Changes in zebrafish (Danio rerio) lens crystallin content during development.

PurposeThe roles that crystallin proteins play during lens development are not well understood. Similarities in the adult crystallin composition of mammalian and zebrafish lenses have made the latter a valuable model for examining lens function. In this study, we describe the changing zebrafish lens proteome during development to identify ontogenetic shifts in crystallin expression that may provide insights into age-specific functions.MethodsTwo-dimensional gel electrophoresis and size exclusion chromatography were used to characterize the lens crystallin content of 4.5-day to 27-month-old zebrafish. Protein spots were identified with mass spectrometry and comparisons with previously published proteomic maps, and quantified with densitometry. Constituents of size exclusion chromatography elution peaks were identified with sodium dodecyl sulfate-polyacrylamide gel electrophoresis.ResultsZebrafish lens crystallins were expressed in three ontogenetic patterns, with some crystallins produced at relatively constant levels throughout development, others expressed primarily before 10 weeks of age (βB1-, βA1-, and γN2-crystallins), and a third group primarily after 10 weeks (α-, βB3-, and γS-crystallins). Alpha-crystallins comprised less than 1% of total lens protein in 4.5-day lenses and increased to less than 7% in adult lenses. The developmental period between 6 weeks and 4 months contained the most dramatic shifts in lens crystallin expression.ConclusionsThese data provide the first two-dimensional gel electrophoresis maps of the developing zebrafish lens, with quantification of changing crystallin abundance and visualization of post-translational modification. Results suggest that some crystallins may play stage specific roles during lens development. The low levels of zebrafish lens α-crystallin relative to mammals may be due to the high concentrations of γ-crystallins in this aquatic lens. Similarities with mammalian crystallin expression continue to support the use of the zebrafish as a model for lens crystallin function

Navigation Instruction Validation Tool and Indoor Wayfinding Training System for People with Disabilities

According to World Health Survey, there are 785 million (15.6%) people in the world that live with a disability. It is a well-known fact that lack of access to public transportation is a barrier for people with disabilities in seeking work or accessing health care. In this research, we seek to increase access to public transportation by introducing a virtual pre-travel training system that enables people with disabilities to get familiar with a public transportation venue prior to arriving at the venue. Using this system, users establish a mental map of the target environment prior to their arrival to the physical space, increasing their confidence and therefore increasing their chances of using public transportation. First, we have to guarantee that all navigation instructions sent to our training system are correct. Since the number of navigation instruction increases dramatically, instruction validation becomes a challenge. We propose a video game based validation tool which includes a game scene that represents in 2D the physical environment and uses a game avatar to verify the navigation instructions automatically in the game scene. The avatar traverses the virtual space following the corresponding navigation instructions. Only in case that it successfully reaches the planned destination, the current navigation instruction can be considered as correct. Then, we introduce a virtual reality based pre-travel wayfinding training system to assist people with disabilities to get familiar with a venue prior to their arrival at the physical space, which provides two modes: 1) Self-Guided mode in which the path between a source and a destination is shown to the user from third person perspective, and 2) Exploration mode in which the user explores and interacts with the environment. In the end, we have implemented visual analytics tools that track and evaluate trainees’ performance and help us optimize the game. These tools identify the difficulties faced by the trainees as well as obtain overall statistics on the trainees’ behavior in the indoor environment, helping us understand how to modify the system and adjust it to different classes of disabilities

A study on fault diagnosis in nonlinear dynamic systems with uncertainties

In this draft, fault diagnosis in nonlinear dynamic systems is addressed. The objective of this work is to establish a framework, in which not only model-based but also data-driven and machine learning based fault diagnosis strategies can be uniformly handled. Instead of the well-established input-output and the associated state space models, stable image and kernel representations are adopted in our work as the basic process model forms. Based on it, the nominal system dynamics can then be modelled as a lower-dimensional manifold embedded in the process data space. To achieve a reliable fault detection as a classification problem, projection technique is a capable tool. For nonlinear dynamic systems, we propose to construct projection systems in the well-established framework of Hamiltonian systems and by means of the normalised image and kernel representations. For nonlinear dynamic systems, process data form a non-Euclidean space. Consequently, the norm-based distance defined in Hilbert space is not suitable to measure the distance from a data vector to the manifold of the nominal dynamics. To deal with this issue, we propose to use a Bregman divergence, a measure of difference between two points in a space, as a solution. Moreover, for our purpose of achieving a performance-oriented fault detection, the Bregman divergences adopted in our work are defined by Hamiltonian functions. This scheme not only enables to realise the performance-oriented fault detection, but also uncovers the information geometric aspect of our work. The last part of our work is devoted to the kernel representation based fault detection and uncertainty estimation that can be equivalently used for fault estimation. It is demonstrated that the projection onto the manifold of uncertainty data, together with the correspondingly defined Bregman divergence, is also capable for fault detection

Energy-Efficient β

As the first priority of query processing in wireless sensor networks is to save the limited energy of sensor nodes and in many sensing applications a part of skyline result is enough for the user’s requirement, calculating the exact skyline is not energy-efficient relatively. Therefore, a new approximate skyline query, β-approximate skyline query which is limited by a guaranteed error bound, is proposed in this paper. With an objective to reduce the communication cost in evaluating β-approximate skyline queries, we also propose an energy-efficient processing algorithm using mapping and filtering strategies, named Actual Approximate Skyline (AAS). And more than that, an extended algorithm named Hypothetical Approximate Skyline (HAS) which replaces the real tuples with the hypothetical ones is proposed to further reduce the communication cost. Extensive experiments on synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings

Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems

This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard method of machine learning technique and widely applied for fault (anomaly) detection and classification. In the context of representation learning, the so-called latent (hidden) variable plays an important role towards an optimal fault detection. In ideal case, the latent variable should be a minimal sufficient statistic. The existing autoencoder-based fault detection schemes are mainly application-oriented, and few efforts have been devoted to optimal autoencoder-based fault detection and explainable applications. The main objective of our work is to establish a framework for learning autoencoder-based optimal fault detection in nonlinear dynamic systems. To this aim, a process model form for dynamic systems is firstly introduced with the aid of control and system theory, which also leads to a clear system interpretation of the latent variable. The major efforts are devoted to the development of a control theoretical solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced for dynamic systems and fault detection specifications. In particular, the existence conditions for such a latent variable are derived, based on which a loss function and further a learning algorithm are developed. This learning algorithm enables optimally training of autoencoders to achieve an optimal fault detection in nonlinear dynamic systems. A case study on three-tank system is given at the end of this paper to illustrate the capability of the proposed autoencoder-based fault detection and to explain the essential role of the latent variable in the proposed fault detection system

Leadless pacemaker implantation and azygos continuation in the inferior vena cava:a case description

Azygos continuation (AC) of the inferior vena cava (IVC), also known as the absence of the hepatic segment of the IVC with AC, is a rare anatomic variant in the general population with an incidence of 0.6% (1). AC of the IVC is congenital and independent from other anatomical variants. It is primarily caused by the absence or hypoplasia of the IVC’s hepatic segment. The IVC below the hepatic segments flows upward through the azygos into the superior vena cava (SVC) and eventually drains into the right atrium. The renal portion of the IVC receives blood flow from the kidneys and lower extremities and drains into the SVC through the azygos vein. The azygos vein, azygos arch, and SVC dilate to accommodate the increase in blood flow (2). Usually, AC in the IVC is asymptomatic and does not affect the functionality of the cardiovascular system. However, it significantly impacts leadless pacemaker (LP) implantation via the femoral vein. The LP is a feasible alternative to the single-ventricle pacemaker; that is, a pacemaker that simply paces the ventricle but not the atrium. LP is a novel technique that differs from traditional pacemakers in terms of the electronic components, implantation procedure, possible complications, and postoperative management. Unlike the conventional pacemaker implantation, where the lead is delivered to the heart by puncturing the subclavian veins to approach the SVC, the LP implantation involves puncturing the femoral vein and delivering the LP to the heart via the IVC (3). If a patient has AC of the IVC, an LP cannot be implanted through the IVC pathway or could be delivered to the wrong location, such as the SVC. Furthermore, routine preoperative examinations (e.g., cardiac ultrasound and chest X-ray) are ineffective in detecting AC. Herein, we report a case of AC of the IVC observed during LP implantation, which resulted in the abandonment of this procedure. Electrocardiogram (ECG)- gated computed tomography (CT) venography was used to identify the anatomic variant of the IVC as an AC. This patient eventually underwent conventional pacemaker implantation via the SVC

The real-world analysis of adverse events with azacitidine: a pharmacovigilance study based on the FAERS and WHO-VigiAccess databases

BackgroundAzacitidine is used to treat myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). It acts as a cytosine analog and DNA methyltransferase inhibitor, inducing DNA hypomethylation to reverse epigenetic modifications and restore normal gene expression. However, adverse events (AEs) associated with azacitidine are mainly reported in clinical trials, with limited real-world evidence. This study aims to assess the AE profile of azacitidine by utilizing data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and WHO-VigiAccess databases.MethodsWe extracted adverse event (AE) reports related to azacitidine from the FAERS and WHO-VigiAccess databases, covering the period from the drug’s market introduction to the third quarter of 2024. We used statistical methods including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM) to analyze the association between azacitidine and documented AEs.ResultsThe investigation unveiled 16,056 azacitidine-related adverse event (AE) reports from FAERS and 19,867 reports from WHO-VigiAccess. The median duration for the occurrence of these AEs during the observation period was 36 days, with an interquartile range (IQR) spanning from 11 to 126 days. Our statistical analysis identified 27 organ systems associated with AEs induced by azacitidine. Among these, the notable System Organ Classes (SOCs) that met four specific criteria included: infections and infestations, blood and lymphatic system disorders, and neoplasms benign, malignant, and unspecified (including cysts and polyps). Four algorithms identified 443 significant disproportionality preferred terms (PTs), including previously unreported AEs such as death, sepsis, septic shock, respiratory failure, cardiac failure, tumor lysis syndrome, bone marrow failure, interstitial lung disease, and pericarditis. Analysis from the WHO-VigiAccess database showed a ROR of 3.65 and a PRR of 3.30 for the SOC of infections and infestations.ConclusionThis research not only confirms the widely acknowledged AEs linked to azacitidine but also uncovers several potentially new safety concerns noted in actual clinical practice. These results may offer important vigilance information for clinicians and pharmacists when addressing safety issues associated with azacitidine

Colonization of Beauveria bassiana 08F04 in root-zone soil and its biocontrol of cereal cyst nematode (Heterodera filipjevi)

Cereal cyst nematodes cause serious yield losses of wheat in Hunaghuai winter wheat growing region in China. Beauveria bassiana 08F04 isolated from the surface of cysts is a promising biological control agent for cereal cyst nematodes. As the colonization capacity is a crucial criteria to assess biocontrol effectiveness for a microbial agent candidate, we aimed to label B. bassiana 08F04 for efficient monitoring of colonization in the soil. The binary pCAM-gfp plasmid containing sgfp and hph was integrated into B. bassiana 08F04 using the Agrobacterium tumefaciens-mediated transformation. The transformation caused a significant change in mycelial and conidial yields, and in extracellular chitinase activity in some transformants. The cultural filtrates of some transformants also decreased acetylcholinesterase activity and the survival of Heterodera filipjevi second-stage juveniles relative to the wild-type strain. One transformant (G10) had a growth rate and biocontrol efficacy similar to the wild-type strain, so it was used for a pilot study of B. bassiana colonization conducted over 13 weeks. Real-time PCR results and CFU counts revealed that the population of G10 increased quickly over the first 3 weeks, then decreased slowly over the following 4 weeks before stabilizing. In addition, the application of wild-type B. bassiana 08F04 and transformant G10 significantly reduced the number of H. filipjevi females in roots by 64.4% and 60.2%, respectively. The results of this study have practical applications for ecological, biological and functional studies of B. bassiana 08F04 and for bionematicide registration.National Special Fund for Agro-scientific Research in the Public Interest of China [201503112]; Scientific and Technological Project of Henan Province [182102110272]; Special Funds for Research and Development of Henan Academy of Agricultural Sciences [2019CY02]This research was financially supported by National Special Fund for Agro-scientific Research in the Public Interest of China (201503112), Scientific and Technological Project of Henan Province (182102110272) and Special Funds for Research and Development of Henan Academy of Agricultural Sciences (2019CY02)

Active site switching on high entropy phosphides as bifunctional oxygen electrocatalysts for rechargeable/robust Zn–air battery

High-entropy materials (HEMs) offer a quasi-continuous spectrum of active sites and have generated great expectations in fields such as electrocatalysis and energy storage. Despite their potential, the complex composition and associated surface phenomena of HEMs pose challenges to their rational design and development. In this context, we have synthesized FeCoNiPdWP high entropy phosphide (HEP) nanoparticles using a low-temperature colloidal method, and explored their application as bifunctional electrocatalysts for the oxygen evolution and reduction reactions (OER/ORR). Our analysis provides a detailed understanding of the individual roles and transformations of each element during OER/ORR operation. Notably, the HEPs exhibit an exceptionally low OER overpotential of 227 mV at 10 mA cm−2, attributed to the reconstructed HEP surface into a FeCoNiPdW high entropy oxyhydroxide with high oxidation states of Fe, Co, and Ni serving as the active sites. Additionally, Pd and W play crucial roles in modulating the electronic structure to optimize the adsorption energy of oxygen intermediates. For the ORR, Pd emerges as the most active component. In the reconstructed catalyst, the strong d–d orbital coupling of especially Pd, Co, and W fine-tunes ORR electron transfer pathways, delivering an ORR half-wave potential of 0.81 V with a pure four-electron reduction mechanism. The practicality of these HEPs catalysts is showcased through the assembly of aqueous zinc–air batteries. These batteries demonstrate a superior specific capacity of 886 mA h gZn−1 and maintain excellent stability over more than 700 hours of continuous operation. Overall, this study not only elucidates the role of each element in HEMs but also establishes a foundational framework for the design and development of next-generation bifunctional oxygen catalysts, broadening the potential applications of these complex materials in advanced energy systems