Inhibition of the Aquaporin-1 Cation Conductance by Selected Furan Compounds Reduces Red Blood Cell Sickling

In sickle cell disease (SCD), the pathological shift of red blood cells (RBCs) into distorted morphologies under hypoxic conditions follows activation of a cationic leak current (Psickle) and cell dehydration. Prior work showed sickling was reduced by 5-hydroxylmethyl-2-furfural (5-HMF), which stabilized mutant hemoglobin and also blocked the Psickle current in RBCs, though the molecular basis of this 5-HMF-sensitive cation current remained a mystery. Work here is the first to test the hypothesis that Aquaporin-1 (AQP1) cation channels contribute to the monovalent component of Psickle. Human AQP1 channels expressed in Xenopus oocytes were evaluated for sensitivity to 5-HMF and four derivatives known to have differential efficacies in preventing RBC sickling. Ion conductances were measured by two-electrode voltage clamp, and osmotic water permeability by optical swelling assays. Compounds tested were: 5-HMF; 5-PMFC (5-(phenoxymethyl)furan-2-carbaldehyde); 5-CMFC (5-(4-chlorophenoxymethyl)furan-2-carbaldehyde); 5-NMFC (5-(2-nitrophenoxymethyl)-furan-2-carbaldehyde); and VZHE006 (tert-butyl (5-formylfuran-2-yl)methyl carbonate). The most effective anti-sickling agent, 5-PMFC, was the most potent inhibitor of the AQP1 ion conductance (98% block at 100 µM). The order of sensitivity of the AQP1 conductance to inhibition was 5-PMFC > VZHE006 > 5-CMFC ≥ 5-NMFC, which corresponded with effectiveness in protecting RBCs from sickling. None of the compounds altered AQP1 water channel activity. Combined application of a selective AQP1 ion channel blocker AqB011 (80 µM) with a selective hemoglobin modifying agent 5-NMFC (2.5 mM) increased anti-sickling effectiveness in red blood cells from human SCD patients. Another non-selective cation channel known to be expressed in RBCs, Piezo1, was unaffected by 2 mM 5-HMF. Results suggest that inhibition of AQP1 ion channels and capacity to modify hemoglobin are combined features of the most effective anti-sickling agents. Future therapeutics aimed at both targets could hold promise for improved treatments for SCD.

Dielectric Properties of Textile Materials: Analytical Approximations and Experimental Measurements—A Review

Deciphering how the dielectric properties of textile materials are orchestrated by their internal components has far-reaching implications. For the development of textile-based electronics, which have gained ever-increasing attention for their uniquely combined features of electronics and traditional fabrics, both performance and form factor are critically dependent on the dielectric properties. The knowledge of the dielectric properties of textile materials is thus crucial in successful design and operation of textile-based electronics. While the dielectric properties of textile materials could be estimated to some extent from the compositional profiles, recent studies have identified various additional factors that have also substantial influence. From the viewpoint of materials characterization, such dependence of the dielectric properties of textile materials have given rise to a new possibility—information on various internal components could be, upon successful correlation, extracted by measuring the dielectric properties. In view of these considerable implications, this invited review paper summarizes various fundamental theories and principles related to the dielectric properties of textile materials. In order to provide an imperative basis for uncovering various factors that intricately influence the dielectric properties of textile materials, the foundations of the dielectrics and polarization mechanisms are first recapitulated, followed by an overview on the concept of homogenization and the dielectric mixture theory. The principal advantages, challenges and opportunities in the analytical approximations of the dielectric properties of textile materials are then discussed based on the findings from the recent literature, and finally a variety of characterization methods suitable for measuring the dielectric properties of textile materials are described. It is among the objectives of this paper to build a practical signpost for scientists and engineers in this rapidly evolving, cross-disciplinary field.

Identification of Reasons Behind Infant Crying Using Acoustic Signal Processing and Deep Neural Network for Neonatal Intensive Care Unit

The infants admitted in the Neonatal Intensive Care Unit (NICU) always need a Hygienic environment and round the clock observations. Infants or the just born babies always express their physical and emotional needs through cry. Thus, the detection of the reasons behind the infant cry plays a vital role in monitoring the health of the babies in the NICU. In this paper, we have proposed a novel approach for detecting the reasons for Infant's cry. In the proposed approach the cry signal of the infant is captured and from this signal, the unique set of features are extracted using MFCCs, LPCCs, and Pitch. This set of features is used to differentiates the patters signals to recognize the reasons for the cry. The reasons for cry such as hunger, pain, sleep, and discomfort are used to represent different classes. The Neural Network Multilayer classifier is designed to recognize the reasons for the cry using the standard dataset of infant cry. The proposed classifier can achieve accuracy of 93.24% from the combined features of MFCCs, LPCCs and Pitch using

Combining Implicit and Explicit Feature Extraction for Eye Tracking: Attention Classification Using a Heterogeneous Input

Statistical measurements of eye movement-specific properties, such as fixations, saccades, blinks, or pupil dilation, are frequently utilized as input features for machine learning algorithms applied to eye tracking recordings. These characteristics are intended to be interpretable aspects of eye gazing behavior. However, prior research has demonstrated that when trained on implicit representations of raw eye tracking data, neural networks outperform these traditional techniques. To leverage the strengths and information of both feature sets, we integrated implicit and explicit eye tracking features in one classification approach in this work. A neural network was adapted to process the heterogeneous input and predict the internally and externally directed attention of 154 participants. We compared the accuracies reached by the implicit and combined features for different window lengths and evaluated the approaches in terms of person- and task-independence. The results indicate that combining implicit and explicit feature extraction techniques for eye tracking data improves classification results for attentional state detection significantly. The attentional state was correctly classified during new tasks with an accuracy better than chance, and person-independent classification even outperformed person-dependently trained classifiers for some settings. For future experiments and applications that require eye tracking data classification, we suggest to consider implicit data representation in addition to interpretable explicit features.

Value-added and Transparent Experiments

Experimental research by political scientists on elites has grown dramatically in recent years. Experimenting on and with elites raises important questions, both practical and ethical. Elites are busy people, doing important work under public scrutiny. Therefore, any experiments that use up political elites’ time, risk impairing their ability to do their jobs as well as possible, or put at risk the larger research community’s access to elites should be avoided. Nevertheless, despite these risks and challenges, we argue experimenting with elites has enough benefits both to the research community and to elites themselves, that it should still be done. The relevant question then becomes how should we think about doing experiments with political elites? We propose a framework of value-added and transparent experiments. Our framework is guided by the following two simple rules: Elite subjects should individually benefit from the process of doing the experiment. It should add value to their role as representatives. Second, the identity of the researchers and purposes of the experiment should be transparent. As we argue, these two combined features can still accommodate a large range of experiments, can creatively spark researchers to think up new designs and can protect access to elites for future research. We review two such examples at the end of this essay.

PATH-02. TWO CASES WITH MULTIPLE HYBRID NERVE SHEATH TUMORS WITH ERBB2 MUTATIONS

Hybrid nerve sheath tumors are new disease entity incorporated in WHO classification of tumors of the central nervous system 2016 version that includes benign peripheral nerve sheath tumors with combined features of more than one conventional type. Ronellenfitsch et al recently reported one case with ERBB2 D769Y mutation with good clinical response to lapatinib in 2020. We report two cases with imaging findings of multiple peripheral nerve sheath tumors with pathology proven hybrid nerve sheath tumors with ERBB2 mutations. Patient 1 has neurofibroma/schwannoma hybrid nerve sheath tumor with ERBB2 D769Y mutation and patient 2 has plexiform neurofibroma/schwannoma hybrid nerve sheath tumor with ERBB2 V777L mutation. Both of our patients have similar clinical features with adult-onset multiple peripheral nerve sheath tumors without other systemic features of neurofibromatosis type1 or vestibular schwannomas or family history. Hybrid nerve sheath tumors are newly recognized disease and more than half of the cases have multiple peripheral nerve sheath tumors, suggestive of tumor syndrome. Patients with multiple peripheral nerve sheath tumors without systemic features of neurofibromatosis type1 or vestibular schwannomas should be screened for genetic/molecular tests including ERBB2 mutation that might provide treatment options for these patients.

Adaptive online terrain classification method for mobile robot based on vibration signals

To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time–frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot’s moving speed and achieve higher terrain classification accuracy.

Fusion in Dissimilarity Space Between RGB D and Skeleton for Person Re Identification

Person re-identification (Re-id) is one of the important tools of video surveillance systems, which aims to recognize an individual across the multiple disjoint sensors of a camera network. Despite the recent advances on RGB camera-based person re-identification methods under normal lighting conditions, Re-id researchers fail to take advantages of modern RGB-D sensor-based additional information (e.g. depth and skeleton information). When traditional RGB-based cameras fail to capture the video under poor illumination conditions, RGB-D sensor-based additional information can be advantageous to tackle these constraints. This work takes depth images and skeleton joint points as additional information along with RGB appearance cues and proposes a person re-identification method. We combine 4-channel RGB-D image features with skeleton information using score-level fusion strategy in dissimilarity space to increase re-identification accuracy. Moreover, our propose method overcomes the illumination problem because we use illumination invariant depth image and skeleton information. We carried out rigorous experiments on two publicly available RGBD-ID re-identification datasets and proved the use of combined features of 4-channel RGB-D images and skeleton information boost up the rank 1 recognition accuracy.