Reversal Of Gradient Research Articles

The potential impact of co-residence structures on socio-demographic inequalities in COVID-19 mortality

During the COVID-19 pandemic, confinement measures were adopted across the world to limit the spread of the virus. In France, these measures were applied between March 17 and May 10. Using high-quality population census data and focusing on co-residence structures on French territory, this article analyzes how co-residence patterns unevenly put different socio-demographic groups at risk of being infected and dying from COVID-19. The research ambition is to quantify the possible impact of co-residence structures heterogeneity on socio-economic inequalities in mortality stemming from within-household transmission of the virus. Using a simulation approach, the article highlights the existence of theoretical pronounced inequalities of vulnerability to COVID-19 related to cohabitation structures as well as a reversal of the social gradient of vulnerability when the age of the infected person increases. Among young age categories, infection is simulated to lead to more deaths in the less educated or foreign-born populations. Among the older ones, the inverse holds with infections having a greater potential to provoke deaths through the transmission of the virus within households headed by a highly educated or a native-born person. Demographic patterns such as the cohabitation of multiple generations and the survival of both partners of a couple help to explain these results. Even though inter-generational co-residence and large households are more common among the lower educated and foreign born in general, the higher educated are more likely to still live with their partner at higher ages.

Strong Southern African Monsoon and weak Mozambique Channel throughflow during Heinrich events: Implication for Agulhas leakage

The Mozambique Channel is a conduit of trade wind-driven throughflow that is a key component of the Agulhas Current and Agulhas leakage, a flux of warm and salty water from the tropical Indo-Pacific to the Atlantic Ocean. Agulhas leakage is thought to modulate Atlantic meridional overturning circulation variability. Previous studies from the Cape Basin suggest that enhanced Agulhas leakage played an important role in accelerating glacial terminations. The southern African monsoon response to abrupt climate changes associated with meltwater-induced reorganizations of the North Atlantic meridional overturning circulation, and its impact on the Mozambique Channel throughflow and, by extension, on the Agulhas leakage is not well understood. Here we present a high-resolution 26,000 year-long hydroclimate record of northern Madagascar, a core region of the southern hemisphere monsoon domain, and a mixed layer temperature reconstruction using sediment cores collected from the runoff-influenced eastern Mozambique Channel. The record indicates precipitation increases centered at 11.7-12.5 thousand years before present (kyr BP), 14.5-19 kyr BP, 23-24 kyr BP, 25-26 kyr BP. Considering age model uncertainties, this is the first strong evidence for southern African monsoon strengthening in response to meltwater-induced northern high latitude climate instabilities during the Younger Dryas (YD), Heinrich Stadial 1 (HS1), HS2 and the HS-like event prior to HS2, in agreement with the results of transient climate simulations. Furthermore, our study shows a reversal of the mixed layer temperature gradient between the western and eastern Mozambique Channel during Heinrich event 1 (HE1). We posit that the gradient reversal indicates a weakening of the trade wind-driven South Equatorial Current and Mozambique Channel throughflow that likely weakened the Agulhas leakage, potentially creating a feedback that may have contributed to the sustained weakening of the AMOC during HE1 by reducing the amount of heat and salt leakage into the Atlantic.

Deep North Atlantic Last Glacial Maximum Salinity Reconstruction

AbstractWe reconstruct deep water‐mass salinities and spatial distributions in the western North Atlantic during the Last Glacial Maximum (LGM, 19–26 ka), a period when atmospheric CO2 was significantly lower than it is today. A reversal in the LGM Atlantic meridional bottom water salinity gradient has been hypothesized for several LGM water‐mass reconstructions. Such a reversal has the potential to influence climate, ocean circulation, and atmospheric CO2 by increasing the thermal energy and carbon storage capacity of the deep ocean. To test this hypothesis, we reconstructed LGM bottom water salinity based on sedimentary porewater chloride profiles in a north‐south transect of piston cores collected from the deep western North Atlantic. LGM bottom water salinity in the deep western North Atlantic determined by the density‐based method is 3.41–3.99 ± 0.15% higher than modern values at these sites. This increase is consistent with: (a) the 3.6% global average salinity change expected from eustatic sea level rise, (b) a northward expansion of southern sourced deep water, (c) shoaling of northern sourced deep water, and (d) a reversal of the Atlantic's north‐south deep water salinity gradient during the LGM.

Investigations on the anthropogenic reversal of the natural ozone gradient between northern and southern midlatitudes

Abstract. Our quantitative understanding of natural tropospheric ozone concentrations is limited by the paucity of reliable measurements before the 1980s. We utilize the existing measurements to compare the long-term ozone changes that occurred within the marine boundary layer at northern and southern midlatitudes. Since 1950 ozone concentrations have increased by a factor of 2.1 ± 0.2 in the Northern Hemisphere (NH) and are presently larger than in the Southern Hemisphere (SH), where only a much smaller increase has occurred. These changes are attributed to increased ozone production driven by anthropogenic emissions of photochemical ozone precursors that increased with industrial development. The greater ozone concentrations and increases in the NH are consistent with the predominant location of anthropogenic emission sources in that hemisphere. The available measurements indicate that this interhemispheric gradient was much smaller and was likely reversed in the pre-industrial troposphere with higher concentrations in the SH. Six Earth system model (ESM) simulations indicate similar total NH increases (1.9 with a standard deviation of 0.3), but they occurred more slowly over a longer time period, and the ESMs do not find higher pre-industrial ozone in the SH. Several uncertainties in the ESMs may cause these model–measurement disagreements: the assumed natural nitrogen oxide emissions may be too large, the relatively greater fraction of ozone injected by stratosphere–troposphere exchange to the NH may be overestimated, ozone surface deposition to ocean and land surfaces may not be accurately simulated, and model treatment of emissions of biogenic hydrocarbons and their photochemistry may not be adequate.

The Dynamical Structure of a Warm Core Ring as Inferred from Glider Observations and Along-Track Altimetry

This study investigates the vertical structure of the dynamical properties of a warm-core ring in the Gulf of Mexico (Loop Current ring) using glider observations. We introduce a new method to correct the glider’s along-track coordinate, which is, in general, biased by the unsteady relative movements of the glider and the eddy, yielding large errors on horizontal derivatives. Here, we take advantage of the synopticity of satellite along-track altimetry to apply corrections on the glider’s position by matching in situ steric height with satellite-measured sea surface height. This relocation method allows recovering the eddy’s azimuthal symmetry, precisely estimating the rotation axis position, and computing reliable horizontal derivatives. It is shown to be particularly appropriate to compute the eddy’s cyclo-geostrophic velocity, relative vorticity, and shear strain, which are otherwise out of reach when using the glider’s raw traveled distance as a horizontal coordinate. The Ertel potential vorticity (PV) structure of the warm core ring is studied in details, and we show that the PV anomaly is entirely controlled by vortex stretching. Sign reversal of the PV gradient across the water column suggests that the ring might be baroclinically unstable. The PV gradient is also largely controlled by gradients of the vortex stretching term. We also show that the ring’s total energy partition is strongly skewed, with available potential energy being 3 times larger than kinetic energy. The possible impact of this energy partition on the Loop Current rings longevity is also discussed.

Deep domain adaptation with adversarial idea and coral alignment for transfer fault diagnosis of rolling bearing

In recent years, transfer learning has become more and more favored by scholars from all walks of life. At present, although transfer learning has achieved certain results in the field of fault diagnosis, the use of transfer learning alone may lead to poor transfer effects or even negative transfer due to the sample gap being under variable conditions in the same machinery. Therefore, deep domain adaptation with adversarial idea and coral alignment (DAACA) is proposed in this paper in order to solve the problem. DAACA is briefly summarized below. The domain adaptation with adversarial idea is added on the basis of transfer learning. The deep coral is then appended to further reduce the distribution difference between the data from the source and the target domain, which improves the invariant features of adversarial domain adaptation learning. In addition, a gradient reversal layer is introduced in the method to achieve gradient reversion and avoid the adversarial disadvantage of fixing parameters separately. It can be seen from the experimental results that the DAACA can not only solve the problem caused by the sample gap in variable conditions, but also achieve higher diagnosis accuracy and generalization ability.

Proactive Privacy-preserving Learning for Retrieval

Deep Neural Networks (DNNs) have recently achieved remarkable performance in image retrieval, yet posing great threats to data privacy. On the one hand, one may misuse a deployed DNNs based system to look up data without consent. On the other hand, organizations or individuals would legally or illegally collect data to train high-performance models outside the scope of legitimate purposes. Unfortunately, less effort has been made to safeguard data privacy against malicious uses of DNNs. In this paper, we propose a data-centric Proactive Privacy-preserving Learning (PPL) algorithm for hashing based retrieval, which achieves the protection purpose by employing a generator to transfer the original data into the adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the adversarial data can confuse menacing retrieval models to make erroneous predictions. Given that the prior knowledge of malicious models is not available, a surrogate retrieval model is instead introduced acting as a fooling target. The framework is trained by a two-player game conducted between the generator and the surrogate model. More specifically, the generator is updated to enlarge the gap between the adversarial data and the original data, aiming to lower the search accuracy of the surrogate model. On the contrary, the surrogate model is trained with the opposing objective that is to maintain the search performance. As a result, an effective and robust adversarial generator is encouraged. Furthermore, to facilitate an effective optimization, a Gradient Reversal Layer (GRL) module is inserted to connect two models, enabling the two-player game in a one-step learning. Extensive experiments on three widely-used realistic datasets prove the effectiveness of the proposed method.

Biased action of the CXCR4-targeting drug plerixafor is essential for its superior hematopoietic stem cell mobilization

Following the FDA-approval of the hematopoietic stem cell (HSC) mobilizer plerixafor, orally available and potent CXCR4 antagonists were pursued. One such proposition was AMD11070, which was orally active and had superior antagonism in vitro; however, it did not appear as effective for HSC mobilization in vivo. Here we show that while AMD11070 acts as a full antagonist, plerixafor acts biased by stimulating β-arrestin recruitment while fully antagonizing G protein. Consequently, while AMD11070 prevents the constitutive receptor internalization, plerixafor allows it and thereby decreases receptor expression. These findings are confirmed by the successful transfer of both ligands’ binding sites and action to the related CXCR3 receptor. In vivo, plerixafor exhibits superior HSC mobilization associated with a dramatic reversal of the CXCL12 gradient across the bone marrow endothelium, which is not seen for AMD11070. We propose that the biased action of plerixafor is central for its superior therapeutic effect in HSC mobilization.

THE IMPACT OF ANTIHYPERTENSIVE DRUGS ON VASCULAR RIGIDITY AND ON THE GRADIENTS OF ARTERIAL STIFFNESS: A PILOT STUDY

Objective: Damages to barosensitive organs would be induced by the reversal of the arterial stiffness gradient. Our team has demonstrated that administration of nitroglycerin reduces regional stiffness of muscle arteries (radial and femoral) without modifying aortic stiffness, resulting in increased aortic-brachial (AB-PWV) and aortic-femoral (AF-PWV) stiffness gradients. The aim of this project is to study the impact of antihypertensive treatment on arterial stiffness, particularly on stiffness gradients. Design and method: This pilot study was performed in 19 hypertensive patients (age: 71 ± 11 years, men: 85%). The aortic, brachial and femoral stiffnesses were respectively measured by the carotid-femoral (cf-PWV), carotid-radial (cr-PWV) and femoral-dorsalis pedis (fd-PWV) pulse wave velocity during 2 visits: with and without medication for high blood pressure. During the drug-free visit, patients were to stop taking their antihypertensive drugs 2-3 days before their assessment, depending on their condition. The AB-PWV and AF-PWV ratios were obtained by dividing cf-PWV by cr-PWV or fd-PWV respectively. Results: At the visit without antihypertensive drugs, the cf-PWV, cr-PWV and fd-PWV were respectively 12.91 ± 0.65 m/s, 9.6 ± 0.4 m/s and 10.3 ± 0.3 m/s, resulting in an AB- ratio PWV of 1.37 ± 0.09 and an AF-PWV ratio of 1.21 ± 0.07. At the visit with medication, a significant decrease in mean arterial pressure of 9.5 mmHg (p < 0.001) was observed. In addition, reductions in cr-PWV of 0.79 m/s (p = 0.034) and fd-PWV of 0.45 m/s (p = 0.29) were noted, with no statistically significant change in cf-PWV (p = 0.83), leading to a non-significant increase in the AB-PWV (p = 0.21) and AF-PWV (p = 0.52) ratios. Conclusions: The preliminary data from this pilot project, although statistically non-significant, evolves in the same direction as those obtained with the administration of nitroglycerin. Indeed, there is a decrease in brachial and femoral stiffness leading to an increase in the gradients, which could have consequences for the microcirculation. These results suggest an impact of antihypertensive drugs on peripheral arteries and the vascular stiffness gradients, which could be confirmed with the continuation of this project on a larger sample.

Modeling of Resistive Forces and Buckling Behavior in Variable Recruitment Fluidic Artificial Muscle Bundles

Fluidic artificial muscles (FAMs), also known as McKibben actuators, are a class of fiber-reinforced soft actuators that can be pneumatically or hydraulically pressurized to produce muscle-like contraction and force generation. When multiple FAMs are bundled together in parallel and selectively pressurized, they can act as a multi-chambered actuator with bioinspired variable recruitment capability. The variable recruitment bundle consists of motor units (MUs)—groups of one of more FAMs—that are independently pressurized depending on the force demand, similar to how groups of muscle fibers are sequentially recruited in biological muscles. As the active FAMs contract, the inactive/low-pressure units are compressed, causing them to buckle outward, which increases the spatial envelope of the actuator. Additionally, a FAM compressed past its individual free strain applies a force that opposes the overall force output of active FAMs. In this paper, we propose a model to quantify this resistive force observed in inactive and low-pressure FAMs and study its implications on the performance of a variable recruitment bundle. The resistive force behavior is divided into post-buckling and post-collapse regions and a piecewise model is devised. An empirically-based correction method is proposed to improve the model to fit experimental data. Analysis of a bundle with resistive effects reveals a phenomenon, unique to variable recruitment bundles, defined as free strain gradient reversal.

Rethinking Couples’ Fertility in Spain: Do Partners’ Relative Education, Employment, and Job Stability Matter?

AbstractFertility decisions among Spanish couples have been strongly driven by economic uncertainty in a context in which dual-earner couples have become the norm and in which the gender gap in education has reversed. However, the partners’ respective jobs do not carry the same weight in such decisions. We explore how homogamous or heterogamous education, employment, and job stability between partners can provide insights into couples’ fertility decisions using data from 2002 to 2018 Spanish Labor Force Survey. The results reveal that among heterogamic couples, the woman’s job stability more than the man’s is key for childbearing decisions, while no differences are found in fertility levels for educationally heterogamous partners. In homogamous couples, the results suggest a reversal of the negative education-fertility gradient and show that highly educated couples have a higher likelihood of having a child than less-educated couples. Dual-earner couples are more likely to be parents than couples affected by unemployment and as likely as those meeting the declining male breadwinner, female caregiver model. We conclude that the role played by female employment in fertility trumps the role played by gender essentialism, highlighting the nonexclusive importance of gender egalitarianism, female employment, and economic uncertainty to fertility.

Arrhythmogenic Mechanisms in Hypokalaemia: Insights From Pre-clinical Models.

Potassium is the predominant intracellular cation, with its extracellular concentrations maintained between 3. 5 and 5 mM. Among the different potassium disorders, hypokalaemia is a common clinical condition that increases the risk of life-threatening ventricular arrhythmias. This review aims to consolidate pre-clinical findings on the electrophysiological mechanisms underlying hypokalaemia-induced arrhythmogenicity. Both triggers and substrates are required for the induction and maintenance of ventricular arrhythmias. Triggered activity can arise from either early afterdepolarizations (EADs) or delayed afterdepolarizations (DADs). Action potential duration (APD) prolongation can predispose to EADs, whereas intracellular Ca2+ overload can cause both EADs and DADs. Substrates on the other hand can either be static or dynamic. Static substrates include action potential triangulation, non-uniform APD prolongation, abnormal transmural repolarization gradients, reduced conduction velocity (CV), shortened effective refractory period (ERP), reduced excitation wavelength (CV × ERP) and increased critical intervals for re-excitation (APD–ERP). In contrast, dynamic substrates comprise increased amplitude of APD alternans, steeper APD restitution gradients, transient reversal of transmural repolarization gradients and impaired depolarization-repolarization coupling. The following review article will summarize the molecular mechanisms that generate these electrophysiological abnormalities and subsequent arrhythmogenesis.

Multiscale Adversarial and Weighted Gradient Domain Adaptive Network for Data Scarcity Surface Defect Detection

Surface defect detection is a challenging task in industrial manufacture. Recent methods using supervised learning need a large-scale dataset to achieve precise detection. However, the time-consuming and the difficulty of data acquisition make it difficult to build a large-scale dataset. This article proposes a domain adaptive network, called multiscale adversarial and weighted gradient domain adaptive network (MWDAN) for data scarcity surface defect detection. By MWDAN, the detection model trained from a small-scale dataset can gain the knowledge of transfer from another large-scale dataset, that is to say, even for a training dataset that is difficult to collect huge amounts of data, a good defect detection model can also be constructed, aided by another dataset that is relatively easy to acquire. The MWDAN is constructed in two levels. In the image level, a multiscale domain feature adaptation approach is proposed to solve the domain shift between the source domain and the target domain. In the instance level, a piecewise weighted gradient reversal layer (PWGRL) is designed to balance the weight of the backpropagation gradient for the hard- and easy-confused samples in domain classification and force confusion. Then, the PWGRL can reduce the local instance difference to further promote domain consistency. The experiments on mental surface defect detection show encourage results by the proposed MWDAN method.

Graph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification

In the text sentiment classification task, some words are seemingly unrelated to the classification task, but they have a direct impact on the performance of classification model. For example, in the sentences “I have terminal cancer” and “Cancer is a very common disease”, it can be clearly found that the word “cancer” has two different sentiment tendencies in the daily life domain and the medical domain. In the daily life domain, the word “cancer” shows an extremely negative sentiment tendency. While in the medical domain, the word “cancer” is just a simple term with a relatively neutral sentiment tendency. Although current deep learning models have already achieved good performance through their powerful feature learning capabilities, there are serious deficiencies in dealing with the above problem. Therefore, from a new perspective, this paper proposes the Graph Domain Adversarial Transfer Network (GDATN) based on the idea of adversarial learning, which uses the labeled source domain data to predict the sentiment label of unlabeled target domain data. Firstly, GDATN extracts feature representations through the Bidirectional Long Short-Term Memory (BiLSTM) Network and Graph Attention Network (GAT) successively. Then, GDATN introduces the domain classifier to capture the domain-shared text feature representation with the Gradient Reversal Layer (GRL). In addition, an auxiliary task named the projection mechanism is constructed to further capture the domain-specific text feature representation in response to the text domain problem. Extensive experimental results on two benchmark datasets show that GDATN proposed in this paper outperforms the other six benchmark sentiment classification models, and GDATN has a better stability on different cross-domain pairs.

Deep Adversarial Capsule Network for Compound Fault Diagnosis of Machinery Toward Multidomain Generalization Task

With advanced measurement technologies and signal analytics algorithms developed rapidly, the past decades have witnessed large amount of successful breakthroughs and applications in the field of intelligent fault diagnosis (IFD). However, the historical IFD methods have difficulties for compound fault diagnosis, when labeled data cannot be collected in advance for new or extreme working conditions. Facing with such challenges, a deep adversarial capsule network (DACN) is proposed to embed multidomain generalization into the intelligent compound fault diagnosis, so that the DACN not only can intelligently decouple the compound fault into multiple single faults for industrial equipment (IE) but also can be generalized from certain working conditions to another new. First, a DACN including feature extractor (FE), decoupling classifier (DC) and multidomain classifier (MC) is constructed for feature learning, fault decoupling, and unsupervised multidomain adaptation, respectively. Second, adversarial training is introduced into the DACN in the training stage via a gradient reversal layer that can build the connection between the FE and MC, which can force the DACN to learn the domain-invariance features. Finally, the DACN is trained using the single-fault data collected under multiple working conditions, and then applied to monitor the health condition of IE under new working conditions. The cross-validation experiments have been implemented on an automobile transmission (AT), which illustrates that the DACN obtains an optimum performance with the highest average accuracy of 97.65% for compound fault diagnosis of IE under multidomain generalization task and outperforms other related methods.

Prediction of enhancer\u2013promoter interactions using the cross-cell type information and domain adversarial neural network

BackgroundEnhancer–promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information.ResultsIn this work, we present a novel Sequence-based method (called SEPT) to predict the enhancer–promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves).ConclusionsSEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.

Enhancing the Reliability of Coating Flaw Detection for Pipes Buried in Soil Using a Multi-Electrode Detector

External corrosion of buried pipes can be controlled using both coating and cathodic protection. However, deterioration of the coating can occur due to several reasons. The detection reliabilty of coating flaw detection methods is affected by interference such as metal objects connected to rectifiers and copper grids. When performing parallel direct current voltage gradient (DCVG) inspection, a sine wave form without potential reversal in voltage gradient appears in the area where the interference exists. However, this area may be not identified using existing methods. The objective of this study was to determine the effect of analyzing direction on the reliability of coating flaw detection of pipes buried in soil using a multi-electrode detector. DCVG on the buried pipe was measured along the buried pipe. This measurement parallel to the pipe was repeated. Measured data were analyzed for parallel, vertical, and diagonal directions. The reliability of coating flaw detection was improved by up to 46.4% compared to the conventional method.

Event-Driven Hyporheic Exchange during Single and Seasonal Rainfall in a Gaining Stream

This study combined a reach-scale field survey and numerical modelling analysis to reveal the pattern of transient hyporheic exchange driven by rainfall events in the Zhongtian River, Southeast China. Field observations revealed hydrodynamic properties and flux variations in surface water (SW)/ groundwater (GW), suggesting that the regional groundwater recharged the study reach. A two-step numerical modelling procedure, including a hydraulic surface flow model and a groundwater flow model, was then used to interpret the transient hyporheic flow system. The hyporheic exchange exhibited strong temporal evolution in the study reach, as indicated by the rainfall event-driven hyporheic exchange. The reversal of the hydraulic gradient and transient hyporheic exchange were simulated using numerical simulation. Anisotropic hydraulic conductivity is the key to generating transient hyporheic exchange. A revised conceptual model was used to interpret the observed temporal patterns in hyporheic exchange. The seasonal rainfall events generate transient hyporheic exchange, and the pattern of transient hyporheic exchange indicates that transient hyporheic exchange appears only after an increased phase of the river stage but does not last long. The temporal pattern of hyporheic exchange can significantly affect the hydrodynamic exchange and the evolution of hydrology in the hyporheic zone for a gaining stream, and these results have important guiding significance for the comprehensive management of surface water and groundwater quantity and quality.

Mechanisms, features, and significance of diastolic mitral regurgitation: a case series.

BackgroundDiastolic mitral regurgitation (DMR) is a type of functional mitral regurgitation. Its occurrence in the diastolic phase of cardiac cycle renders DMR an easily ignored entity. Confusing it with systolic mitral regurgitation occasionally happens. The reversal of left atrioventricular pressure gradient during diastole and the incomplete closure of mitral valve are the essential conditions for DMR. Diastolic mitral regurgitation develops under various situations, where the mechanisms of diastolic reversal of left atrioventricular pressure gradient differ.Case summaryPatient 1 was a 50-year-old man diagnosed with 2:1 second-degree atrioventricular block (AVB). Patient 2 was a 70-year-old man diagnosed with first-degree AVB. Patient 3 was a 66-year-old man diagnosed with atrial fibrillation with long intermission and occasional atrial flutter with unequal conduction. Patient 4 was a 54-year-old woman diagnosed with dilated cardiomyopathy with complete left bundle branch block. Patient 5 was a 36-year-old man diagnosed with severe acute aortic regurgitation secondary to subacute bacterial endocarditis.DiscussionAlthough the degree of DMR is relatively mild, its appearance generally prompts further clinical considerations. The appreciation of DMR has an incremental value for diagnosing and evaluating the underlying cardiovascular disease.

Comparison between the kinetics of cyclohexane absorption and desorption for heterogeneous bitumen nanofilms

We used a gravimetric technique to study the kinetics of cyclohexane absorption and desorption for bitumen films with thicknesses ranging from ~50 nm to ~1000 nm by monitoring their relative mass uptake and release rates at 25 ℃ and 1 atm, respectively. In the absorption experiments, bitumen films were saturated with cyclohexane by exposing them to a carrier gas containing cyclohexane vapor at two relative saturations (RS) – 20% and 90% separately. These films were then used in the following desorption experiments. The absorption rate was about two times faster than the desorption rate. Furthermore, the absorption rate exhibited a linear inverse film thickness dependence while desorption a non-linear one. The cyclohexane diffusivity in the films exhibited a linear positive film thickness dependence with it being higher in absorption than that in desorption. The observations implied that amounts of cyclohexane dissolved and evaporated controlled the overall absorption and desorption rates, respectively. The inverse film thickness dependence of such rates was due to the decreasing influence of the hydrophilic substrate on the concentration gradient of bitumen molecules with different degrees of polarity in the films. Mass uptake was observed in the early stage of the desorption experiments of the 20% RS samples, especially those with low bitumen coverages. This observation seemed to be a feature of nanoscale films. We speculated that cyclohexane evaporated into the carrier gas adjacent to the sample surface dissolved back into the bitumen film as a result of a cyclohexane concentration gradient reversal.