Detection Of Signatures Research Articles

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

BackgroundMetabolic 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.MethodsWe 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.ResultsThis 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.ConclusionOur 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.

Prediction of spatter-related defects in metal laser powder bed fusion by analytical and machine learning modelling applied to off-axis long-exposure monitoring

Laser powder bed fusion of metals is increasingly used for fabricating complex parts requiring good mechanical properties. Simultaneously, researchers in the field are intensifying the efforts to reduce defects, such as internal porosities, which hinder a wider industrial adoption of this technology, urging process monitoring to a pivotal role in defect identification and mitigation. Therefore, understanding the correlation between in-process monitoring signals and post-process actual defects is fundamental to taking informed decisions and potential corrective actions during the process.This work focuses on developing models to predict spatter-related defects from specific process signatures detected through off-axis long-exposure imaging. Layer-wise images were properly aligned with corresponding cross-sections from tomographic reconstructions to investigate the relationship between spatter-related signatures and actual defects measured by X-ray computed tomography. This relationship was used as a knowledge basis to develop an analytical image-processing approach and a machine learning-based methodology, which were then compared in terms of their correlation performances. The advantages and limitations of both methods are discussed in the paper.Both approaches led to promising results in the prediction of lack-of-fusion defects caused by spatters, with the machine learning approach showing a prediction accuracy in the order of 90% for defects with equivalent diameter above 90µm, while the analytical model needed equivalent diameters larger than 130µm to reach a prediction accuracy in the order of 80%. Furthermore, the machine learning method led to strong results regarding early defect detection, with most of the investigated defects properly predicted by analysing two consecutive layers after the signature detection.

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.

Exploring circulating MiRNA signature for osteosarcoma detection: Bioinformatics-based analyzing and validation

Early detection followed by efficient treatment still remain a considerable challenge for osteosarcoma (OS), indicating the importance of emerging innovative diagnostic methods. Circulating miRNAs offer a promising and non-invasive approach to assess the OS molecular landscapes. This study utilized RNAseq data from OS plasma miRNA expression profiles (PRJEB30542) and PCR Array data (GSE65071) from GEO and ENA databases. In total, 43 miRNAs demonstrated significant differential expression in OS samples of training dataset. A diagnostic model, including hsa-miR-30a-5p, hsa-miR-556–3p, hsa-miR-200a-3p, and hsa-miR-582–5p was identified through multivariate logistic regression analysis and demonstrated significant efficacy in differentiating OS patients from healthy controls in the validation group (AUC: 0.917, sensitivity: 1, specificity: 0.85). The result of target gene prediction and functional enrichment analyses revealed significant associations with terms such as epithelial morphogenesis, P53 and Wnt signaling pathways, and neoplasm metastasis. Further bioinformatics-based evaluations showed that the down-regulation of these miRNAs significantly correlates with poor prognosis and lower survival rate in OS patients and propose their tumor suppressor function in pathogenesis of OS. Furthermore, the study developed a miRNA-mRNA subnetwork that connects these miRNAs to the P53 and Wnt signaling pathways, which are critical pathways with oncogenic effects on OS progression. This comprehensive approach not only presents a promising diagnostic model but also proposes potential molecular markers for OS early diagnosis, making prognosis, and targeted therapy. The identified miRNA-mRNA functional axis holds promise as a valuable resource for further research in understanding OS pathogenesis and establishing therapeutic modalities.

Probing light inelastic dark matter from direct detection

For dark matter (DM) direct detections, the kinematic effects such as those of the inelastic scattering can play important role in light DM searches. The light DM detection is generally difficult because of its small recoil energy. But the recoil energy of the exothermic inelastic DM scattering could exceed the detection threshold due to the contribution from the DM mass-splitting, making the direct detection of sub-GeV DM feasible. In this work, we systematically study signatures of the light exothermic inelastic DM from the recoil spectra including both the DM-electron scattering and Migdal effect. Such inelastic DM has mass around (sub-)GeV scale with DM mass-splitting of O(1−102) keV. We analyze the direct detection sensitivities to such light inelastic DM. For different inelastic DM masses and mass-splittings, we find that the DM-electron recoil and Migdal effect can contribute significantly and differently to the direct detection signatures. The DM-lepton and/or DM-quark interactions may vary for different DM models, and their interplay leads to a diversity in the recoil spectra. Hence, it is important to perform a combined analysis to include both the DM-electron recoil and Migdal effect. We further demonstrate that this analysis has strong impacts on the cosmological and laboratory bounds for the inelastic DM.

Abstract B023: Unravelling fibroinflammatory and immune signatures for pancreatic cancer early detection

Abstract Pancreatic cancer is associated with late-stage diagnosis and poor patient prognosis. The disease is characterised by chronic dysregulated immunity and inflammation. Moreover, chronic pancreatitis is linked with an increased risk of pancreatic cancer. Tissue remodelling during disease progression involves a complex network of cells and a crosstalk between cancer cells, stromal components and immune cells, but it remains poorly explored. Thus, to improve the outcome of patients, there is a need for better understanding of biological changes associated with the initiation and progression of pancreatic cancer at the molecular and spatial levels. Using samples obtained from our Accelerated Diagnosis of neuroEndocrine and Pancreatic TumourS (ADEPTS) biobank, we have evaluated blood samples for the discovery of non- invasive biomarkers to discriminate patients with pancreatic cancer from symptomatic patients without cancer. In this pilot study, the transcriptome of peripheral blood mononuclear cells (PBMCs), isolated using a ficoll gradient was explored to identify PDAC-associated mRNA signatures. Moreover, the Olink® Explore Inflammation and Oncology panels were performed in a cohort of 40 patients with early stage (I-II) PDAC, 40 chronic pancreatitis samples and 40 control patients with benign symptomatic upper GI diseases. To validate and enrich these results, a panel of inflammatory and immune markers including immunoglobulins, cytokines and chemokines was also run using multiplex techniques. Symptoms observed in our cohort were also analysed together with clinical (e.g diabetic status, biochemistry profile) and demographic data (age, sex, ethnicity, post code etc). We have identified mRNA transcriptome signatures and differentially expressed proteins associated with inflammatory and immunological processes in PDAC compared to benign symptomatic samples. Our results offer potential diagnostic utility for early-stage disease. Additionally, our unique cohort of patient samples comprising of those with confirmed PDAC and those presenting to clinic with benign disease and similar non-specific symptoms, allows for the development of non-invasive diagnostic tests with better translational applicability. Future work will involve the validation of these findings in a larger patient cohort and the use of NanoString GeoMx® Digital Spatial Profiler to perform whole transcriptome and proteome profiling in human chronic pancreatic and pancreatic cancer tissue samples. Citation Format: Kyra Fraser, Maria Rosado, Stephen P Pereira, Pilar Acedo. Unravelling fibroinflammatory and immune signatures for pancreatic cancer early detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B023.

Abstract B027: A blood-based, multiplex protein biomarker signature shows superior performance in detection of early-stage pancreatic ductal adenocarcinoma (PDAC)

Abstract BACKGROUND: A sensitive and specific blood biomarker test is needed to improve survival in PDAC through early detection. An initial discovery study analyzing more than 3000 proteins using both pre-selected immunoassays and the Olink proteomics platform identified 15 promising protein candidates, of which ten were successfully pre-verified for ELISA immunoassay analysis. This model-development study was conducted to develop and pre- validate a specific and sensitive biomarker signature for the early detection of PDAC. METHODS: This multicenter case-control study was performed using existing bio-banked pretreatment samples from patients with newly diagnosed PDAC (Stages I-II) and participants in PDAC high-risk surveillance programs as controls. Blinded quantitative measurement of protein biomarkers was performed using ELISA-based immunoassays. CA19-9 was measured on a Roche COBAS instrument. An AI-derived predictive mathematical biomarker signature incorporating multiple protein biomarkers was developed and assessed using standard cross- validation techniques. RESULTS: 128 Stage 1 and 2 PDACs and 495 high-risk control serum samples were analyzed with 10 predetermined biomarkers to develop a four-protein biomarker signature. The model predicted PDAC versus non-PDAC with 84% sensitivity at 98% specificity. Model performance was significantly better when compared to CA19-9 alone (sensitivity 65% at 98% specificity, p<0.001). In those patients with low CA19-9 (<37 U/ml), the model predicted PDAC with 62% sensitivity at 98% specificity, performing significantly better than CA19-9 alone (9% sensitivity at similar 98% specificity (p<0.001). Additionally, in patients >65 years old, the signature predicted PDAC with 90% sensitivity at 98% specificity, which was significantly more sensitive than CA19-9 alone (75% sensitivity at 98% specificity, p<0.001). Test accuracy was not affected by diabetes status. CONCLUSIONS: This blood-based, multiplexed biomarker signature showed high sensitivity and specificity and significantly outperformed CA19-9 for early-stage PDAC. This biomarker model has the potential to improve outcomes in PDAC patients through early detection. Analytical and clinical validation studies are planned. Citation Format: Norma Palma, Ralph Schiess, Thomas King, Alcibiade Athanasiou, Natasha Kureshi, Lisa Ford, Aimee Lucas, Bryson Katona, Randall Brand, Diane Simeone. A blood-based, multiplex protein biomarker signature shows superior performance in detection of early-stage pancreatic ductal adenocarcinoma (PDAC) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B027.

Towards a universal analytical model for Population III star formation: interplay between feedback and fragmentation

ABSTRACT JWST has brought us new insights into Cosmic Dawn with tentative detection of the unique signatures of metal-free Population III (Pop III) stars, such as strong He II emission, extremely blue ultraviolet spectrum, and enhanced nitrogen abundance. Self-consistent theoretical predictions of the formation rates, sites, and masses of Pop III stars are crucial for interpreting the observations, but are challenging due to complex physical processes operating over the large range of length-scales involved. One solution is to combine analytical models for the small-scale star formation process with cosmological simulations that capture the large-scale physics such as structure formation, radiation backgrounds, and baryon-dark matter streaming motion that regulate the conditions of Pop III star formation. We build an analytical model to predict the final masses of Pop III stars/clusters from the properties of star-forming clouds, based on the key results of small-scale star formation simulations and stellar evolution models. Our model for the first time considers the interplay between feedback and fragmentation and covers different modes of Pop III star formation ranging from ordinary small ($\sim\!{10{-}2000}\ \rm M_\odot$) clusters in molecular-cooling clouds to massive ($\gtrsim\!{10^{4}}\ \rm M_\odot$) clusters containing supermassive ($\sim\!{10^{4}{-}3}\times 10^{5}\ \rm M_\odot$) stars under violent collapse of atomic-cooling clouds with large gas accretion rates of $\gtrsim\!{0.1}\ \rm M_\odot \ yr^{-1}$. As an example, the model is applied to the Pop III star-forming clouds in the progenitors of typical haloes hosting high-z luminous quasars ($M_{\rm h}\sim 10^{12}\ \rm M_\odot$ at $z\sim 6$), which shows that formation of Pop III massive clusters is common ($\sim\!{20{-}70}{{\ \rm per\ cent}}$) in such biased ($\sim\!{4}\sigma$) regions, and the resulting heavy black hole seeds from supermassive stars can account for a significant fraction of observed luminous ($\gtrsim\!{10^{46}}\ \rm erg\ s^{-1}$) quasars at $z\sim 6$.

Establishment and validation of circulating cell-free DNA signatures for nasopharyngeal carcinoma detection

Early detection of nasopharyngeal carcinoma (NPC) poses a significant challenge. The absence of highly sensitive and specific diagnostic biomarkers for nasopharyngeal carcinoma contributes to the unfavourable prognosis of NPC patients. Here, we aimed to establish a non-invasive approach for detecting NPC using circulating cell-free DNA (cfDNA). We investigated the potential of next-generation sequencing (NGS) of peripheral blood cells as a diagnostic tool for NPC. We collected data on genome-wide nucleosome footprint (NF), 5'-end motifs, fragmentation patterns, CNV information, and EBV content from 553 Chinese subjects, including 234 NPC patients and 319 healthy individuals. Through case-control analysis, we developed a diagnostic model for NPC, and validated its detection capability. Our findings revealed that the frequencies of NF, fragmentation, and motifs were significantly higher in NPC patients compared to healthy controls. We developed an NPC score based on these parameters that accurately distinguished NPC from non-NPC cases according to the American Joint Committee on Cancer staging system from non-NPC (validation set: area under curve (AUC)=99.9% (95% CI: 99.8%-100%), se: 98.15%, sp: 100%). This model showed superior performance over plasma EBV DNA. Additionally, the NPC score effectively differentiated between NPC patients and healthy controls, even after clinical treatment. Furthermore, the NPC score was found to be independent of potential confounders such as age, sex, or TNM stage. We have developed and verified a non-invasive approach with substantial potential for clinical application in detecting NPC. A full list of funding bodies that contributed to this study can be found in Funding section.

Dark branes for dark matter

We propose a setup for the origin of dark matter based on spacetime with a warped extra dimension and three branes: the Planck brane, the TeV brane, at a (few) TeV scale ρT, and a dark brane, at a (sub-)GeV scale ρ1≲100 GeV≪ρT. The Standard Model (SM) is localized in the TeV brane, thus solving the Higgs hierarchy problem, while the dark matter χ, a Dirac fermion with mass mχ<ρ1, is localized in the dark brane. The radion, with mass mr<mχ, interacts strongly [∼mχ/ρ1∼O(1)] with dark matter and very weakly (∼mfρ1/ρT2≪1) with the Standard Model matter f. The generic conflict between the bounds on its detection signatures and its proper relic abundance is avoided as dark matter annihilation is p-wave suppressed. The former is determined by its very weak interactions with the SM and the latter by its much stronger annihilation into radions. Therefore, there is a vast range in the dark matter’s parameter space where the correct relic abundance is achieved consistently with the existing bounds. Moreover, for the dark brane with ρ1≲3 GeV, a confinement/deconfinement first order phase transition, where the radion condensates, produces a stochastic gravitational wave background at the nanohertz frequencies, which can be identified with the signal detected by the Pulsar Timing Array (PTA) experiments. In the PTA window, for 0.15≲mχ≲2 GeV the relic abundance is reproduced and all constraints are satisfied. Published by the American Physical Society 2024

Artificial Intelligence Based Multi-Layer Approach for Finding Unknown Attacks in Cloud Network by Using Hybrid Intrusion Detection

Objectives : The objective of this study is to explore Intrusion Detection Systems and their various types by gathering research from previously published articles in refereed journals. The focus is on developing a proposed model capable of identifying unknown attacks in cloud networks using Signature and Anomaly-based Intrusion Detection Systems. Subsequently, the efficiency of the proposed model will be assessed, and a comparison with existing models will be conducted. The paper's main objective is to identify unknown attacks in a cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach. Methods: Leveraging insights from existing literature, the proposed model combines signature-based IDS for known threat detection and anomaly-based IDS for detecting unusual behavioral patterns indicative of new or unseen attacks. Experimental evaluations using NSL-KDD and ADFA datasets demonstrate competitive accuracy and detection rates, with the proposed artificial intelligence-based Hybrid IDS achieving high performance in detecting both normal and malicious activities. Findings: This model produces above 90%, 96%, and 98% efficiency in the wired, Wireless, and Cloud networks respectively, and this model finds known attacks effectively while using parameters like event logs, file transferring time, TCP and UDP addresses, CPU Usage, Weak and synthetic data, IP and MAC address. Existing literature said that the existing model using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The findings suggest that the artificial intelligence-based multi-layered approach offers a promising solution for enhancing cloud network security, with the potential for further optimization and integration of advanced technologies in future research endeavors. Novelty: This study presents an artificial intelligence-based multi-layered approach for detecting unknown attacks in cloud networks by integrating signature-based and anomaly-based intrusion detection systems (IDS). The authors developed the model to detect the intrusion by using the Behaviour Profiling algorithm and dynamically prevent the data from intrusion by using the Statistical approach model. The authors trying to find unknown attacks, therefore the authors defined the objective of this paper as to find the unknown attacks in cloud networks by using the combination of signature and anomaly-based intrusion detection systems. The objective is to develop a model capable of effectively identifying cyber threats in cloud environments. The existing models do not concentrate on identifying unknown attacks by using Signature-based Intrusion Detection. Very few of the literature said that known attacks can be identified easily by using Signature-based Intrusion Detection but the unknown attacks identifying process is hard. Some of the Literature said that using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The current paper identifies unknown attacks in the cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach and it produced above 97% accuracy. The unique feature of the model is artificial intelligence-based multi-layered approach and dynamic key have been utilized to avoid malicious activities in the network. Keywords: Anomaly based IDS, Cloud Network Security, Hybrid IDS, Multi-Layer Approach, Signature based IDS

Evaluation and integration of cell-free DNA signatures for detection of lung cancer

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915–0.981), with a sensitivity of 79.3 % (95 % CI: 71.5–87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6–100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3–85.8 %) in stage I patients, 100 % (95 % CI: 100.0–100.0 %) in stage II patients, 100 % (95 % CI: 100.0–100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%–100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949–0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.