes among four composite behavior domains — Fear, Attention/Excitability, Aggression, and Trainability — we find that time (year of enrollment) had the highest influence on Trainability, wherein dogs enrolled in all three years after 2020 (2021–2023) had lower average reported scores than dogs enrolled in 2020. Several other variables, including breed, life stage, sex, spay/neuter status, size, primary residence, and primary activities, have positive and negative statistical associations with mean behavioral scores in all four domains.

The Pandemic Journaling Project: A new dataset of first-person accounts of the COVID-19 pandemic

Background The rapid global spread of the COVID-19 pandemic affected different regions, communities, and individuals in vastly different ways that interdisciplinary social scientists are well-positioned to document and investigate. This paper describes an innovative mixed-methods dataset generated by a research study that was designed to chronicle and preserve evidence of the pandemic’s divergent effects: the Pandemic Journaling Project (PJP). The dataset was generated by leveraging digital technology to invite ordinary people around the world to document the impact of the COVID-19 pandemic on their everyday lives over a two-year period (May 2020-May 2022) using text, images, and audio. Methods and findings PJP’s weekly, online, bilingual (English/Spanish) journaling platform was open to anyone with access to a smartphone or computer, including teens aged 15–17 with permission of a parent or guardian. Participants first completed a baseline quantitative survey, after which they were invited to create two journal entries in response to suggested narrative prompts. In each subsequent week, participants received weekly invitations to contribute via their choice of email or SMS (text message). Each invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join the project at any point between May 2020 and May 2022. PJP employed a cohort design. Regardless of when they joined, all received the narrative prompts and accompanying survey questions in the same order. Participants could stop participating at any point, and they could later restart if they wished. The project welcomed any interested participant and sought to capture as broad a range of perspectives as possible, while also taking measures to include voices that might not otherwise be preserved in the historical record. The project launched in May 2020. In the two years it operated as a weekly journaling platform, PJP generated nearly 27,000 individual journal entries from over 1,800 people in 55 countries around the world. Data from PJP’s first phase (PJP-1) are now accessible at the Qualitative Data Repository (QDR) at Syracuse University. Conclusions The first phase of the Pandemic Journaling Project has produced an innovative multimedia dataset that can support studies of how the COVID-19 pandemic has affected a wide range of communities across a wide range of outcomes including mental health, reproductive health, vaccine hesitancy, and trust in health professionals, among others. The dataset is available to researchers who follow established data protection protocols and procedures. These data protection measures will be in place for 25 years, through 2049, after which all PJP-1 data will become a fully accessible public archive via QDR.

Beyond lethal temperatures: Factors behind the disappearance of chum salmon from their southern margins under climate change

The Tone River in Japan represents one of the southern limit distributions of chum salmon (Oncorhynchus keta) on the western side of the North Pacific, but the number of adult chum salmon observed here has declined dramatically since 2013 and reached zero in 2024. The factors behind the recent decline of the chum salmon population in the Tone River were investigated by using ocean reanalysis data and a 20-year particle-tracking simulation. Virtual chum salmon fry were released at the mouth of the Tone River in spring each year with six different swimming strategies to evaluate the effects of ocean currents on the population growth rate of salmon. None of the simulated scenarios reproduced the recent decline in the population, regardless of the swimming strategy and addition of lethal temperature thresholds. Instead, the decline in population growth rate appears to be correlated with warming water temperature and reduced zooplankton abundance caused by the northward shifts of the Kuroshio/Kuroshio Extension and Oyashio. Along the coast of northeastern Japan, the warm, nutrient-poor Kuroshio/Kuroshio Extension replaced the cold, nutrient-rich Oyashio on the migration route of chum salmon fry, increasing the water temperature and reducing zooplankton abundance. Partial correlation analysis of the water temperature and zooplankton abundance indicated that the latter was the main influencing factor coherently related to the population growth rate of salmon. The reduced zooplankton abundance would affect the growth and survival of chum salmon fry, which would result in a decline in population growth. The northward shift of the Kuroshio/Kuroshio Extension and Oyashio may continue or return southward depending on the effects of climate change, which will greatly influence the future population growth of chum salmon and whether they come back to the Tone River.

A novel hybrid model for species distribution prediction using probabilistic random forest, principal component analysis and genetic algorithm

Probabilistic Random Forest is an extension of the traditional Random Forest machine learning algorithm that is one of the frequently used machine learning algorithms employed for species distribution modeling. However, with the use of complex dataset for predicting the presence or absence of the species, It is essential that feature extraction is important to generate optimal prediction that can affect the model accuracy and AUC score of the model simulation. In this paper, we integrated the Genetic Algorithm Optimization technique, which is popular for its excellent feature extraction technique, to enhance the predictive performance of the PRF Model. a novel hybrid algorithm the genetically optimized probabilistic random forest algorithm, designed for predicting the distribution of mastomys natalensis in Nigeria. The model was also compared with existing models for dimensionality reduction with other optimization techniques, such as Principal Component Analysis, Grey Wolf, Optimizer optimized backpropagation neural network algorithm (GNNA), Butterfly Optimization Algorithm. These models were evaluated using four performance metrics, accuracy, the areas under curve, sensitivity, specificity, F1_score and precision. We also examined the spatial predictive distribution of the models. The results generated that the predictive performance of PRFGA, significantly improved compared to PRFPCA, GNNA and PRFBOA in predicting the presence or absence of mastomys natalensis with a presence only and pseudo-absence sample set. the PRFGA demonstrated a high predictive power in predicting the spatial distribution of the presence or absence of mastomys natalensis in Nigeria. The integration of the Genetic Algorithm optimization technique, stems from its renowned ability to address the specific challenges of data uncertainty and high-dimensionality reduction in feature extraction sets of SDMs, to enhance the performance of the PRF model.

Impact of skin tone, environmental, and technical factors on thermal imaging

Background Erythema, an early visual indicator of tissue damage preceding pressure injuries (PrIs), presents as redness in light skin tones but is harder to detect in dark skin tones. While thermography shows promise for early PrI detection, validation across different skin tones remains limited. Furthermore, most protocols and models have been developed under highly controlled conditions. Objective To evaluate how environmental and technical factors (i.e., patient positioning, lighting, distance, camera type) and skin tone impact thermal imaging measurements and temperature change. Methods This pre-post experimental study enrolled 35 healthy adults (30 with Monk Skin Tone Scale ≥6). Melanin Index was measured on the volar forearm using the SkinColorCatch ® . After baseline imaging, a 15.5°C cooling stone was placed on one posterior superior iliac spine (PSIS) for 5 minutes. Thermal images were then collected with either the FLIR E8-XT or the FLIR ONE Pro camera under varied conditions: two lighting types (overhead room versus localized LED ring light), three postures (side-lying, side-lying with forward knee placement, and side-lying with rearward knee placement), and two camera-to-body distances (35 cm and 50 cm from the PSIS). The cooling/imaging procedure was repeated using the alternate camera, and data were analyzed using mixed-methods model. Results Temperature change was effectively detected across all skin tones, with cooling resulting in a −3.7 ± 1.2°C difference between the region of interest (ROI) and control region. Camera type significantly affected measurements, with the ONE Pro recording 1.04°C less cooling than the E8-XT. Distance had minimal impact (0.11°C cooler at 50 cm vs 35 cm at baseline), with no significant difference when comparing ROI to control regions, while lighting and posture had no impact on measurements. Skin tone varied by cooling measurement, with higher melanin levels showing greater temperature changes. A 0.98°C difference was observed between the lightest and darkest skin tone groups. Discussion Our findings confirm thermal imaging’s robustness across varied environments, with the minor distance effects mitigated through perpendicular measurements and relative temperature comparisons. Significant discrepancies between thermal cameras (>1°C) highlight that these technologies cannot be used interchangeably when establishing thresholds. While effective across all skin tones, the observed differences in cooling response suggest tailored thresholds may be necessary for darker skin tones. Future research should focus on clinical validation across diverse populations to enhance PrI detection accuracy.

More than scientists: How message and messenger attributes influence viewers’ climate change intentions

Effectively motivating public action on climate change remains a central challenge for science communicators. This study investigated how message and messenger attributes shape viewers’ motivation to act on climate change, and whether these effects vary as a function of political orientation. Using a policy-capturing design, 581 U.S. adults each viewed six randomly selected short videos from the More than Scientists website, in which climate scientists described the personal relevance of climate change. Linguistic features of the messages were analyzed using the Linguistic Inquiry and Word Count (LIWC) software, and messenger attributes (e.g., age, sex, attractiveness) were independently coded. Multilevel modeling revealed that messenger characteristics—particularly being older, male, attractive, and filmed in natural settings—were the strongest predictors of viewer motivation, explaining over 21% of within-person variance. By contrast, linguistic message attributes had weak predictive power overall, though messages with future-focused language and greater length were modestly more motivating. Political orientation moderated some message effects: affiliation-oriented language increased motivation for left-leaning viewers, while achievement-oriented language was more effective for right-leaning viewers. These findings underscore the importance of peripheral cues in climate communication and support targeted messaging strategies that align with audience values and identities.

Epidemiological patterns of SARS-CoV-2 reinfections in Espírito Santo, Brazil: A population-based analysis using integrated surveillance and vaccination data

Background Reinfections with SARS-CoV-2 have gained increasing relevance in the context of emerging immune-evasive variants and waning population immunity. Understanding their frequency and distribution is essential to guide public health strategies, particularly in middle-income countries. This study investigates the epidemiological patterns of SARS-CoV-2 reinfections in Espírito Santo, Brazil, using integrated notification and vaccination databases. Methods We conducted a retrospective population-based study using state-level surveillance and immunization data from September 2020 to February 2023. Deterministic record linkage was performed to identify reinfections based on four hierarchical definitions, all requiring a minimum 90-day interval between episodes. Reinfection rates were analyzed by demographic, temporal, regional, and vaccination-related variables. Specific contexts such as Indigenous communities and long-term care facilities were also explored. Results Reinfection estimates varied widely depending on the definition used—from 0.27% under the strictest criteria to 9.72% under the most inclusive. Reinfections were more common among women and adults aged 30–49 years. The highest proportions occurred during the second and fourth waves, driven by the Gamma and Omicron variants, respectively. Reinfection rates were particularly elevated in Indigenous communities (16.5%) and long-term care facilities (14%), highlighting structural vulnerabilities. In contrast, no increased reinfection risk was observed among incarcerated populations, possibly due to data limitations. Reinfections were frequent among vaccinated individuals, especially those who had received two doses without boosters. However, this likely reflects exposure patterns and time since vaccination, rather than reduced vaccine effectiveness. Brazil’s use of inactivated-virus vaccines, such as CoronaVac, in the early phases may also have influenced susceptibility. Reinfections occurred predominantly after the emergence of Omicron, consistent with findings from national and global studies. Conclusions This study shows that the cumulative frequency of reinfections increased over the study period, reaching up to ~10% under the most inclusive definition, while remaining modest under the strictest criteria. These findings reinforce the importance of continued genomic surveillance, timely booster campaigns, and targeted interventions in high-risk environments. The integration of multiple data sources and the use of variable reinfection definitions allowed for a nuanced analysis, offering valuable insights to inform public health planning in Brazil and similar contexts.

In vitro characterization of Trichophyton rubrum biofilm by combined anti-biofilm enzymes

Trichophyton rubrum, a dermatophyte, demonstrates a notable ability to form mature biofilms on skin and associated surfaces, strengthening its resistance to antifungal agents. This characteristic poses intricate challenges in dermatological research and therapeutic strategies, underscoring the need for innovative approaches to effectively manage fungal infections. This work assessed the impact of the anti-biofilm enzymes, i.e., cellulase, protease, and amylase, individually and in combination, on the eradication and inhibition of T. rubrum biofilm. After 168 hours of incubation, T. rubrum biofilm formation matured, and the anti-biofilm enzymes significantly reduced the rate of biofilm development. The rates of biofilm inhibition and eradication for cellulase, protease, and amylase were 64%, 38%, and 28% at 72 hours of incubation, and 47%, 25.87%, and 17%, respectively, at 168 hours. However, the combined anti-biofilm enzymes (cellulase, protease, and amylase) had a 60.62% biofilm suppression rate. SEM analysis revealed marked reductions in T. rubrum conidial density, disrupted hyphal structures, and diminished biofilm adherence in enzyme-treated samples compared to untreated controls, visually supporting the inhibitory effect observed in quantitative assays. These morphological alterations indicate compromised fungal viability and structural disintegration of the biofilm matrix. Collectively, the SEM findings reinforce the therapeutic potential of enzyme-based strategies against dermatophytic biofilms. Additionally, these anti-biofilm enzymes have shown strong efficacy in reducing the exopolysaccharide (EPS) content and degrading the complex EPS matrix network. The T. rubrum biofilm treated with anti-biofilm enzymes, such as cellulase and protease, resulted in EPS degradation by FTIR analysis. The antibiofilm enzyme cellulase showed notable degradation of the beta 1–4 linkage within the glycosidic bond. A significant degradation is observed when T. rubrum biofilm is treated with combined enzymes (cellulase, protease, and amylase). The combined enzymatic treatment disrupted the EPS matrix, indicating its potential as an effective strategy for inhibiting biofilm formation by T. rubrum.

Preliminary study on metabolite differences between two obesity syndromes based on Q Exactive liquid chromatography–tandem mass spectrometry metabolomics

Background According to traditional Chinese medicine, based on the pathogenesis, clinical manifestation, and treatment, obesity can be classified into two broad types, namely, “shi obesity” and “xu obesity.” The aim of this study was to explore the differences in metabolite levels between these two types of obesity. Methods Q Exactive liquid chromatography–tandem mass spectrometry was used to analyze the metabolites in the sera of 30 healthy adults, 30 adults with shi obesity, and 30 adults with xu obesity. Possible metabolic pathways associated with biological alterations were identified using the Human Metabolome Database and Kyoto Encyclopedia of Genes and Genomes analyses. Results Fifteen important potential biomarkers were identified, 14 of which were upregulated. Furthermore, the biomarkers phenylalanine, L-tyrosine, glycerophosphocholine, and LysoPC(O-18:0/0:0) were significantly correlated with both shi and xu obesity. Of note, L-tyrosine and glycerophosphocholine were screened and used as potential biomarkers of shi and xu obesity for the first time. Conclusion This study verified the differences in serum metabolites associated with shi and xu obesity, demonstrating potential therapeutic strategies for the two types.

Utilizing CNNs for classification and uncertainty quantification for 15 families of European fly pollinators

Pollination is essential for maintaining biodiversity and ensuring food security, and in Europe it is primarily mediated by four insect orders (Coleoptera, Diptera, Hymenoptera, Lepidoptera). However, traditional monitoring methods are costly and time consuming. Although recent automation efforts have focused on butterflies and bees, flies, a diverse and ecologically important group of pollinators, have received comparatively little attention, likely due to the challenges posed by their subtle morphological differences. In this study, we investigate the application of Convolutional Neural Networks (CNNs) for classifying 15 European pollinating fly families and quantifying the associated classification uncertainty. In curating our dataset, we ensured that the images of Diptera captured diverse visual characteristics relevant for classification, including wing morphology and general body habitus. We evaluated the performance of three CNNs, ResNet18, MobileNetV3, and EfficientNetB4 and estimated the prediction confidence using Monte Carlo methods, combining test-time augmentation and dropout to approximate both aleatoric and epistemic uncertainty. We demonstrate the effectiveness of these models in accurately distinguishing fly families. We achieved an overall accuracy of up to 95.61%, with a mean relative increase in accuracy of 5.58% when comparing uncropped to cropped images. Furthermore, cropping images to the Diptera bounding boxes not only improved classification performance across all models but also increased mean prediction confidence by 8.56%, effectively reducing misclassifications among families. This approach represents a significant advance in automated pollinator monitoring and has promising implications for both scientific research and practical applications.

YOLOv11-WBD: A wavelet-bidirectional network with dilated perception for robust metal surface defect detection

In the field of quality control, metal surface defect detection is an important yet challenging task. Although YOLO models perform well in most object detection scenarios, metal surface images under operational conditions often exhibit coexisting high-frequency noise components and spectral aliasing background textures, and defect targets typically exhibit characteristics such as small scale, weak contrast, and multi-class coexistence, posing challenges for automatic defect detection systems. To address this, we introduce concepts including wavelet decomposition, cross-attention, and U-shaped dilated convolution into the YOLO framework, proposing the YOLOv11-WBD model to enhance feature representation capability and semantic mining effectiveness. To improve robustness, a plug-and-play Wavelet-Attentive Multiband Fusion Module (WAMF) is designed, achieving decoupling of low-frequency and high-frequency features and adaptive multi-frequency fusion. To effectively aggregate multi-scale features, a Bottleneck-Enhanced Dilated U-Conv Module (BEDU) is designed, fusing global and local information with lower computational resource consumption. To address feature fusion, a Bidirectional Depthwise Cross-Attention Module (BDCA) is designed to replace simple concatenation and convolution operations, achieving adaptive feature fusion. YOLOv11-WBD undergoes rigorous evaluation on the public NEU-DET and GC10-DET datasets; experimental results show that the improved model achieves performance gains on both datasets: the mAP@0.5 metric increased by 5.8% and 2.8% respectively. Furthermore, the improved model demonstrates stronger noise tolerance, maintaining high defect detection capability even in moderate noise environments, providing a valuable solution for industrial applications.

Matrix games between full siblings in Mendelian populations

We develop a model that integrates evolutionary matrix game theory with Mendelian genetics. Within this framework, we define the genotype dynamics that describes how the frequencies of genotypes change in sexual diploid populations. We show that our formal definition of evolutionary stability for genotype distributions implies the stability of the corresponding interior equilibrium point in the genotype dynamics. We apply our findings to a model of familial selection, where the survival rates of siblings in monogamous families are determined by a matrix game between them. According to Mendelian inheritance, the behaviour associated with each genotype is uniquely determined by an autosomal (recessive-dominant or intermediate) allele pair. We provide general conditions for the evolutionary stability of homozygote populations. We find that the payoff matrix and the genotype-phenotype map together determine this stability. In numerical examples we consider the prisoner’s dilemma between siblings. Based on the evolutionary stability of the pure cooperator and defector states, we provide a potential classification of the genotype dynamics. We distinguish between two cases: one in which the total survival rate is higher in cooperator-cooperator interactions (“coordinated” case), and another in which it is higher in cooperator-defector interactions (“anti-coordinated” case). In the coordinated case, global stability of cooperator homozygote population is possible but not necessary, since bistability, stable coexistence of cooperators and defectors, and even global stability of the defector homozygote state are all possible, depending on the interaction between the phenotypic payoff matrix and the genotype-phenotype mapping. In the anti-coordinated case, the cooperator homozygote population cannot be stable. Thus, similarly to the group selection theory, the welfare of the family (the sum of the survival rates of siblings) governs the emergence of cooperative behavior among family members. Finally, in the case of the donation game, the classical Hamilton’s rule determines whether the homozygous cooperator or the homozygous defector population is stable; bistability or stable coexistence are impossible.

Correction: Game bird carcasses are less persistent than raptor carcasses, but can predict raptor persistence dynamics

Multiproxy analysis unwraps origin and fabrication biographies of Sardinian figurines: On the trail of metal-driven interaction and mixing practices in the early first millennium BCE

This article presents a multiproxy investigation of metal samples obtained from 48 Nuragic figurines (so-called bronzetti) and three copper bun ingots. These objects originate from three prominent Sardinian sanctuaries and one unidentified site, dating to the late Nuragic period of the early first millennium BCE. The dataset significantly expands the existing scientific database and unwraps the complex fabrication biographies of the figurines from ore to finished object. The investigation employs an advanced archaeometallurgical approach, integrating conventional trace-elemental and lead isotope analyses with rarely used copper, tin, and osmium isotope measurements. This methodological combination allows for a more reliable identification of the original metal sources used in the production of the objects, namely copper from the Iglesiente-Sulcis district in southwest Sardinia, with the Sa Duchessa mine as the most likely supplier, in addition to copper from the Alcudia valley or the Linares district in the Iberian Peninsula. Notably, the combination of analytical proxies reveals the mixing of copper from these distinct regions, while ruling out the exploitation of Sardinian tin resources. Furthermore, the osmium isotope ratios confirm the use of Sardinian copper and exclude the alloying of local lead with imported copper. These results shed light on local metallurgical practices and distribution strategies in Nuragic Sardinia, but also on Sardinia’s broader role and position in the Mediterranean world during the transition from the Late Bronze Age to the Early Iron Age.

Correction: Barriers and facilitators of access to sexual and reproductive health services among migrant, internally displaced, asylum seeking and refugee women: A scoping review

Trust or money? Barriers to health and healthcare behavior during the COVID-19 pandemic

This study aimed to examine how trust in institutions and changes in household finances were associated with healthcare utilization and preventive behaviors during and immediately after the COVID-19 pandemic. The COVID-19 pandemic worsened health disparities, ignited distrust in healthcare systems, and contributed to household economic shifts for many United States (US) residents. To examine these issues, we surveyed a nationally representative sample of US residents in July 2020 (n = 1,085) and May 2023 (n = 2,189). These repeated cross-sectional surveys enabled investigation of how trust in key stakeholders (e.g., federal government, the healthcare system) and household finances were linked with various types of healthcare utilization (e.g., annual preventive visits, receipt of pharmacy-based healthcare), preventive health care (e.g., influenza vaccination), and preventive behaviors (e.g., exercise, healthy eating). In 2023, the likelihoods of using some types of healthcare (annual health check and pharmacy-based healthcare) and engaging in preventive health behaviors increased relative to 2020. Improved household finances were associated with greater odds of healthy eating, exercising, and receiving annual preventive visits. Trust in the healthcare system was positively associated with all healthcare use types examined including preventive care such as influenza immunization and the individual prevention behavior of healthy eating but not exercise. Findings highlight the important role healthcare systems can have as trusted entities in potentially supporting healthcare utilization and prevention in the post-pandemic environment. Policy implications of these findings include increased efforts by payers and healthcare systems to facilitate positive health behaviors for US residents via specific strategies, such as making annual preventive health checks more accessible. At the same time, it is critical to support maintaining and building trust in healthcare systems to promote appropriate healthcare utilization.

Global transcriptional analysis of human FHs 74 Int intestinal epithelial cells after exposure to advanced glycation end products

Advanced glycation end products (AGEs) and reactive intermediates, such as methylglyoxal, are formed during thermal processing of foods and have been implicated in the pathogenesis of a series of chronic inflammatory diseases. AGEs are thought to directly interact with the intestinal epithelium upon ingestion of thermally processed foods, but their effects on intestinal epithelial cells are poorly understood. This study investigated transcriptomic changes in human intestinal epithelial FHs 74 Int cells after exposure to AGE-modified human serum proteins (AGE-HS), S100A12, a known RAGE ligand, and unmodified human serum proteins (HS). In contrast to previous studies employing cancer cell lines, RNA sequencing of FHs 74 Int cells treated with AGE-HS did not reveal transcriptional changes associated with increased proliferation, increased expression of tight junction proteins or proinflammatory responses. Surprisingly, neither AGE-HS nor S100A12 treatments resulted in significant differential gene expression at standard analysis thresholds, while unmodified HS induced minor transcriptional changes. Gene set enrichment analysis revealed that AGE-HS treatment induced downregulation of gene sets linked to MYC, interferon responses, and oxidative phosphorylation, as well as pathways related to neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s disease, paralleling some effects observed with S100A12. This is the first global transcriptomic analysis of FHs 74 Int cells and the first unbiased investigation of signaling pathway alterations in intestinal epithelial cells exposed to AGEs. In contrast to previous studies, this analysis did not reveal any significantly differentially expressed genes, thus challenging previous reports of robust AGE-induced inflammatory and proliferative effects and emphasizing the importance of an isolated experimental setting and rigorous endotoxin testing.

The Minoan Thera eruption predates Pharaoh Ahmose: Radiocarbon dating of Egyptian 17th to early 18th Dynasty museum objects

The huge volcanic eruption at Thera (Santorini), situated in the Aegean Sea, occurred within the Late Minoan IA archaeological period. However, its temporal association with Egyptian history has long been a controversial subject. Traditionally, the eruption was placed in the early 18th Dynasty, associated with Pharaoh Thutmose III as the youngest option or with Pharaoh Nebpehtire Ahmose as the oldest possibility. We investigated museum objects from the 17th and early 18th Dynasty, at the transition from the Second Intermediate Period to the New Kingdom, a period hardly studied with radiocarbon dating. Our research facilitated the first-ever direct radiocarbon time comparison between this Dynastic transition period and the Minoan Thera eruption. Detailed results are presented of a mudbrick from the Ahmose Temple at Abydos (British Museum), a linen burial cloth associated with Satdjehuty (British Museum), and wooden stick shabtis from Thebes (Petrie Museum), evaluated within a comprehensive context of historical Egyptian chronology options. Since the above items cannot be arranged in a stratigraphic sequence, Bayesian analysis could not be used. We adopted an alternative strategy within radiocarbon time space. Comparing our uncalibrated dates of 17th and early 18th Dynasty objects with a robust series of uncalibrated radiocarbon dates for the Minoan Thera eruption, it becomes clear that the two data sets have a different time signature. The Minoan eruption is older than the reign of Nebpehtire Ahmose, the first king of the 18th Dynasty, who reunited Upper and Lower Egypt. Our calibrated results support a low chronology for his reign and the beginning of the New Kingdom. Previous radiocarbon dates of king Senusert III support a high chronology for the Middle Kingdom. Therefore, the Second Intermediate Period, sandwiched in between these united Egyptian Kingdoms, embodies a significant time interval, as also indicated by Bennett’s genealogical studies of the El-Kab governors.