Natural channel protein inserts and functions in a completely artificial, solid-supported bilayer membrane

Reconstitution of membrane proteins in artificial membrane systems creates a platform for exploring their potential for pharmacological or biotechnological applications. Previously, we demonstrated amphiphilic block copolymers as promising building blocks for artificial membranes with long-term stability and tailorable structural parameters. However, the insertion of membrane proteins has not previously been realized in a large-area, stable, and solid-supported artificial membrane. Here, we show the first, preliminary model of a channel membrane protein that is functionally incorporated in a completely artificial polymer, tethered, solid-supported bilayer membrane (TSSBM). Unprecedented ionic transport characteristics that differ from previous results on protein insertion into planar, free-standing membranes, are identified. Our findings mark a change in understanding protein insertion and ion flow within natural channel proteins when inserted in an artificial TSSBM, thus holding great potential for numerous applications such as drug screening, trace analyzing, and biosensing

Large-scale BN tunnel barriers for graphene spintronics

We have fabricated graphene spin-valve devices utilizing scalable materials made from chemical vapor deposition (CVD). Both the spin-transporting graphene and the tunnel barrier material are CVD-grown. The tunnel barrier is realized by h-BN, used either as a monolayer or bilayer and placed over the graphene. Spin transport experiments were performed using ferromagnetic contacts deposited onto the barrier. We find that spin injection is still greatly suppressed in devices with a monolayer tunneling barrier due to resistance mismatch. This is, however, not the case for devices with bilayer barriers. For those devices, a spin relaxation time of 260 ps intrinsic to the CVD graphene material is deduced. This time scale is comparable to those reported for exfoliated graphene, suggesting that this CVD approach is promising for spintronic applications which require scalable materials.Comment: 13 pages, 3 figure

Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation. To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach. We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git

Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space, in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities.We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation.To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach.We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git

Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent

Feature selection prepares the AI-readiness of data by eliminating redundant features. Prior research falls into two primary categories: i) Supervised Feature Selection, which identifies the optimal feature subset based on their relevance to the target variable; ii) Unsupervised Feature Selection, which reduces the feature space dimensionality by capturing the essential information within the feature set instead of using target variable. However, SFS approaches suffer from time-consuming processes and limited generalizability due to the dependence on the target variable and downstream ML tasks. UFS methods are constrained by the deducted feature space is latent and untraceable. To address these challenges, we introduce an innovative framework for feature selection, which is guided by knockoff features and optimized through reinforcement learning, to identify the optimal and effective feature subset. In detail, our method involves generating "knockoff" features that replicate the distribution and characteristics of the original features but are independent of the target variable. Each feature is then assigned a pseudo label based on its correlation with all the knockoff features, serving as a novel metric for feature evaluation. Our approach utilizes these pseudo labels to guide the feature selection process in 3 novel ways, optimized by a single reinforced agent: 1). A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process in feature selection. 2). We introduce unsupervised rewards to evaluate the feature subset quality based on the pseudo labels and the feature space reconstruction loss to reduce dependencies on the target variable. 3). A new {\epsilon}-greedy strategy is used, incorporating insights from the pseudo labels to make the feature selection process more effective

Recognition of dipole-induced electric field in 2D materials for surface-enhanced Raman scattering

The application of two-dimensional (2D) materials, including metallic graphene, semiconducting transition metal dichalcogenides, and insulating hexagonal boron nitride (h-BN) for surface-enhancement Raman spectroscopy has attracted extensive research interest. This article provides a critical overview of the recent developments in surface-enhanced Raman spectroscopy using 2D materials. By re-examining the relationship between the lattice structure and Raman enhancement characteristics, including vibration selectivity and thickness dependence, we highlight the important role of dipoles in the chemical enhancement of 2D materials

Anatomical study of simple landmarks for guiding the quick access to humeral circumflex arteries

BACKGROUND: The posterior and anterior circumflex humeral artery (PCHA and ACHA) are crucial for the blood supply of humeral head. We aimed to identify simple landmarks for guiding the quick access to PCHA and ACHA, which might help to protect the arteries during the surgical management of proximal humeral fractures. METHODS: Twenty fresh cadavers were dissected to measure the distances from the origins of PCHA and ACHA to the landmarks (the acromion, the coracoid, the infraglenoid tubercle, the midclavicular line) using Vernier calipers. RESULTS: The mean distances from the origin of PCHA to the infraglenoid tubercle, the coracoid, the acromion and the midclavicular line were 27.7 mm, 50.2 mm, 68.4 mm and 75.8 mm. The mean distances from the origin of ACHA to the above landmarks were 26.9 mm, 49.2 mm, 67.0 mm and 74.9 mm. CONCLUSION: Our study provided a practical method for the intraoperative identification as well as quick access of PCHA and ACHA based on a series of anatomical measurements

Charge transport in a single molecule transistor probed by scanning tunneling microscopy

We report on the scanning tunneling microscopy/spectroscopy (STM/STS) study of cobalt phthalocyanine (CoPc) molecules deposited onto a back-gated graphene device. We observe a clear gate voltage ( V g ) dependence of the energy position of the features originating from the molecular states. Based on the analysis of the energy shifts of the molecular features upon tuning  V g , we are able to determine the nature of the electronic states that lead to a gapped differential conductance. Our measurements show that capacitive couplings of comparable strengths exist between the CoPc molecule and the STM tip as well as between CoPc and graphene, thus facilitating electronic transport involving only unoccupied molecular states for both tunneling bias polarities. These findings provide novel information on the interaction between graphene and organic molecules and are of importance for further studies, which envisage the realization of single molecule transistors with non-metallic electrodes

Competing surface reactions limiting the performance of ion-sensitive field-effect transistors

© 2015 Elsevier B.V. All rights reserved.Ion-sensitive field-effect transistors based on silicon nanowires are promising candidates for the detection of chemical and biochemical species. These devices have been established as pH sensors thanks to the large number of surface hydroxyl groups at the gate dielectrics which makes them intrinsically sensitive to protons. To specifically detect species other than protons, the sensor surface needs to be modified. However, the remaining hydroxyl groups after functionalization may still limit the sensor response to the targeted species. Here, we describe the influence of competing reactions on the measured response using a general site-binding model. We investigate the key features of the model with a real sensing example based on gold-coated nanoribbons functionalized with a self-assembled monolayer of calcium-sensitive molecules. We identify the residual pH response as the key parameter limiting the sensor response. The competing effect of pH or any other relevant reaction at the sensor surface has therefore to be included to quantitatively understand the sensor response and prevent misleading interpretations