Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics. Unfortunately, the number of available structures is orders of magnitude lower than the training data sizes commonly used in computer vision and machine learning. Moreover, this number is reduced even further, when only annotated protein structures can be considered, making the training of existing models difﬁcult and prone to over-ﬁtting. To address this challenge, we introduce a new representation learning framework for 3D protein structures. Our framework uses unsupervised contrastive learning to learn meaningful representations of protein structures, making use of proteins from the Protein Data Bank. We show, how these representations can be used to solve a large variety of tasks, such as protein function prediction, protein fold classiﬁcation, structural similarity prediction, and protein-ligand binding afﬁnity prediction. Moreover, we show how ﬁne-tuned networks, pre-trained with our algorithm, lead to signiﬁcantly improved task performance, achieving new state-of-the-art results in many tasks.

Contrastive Representation Learning for 3D Protein Structures

Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.

A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents.

The implications of atomically thin-layered two-dimensional (2D) materials for electrical, optical, and flexible applications have great potential for a new-generation technology. Here, we successfully fabricated a flexible and lateral homojunction photodiode using a single molybdenum ditelluride (MoTe2) flake with varying thicknesses (thin–thick). The gate voltage manipulation allowed a significant separation of photocarriers and created a built-in field at the interface, resulting in broadband photodetection from ultraviolet to near-infrared wavelengths. The study found that thin-MoTe2 and thick-MoTe2 act as strongly p- and n-type semiconductors, respectively. The effect of bromine (Br) on the MoTe2 surface showed efficient p-type doping on thin-MoTe2, leading to an improved current rectification ratio. The rectification ratio of the pristine device (1.18 × 103) increased by the order of 102 after bromine adsorption (4.66 × 105) at Vg = −20 V. The gate-tunable photodetection of the lateral homojunction MoTe2 diode demonstrated remarkable photoresponsivity (58.4 AW–1), external quantum efficiency (19,840.95%), and detectivity (2.53 × 1011 Jones). We also successfully demonstrated the fabrication of the homojunction MoTe2 photodiode on a flexible substrate, holding promise for wearable medical optoelectronic devices. Performance evaluation under different bending radii showed slightly reduced responsivity (25.4 AW–1) compared to that of the flat surface (27.3 AW–1). Thus, this research presents an intriguing approach for high-performance and flexible photodetectors based on MoTe2 lateral homojunctions, with potential applications in optoelectronics and future wearable technology. 

Broadband and Flexible Photodiode of a Bromine-Doped Lateral MoTe2 Homojunction

The control of movement in living organisms represents a fundamental task that the brain has evolved to solve. One crucial aspect is how the nervous system organizes the transformation of sensory information into motor commands. These commands lead to muscle activation and subsequent animal movement, which can exhibit complex patterns. One example of such movement is locomotion, which involves the translation of the entire body through space. Central Pattern Generators (CPGs) are neuronal circuits that provide control signals for these movements. Compared to the intricate circuits found in the brain, CPGs can be simplified into networks of neurons that generate rhythmic activation, coordinating muscle movements. Since the 1990s, researchers have developed numerous models of locomotive circuits to simulate different types of animal movement, including walking, flying, and swimming. Initially, the primary goal of these studies was to construct biomimetic robots. However, it became apparent that simplified CPGs alone were not sufficient to replicate the diverse range of adaptive locomotive movements observed in living organisms. Factors such as sensory modulation, higher-level control, and cognitive components related to learning and memory needed to be considered. This necessitated the use of more complex, high-dimensional circuits, as well as novel materials and hardware, in both modeling and robotics. With advancements in high-power computing, artificial intelligence, big data processing, smart materials, and electronics, the possibility of designing a new generation of true bio-mimetic robots has emerged. These robots have the capability to imitate not only simple locomotion but also exhibit adaptive motor behavior and decision-making. This motivation serves as the foundation for the current review, which aims to analyze existing concepts and models of movement control systems. As an illustrative example, we focus on underwater movement and explore the fundamental biological concepts, as well as the mathematical and physical models that underlie locomotion and its various modulations. 

Control of movement of underwater swimmers: animals, simulated animates and swimming robots

Maximizing light‐to‐electricity conversion efficiency is a crucial challenge for the practical applications of 2D material photodetectors. However, due to the lack of stable and precise electronic structure control methods for 2D materials, the driving force of photogenerated carriers is insufficient that severely hinders the efficiency of separation and transport. Herein, a gradient‐modulated, stable and precise strain applied strategy for 2D materials is designed and constructed, which results in a significant improvement in the detect efficiency of ZnO/WSe2/graphene van der Waals heterojunction photodetectors. Different from the overall strain of all‐component materials in typical photodetectors, biaxial tensile strain is applied to WSe2 that can be precisely modulated by controlling the height of ZnO nanorods, while the strain is nearly unaffected to ZnO. As the strain modulation increases from 1.3% to 4.0%, the external quantum efficiency (EQE) of the heterojunction increases from 11.4% to 35.3%, representing a threefold increase. Furthermore, with increasing strain, the EQE can reach higher levels. Moreover, the strain‐enhanced conversion efficiency mechanism is elucidated that results from the synergistic effect of the strain‐induced WSe2 exciton convergence and the strain‐increased ZnO/WSe2 interface barrier, which enhances the carrier interface separation efficiency.

Gradient‐Strained Van Der Waals Heterojunctions for High‐Efficient Photodetectors

Self‐powered ultrafast 2D photodetectors have demonstrated great potential in imaging, sensing, and communication. Understanding the intrinsic ultrafast charge carrier generation and separation processes is essential for achieving high‐performance devices. However, probing and manipulating the ultrafast photoresponse is limited either by the temporal resolution of the conventional methods or the required sophisticated device configurations. Here, van der Waals heterostructure photodetectors are constructed based on MoS2/WSe2 p–n and n–n junctions and manipulate the picosecond photoresponse by combining photovoltaic (PV) and photothermoelectric (PTE) effects. Taking time‐resolved photocurrent (TRPC) measurements, a TRPC peak at zero time delay is observed with decay time down to 4 ps in the n–n junction device, in contrast to the TRPC dip in the p–n junction and pure WSe2 devices, indicating an opposite current polarity between PV and PTE. More importantly, with an ultrafast photocurrent modulation, a transition from a TRPC peak to a TRPC dip is realized, and detailed carrier transport dynamics are analyzed. This study provides a deeper understanding of the ultrafast photocurrent generation mechanism in van der Waals heterostructures and offers a new perspective in instruction for designing more efficient self‐powered photodetectors.

Manipulating Picosecond Photoresponse in van der Waals Heterostructure Photodetectors

Abstract The elegant water shrew, Nectogale elegans, is one of the small mammal species most adapted to a semi‐aquatic lifestyle. The taxonomy of the genus Nectogale has received little attention due to difficulties in specimen collection. In this study, we sequenced one mitochondrial and eight nuclear genes to infer the phylogenetic relationship of Nectogale. Phylogenetic analyses revealed two large clades within Nectogale. One clade represented N. elegans, and the other was regarded as N. sikhimensis. The split between N. elegans and N. sikhimensis dated back to the early Pleistocene (2.15 million years ago [Ma]), which might be relevant to the Qinghai‐Tibet Plateau (QTP) uplift. The morphological comparison showed several distinguishing characters within Nectogale: the shape of the mastoids, the first lower unicuspid (a1), and the second upper molar (M2). Overall, the molecular and the morphological evidences supported that the genus Nectogale consists of two valid species: N. elegans and N. sikhimensis.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.9404

Molecular phylogeny and taxonomy of the genus Nectogale (Mammalia: Eulipotyphla: Soricidae)

Structure and function in artificial, zebrafish and human neural networks.

Protein Shape Retrieval Contest

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Manipulating Picosecond Photoresponse in van der Waals Heterostructure Photodetectors

Molecular phylogeny and taxonomy of the genus Nectogale (Mammalia: Eulipotyphla: Soricidae)

Structure and function in artificial, zebrafish and human neural networks.

Protein Shape Retrieval Contest