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A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.This Perspective describes the application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process.

The first order and second order corrected photoluminescence quantum yields are computed and compared to experiment for naphthalene in this manuscript discussing negative results. Results for anthracene and tetracene are recalled from previous work (Manian et al. in J Chem Phys 155:054108, 2021), and the results for all three polyacenes are juxtaposed to each other. While at the Franck–Condon point, each of the three noted polyacenes were found to possess a quantum yield near unity. Following the consideration of Herzberg–Teller effects, quantum yields stabilised for anthracene and tetracene to 0.19 and 0.08, respectively. Conversely, the second order corrected quantum yield for naphthalene was found to be 0.91. Analysis of this result showed that while the predicted non-radiative pathways correlate well with what should be expected, the approximation used to calculate second order corrected fluorescence, which yielded very positive results for many other molecular systems, here is unable to account for strong second order contributions, resulting in a grossly overestimated rate of fluorescence. However, substitution of an experimental radiative rate results in a quantum yield of 0.33. This work extols the importance of Herzberg–Teller terms in photophysical descriptions of chromophores, and highlights those cases in which a treatment beyond the above approximation is required.

The predicted strong piezoelectricity for monolayers of group IV monochalcogenides, together with their inherent flexibility, makes them likely candidates for developing flexible nanogenerators. Within this group, SnS is a potential choice for such nanogenerators due to its favourable semiconducting properties. To date, access to large-area and highly crystalline monolayer SnS has been challenging due to the presence of strong inter-layer interactions by the lone-pair electrons of S. Here we report single crystal across-the-plane and large-area monolayer SnS synthesis using a liquid metal-based technique. The characterisations confirm the formation of atomically thin SnS with a remarkable carrier mobility of ~35 cm 2 V −1 s −1 and piezoelectric coefficient of ~26 pm V −1 . Piezoelectric nanogenerators fabricated using the SnS monolayers demonstrate a peak output voltage of ~150 mV at 0.7% strain. The stable and flexible monolayer SnS can be implemented into a variety of systems for efficient energy harvesting. The presence of strong inter-layer interactions has hindered the synthesis efforts towards large-area and highly crystalline monolayer SnS. Here, the authors report synthesis of large-area monolayer SnS using a liquid metal-based technique, and fabricate piezoelectric nano-generators with average peak output voltage of 150 mV at 0.7% strain.

Catalytic solvent regeneration has attracted broad interest owing to its potential to reduce energy consumption in CO 2 separation, enabling industry to achieve emission reduction targets of the Paris Climate Accord. Despite recent advances, the development of engineered acidic nanocatalysts with unique characteristics remains a challenge. Herein, we establish a strategy to tailor the physicochemical properties of metal-organic frameworks (MOFs) for the synthesis of water-dispersible core-shell nanocatalysts with ease of use. We demonstrate that functionalized nanoclusters (Fe 3 O 4 -COOH) effectively induce missing-linker deficiencies and fabricate mesoporosity during the self-assembly of MOFs. Superacid sites are created by introducing chelating sulfates on the uncoordinated metal clusters, providing high proton donation capability. The obtained nanomaterials drastically reduce the energy consumption of CO 2 capture by 44.7% using only 0.1 wt.% nanocatalyst, which is a ∽10-fold improvement in efficiency compared to heterogeneous catalysts. This research represents a new avenue for the next generation of advanced nanomaterials in catalytic solvent regeneration. Catalytic solvent regeneration is of interest to reduce energy consumption in CO2 separation, however, the development of engineered nanocatalysts remains a challenge. Here, a new avenue is presented for the next generation of advanced metal-organic frameworks (MOFs) in energy-efficient CO2 capture.

Medical education is highly demanding and evidence shows that medical students are three times more susceptible to deteriorating physical and mental health than the average college student. While trait anxiety may further increase such risk, little is known about the role of trait mindfulness in mitigating these effects. Here we examine the protective role of specific mindfulness facets as mediators in pathways from trait anxiety to perceived stress, psychosomatic burden and sleep-wake quality in medical students, across repeated measurements throughout the first trimester of the school year. Preclinical medical students enrolled in the second year of the Medical School of University of Bologna completed self-report questionnaires examining personality traits as well as physical and psychological wellbeing. Data were collected at the beginning (Time 1: N = 349) and the end of the first trimester (Time 2: N = 305). As students approached the end of the trimester and upcoming exams, reported levels of perceived stress, psychosomatic problems and difficulties in wakefulness increased significantly compared to the beginning of the trimester. Mediation results showed that trait anxiety predicted such outcomes whereas the protective role of mindfulness facets in mitigating these effects was significant only at Time 2. Specific facets of Nonjudging of inner experience and Acting with awareness proved to be the most effective mediators. Findings highlight that the beneficial role of mindfulness facets in mitigating negative consequences of trait anxiety on medical student wellbeing is revealed in high-pressure periods and when self-regulation is needed the most. Cultivating awareness and nonjudgmental acceptance of one’s inner experiences is a crucial self-regulation resource that can help medical students sustain their wellbeing as they learn and throughout their high-pressure education and professional careers.

A variety of deposition methods for two-dimensional crystals have been demonstrated; however, their wafer-scale deposition remains a challenge. Here we introduce a technique for depositing and patterning of wafer-scale two-dimensional metal chalcogenide compounds by transforming the native interfacial metal oxide layer of low melting point metal precursors (group III and IV) in liquid form. In an oxygen-containing atmosphere, these metals establish an atomically thin oxide layer in a self-limiting reaction. The layer increases the wettability of the liquid metal placed on oxygen-terminated substrates, leaving the thin oxide layer behind. In the case of liquid gallium, the oxide skin attaches exclusively to a substrate and is then sulfurized via a relatively low temperature process. By controlling the surface chemistry of the substrate, we produce large area two-dimensional semiconducting GaS of unit cell thickness (∼1.5 nm). The presented deposition and patterning method offers great commercial potential for wafer-scale processes. One of the key challenges 2D materials still face is their uniform wafer-scale deposition. Here, the authors present a deposition method for post-transition metal dichalcogenides, based on transformation of an ultra-thin oxide layer on the surface of liquid elemental gallium onto an oxide-coated substrate.

Two-dimensional piezotronics will benefit from the emergence of new crystals featuring high piezoelectric coefficients. Gallium phosphate (GaPO 4 ) is an archetypal piezoelectric material, which does not naturally crystallise in a stratified structure and hence cannot be exfoliated using conventional methods. Here, we report a low-temperature liquid metal-based two-dimensional printing and synthesis strategy to achieve this goal. We exfoliate and surface print the interfacial oxide layer of liquid gallium, followed by a vapour phase reaction. The method offers access to large-area, wide bandgap two-dimensional (2D) GaPO 4 nanosheets of unit cell thickness, while featuring lateral dimensions reaching centimetres. The unit cell thick nanosheets present a large effective out-of-plane piezoelectric coefficient of 7.5 ± 0.8 pm V − 1 . The developed printing process is also suitable for the synthesis of free standing GaPO 4 nanosheets. The low temperature synthesis method is compatible with a variety of electronic device fabrication procedures, providing a route for the development of future 2D piezoelectric materials. Two-dimensional piezoelectric materials hold promise for nano-electromechanical technologies, yet it is challenging to prepare them in large areas with high sample homogeneity. Syed et al. surface print GaPO 4 sheets with unit cell thickness over centimetres using a liquid metal-based synthesis process.

Background Healthcare associated infections are of significant burden in Australia and globally. Previous estimates in Australia have relied on single-site studies, or combinations thereof, which have suggested the burden of these infections is high in Australia. Here, we estimate the burden of five healthcare associated infections (HAIs) in Australian public hospitals using a standard international framework, and compare these estimates to those observed in Europe. Methods We used data from an Australian point prevalence survey to estimate the burden of HAIs amongst adults in Australian public hospitals using an incidence-based approach, introduced by the ECDC Burden of Communicable Diseases in Europe. Results We estimate that 170,574 HAIs occur in adults admitted to public hospitals in Australia annually, resulting in 7583 deaths. Hospital acquired pneumonia is the most frequent HAI, followed by surgical site infections, and urinary tract infections. We find that blood stream infections contribute a small percentage of HAIs, but contribute the highest number of deaths (3207), more than twice that of the second largest, while pneumonia has the higher impact on years lived with disability. Conclusion This study is the first time the national burden of HAIs has been estimated for Australia from point prevalence data collected using validated surveillance definitions. Per-capita, estimates are similar to that observed in Europe, but with significantly higher occurrences of bloodstream infections and healthcare-associated pneumonia, primarily amongst women. Overall, the estimated burden is high and highlights the need for continued investment in HAI prevention.

Xanthione is a sulfated polycyclic aromatic hydrocarbon which exhibits unique anti-Kasha properties and substantial sensitivity to its medium. Due to this sensitivity however, this makes xanthione-based systems very difficult to simulate. Further, xanthione’s is understood to be come more photostable in the presence of a highly polar medium, however whether these photophysical properties could be taken advantage of for certain applications remains to be seen. In clarifying long-held beliefs of specific solvent effects, we apply a rigorous theoretical solvent analysis in both implicit and explicit solvent mediums to elucidate a more complete description of solvent polarity sensitivity in xanthione using both quantum chemical and molecular dynamics techniques. Not only was it found that explicit solvation methods are vital in an accurate description of the system, only a handful of explicit solvent molecules in the simulation are required to yield an appropriate electronic description. This short work is vital to devising future applications for xanthione-based and other quantum technologies, and is an important foundation stone on this journey.

Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal–organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.