Polytypic Two-Dimensional FeAs with High Anisotropy.

In the realm of two-dimensional (2D) crystal growth, the chemical composition often determines the thermodynamically favored crystallographic structures. This relationship poses a challenge in synthesizing novel 2D crystals without altering their chemical elements, resulting in the rarity of achieving specific crystallographic symmetries or lattice parameters. We present 2D polymorphic FeAs crystals that completely differ from bulk orthorhombic FeAs (Pnma), differing in the stacking sequence, i.e., polytypes. Preparing polytypic FeAs outlines a strategy for independently controlling each symmetry operator, which includes the mirror plane for 2Q-FeAs (I4/mmm) and the glide plane for 1Q-FeAs (P4/nmm). As such, compared to bulk FeAs, polytypic 2D FeAs shows highly anisotropic properties such as electrical conductivity, Young's modulus, and friction coefficient. This work represents a concept of expanding 2D crystal libraries with a given chemical composition but various crystal symmetries.

Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection.

Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. The Tryp dataset provides bounding box annotations for tightly enclosed regions containing the parasite for 3,085 positive images, and 93 images collected from negative blood samples. The Tryp dataset represents the largest of its kind. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. Overall, the availability of the Tryp dataset is expected to facilitate research advancements in diagnostic screening for this disease, which may lead to improved healthcare outcomes for the communities impacted.

CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins.

BACKGROUND: CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. RESULTS: CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. CONCLUSION: CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA-protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at www.crisprcasdocker.org as a web server, and at https://github.com/hshimlab/CRISPR-Cas-Docker as an open-source tool.

Enhancing Li Ion Battery Performance by Mechanical Resonance.

The quest for safe and high-performance Li ion batteries (LIBs) motivates intense efforts seeking a high-energy but reliable anode, cathode, and nonflammable electrolyte. For any of these, exploring new electrochemistry methods that enhance safety and performance by employing well-designed electrodes and electrolytes are required. Electrolyte wetting, governed by thermodynamics, is another critical issue in increasing Li ion transport through the separator. Herein, we report an approach to enhancing LIB performance by applying mechanical resonant vibration to increase electrolyte wettability on the separator. Wetting is activated at a resonant frequency with a capillary wave along the surface of the electrolyte, allowing the electrolyte to infiltrate into the porous separator by inertia force. This mechanical resonance, rather than electrochemistry, leads to the high specific capacity, rate capability, and cycling stability of LIBs. The concept of the mechanical approach is a promising yet simple strategy for the development of safer LIBs using liquid electrolytes.