A generalizability score for aggregate causal effect.

Scientists frequently generalize population level causal quantities such as average treatment effect from a source population to a target population. When the causal effects are heterogeneous, differences in subject characteristics between the source and target populations may make such a generalization difficult and unreliable. Reweighting or regression can be used to adjust for such differences when generalizing. However, these methods typically suffer from large variance if there is limited covariate distribution overlap between the two populations. We propose a generalizability score to address this issue. The score can be used as a yardstick to select target subpopulations for generalization. A simplified version of the score avoids using any outcome information and thus can prevent deliberate biases associated with inadvertent access to such information. Both simulation studies and real data analysis demonstrate convincing results for such selection.

Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach.

Cyclometalated Pt(II) complexes are popular phosphorescent emitters with color-tunable emissions. To render their practical applications as organic light-emitting diodes emitters, it is required to develop Pt(II) complexes with high radiative decay rate constant and photoluminescence (PL) quantum yield. Here, a general protocol is developed for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield based on the combination of first-principles quantum mechanical method, machine learning, and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than 200 samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen and ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients are 0.96, 0.81, and 0.67 for the predictions of emission wavelength, PL quantum yield and radiative decay rate constant, respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.

A Network Meta-Analysis of the Systemic Therapies in Unresectable Head and Neck Squamous Cell Carcinoma.

The current standard treatment for locally advanced squamous cell carcinoma of the head and neck (LASCCHN) comprises concurrent radiotherapy (CRT) alongside platinum-based chemotherapy. However, innovative therapeutic alternatives are being evaluated in phase II/III randomized trials. This study employed a Bayesian network meta-analysis (NMA) using fixed effects to provide both direct and indirect comparisons of all existing treatment modalities for unresectable LASCCHN. METHODS: We referenced randomized controlled trials (RCTs) from January 2000 to July 2023 by extensively reviewing PubMed, EMBASE, and Web of Science databases, adhering to the Cochrane methodology. Relevant data, including summary estimates of overall survival (OS) and progression-free survival (PFS), were extracted from these selected studies and recorded in a predefined database sheet. Subsequently, we conducted a random effects network meta-analysis using a Bayesian framework. RESULTS: Based on the Surface Under the Cumulative Ranking (SUCRA) values, the league table organizes the various treatments for OS in the following order: IC + RT&MTT, MTT-CRT, IC + CRT&MTT, CRT, IC + CRT, MTT-RT, IC + MTT-RT, and RT. In a similar order, the treatments rank as follows according to the league table: IC + CRT&MTT, MTT-CRT, IC + CRT, IC + RT&MTT, CRT, IC + MTT-RT, MTT-RT, and RT. Notably, none of these treatments showed significant advantages over concurrent chemoradiotherapy. CONCLUSION: Despite concurrent chemoradiotherapy being the prevailing treatment for LASCCHN, our findings suggest the potential for improved outcomes when concurrent chemoradiotherapy is combined with targeted therapy or induction chemotherapy.

The current standard treatment for advanced head and neck cancer involves combining radiation therapy with chemotherapy. However, there are ongoing trials exploring alternative therapies. In this study, we conducted a comprehensive analysis of existing treatments using a statistical method called network meta-analysis. Our analysis included data from randomized controlled trials published between January 2000 and July 2023. We focused on overall survival and progression-free survival as key outcome measures. The results of our analysis showed that none of the alternative treatments demonstrated significant advantages over the standard concurrent chemoradiotherapy. Nevertheless, there is potential for improved outcomes when targeted therapy or induction chemotherapy is combined with concurrent chemoradiotherapy.

The Influence of Rapid Influenza Diagnostic Testing on Clinician Decision-Making for Patients With Acute Respiratory Infection in Urgent Care.

BACKGROUND: The potential benefits of using rapid influenza diagnostic tests (RIDTs) in urgent care facilities for clinical care and prescribing practices are understudied. We compared antiviral and antibiotic prescribing, imaging, and laboratory ordering in clinical encounters with and without RIDT results. METHODS: We compared patients with acute respiratory infection (ARI) symptoms who received an RIDT and patients who did not at 2 urgent care facilities. Primary analysis using 1-to-1 exact matching resulted in 1145 matched pairs to which McNemar 2 × 2 tests were used to assess the association between the likelihood of prescribing, imaging/laboratory ordering, and RIDT use. Secondary analysis compared the same outcomes using logistic regression among the RIDT-tested population between participants who tested negative (RIDT(-)) and positive (RIDT(+)). RESULTS: Primary analysis revealed that compared to the non-RIDT-tested population, RIDT(+) patients were more likely to be prescribed antivirals (OR, 10.23; 95% CI, 5.78-19.72) and less likely to be prescribed antibiotics (OR, 0.15; 95% CI, .08-.27). Comparing RIDT-tested to non-RIDT-tested participants, RIDT use increased antiviral prescribing odds (OR, 3.07; 95% CI, 2.25-4.26) and reduced antibiotic prescribing odds (OR, 0.52; 95% CI, .43-.63). Secondary analysis identified increased odds of prescribing antivirals (OR, 28.21; 95% CI, 18.15-43.86) and decreased odds of prescribing antibiotics (OR, 0.20; 95% CI, .13-.30) for RIDT(+) participants compared with RIDT(-). CONCLUSIONS: Use of RIDTs in patients presenting with ARI symptoms influences clinician diagnostic and treatment decision-making, which could lead to improved patient outcomes, population-level reductions in influenza burden, and a decreased threat of antibiotic resistance.

Surface Lattice Modulation through Chemical Delithiation toward a Stable Nickel-Rich Layered Oxide Cathode.

Nickel-rich layered oxides (NLOs) are considered as one of the most promising cathode materials for next-generation high-energy lithium-ion batteries (LIBs), yet their practical applications are currently challenged by the unsatisfactory cyclability and reliability owing to their inherent interfacial and structural instability. Herein, we demonstrate an approach to reverse the unstable nature of NLOs through surface solid reaction, by which the reconstructed surface lattice turns stable and robust against both side reactions and chemophysical breakdown, resulting in improved cycling performance. Specifically, conformal La(OH)3 nanoshells are built with their thicknesses controlled at nanometer accuracy, which act as a Li+ capturer and induce controlled reaction with the NLO surface lattices, thereby transforming the particle crust into an epitaxial layer with localized Ni/Li disordering, where lithium deficiency and nickel stabilization are both achieved by transforming oxidative Ni3+ into stable Ni2+. An optimized balance between surface stabilization and charge transfer is demonstrated by a representative NLO material, namely, LiNi0.83Co0.07Mn0.1O2, whose surface engineering leads to a highly improved capacity retention and excellent rate capability with a strong capability to inhibit the crack of NLO particles. Our study highlights the importance of surface chemistry in determining chemical and structural behaviors and paves a research avenue in controlling the surface lattice for the stabilization of NLOs toward reliable high-energy LIBs.

High-performance plasmonic polymer modulators through mode hybridization and electro-thermomechanical effects.

In this work, an electro-optical polymer modulator with double-layered gold nanostrips, a polymer nanograting, and a metal substrate is proposed and designed. Interestingly, mode hybridization between the Fabry-Pérot (F-P) and anti-bonding modes is formed, and strongly depends on the nanograting size, which can be controllably modulated by an injection current. The simulation and calculation results show that the temperature sensitivity and large structural sensitivity for the polymer modulator could remain constant during the current-tuning process, and a near-zero reflectance and a low linewidth of 13.8 nm in the red region corresponding to a high quality (Q) factor of 51 is achieved. In addition, a large redshift of 60.7 nm and a super-high modulation depth of 424 are obtained at only 8 µA.

Entropy balancing for causal generalization with target sample summary information.

In this paper, we focus on estimating the average treatment effect (ATE) of a target population when individual-level data from a source population and summary-level data (e.g., first or second moments of certain covariates) from the target population are available. In the presence of the heterogeneous treatment effect, the ATE of the target population can be different from that of the source population when distributions of treatment effect modifiers are dissimilar in these two populations, a phenomenon also known as covariate shift. Many methods have been developed to adjust for covariate shift, but most require individual covariates from a representative target sample. We develop a weighting approach based on the summary-level information from the target sample to adjust for possible covariate shift in effect modifiers. In particular, weights of the treated and control groups within a source sample are calibrated by the summary-level information of the target sample. Our approach also seeks additional covariate balance between the treated and control groups in the source sample. We study the asymptotic behavior of the corresponding weighted estimator for the target population ATE under a wide range of conditions. The theoretical implications are confirmed in simulation studies and a real-data application.

A hybrid system to understand the relations between assessments and plans in progress notes.

OBJECTIVE: The paper presents a novel solution to the 2022 National NLP Clinical Challenges (n2c2) Track 3, which aims to predict the relations between assessment and plan subsections in progress notes. METHODS: Our approach goes beyond standard transformer models and incorporates external information such as medical ontology and order information to comprehend the semantics of progress notes. We fine-tuned transformers to understand the textual data and incorporated medical ontology concepts and their relationships to enhance the model's accuracy. We also captured order information that regular transformers cannot by taking into account the position of the assessment and plan subsections in progress notes. RESULTS: Our submission earned third place in the challenge phase with a macro-F1 score of 0.811. After refining our pipeline further, we achieved a macro-F1 of 0.826, outperforming the top-performing system during the challenge phase. CONCLUSION: Our approach, which combines fine-tuned transformers, medical ontology, and order information, outperformed other systems in predicting the relationships between assessment and plan subsections in progress notes. This highlights the importance of incorporating external information beyond textual data in natural language processing (NLP) tasks related to medical documentation. Our work could potentially improve the efficiency and accuracy of progress note analysis.

Construct exchange-correlation functional via machine learning.

Density functional theory has been widely used in quantum mechanical simulations, but the search for a universal exchange-correlation (XC) functional has been elusive. Over the last two decades, machine-learning techniques have been introduced to approximate the XC functional or potential, and recent advances in deep learning have renewed interest in this approach. In this article, we review early efforts to use machine learning to approximate the XC functional, with a focus on the challenge of transferring knowledge from small molecules to larger systems. Recently, the transferability problem has been addressed through the use of quasi-local density-based descriptors, which are rooted in the holographic electron density theorem. We also discuss recent developments using deep-learning techniques that target high-level ab initio molecular energy and electron density for training. These efforts can be unified under a general framework, which will also be discussed from this perspective. Additionally, we explore the use of auxiliary machine-learning models for van der Waals interactions.

A semilocal machine-learning correction to density functional approximations.

Machine learning (ML) has demonstrated its potential usefulness for the development of density functional theory methods. In this work, we construct an ML model to correct the density functional approximations, which adopts semilocal descriptors of electron density and density derivative and is trained by accurate reference data of relative and absolute energies. The resulting ML-corrected functional is tested on a comprehensive dataset including various types of energetic properties. Particularly, the ML-corrected Becke's three parameters and the Lee-Yang-Parr correlation (B3LYP) functional achieves a substantial improvement over the original B3LYP on the prediction of total energies of atoms and molecules and atomization energies, and a marginal improvement on the prediction of ionization potentials, electron affinities, and bond dissociation energies; whereas, it preserves the same level of accuracy for isomerization energies and reaction barrier heights. The ML-corrected functional allows for fully self-consistent-field calculation with similar efficiency to the parent functional. This study highlights the progress of building an ML correction toward achieving a functional that performs uniformly better than B3LYP.

Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease.

Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD), but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome-wide association study in 18,385 European-ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta-GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04-1.18; P 8.43 × 10-4) and 1.13 (95% CI, 1.07-1.20; P 4.51 × 10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re-computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03-1.15; P 0.002), while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95-1.06; P 0.97). Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European-ancestry participants (1598 incident CAD cases). In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99-1.14; P = 0.07) and 1.07 (1.01-1.15; P = 0.03), respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.19 kcal/mol to ∼0.2 kcal/mol for a NCI molecular database (>1000 molecules). The representativeness of the joint features can be visualized by t-distributed stochastic neighbor embedding (t-SNE), where they can distinguish categorized NCI systems quite well. Therefore, the fused model performs better than its component networks. In addition, the 3D CNN takes electron density as inputs that are in the same range, despite the size of molecular systems, so it can promote model applicability and transferability. To clarify the applicability of DeepNCI, an application domain (AD) has been defined with merged features using the K-nearest-neighbor method. The calculations for external test sets are shown that AD can properly monitor the reliability for a prediction. The model transferability is tested with a small database of homolysis bond dissociation energy including only dozens of samples. With NCI database pretrained parameters, the same or better performance than the reported results is achieved by transfer learning. This suggests that the DeepNCI model is transferable and it may transfer to other relative tasks, which possibly can resolve some small sampling problems. The source code of DeepNCI can be freely accessed at https://github.com/wenzelee/DeepNCI.

Improving Density Functional Prediction of Molecular Thermochemical Properties with a Machine-Learning-Corrected Generalized Gradient Approximation.

The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density. The resulting ML-corrected PBE is immediately applicable to any real molecule and yields significantly improved heats of formation while preserving the accuracy for other thermochemical and kinetic properties. This work highlights the prospect of combining the power of data-driven ML methods with physics-inspired derivations for reaching the heaven of chemical accuracy.

Predicting Experimental Heats of Formation via Deep Learning with Limited Experimental Data.

When it comes to predicting experimental values of molecular properties with deep learning, the key problem is the lack of sufficient experimental data for training. We propose a method that consists of pretraining a graph neural network that aims to reproduce first-principles quantum mechanical results, followed by fine-tuning of a fully connected neural network against experimental results. The combined pretraining and fine-tuning model is expected to yield molecular properties close to experimental accuracy. This is made possible because first-principles quantum mechanical methods are often qualitatively correct or semiquantitatively accurate; thus, a calibration of the calculation results against high-precision but limited experiment data can improve accuracy greatly. Moreover, the method is highly efficient, as first-principles quantum mechanical calculation is bypassed. To demonstrate this, we apply the combined model to determine the experimental heats of formation of organic molecules made of H, C, O, N, or F atoms (up to 30 atoms), where mere 405 experimental data are used. The overall mean absolute error is 1.8 kcal/mol for these molecules.

Characteristics and context of fentanyl test strip use among syringe service clients in southern Wisconsin.

BACKGROUND: Fentanyl adulteration of illicit drugs is a major driver of opioid-involved overdose in the USA. Fentanyl test strips are increasingly used by people who use drugs to check for fentanyl. However, little is known about factors that influence test strip use in this population. METHODS: In this mixed-methods study employing semi-structured open-ended interviews (n = 29) and a structured survey (n = 341), we examined characteristics associated with test strip use, characteristics of test strip use, and situational, logistical and psychosocial factors influencing test strip use. Respondents were recruited from a syringe service program in southern Wisconsin. Bivariate tests of association and multivariable logistic regression examined the relationship between respondent characteristics and test strip use. Summary statistics were used to describe how situational, logistical and psychosocial factors impact test strip use. RESULTS: Most respondents were male (59.6%), non-Hispanic white (77.4%), young (mean 35.7 years), reported heroin as their primary drug (70.7%), injection as their primary route (87.9%), and use ≥ 3 times daily (78.6%). In multivariable models, site, race and ethnicity, drug of choice, and seeking fentanyl were associated with test strip use. Among test strip users, 36.5% use them most of the time or more and 80.6% get positive results half the time or more. Among individuals reporting heroin, fentanyl, methamphetamine, or cocaine or crack cocaine at least once per month, 99.1%, 56.8%, 42.2%, and 55.7% reported testing these drugs, respectively. Test strip use is supported by information from suppliers, regular transportation, diverse distribution locations, recommendations from harm reduction staff, and having a safe or private place to use. CONCLUSIONS: We found that individuals who use fentanyl test strips are more often non-Hispanic white, use heroin, and seek drugs with fentanyl relative to individuals without test strip use. Findings confirm high fentanyl penetration in the Wisconsin drug supply. Low rates of stimulant testing suggest inadequate awareness of fentanyl penetration. Findings support outreach to key populations, increased diversity of distributing locations, efforts to correct misperceptions about drug wasting, emphasis on pre-consumption testing, and the importance of adjunct behaviors to prevent overdose given high rates of intentional fentanyl use.

Carrier-Envelope-Phase Modulated Currents in Scanning Tunneling Microscopy.

Carrier-envelope-phase (CEP) stable optical pulses combined with state-of-the-art scanning tunneling microscopy (STM) can track and control ultrafast electronic tunneling currents. On the basis of nonequilibrium Green's function formalism, we present a time and frequency domain theoretical study of CEP-stable pulse-induced tunneling currents between an STM tip and a metal substrate. It is revealed that the experimentally observed phase shift between the maximum tunneling current and maximum electric field is caused by the third-order response to the electric field. The shift is also found to be sensitive to the duration of pulses. The tunneling process can thus be precisely manipulated by varying the phase and duration of these pulses.

Vibration-mediated resonant charge separation across the donor-acceptor interface in an organic photovoltaic device.

Examination of a recent open-system Ehrenfest dynamics simulation suggests that a vibration-mediate resonance may play a pivotal role in the charge transfer across a donor-acceptor interface in an organic solar cell. Based on this, a concise dissipative two-level electronic system coupled to a molecular vibrational mode is proposed and solved quantum mechanically. It is found that the charge transfer is enhanced substantially when the vibrational energy quanta is equal to the electronic energy loss across the interface. This vibration-mediate resonant charge transfer process is ultrafast, occurring within 100 fs, comparable to experimental findings. The open-system Ehrenfest dynamics simulation of the two-level model is carried out, and similar results are obtained, which confirms further that the earlier open-system Ehrenfest dynamics simulation indeed correctly predicted the occurrence of the resonant charge transfer across the donor-acceptor interface.

Time-dependent simulation of photocurrent-detected two-dimensional spectroscopy of open systems.

A new time-domain simulation protocol of two-dimensional electronic spectra with photocurrent detection is presented. Time-dependent density functional theory for open systems at finite temperature is applied to evaluate the photocurrent response to four laser pulses, and a non-perturbative phase-matching approach is implemented to extract the fourth-order photocurrent signal with a desired phase-matching condition. Simulations for an open three-level model indicates that transition dipoles interact resonantly with the incident pulses and that different sample-electrode couplings may be identified by appearance of different peaks/valleys in photocurrent spectra from different electrodes. Moreover, qualitative reproduction of experimental spectra of a PbS quantum dot photocell [Karki et al., Nat. Commun. 5(1), 5869 (2014)] reveals the stimulated electron dynamics.

Capillary electrophoresis-based methodology for screening of oligonucleotide aptamers.

As a new molecular recognition element, oligonucleotide aptamer not only has higher affinity and specificity to target molecules, but also has the advantages of wide recognition range, in vitro synthesis and chemical stability compared with conventional antibodies. Since a kind of screening method termed systematic evolution of ligands by exponential enrichment (SELEX) was reported, scientists have extensively researched the methodology of how to highly and efficiently screen out aptamers from a library consisting of a large number of random oligonucleotides. Certainly capillary electrophoresis-based screening methodologies, including nonequilibrium capillary electrophoresis of equilibrium mixtures, equilibrium capillary electrophoresis of equilibrium mixtures, non-SELEX, ideal-filter capillary electrophoresis, capillary transient isotachophoresis, etc., are revolutionary. Compared with conventional SELEX, these capillary electrophoresis-based methodologies show incomparable advantages such as the single-round screening of aptamers and increased successful screening rate. Methodology studies on the screening process of aptamers are comprehensively reviewed.

Obesity and BMI Cut Points for Associated Comorbidities: Electronic Health Record Study.

BACKGROUND: Studies have found associations between increasing BMIs and the development of various chronic health conditions. The BMI cut points, or thresholds beyond which comorbidity incidence can be accurately detected, are unknown. OBJECTIVE: The aim of this study is to identify whether BMI cut points exist for 11 obesity-related comorbidities. METHODS: US adults aged 18-75 years who had ≥3 health care visits at an academic medical center from 2008 to 2016 were identified from eHealth records. Pregnant patients, patients with cancer, and patients who had undergone bariatric surgery were excluded. Quantile regression, with BMI as the outcome, was used to evaluate the associations between BMI and disease incidence. A comorbidity was determined to have a cut point if the area under the receiver operating curve was >0.6. The cut point was defined as the BMI value that maximized the Youden index. RESULTS: We included 243,332 patients in the study cohort. The mean age and BMI were 46.8 (SD 15.3) years and 29.1 kg/m2, respectively. We found statistically significant associations between increasing BMIs and the incidence of all comorbidities except anxiety and cerebrovascular disease. Cut points were identified for hyperlipidemia (27.1 kg/m2), coronary artery disease (27.7 kg/m2), hypertension (28.4 kg/m2), osteoarthritis (28.7 kg/m2), obstructive sleep apnea (30.1 kg/m2), and type 2 diabetes (30.9 kg/m2). CONCLUSIONS: The BMI cut points that accurately predicted the risks of developing 6 obesity-related comorbidities occurred when patients were overweight or barely met the criteria for class 1 obesity. Further studies using national, longitudinal data are needed to determine whether screening guidelines for appropriate comorbidities may need to be revised.