Traditional Signature Research Articles

Collecting Digital Data with the Assurance of Integrity.

It is very common today that systems collect and store sensitive information. The database administrators of these types of systems have access to this sensitive information and can manipulate it. Therefore, data integrity is of core importance in these systems, and methods to detect fraudulent behavior need to be implemented. The objective of this article is to evaluate the features and performance impact of different methods for achieving and implementing data integrity in a database during data collection to improve assurance. Five methods for achieving data integrity were tested. The methods were tested in a controlled environment. This paper evaluates traditional Digital signature, Linked timestamping applied to a Merkle hash tree, and Auditing performance impact and feature impact wise. Two more methods were implemented and tested in a controlled environment, Merkle hash tree and Digital watermarking. In the evaluation, the researcher proved that traditional Digital signature is faster than Linked timestamping. In this study, it was concluded that when choosing a data integrity method to implement, it is of great importance to know which type of operation is more frequently used. The experiments show that the Digital signature method performed better than Linked timestamping and Auditing. Keyword: Data Collection, Integrity, Assurance, Security, Cyber space, cybercrimes,

Comparative Analysis of Dynamic Characteristics between Electronic Signature and Conventional Signature Based on Computer Vision Technology.

During the last two or three decades where innovations in technology have been dominant, especially the rapid development of electronic information technology, various types of electronic devices have been developed for different application areas. It is this technological-assisted equipment that has drastic effects on the lifestyle of every creature in general and human beings in particular. In addition to the other activities or services, technology has enabled human beings to write on electronic devices, which is due to the fact that these devices will generate electronic signature handwriting that is extremely useful for human beings. They may effectively cope with the electronization of traditional signature handwriting and ease the difficulties of authenticating the identification information of the signatory of electronic documents when used in conjunction with electronic documents. This method is frequently utilized in e-government, e-commerce, banking and insurance, medical care, and other industries. This study uses the current mainstream computer vision technology to compare and analyze the handwriting dynamic characteristics of electronic signature and conventional signature. It uses the electronic signature God and software to collect and extract the original characteristic data of user's electronic signature and then extracts the characteristics of average writing speed, duration, and average pressure on the basis of these data for analysis. Among these techniques, the writing time of electronic signature is longer than that of conventional signature, and the average speed of conventional signature notes is higher than that of electronic signature, and when analyzing the average pressure characteristics, the conventional signature pressure is greater than the electronic signature pressure.

Hypoxia classifier for transcriptome datasets

Molecular gene signatures are useful tools to characterize the physiological state of cell populations, but most have developed under a narrow range of conditions and cell types and are often restricted to a set of gene identities. Focusing on the transcriptional response to hypoxia, we aimed to generate widely applicable classifiers sourced from the results of a meta-analysis of 69 differential expression datasets which included 425 individual RNA-seq experiments from 33 different human cell types exposed to different degrees of hypoxia (0.1-5\%O\textsubscript{2}) for 2-48h. The resulting decision trees include both gene identities and quantitative boundaries, allowing for easy classification of individual samples without control or normoxic reference. Each tree is composed by 3-5 genes mostly drawn from a small set of just 8 genes (EGLN1, MIR210HG, NDRG1, ANKRD37, TCAF2, PFKFB3, BHLHE40, and MAFF). In spite of their simplicity, these classifiers achieve over 95\% accuracy in cross validation and over 80\% accuracy when applied to additional challenging datasets. Our results indicate that the classifiers are able to identify hypoxic tumor samples from bulk RNAseq and hypoxic regions within tumor from spatially resolved transcriptomics datasets. Moreover, application of the classifiers to histological sections from normal tissues suggest the presence of a hypoxic gene expression pattern in the kidney cortex not observed in other normoxic organs. Finally, tree classifiers described herein outperform traditional hypoxic gene signatures when compared against a wide range of datasets. This work describes a set of hypoxic gene signatures, structured as simple decision tress, that identify hypoxic samples and regions with high accuracy and can be applied to a broad variety of gene expression datasets and formats.

Hypoxia classifier for transcriptome datasets

Molecular gene signatures are useful tools to characterize the physiological state of cell populations, but most have developed under a narrow range of conditions and cell types and are often restricted to a set of gene identities. Focusing on the transcriptional response to hypoxia, we aimed to generate widely applicable classifiers sourced from the results of a meta-analysis of 69 differential expression datasets which included 425 individual RNA-seq experiments from 33 different human cell types exposed to different degrees of hypoxia (0.1–5%hbox {O}_{2}) for 2–48 h. The resulting decision trees include both gene identities and quantitative boundaries, allowing for easy classification of individual samples without control or normoxic reference. Each tree is composed of 3–5 genes mostly drawn from a small set of just 8 genes (EGLN1, MIR210HG, NDRG1, ANKRD37, TCAF2, PFKFB3, BHLHE40, and MAFF). In spite of their simplicity, these classifiers achieve over 95% accuracy in cross validation and over 80% accuracy when applied to additional challenging datasets. Our results indicate that the classifiers are able to identify hypoxic tumor samples from bulk RNAseq and hypoxic regions within tumor from spatially resolved transcriptomics datasets. Moreover, application of the classifiers to histological sections from normal tissues suggest the presence of a hypoxic gene expression pattern in the kidney cortex not observed in other normoxic organs. Finally, tree classifiers described herein outperform traditional hypoxic gene signatures when compared against a wide range of datasets. This work describes a set of hypoxic gene signatures, structured as simple decision tress, that identify hypoxic samples and regions with high accuracy and can be applied to a broad variety of gene expression datasets and formats.

Metabolomics can spot the difference: Dried Blood Spot (DBS) coming of age in a metabolomics era

In the wake of the birth of personalized medical treatment, the need for validated in‐home bio‐specimen collection options from patients are required. New ways of collecting and storing bio‐specimens to retain important biological information are on the rise. For this reason, dried blood spots (DBSs) have gained increasing attention especially in the field of metabolomics. Metabolomics systematically captures the phenotype of disease, interventions, microbiome, etc. if the most relevant biological pathways are represented broadly, as has traditionally been demonstrated in clinical EDTA plasma. Here we demonstrated results from a study comparing DBS against the golden standard in blood collection, EDTA plasma. The study showed that even though DBS had a reduction of overall metabolites coverage compared to plasma (̴ 800 vs ̴1200 metabolites, respectively), all major pathways and more than 95% of the metabolic sub‐pathways that are routinely detected in plasma are conserved in DBS. Traditional biological signatures, including exercise biomarkers, dietary biomarkers, and even the cyclical trend of the female hormonal biomarkers, are equally well represented in DBS compared to plasma. This evidently showed that metabolic signatures of disease that have been well characterized in plasma are maintained in DBS. The analysis of DBS for metabolomics has come a long way and this study showed DBS to be a valuable alternative to EDTA plasma and ought be considered when plasma is impractical to collect and store i.e., at‐home collections, remote localities, resource limited settings, pediatrics, and IEM screening to name a few. DBS‐based metabolomics provides a powerful tool for personalized medicine and an effective way of bringing metabolomics into our homes.

Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins.

Currently, contrast-enhancing margins on T1WI are used to guide treatment of gliomas, yet tumor invasion beyond the contrast-enhancing region is a known confounding factor. Therefore, this study used postmortem tissue samples aligned with clinically acquired MRIs to quantify the relationship between intensity values and cellularity as well as to develop a radio-pathomic model to predict cellularity using MR imaging data. This single-institution study used 93 samples collected at postmortem examination from 44 patients with brain cancer. Tissue samples were processed, stained with H&E, and digitized for nuclei segmentation and cell density calculation. Pre- and postgadolinium contrast T1WI, T2 FLAIR, and ADC images were collected from each patient's final acquisition before death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed-effects models were used to assess the relationship between single-image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 × 5 voxel tiles from each image, with a two-thirds to one-third train-test split for validation. Single-image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship in patients with glioblastoma. The radio-pathomic model accurately predicted cellularity in the test set (root mean squared error = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast-enhancing region. A radio-pathomic model for cellularity trained with tissue samples acquired at postmortem examination is able to identify regions of hypercellular tumor beyond traditional imaging signatures.

Non-Coding RNAs Are Brokers in Breast Cancer Interactome Networks and Add Discrimination Power between Subtypes.

Despite the power of high-throughput genomics, most non-coding RNA (ncRNA) biotypes remain hard to identify, characterize, and validate. This is a clear indication that intensive next-generation sequencing research has led to great efficiency and accuracy in detecting ncRNAs, but not in their functionalization. Computational scientists continue to support the discovery process by spotting significant data features (expression or mutational profiles), elucidating phenotype uncertainty, and delineating complex regulation landscapes for biological pathways and pathophysiological processes. With reference to transcriptome regulation dynamics in cancer, this work introduces a novel network-driven inference approach designed to reveal the potential role of computationally identified ncRNAs in discriminating between breast cancer (BC) subtypes beyond the traditional gene expression signatures. As heterogeneity cast in the subtypes is a characteristic of most cancers, the proposed approach is generalizable beyond BC. Expression profiles of a wide transcriptome spectrum were obtained for a number of BC patients (and controls) listed in TCGA and processed with RNA-Seq. The well-known PAM50 subtype signature was available for the samples and used to move from differentially expressed transcript profiles to subtype-specific biclusters associating gene patterns with patients. Co-expressed gene networks were then generated and annotations were provided, focusing on the biclusters with basal and luminal signatures. These were used to build template maps, i.e., networks in which to embed the ncRNAs and contextually functionalize them based on their interactors. This inference approach is able to assess the influence of ncRNAs at the level of BC subtype. Network topology was considered through the brokerage measure to account for disruptiveness effects induced by the removal of nodes corresponding to ncRNAs. Equivalently, it is shown that ncRNAs can act as brokers of network interactome dynamics, and removing them allows the refinement of subtype-related characteristics previously obtained by gene signatures only. The results of the study elucidate the role of pseudogenes in two major BC subtypes, considering the contextual annotations. Put into a wider perspective, ncRNA brokers may help predictive functionalization studies targeted to new disease phenotypes, for instance those linked to the tumor microenvironment or metabolism, or those specifically involving metastasis. Overall, the approach may represent an in silico prioritization strategy toward the systems identification of new diagnostic and prognostic biomarkers.

Hash-Based Signature for Flexibility Authentication of IoT Devices

5G provides a unified authentication architecture and access management for IoT (Internet of Things) devices. But existing authentication services cannot cover massive IoT devices with various computing capabilities. In addition, with the development of quantum computing, authentication schemes based on traditional digital signature technology may not be as secure as we expected. This paper studies the authentication mechanism from the user equipment to the external data network in 5G and proposed an authentication protocol prototype that conforms to the Third Generation Partnership Program (3GPP) standard. This prototype can accommodate various Hash-based signature technologies, applying their advantages in resource consumption to meet the authentication requirements of multiple types of IoT devices. The operation of the proposed authentication scheme is mainly based on the Hash function, which is more efficient than the traditional authentication scheme. It provides flexible and high-quality authentication services for IoT devices cluster in the 5G environment combining the advantages of Hash-based signature technology and 5G architecture.

Fast Detection for Cyber Threats in Electric Vehicle Traction Motor Drives

While cyber-physical security of electric vehicles (EV) is gaining increased concerns due to the fast development of vehicle onboard communication networks, the existing literature focuses on the vehicle level and it does not explicitly address cyber-threat detection for the EV powertrain traction motor drives. Therefore, in this paper, we propose a fast, model-free approach to detect cyber-threats in electric vehicle traction motor drives with only four easy-to-get, trustworthy sensor signals. First, the trustworthy motor current signals are selected to undermine the impacts of the vehicle’s random driving cycles. Then a set of innovative time-domain current features that are the most sensitive to a wide range of anomalies are selected to reduce the number of observations needed, thus vastly reducing the computational burden and the time-to-detect. Next, four binary classifiers are developed to detect cyber-threats while a majority vote mechanism is adopted to reduce the false alarm rate. Finally, the proposed method is validated by the real-time hardware-in-the-loop simulations. Validation results show that the proposed detection method achieves much faster detection compared to traditional current signature analysis. Furthermore, the proposed detection methods achieve an accuracy higher than 98% with the false alarm rate less than 0.01%.

Revocable identity-based fully homomorphic signature scheme with signing key exposure resistance

Fully homomorphic signature schemes in identity-based settings can provide authenticity, homomorphism, and non-repudiation as do traditional digital signatures, while simplifying the public key infrastructure (PKI) requirements, in which each user in the system can use his or her identity as a public key. As identity-based systems (IBS) have a natural link between unique identity information and the user, allowing user revocation is usually more difficult in IBS than in PKI settings. Although several revocable identity-based fully homomorphic signature (RIBFHS) schemes have been proposed, these schemes are vulnerable to signing key exposure. With this study, we are the first to consider a realistic threat signing key exposure in RIBFHS systems. In addition, we introduce a new security definition of RIBFHS with signing key exposure resistance. Then, we employ Agrawal’s left–right lattices and delegation technology in fixed dimensions to construct a new RIBFHS scheme over the lattice with regularly broadcasted update keys, which not only resists signing key exposure, but also meets scalability and context hiding properties. Finally, we prove that our construction is existentially unforgeable against chosen message attacks under the standard short integer solution (SIS) assumption in the random oracle model.

A Survey on Group Signatures and Ring Signatures: Traceability vs. Anonymity

This survey reviews the two most prominent group-oriented anonymous signature schemes and analyzes the existing approaches for their problem: balancing anonymity against traceability. Group signatures and ring signatures are the two leading competitive signature schemes with a rich body of research. Both group and ring signatures enable user anonymity with group settings. Any group user can produce a signature while hiding his identity in a group. Although group signatures have predefined group settings, ring signatures allow users to form ad-hoc groups. Preserving user identities provided an advantage for group and ring signatures. Thus, presently many applications utilize them. However, standard group signatures enable an authority to freely revoke signers’ anonymity. Thus, the authority might weaken the anonymity of innocent users. On the other hand, traditional ring signatures maintain permanent user anonymity, allowing space for malicious user activities; thus achieving the requirements of privacy-preserved traceability in group signatures and controlled anonymity in ring signatures has become desirable. This paper reviews group and ring signatures and explores the existing approaches that address the identification of malicious user activities. We selected many papers that discuss balancing user tracing and anonymity in group and ring signatures. Since this paper scrutinizes both signatures from their basic idea to obstacles including tracing users, it provides readers a broad synthesis of information about two signature schemes with the knowledge of current approaches to balance excessive traceability in group signatures and extreme anonymity in ring signatures. This paper will also shape the future research directions of two critical signature schemes that require more awareness.

Particle swarm optimization artificial intelligence technique for gene signature discovery in transcriptomic cohorts

The development of gene signatures is key for delivering personalized medicine, despite only a few signatures being available for use in the clinic for cancer patients. Gene signature discovery tends to revolve around identifying a single signature. However, it has been shown that various highly predictive signatures can be produced from the same dataset. This study assumes that the presentation of top ranked signatures will allow greater efforts in the selection of gene signatures for validation on external datasets and for their clinical translation. Particle swarm optimization (PSO) is an evolutionary algorithm often used as a search strategy and largely represented as binary PSO (BPSO) in this domain. BPSO, however, fails to produce succinct feature sets for complex optimization problems, thus affecting its overall runtime and optimization performance. Enhanced BPSO (EBPSO) was developed to overcome these shortcomings. Thus, this study will validate unique candidate gene signatures for different underlying biology from EBPSO on transcriptomics cohorts. EBPSO was consistently seen to be as accurate as BPSO with substantially smaller feature signatures and significantly faster runtimes. 100% accuracy was achieved in all but two of the selected data sets. Using clinical transcriptomics cohorts, EBPSO has demonstrated the ability to identify accurate, succinct, and significantly prognostic signatures that are unique from one another. This has been proposed as a promising alternative to overcome the issues regarding traditional single gene signature generation. Interpretation of key genes within the signatures provided biological insights into the associated functions that were well correlated to their cancer type.

Attribute-Based Blind Signature Scheme Based on Elliptic Curve Cryptography

Blind signature is a special digital signature that allows the signer to sign a document without knowing its content. However, in many situations, multiple people need to blindly sign messages. At this time, the traditional blind signature can no longer satisfy the application requirements. To solve this problem, attribute-based cryptography has been combined with the blind signature. The concept of the attribute-based blind signature is generated. At present, all attribute-based blind signature schemes require the support of bilinear pairing technology, which involves several complex pairing and exponential operations in the signature and verification processes, and the computational efficiency is not high. In this paper, we present an attribute-based blind signature scheme based on elliptic curve cryptography (ECC), and the security of new scheme is proved under the intractability of elliptic curve discrete logarithm problem (ECDLP). Our scheme is a key policy attribute-based signature (KP-ABS). The new scheme uses linear secret sharing scheme (LSSS) matrix technology that does not require recursive operation to achieve more flexible and fine-grained access control. In addition, the scheme is based on Elliptic Curve Cryptography (ECC) using scalar multiplication on an elliptic curve instead of a bilinear pairing operation. Our scheme has significant advantages in terms of computational efficiency and storage compared with existing attribute-based blind signature schemes.

Efficient Pairing-Free Attribute-Based Blind Signature Scheme Based on Ordered Binary Decision Diagram

Attribute-based blind signature is used to realize the blind signature of messages by multiple people, and it is suitable for electronic payment services in the cloud. Most existing attribute-based blind signature schemes have complex bilinear pairing operations in the process of signature and verification, which leads to low algorithm efficiency. To solve this problem, in this study, an efficient pairing-free attribute-based blind signature scheme based on ordered binary decision diagram is proposed, and the security analysis of the new scheme is given under the difficulty of the elliptic curve discrete logarithm problem (ECDLP). The proposed scheme is based on elliptic curve cryptography (ECC). In this scheme, scalar multiplication on the elliptic curve, which is relatively lightweight, is used to replace bilinear pairing with a large computational cost in traditional attribute-based blind signature schemes. The computational overhead in the signature and verification processes is reduced, making the solution more suitable for resource-constrained cloud terminal user scenarios. On the other hand, in order to improve the computational efficiency of the attribute-based blind signature scheme, a more expressive and computationally efficient ordered binary decision diagram (OBDD) structure is used to construct the access structure of the signer. The OBDD access structure can simultaneously support the positive and negative values of attributes in the access structure at the same time. Compared with existing attribute-based blind signature schemes, the proposed scheme has better performance and storage, and meets the needs of resource-constrained cloud end users.

Provably Secure Lattice-Based Self-Certified Signature Scheme

Digital signatures are crucial network security technologies. However, in traditional public key signature schemes, the certificate management is complicated and the schemes are vulnerable to public key replacement attacks. In order to solve the problems, in this paper, we propose a self-certified signature scheme over lattice. Using the self-certified public key, our scheme allows a user to certify the public key without an extra certificate. It can reduce the communication overhead and computational cost of the signature scheme. Moreover, the lattice helps prevent quantum computing attacks. Then, based on the small integer solution problem, our scheme is provable secure in the random oracle model. Furthermore, compared with the previous self-certified signature schemes, our scheme is more secure.

Functional signatures: new definition and constructions

Functional signatures (FS) enable a master authority to delegate its signing privilege to an assistant. Concretely, the master authority uses its secret key skF to issue a signing key skf for a designated function \(f \in {{\cal F}_{{\rm{FS}}}}\) and sends both f and skf to the assistant \({\cal E}\), which is then able to compute a signature σf with respect to pkF for a message y in the range of f. In this paper, we modify the syntax of FS slightly to support the application scenario where a certificate of authorization is necessary. Compared with the original FS, our definition requires that \({{\cal F}_{{\rm{FS}}}}\) is an injective function family and for any f0, \({f_1} \in {{\cal F}_{{\rm{FS}}}}\) there does not exist an intersection between range(f0) and range(f1). Accordingly, we redefine the security of FS and introduce two additional security notions, called unlinkability and accountability. Signatures σf in our definition do not expose the intention of the master authority. We propose two constructions of FS. The first one is a generic construction based on signatures with perfectly re-randomizable keys, non-interactive zero-knowledge proof (NIZK) and traditional digital signatures, and the other is based on RSA (Rivest-Shamir-Adleman) signatures with full domain hash and NIZK. We prove that both schemes are secure under the given security models.

False signals of chaos from quantum probes

We demonstrate that two-time correlation functions, which are generalizations of out-of-time-ordered correlators (OTOCs), can show 'false-flags' of chaos by exhibiting behaviour predicted by random matrix theory even in a system with classically regular dynamics. In particular, we analyze a system of bosons trapped in a double-well potential and probed by a quantum dot which is coupled to the bosons dispersively. This is an integrable system (considered both as separate parts and in total). Despite the continuous time evolution generated by the actual Hamiltonian, we find that the n-fold two-time correlation function for the probe describes an effective stroboscopic or Floquet dynamics whereby the bosons appear to be alternately driven by two different non-commuting Hamiltonians in a manner reminiscent of the Trotterized time evolution that occurs in digital quantum simulation. The classical limit of this effective dynamics can have a nonzero Lyapunov exponent, while the effective level statistics and return probability show traditional signatures of chaotic behaviour. In line with several other recent studies, this work highlights the fact that the behavior of OTOCs and their generalizations must be interpreted with some care.

A Novel Six Autophagy-Related Genes Signature Associated With Outcomes and Immune Microenvironment in Lower-Grade Glioma.

Since autophagy and the immune microenvironment are deeply involved in the tumor development and progression of Lower-grade gliomas (LGG), our study aimed to construct an autophagy-related risk model for prognosis prediction and investigate the relationship between the immune microenvironment and risk signature in LGG. Therefore, we identified six autophagy-related genes (BAG1, PTK6, EEF2, PEA15, ITGA6, and MAP1LC3C) to build in the training cohort (n = 305 patients) and verify the prognostic model in the validation cohort (n = 128) and the whole cohort (n = 433), based on the data from The Cancer Genome Atlas (TCGA). The six-gene risk signature could divide LGG patients into high- and low-risk groups with distinct overall survival in multiple cohorts (all p < 0.001). The prognostic effect was assessed by area under the time-dependent ROC (t-ROC) analysis in the training, validation, and whole cohorts, in which the AUC value at the survival time of 5 years was 0.837, 0.755, and 0.803, respectively. Cox regression analysis demonstrated that the risk model was an independent risk predictor of OS (HR > 1, p < 0.05). A nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its robust predictive capacity. KM analysis revealed a significant difference in the subgroup analyses’ survival. Functional enrichment analysis revealed that these autophagy-related signatures were mainly involved in the phagosome and immune-related pathways. Besides, we also found significant differences in immune cell infiltration and immunotherapy targets between risk groups. In conclusion, we built a powerful predictive signature and explored immune components (including immune cells and emerging immunotherapy targets) in LGG.

Controllable anonymous authentication scheme based on blockchain and decentralized traceable attribute-based signature

The development of information technology and the enhancement of peoples attention to personal privacy security put forward higher requirements for the identity authentication mechanism of traditional Internet applications, which not only needs to realize the users identity authentication, but also protects the users privacy information to the greatest extent. Compared with the traditional digital signature schemes, the attribute-based signature scheme has the characteristics of anonymity, that is, the attribute-based signature can hide the signers identity information, and other users cannot link a signature to the signers identity (attribute set). In practical application scenarios, we usually need to monitor the identity of users to prevent the occurrence of anonymous abuse and dishonesty. Therefore, in order to balance these two seemingly contradictory requirements, this paper proposes a controllable anonymous authentication scheme based on blockchain and decentralized traceable attribute-based signature. By properly improving the existing traceable attribute-based signature scheme, the decentralized attribute authority is realized without affecting the efficiency, so as to prevent the corruption of single attribute authority on system security. The results show that the proposed scheme is suitable for electronic voting systems and other scenarios with decentralized controllable anonymous authentication requirements.

Preliminary Observations of Weak Three-Body Scatter Spikes Associated With Low-End Severe Hail

he three-body scatter spike (TBSS), an artifact caused by radar microwave scattering associated with large hydrometeors, traditionally has been utilized in the National Weather Service as an operational indicator of destructive hail. Severe weather warning strategies based on the TBSS were derived using a lower-bound reflectivity threshold of 5 dBZ. With recent WSR-88D workstation software and product upgrades, the operational display of very low value reflectivity data to -30 dBZ has allowed meteorologists to observe more subtle, but meaningful, atmospheric targets and artifacts. Since these upgrades, radar-based interrogations of thunderstorms over west Texas and South Dakota (among other places) have revealed weak TBSS signatures characterized by faint radar returns as low as -14 dBZ. A sufficient dataset of weak TBSS cases does not exist currently to support a robust statistical analysis. Preliminary observations, however, indicate that the artifact may occur prior to and during low-end severe hail measuring between 1.9 cm and 2.5 cm in diameter, especially when recognized to emanate from pulse-type convective storms sampled at close range over the Great Plains. These observations suggest that an extension to the current conceptual use of the conventional TBSS model, which associates the traditional signature to a likelihood of “very large hail,” should be investigated.