Detection Of Signatures Research Articles

Deciphering genetic characteristics of South China and North China indigenous pigs through selection signatures

BackgroundIndigenous pig breeds in China have accumulated significant genetic diversity due to regional selection pressures. Investigating the selection signatures of these populations helps to understand their adaptive evolution and contributes to genetic improvement programs.ResultsWe collected whole-genome sequencing data from 133 individuals, including South China and North China indigenous pigs and Asian wild boars. After data filtering, we retained 31,521,978 high-quality SNPs. Population structure analysis using PCA revealed distinct genetic clustering among these populations. Selection signature detection identified 5,227 loci under selection in South China indigenous pigs and 5,800 in North China indigenous pigs compared to Asian wild boars. Candidate genes were enriched in immune response pathways, reproductive traits, and pigmentation pathways. South China indigenous pigs exhibited selection signals for fat deposition and immune responses, while North China indigenous pigs showed stronger signals related to growth, blood physiology, and reproductive performance. Additionally, key genes such as MC1R and KIT were associated with coat color variation, and IGF1R and IGF2R were linked to growth regulation.ConclusionOur results demonstrate that indigenous pigs in China have undergone selection for distinct traits aligned with their regional environments and farming systems. South China indigenous pigs have been selected for traits related to fat deposition and immunity, while North China indigenous pigs have been selected for growth and reproductive traits. The findings offer crucial insights into the genetic architecture of indigenous pig breeds, providing a valuable foundation for future genetic breeding programs.

Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder

Thermal wave imaging uses the temporal temperature distribution over the object’s surface for subsurface analysis. However, the noise generated during experimentation corrupts this temporal history and hampers the detection of defect signatures. As denoising of the temporal thermal history enhances the defect detectability, this study offers a stacked denoising convolution autoencoder (SDCAE) in frequency-modulated thermal wave imaging with one-dimensional convolution layers to reduce noise in temporal thermal evolution and train high-level features resulting in improved defect signs. Experimental results on mild steel and carbon fiber reinforced polymer specimens with different sizes of defects at various depths demonstrate that integrating temporal denoising and deep feature learning techniques into a single novel framework significantly improved defect detectability. In addition, defect signal-to-noise ratios of the denoised thermal data and latent space of the proposed model compared to conventional autoencoder and dimensionality reduction techniques recommend the superiority of the proposed method.

LET-7a and LET-7b as Potential Signatures for Glioma Recurrence, Regardless of WHO Tumor Grade.

Micro RNAs (miRs; miRNAs) are small, non-coding RNA segments that influence gene expression. To do this, they bind to corresponding mRNA segments and inhibit the subsequent protein translation. Currently, about 200 transcripts are thought to be regulated by a single miRNA. Dysregulated miRNAs can act as tumor promoters as well as tumor suppressors. The aim of this study was to verify miR-LET-7a and miR-LET-7b as a signature for glioma recurrence detection by analyzing their expression in glioma tissue. Only patients with two or more recurrences of glioma (n=25) were included in the study (tissue samples, n=89). Tissue was obtained during neurosurgical procedures and shock-frozen in liquid nitrogen. The patient cohort (female:male=12:13) had an average age of 35 years when first diagnosed (primary tumor: 13 WHO grade II, 7 WHO grade III and 5 WHO grade IV) and 48 years in the fifth relapse. Quantitative real-time polymerase chain reaction was performed to analyze miRNA expression. miR-151a-3p was used as an internal reference. Expression of miR-LET-7a and miR-LET-7b was significantly lower in the first recurrence than in the primary tumor. The expression of miR-LET-7a and -7b significantly decreased with increasing tumor grade. The strongest down-regulation was found between WHO grade III and IV. In paired samples in which tumor grade did not change between the primary and the first recurrence, the expression of both miRNAs remained significantly lower in the recurrent than in the primary tumor. miR-LET-7a and miR-LET-7b represent a potential signature for glioma recurrence regardless of WHO grade.

Supernova electron-neutrino interactions with xenon in the nEXO detector

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double-β decay detector (∼5 metric ton, 90% Xe136, 10% Xe134) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the Model of Argon Reaction Low Energy Yields (MARLEY) event generator. We find good agreement between MARLEY’s predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector’s efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5–8 kpc from Earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae. Published by the American Physical Society 2024

Liquid biopsy to identify Barrett’s oesophagus, dysplasia and oesophageal adenocarcinoma: the EMERALD multicentre study

BackgroundThere is no clinically relevant serological marker for the early detection of oesophageal adenocarcinoma (EAC) and its precursor lesion, Barrett’s oesophagus (BE).ObjectiveTo develop and test a blood-based assay for EAC...

An Efficient In Vitro Regeneration Protocol and the Feature of Root Induction with Phloroglucinol in Paeonia ostii.

Paeonia ostii, a plant of substantial economic significance, continues to face constraints in achieving large-scale propagation. In vitro propagation offers a promising avenue for the production of disease-free plants and the genetic transformation of peonies to instill novel traits. However, significant challenges persist in tissue culture, particularly with regards to the reproduction coefficient of shoots and the rooting process. This study reports an efficacious protocol for P. ostii micropropagation, focusing on in vitro root development facilitated through the application of phloroglucinol (PG). Furthermore, the study unveils the molecular signature of P. ostii during in vitro root development. The results indicate that the modified Y3 medium (Y3M), supplemented with 1 mg/L 6-benzyladenine (BA) and 0.1 mg/L α-naphthaleneacetic acid (NAA), is optimal for adventitious bud induction, achieving a 96.67% induction rate and an average of 16.03 adventitious shoots per sample. The highest elongation percentage (92.15%) and the longest average shoot length (3.87 cm) were obtained with Y3M containing 0.3 mg/L BA and 0.03 mg/L NAA. Additionally, the optimal medium for inducing root formation in P. ostii was identified as WPM supplemented with 3 mg/L indole-3-butyric acid (IBA) and 100 mg/L phloroglucinol (PG). Lignin content detection, microscope inspection, and molecular signature results demonstrated that PG enhanced lignin biosynthesis, thereby promoting in vitro rooting of P. ostii.

Unravelling mutational signatures with plasma circulating tumour DNA

The use of circulating tumour DNA (ctDNA) to profile mutational signatures represents a non-invasive opportunity for understanding cancer mutational processes. Here we present MisMatchFinder, a liquid biopsy approach for mutational signature detection using low-coverage whole-genome sequencing of ctDNA. Through analysis of 375 plasma samples across 9 cancers, we demonstrate that MisMatchFinder accurately infers single-base and doublet-base substitutions, as well as insertions and deletions to enhance the detection of ctDNA and clinically relevant mutational signatures.

EPID-22. MUTATIONAL SIGNATURE ASSOCIATED WITH HALOALKANE EXPOSURE ENRICHED IN FIREFIGHTERS IN THE ADULT GLIOMA STUDY

Abstract Using data from The Cancer Genome Atlas (TCGA) and Glioma Longitudinal Analysis (GLASS), we identified specific environmentally related mutational signatures in glioma including a signature associated with haloalkane exposure (SBS42) from the Catalogue of Somatic Mutational Signatures (COSMIC). Haloalkanes are widely used commercially including in flame retardants and fire extinguishants and are an intriguing finding given an observed increased glioma risk in firefighters. To validate our findings, we use new study participants with data on occupation (not available in either TCGA/GLASS) from the University of California, San Francisco Adult Glioma Study (AGS) to compare mutational signatures in tumors from 17 persons with glioma and documented occupational exposure to firefighting with those of 18 matched glioma patients without history of firefighting. Using whole exome sequencing, we identified environmental exposure signatures in tumor tissue from firefighters as well as in non-firefighters who had other relevant occupational exposure (e.g. auto/truck mechanic work). In these data, glioma is largely associated with aging and mutational signatures relating to endogenous mutational processes that correlate with age, such as spontaneous or enzymatic deamination of 5-methylcytosine. However, some gliomas have detectable signatures associated with exogenous mutational processes, such as SBS42 haloalkanes. Thus, we confirm detection of haloalkane signatures in persons likely highly exposed, i.e. long-term firefighters. Identifying exogenous mutational processes in cancers are important as they may inform public health intervention strategy to reduce mutagenesis and prevent cancer inception. Identifying occupational correlates with SBS42, associated with occupational exposure to haloalkanes, will pinpoint occupational hazards that may be avoidable. This is especially important for glioma and other cancers where exogenous mutagenesis is not well established.

Dark matter raining on DUNE and other large volume detectors

Direct detection is a powerful means of searching for particle physics evidence of dark matter (DM) heavier than about a GeV with \U0001d4aa(kiloton) volume, low-threshold detectors. In many scenarios, some fraction of the DM may be boosted to large velocities enhancing and generally modifying possible detection signatures. We investigate the scenario where 100% of the DM is boosted at the Earth due to new attractive long-range forces. This leads to two main improvements in detection capabilities: (1) the large boost allows for detectable signatures of DM well below a GeV at large-volume neutrino detectors, such as DUNE, Super-K, Hyper-K, and JUNO, as possible DM detectors, and (2) the flux at the Earth’s surface is enhanced by a focusing effect. In addition, the model leads to a significant anisotropy in the signal with the DM flowing dominantly vertically at the Earth’s surface instead of the typical approximately isotropic DM signal. We develop the theory behind this model and also calculate realistic constraints using a detailed GENIE simulation of the signal inside detectors.

Mild cognitive impairment in amyotrophic lateral sclerosis: current view.

Amyotrophic lateral sclerosis (ALS) is a fatal multi-system neurodegenerative disorder with no effective treatment or cure. Although primarily characterized by motor degeneration, cognitive dysfunction is an important non-motor symptom that has a negative impact on patient and caregiver burden. Mild cognitive deficits are present in a subgroup of non-demented patients with ALS, often preceding motor symptoms. Detailed neuropsychological assessments reveal deficits in a variety of cognitive domains, including those of verbal fluency and retrieval, language, executive function, attention and verbal memory. Mild cognitive impairment (MCI), a risk factor for developing dementia, affects between 10% and over 50% of ALS patients. Neuroimaging revealed atrophy of frontal and temporal cortices, disordered white matter Integrity, volume reduction in amygdala and thalamus, hypometabolism in the frontal and superior temporal gyrus and anterior insula. Neuronal loss in non-motor brain areas, associated with TDP-43 deposition, one of the morphological hallmarks of ALS, is linked to functional disruption of frontostriatal and frontotemporo-limbic connectivities as markers for cognitive deficits in ALS, the pathogenesis of which is still poorly understood. Early diagnosis by increased cerebrospinal fluid or serum levels of neurofilament light/heavy chain or glial fibrillary acidic protein awaits confirmation for MCI in ALS. These fluid biomarkers and early detection of brain connectivity signatures before structural changes will be helpful not only in establishing early premature diagnosis but also in clarifying the pathophysiological mechanisms of MCI in ALS, which might serve as novel targets for prohibition/delay and future adequate treatment of this debilitating disorder.

Regional microbial content of fermented traditional and industrial East Mediterranean sausages from the islands of Cyprus and Mytilini

Objective: To characterize the microbial biodiversity of fermented sausages from the East Mediterranean islands of Cyprus and Mytilini, and to identify differences between the microbial diversity of traditionally and industrially produced Cypriot sausages. Method: The microbial diversity of thirty sausages from Cyprus and Mytilini (traditionally and industrially produced) was analyzed using high throughput sequencing (HTS) (amplicon sequencing) of 16S rRNA gene and ITS loci fragments. By applying microbial signature detection and machine learning algorithms, we identified key microbes that distinguish traditionally produced sausages from those produced industrially. Focusing on selected microbial taxa and using interaction network analysis, we identified associations among the sausages’ microbiota that may affect the shaping of the sausages’ microbial consortia. Results: Cypriot and Mytilini sausages indicated increased relative representation of Lactobacillus and Leuconostoc . Cypriot sausages were distinguished by the presence of the fungi Debaryomyces hansenii and Candida spp. , whereas Mytilini sausages by the bacteria Lactococcus and Streptococcus . Traditionally produced sausages from the Pitsilia region of Cyprus were differentiated by the presence of the species Lactobacillus helveticus , whereas industrially produced sausages were differentiated by the species Leuconostoc mesenteroides . Focusing mainly on Lactobacillus and Leuconostoc , negative associations with pathogenic bacteria, such as Salmonella , and spoilage fungi, such as Fusarium poae , were revealed. Conclusion: The present metataxonomic analysis provided insights into the microbial communities that characterize Cypriot and Mytilini sausages. The findings provide an indication that the microbial diversity might be applied as an additional marker of Cypriot sausages’ authenticity.

The glow of axion quark nugget dark matter

Context. The existence of axion quark nuggets is a potential consequence of the axion field, which provides a possible solution to the charge-conjugation parity violation in quantum chromodynamics. In addition to explaining the cosmological discrepancy of matter-antimatter asymmetry and a visible-to-dark-matter ratio of Ωdark/Ωvisible ≃ 5, these composite compact objects are expected to represent a potentially ubiquitous electromagnetic background radiation by interacting with ordinary baryonic matter. We conducted an in-depth analysis of axion quark nugget-baryonic matter interactions in the environment of the intracluster medium in the constrained cosmological Simulation of the LOcal Web (SLOW). Aims. Here, we aim to provide upper limit predictions on electromagnetic counterparts of axion quark nuggets in the environment of galaxy clusters by inferring their thermal and non-thermal emission spectrum originating from axion quark nugget-cluster gas interactions. Methods. We analyzed the emission of axion quark nuggets in a large sample of 161 simulated galaxy clusters using the SLOW simulation. These clusters are divided into a sub-sample of 150 galaxy clusters, ordered in five mass bins ranging from 0.8 to 31.7 × 1014 M⊙, along with 11 cross-identified galaxy clusters from observations. We investigated dark matter-baryonic matter interactions in galaxy clusters in their present stage at the redshift of z = 0 by assuming all dark matter consists of axion quark nuggets. The resulting electromagnetic signatures were compared to thermal Bremsstrahlung and non-thermal cosmic ray (CR) synchrotron emission in each galaxy cluster. We further investigated individual frequency bands imitating the observable range of the WMAP, Planck, Euclid, and XRISM telescopes for the most promising cross-identified galaxy clusters hosting detectable signatures of axion quark nugget emission. Results. We observed a positive excess in the low- and high-energy frequency windows, where thermal and non-thermal axion quark nugget emission can significantly contribute to (or even outshine) the emission of the intracluster medium (ICM) in frequencies up to νT ≲ 3842.19 GHz and νT ϵ [3.97, 10.99] × 1010GHz, respectively. Emission signatures of axion quark nuggets are found to be observable if CR synchrotron emission of individual clusters is sufficiently low. The degeneracy in the parameters contributing to an emission excess makes it challenging to offer predictions with respect to pinpointing specific regions of a positive axion quark nugget excess; however, a general increase in the total galaxy cluster emission is expected based on this dark matter model. Axion quark nuggets constitute an increment of 4.80% of the total galaxy cluster emission in the low-energy regime of νT ≲ 3842.19 GHz for a selection of cross-identified galaxy clusters. We propose that the Fornax and Virgo clusters represent the most promising candidates in the search for axion quark nugget emission signatures. Conclusions. The results from our simulations point towards the possibility of detecting an axion quark nugget excess in galaxy clusters in observations if their signatures can be sufficiently disentangled from the ICM radiation. While this model proposes a promising explanation for the composition of dark matter, with the potential to have this outcome verified by observations, we propose further changes that are aimed at refining our methods. Our ultimate goal is to identify the extracted electromagnetic counterparts of axion quark nuggets with even greater precision in the near future.

Static analysis framework for permission-based dataset generation and android malware detection using machine learning

Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques. However, the issues presented by modern malware cannot be met by traditional signature or heuristic-based malware detection methods. Previous research suggests that machine-learning classifiers can be utilised to analyse permissions, making it possible to differentiate between malicious and benign applications on the Android platform. There exist machine-learning methods that utilise permission-based attributes to build models for the detection of malware on Android devices. Nevertheless, the performance of these detection methods is dependent on the raw or feature datasets. Android malware research frequently faces a major obstacle due to the lack of adequate and up-to-date raw malware datasets. In this paper, we put forward a systematic approach to generate an Android permission-based dataset using static analysis. To create the dataset, we collect recent raw malware samples (APK files) and focus on the reverse engineering approach and permission-based features extraction. We also conduct a thorough feature analysis to determine the important Android permissions and present a machine-learning-based Android malware detection mechanism. The experimental result of our study demonstrates that with just 48 features, the random forest classifier-based Android malware detection model obtains the best accuracy of 97.5%.

Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine learning.

Metabolic dysfunction-associated steatohepatitis (MASH) is a highly prevalent liver disease globally, with a significant risk of progressing to cirrhosis and even liver cancer. Efferocytosis, a process implicated in a broad spectrum of chronic inflammatory disorders, has been reported to be associated with the pathogenesis of MASH; however, its precise role remains obscure. Thus, we aimed to identify and validate efferocytosis linked signatures for detection of MASH. We retrieved gene expression patterns of MASH from the GEO database and then focused on assessing the differential expression of efferocytosis-related genes (EFRGs) between MASH and control groups. This analysis was followed by a series of in-depth investigations, including protein-protein interaction (PPI), correlation analysis, and functional enrichment analysis, to uncover the molecular interactions and pathways at play. To screen for biomarkers for diagnosis, we applied machine learning algorithm to identify hub genes and constructed a clinical predictive model. Additionally, we conducted immune infiltration and single-cell transcriptome analyses in both MASH and control samples, providing insights into the immune cell landscape and cellular heterogeneity in these conditions. This research pinpointed 39 genes exhibiting a robust correlation with efferocytosis in MASH. Among these, five potential diagnostic biomarkers-TREM2, TIMD4, STAB1, C1QC, and DYNLT1-were screened using two distinct machine learning models. Subsequent external validation and animal experimentation validated the upregulation of TREM2 and downregulation of TIMD4 in MASH samples. Notably, both TREM2 and TIMD4 demonstrated area under the curve (AUC) values exceeding 0.9, underscoring their significant potential in facilitating the diagnosis of MASH. Our study comprehensively elucidated the relationship between MASH and efferocytosis, constructing a favorable diagnostic model. Furthermore, we identified potential therapeutic targets for MASH treatment and offered novel insights into unraveling the underlying mechanisms of this disease.

Uniqueness, Reproducibility and Discrimination of Nuclear Warhead Gamma Signatures

Nuclear verification includes confirmation measures of nuclear weapons and fissile material, which often rely on the detection of radiation signatures. It is an open research question whether these signatures are unique. Or in reverse, could a malicious actor imitate these signatures and compromise verification of disarmament processes by using hoax objects instead of real warheads? This article presents several warhead emission models and a simulation framework to assess the uniqueness of their passive gamma radiation signatures. A red team approach includes detailed simulations of three proposed confirmation systems: The Information Barrier eXperimental, and the Information Barrier eXperimental II, both developed at Princeton University, and the Trusted Radiation Identification System, developed at the Sandia National Laboratories. For these simulations, various hoaxes are analyzed: warheads using reactor-grade plutonium instead of weapon-grade plutonium, combinations of different gamma emitting isotopes, and combinations of neutron sources with radioactive isotopes. Our findings reveal that numerous radioactive signatures are not unique and may be susceptible to imitation. Moreover, confirmation systems based on passive gamma radiation signatures using low bin numbers demonstrate a limited ability to accurately handle warheads of varying ages.

Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue.

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein-protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922-0.959 with predictive accuracy of 84.2%-89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.

The spectrum of behavioral disorders in amyotrophic lateral sclerosis: current view.

Behavioral disorders, with an average prevalence of 30-60% are important non-motor symptoms in amyotrophic lateral sclerosis (ALS) that have a negative impact on prognosis, management and quality of life, yet the underlying neurobiology is poorly understood. Among people with ALS, apathy, fatigue, anxiety, irritability and other behavioral symptoms are the most prominent, although less frequent than cognitive impairment. The present review explores the current understanding of behavioral changes in ALS with particular emphasis on our current knowledge about their structural and functional brain correlates, substantiating a multisystem degeneration with particular dysfunction of frontal-subcortical circuits and dysfunction of fronto-striatal, frontotemporal and other essential brain systems. The natural history of behavioral dysfunctions in ALS and their relationship to frontotemporal lobe degeneration (FTLD) are not fully understood, although they form a clinical continuum, suggesting a differential vulnerability of non-motor brain networks, ALS being considered a brain network disorder. An assessment of risks or the early detection of brain connectivity signatures before structural changes may be helpful in investigating the pathophysiological mechanisms of behavioral impairment in ALS. Treatment of both ALS and co-morbid behavioral disorders is a multidisciplinary task, but whereas no causal or disease-modifying therapies for ALS are available, symptomatic treatment of a variety of behavioral symptoms plays a pivotal role in patient care, although the management of behavioral symptoms in clinical care still remains limited.

Detection of a stress related acoustic signature by passive acoustic monitoring in Atlantic salmon farming

Crowding is a necessary operation for moving fish out of or between sea cages in Atlantic salmon farming. These operations consist of gradually reducing available volume in the nets to increase the biomass density. This is known to stress the fish, increase risk of injury, and can in extreme cases increase mortality rates. To reduce the negative impacts of crowding, the operation must be adjusted based on real-time monitoring of fish welfare. Today, this is mainly done by manually monitoring surface activity, as the high fish density limits the use of available tools. In this paper a new approach for monitoring was applied to crowding of meso-scale cages containing 40 fish each, using automatic detection of passive acoustic signatures. Four synchronised hydrophones were deployed and the acoustic signature was defined from the recordings. The automatic detection algorithm recorded an extreme increase in registered acoustic events during crowding, as high as 1000 events per 10 min period, compared to only 20 events per 10 min period on the day following crowding. Correlating the number of acoustic events to recorded heart rate and activity measures from implants in the salmon indicate that automatically registering acoustic events is a promising method for monitoring farmed fish.

Measuring very low radiation doses in PTFE for nuclear forensic enrichment reconstruction

Every country that has made nuclear weapons has used uranium enrichment to do so. Despite the centrality of this technology to international security, there is still no reliable physical marker of past enrichment in the open literature that can be used to perform forensic verification of historically produced weapons on gas centrifuges. We show that the extremely low radioactivity from uranium alpha emissions during enrichment leaves detectable and irreversible calorimetric signatures in the common enrichment gasket material PTFE, allowing for historical reconstruction of past enrichment activities at a sensitivity better than one weapon's quantity of highly enriched uranium. Fast scanning calorimetry also enables the measurement of recrystallization enthalpies of sequentially microtomed slices, confirming the magnitude and the type of radiation exposure while also providing detection of tampering and a method for analyzing field samples useful for treaty verification. This work opens the door for common items to be turned into precise dosimeters to detect the past presence of radioactivity, nuclear materials, and related activities with high confidence.

A hybrid framework for Detection of Multivariate porphyry Cu Signatures and Anomaly Enhancement: Incorporation of SFA, GMPI, and Grey Wolf Optimization

Geochemical data from stream sediment are commonly employed for regional scale mineral exploration, as they can be utilized to detect geochemical anomalies associated with mineralization processes. However, recognizing efficient multielement geochemical signatures related to target mineralization using stream sediment geochemical data can be challenging due to the intricate nature of mineralization events. To achieve this goal, this article proposes a combination of statistics, logistic functions and machine learning techniques. Initially, the staged factor analysis (SFA) approach was applied to the data to determine the geochemical footprints associated with porphyry copper mineralization in the Varzaghan area. Subsequently, the geochemical mineralization probability index (GMPI) was utilized to facilitate appropriate separation, more precise identification, and the transfer of data into a fuzzy space. By using a combination of SFA and GMPI, it was feasible to enhance the detection of geochemical halos and concealed anomalies. Ultimately, the output of these two approaches was subjected to an improved clustering technique known as GWO-K-means to detect geochemical anomalies. The findings indicated that there is a strong relationship between known mineral occurrences (KMOs) and the identified anomalous areas. The success rate curve demonstrated that the optimized model outperformed the K-means model, proposing that optimization can boost the accuracy in identifying geochemical targets. Overall, the outcomes of this study can be beneficial for proper planning of future exploration activities.