ARTICLE | doi:10.20944/preprints202202.0238.v1
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: homomorphic; digital signature; IoT; authentication
Online: 18 February 2022 (17:04:53 CET)
In this paper, we address the problem of compatibility between digital signature schemes and in-network aggregation approaches. In the IoT world, the gateways alter the signed network flows when performing in-network aggregation. Therefore, existing conventional approaches are not suitable for verifying the authenticity of the original flows. This raises the need for energy-effective and secure schemes that enable the destination to validate aggregated network flows. In this regard, a lightweight homomorphic signature scheme is proposed which supports the implementation of aggregation procedures without affecting the verification process. We demonstrate the unforgeability and the privacy of our scheme. We also perform an analytical study of its energy-efficiency. The results suggest that the proposed scheme considerably decreases the processing overhead of the existing set-homomorphic signature schemes. Moreover, it does not add any communication overhead to traditional (non-homomorphic) signature schemes. This, in turn, improves the energy consumption by 30% compared to existing homomorphic signature techniques.
ARTICLE | doi:10.20944/preprints202106.0658.v1
Subject: Physical Sciences, Acoustics Keywords: vacuum, physics of a vacuum, fully geometrized physics, vacuum balance, signature, algebra of signature
Online: 28 June 2021 (13:58:31 CEST)
The aim of the article is to develop geometrized physics of a vacuum on the basis of two basic postulates: 1) the constancy of the speed of light (more precisely, the speed of propagation of electromagnetic waves) in the vacuum; 2) the ‘vacuum balance condition’ associated with the statement that only mutually opposite formations are born from the vacuum, so that, on average, they completely compensate of the manifestations of each other. The Algebra of signatures is proposed as a mathematical basis for geometrized physics of a vacuum.
ARTICLE | doi:10.20944/preprints202105.0179.v2
Subject: Mathematics & Computer Science, General & Theoretical Computer Science Keywords: Information Centric Network; Authentication; Digital Signature
Online: 7 September 2021 (07:51:56 CEST)
The world is growing very rapidly concerning technology. In the next-generation Internet, the existing architecture requires to be upgraded from Host-Centric Networking paradigm to Information-centric networking architecture. The unique aspect of information-centric networking is in-network cashing. Due to the system augmentation and In-network cashing technique, this novel system needs extremely high content security to ensure system integrity and maintenance. 5G network may be supported by the Information-Centric Network due to its high data transmission rate. In order to handle the serious security issues such as attack on confidentiality, authentication and integrity of the content, a Digital Signature based Access Control Mechanism in Information-Centric Network (DSAC) scheme is proposed to enhance security of ICN. Briefly, this new scheme uses Digital Signature, hash function, Trusted Third Party (TTP) and Proxy TTP. The client request for content, after receiving a request, the content provider generates and encrypts content with the digital signature and random value ‘k’ hash function and send it to TTP. After the signing process, the TTP sends the encryption hash key to Proxy TTP. In this proposed scheme authentication, confidentiality, the integrity aspects of the content security are improved.
ARTICLE | doi:10.20944/preprints202201.0346.v1
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: pyroptosis; ovarian cancer; prognosic; immune microenvironment; signature
Online: 24 January 2022 (11:19:53 CET)
Background: LncRNA and pyroptosis play important roles in cancer development and tumor immune microenviroment. However, pyroptosis-related lncRNAs (PRLs) in ovarian cancer have not been identified and its impact on prognosis and immune response are not fully understood. Methods: Using pearson correlation analysis, PRLs were screened. Subsequently, we constructed a prognosis signature by using LASSO cox regression. In addition, the association between risk score and cancer immune environment was analyzed. Results: In TCGA-RNA-seq cohort (n=377), 32 prognostic PRLs were selected and a 7-gene signature were developed and had high accuracy in predicting the OS of ovarian cancer patients. Stratification analysis suggested that it might serve as an independent prognostic indicator. Except to clinical outcome, the signature was significantly associated with tumor immune microenvironment. Patients with high risk score exhibited lower infiltration abundance of MHC class Ⅰ cells, Type Ⅰ IFN response and immunotherapy response. In ovarian cancer, TYMSOS was highly expressed and its high expression was associated with worse OS. TYMSOS deletion in ovarian cancer cell lines inhibited the cell proliferation, invasion and migration, indicating that it might serve as a novel biomarker in ovarian cancer. Conclusions: The prognostic PRLs signature constructed in this work is available for prognostic prediction and immune microenvironment infiltration in ovarian cancer.
ARTICLE | doi:10.20944/preprints202209.0342.v1
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: anoikis; Low-grade glioma; signature; prognosis; immune microenvironment
Online: 22 September 2022 (10:38:43 CEST)
Low-grade glioma (LGG) is a highly aggressive disease in the skull. On the other hand, anoikis, a specific form of cell death induced by the loss of cell contact with the extracellular matrix, plays a key role in cancer metastasis. In this study，anoikis-related genes (ANRGs) were used to identify LGG subtypes and to construct a prognostic model for LGG patients. In addition, we explored the immune microenvironment and enrichment pathways between different subtypes. We constructed an anoikis-related gene signature using the TCGA cohort and investigated the differences in clinical features, mutational landscape, immune cell infiltration, etc. between different risk groups. Kaplan-Meier analysis showed that the characteristics of ANRGs in the high-risk group were associated with poor prognosis in LGG patients. The risk score was identified as an independent prognostic factor. The high-risk group had higher immune cell infiltration, tumor mutation load, immune checkpoint gene expression, and ICB treatment response. Functional analysis showed that these high- and low-risk groups had different immune statuses and drug sensitivity. Risk scores were used together with LGG clinicopathological features to construct a nomogram, and DCA analysis showed that the model could enable patients to benefit from clinical treatment strategies.
ARTICLE | doi:10.20944/preprints202012.0404.v1
Subject: Life Sciences, Biochemistry Keywords: microenvironment signature; prognostic model; glioma; CGGA; ESTIMATE algorithm
Online: 16 December 2020 (11:01:08 CET)
Glioma accounts for the highest proportion of primary intracranial malignant tumors. Microenvironment enormously influences the process of glioma progression. Our study is to establish an individualized prognostic nomogram for glioma patients with microenvironment signature. Glioma samples of Chinese Glioma Genome Atlas (CGGA) were grouped by the immune and stromal score based on ESTIMATE algorithm. Microenvironment-related genes (MRGs) in glioma were analyzed by R. To determine the best prognostic correlation genes, univariate and multivariate Cox regression analysis were used to analyze MRGs. Use the selected genes (CHI3L1, SOCS3, SLC47A2, COL3A1, SRPX2 and SERPINA3), we established the prognostic risk score model (microenvironment signature) and validated it. Gene Set Enrichment Analysis (GSEA) showed that the high-risk group was mainly enriched in immune and stromal function KEGG pathways. Finally, the nomogram was constructed and evaluated. The receiver operating characteristic (ROC) curve, Calibration plots and decision curve analysis (DCA) of training and validation set indicated the excellent predictive performance of nomogram. In conclusion, the 6-gene microenvironment signature can not only provide directions for the basic research of glioma, but also can be included as an independent prognostic index in nomogram for individual prediction to guide clinical treatment.
ARTICLE | doi:10.20944/preprints202107.0257.v1
Subject: Keywords: Hyperspectral images, unsupervised Algorithm, clustering,K-means algorithm, spectral signature.
Online: 12 July 2021 (12:14:58 CEST)
Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.
ARTICLE | doi:10.20944/preprints202112.0472.v2
Subject: Mathematics & Computer Science, Other Keywords: Post-quantum cryptography; Blockchain; Code-based cryptography; Adaptor signature; Scriptless scripts.
Online: 31 December 2021 (11:46:58 CET)
An adaptor signature can be viewed as a signature concealed with a secret value and, by design, any two of the trio yield the other. In a multiparty setting, an initial adaptor signature allows each party create additional adaptor signatures without the original secret. Adaptor signatures help address scalability and interoperabity issues in blockchain. They can also bring some important advantages to cryptocurrencies, such as low on-chain cost, improved transaction fungibility, and less limitations of a blockchain’s scripting language. In this paper, we propose a new two-party adaptor signature scheme that relies on quantum-safe hard problems in coding theory. The proposed scheme uses a hash-and-sign code-based signature scheme introduced by Debris-Alazard et al. and a code-based hard relation defined from the well-known syndrome decoding problem. To achieve all the basic properties of adaptor signatures formalized by Aumayr et al., we introduce further modifications to the aforementioned signature scheme. We also give a security analysis of our scheme and its application to the atomic swap. After providing a set of parameters for our scheme, we show that it has the smallest pre-signature size compared to existing post-quantum adaptor signatures.
ARTICLE | doi:10.20944/preprints202004.0392.v1
Subject: Mathematics & Computer Science, Applied Mathematics Keywords: Multivariate Public Key Cryptosystem; Random polynomial; Oil Vinegar signature; Provable Security
Online: 22 April 2020 (06:09:50 CEST)
An oil and vinegar scheme is a signature scheme based on multivariate quadratic polynomials over finite fields. The system of polynomials contains $n$ variables, divided into two groups: $v$ vinegar variables and $o$ oil variables. The scheme is called balanced (OV) or unbalanced (UOV), depending on whether $v = 0$ or not, respectively. These schemes are very fast and require modest computational resources, which make them ideal for low-cost devices such as smart cards. However, the OV scheme has been already proven to be insecure and the UOV scheme has been proven to be very vulnerable for many parameter choices. In this paper, we propose a new multivariate public key signature whose central map consists of a set of polynomials obtained from the multiplication of block matrices. Our construction is motivated by the design of the Simple Matrix Scheme for Encryption and the UOV scheme. We show that it is secure against the Separation Method, which can be used to attack the UOV scheme, and against the Rank Attack, which is one of the deadliest attacks against multivariate public-key cryptosystems. Some theoretical results on matrices with polynomial entries are also given, to support the construction of the scheme.
ARTICLE | doi:10.20944/preprints202206.0183.v1
Subject: Materials Science, Biomaterials Keywords: Biomarkers; Drug Signature Identification; Key pathways; Oral Cancer; Oral Squamous Cell Carcinoma
Online: 13 June 2022 (10:14:58 CEST)
Background: Oral cancer (OC) is serious health concerning issue that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. Methods: Publicly available repository the Gene Expression Omnibus (GEO) was responsible to collect OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected to apply analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to figure out the up-regulated and down-regulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and Pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein-protein interaction (PPI) networks were built to identify the most performed genes, clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study deeply about OC-associated DEGs. Finally, top gene performers from PPI networks were used to apply drug signature analysis. Results: After applying filtration and cut-off criteria 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the Cell cycle and Influenza A. The PPI network holds 88 nodes and 492 edges and CDC6 had the highest number of connections. 4 clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of the pathophysiology and the development of new therapies for OC.
ARTICLE | doi:10.20944/preprints202108.0409.v1
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: Renal cancers; oncocytoma; chromophobe; transcriptomics; machine learning; clustering; gene signature; unsupervised learning
Online: 20 August 2021 (11:23:43 CEST)
Chromophobe renal cell carcinoma (chRCC) and oncocytoma (RO) are renal tumor types originating from alpha intercalated cells of the collecting ducts of the kidney. Both tumor types have similar gross histological morphology and increased mitochondria, which leads to difficulties differentiating between these tumors, especially with core biopsy samples. This study aims to apply a machine learning approach to develop a molecular classifier based on transcriptomics data. Here we generated a meta-data set containing 62 chRCC and 45 RO gene expression arrays. Arrays were subjected to quality control steps, and genes were selected based on differential expression and ROC analysis. The final gene list was evaluated with UMAP based dimension reduction followed by density-based clustering with 95.5% accuracy. Molecular profiling by KEGG pathway analysis identified enrichment of fatty acid oxidation pathway in RO. We finally identified and validated the 30-gene signature, with an accuracy of 94.4% to distinguish chRCC from RO on UMAP analysis. Our results show that chRCC and RO have a distinct gene signature that can differentiate these tumors and complement histology for routine diagnosis of these two tumors.
REVIEW | doi:10.20944/preprints202012.0700.v1
Subject: Life Sciences, Biochemistry Keywords: renal progenitors; molecular mechanisms; kidney injury; single cell RNA sequencing; molecular signature
Online: 28 December 2020 (12:40:17 CET)
Kidneys of mice, rats and humans possess progenitors that maintain daily homeostasis and take part in endogenous regenerative processes following injury, owing to their capacity to proliferate and differentiate. In the glomerular and tubular compartments of the nephron, consistent studies demonstrated that well-characterized, distinct populations of progenitor cells, localized in the parietal epithelium of Bowman capsule and scattered in the proximal and distal tubules, could generate segment-specific cells in physiological conditions and following tissue injury. However, defective or abnormal regenerative responses of these progenitors can contribute to pathologic conditions. The molecular characteristics of renal progenitors have been extensively studied, revealing that numerous classical and evolutionarily conserved pathways, such as Notch or Wnt/β-catenin, play a major role in cell regulation. Others, such as retinoic acid, renin-angiotensin-aldosterone system, TLR2 (Toll-Like Receptor 2) and leptin, are also important in this process. In this review, we summarize the plethora of molecular mechanisms directing renal progenitor responses during homeostasis and following kidney injury. Finally, we will explore how single cell RNA sequencing could bring the characterization of renal progenitors to the next level, while knowing their molecular signature is gaining relevance in the clinic.
ARTICLE | doi:10.20944/preprints202006.0065.v1
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: Cyber Attacks; Network Security; Network Performance; Network Traffic; Anomaly Detection; Signature Detection
Online: 7 June 2020 (07:58:18 CEST)
This paper incorporates the definition of Intrusion Detection Systems and the methodologies utilised by these systems. As well as this, this research paper also encompasses a taxonomy and a survey of IDS and the specific strategies and principles. Finally, this paper also includes a discussion amongst other authors for instance what the authors differ and agree on, along with the previously related studies.
Subject: Earth Sciences, Geophysics Keywords: ULFgeomagnetic signature, Mw6.4 earthquake, geomagnetic data, BPOL, BPOL* and BPOL*(PAG-SUA) time series
Online: 15 January 2021 (12:57:21 CET)
An earthquake of Mw6.4 hit the coastal zone of Albania on 26 November 2019, at 02:54:11 UTC. It was intensively felt at about 34km far away, in Tirana City, where damages and lives lost occurred. To emphasize a geomagnetic signature before the onset of this earthquake, the data collected on the interval 15 October–30 November 2019, at the Panagjurishte (PAG)-Bulgaria and Surlari (SUA)-Romania observatories are analyzed by using both the polarization parameter (BPOL)-time invariant in non-seismic conditions, becoming unstable before this seismic event, and the strain effect for geomagnetic signal identification. Consequently, BPOL time series and its standard deviations are performed for the both sites using ULF-FFT band-pass filtering. A statistical analysis, based on a standardized random variable equation, was applied to emphasize on the BPOL*(PAG) and ABS BPOL*(PAG) time series the anomalous signal’s singularity and, to differentiate the transient local anomalies due to Mw6.4earthquake, from the internal and external parts of the geomagnetic field, taken PAG observatory as reference. Finally, the ABS BPOL*(PAG-SUA) time series are obtained on the interval 1-30 November, 2019, where a geomagnetic signature greater than 2.0, was detected on 23 November and the lead time was 3 days before the onset of Mw6.4earthquake.
ARTICLE | doi:10.20944/preprints202105.0176.v1
Subject: Keywords: Video Steganography, Least Significant Bit (LSB) Coding, Double key Encryption, Decryption, Password Verification, Signature Verification
Online: 10 May 2021 (11:21:29 CEST)
In today’s digital media data communication over the internet increasing day by day. Therefore the data security becomes the most important issue over the internet. With the increase of data transmission, the number of intruders also increases. That’s the reason it is needed to transmit the data over the internet very securely. Steganography is a popular method in this field. This method hides the secret data with a cover medium in a way so that the intruders cannot predict the existence of the data. Here a steganography method is proposed which uses a video file as a cover medium. This method has five main steps. First, convert the video file into video frames. Then a particular frame is selected for embedded the secret data. Second, the Least Significant Bit (LSB) Coding technique is used with the double key security technique. Third, an 8 characters password verification process. Fourth, reverse the encrypted video. Fifth, signature verification process to verify the encryption and decryption process. These five steps are followed by both the encrypting and decrypting processes.
Subject: Engineering, Electrical & Electronic Engineering Keywords: arm motion recognition; micro-doppler signature; time series analysis; dynamic time warping; long short-term memory
Online: 16 December 2019 (11:42:44 CET)
Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use time series analysis method for accurately measuring the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply the dynamic time warping (DTW) method and compare its performance with that of the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and trends of time series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.
ARTICLE | doi:10.20944/preprints201907.0051.v1
Subject: Life Sciences, Genetics Keywords: lung cancer; molecular signature; molecular pathway; differentially expressed genes; protein-protein interaction; reporter biomolecules and bioinformatics
Online: 3 July 2019 (08:54:37 CEST)
Lung cancer is one of the most important health risks worldwide for human. Non-small cell lung cancer (NSCLC) is the most common cause of premature death from malignant disease. This study provides in-depth insights from systems biology analyses to identify molecular to inform systemic drug targeting in NSCLC. Gene expression profiles from non small cell lung cancer were analyzed with genome-scale biomolecular networks (I,e., protein-protein interaction, transcriptional and post transcriptional regulatory networks). The aim of the study was to determine the pathways and expression profile of the genes to discover molecular signature at RNA and protein levels which could serve as potential drug targets for therapeutics innovation and the identification of novel targets. Eight proteins, six TFs and seven miRNAs came into prominence as potential drug targets. The differential expression profiles of these reporter biomolecules were cross-validated by independent RNA-Seq and miRNA-Seq. Risk discrimination performance of the reporter biomolecules NPR3, JUN, PPARG, TP53, CKMT1A, SP3 and TFAP2A were also evaluated. Total 213 drugs and 7 proteins were found for non small cell lung cancer through dgidb. Among these identified drugs seven drugs such as- Gemcitabine, Carboplatin, paclitaxel, Docetaxel, Crizotinib, Bevacizumab and Gemcitabine is used for NSCLC which is approved by National Cancer Institute. The molecular signatures and repurposed drugs presented here permit further attention for experimental studies which are offer significant potential as biomarkers and candidate therapeutics for precision medicine approaches to clinical management of NSCLC.
ARTICLE | doi:10.20944/preprints202104.0475.v1
Subject: Medicine & Pharmacology, Allergology Keywords: drug repurposing; virtual screening; multiscale; multitargeting; polypharmacology; computational biology; drug repositioning; structural bioinformatics; molecular docking; proteomic signature
Online: 19 April 2021 (12:22:05 CEST)
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multidisease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines via large scale modelling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is then compared to all other signatures that are then sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions in the platform used to create the drug-proteome signatures may be determined by any screening or docking method but the primary approach used thus far has been an in house similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and cheminformatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the corresponding two docking-based pipelines it was synthesized from, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking based signature generation methods can capture unique and useful signal for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.
ARTICLE | doi:10.20944/preprints202207.0247.v2
Subject: Engineering, Other Keywords: fault detection; retraction/extension (R/E) hydraulic system; bond graph-linear fractional trans-formation technique; interval analytic redundancy relations; uncertainty; fault signature matrix; residuals; thresholds
Online: 17 August 2022 (03:53:54 CEST)
Various factors, such as uncertainty of component parameters and uncertainty of sensor meas-urement values, contribute to the difficulty of fault detection in the landing gear retrac-tion/extension hydraulic system. In this paper, we introduce linear fractional transformation technology and uncertainty analysis theory for the construction of the diagnostic bond graph of the landing gear retraction/extension hydraulic system. In this way, interval analytical redundancy relations and fault signature matrix can be derived. Using the fault signature matrix, existing faults of the system can be preliminarily detected and isolated. Additionally, interval analytical re-dundancy relations can be used to detect system faults in detail, and cases analysis can be carried out to determine if the actuator is externally or internally leaky, and if the landing gear selector valve is reversing stuck. Compared to the traditional analytical redundancy relations, this method takes into account the negative factors of uncertainty; and compared to the traditional absolute diagnostic threshold, the interval diagnostic threshold is more accurate and sensitive.
ARTICLE | doi:10.20944/preprints202110.0297.v1
Subject: Medicine & Pharmacology, Oncology & Oncogenics Keywords: gastric cancer; immunotherapy; immune checkpoint blockade (ICB); immune checkpoint inhibitors (ICI),; DNA repair gene signature (DRGS); prognostic biomarker; score system
Online: 20 October 2021 (22:47:19 CEST)
Gastric cancer is a heterogeneous group of diseases with only a fraction of patients responds to immunotherapy. The relationships between tumor DNA damage response, the immune system and immunotherapy have recently attracted attention. Accumulating evidence indicate that DNA repair landscape is a significant factor in driving response to immune checkpoint blockade (ICB) therapy. In this study, to explore new prognostic and predictive biomarkers for gastric cancer patients who are sensitive and responsible to immunotherapy, we developed a novel 15-DNA repair gene signature (DRGS) and its related scoring system and evaluated the efficiency of DRGS in discriminating different molecular and immune characteristics and therapeutic outcomes of gastric adenocarcinoma. The results showed that DRGS high score patients showed significantly better therapeutic outcomes compared to DRGS low score patients (P < 0.001). Integrated analysis of multi-omics data demonstrated that the patients with high DRGS score were characteristic of high levels of anti-tumor lymphocytes infiltration, tumor mutation burden (TMB) and PD-L1 expression, and these patients exhibited a longer overall survival and may benefit more from ICB therapy, as compared to the low-score patients. Therefore, the DRGS and its scoring system may have implications in tailoring immunotherapy in gastric cancers.
REVIEW | doi:10.20944/preprints201709.0027.v1
Subject: Life Sciences, Biotechnology Keywords: EVs; endothelial-derived microparticles; platelet-derived microparticles; non-invasive biomarkers; miRNAs signature; diabetes associated complications; micro-macrovascular damage; diabetic nephropathy
Online: 8 September 2017 (09:38:47 CEST)
Extracellular vesicles (EVs) represent a heterogeneous population of small vesicles, consisting of a phospholipidic bilayer surrounding a soluble interior cargo. Almost all cell types release EVs, thus they are naturally present in all body fluids. Among the several potential applications, EVs could be used as drug delivery vehicles in disease treatment, in immune therapy because of their immunomodulatory properties and in regenerative medicine. In addition to general markers, EVs are characterized by the presence of specific biomarkers (proteins, miRNAs) that allow the identification of their cell- or tissue-origin. For these features, they represent a potential powerful diagnostic tool to monitor state and progression of specific diseases. As regards, a large body of studies supports the idea that endothelial derived (EMPs) together with platelet-derived microparticles (PMPs) are deeply involved in the pathogenesis of diseases characterized by micro- and macrovascular damages, including diabetes. Existing literature suggests that the detection of circulating EMPs and PMPs and their specific miRNA profile may represent a very useful non-invasive signature to achieve informations about the onset of peculiar disease manifestations. In this Review, we discuss the possible utility of EVs in the early diagnosis of diabetes-associated microvascular complications, specifically related to kidney.
REVIEW | doi:10.20944/preprints202106.0458.v1
Subject: Life Sciences, Biochemistry Keywords: Arabidopsis; amidase signature superfamily; growth; stress; auxin; abscisic acid; amidase; indole-3-acetamide; indole-3-acetic acid; fatty acid amide hydrolase
Online: 17 June 2021 (12:10:54 CEST)
The evolutionary success of land plants largely relies on their ability to cope with constant environmental fluctuations, which negatively impact their reproductive fitness and trigger adaptational responses to biotic and abiotic stresses. In this challenging scenario, comprehensive research efforts so far aimed at depicting the roles of well-known phytohormones, mainly auxins, along with brassinosteroids, jasmonates, and abscisic acid, although the signaling networks coordinating the crosstalk among them remains vaguely understood. Accordingly, this review focuses on the Arabidopsis Amidase Signature (AS) superfamily members, highlighting the hitherto relatively underappreciated functions of AMIDASE1 (AMI1) and FATTY ACID AMIDE HYDROLASE (FAAH), as crucial coordinators of the growth-defense response trade-off by modulating auxin and ABA homeostasis.
ARTICLE | doi:10.20944/preprints201908.0320.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: Electrocardiography Analysis; Persistence Landscape; Signal Analysis; Machine Learning; Topological Data Analysis; Topological Signal Signature; Classification; Time Series Analysis; Biomedical Signal Analysis; Persistence Homology
Online: 30 August 2019 (09:51:40 CEST)
Data can be illustrated in shapes, and the shapes could provide insight for data modeling and information extraction. Topological data analysis provides an alternative insight in biomedical data analysis and knowledge discovery with the algebra topology tools. In present work, we study the application of topological data analysis for personalized electrocardiographic signal classification toward arrhythmia analysis. Using phase space reconstruction technique, the signal samples are converted into point clouds for topological analysis facility. With topological techniques the persistence landscapes from the point clouds are extracted as features to perform the arrhythmia classification task. We find that the proposed method is robust to the training set size, with only a training set size of 20% percents, the normal heartbeat class are 100% recognized, ventricular beats for 97.13%, supra-ventricular beats for 94.27% and fusion beats for 94.27% within the corresponding experiments. The property of keeping high performance when using smaller training sample proves that the proposed method is especially applicable to personalized analysis. With the present study, we show that the topological data analysis technique could be a useful tool in biomedical signal analysis, and provide powerful ability in personalized analysis.
ARTICLE | doi:10.20944/preprints202112.0287.v1
Subject: Earth Sciences, Environmental Sciences Keywords: foodshed archipelago; proximity food supply chains; spatial signature; city-region; food self-sufficiency; regional food security; agricultural diversification; food planning; regional food system; food policy
Online: 17 December 2021 (11:37:50 CET)
: Foodshed approaches allow the assessment of the theoretical food self-sufficiency capacity of a specific region based on biophysical conditions. Recent analyses show that the focus needs to be shifted from foodshed size portrayed as an isotropic circle to a commodity-group-specific spatial configuration of the foodshed that takes into account the socio-economic and biophysical conditions essential to the development of local food supply chains. We focus on a specific animal product (beef) and use an innovative modeling approach based on spatial analysis to detect the areas of the foodshed dedicated to beef feeding (forage, pasture, and grassland), considering the foodshed as a complex of complementary areas called an archipelago. We use available statistical data including a census to address the city-region of Avignon (France) covering a 100 km radius. Our results show that the factors driving the use of short supply chains for beef feeding areas are the foodshed archipelago’s number of patches, the connectivity between them, and the rugosity of the boundaries. In addition, our beef self-sufficiency assessment results differ depending on geographical context. For instance, being located within the perimeters of a nature park seems to help orient beef production towards short supply chains. We discuss possible leverage for public action to reconnect beef production areas to consumption areas (the city) via short supply chains (e.g. green, home-grown school food programs) so as to increase local food security through increased local food self-sufficiency.
ARTICLE | doi:10.20944/preprints202006.0165.v2
Subject: Life Sciences, Virology Keywords: Conserved signature indels (CSIs) specific for SARS and SARS-CoV-2-related viruses. Molecular markers distinguishing different clades of Sarbecovirus, Evolutionary relationships between SARS and SARS-CoV-2-related viruses, Origin of SARS-CoV-2 and Pangolin CoV_MP789 viruses, Novel sequence and structural features of spike and nucleocapsid proteins. Genetic recombination.
Online: 26 August 2020 (10:17:16 CEST)
Both SARS-CoV-2 (COVID-19) and SARS coronaviruses (CoVs) are members of the subgenus Sarbecovirus. To understand the origin of SARS-CoV-2, protein sequences from sarbecoviruses were analyzed to identify highly-specific molecular markers consisting of conserved inserts or deletions (termed CSIs) in the spike (S) and nucleocapsid (N) proteins that are specific for either particular clusters/lineages of these viruses or are commonly shared by specific lineages. Three novel CSIs in the N-terminal domain of the spike protein S1-subunit (S1-NTD) are uniquely shared by the SARS-CoV-2, BatCoV-RaTG13 and most pangolin CoVs, distinguishing this cluster of viruses (SARS-CoV-2r) from all others. In the same positions, where these CSIs are found, related CSIs are also present in two other sarbecoviruses (viz. CoVZXC21 and CoVZC45 forming CoVZC cluster), which form an out group of the SARS-CoV-2r cluster. These three CSIs are not found in the SARS-CoVs. However, both SARS and SARS-CoV-2r CoVs contain two large CSIs in the C-terminal domain of S1 (S1-CTD), which binds the human ACE-2 receptor, that are absent in the CoVZC cluster of CoVs. These results indicate that while the S1-NTD of the SARS-CoV-2r viruses possesses the sequence characteristics of the CoVZC cluster of CoVs, their S1-CTD resembles the SARS viruses. Thus, the spike protein of SARS-CoV-2r viruses has likely originated from a recombination event between the S1-NTD of the CoVZC viruses and the S1-CTD of SARS viruses. This inference is also supported by the amino acid sequence similarity of the S1-NTD and S1-CTD from SARS-CoV-2 compared to the CoVZC and SARS CoVs. We also present evidence that one of the pangolin-CoV_MP789, whose receptor-binding domain is most similar to the SARS-CoV-2, is also derived by a recent recombination between the S1-NTD of the CoVZC CoVs and the S1-CTD of a SARS-CoV-2 related virus. Several other identified CSIs are specific for others clusters of sarbecoviruses including a clade consisting of bat SARS-CoVs (BM48-31/BGR/2008 and SARS_BtKY72). Structural mappings studies show that the identified CSIs are located within surface-exposed loops and form distinct patches on the surface of the spike protein. These surface loops/patches are predicted to interact with other host components and play important role in the biology/pathology of SARS-CoV-2 virus. Lastly, the CSIs specific for the SARS-CoV-2r clade provide novel means for development of new diagnostic and therapeutic targets for these viruses.
ARTICLE | doi:10.20944/preprints202112.0323.v1
Subject: Mathematics & Computer Science, Probability And Statistics Keywords: Intrusion Detection System (IDS); HNADAM-SDG(Hybrid Nestrov-Accelerated Adaptive Moment Estimation –Stochastic Gradient Descent); Network-based Intrusion Detection System (NIDS); Host-based Intrusion Detection System (HIDS); Signature-based Intrusion Detection System (SIDS); Anomaly-based Intrusion Detection System (AIDS); Algorithms; Machine Learning.
Online: 21 December 2021 (11:45:39 CET)
A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques