Graphene via Microwave Expansion of Graphite Followed by Cryo-Quenching and its Application in Electrostatic Droplet Switching.

Monoelemental atomic sheets (Xenes) and other 2D materials offer record electronic mobility, high thermal conductivity, excellent Young's moduli, optical transparency, and flexural capability, revolutionizing ultrasensitive devices and enhancing performance. The ideal synthesis of these quantum materials should be facile, fast, scalable, reproducible, and green. Microwave expansion followed by cryoquenching (MECQ) leverages thermal stress in graphite to produce high-purity graphene within minutes. MECQ synthesis of graphene is reported at 640 and 800 W for 10 min, followed by liquid nitrogen quenching for 5 and 90 min of sonication. Microscopic and spectroscopic analyses confirmed the chemical identity and phase purity of monolayers and few-layered graphene sheets (200-12 µm). Higher microwave power yields thinner layers with enhanced purity. Molecular dynamics simulations and DFT calculations support the exfoliation under these conditions. Electrostatic droplet switching is demonstrated using MECQ-synthesized graphene, observing electrorolling of a mercury droplet on a BN/graphene interface at voltages above 20 V. This technique can inspire the synthesis of other 2D materials with high purity and enable new applications.

Machine Learning-Assisted Exploration of Intrinsically Spin-Ordered Two-Dimensional (2D) Nanomagnets.

The existence of spontaneous spin-ordering in two-dimensional (2D) nanomagnets holds significant importance due to their several unique and promising properties that distinguish them from conventional 2D materials. In recent times, machine learning (ML) has emerged as a powerful tool for effectively exploring and identifying the optimal 2D materials for specific applications or properties within a limited span of time. Here, we have introduced ML-accelerated approaches to specifically estimate the properties, such as the HSE bandgap and magnetoanisotropic energy (MAE) of 2D magnetic materials. Supervised ML algorithms were employed to derive the descriptors that are capable of predicting the properties of intrinsic 2D magnetic materials. Furthermore, the feature selection score is also calculated to reduce the feature space complexity and improve the model accuracy. The input features were obtained from the C2DB database, and models were constructed using linear regression, Lasso, decision tree, random forest, XG Boost, and support vector machine algorithms. The random forest model predicted the HSE band gaps with an unprecedented low root-mean-square error (RMSE) of 0.22 eV, while the linear regression gives the best fit with RMSEs of 0.25 and 0.22 meV for the MAE(x) and MAE(y), respectively. Therefore, the integration of interpretable analytical models with density functional theory offers a swift and reliable approach for uncovering the properties of intrinsic 2D magnetic materials. This collaborative methodology not only ensures speed in analysis but also enriches the material space.

Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers' awareness?

Artificial intelligence in the workplace is becoming increasingly common. These tools are sometimes used to aid users in performing their task, for example, when an artificial intelligence tool assists a radiologist in their search for abnormalities in radiographic images. The use of artificial intelligence brings a wealth of benefits, such as increasing the efficiency and efficacy of performance. However, little research has been conducted to determine how the use of artificial intelligence assistants might affect the user's cognitive skills. In this theoretical perspective, we discuss how artificial intelligence assistants might accelerate skill decay among experts and hinder skill acquisition among learners. Further, we discuss how AI assistants might also prevent experts and learners from recognizing these deleterious effects. We then discuss the types of questions: use-inspired basic cognitive researchers, applied researchers, and computer science researchers should seek to answer. We conclude that multidisciplinary research from use-inspired basic cognitive research, domain-specific applied research, and technical research (e.g., human factors research, computer science research) is needed to (a) understand these potential consequences, (b) design artificial intelligence systems to mitigate these impacts, and (c) develop training and use protocols to prevent negative impacts on users' cognitive skills. Only by answering these questions from multidisciplinary perspectives can we harness the benefits of artificial intelligence in the workplace while preventing negative impacts on users' cognitive skills.

Eco-Friendly Biomemristive Nonvolatile Memory: Harnessing Organic Waste for Sustainable Technology.

Organic material-based bioelectronic nonvolatile memory devices have recently received a lot of attention due to their environmental compatibility, simple fabrication recipe, preferred scalability, low cost, low power consumption, and numerous additional advantages. Resistive random-access memory (RRAM) devices work on the principle of resistive switching, which has the potential for applications in memory storage and neuromorphic computing. Here, natural organically grown orange peel was used to extract biocompatible pectin to design a resistive switching-based memory device of the structure Ag/Pectin/Indium tin oxide (ITO), and the behavior was studied between a temperature range of 10K and 300K. The microscopic characterization revealed the texture of the surface and thickness of the layers. The memristive current-voltage characteristics performed over 1000 consecutive cycles of repeated switching revealed sustainable bipolar resistive switching behavior with a high ON/OFF ratio. The underlying principle of Resistive Switching behavior is based on the formation of conductive filaments between the electrodes, which is explained in this work. Further, we have also designed a 2 × 2 crossbar array of RRAM devices to demonstrate various logic circuit operations useful for neuromorphic computing. The robust switching characteristics suggest possible uses of such devices for the design of ecofriendly bioelectronic memory applications and in-memory computing.

Development of a novel self-healing Zn(II)-metallohydrogel with wide bandgap semiconducting properties for non-volatile memory device application.

A rapid and effective strategy has been devised for the swift development of a Zn(II)-ion-based supramolecular metallohydrogel, termed Zn@PEH, using pentaethylenehexamine as a low molecular weight gelator. This process occurs in an aqueous medium at room temperature and atmospheric pressure. The mechanical strength of the synthesized Zn@PEH metallohydrogel has been assessed through rheological analysis, considering angular frequency and oscillator stress dependencies. Notably, the Zn@PEH metallohydrogel exhibits exceptional self-healing abilities and can bear substantial loads, which have been characterized through thixotropic analysis. Additionally, this metallohydrogel displays injectable properties. The structural arrangement resembling pebbles within the hierarchical network of the supramolecular Zn@PEH metallohydrogel has been explored using FESEM and TEM measurements. EDX elemental mapping has confirmed the primary chemical constituents of the metallohydrogel. The formation mechanism of the metallohydrogel has been analyzed via FT-IR spectroscopy. Furthermore, zinc(II) metallohydrogel (Zn@PEH)-based Schottky diode structure has been fabricated in a lateral metal-semiconductor-metal configuration and  it's charge transport behavior has also been studied. Notably, the zinc(II) metallohydrogel-based resistive random access memory (RRAM) device (Zn@PEH) demonstrates bipolar resistive switching behavior at room temperature. This RRAM device showcases remarkable switching endurance over 1000 consecutive cycles and a high ON/OFF ratio of approximately 270. Further, 2 × 2 crossbar array of the RRAM devices were designed to demonstrate OR and NOT logic circuit operations, which can be extended for performing higher order computing operations. These structures hold promise for applications in non-volatile memory design, neuromorphic and in-memory computing, flexible electronics, and optoelectronic devices due to their straightforward fabrication process, robust resistive switching behavior, and overall system stability.

Prediction of Postoperative Urinary Tract Infection Following Benign Gynecologic Surgery.

INTRODUCTION AND HYPOTHESIS: The objective was to develop a prediction model for urinary tract infection (UTI) after pelvic surgery. METHODS: We utilized data from three tertiary care centers of women undergoing pelvic surgery. The primary outcome was a UTI within 8 weeks of surgery. Additional variables collected included procedural data, severity of prolapse, use of mesh, anti-incontinence surgery, EBL, diabetes, steroid use, estrogen use, postoperative catheter use, PVR, history of recurrent UTI, operative time, comorbidities, and postoperative morbidity including venous thromboembolism, surgical site infection. Two datasets were used for internal validation, whereas a third dataset was used for external validation. Algorithms that tested included the following: multivariable logistic regression, decision trees (DTs), naive Bayes (NB), random forest (RF), gradient boosting (GB), and multilayer perceptron (MP). RESULTS: For the training dataset, containing both University of British Columbia and Mayo Clinic Rochester data, there were 1,657 patients, with 172 (10.4%) UTIs; whereas for the University of Calgary external validation data, there were a total of 392 patients with a UTI rate of 16.1% (n = 63). All models performed well; however, the GB, DT, and RF models all had an area under the curve (AUC) > 0.97. With external validation the model retained high discriminatory ability, DT: AUC = 0.88, RF: AUC = 0.88, and GB: AUC = 0.90. CONCLUSIONS: A model with high discriminatory ability can predict UTI within 8 weeks of pelvic surgery. Future studies should focus on prospective validation and application of randomized trial models to test the utility of this model in the prevention of postoperative UTI.

Novel supramolecular luminescent metallogels containing Tb(iii) and Eu(iii) ions with benzene-1,3,5-tricarboxylic acid gelator: advancing semiconductor applications in microelectronic devices.

A novel strategy was employed to create supramolecular metallogels incorporating Tb(iii) and Eu(iii) ions using benzene-1,3,5-tricarboxylic acid (TA) as a gelator in N,N-dimethylformamide (DMF). Rheological analysis demonstrated their mechanical robustness under varying stress levels and angular frequencies. FESEM imaging revealed a flake-like hierarchical network for Tb-TA and a rod-shaped architecture for Eu-TA. EDX analysis confirmed essential chemical constituents within the metallogels. FT-IR, PXRD, Raman spectroscopy, and thermogravimetric analysis assessed their gelation process and material properties, showing semiconducting characteristics, validated by optical band-gap measurements. Metal-semiconductor junction-based devices integrating Al metal with Tb(iii)- and Eu(iii)-metallogels exhibited non-linear charge transport akin to a Schottky diode, indicating potential for advanced electronic device development. Direct utilization of benzene-1,3,5-tricarboxylic acid and Tb(iii)/Eu(iii) sources underscores their suitability as semiconducting materials for device fabrication. This study explores the versatile applications of Tb-TA and Eu-TA metallogels, offering insights for material science researchers.

CrXY (X/Y = S, Se, Te) monolayers as efficient anode materials for Li and Na-ion batteries: a first-principles study.

Over the last decade, two-dimensional (2D) materials have been of great interest in the energy storage field. Large-scale electrochemical energy storage is based on the intercalation of metal ions in layered materials having van der Waals gaps. In this work, by means of first-principles calculations, we explored the use of 2D Janus transition metal dichalcogenides (TMDs) CrSSe, CrSTe and CrSeTe as anode materials for lithium and sodium-ion batteries. To examine the electronic properties and electrochemical performance, density functional theory (DFT) calculation was used. Our research shows that lithium diffuses easily with short diffusion distances and prefers to bind effectively to the monolayer. These structures are metallic in their bare phases. The highest adsorption energy shown by CrSSe, CrSTe, and CrSeTe is -1.86 eV, -1.66 eV, -2.15 eV with a low diffusion barrier of 0.3 eV, 0.6 eV, and 0.1 eV for the Li atoms and 0.54 eV, 0.32 eV and 0.15 eV for the Na atoms, respectively. At different chemical stoichiometries, we discovered negligible average open-circuit voltages of 1.0 V, 0.52 V, 0.6 V for lithium and 0.1 V, 0.49 V, and 0.51 V for sodium atoms respectively. The storage capacities shown by CrSSe, CrSTe, and CrSeTe are 348 mA h g-1, 254 mA h g-1, 208 mA h g-1 for the Li atoms and 260 mA h g-1, 198 mA h g-1, 177 mA h g-1 for the Na atoms respectively.

Characterization of Regolith And Trace Economic Resources (CRATER): An Orbitrap-based laser desorption mass spectrometry instrument for in situ exploration of the Moon.

RATIONALE: Characterization of Regolith And Trace Economic Resources (CRATER), an Orbitrap™-based laser desorption mass spectrometry instrument designed to conduct high-precision, spatially resolved analyses of planetary materials, is capable of answering outstanding science questions about the Moon's formation and the subsequent processes that have modified its (sub)surface. METHODS: Here, we describe the baseline design of the CRATER flight model, which requires <20 000 cm3  volume, <10 kg mass, and <60 W peak power. The analytical capabilities and performance metrics of a prototype that meets the full functionality of the flight model are demonstrated. RESULTS: The instrument comprises a high-power, solid-state, pulsed ultraviolet (213 nm) laser source to ablate the surface of the lunar sample, a custom ion optical interface to accelerate and collimate the ions produced at the ablation site, and an Orbitrap mass analyzer capable of discriminating competing isobars via ultrahigh mass resolution and high mass accuracy. The CRATER instrument can measure elemental and isotopic abundances and characterize the organic content of lunar surface samples, as well as identify economically valuable resources for future exploration. CONCLUSION: An engineering test unit of the flight model is currently in development to serve as a pathfinder for near-term mission opportunities.

Twistronics in two-dimensional transition metal dichalcogenide (TMD)-based van der Waals interface.

Transition metal dichalcogenides (TMD) based heterostructures have gained significant attention lately because of their distinct physical properties and potential uses in electronics and optoelectronics. In the present work, the effects of twist on the structural, electronic, and optical properties (such as the static dielectric constant, refractive index, extinction coefficient, and absorption coefficient) of vertically stacked TMD heterostructures, namely MoSe2/WSe2, WS2/WSe2, MoSe2/WS2 and MoS2/WSe2, have been systematically studied and a thorough comparison is done among these heterostructures. In addition, the absence of negative frequency in the phonon dispersion curve and a low formation energy confirm the structural and thermodynamical stability of all the proposed TMD heterostructures. The calculations are performed using first-principles-based density functional theory (DFT) method. Beautiful Moiré patterns are formed due to the relative rotation of the layers as a consequence of the superposition of the periodic structures of the TMDs on each other. Twist engineering allows the modulation of bandgaps and a phase change from direct to indirect band gap semiconductors as well. The high optical absorption in the visible range of spectrum makes these twisted heterostructures very promising candidates in photovoltaic applications.

Rapid Semiconducting Supramolecular Mg(II)-Metallohydrogel: Exploring Its Potential in Nonvolatile Resistive Switching Applications and Antiseptic Wound Healing Properties.

An effective strategy was employed for the rapid development of a supramolecular metallohydrogel of Mg(II) ion (i.e., Mg@PEHA) using pentaethylenehexamine (PEHA) as a low-molecular-weight gelator in aqueous medium under ambient conditions. The mechanical stability of the synthesized Mg@PEHA metallohydrogel was characterized by using rheological analysis, which showed its robustness across different angular frequencies and oscillator stress levels. The metallohydrogel exhibited excellent thixotropic behavior, which signifies that Mg@PEHA has a self-healing nature. Field emission scanning electron microscopy and transmission electron microscopy images were utilized to explore the rectangular pebble-like hierarchical network of the Mg@PEHA metallohydrogel. Elemental mapping through energy-dispersive X-ray spectroscopy analysis confirmed the presence of primary chemical constituents in the metallohydrogel. Fourier transform infrared spectroscopy spectroscopy provided insights into the possible formation strategy of the metallohydrogel. In this work, Schottky diode structures in a metal-semiconductor-metal geometry based on a magnesium(II) metallohydrogel (Mg@PEHA) were constructed, and the charge transport behavior was observed. Additionally, a resistive random access memory (RRAM) device was developed using Mg@PEHA, which displayed bipolar resistive switching behavior at room temperature. The researchers investigated the switching mechanism, which involved the formation or rupture of conduction filaments, to gain insights into the resistive switching process. The RRAM device demonstrated excellent performance with a high ON/OFF ratio of approximately 100 and remarkable endurance of over 5000 switching cycles. RRAM devices exhibit good endurance, meaning they can endure a large number of read and write cycles without significant degradation in performance. RRAM devices have shown promising reliability in terms of long-term performance and stability, making them suitable for critical applications that require reliable memory solutions. Significant inhibitory activity against the drug-resistant Klebsiella pneumonia strain and its biofilm formation ability was demonstrated by Mg@PEHA. The minimum inhibitory concentration value of the metallohydrogel was determined to be 3 mg/mL when it was dissolved in 1% DMSO. To study the antibiofilm activity, an MTT assay was performed, revealing that biofilm inhibition (60%) commenced at 1 mg/mL of Mg@PEHA when dissolved in 1% DMSO. Moreover, in the mouse excisional wound model, Mg@PEHA played a crucial role in preventing postoperative wound infections and promoting wound healing.

On the Relationship between Feature Selection Metrics and Accuracy.

Feature selection metrics are commonly used in the machine learning pipeline to rank and select features before creating a predictive model. While many different metrics have been proposed for feature selection, final models are often evaluated by accuracy. In this paper, we consider the relationship between common feature selection metrics and accuracy. In particular, we focus on misorderings: cases where a feature selection metric may rank features differently than accuracy would. We analytically investigate the frequency of misordering for a variety of feature selection metrics as a function of parameters that represent how a feature partitions the data. Our analysis reveals that different metrics have systematic differences in how likely they are to misorder features which can happen over a wide range of partition parameters. We then perform an empirical evaluation with different feature selection metrics on several real-world datasets to measure misordering. Our empirical results generally match our analytical results, illustrating that misordering features happens in practice and can provide some insight into the performance of feature selection metrics.

Exploration of a wide bandgap semiconducting supramolecular Mg(II)-metallohydrogel derived from an aliphatic amine: a robust resistive switching framework for brain-inspired computing.

A rapid metallohydrogelation strategy has been developed of magnesium(II)-ion using trimethylamine as a low molecular weight gelator in water medium at room temperature. The mechanical property of the synthesized metallohydrogel material is established through the rheological analysis. The nano-rose like morphological patterns of Mg(II)-metallohydrogel are characterized through field emission scanning electron microscopic study. The energy dispersive X-ray elemental mapping analysis confirms the primary gel forming elements of Mg(II)-metallohydrogel. The possible metallohydrogel formation strategy has been analyzed through FT-IR spectroscopic study. In this work, magnesium(II) metallohydrogel (Mg@TMA) based metal-semiconductor-metal structures have been developed and charge transport behaviour is studied. Here, it is confirmed that the magnesium(II) metallohydrogel (Mg@TMA) based resistive random access memory (RRAM) device is showing bipolar resistive switching behaviour at room temperature. We have also explored the mechanism of resistive switching behaviour using the formation (rupture) of conductive filaments between the metal electrodes. This RRAM devices exhibit excellent switching endurance over 10,000 switching cycles with a large ON/OFF ratio (~ 100). The easy fabrication techniques, robust resistive switching behaviour and stability of the present system makes these structures preferred candidate for applications in non-volatile memory design, neuromorphic computing, flexible electronics and optoelectronics etc.

A 5-aminoisophthalic acid low molecular weight gelator based novel semiconducting supramolecular Zn(ii)-metallogel: unlocking an efficient Schottky barrier diode for microelectronics.

A novel method has been successfully developed for creating supramolecular metallogels using zinc(ii) ions and 5-aminoisophthalic acid as the gelator (low molecular weight gelator) in a dimethylformamide (DMF) solvent at room temperature. Comprehensive rheological investigations confirm the robust mechanical strength of the resulting zinc(ii)-metallogel. Microstructural analysis conducted through field-emission scanning electron microscopy (FESEM) unveils a unique flake-like morphology, with energy-dispersive X-ray (EDX) elemental mapping confirming the prevalence of zinc as the primary constituent of the metallogel. To understand the formation mechanism of this metallogel, Fourier-transform infrared (FT-IR) spectroscopy was employed. Notably, these supramolecular zinc(ii)-metallogel assemblies exhibit electrical conductivity reminiscent of metal-semiconductor (MS) junction electronic components. Surprisingly, the metallogel-based thin film device showcases an impressive electrical conductivity of 1.34 × 10-5 S m-1. The semiconductor characteristics of the synthesized zinc(ii)-metallogel devices, including their Schottky barrier diode properties, have been extensively investigated. This multifaceted study opens up a promising avenue for designing functional materials tailored for electronic applications. It harnesses the synergistic properties of supramolecular metallogels and highlights their significant potential in the development of semiconductor devices. This work represents a novel approach to the creation of advanced materials with unique electronic properties, offering exciting prospects for future innovations in electronic and semiconductor technologies.

Two novel low molecular weight gelator-driven supramolecular metallogels efficient in antimicrobial activity applications.

A remarkable ultrasonication technique was successfully employed to create two novel metallogels using citric acid as a low molecular weight gelator, in combination with cadmium(ii)-acetate and mercury(ii)-acetate dissolved in N,N-dimethyl formamide at room temperature and under ambient conditions. The mechanical properties of the resulting Cd(ii)- and Hg(ii)-metallogels were rigorously examined through rheological analyses, which revealed their robust mechanical stability under varying angular frequencies and shear strains. Detailed characterization of the chemical constituents within these metallogels was accomplished through EDX mapping experiments, while microstructural features were visualized using field emission scanning electron microscope (FESEM) images. Additionally, FT-IR spectroscopic analysis was employed to elucidate the metallogel formation mechanism. Significantly, the antimicrobial efficacy of these novel metallogels was assessed against a panel of bacteria, including Gram-positive strains such as Bacillus subtilis and Staphylococcus epidermidis, as well as Gram-negative species like Escherichia coli and Pseudomonas aeruginosa. The results demonstrated substantial antibacterial activity, highlighting the potential of Cd(ii) and Hg(ii)-based citric acid-mediated metallogels as effective agents against a broad spectrum of bacteria. In conclusion, this study provides a comprehensive exploration of the synthesis, characterization, and antimicrobial properties of Cd(ii) and Hg(ii)-based citric acid-mediated metallogels, shedding light on their promising applications in combating both Gram-positive and Gram-negative bacterial infections. These findings open up exciting prospects for the development of advanced materials with multifaceted industrial and biomedical uses.

Resonant transport in a highly conducting single molecular junction via metal-metal covalent bond.

Achieving highly transmitting molecular junctions through resonant transport at low bias is key to the next-generation low-power molecular devices. Although resonant transport in molecular junctions was observed by connecting a molecule between the metal electrodes via chemical anchors by applying a high source-drain bias (>1 V), the conductance was limited to <0.1G0, G0 being the quantum of conductance. Herein, we report electronic transport measurements by directly connecting a ferrocene molecule between Au electrodes under ambient conditions in a mechanically controllable break junction setup (MCBJ), revealing a conductance peak at ∼0.2G0 in the conductance histogram. A similar experiment was repeated for ferrocene terminated with amine (-NH2) and cyano (-CN) anchors, where conductance histograms exhibit an extended low conductance feature, including the sharp high conductance peak, similar to pristine ferrocene. The statistical analysis of the data and density functional theory-based transport calculation suggest a possible molecular conformation with a strong hybridization between the Au electrodes, and that the Fe atom of ferrocene is responsible for a near-perfect transmission in the vicinity of the Fermi energy, leading to the resonant transport at a small applied bias (<0.5 V). Moreover, calculations including van der Waals/dispersion corrections reveal a covalent-like organometallic bonding between Au and the central Fe atom of ferrocene, having bond energies of ∼660 meV. Overall, our study not only demonstrates the realization of an air-stable highly transmitting molecular junction, but also provides important insights about the nature of chemical bonding at the metal/organo-metallic interface.

Discovery and Optimization of a STING Agonist Platform for Application in Antibody Drug Conjugates.

While STING agonists have proven to be effective preclinically as anti-tumor agents, these promising results have yet to be translated in the clinic. A STING agonist antibody-drug conjugate (ADC) could overcome current limitations by improving tumor accessibility, allowing for systemic administration as well as tumor-localized activation of STING for greater anti-tumor activity and better tolerability. In line with this effort, a STING agonist ADC platform was identified through systematic optimization of the payload, linker, and scaffold based on multiple factors including potency and specificity in both in vitro and in vivo evaluations. The platform employs a potent non-cyclic dinucleotide STING agonist, a cleavable ester-based linker, and a hydrophilic PEG8-bisglucamine scaffold. A tumor-targeted ADC built with the resulting STING agonist platform induced robust and durable anti-tumor activity and demonstrated high stability and favorable pharmacokinetics in nonclinical species.

Detection of Short Peptides as Putative Biosignatures of Psychrophiles via Laser Desorption Mass Spectrometry.

Studies of psychrophilic life on Earth provide chemical clues as to how extraterrestrial life could maintain viability in cryogenic environments. If living systems in ocean worlds (e.g., Enceladus) share a similar set of 3-mer and 4-mer peptides to the psychrophile Colwellia psychrerythraea on Earth, spaceflight technologies and analytical methods need to be developed to detect and sequence these putative biosignatures. We demonstrate that laser desorption mass spectrometry, as implemented by the CORALS spaceflight prototype instrument, enables the detection of protonated peptides, their dimers, and metal adducts. The addition of silicon nanoparticles promotes the ionization efficiency, improves mass resolving power and mass accuracies via reduction of metastable decay, and facilitates peptide de novo sequencing. The CORALS instrument, which integrates a pulsed UV laser source and an Orbitrap™ mass analyzer capable of ultrahigh mass resolving powers and mass accuracies, represents an emerging technology for planetary exploration and a pathfinder for advanced technique development for astrobiological objectives. Teaser: Current spaceflight prototype instrument proposed to visit ocean worlds can detect and sequence peptides that are found enriched in at least one strain of microbe surviving in subzero icy brines via silicon nanoparticle-assisted laser desorption analysis.

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures.

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a model based on low prior structural information. In order to address this, we have developed an AlphaFold2 version where we exclude all structural templates with more than 30% sequence identity from the model-building process. In a previous study, we used those models in conjunction with state-of-the-art free energy perturbation methods and demonstrated that it is possible to obtain quantitatively accurate results. In this work, we focus on using these structures in rigid receptor-ligand docking studies. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario for virtual screening campaigns; in fact, we strongly recommend to include some post-processing modeling to drive the binding site into a more realistic holo model.

Development of a machine learning-based predictive model for prediction of success or failure of medical management for benign prostatic hyperplasia.

OBJECTIVE: To develop a novel predictive model for identifying patients who will and will not respond to the medical management of benign prostatic hyperplasia (BPH). METHODS: Using data from the Medical Therapy of Prostatic Symptoms (MTOPS) study, several models were constructed using an initial data set of 2172 patients with BPH who were treated with doxazosin (Group 1), finasteride (Group 2), and combination therapy (Group 3). K-fold stratified cross-validation was performed on each group, Within each group, feature selection and dimensionality reduction using nonnegative matrix factorization (NMF) were performed based on the training data, before several machine learning algorithms were tested; the most accurate models, boosted support vector machines (SVMs), being selected for further refinement. The area under the receiver operating curve (AUC) was calculated and used to determine the optimal operating points. Patients were classified as treatment failures or responders, based on whether they fell below or above the AUC threshold for each group and for the whole data set. RESULTS: For the entire cohort, the AUC for the boosted SVM model was 0.698. For patients in Group 1, the AUC was 0.729, for Group 2, the AUC was 0.719, and for Group 3, the AUC was 0.698. CONCLUSION: Using MTOPS data, we were able to develop a prediction model with an acceptable rate of discrimination of medical management success for BPH.