When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

A Methylazanediyl Bisacetamide Derivative Sensitizes Staphylococcus aureus Persisters to a Combination of Gentamicin And Daptomycin

Infections caused by Staphylococcus aureus, notably methicillin-resistant S. aureus (MRSA), pose treatment challenges due to its ability to tolerate antibiotics and develop antibiotic resistance. The former, a mechanism independent of genetic changes, allows bacteria to withstand antibiotics by altering metabolic processes. Here, a potent methylazanediyl bisacetamide derivative, MB6, is described, which selectively targets MRSA membranes over mammalian membranes without observable resistance development. Although MB6 is effective against growing MRSA cells, its antimicrobial activity against MRSA persisters is limited. Nevertheless, MB6 significantly potentiates the bactericidal activity of gentamicin against MRSA persisters by facilitating gentamicin uptake. In addition, MB6 in combination with daptomycin exhibits enhanced anti-persister activity through mutual reinforcement of their membrane-disrupting activities. Crucially, the "triple" combination of MB6, gentamicin, and daptomycin exhibits a marked enhancement in the killing of MRSA persisters compared to individual components or any double combinations. These findings underscore the potential of MB6 to function as a potent and selective membrane-active antimicrobial adjuvant to enhance the efficacy of existing antibiotics against persister cells. The molecular mechanisms of MB6 elucidated in this study provide valuable insights for designing anti-persister adjuvants and for developing new antimicrobial combination strategies to overcome the current limitations of antibiotic treatments.

Data-Driven Intelligent Manipulation of Particles in Microfluidics

Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced-order physical model characterizing the field-driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data-driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult-to-derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data-driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence.

Efficacy, toxicity, and quality-of-life outcomes of ultrahypofractionated radiotherapy in patients with localized prostate cancer: A single-arm phase 2 trial from Asia

AIMS

Ultra-hypofractionated radiotherapy (UHF-RT) is widely utilized in men with localized prostate cancer (PCa). There are limited data in Asian cohorts. We report the outcomes of a single-arm, phase II trial of UHF-RT from an Asian center.

METHODS

We recruited men with histologically confirmed, nonmetastatic localized PCa. UHF-RT regimens were 36.25 Gy (Cohort A) and 37.5 Gy (Cohort B) delivered in five fractions every other day over 1.5-2.5 weeks. Primary endpoint was physician-scored late genitourinary (GU) and gastrointestinal (GI) adverse events (AEs). Quality-of-life (QoL) was assessed by Expanded Prostate Cancer Index Composite (EPIC) at baseline, 1- and 2-year post-UHF-RT.

RESULTS

Between March 2014 and August 2019, 105 men were recruited; four were subsequently excluded from analysis. Median age was 68.0 (Interquartile range (IQR): 63.8-73.0) years. 26 (24.8%) and 68 (64.8%) men had NCCN-defined low-and intermediate-risk PCa, respectively. No late ≥G3 GU or GI toxicities were reported in both cohorts. Peak incidence of acute ≥G2 GU AEs at 14 days post-UHF-RT was 23.6% (17/72) and 24.0% (6/25) in Cohorts A and B, respectively; ≥G2 GI AEs were observed in 9.7% (7/72) and 36.0% (9/25), respectively. Late ≥G2 GU and GI AEs occurred in 4.7% and 3.1% of Cohort A patients, and 5.0% in Cohort B at 12 months, with no AEs at 24 months. EPIC scores changed minimally across all domains. At a median follow-up of 44.9 months, we recorded one (1.3%) biochemical relapse by the Phoenix criteria (Cohort A).

CONCLUSION

UHF-RT is well tolerated in Asian men and can be a recommended fractionation schema for localized PCa.

Discovery of Dome-Shaped Superconducting Phase and Anisotropic Transport in a van der Waals Layered Candidate NbIrTe4 under Pressure

The unique electronic structure and crystal structure driven by external pressure in transition metal tellurides (TMTs) can host unconventional quantum states. Here, the discovery of pressure-induced phase transition at ≈2 GPa, and dome-shaped superconducting phase emerged in van der Waals layered NbIrTe4 is reported. The highest critical temperature (Tc ) is ≈5.8 K at pressure of ≈16 GPa, where the interlayered Te-Te covalent bonds form simultaneously derived from the synchrotron diffraction data, indicating the hosting structure of superconducting evolved from low-pressure two-dimensional (2D) phase to three-dimensional (3D) structure with pressure higher than 30 GPa. Strikingly, the authors have found an anisotropic transport in the vicinity of the superconducting state, suggesting the emergence of a "stripe"-like phase. The dome-shaped superconducting phase and anisotropic transport are possibly due to the spatial modulation of interlayer Josephson coupling .

Collaborative Equilibrium Coupling of Catalytic DNA Nanostructures Enables Programmable Detection of SARS-CoV-2

Accessible and adaptable nucleic acid diagnostics remains a critical challenge in managing the evolving COVID-19 pandemic. Here, an integrated molecular nanotechnology that enables direct and programmable detection of SARS-CoV-2 RNA targets in native patient specimens is reported. Termed synergistic coupling of responsive equilibrium in enzymatic network (SCREEN), the technology leverages tunable, catalytic molecular nanostructures to establish an interconnected, collaborative architecture. SCREEN mimics the extraordinary organization and functionality of cellular signaling cascades. Through programmable enzyme-DNA nanostructures, SCREEN activates upon interaction with different RNA targets to initiate multi-enzyme catalysis; through system-wide favorable equilibrium shifting, SCREEN directly transduces a single target binding into an amplified electrical signal. To establish collaborative equilibrium coupling in the architecture, a computational model that simulates all reactions to predict overall performance and optimize assay configuration is developed. The developed platform achieves direct and sensitive RNA detection (approaching single-copy detection), fast response (assay reaction is completed within 30 min at room temperature), and robust programmability (across different genetic loci of SARS-CoV-2). When clinically evaluated, the technology demonstrates robust and direct detection in clinical swab lysates to accurately diagnose COVID-19 patients.