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In this study, we compare equilibrium-line altitudes (ELAs) calculated using the area–altitude balance ratio (AABR) and the accumulation–area ratio (AAR) methods, with measured ELAs derived from direct field observations. We utilise a GIS toolbox to calculate the ELA for 64 extant glaciers by applying the AABR and AAR methods to DEMs and polygons of their geometry. The calculated ELAs (c-ELAs) are then compared to measured zero-net balance ELAs (znb-ELAs) obtained from mass-balance time series held by the WGMS for the same glaciers. The correlation between znb-ELAs and AABR (1.56)/AAR (0.58) c-ELAs is very strong, with an r2 = 0.99. The smallest median difference between znb-ELAs and c-ELAs (i.e. 65.5 m) is obtained when a globally representative AABR of 1.56 is used. When applied to palaeoglacier-climate applications, this difference translates to ~0.42°C, well within the uncertainty of palaeotemperature proxies used to determine mean summer temperature at the ELA. The more widely used mean AABR of 1.75 is shown to be statistically invalid due to the skewness of the dataset. On this basis, when calculating glacier ELAs, we recommend the use of a global AABR value of 1.56.

Background In the last decade, increasing evidence has shown that changes in human gut microbiota are associated with diseases, such as obesity. The excreted/secreted proteins (secretome) of the gut microbiota affect the microbial composition, altering its colonization and persistence. Furthermore, it influences microbiota-host interactions by triggering inflammatory reactions and modulating the host's immune response. The metatranscriptome is essential to elucidate which genes are expressed under diseases. In this regard, little is known about the expressed secretome in the microbiome. Here, we use a metatranscriptomic approach to delineate the secretome of the gut microbiome of Mexican children with normal weight (NW) obesity (O) and obesity with metabolic syndrome (OMS). Additionally, we performed the 16S rRNA profiling of the gut microbiota. Results Out of the 115,712 metatranscriptome genes that codified for proteins, 30,024 (26%) were predicted to be secreted, constituting the Secrebiome of the gut microbiome. The 16S profiling confirmed an increased abundance in Firmicutes and decreased in Bacteroidetes in the obesity groups, and a significantly higher richness and diversity than the normal weight group. We found novel biomarkers for obesity with metabolic syndrome such as increased Coriobacteraceae, Collinsela , and Collinsella aerofaciens ; Erysipelotrichaceae, Catenibacterium and Catenibacterium sp. , and decreased  Parabacteroides distasonis , which correlated with clinical and anthropometric parameters associated to obesity and metabolic syndrome. Related to the Secrebiome, 16 genes, homologous to F. prausniitzi , were overexpressed for the obese and 15 genes homologous to Bacteroides, were overexpressed in the obesity with metabolic syndrome. Furthermore, a significant enrichment of CAZy enzymes was found in the Secrebiome. Additionally, significant differences in the antigenic density of the Secrebiome were found between normal weight and obesity groups. Conclusions These findings show, for the first time, the role of the Secrebiome in the functional human-microbiota interaction. Our results highlight the importance of metatranscriptomics to provide novel information about the gut microbiome’s functions that could help us understand the impact of the Secrebiome on the homeostasis of its human host. Furthermore, the metatranscriptome and 16S profiling confirmed the importance of treating obesity and obesity with metabolic syndrome as separate conditions to better understand the interplay between microbiome and disease.

Data collected by autonomous sensors, including camera traps and acoustic recorders, have enormous potential to generate new scientific insights in ecology and related fields. Modern machine learning and AI classification methods are critical to analysing these often immense data streams. Accordingly, considerable effort has been dedicated to building AI models that accurately detect and classify species and events of interest in these data. These AI models, however, form only one part of a larger research framework that is needed to answer ecological questions using sensor data. We argue that a deep understanding of this research context is required to develop and apply appropriate AI models that can support scientific advances in ecology and evolution. In this manuscript, we contextualize the use of AI methods in autonomous biodiversity surveys, focusing on terrestrial bioacoustics as a case study, by discussing six sequential areas that together form a research project: hardware, field deployments, data management, detection and classification using AI and related models, statistical analysis and ecological insight. For each area, we briefly highlight several ways in which decisions made in that area can constrain, support or interact with the development and application of AI models. We conclude with several suggestions for better development and integration of AI models into ecological research, including the need for additional research at the interface of AI models and statistical analysis, the question of achieving human‐level performance with AI models and the sources of future methodological advances in AI for ecology.

The impact of recombinant protein production (RPP) on host cells and the metabolic burden associated with it undermine the efficiency of the production system. This study utilized proteomics to investigate the dynamics of parent and recombinant cells induced at different time points for RPP. The results revealed significant changes in both transcriptional and translational machinery that may have impacted the metabolic burden, growth rate of the culture and the RPP. The timing of protein synthesis induction also played a critical role in the fate of the recombinant protein within the host cell, affecting protein and product yield. The study identified significant differences in the expression of proteins involved in fatty acid and lipid biosynthesis pathways between two E. coli host strains (M15 and DH5⍺), with the E. coli M15 strain demonstrating superior expression characteristics for the recombinant protein. Overall, these findings contribute to the knowledge base for rational strain engineering for optimized recombinant protein production.

As technologies advance and applications for uncrewed aircraft increase, the capability to conduct automated air-to-air refueling becomes increasingly important. This paper provides a review of required sensors to enable automated air-to-air refueling for an uncrewed aircraft, as well as a review of published research on the topic. Automated air-to-air refueling of uncrewed aircraft eliminates the need for ground infrastructure for intermediate refueling, as well as the need for on-site personnel. Automated air-to-air refueling potentially supports civilian applications such as weather monitoring, surveillance for wildfires, search and rescue, and emergency response, especially when airfields are not available due to natural disasters. For military applications, to enable the Air Wing of the Future to strike at the ranges required for the mission, both crewed and uncrewed aircraft must be capable of air-to-air refueling. To cover the sensors required to complete automated air-to-air refueling, a brief history of air-to-air refueling is presented, followed by a concept of employment for uncrewed aircraft refueling, and finally, a review of the sensors required to complete the different phases of automated air-to-air refueling. To complete uncrewed aircraft refueling, the uncrewed receiver aircraft must have the sensors required to establish communication, determine relative position, decrease separation to astern position, transition to computer vision, position keep during refueling, and separate from the tanker aircraft upon completion of refueling. This paper provides a review of the twelve sensors that would enable the uncrewed aircraft to complete the seven tasks required for automated air-to-air refueling.

Interactions of hydrothermal and deformation processes in active fault zones are important in light of seismic and geothermal activities. This study investigates different tectonites of the Grimselpass breccia fault, a hydrothermally active strike-slip structure in the Central Alps (Switzerland). We combine microstructural and geochemical investigations to decipher the interaction of grain-size reduction and associated dilation (volume gain) during frictional deformation with geochemical processes such as mass gain via precipitation, grain growth and healing. Three types of tectonites were investigated in the fault zone: (1) cockade-bearing tectonites, (2) cataclasites and (3) fault gouges. They differ in terms of microstructure of clasts, mass and volume gain/loss, as well as type and degree of cementation. Clast size distributions show decreasing median values from low-strain cataclasites to high-strain cataclasites. Mass/volume gains are up to 20 %, with samples of smallest clast sizes often showing the highest mass gain. In the case of cockade-bearing tectonites large volume and mass gain occur. In particular, the precipitation of SiO2-rich cements is inferred to (i) reduce permeability and (ii) induce a gain in strength of the respective fault rocks. Location, timing and degree of healing directly control the fault architecture by deactivating fault strands from the circulation of hydrothermal fluids. In this case, new rupturing events are necessary to reactivate locked fault strands. Such feedback cycles of episodic deformation, mass/volume and clogging preserved hydrothermal circulation and deformation over the last 3.4 Ma, representing a process chain for the long-term preservation of orogenic hydrothermal systems.

Abstract In recent years \"religious literacy\" advocacy has gained a significant following, both academic and non-academic. Furthermore, it is widely accepted that attainment of religious literacy (and the reduction of religious illiteracy - ignorance about religions, either explicitly or by implication) will bring social benefits or cure societal ills. Yet, the term \"religious literacy\" itself remains poorly defined; neither have the benefits touted by advocates of \"religious literacy\" been subjected to empirical testing. Instead, reasons for the ready adoption of the term can be found in its particular genealogy, and in its relation to advocacies for other \"literacies\". Moreover, especially the advocacy of basic literacy (i.e., of literacy, literally speaking) is historically entangled with Christian theologies and other ideologies, a history giving rise to what Harvey Graff once called the \"literacy myth\" linking increased literacy to social improvements or progress. Thus, finally, the particular resonance of \"religious literacy\" in Religious Studies and related academic circles may reflect the inescapable historical character of our particular academic enterprise as \"ex-theological.\"

Background Reliable genotyping tools are essential for understanding genomic diversity and tracking strain variation in Escherichia coli . Traditional typing approaches, including SNP and MLST based methods, focus mainly on conserved regions of the genome and may overlook potential changes in the unconserved gene content. To address this gap, this study proposed a new genotyping approach that combines diversity profiles observed from repeat-rich protein sequence and genomic neighborhood structural organization. Results Comparative genomic analysis was conducted on 83 publicly available E. coli genomes. Sequence comparison identified a repeat hotspot within the RSXC protein, while gene neighborhood (synteny) analysis revealed three variable genomic regions (hotspots A–C). Differences in RSXC repeat copy number and terminal motifs allowed the strains to be classified into seven repeat types (R7a, R7b, R6a, R6b, R5a, R5b, and R4a). Structural variation within genomic hotspots A, B, and C was characterized by the presence, absence, and arrangement of intermediate genes and classified into 7, 8, and 4 structure types, respectively. Integration of RSXC repeat profiles with genomic hotspot patterns generated 14 distinct sequence types (ST1–ST14). RSXC repeat patterns and the combined genotyping scheme both showed good discriminatory power (Simpson’s diversity index of 0.726), which is comparable to MLST typing (0.723) among the 83 E. coli strains analysed. Virulence and antimicrobial resistance (AMR) profiling of the examined strains further supported biological relevance of the proposed method. Strains with higher RSXC repeat numbers (R7 and R6) positively linked with virulence genes presence and negatively associated with AMR gene counts as supported by a significant positive correlation with virulence genes (ρ = 0.323, p  = 0.0026) and a negative correlation with AMR genes (ρ = −0.276, p  = 0.0105). Conclusions This repeat–synteny based genotyping approach captures genomic diversity in the 83 E. coli strains that is often overlooked by conventional typing approaches and provides a practical framework for discriminating E. coli strains. The strong association with MLST, combined with meaningful relationships to virulence and AMR profiles, suggests that this approach may serve as a useful marker for epidemiological identifications.

Background: Better survival for overweight and obese patients after ST-segment elevation myocardial infarction (STEMI) has been demonstrated. The association between body mass index (BMI), microvascular obstruction (MVO), and area at risk (AAR) after STEMI was evaluated. Methods: A prospective observational study was performed to enrolled patients undergoing primary percutaneous coronary intervention (pPCI) for STEMI and cardiac magnetic resonance was performed within 5-7 days. Patients were classified as normal weight (18.5 [less than or equal to]BMI <24.0 kg/m ), overweight (24.0 [less than or equal to]BMI <28.0 kg/[m.sup.2]), or obese (BMI [greater than or equal to]28 kg/m ). Results: Among 225 patients undergoing pPCI, 67 (30.00%) were normal weight, 113 (50.22%) were overweight, and 45 (20.00%) were obese. BMI [greater than or equal to]28 kg/[m.sup.2] was significantly associated with less risk of MVO when compared with a normal BMI after multivariable adjustment (overweight: HR 0.29, 95% CI 0.13-0.68, p = 0.004). Compared with normal weight patients, obese and overweight patients tend to have larger hearts (greater left ventricular end-diastolic volume [LVEDV] and left ventricular [LV] mass). In adjusted analysis, increased BMI was significantly associated with a smaller AAR. In addition, obese patients had a smaller AAR ([beta] = -0.252, 95% CI -20.298- -3.244, p = 0.007) and AAR, % LV mass ([beta] = -0.331, 95% CI -0.211- -0.062, p < 0.001) than normal weight patients. Conclusion: Obesity (BMI [greater than or equal to]28 kg/[m.sup.2]) is independently associated with lower risks of MVO and a smaller AAR, % LV mass than normal weight patients among subjects undergoing pPCI for STEMI. Keywords: ST-segment elevation myocardial infarction, STEMI, microvascular obstruction, MVO, cardiac magnetic resonance, CMR, body mass index, BMI, area at risk, AAR

Histological analysis with 2,3,5-triphenyltetrazolium chloride (TTC) staining is the most frequently used tool to detect myocardial ischemia/reperfusion injury. However, its practicality is often challenged by poor image quality in gross histology, leading to an equivocal infarct-boundary delineation and potentially compromised measurement accuracy. Here, we introduce several crucial refinements in staining protocol and sample processing, which enable TTC images to be analyzed with light microscopy. The refined protocol involves a two-step TTC staining process (perfusion and immersion) and subsequent Zamboni fixation to differentiate myocardial viability and necrosis, and use of Coomassie brilliant blue to label area-at-risk. After the duo-staining steps were completed, the heart sample was embedded and sliced transversally by a cryostat into a series of thin sections (50 µm) for microscopic analysis. The refined TTC (redTTC) assay yielded remarkably high-quality images with striking color intensity and sharply defined boundaries, permitting unambiguous and reliable delineation of the infarct and area-at-risk. In the same animals, the redTTC assay showed good agreement with the in-vivo gold standard measurements (LGE and MEMRI). Meanwhile, redTTC imaging allows tracking of viable cardiomyocytes at cellular resolution, and with this enhanced capability, we convincingly demonstrated the pro-survival action of stem cells based-therapy. Therefore, the redTTC assay represents a significant technical advance that permits precise detection of the true extent of cardiac injury and cardiomyocyte viability. This approach is cost-effective and may be adapted for use in diverse applications, making it highly appealing to many laboratories performing ischemia/reperfusion injury experiments.