Risk Of Attack Research Articles

Pattern Matters in the Aposematic Colouration of Papilio polytes Butterflies.

Many toxic animals display bright colour patterns to warn predators about their toxicity. This sometimes leads other sympatric palatable organisms to evolve mimetic colour patterns to also evade predation. These mimics, however, are often imperfect, and it is unclear how much their colour patterns can vary away from the model before they become ineffective. In this study, we investigated how predation risk of the palatable Common Mormon butterfly (Papilio polytes) is affected by two alterations of its wing pattern that make it progressively more distinct from its model, the Common Rose (Pachliopta aristolochiae). We deployed butterfly paper models in the field, where all models displayed the same colours but had different patterns. In the first modification from the Wildtype pattern, we exchanged the position of the red and white colour patches but kept the overall pattern constant. In the second modification, we created an eyespot-like shape from the pre-existing pattern elements by moving their positions in the wing, altering the overall wing pattern. Both modifications increased attack risk from predators relative to Wildtype patterns, with the eyespot-like modification having the highest predation risk. Our results show that avian predators can distinguish between all three patterns tested, and that pattern is important in aposematic signals. Predators learn to avoid aposematic colours, not in isolation, but as part of specific patterns.

The effect of lithium variation coefficient on the risk of attack in patients with bipolar disorder: A pilot study.

This study examines the association between the coefficient of variation (%CV) of lithium levels and episode risk and frequency in bipolar patients maintaining serum lithium levels within the therapeutic range. We retrospectively reviewed patients with bipolar disorder under care from 2018 to 2022. Inclusion criteria were at least 2years of follow-up, a minimum of three annual lithium level measurements within the therapeutic range. Patients were categorized based on seizure status. We calculated mean lithium levels, standard deviation (SD), and %CV. The study included 75 patients (patients with-without episodes, 39-36). Demographic data revealed no significant differences. While mean lithium levels showed no significant disparity between groups, SD and %CV were notably higher in patients with episodes (P < .05). ROC analysis demonstrated AUC values of 0.722 (95% CI: 0.607-0.836 P = .001) for %CV and 0.709 (95% CI: 0.593-0.826; P = .002) for SD. The optimal %CV cutoff was 17.39, with 67% sensitivity and 69% specificity. A weak correlation was found between %CV and the number of episodes (P = .001, r = 0.376). The post-hoc power analysis for this study was 0.78. Despite acceptable lithium levels, patients with recent episodes exhibited significant lithium level fluctuations. Integrating %CV with real-time lithium measurements during bipolar disorder follow-up may enhance clinical monitoring and seizure prediction.

Targeting Cancers: Uncovering the Potential Roles of Potato Tissue Culture as Anti-Cancer Agents — A Secondary Publication

Plant tissue culture is a technique that enhances the quality and quantity of potatoes. Potatoes are a significant crop and are primarily used in the world. It is a staple food in many countries, where millions of tonnes are produced annually. It is an essential source of many nutrients, such as proteins, carbohydrates, vitamins, and beta-carotene. In addition, potatoes are being used as therapeutic agents against cancer and other human diseases as well. Potatoes are on the third list after wheat and rice. To overcome food shortages and malnutrition, there are two methods used for producing potatoes: the first is sexual, which is seed propagation, and the second is asexual, which is plant tissue culture propagation. Conventional potato breeding is a uniform method, but it is unsafe because there is a risk of pathogen attack. In a laboratory setting, the tissue culture of potatoes produced millions of plants with nutrient-rich medium under controlled environmental conditions that prevent pest attacks. Some environmental stresses, such as salinity and water scarcity, affect potato yield and production; however, applying nanoparticles like organic, inorganic, and silicon dioxide enhances potato quality and combats stress. Biotechnology has proven to be helpful in addressing all these issues. This review discusses the significance of potatoes, their production through the tissue culture technique, and the application of nanoparticles to improve the growth, and impact of potatoes on human health.

Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures.

The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25th percentile compared to their 50th percentile and a positive association of 0.0285 units when all the metals were at their 75th percentile compared to their 50th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.

RWHO: A hybrid of CNN architecture and optimization algorithm to predict basal cell carcinoma skin cancer in dermoscopic images

Basal cell carcinoma (BCC) is a type of skin cancer that initiates from the epithelial cells of our skin. Compared to other forms of cancer, BCC infrequently spreads to other parts of the body. It has a risk of local attack and demolition of surrounding tissues. Typically, BCC shows as one or numerous small, glowing nodules exhibiting central depressions. These knots are commonly found on the sun-exposed skin areas of older adults. Many dermatoscopic methods are available for diagnosing and predicting such kinds of skin cancers. But, medical professionals find it difficult to diagnose at some kind of images at the early stages. An automated methodology to predict such types of skin lesions would be better for such a diagnosis. In the present work, a new computer-assisted algorithm called RESNET50-WHO (RWHO) has been introduced to predict and diagnose BCC skin cancer. The method uses a combination of deep learning algorithm RESNET 50 and a metaheuristic algorithm, called wildebeest herd optimization (WHO) Algorithm to do prediction. The initial features from the images are extracted using RESNET 50. The output is given to the WHO algorithm to extract the beneficial features to reduce the time complexity. The method is tested using the PH2 dataset. The results obtained using the proposed algorithm is compared with the state-of-art optimization algorithms and evaluated. The conclusive findings specify that the proposed algorithm beats the comparative methods, yielding superior results

Web 3.0 security: Backdoor attacks in federated learning-based automatic speaker verification systems in the 6G era

With the advent of Next-Generation Web 3.0 and the integration of 6G technologies, digital industrial applications are undergoing unprecedented transformations. Among these, the field of intelligent voice recognition, particularly Federated Learning-based Automatic Speaker Verification (FL-ASV) systems, stands out by collaboratively training robust ASV models across systems while protecting sensitive voiceprint data. However, the aspect of security within such systems is still largely unexplored and presents potential vulnerabilities. To bridge this gap, we design a voiceprint-driven backdoor attack for FL-ASV, termed FedCTS. Concretely, we employ contrastive learning techniques to significantly improve the feature extraction process for individual speakers. This enhancement not only maintains the inherent performance of FL-ASV systems but also introduces a level of complexity that can mislead even the most skilled defenders. Furthermore, we intricately obfuscate the triggers by subtly embedding voice and backdoor clips within the utterances. This is achieved by dividing the utterances into chronological segments through a meticulously devised time-series injection strategy, thereby ensuring the triggers remain undetectable. Additionally, we have conceptualized a unique defense mechanism tailored to counter such attacks. This defense mechanism operates by scrutinizing the speaker’s frequencies and filtering out any suspicious frequencies that fall outside the normal range of human voice, helping to mitigate the risk of backdoor attacks without compromising the system’s functionality. In the context of the fast-evolving digitalized industrial landscape, our attack strategy, FedCTS, has demonstrated a significant improvement in effectiveness. It achieves an average increase of 7.03% in the attack success rate when compared to existing state-of-the-art methods.

Integrated energy cyber–physical system fusion modeling and operation state analysis under the cooperative attack

With the increasingly close coupling between the cyber system and the physical system, cyber attacks have a significant impact on the physical system by attacking the cyber system. As a cyber–physical system with multiple energy sources, integrated energy cyber–physical system (IECPS) is facing the risk of cyber attacks. However, the research on IECPS is still in the direction of operation optimization at the physical level, which cannot meet the requirements of system security and stability analysis under cyber cooperative attacks. Firstly, this paper analyzes the interaction mechanism between the energy network composed of electricity, heat and gas and the cyber network in IECPS, and put forward a layered modeling method of IECPS to analyze the influence of abnormal information flow caused by cyber attacks on energy flow. Secondly, based on the energy circuit method (ECM), a hybrid calculation method of IECPS energy-information flow is proposed to solve the model. Then, the load shedding state of IECPS and the tampering process of system control commands after the fake data injection attack are analyzed by using the proposed calculation method. Finally, the simulation analysis is carried out in an integrated energy system composed of IEEE 39-node power grid, 6-node heating network and 7-node gas network. The results show that, compared with the traditional Newton–Raphson method, the proposed IECPS hybrid calculation method can better obtain the change results of energy flow in the steady-state scenario and the cyber cooperative attack scenario, and identify the high-risk branches in the system, which is of great significance to the safe and stable operation of the energy system.

Corrosion fatigue analysis of NREL’s 15-MW offshore wind turbine with time-varying stress concentration factors

Over the last twenty years, significant development in wind turbine technologies has led to a dramatic increase in the scale of wind turbines with many now beginning to be installed in offshore locations. Consequently, modern multi-megawatt offshore wind turbines are exposed to increased cyclic loading in addition to an increased risk of corrosion attack. The combination of these two factors may result in wind turbine support structures becoming increasingly vulnerable to fatigue corrosion. The objective of this work is to investigate the impact of material thinning in fatigue-prone areas with respect to fatigue loading and ultimately to examine the potential repercussions on the lifespan of wind turbine support structures. To achieve this, a composite model is constructed coupling results from a multi-body structural dynamic model with time-varying Stress Concentration Factors (SCF) obtained from a finite element model (FEM) of NREL’s 15-MW monopile-based offshore wind turbine. The nonlinear aeroelastic multi-body dynamic model of the wind turbine is used to generate stress time histories for a set of environmental conditions based on the operational conditions of the wind turbine. The finite element model of the wind turbine is then used to identify fatigue-vulnerable regions in the wind turbine support structure and calculate SCFs for these specific areas. The integration of SCFs into the fatigue calculations reduced the lifespan of the turbine tower by a factor of 4, demonstrating the importance of precisely modelling such local stress concentrations for effective fatigue analysis. A novelty of this work arises in the ability of the finite element model to update the SCFs of the fatigue-prone areas over time as corrosion-induced wastage alters the substructure’s geometry, thereby inducing a global redistribution of stresses. A fatigue analysis is carried out availing of the SCFs which vary annually in addition to the stress-time histories produced by the multi-body dynamic model. The results illustrate that the phenomenon of corrosion thinning induced an 8.9% reduction in the fatigue life of the wind turbine tower, thus emphasising the significant importance of proactive maintenance strategies to mitigate the impact of corrosion.

Research on Data Security Measures in Romania

Abstract Information from the online environment is constantly increasing, as well as its value. In this context, for companies data security becomes a central pillar to protect sensitive information and to ensure its integrity, confidentiality and availability (CIA Triangle). The security measures adopted by the companies also increase the degree of trust of their partners and customers. But data security is not just about implementing the right technologies and policies; equally important is the awareness and proper training of personnel who interact with this data or the applications that contain it. Human resource in companies playes an important role in protecting sensitive information by understanding the negative conseques of not securing and manipulating in a risky manner the confidential data used in their daily activies. To prepare the human resource and educate the employees in order to raise their awarness towards which is considered sensitive data, what measures and tools must be applied when handling it, trainings by specialisez personnel can be implemented. In this manner, the risks of cyber security attacks, data leak, are minimized and the business environment is strengthened. In this research it is presented a review of specialized literature that treat the subject of data security to highlight the current stage of development and have a general landscape from a scientific point of view. Having this as a base, the authors have developed a questionnaire that was distributed to romanian companies to find out the business perspective towards this subject. Therefore, by analysing the two perspectives, this study has the goal to present the dynamics of data security, the challenges brought by it and the good practices that can be addopted.

Ticagrelor vs Clopidogrel in Clopidogrel-Naive Patients With Chronic Coronary Syndrome

BackgroundWhether ticagrelor may reduce periprocedural myocardial necrosis after elective percutaneous coronary intervention (PCI) in patients with and without chronic clopidogrel therapy is unclear. ObjectivesThis study sought to compare ticagrelor vs clopidogrel in patients with and without chronic clopidogrel therapy before undergoing elective PCI. MethodsIn this prespecified analysis of the ALPHEUS (Assessment of Loading With the P2Y12 Inhibitor Ticagrelor or Clopidogrel to Halt Ischemic Events in Patients Undergoing Elective Coronary Stenting) trial, patients were defined as clopidogrel(+) and clopidogrel(−) according to the presence and absence of clopidogrel treatment for ≥7 days before PCI, respectively. The primary endpoint was the composite of PCI-related myocardial infarction and major injury as defined by the third and fourth universal definition 48 hours after PCI. ResultsA total of 1,882 patients were included, 805 (42.7%) of whom were clopidogrel(+). These patients were older, had more comorbidities, and had more frequent features of complex PCI. The primary endpoint was less frequently present in clopidogrel(−) compared to clopidogrel(+) patients (32.8% vs 40.0%; OR: 0.73; 95% CI: 0.60-0.88), but no significant differences were reported for the risk of death, myocardial infarction, stroke, or transient ischemic attack at 48 hours or 30 days. Ticagrelor did not reduce periprocedural myocardial necrosis or the risk of adverse outcomes, and there was no significant interaction regarding the presence of chronic clopidogrel treatment. ConclusionsClopidogrel-naive patients presented less periprocedural complications compared to clopidogrel(+) patients, a difference related to a lower risk profile and less complex PCI. The absence of clopidogrel at baseline did not affect the absence of a difference between ticagrelor and clopidogrel in terms of PCI-related complications supporting the use of clopidogrel as the standard of care in elective PCI in patients with or without chronic clopidogrel treatment.

Blood-based inflammatory protein biomarker panel for the prediction of relapse and severity in patients with neuromyelitis optica spectrum disorder: A prospective cohort study.

To date, most existing models for predicting neuromyelitis optica spectrum disorder (NMOSD) are based primarily on clinical characteristics. Blood-based NMOSD severity and prognostic predictive immune- and inflammation-related biomarkers are needed. We aimed to investigate the associations between plasma inflammatory biomarkers and relapse and attack severity in NMOSD. This two-step, single-center prospective cohort study included discovery and validation cohorts. We quantified 92 plasma inflammatory proteins by using Olink's proximity extension assay and identified differentially expressed proteins in the relapse group (relapse within 1 year of follow-up) and severe attack group. To define a new molecular prognostic model, we calculated the risk score of each patient based on the key protein signatures and validated the results in the validation cohort. The relapse prediction model, including FGF-23, DNER, GDNF, and SLAMF1, predicted the 1-year relapse risk. The severe attack prediction model, including PD-L1 and MCP-2, predicted the severe clinical attack risk. Both the relapse and severe attack prediction models demonstrated good discriminative ability and high accuracy in the validation cohort. Our discovered biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression.

Blockchian Based Hybrid Consensus Algorithm for Data Security in Edge Computing

Abstract: Edge computing has emerged as a paradigm to process data closer to its source, enabling low-latency and real-time applications. However, the distributed and heterogeneous nature of edge environments poses significant challenges for ensuring data security and integrity. Blockchain technology offers a promising solution by providing a decentralized and immutable ledger for recording transactions. This abstract proposes a consensus algorithm tailored for securing data in edge computing environments using blockchain technology. The proposed consensus algorithm leverages a hybrid approach combining Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT). PoA ensures that only trusted nodes within the edge network are eligible to participate in the consensus process, mitigating the risk of malicious attacks. PBFT enhances the efficiency and scalability of the consensus algorithm by allowing fast decision-making among a subset of nodes, thereby reducing latency in data validation and verification. Furthermore, smart contracts are employed to enforce data access control and execute predefined rules for data processing and sharing. These smart contracts facilitate secure and transparent interactions among edge devices, ensuring that only authorized parties can access and manipulate sensitive data. The integration of blockchain technology with edge computing offers several benefits, including enhanced data security, traceability, and accountability. By employing a tailored consensus algorithm, edge computing environments can effectively mitigate security threats while maintaining low-latency data processing capabilities. Future research directions may explore optimization techniques to further improve the performance and scalability of the proposed solution in large-scale edge networks.

Securing the network in e-retailing and manufacturing: a balanced decision analysis under competition and cooperation modes

With the continuous emergence of network problems, the e-commerce supply chain faces the risk of network attacks. This paper studies a dual-channel supply chain, where manufacturers can earn revenue through online channels (i.e. manufacturers sell products through e-retailers) and offline channels (i.e. manufacturers sell products directly to consumers). In the online channel, we analysed two modes. The first is the competitive model, where e-tailers buy products from manufacturers and resell them to consumers. The second is a cooperative model, in which manufacturers pay a commission to sell products to consumers on e-retailer platforms. We divide the network risk prevention methods into three models: the M model (wherein the manufacturer conducts risk defense blocking), the E model (wherein e-retailers conduct risk defense blocking), and the ME model (wherein e-retailers and manufacturers conduct risk defense blocking simultaneously). These cases are discussed in this paper. The results show that manufacturers and e-retailers will gain the highest profit in the ME model, regardless of competition mode or cooperation mode. As a result, manufacturers and retailers can simultaneously mount cyber defenses seeking to maximize profits. In addition, government subsidies have a positive effect on encouraging supply chain members to take defensive measures.

P.009 Long-term comparative efficacy of inebilizumab from N-MOmentum participants versus azathioprine and immunosuppressants and placebo in NMOSD patients

Background: Long-term efficacy of inebilizumab (INEB), an anti-CD19+ B cell-depleting antibody approved for the treatment of seropositive-aquaporin-4-antibody (AQP4+) neuromyelitis optica spectrum disorder (NMOSD) was evaluated over N-MOmentum (NCT02200770) open-label period (OLP) vs azathioprine and other immunosuppressants (AZA/IST) and vs PBO. Methods: Two historical comparator groups (HCGs), AZA/IST (N=132) and PBO (N=106), derived from published NMOSD studies, were used to compare efficacy of INEB (N=208) over the OLP. Hazard ratios (HR) for INEB vs HCGs were estimated using Cox proportional hazards (PH) regression. Time to NMOSD attack was analysed using parametric and flexible survival (spline) models. Results: Time to NMOSD attack for N-MOmentum PBO compared to PBO was HR 1.15;(95% CI:0.67–1.91; P=0.58). The HRs for time to NMOSD attack for INEB vs AZA/IST and PBO groups were 0.29(95% CI:0.17, 0.42; P&lt;0.001) and 0.15 (95% CI:0.10, 0.21; P&lt;0.001). At 4 years, estimated attack-free survival was 77% (95% CI:71, 83) for INEB, 36% (95% CI:27, 46) for AZA/IST, and 12% (95% CI:7, 20) for PBO. Conclusions: INEB was associated with a statistically significant reduction in risk of an NMOSD attack and provided a long-term attack-free probability over the OLP compared to the relative short-term benefit observed with AZA/IST.

Comparative Prevalence of Cerebrovascular Disease in Vietnamese Communities in South-Western Sydney.

Culturally and linguistically diverse (CALD) communities are growing globally. Understanding patterns of cerebrovascular disease in these communities may improve health outcomes. We aimed to compare the rates of transient ischaemic attack (TIA), ischaemic stroke (IS), intracerebral haemorrhage (ICH), intracranial atherosclerosis (ICAD), and stroke risk factors in Vietnamese-born residents of South-Western Sydney (SWS) with those of an Australian-born cohort. A 10-year retrospective analysis (2011-2020) was performed using data extracted from the Health Information Exchange database characterising stroke presentations and risk factor profiles. The rates of hypertension (83.7% vs. 70.3%, p < 0.001) and dyslipidaemia (81.0% vs. 68.2%, p < 0.001) were significantly higher in Vietnamese patients, while the rates of ischaemic heart disease (10.4% vs. 20.3%, p < 0.001), smoking (24.4% vs. 40.8%, p < 0.001), and alcohol abuse (>1 drink/day) (9.6% vs. 15.9%, p < 0.001) were lower. The rates of ICAD and ICH were higher in Vietnamese patients (30.9% vs. 6.9%, p < 0.001 and 24.7% vs. 14.4%, p = 0.002). Regression analysis revealed that diabetes (OR: 1.86; 95% CI: 1.14-3.04, p = 0.014) and glycosylated haemoglobin (OR: 1.51; 95% CI: 1.15-1.98, p = 0.003) were predictors of ICAD in Vietnamese patients. Vietnamese patients had higher rates of symptomatic ICAD and ICH, with unique risk factor profiles. Culturally specific interventions arising from these findings may more effectively reduce the community burden of disease.

Blast protection of underwater tunnels with 3D auxetic materials

Abstract In recent years, the risk of blast attacks on underwater tunnels, important transportation routes, has increased due to the growing prevalence of vicious regional conflicts and global terrorist activities. In this work, we investigate the blast impact behavior of underwater tunnels filled with honeycomb core and covered by solid panels. The core is composed of 3D auxetic materials with high energy absorption compared to conventional honeycombs. In order to improve the stiffness of the auxetic structures, a pair of crossed rods is introduce to each cell. The relative densities of 3D auxetic structures are derived theoretically. The coupling effects of the geometrical parameters on the relative density are investigated. Then the deformation patterns of the underwater tunnels at different blast heights are analyzed. The kinetic energy and absorbed energy are discussed for tunnels with 3D auxetic materials and solid materials. Results show that tunnels composed with 3D reinforced auxetic structures can absorb much more energy than solid ones. Moreover, localized damage is observed which means greater chance of survival and smaller repairs after extreme impact. Finally, a stiffness-improved 3D reinforced auxetic structure is presented to enhance the tunnel’s strength and stability further.

Sodium content in fermented shrimp paste with varying packaging materials

Shrimp and fish paste are fermented products with high amounts of added salt. Sodium (Na+ ) ions in salt are growth factors for halophilic lactic acid bacteria (LAB), essential in shrimp paste. However, an overabundance of Na+ negatively affects the human body and may increase blood pressure and the risk of heart attacks, strokes, kidney damage, and other health issues. Commercial shrimp paste can be found in various packaging materials. The permeability of the packaging material affects air availability during fermentation, which affects LAB growth and sodium levels. This study aimed to determine the effect different packaging materials have on rebon (Acetes sp.) shrimp paste sodium levels, precisely that of teak leaf, paper, and plastic. A completely randomized design and an experimental laboratory were used for the experiments. The data were analyzed using ANOVA and Honest Significant Differences tests. The different packaging materials significantly affected (P &lt; 5%) the acidity, sodium levels, and total plate count (TPC) of LAB but did not affect the water activity or salt content. Shrimp paste consisted of 1.32±0.12% sodium when packaged in teak leaves, 1.51±0.08% in paper packaging, and 1.69±0.07% in plastic packaging. Teak leaves packaging resulted in shrimp paste with the lowest sodium content because of its many pores and high gas permeability, thus supporting the growth of microbes that use Na+ .

Federated Learning on Distributed and Encrypted Data for Smart Manufacturing

Abstract Industry 4.0 drives exponential growth in the amount of operational data collected in factories. These data are commonly distributed and stored in different business units or cooperative companies. Such data-rich environments increase the likelihood of cyber attacks, privacy breaches, and security violations. Also, this poses significant challenges on developing machine learning models on sensitive data that are distributed among different business units. To fill this gap, this paper presents a novel privacy-preserving framework to enable federated learning on siloed and encrypted data for smart manufacturing. Specifically, we leverage fully homomorphic encryption (FHE) to allow for computation on ciphertexts and generate encrypted results which, when decrypted, match the results of mathematical operations performed on the plaintexts. Multi-layer encryption and privacy protection reduce the likelihood of data breaches while maintaining the prediction performance of machine learning models. Experimental results in real-world case studies show that the proposed framework yields superior performance to reduce the risk of cyber attacks and harness siloed data for smart manufacturing.

Hormone replacement therapy perspectives.

Hormone replacement therapy (HRT), also known as menopausal hormone therapy (MHT), was looked upon as a fountain of youth that kept women young and reduced cardiovascular disease. This led to a large-scale study called the Women's Health Initiative (WHI) that was conducted to show the cardiovascular benefits of HRT. This study was suspended early because of adverse side effects. The USFDA responded by slapping a "black box" warning on all HRT products. USFDA-approved bioidentical HRT formulations are safe and effective. We propose that these formulations have the "black box" warning removed so that doctors feel more confident in prescribing these products for symptoms of menopause and chronic conditions such as cardiovascular health. We propose eliminating the sale of products containing medroxyprogesterone acetate (MPA) because of the increased risk of heart attacks and breast cancers associated with this medication.

On the security risk of pre-image attack on cancelable biometrics

Over the years, a number of biometric template protection schemes, often based on the notion of “cancelable biometrics”, have been proposed. An ideal cancelable biometric algorithm complies with four criteria: irreversibility, revocability, unlinkability, and performance preservation. Cancelable biometrics employs an irreversible but distance preserving transformation to convert the original biometric templates into protected templates. Matching in the transform domain can be accomplished due to the property of distance preservation. However, distance preservation also entails security issues, a point often overlooked in existing research. In this paper, we have conducted a comprehensive security analysis of distance preservation in cancelable biometrics for the first time. The analysis is based on a pre-image attack, which is launched to break the security of cancelable biometrics under Kerckhoffs’s assumption. Furthermore, we propose a general security analysis framework under the single and cross-transformation attacks, which also employs an information leakage estimation strategy based on mutual information as a complement. The experimental results performed on real face, iris, and fingerprint data demonstrate that the risks originating from the matching scores computed from the distance/similarity of two cancelable templates greatly compromise the security of cancelable biometric schemes, including the classic Biohashing, Index-of-max hashing, Non-linear multi-dimensional spectral hashing, Indexing-First-One hashing, Bloom Filter and Two-factor Protected Minutia Cylinder-Code. The security versus accuracy trade-off is discussed and recommendations for designing a biometric system secure against pre-image attacks are also proposed. The source code is available at github.com/biometricsecurity/CBrisks.