Oxidation-induced superelasticity in metallic glass nanotubes.

Although metallic nanostructures have been attracting tremendous research interest in nanoscience and nanotechnologies, it is known that environmental attacks, such as surface oxidation, can easily initiate cracking on the surface of metals, thus deteriorating their overall functional/structural properties1-3. In sharp contrast, here we report that severely oxidized metallic glass nanotubes can attain an ultrahigh recoverable elastic strain of up to ~14% at room temperature, which outperform bulk metallic glasses, metallic glass nanowires and many other superelastic metals hitherto reported. Through in situ experiments and atomistic simulations, we reveal that the physical mechanisms underpinning the observed superelasticity can be attributed to the formation of a percolating oxide network in metallic glass nanotubes, which not only restricts atomic-scale plastic events during loading but also leads to the recovery of elastic rigidity on unloading. Our discovery implies that oxidation in low-dimensional metallic glasses can result in unique properties for applications in nanodevices.

UPP2: fast and accurate alignment of datasets with fragmentary sequences.

MOTIVATION: Multiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. Ultra-large multiple sequence alignment using Phylogeny-aware Profiles (UPP) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full-length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets. RESULTS: We present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show that UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity and is among the most accurate otherwise. AVAILABILITY AND IMPLEMENTATION: https://github.com/gillichu/sepp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ultrastrong yet Ductile 2D Titanium Nanomaterial for On-Skin Conformal Triboelectric Sensing.

Conventional titanium (e.g., bulk or thin films) is well-known for its relatively high mechanical strength, excellent corrosion resistance, and superior biocompatibility, which are suitable for biomedical engineering and wearable devices. However, the strength of conventional titanium often trades off its ductility, and their use in wearable devices has not been explored yet. In this work, we fabricated a series of large-sized 2D titanium nanomaterials with the method of polymer surface buckling enabled exfoliation (PSBEE), which possess a unique heterogeneous nanostructure containing nanosized titanium, titanium oxide, and MXene-like phases. As a result, these 2D titaniums exhibit both superb mechanical strength (6-13 GPa) and remarkable ductility (25-35%) at room temperature, outperforming all other titanium-based materials reported so far. More interestingly, we demonstrate that the 2D titanium nanomaterials also showed good performance in triboelectric sensing and can be used to fabricate self-powered, on-skin conformal triboelectric sensors with good mechanical reliability.

Strong, Ductile, and Tough Nanocrystal-Assembled Freestanding Gold Nanosheets.

The structural and mechanical properties of low-dimensional nanostructured metals have been attracting tremendous interest in the fast-growing fields of nanosciences and nanotechnologies. However, it still remains a challenge today to develop strong yet ductile low-dimensional metals that can support the further development of nanodevices. Here, through the polymer-assisted assembly of gold nanocrystals, we successfully fabricated the freestanding, ultrathin gold nanomaterial. Unlike conventional bulk gold or other low-dimensional gold nanostructures (i.e., nanowires and nanosheets), these gold nanosheets are composed of highly distorted gold nanocrystals that are 3-5 nm in size, which are joined together through nanosized amorphous carbon interphases. As a result, the gold nanosheets exhibit superb strength (up to 1.2 GPa), excellent ductility (>50%), and superior fracture toughness (>100 J/m2), outperforming various gold nanostructures hitherto reported.

Enzyme activity engineering based on sequence co-evolution analysis.

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3'-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substrate-binding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver.

Etching-Free Ultrafast Fabrication of Self-Rolled Metallic Nanosheets with Controllable Twisting.

The rapid development of state-of-the-art nanotechnology is driven by the emerging novel nanofabrication methods, such as self-rolling of 2D materials or nanosheets. Nonetheless, the traditional chemical etching-based "roll-up" technologies suffer from a low fabrication efficiency and generally produce only scroll-like structures. In this work, we develop a versatile, ultrafast, and etching-free method to synthesize self-rolled metallic nanostructures through hydrogel surface buckling enabled exfoliation, which enables rapid exfoliation and self-rolling of metallic nanosheets at a rate about 1 to 2 orders of magnitude faster than other methods. Furthermore, we observe a scroll-helix-scroll transition through the twisting of the self-rolled nanosheets. Through extensive finite element simulations and experiments, we reveal the thermodynamics underpinning these configurational transitions.

Influence of Hemodialysis Catheter Insertion on Hemodynamics in the Central Veins.

Central venous catheter (CVC) related thrombosis is a major cause of CVC dysfunction in patients under hemodialysis. The aim of our study was to investigate the impact of CVC insertion on hemodynamics in the central veins and to examine the changes in hemodynamic environments that may be related to thrombus formation due to the implantation of CVC. Patient-specific models of the central veins with and without CVC were reconstructed based on computed tomography images. Flow patterns in the veins were numerically simulated to obtain hemodynamic parameters such as time-averaged wall shear stress (TAWSS), oscillating shear index (OSI), relative residence time (RRT), and normalized transverse wall shear stress (transWSS) under pulsatile flow. The non-Newtonian effects of blood flow were also analyzed using the Casson model. The insertion of CVC caused significant changes in the hemodynamic environment in the central veins. A greater disturbance and increase of velocity were observed in the central veins after the insertion of CVC. As a result, TAWSS and transWSS were markedly increased, but most parts of OSI and RRT decreased. Newtonian assumption of blood flow would overestimate the increase in TAWSS after CVC insertion. High wall shear stress (WSS) and flow disturbance, especially the multidirectionality of the flow, induced by the CVC may be a key factor in initiating thrombosis after CVC insertion. Accordingly, approaches to decrease the flow disturbance during CVC insertion may help restrain the occurrence of thrombosis. More case studies with pre-operative and postoperative modeling and clinical follow-up need to be performed to verify these findings. Non-Newtonian blood flow assumption is recommended in computational fluid dynamics (CFD) simulations of veins with CVCs.

Ultra-Efficient and Cost-Effective Platinum Nanomembrane Electrocatalyst for Sustainable Hydrogen Production.

Hydrogen production through hydrogen evolution reaction (HER) offers a promising solution to combat climate change by replacing fossil fuels with clean energy sources. However, the widespread adoption of efficient electrocatalysts, such as platinum (Pt), has been hindered by their high cost. In this study, we developed an easy-to-implement method to create ultrathin Pt nanomembranes, which catalyze HER at a cost significantly lower than commercial Pt/C and comparable to non-noble metal electrocatalysts. These Pt nanomembranes consist of highly distorted Pt nanocrystals and exhibit a heterogeneous elastic strain field, a characteristic rarely seen in conventional crystals. This unique feature results in significantly higher electrocatalytic efficiency than various forms of Pt electrocatalysts, including Pt/C, Pt foils, and numerous Pt single-atom or single-cluster catalysts. Our research offers a promising approach to develop highly efficient and cost-effective low-dimensional electrocatalysts for sustainable hydrogen production, potentially addressing the challenges posed by the climate crisis.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.

HMMerge: an ensemble method for multiple sequence alignment.

Motivation: Despite advances in method development for multiple sequence alignment over the last several decades, the alignment of datasets exhibiting substantial sequence length heterogeneity, especially when the input sequences include very short sequences (either as a result of sequencing technologies or of large deletions during evolution) remains an inadequately solved problem. Results: We present HMMerge, a method to compute an alignment of datasets exhibiting high sequence length heterogeneity, or to add short sequences into a given 'backbone' alignment. HMMerge builds on the technique from its predecessor alignment methods, UPP and WITCH, which build an ensemble of profile HMMs to represent the backbone alignment and add the remaining sequences into the backbone alignment using the ensemble. HMMerge differs from UPP and WITCH by building a new 'merged' HMM from the ensemble, and then using that merged HMM to align the query sequences. We show that HMMerge is competitive with WITCH, with an advantage over WITCH when adding very short sequences into backbone alignments. Availability and implementation: HMMerge is freely available at https://github.com/MinhyukPark/HMMerge. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans.

BACKGROUND: Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. METHODS: Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. FINDINGS: Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. INTERPRETATION: The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. FUNDING: S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).

Development of bioinformatics and multi-omics analyses in organoids.

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer.

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

WITCH: Improved Multiple Sequence Alignment Through Weighted Consensus Hidden Markov Model Alignment.

Accurate multiple sequence alignment is challenging on many data sets, including those that are large, evolve under high rates of evolution, or have sequence length heterogeneity. While substantial progress has been made over the last decade in addressing the first two challenges, sequence length heterogeneity remains a significant issue for many data sets. Sequence length heterogeneity occurs for biological and technological reasons, including large insertions or deletions (indels) that occurred in the evolutionary history relating the sequences, or the inclusion of sequences that are not fully assembled. Ultra-large alignments using Phylogeny-Aware Profiles (UPP) (Nguyen et al. 2015) is one of the most accurate approaches for aligning data sets that exhibit sequence length heterogeneity: it constructs an alignment on the subset of sequences it considers "full-length," represents this "backbone alignment" using an ensemble of hidden Markov models (HMMs), and then adds each remaining sequence into the backbone alignment based on an HMM selected for that sequence from the ensemble. Our new method, WeIghTed Consensus Hmm alignment (WITCH), improves on UPP in three important ways: first, it uses a statistically principled technique to weight and rank the HMMs; second, it uses k > 1 HMMs from the ensemble rather than a single HMM; and third, it combines the alignments for each of the selected HMMs using a consensus algorithm that takes the weights into account. We show that this approach provides improved alignment accuracy compared with UPP and other leading alignment methods, as well as improved accuracy for maximum likelihood trees based on these alignments.

Network-based machine learning approach to predict immunotherapy response in cancer patients.

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types-melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Comparison of balance ability and physical fitness according to the growth period in taekwondo players.

The purpose of this study was to investigate and evaluate balance ability, isokinetic moments, and physical fitness according to the growth period from middle school to university in male taekwondo players. Thirty-five taekwondo players (middle school, n=11; high school, n=15; university, n=9) participated in this study. The anteriorposterior and medial-lateral CoP displacement at balance ability was better in university players than in high school and middle school players. The isokinetic knee joint muscle strength of the right extension and flexion % body weight (BW) at 60°/s was higher in university players than in high school players. The average power of right flexion BW at 180°/sec was higher in middle school players than in high school players. Back strength was higher in university players than in middle and high school players. The repeated jump was higher in high school players than in middle school players. The standing broad jump was higher in university players than in middle school players. The side-step was higher in university players than in high school players. The average power and total energy of anaerobic power were higher in university and high school players than in middle school players. The peak drop in anaerobic power was higher in middle school players than in high school players. Based on the results of the present study, the growth period should be considered when applying training such as balance, isokinetic moments, and physical fitness to male taekwondo players.

Quality of Life and Physical Ability Changes After Hospital-Based Cardiac Rehabilitation in Patients With Myocardial Infarction.

OBJECTIVE: To evaluate the effect of hospital-based cardiac rehabilitation (CR) on quality of life (QOL) and physical ability in patients with myocardial infarction (MI). METHODS: Patients with MI who were referred to the Cardiac Health and Rehabilitation Center 2 weeks after percutaneous coronary intervention were divided into CR and non-CR groups. The CR group performed supervised exercises 3 times a week for 2 months. QOL assessment, using the 36-item Short-Form Health Survey (SF-36) and physical ability evaluation were performed at the beginning and end of CR. RESULTS: The CR group demonstrated statistically significant improvements in physical functioning (PF), physical role functioning (RP), bodily pain (BP), general health perceptions (GH), vitality (VT), social role functioning (SF), emotional role functioning (RE), mental health (MH), physical component summary (PCS), and mental component summary (MCS). The non-CR group showed improvement in RP. Secondary outcomes, including resting heart rate (RHR), maximal oxygen consumption (VO2max), metabolic equivalent of task (MET), maximal exercise time (ETmax), stage 3 Borg rating of perceived exertion (3RPE), maximal Borg rating of perceived exertion (RPEmax), and stage 3 rate pressure product (3RPP), improved in the CR group. The non-CR group showed improvements in VO2max, MET, ETmax, and 3RPE. There were significant differences in improvements in PF, RP, BP, VT, SF, MH, MCS, RHR, VO2max, MET, ETmax, 3RPE, and 3RPP between the two groups. CONCLUSION: Male patients with MI demonstrated improvements in QOL and physical ability following hospital-based CR; the impact on the mental component was greater than that on the physical component.