Notes

Drinking alcohol, cannabis employ, and psychopathology symptoms among college students both before and after COVID-19.
The phenomenon of field-induced superconductor-to-insulator transitions observed experimentally in electron-doped SrTiO3/LaAlO3interfaces, analyzed recently by menas of 2D superconducting fluctuations theory, is revisited with new insights associating it with the appearnace at low temperatures of field-induced boson insulating states. Within the framework of the time-dependent Ginzburg-Landau functional approach, we pinpoint the origin of these states in field-induced extreme softening of fluctuation modes over a large region in momentum space, upon diminishing temperature, which drives Cooper-pair fluctuations to condense into mesoscopic puddles in real space. Dynamical quantum tunneling of Cooper-pair fluctuations out of these puddles, introduced within a phenomenological approach, which break into mobile single-electron states, contains the high-field resistance onset predicted by the exclusive boson theory.Osteocytes are considered the primary mechanical sensor in bone tissue and orchestrate the coupled bone remodeling activity of adjacent osteoblast and osteoclast cells.In vivoinvestigation of mechanically induced signal propagation through networks of interconnected osteocytes is confounded by their confinement within the mineralized bone matrix, which cannot be modeled in conventional culture systems. Selleckchem Phenol Red sodium In this study, we developed a new model that mimics thisin vivoconfinement using gelatin methacrylate (GelMA) hydrogel or GelMA mineralized using osteoblast-like model cells. This model also enables real-time optical examination of osteocyte calcium (Ca2+) signaling dynamics in response to fluid shear stimuli cultured under confined conditions. Using this system, we discovered several distinct and previously undescribed patterns of Ca2+responses that vary across networks of interconnected osteocytes as a function of space, time and connectivity. Heterogeneity in Ca2+signaling may provide new insights into bone remodeling in response to mechanical loading. Overall, such a model can be extended to study signaling dynamics within cell networks exposed to flow-induced mechanical stimuli under confined conditions.Crystalline titanium oxides have shown photocatalytic activity (PCA) and the formation of antibacterial reactive oxygen species (ROS) when stimulated with UV light. Polyaniline (PANI) is a conductive polymer that has shown antibacterial effects. Previously, titanium oxides have been PANI-doped using a multi-step approach. In the present study, we compared PANI-doped specimens produced with a two-step method (ACV), to PANI-doped specimens produced by a novel single-step direct anodization (AAn) method, and a control group of anodized un-doped specimens. The surface morphology, oxide crystallinity, surface elemental composition, surface roughness, surface wettability, oxide adhesion, corrosion resistance, PCA, and ROS generation of each oxide group were evaluated. All groups exhibited mixed anatase and rutile phase oxides. The AAn group revealed less anatase and rutile, but more PANI-surface coverage. The AAn group exhibited significantly increased PCA after 60 minutes of direct UVA illumination compared to the applicable to a wide variety of implant applications.We demonstrate a highly selective and sensitive Cupric oxide (CuO) thin film-based low concentration Hydrogen sulfide (H2S) sensor. The sensitivity was improved around three times by decorating with reduced graphene oxide (rGO) nanosheets. CuO thin films were deposited by Chemical Vapor Deposition followed by inter-digital electrode fabrication by a thermal evaporations system. The crystal structure of CuO was confirmed by x-ray diffraction. The sensing response of pristine CuO was found around 54% at 100 °C to 100 ppm of H2S. In contrast, the sensing response was enhanced to 167% by decorating with rGO of 1.5 mg ml-1concentration solution. The sensing was improved due to the formation of heterojunctions between the rGO and CuO. The developed sensor was examined under various gas environments and found to be highly selective towards H2S gas. The improvement in sensing response has been attributed to increased hole concentration in CuO in the presence of rGO due to the Fermi level alignment and increased absorption of H2S molecules at the rGO/CuO heterojunction. Further, electronic structure calculations show the physisorption behavior of H2S molecules on the different adsorption sites. Detailed insight into the gas sensing mechanism is discussed based on experimental results and electronic structure calculations.Objective.Retinal biomarker in optical coherence tomography (OCT) images plays a key guiding role in the follow-up diagnosis and clinical treatment of eye diseases. Although there have been many deep learning methods to automatically process retinal biomarker, the detection of retinal biomarkers is still a great challenge due to the similar characteristics to normal tissue, large changes in size and shape and fuzzy boundary of different types of biomarkers. To overcome these challenges, a novel contrastive uncertainty network (CUNet) is proposed for retinal biomarkers detection in OCT images.Approach.In CUNet, proposal contrastive learning is designed to enhance the feature representation of retinal biomarkers, aiming at boosting the discrimination ability of network between different types of retinal biomarkers. Furthermore, we proposed bounding box uncertainty and combined it with the traditional bounding box regression, thereby improving the sensitivity of the network to the fuzzy boundaries of retinal biomarkers, and to obtain a better localization result.Main results.Comprehensive experiments are conducted to evaluate the performance of the proposed CUNet. The experimental results on two datasets show that our proposed method achieves good detection performance compared with other detection methods.Significance.We propose a method for retinal biomarker detection trained by bounding box labels. The proposal contrastive learning and bounding box uncertainty are used to improve the detection of retinal biomarkers. The method is designed to help reduce the amount of work doctors have to do to detect retinal diseases.Objective Gliomas are the most common primary brain tumors. Approximately 70% of the glioma patients diagnosed with glioblastoma have an averaged overall survival (OS) of only ∼16 months. Early survival prediction is essential for treatment decision-making in glioma patients. Here we proposed an ensemble learning approach to predict the post-operative OS of glioma patients using only pre-operative MRIs.Approach Our dataset was from the Medical Image Computing and Computer Assisted Intervention Brain Tumor Segmentation challenge 2020, which consists of multimodal pre-operative MRI scans of 235 glioma patients with survival days recorded. The backbone of our approach was a Siamese network consisting of twinned ResNet-based feature extractors followed by a 3-layer classifier. During training, the feature extractors explored traits of intra and inter-class by minimizing contrastive loss of randomly paired 2D pre-operative MRIs, and the classifier utilized the extracted features to generate labels with cost defined by cross-entropy loss. During testing, the extracted features were also utilized to define distance between the test sample and the reference composed of training data, to generate an additional predictor via K-NN classification. The final label was the ensemble classification from both the Siamese model and the K-NN model.Main results Our approach classifies the glioma patients into 3 OS classes long-survivors (>15 months), mid-survivors (between 10 and 15 months) and short-survivors ( less then 10 months). The performance is assessed by the accuracy (ACC) and the area under the curve (AUC) of 3-class classification. The final result achieved an ACC of 65.22% and AUC of 0.81.Significance Our Siamese network based ensemble learning approach demonstrated promising ability in mining discriminative features with minimal manual processing and generalization requirement. This prediction strategy can be potentially applied to assist timely clinical decision-making.A simpleα-cyanostilbene-functioned salicylaldehyde-based Schiff-base probe, which exhibited outstanding 'aggregation-induced emission and excited state intramolecular proton transfer (AIE + ESIPT)' emission in solution, aggregation and solid states, was synthesized in high yield of 87%. Its solid-states with different morphologies emitted different fluorescence after crystallization in EtOH/H2O (1/2, v/v) mixtures or pure EtOH solvent. Besides, it exhibited an obvious spectro-photometrical fluorescence quenching for highly selective sensing of Co2+in THF/water system (ƒw= 60%, pH = 7.4), accompanied by an intense green fluorescence turn-off behavior under UV365nmillumination. The binding stochiometry between the ligand and Co2+was found to be 21, and the detection limit (DL) was calculated to be 0.41 × 10-8M. In addition, it could be applied to detect Co2+in real water samples and on silica gel testing strip.Nitride complexes have been invoked as catalysts and intermediates in a wide variety of transformations and are noted for their tunable acid/base properties. A density functional theory study is reported herein that maps the basicity of 3d and 4d transition metals that routinely form nitride complexes V, Cr, Mn, Nb, Mo, Tc, and Ru. Complexes were gathered from the Cambridge Structural Database, and from the free energy of protonation, the pKb(N) of the nitride group was calculated to quantify the impact of metal identity, oxidation state, coordination number, and supporting ligand type upon metal-nitride basicity. In general, the basicity of transition metal nitrides decreases from left to right across the 3d and 4d rows and increases from 3d metals to their 4d congeners. Metal identity and oxidation state primarily determine basicity trends; however, supporting ligand types have a substantial impact on the basicity range for a given metal. Synergism of these factors in determining the overall pKb(N) values is discussed, as are the implications for the catalytic reactivity of metal nitrides.Activating mutations in the epidermal growth factor receptor (EGFR) are frequent oncogenic drivers of non-small-cell lung cancer (NSCLC). The most frequent alterations in EGFR are short in-frame deletions in exon 19 (Del19) and the missense mutation L858R, which both lead to increased activity and sensitization of NSCLC to EGFR inhibition. The first approved EGFR inhibitors used for first-line treatment of NSCLC, gefitinib and erlotinib, are quinazoline-based. However, both inhibitors have several known off-targets, and they also potently inhibit wild-type (WT) EGFR, resulting in side effects. Here, we applied a macrocyclic strategy on a quinazoline-based scaffold as a proof-of-concept study with the goal of increasing kinome-wide selectivity of this privileged inhibitor scaffold. Kinome-wide screens and SAR studies yielded 3f, a potent inhibitor for the most common EGFR mutation (EGFR Del19 119 nM) with selectivity against the WT receptor (EGFR >10 μM) and the kinome.Objective. The limited functionality of hand prostheses remains one of the main reasons behind the lack of its wide adoption by amputees. Indeed, while commercial prostheses can perform a reasonable number of grasps, they are often inadequate for manipulating the object once in hand. This lack of dexterity drastically restricts the utility of prosthetic hands. We aim at investigating a novel shared control strategy that combines autonomous control of forces exerted by a robotic hand with electromyographic (EMG) decoding to perform robust in-hand object manipulation.Approach. We conduct a three-day long longitudinal study with eight healthy subjects controlling a 16-degrees-of-freedom robotic hand to insert objects in boxes of various orientations. EMG decoding from forearm muscles enables subjects to move, proportionally and simultaneously, the fingers of the robotic hand. The desired object rotation is inferred using two EMG electrodes placed on the shoulder that record the activity of muscles responsible for elevation and depression.
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