Leakage Measurements Research Articles

Mono-N-alkylation of Amphotericin B and Nystatin A1 and Its Amides: Effect on the In Vitro Activity, Cytotoxicity and Permeabilization of Model Membranes

Objectives: In 2022, the World Health Organization highlighted the necessity for the development of new antifungal agents. Polyene antibiotics are characterized by a low risk of drug resistance; however, their use is limited by low solubility and severe side effects. Methods: A series of N-alkylated derivatives of amphotericin B and nystatin A1 as well as their N-(2-hydroxyethyl)amides were synthesized. Their antifungal activity was evaluated against various Candida strains and Aspergillus fumigatus using the broth microdilution method. Cytotoxicity was assessed using an MTT assay on human embryonic kidney cells HEK293 and human skin fibroblast cells hFB-hTERT6, as well as a hemolysis assay on erythrocytes. Membrane activity was analyzed by fluorimetric measurement of calcein leakage from model liposomes. Results: Derivatives containing the N-(hydroxyethyl)amino)ethyl fragment (compounds 3 and 4) exhibited relatively high antifungal activity, as did N-(2-hydroxyethyl)amides 5 and 9. Bis-modified compounds 6 and 10 did not outperform their mono-modified analogues in terms of activity or cytotoxicity. The mono-N-alkylated compound 3 showed the highest activity/toxicity ratio, which correlated well with its selectivity for ergosterol-containing model membranes. Discussion: Combining two successful modifications does not necessarily improve the activity/toxicity ratio of polyenes. Further studies can be performed for the optimization of carboxyl group of 3.

Barycentric and pairwise Rényi quantum leakage with application to privacy-utility trade-off

Barycentric and pairwise quantum Rényi leakages are proposed as two measures of information leakage for privacy and security analysis in quantum computing and communication systems. These quantities both require minimal assumptions on the eavesdropper, i.e. they do not make any assumptions on the eavesdropper’s attack strategy or the statistical prior on the secret or private classical data encoded in the quantum system. They also satisfy important properties of positivity, independence, post-processing inequality and unitary invariance. The barycentric quantum Rényi leakage can be computed by solving a semi-definite program; the pairwise quantum Rényi leakage possesses an explicit formula. The barycentric and pairwise quantum Rényi leakages form upper bounds on the maximal quantum leakage, the sandwiched quantum α -mutual information, the accessible information and the Holevo’s information. Furthermore, differentially private quantum channels are shown to bound these measures of information leakage. Global and local depolarizing channels, that are common models of noise in quantum computing and communication, restrict private or secure information leakage. Finally, a privacy-utility trade-off formula in quantum machine learning using variational circuits is developed. The privacy guarantees can only be strengthened, i.e. information leakage can only be reduced, if the performance degradation grows larger and vice versa .

Reducing Leakage Effects and Measurement Time in Low-frequency Noise Measurements

We propose a modified approach for DFT-based spectra and cross-spectra estimation that allows to significantly reduce the problem of spectral leakage at very low frequencies. This results in the ability to select larger resolution bandwidths that, in turn, allow for reduced measurement time for a given maximum level of statistical error in spectral points at very low frequencies.

Structural Reorganizations and Nanodomain Emergence in Lipid Membranes Driven by Ionic Liquids.

Ionic liquids (ILs) have promising applications in pharmaceuticals and green chemistry, but their use is limited by toxicity concerns, mainly due to their interactions with cell membranes. This study examines the effects of imidazolium-based ILs on the microscopic structure and phase behavior of a model cell membrane composed of zwitterionic dipalmitoylphosphatidylcholine (DPPC) lipids. Small-angle neutron scattering and dynamic light scattering reveal that the shorter-chain IL, 1-hexyl-3-methylimidazolium bromide (HMIM[Br]), induces the aggregation of DPPC unilamellar vesicles. In contrast, this aggregation is absent with the longer alkyl chain IL, 1-decyl-3-methylimidazolium bromide (DMIM[Br]). Instead, DMIM[Br] incorporation leads to the formation of distinct IL-poor and IL-rich nanodomains within the DPPC membrane, as evidenced by X-ray reflectivity, differential scanning calorimetry, and molecular dynamics simulations. The less evident nanodomain formation with HMIM[Br] underscores the role of hydrophobic interactions between lipid alkyl tails and ILs. Our findings demonstrate that longer alkyl chains in ILs significantly enhance their propensity to form membrane nanodomains, which is closely linked to enhanced membrane permeability, as shown by dye leakage measurements. This heightened permeability likely underlies the greater cytotoxicity of longer-chain ILs. This crucial link between nanodomains and toxicity provides valuable insights for designing safer, more environmentally friendly ILs, and promoting their use in biomedical applications and sustainable industrial processes.

Enhancing Aerosol Mitigation in Medical Procedures: A CFD-Informed Respiratory Barrier Enclosure.

The COVID-19 pandemic has highlighted the significant infection risks posed by aerosol-generating procedures (AGPs), such as intubation and cardiopulmonary resuscitation (CPR). Despite existing protective measures, high-risk environments like these require more effective safety solutions. In response, our research team has focused on developing a novel respiratory barrier enclosure designed to enhance the safety of healthcare workers and patients during AGPs. We developed a hood that covers the patient's respiratory area, incorporating a negative pressure system to contain aerosols. Using computational fluid dynamics (CFD) analysis, we optimized the hood's design and adjusted the negative pressure levels based on simulations of droplet dispersion. To test the design, Polyalphaolefin (PAO) particles were generated inside the hood, and leakage was measured every 10 s for 90 s. The open side of the hood was divided into nine sections for consistent leakage measurements, and a standardized structure was implemented to ensure accuracy. Our target was to maintain a leakage rate of less than 0.3%, in line with established filter-testing criteria. Through iterative improvements based on leakage rates and intubation efficiency, we achieved significant results. Despite reducing the hood's size, the redesigned enclosure showed a 36.2% reduction in leakage rates and an approximately 3204.6% increase in aerosol extraction efficiency in simulations. The modified hood, even in an open configuration, maintained a droplet leakage rate of less than 0.3%. These findings demonstrate the potential of a CFD-guided design in developing respiratory barriers that effectively reduce aerosol transmission risks during high-risk medical procedures. This approach not only improves the safety of both patients and healthcare providers but also provides a scalable solution for safer execution of AGPs in various healthcare settings.

Deletion within ameloblastin multitargeting domain reduces its interaction with artificial cell membrane

In human, mutations in the gene encoding the enamel matrix protein ameloblastin (Ambn) have been identified in cases of amelogenesis imperfecta. In mouse models, perturbations in the Ambn gene have caused loss of enamel and dramatic disruptions in enamel-making ameloblast cell function. Critical roles for Ambn in ameloblast cell signaling and polarization as well as adhesion to the nascent enamel matrix have been supported. Recently, we have identified a multitargeting domain (MTD) in Ambn that interacts with cell membrane, with the majority enamel matrix protein amelogenin, and with itself. This domain includes an amphipathic helix (AH) motif that directly interacts with cell membrane. In this study, we analyzed the sequence of the MTD for evolutionary conservation and found high conservation among mammals within the MTD and particularly within the AH motif. We computationally predicted that the AH motif lost its hydrophobic moment upon deleting hydrophobic but not hydrophilic residues from the motif. Furthermore, we rationally designed peptides that encompassed the Ambn MTD and contained deletions of largely hydrophobic or hydrophilic stretches of residues. To assess their AH-forming and membrane-binding abilities, we combined those peptides with synthetic phospholipid membrane vesicles and performed circular dichroism, membrane leakage, and vesicle clearance measurements. Circular dichroism showed retention of α-helix formation in all peptides except the one with the largest deletion of eleven amino acids including seven that were hydrophobic. This same peptide variant failed to cause leakage or clearance of synthetic membranes, while smaller deletions yielded intermediate membrane interaction as measured by leakage and clearance assays. Our data revealed that deletion of key hydrophobic residues from the AH leads to the most dramatic loss of Ambn–membrane interaction. Pinpointing roles of residues within the MTD has important implications for the multifunctionality of Ambn.

Plaintext-based Side-channel Collision Attack

Side-channel Collision Attacks (SCCA) is a classical method that exploits information dependency leaked during cryptographic operations. Unlike collision attacks that seek instances where two different inputs to a cryptographic algorithm yield identical outputs, SCCAs specifically target the internal state, where identical outputs are more likely. Although SCCA does not rely on the pre-assumption of the leakage model, it explicitly operates on precise trace segments reflecting the target operation, which is challenging to perform when the leakage measurements are noisy. Besides, its attack performance may vary dramatically, as it relies on selecting a reference byte (and its corresponding leakages) to “collide” other bytes. A poor selection would lead to many bytes unrecoverable. These two facts make its real-world application problematic. This paper addresses these challenges by introducing a novel plaintext-based SCCA. We leverage the bijective relationship between plaintext and secret data, using plaintext as labels to train profiling models to depict leakages from varying operations. By comparing the leakage representations produced by the profiling model instead of the leakage segmentation itself, all secret key differences can be revealed simultaneously without processing leakage traces. Furthermore, we propose a novel error correction scheme to rectify false predictions further. Experimental results show that our approach significantly surpasses the state-of-the-art SCCA in both attack performance and computational complexity (e.g., training time reduced from approximately three hours to five minutes). These findings underscore our method's effectiveness and practicality in real-world attack scenarios.

A Framework for Enhancing Social Media Misinformation Detection with Topical-Tactics

Recent years have seen advancements in machine learning methods for the detection of misinformation on social media. Yet, these methods still often ignore or improperly incorporate key information on the topical-tactics used by misinformation agents. To what extent does this affect the (non)detection of misinformation? We investigate how supervised machine learning approaches can be enhanced to better detect misinformation on social media. Our aim in this regard is to enhance the abilities of academics and practitioners to understand, anticipate, and preempt the sources and impacts of misinformation on the web. To do so, this article leverages a large sample of verified Russian state-based misinformation tweets and non-misinformation tweets from Twitter. It first assesses standard supervised approaches for detecting Twitter-based misinformation both quantitatively (with respect to classification) and qualitatively (with respect to topical-tactics of Russian misinformation). It then presents a novel framework for integrating topical-tactics of misinformation into standard “bag of words”-oriented classification approaches in a manner that avoids data leakage and related measurement challenges. We find that doing so substantially improves the out-of-sample detection of Russian state-based misinformation tweets.

Failure law of hydraulic pipe joints sealing performance under vibration loads

Aviation hydraulic pipe joints are inevitably affected by vibration loads during service. Fatigue relaxation occurs in the pipe joint structure by vibration, and fretting fatigue wear occurs in its sealing interface, leading to a decline in sealing performance. In order to investigate the sealing performance of hydraulic pipe joints affected by vibration load, this paper first analyzes the modal distribution of the sealing system pipeline under different hydraulic pressures and hydraulic fluid flow rates through vibration frequency sweeping experiments. Secondly, the vibration load is applied to the pipe joints for different numbers of cycles, which reveals the microscopic wear behavior of the sealing interfaces under different tightening torques. Finally, by carrying out the leakage measurement experiment under vibration load, the evolution law of the sealing performance of pipe joints under different hydraulic fluid pressures and tightening torques was obtained.

Aggression to Biomembranes by Hydrophobic Tail Chains under the Stimulus of Reductant.

Stimulus-responsive materials hold significant promise for antitumor applications due to their variable structures and physical properties. In this paper, a series of peptides with a responsive viologen derivative, Pep-CnV (n = 1, 2, 3) were designed and synthesized. The process and mechanism of the interaction were studied and discussed. An ultraviolet-visible (UV) spectrophotometer and fluorescence spectrophotometer were used to study their redox responsiveness. Additionally, their secondary structures were measured by Circular Dichroism (CD) in the presence or absence of the reductant, Na2SO3. DPPC and DPPG liposomes were prepared to mimic normal and tumor cell membranes. The interaction between Pep-CnV and biomembranes was investigated by the measurements of surface tension and cargo leakage. Results proved Pep-CnV was more likely to interact with the DPPG liposome and destroy its biomembrane under the stimulus of the reductant. And the destruction increased with the length of the hydrophobic tail chain. Pep-CnV showed its potential as an intelligent antitumor agent.

Feasibility experimental study on plugging leakage in goaf based on two-phase foam

Air leakage in goaf often leads to coal spontaneous combustion (CSC), which not only directly affects the safety production of mines but also causes significant environmental damage. Therefore, effectively sealing the airflow in goaf is crucial for preventing CSC. Feasibility experiments on using two-phase foam to seal air leakage in goaf were conducted, leveraging the advantages of large flow rate, wide diffusion range, and good accumulation characteristics of two-phase foam. The research results indicate that continuous injection of foam into loose media with maintained ventilation can completely seal the air leakage, with the foam capable of withstanding wind pressures of nearly 600 Pa. When the foam is used for one-time sealing with a length of 2 m, it remains effective for 60 min, and the sealing effectiveness improves with longer distances sealed against air leakage. Numerical simulation analysis and field measurements of airflow leakage in mine working faces reveal that effectively sealing the airflow passage in the goaf behind the corner of the return airway is crucial for preventing CSC. Two methods are proposed for sealing external airflow during coal mining: foam injection using a point drilling method near the heading and an incremental buried pipe injection method. Finally, the feasibility of two-phase foam sealing technology for goaf airflow leakage is analyzed from multiple perspectives including sealing effectiveness, practicality, economy, foaming process, and engineering implementation. The research findings provide new insights into goaf sealing technology, aiding in addressing safety and environmental issues caused by spontaneous combustion in goaf areas.

Measurement of inward leakage of full-face masks in EN and ISO standards: comparison of gas and aerosol test agents.

In Europe, respiratory protective devices must be certified before they can be marketed. Among the parameters of interest, inward leakage (IL) characterizes the tightness between the face seal and the face, to verify that the device is well-designed. European standard EN 13274-1 (2001) and International Organization for Standardization (ISO) standard ISO 16900-1 (2019) specify that IL should be measured using sodium chloride (NaCl) aerosol or sulfur hexafluoride (SF6) gas. For reusable masks made of nonporous materials, both test agents are considered equally acceptable. However, the few studies that have compared IL values measured with various aerosols and gases have come to divergent conclusions. This work then aimed to measure IL with the test agents recommended by the standards to determine whether they are really equivalent. Since krypton (Kr) is an interesting candidate for replacing SF6 in standard tests, IL was assessed with SF6 and Kr simultaneously, and with NaCl aerosol using various calculation methods. Tests were carried out on 5 models of full-face masks donned on a headform connected to a breathing machine simulating 3 sinusoidal breathing rates of various intensities. The respirator fit on the headform was evaluated using a controlled negative pressure method to determine a manikin fit factor. Four scenarios were then tested to represent very poor, bad, good, and excellent fit. Gas concentration was measured using a mass spectrometer, and IL was calculated for SF6 and Kr. A combination of 3 devices allowed the determination of the number-based concentration of particles with diameters between 20 nm and 2 µm, and IL was calculated for each of the 33 channels, as well as using a cumulative number concentration. In addition, to comply with standards, a conversion was carried out to calculate IL using a cumulative mass concentration. The results of this work evidenced that the IL values measured with NaCl were systematically lower than those determined with gases. IL was also shown to vary with particle size, with a maximum value exceeding that calculated with cumulative concentrations (in number or mass). As part of the revision of the standards, protocols for measuring inward leakage should be redefined. On the one hand, acceptability thresholds should be re-evaluated according to the nature of the test agent (gas or aerosol), as it is clear that the 2 options do not give the same results for a given configuration. On the other hand, the aerosol leakage measurement protocol needs to be reworked to enable the measurement of a well-defined, robust, and reproducible inward leakage value.

Measurement of CO2 leakage from pipelines under CCS conditions through acoustic emission detection and data driven modeling

CO2 leakage from carbon capture and storage (CCS) networks may lead to ecological hazards, bodily injury and economic losses. In addition, captured CO2 often contains impurities which affect the leakage behavior of CO2. This paper presents a method for continuous and quantitative measurements of CO2 leakage flowrate and the volume fraction of impurities by combining data-driven models with acoustic emission (AE) and temperature sensors. Three data-driven models based on artificial neural network (ANN), random forest (RF), and least squares support vector machine (LS-SVM) algorithms are established. The outputs from the three data-driven models are then integrated to give improved results. Experimental work was conducted on a purpose-built CO2 leakage test rig under a range of conditions. N2 was injected to the CO2 gas stream as an impurity medium. Results show that the integrated model yields a relative error within ±4.0 % for leakage flowrate and ±3.4 % for volume fraction of N2.

The Simulation and Analysis of Leakage, Diffusion Behavior, and Risk Mitigation Measures in a Hydrogen‐Refueling Station

As an component of hydrogen energy utilization, hydrogen‐refueling stations require considerable attention regarding their safety. In this study, the influence of wind conditions is analyzed, specifically no wind and 10 m s−1 wind speed, on hydrogen diffusion characteristics following a 70 MPa hydrogen‐filling machine leakage. In the results, it is suggested that, during the initial stage of leakage, hydrogen exists in the form of an under‐expanded jet, later transitioning to diffusion dominated by buoyancy and wind conditions after moving upward for a certain distance. When there is no wind, hydrogen is significantly affected by buoyancy and obstacles, which leads to the formation of flammable clouds. In windy conditions, the volume fraction of hydrogen in the station is smaller than that in the no‐wind condition at the same leakage time, but the flammable clouds still predominantly distribute in the direction of the hydrogen jet. In this study, risk mitigation measures based on the distribution of flammable clouds are proposed. In the results, it is shown that after implementing these measures, the volume of flammable clouds is reduced from0.135 to 0.014 m3, manifesting the effectiveness of risk mitigation measures and minimizing the risk of hydrogen explosion.

Experimental study on flow characteristics and burning behavior for fire of flammable gases leakage underwater

The occurrence of subsea pipeline leakage incidents has gradually increased with the rapid development of the offshore oil and gas industry. Research on the fire dynamics of flammable gases leakage underwater is very limited, particularly in understanding the coupling mechanism of flow and combustion on the water surface. This paper presents a systematical investigation on flow characteristics and burning behavior for fire of flammable gases leakage underwater. A series of laboratory-scale fire behavior experiments were conducted through a self-designed experimental platform, consisting of the leakage control module, water tank and measurement apparatus. It was revealed that the leakage flow rate and water depth would influence the flow characteristics and burning behavior significantly. Two different burning conditions were found for the flame burning above the water surface (FBWS), i.e., the stable and self-extinguishing (unstable) burning states. The dimensionless analysis is conducted for the burning conditions, where the critical criterion is developed to distinguish the stable and unstable burning states. The base diameter of stable FBWS is almost 1.6 times of the diameter of bubble plume on the water surface. The flame pulsation frequency and dimensionless flame height (the ratio of height to base diameter of flame) of the stable FBWS are different from thoes of liquid pool fires and gas diffusion fires. Consequently, a correlation between the Strouhal number and the Froude number is correlated. Finally, the correlation between the dimensionless flame height and the dimensionless heat release rate is proposed for the stable FBWS.

Development and experimental evaluation of new building air leakage measurement methods: measurement of interior air leaks and comparison to conventional methods

Building air leaks (both through exterior and interior surfaces) can have a significant impact on energy consumption, indoor air quality, fire safety, and moisture accumulation affecting structural durability. Blower door testing has been used to measure leaks in buildings, but commonly used testing methods do not directly measure interior leaks. In this paper, new testing methods (guarded interior test and zonal multipoint pressure testing method) are presented that directly measure these interior leaks, utilizing common blower door equipment for both single and multi-point testing. These new methods are compared to conventional methods in terms of the information provided, limitations and time/effort needed. In addition, building leak measurement results are analyzed to reveal (a) coupling between power law model values (exponent and coefficient) for an ensemble of buildings, (b) the error in using single point testing when estimating low pressure leakage, and (c) how building power law models vary from low to high pressure ranges.

A Deep-Learning-Based Low-Cost Micro-Leakage Measurement System for Industrial Applications

Serious leakage will cause environmental pollution and safety problems, so strict leakage measurement is necessary for sealed products before leaving the factory. In this article, a semantic-segmentation-based micro-leakage measurement framework (MLMF) is proposed, and the core modules in MLMF are designed and analyzed. First, the DeepLabv3+ algorithm is improved from three aspects, including the design of the fusion layer, the introduction of the attention mechanism of the segmentation model, and the reconstruction of the loss function, which improve the precision of image segmentation tasks. Then, the volume of the intermediate cavity (ICV), which affects the accuracy of leakage measurement, is studied, and the reasonable selection interval of the ICV has been obtained. Finally, through the experimental analysis of the influencing factors of the bubble characteristics in the two-phase flow, the key parameters affecting the bubble shape are found, and the control of the initial shape, generation period, and oscillation process of the bubble is realized. The experimental analysis is carried out through the established experimental platform to verify the effectiveness of the proposed MLMF. The bubble method is innovatively combined with deep learning, and a series of problems, such as bubble shape control and liquid media selection, are solved. It provides a novel low-cost dry measurement mechanism for the field of micro-leakage measurement of sealed vessels.

Qualitative and quantitative diagnosis of internal hydrogen leakage in fuel cell based on air flowrate control and open-circuit voltage measurements

Internal hydrogen leakage faults in fuel cells can cause severe performance loss and safety risks. The aim of this study is to achieve qualitative and quantitative diagnoses of internal leakage in fuel cell based on air flowrate control and transient voltage measurements. The hydrogen crossover characteristics of a fault-free and defective fuel cell are both investigated, and the effectiveness of the diagnosis method is evaluated by comparing and analyzing the results before and after the leakage fault introduction. The results suggest that hydrogen leakage failure inside fuel cell is dominated by convection processes. In airflow interruption tests, where the fuel cell is operated under open-circuit conditions with given anode overpressures, the fall time of the cell voltage is used as a qualitative measure of internal hydrogen leakage, and fault occurrence can be recognized by a considerable decrease in this indicator. Through cyclic staircase flowrate sweep tests, we obtain good estimates of internal leakage rates in the defective fuel cell. These diagnostic methods can be performed directly under hydrogen/air conditions, and there is no need for additional power source equipment dedicated to generating excitation signals for fuel cell, giving them great potential in on-board applications.

Near-Infrared Off-Axis integrated cavity output spectroscopic sensor system for ammonia leakage measurement in cold store using embedded Multi-Core processor

A near-infrared off-axis integrated cavity output spectroscopic (OA-ICOS) sensor system for ammonia (NH3) leakage detection was developed. A distributed feedback (DFB) laser was used to target the absorption line of NH3 at 6612.167 cm−1. Experiments were conducted to evaluate the performance of the sensor system. When the averaging time is 0.8 s, the detection limit (1σ) of NH3 is ∼ 1.21 parts-per-million by volume (ppmv) using wavelength modulation spectroscopy (WMS). The 10–90 % response time of the NH3 sensor system is ∼ 7.2 s. Based on the developed local monitoring and control platform (LMCP), cloud server, remote monitoring and control end (RMCE), the unattended NH3 leakage detection experiments were carried out in a cold store located in Jilin City. The experimental results confirmed the rapid detection capability of the reported sensor system for NH3 leakage as well as the capability of remote monitoring and control.

A high-precision TMR sensor array system for detecting surface and internal defects in thin sheet of steel

The online detection of small defects within thin steel holds significant importance in the field of steel manufacturing. This paper presents a high-precision flux leakage measurement system based on tunneling magnetoresistance sensors for detecting small defects in thin steel. We optimized the probe size and magnetic circuit design through numerical simulations and used the developed instrument to detect a standard mimicking through-hole defect plate, achieving a high signal-to-noise ratio and successfully detecting artificial defects small to a diameter of 50 μm. Additionally, we detected an actual internal defect in thin steel provided by the steel plant, meeting the required industrial accuracy. The data acquisition process in this study has been optimized to achieve real-time display and defect localization. The measurement scheme proposed in this paper has great potential for online monitoring of the thin steel production process.