Uniaxial Strain-Induced Stacking Order Change in Trilayer Graphene.

The layer stacking order in two-dimensional heterostructures, like graphene, affects their physical properties and potential applications. Trilayer graphene, specifically ABC-trilayer graphene, has captured significant interest due to its potential for correlated electronic states. However, achieving a stable ABC arrangement is challenging due to its lower thermodynamic stability compared to the more stable ABA stacking. Despite recent advancements in obtaining ABC graphene through external perturbations, such as strain, the stacking transition mechanism remains insufficiently explored. In this study, we unveil a universal mechanism to achieve ABC stacking, applicable for understanding ABA to ABC stacking changes induced by any mechanical perturbations. Our approach is based on a novel strain engineering technique that induces interlayer slippage and results in the formation of stable ABC domains. We investigate the underlying interfacial mechanisms of this stacking change through computational simulations and experiments. Our findings demonstrate a highly anisotropic and significant transformation of ABA stacking to large and stable ABC domains facilitated by interlayer slippage. Through atomistic simulations and local energy analysis, we systematically demonstrate the mechanism for this stacking transition, that is dependent on specific loading orientation. Understanding such a mechanism allows this material system to be engineered by design compatible with industrial techniques on a device-by-device level. We conduct Raman studies to validate and characterize the formed ABC stacking, highlighting its distinct features compared to the ABA region. Our results contribute to a clearer understanding of the stacking change mechanism and provide a robust and controllable method for achieving stable ABC domains, facilitating their use in developing advanced optoelectronic devices.

Patternable Process-Induced Strain in 2D Monolayers and Heterobilayers.

Strain engineering in two-dimensional (2D) materials is a powerful but difficult to control approach to tailor material properties. Across applications, there is a need for device-compatible techniques to design strain within 2D materials. This work explores how process-induced strain engineering, commonly used by the semiconductor industry to enhance transistor performance, can be used to pattern complex strain profiles in monolayer MoS2 and 2D heterostructures. A traction-separation model is identified to predict strain profiles and extract the interfacial traction coefficient of 1.3 ± 0.7 MPa/µm and the damage initiation threshold of 16 ± 5 nm. This work demonstrates the utility to (1) spatially pattern the optical band gap with a tuning rate of 91 ± 1 meV/% strain and (2) induce interlayer heterostrain in MoS2-WSe2 heterobilayers. These results provide a CMOS-compatible approach to design complex strain patterns in 2D materials with important applications in 2D heterogeneous integration into CMOS technologies, moiré engineering, and confining quantum systems.

An Atomistic Insight into Moiré Reconstruction in Twisted Bilayer Graphene beyond the Magic Angle.

Twisted bilayer graphene exhibits electronic properties strongly correlated with the size and arrangement of moiré patterns. While rigid rotation of the two graphene layers results in a moiré interference pattern, local rearrangements of atoms due to interlayer van der Waals interactions result in atomic reconstruction within the moiré cells. Manipulating these patterns by controlling the twist angle and externally applied strain provides a promising route to tuning their properties. Atomic reconstruction has been extensively studied for angles close to or smaller than the magic angle (θ m = 1.1°). However, this effect has not been explored for applied strain and is believed to be negligible for high twist angles. Using interpretive and fundamental physical measurements, we use theoretical and numerical analyses to resolve atomic reconstruction in angles above θ m . In addition, we propose a method to identify local regions within moiré cells and track their evolution with strain for a range of representative high twist angles. Our results show that atomic reconstruction is actively present beyond the magic angle, and its contribution to the moiré cell evolution is significant. Our theoretical method to correlate local and global phonon behavior further validates the role of reconstruction at higher angles. Our findings provide a better understanding of moiré reconstruction in large twist angles and the evolution of moiré cells under the application of strain, which might be potentially crucial for twistronics-based applications.

Field Visit Contact Rate by Mobile Crisis Teams as a Crisis System Performance Metric.

OBJECTIVE: The authors investigated associations between rates of contact with individuals in distress during field visits by mobile crisis teams and client and referral source characteristics. METHODS: In this retrospective observational study of an urban mobile crisis program, call logs (N=2,581) were coded for whether an attempted field visit resulted in a client evaluation. Logistic regression analyses examined potential associations with client age, gender, race-ethnicity, primary language, living situation, insurance, and referral source. RESULTS: Contact was made with 77% of adults and 97% of children referred to mobile crisis teams. Field visit contact rates differed by age. Unsuccessful visits were more likely when the referral source was from institutional settings than from individuals. CONCLUSIONS: Approximately one-quarter of attempted field visits with adults by an urban mobile crisis team were not completed, particularly among referrals from institutional settings. As mobile crisis services proliferate, field visit contact rate could be a key performance metric for these critical services.

An Evaluation of the Relationship Between Medication Regimen Complexity as Measured by the MRC-ICU to Medication Errors in Critically Ill Patients.

Purpose: The purpose of this study was to determine the relationship between medication regimen complexity-intensive care unit (MRC-ICU) score at 24 hours and medication errors identified throughout the ICU. Methods: A single-center, observational study was conducted from August to October 2021. The primary outcome was the association between MRC-ICU at 24 hours and total medication errors identified. During the prospective component, ICU pharmacists recorded medication errors identified over an 8-week period. During the retrospective component, the electronic medical record was reviewed to collect patient demographics, outcomes, and MRC-ICU score at 24 hours. The primary outcome of the relationship of MRC-ICU at 24 hours to medication errors was assessed using Pearson correlation. Results: A total of 150 patients were included. There were 2 pharmacists who recorded 634 errors during the 8-week study period. No significant relationship between MRC-ICU and medication errors was observed (r2 = .13, P = .11). Exploratory analyses of MRC-ICU relationship to major interventions and harm scores showed that MRC-ICU scores >10 had more major interventions (27 vs 14, P = .27) and higher harm scores (15 vs 7, P = .33), although these values were not statistically significant. Conclusion: Medication errors appear to occur independently of medication regimen complexity. Critical care pharmacists were responsible for mitigating a large number of medication errors.

Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices.

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moiré pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moiré engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field.

Functional Output Regression for Machine Learning in Materials Science.

In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of material data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we consider two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in material science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modeling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it effective in modeling even with small amounts of data. We demonstrate the proposed methods through several case studies in materials science.

Proton Therapy for Prostate Cancer: Challenges and Opportunities.

The dosimetric advantages of proton therapy (PT) treatment plans are demonstrably superior to photon-based external beam radiotherapy (EBRT) for localized prostate cancer, but the reported clinical outcomes are similar. This may be due to inadequate dose prescription, especially in high-risk disease, as indicated by the ASCENDE-RT trial. Alternatively, the lack of clinical benefits with PT may be attributable to improper dose delivery, mainly due to geometric and dosimetric uncertainties during treatment planning, as well as delivery procedures that compromise the dose conformity of treatments. Advanced high-precision PT technologies, and treatment planning and beam delivery techniques are being developed to address these uncertainties. For instance, external magnetic resonance imaging (MRI)-guided patient setup rooms are being developed to improve the accuracy of patient positioning for treatment. In-room MRI-guided patient positioning systems are also being investigated to improve the geometric accuracy of PT. Soon, high-dose rate beam delivery systems will shorten beam delivery time to within one breath hold, minimizing the effects of organ motion and patient movements. Dual-energy photon-counting computed tomography and high-resolution Monte Carlo-based treatment planning systems are available to minimize uncertainties in dose planning calculations. Advanced in-room treatment verification tools such as prompt gamma detector systems will be used to verify the depth of PT. Clinical implementation of these new technologies is expected to improve the accuracy and dose conformity of PT in the treatment of localized prostate cancers, and lead to better clinical outcomes. Improvement in dose conformity may also facilitate dose escalation, improving local control and implementation of hypofractionation treatment schemes to improve patient throughput and make PT more cost effective.

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach.

Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.

Porcine reproductive and respiratory syndrome virus genetic variability a management and diagnostic dilemma.

As genetic analysis becomes less expensive, more comprehensive diagnostics such as whole genome sequencing (WGS) will become available to the veterinary practitioner. The WGS elucidates more about porcine reproductive and respiratory syndrome virus (PRRSV) beyond the traditional analysis of open reading frame (ORF) 5 Sanger sequencing. The veterinary practitioner will require a more complete understanding of the mechanics and consequences of PRRSV genetic variability to interpret the WGS results. More recently, PRRSV recombination events have been described in the literature. The objective of this review is to provide a comprehensive outlook for swine practitioners that PRRSV mutates and recombines naturally causing genetic variability, review the diagnostic cadence when suspecting recombination has occurred, and present theory on how, why, and where industry accepted management practices may influence recombination. As practitioners, it is imperative to remember that PRRS viral recombination is occurring continuously in swine populations. Finding a recombinant by diagnostic analysis does not ultimately declare its significance. The error prone replication, mutation, and recombination of PRRSV means exact clones may exist; but a quasispecies swarm of variable strains also exist adding to the genetic diversity. PRRSV nonstructural proteins (nsps) are translated from ORF1a and ORF1b. The arterivirus nsps modulate the hosts' immune response and are involved in viral pathogenesis. The strains that contribute the PRRSV replicase and transcription complex is driving replication and possibly recombination in the quasispecies swarm. Furthermore, mutations favoring the virus to evade the immune system may result in the emergence of a more fit virus. More fit viruses tend to become the dominant strains in the quasispecies swarm. In theory, the swine management practices that may exacerbate or mitigate recombination include immunization strategies, swine movements, regional swine density, and topography. Controlling PRRSV equates to managing the quasispecies swarm and its interaction with the host. Further research is warranted on the frequency of recombination and the genome characteristics impacting the recombination rate. With a well-defined understanding of these characteristics, the clinical implications from recombination can be detected and potentially reduced; thus, minimizing recombination and perhaps the emergence of epidemic strains.

A Discrete Joint Model for Entity and Relation Extraction from Clinical Notes.

Extracting clinical concepts and their relations from clinical narratives is one of the fundamental tasks in clinical natural language processing. Traditional solutions often separate this task into two subtasks with a pipeline architecture, which first recognize the named entities and then classify the relations between any possible entity pairs. The pipeline architecture, although widely used, has two limitations: 1) it suffers from error propagation from the recognition step to the classification step, 2) it cannot utilize the interactions between the two steps. To address the limitations, we investigated a discrete joint model based on structured perceptron and beam search to jointly perform named entity recognition (NER) and relation classification (RC) from clinical notes.

Establishing RNA-RNA interactions remodels lncRNA structure and promotes PRC2 activity.

Human Polycomb Repressive Complex 2 (PRC2) catalysis of histone H3 lysine 27 methylation at certain loci depends on long noncoding RNAs (lncRNAs). Yet, in apparent contradiction, RNA is a potent catalytic inhibitor of PRC2. Here, we show that intermolecular RNA-RNA interactions between the lncRNA HOTAIR and its targets can relieve RNA inhibition of PRC2. RNA bridging is promoted by heterogeneous nuclear ribonucleoprotein B1, which uses multiple protein domains to bind HOTAIR regions via multivalent protein-RNA interactions. Chemical probing demonstrates that establishing RNA-RNA interactions changes HOTAIR structure. Genome-wide HOTAIR/PRC2 activity occurs at genes whose transcripts can make favorable RNA-RNA interactions with HOTAIR. We demonstrate that RNA-RNA matches of HOTAIR with target gene RNAs can relieve the inhibitory effect of a single lncRNA for PRC2 activity after B1 dissociation. Our work highlights an intrinsic switch that allows PRC2 activity in specific RNA contexts, which could explain how many lncRNAs work with PRC2.

RNA matchmaking in chromatin regulation.

Beyond being the product of gene expression, RNA can also influence the regulation of chromatin. The majority of the human genome has the capacity to be transcribed and the majority of the non-protein-coding transcripts made by RNA Polymerase II are enriched in the nucleus. Many chromatin regulators can bind to these ncRNAs in the nucleus; in some cases, there are clear examples of direct RNA-mediated chromatin regulation mechanisms stemming from these interactions, while others have yet to be determined. Recent studies have highlighted examples of chromatin regulation via RNA matchmaking, a term we use broadly here to describe intermolecular base-pairing interactions between one RNA molecule and an RNA or DNA match. This review provides examples of RNA matchmaking that regulates chromatin processes and summarizes the technical approaches used to capture these events.

Bayesian Algorithm for Retrosynthesis.

The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion of the entire reaction space. At present, emerging machine learning technologies are reformulating the process of retrosynthetic planning. This study aimed to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of the training of a deep neural network, which is used to forwardly predict a product of the given reactants with a high level of accuracy, followed by inversion of the forward model into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. With a forward model prediction accuracy of approximately 87%, the Bayesian retrosynthesis algorithm successfully rediscovered 81.8 and 33.3% of known synthetic routes of one-step and two-step reactions, respectively, with top-10 accuracy. Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We also investigated the potential applicability of such diverse candidates based on expert knowledge of synthetic organic chemistry.

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.

BACKGROUND: Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients' quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. OBJECTIVE: This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. METHODS: We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. RESULTS: The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. CONCLUSIONS: The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual's level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes.

OBJECTIVE: This study aims to develop and evaluate effective methods that can normalize diagnosis and procedure terms written by physicians to standard concepts in International Classification of Diseases(ICD) in Chinese, with the goal to facilitate automated medical coding in China. METHODS: We applied the entity-linking framework to normalize Chinese diagnosis and procedure terms, which consists of two steps - candidate concept generation and candidate concept ranking. For candidate concept generation, we implemented both the traditional BM25 algorithm and an extended version that integrates a synonym knowledgebase. For candidate concept ranking, we investigated a number of different algorithms: (1) the BM25 algorithm, (2) ranking support vector machines (RankSVM), (3) a previously reported Convolutional Neural Network (CNN) approach, (4) 11 deep ranking-based methods from the MatchZoo toolkit, and (5) a new BERT (Bidirectional Encoder Representations from Transformers) based ranking method. Using two manually annotated datasets (8,547 diagnoses and 8,282 procedures) collected from a Tier 3A hospital in China, we evaluated above methods and reported their performance (i.e., accuracy) at different cutoffs. RESULTS: The coverage of candidate concept generation was greatly improved after integrating the synonym knowledgebase, achieving 97.9% for diagnoses and 93.4% for procedures respectively. Overall the new BERT-based ranking method achieved the best performance on both diagnosis and procedure normalization, with the best accuracy of 92.1% for diagnosis and 80.1% for procedure, when the top one concept and exact match criteria were used. CONCLUSIONS: This study developed and compared diverse entity-linking methods to normalize clinical terms in Chinese and our evaluation shows good performance on mapping disease terms to ICD codes, demonstrating the feasibility of automated encoding of clinical terms in Chinese.

Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans.

BACKGROUND: Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons. RESULTS: Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging. CONCLUSION: Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.

Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering.

Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding, which can provide a more global and accurate understanding of biological phenomenon. In many problems, different domains may have different cluster structures. Due to rapid growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering. In particular, with consistency weights calculation, the proposed algorithm automatically identify domains relevant to each other strongly (or weakly) by assigning them larger (or smaller) weights. This approach not only significantly improve clustering accuracy but also understand multi-domain networks association. In each iteration of the proposed algorithm, we update consistency weights based on cluster structure of each domain, and then make use of different sets of eigenvectors to obtain different cluster structures in each domain. Experimental results on both synthetic data sets and real data sets (including neuron activity data and gene expression data) empirically demonstrate the effectiveness of the proposed algorithm in clustering performance and in domain association capability.

A study of deep learning approaches for medication and adverse drug event extraction from clinical text.

OBJECTIVE: This article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 National NLP Clinical Challenges (n2c2) shared task. MATERIALS AND METHODS: The clinical corpus used in this study was from the MIMIC-III database and the organizers annotated 303 documents for training and 202 for testing. Our system consists of 2 components: a named entity recognition (NER) and a relation classification (RC) component. For each component, we implemented deep learning-based approaches (eg, BI-LSTM-CRF) and compared them with traditional machine learning approaches, namely, conditional random fields for NER and support vector machines for RC, respectively. In addition, we developed a deep learning-based joint model that recognizes ADEs and their relations to medications in 1 step using a sequence labeling approach. To further improve the performance, we also investigated different ensemble approaches to generating optimal performance by combining outputs from multiple approaches. RESULTS: Our best-performing systems achieved F1 scores of 93.45% for NER, 96.30% for RC, and 89.05% for end-to-end evaluation, which ranked #2, #1, and #1 among all participants, respectively. Additional evaluations show that the deep learning-based approaches did outperform traditional machine learning algorithms in both NER and RC. The joint model that simultaneously recognizes ADEs and their relations to medications also achieved the best performance on RC, indicating its promise for relation extraction. CONCLUSION: In this study, we developed deep learning approaches for extracting medications and their attributes such as ADEs, and demonstrated its superior performance compared with traditional machine learning algorithms, indicating its uses in broader NER and RC tasks in the medical domain.

Deep learning in clinical natural language processing: a methodical review.

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a "long tail" of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. DISCUSSION: Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). CONCLUSION: Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.