AI can help people feel heard, but an AI label diminishes this impact.

People want to "feel heard" to perceive that they are understood, validated, and valued. Can AI serve the deeply human function of making others feel heard? Our research addresses two fundamental issues: Can AI generate responses that make human recipients feel heard, and how do human recipients react when they believe the response comes from AI? We conducted an experiment and a follow-up study to disentangle the effects of actual source of a message and the presumed source. We found that AI-generated messages made recipients feel more heard than human-generated messages and that AI was better at detecting emotions. However, recipients felt less heard when they realized that a message came from AI (vs. human). Finally, in a follow-up study where the responses were rated by third-party raters, we found that compared with humans, AI demonstrated superior discipline in offering emotional support, a crucial element in making individuals feel heard, while avoiding excessive practical suggestions, which may be less effective in achieving this goal. Our research underscores the potential and limitations of AI in meeting human psychological needs. These findings suggest that while AI demonstrates enhanced capabilities to provide emotional support, the devaluation of AI responses poses a key challenge for effectively leveraging AI's capabilities.

In situ visualizing reveals potential drive of lattice expansion on defective support toward efficient removal of nitrogen oxides.

As a sustainable and promising approach of removing of nitrogen oxides (NOx), catalytic reduction of NOx with H2 is highly desirable with a precise understanding to the structure-activity relationship of supported catalysts. In particular, the dynamic evolution of support at microscopic scale may play a critical role in heterogeneous catalysis, however, identifying the in situ structural change of support under working condition with atomic precision and revealing its role in catalysis is still a grand challenge. Herein, we visually capture the surface lattice expansion of WO3-x support in Pt-WO3-x catalyst induced by NO in the exemplified reduction of NO with H2 using in situ transmission electron microscopy and first reveal its important role in enhancing catalysis. We find that NO can adsorb on the oxygen vacancy sites of WO3-x and favorably induce the reversible stretching of W-O-W bonds during the reaction, which can reduce the adsorption energy of NO on Pt4 centers and the energy barrier of the rate-determining step. The comprehensive studies reveal that lattice expansion of WO3-x support can tune the catalytic performance of Pt-WO3-x catalyst, leading to 20% catalytic activity enhancement for the exemplified reduction of NO with H2. This work reveals that the lattice expansion of defective support can tune and optimize the catalytic performance at the atomic scale.

Multiscale embedded printing of engineered human tissue and organ equivalents.

Creating tissue and organ equivalents with intricate architectures and multiscale functional feature sizes is the first step toward the reconstruction of transplantable human tissues and organs. Existing embedded ink writing approaches are limited by achievable feature sizes ranging from hundreds of microns to tens of millimeters, which hinders their ability to accurately duplicate structures found in various human tissues and organs. In this study, a multiscale embedded printing (MSEP) strategy is developed, in which a stimuli-responsive yield-stress fluid is applied to facilitate the printing process. A dynamic layer height control method is developed to print the cornea with a smooth surface on the order of microns, which can effectively overcome the layered morphology in conventional extrusion-based three-dimensional bioprinting methods. Since the support bath is sensitive to temperature change, it can be easily removed after printing by tuning the ambient temperature, which facilitates the fabrication of human eyeballs with optic nerves and aortic heart valves with overhanging leaflets on the order of a few millimeters. The thermosensitivity of the support bath also enables the reconstruction of the full-scale human heart on the order of tens of centimeters by on-demand adding support bath materials during printing. The proposed MSEP demonstrates broader printable functional feature sizes ranging from microns to centimeters, providing a viable and reliable technical solution for tissue and organ printing in the future.

Steering lithium and potassium storage mechanism in covalent organic frameworks by incorporating transition metal single atoms.

Organic electrodes mainly consisting of C, O, H, and N are promising candidates for advanced batteries. However, the sluggish ionic and electronic conductivity limit the full play of their high theoretical capacities. Here, we integrate the idea of metal-support interaction in single-atom catalysts with π-d hybridization into the design of organic electrode materials for the applications of lithium (LIBs) and potassium-ion batteries (PIBs). Several types of transition metal single atoms (e.g., Co, Ni, Fe) with π-d hybridization are incorporated into the semiconducting covalent organic framework (COF) composite. Single atoms favorably modify the energy band structure and improve the electronic conductivity of COF. More importantly, the electronic interaction between single atoms and COF adjusts the binding affinity and modifies ion traffic between Li/K ions and the active organic units of COFs as evidenced by extensive in situ and ex situ characterizations and theoretical calculations. The corresponding LIB achieves a high reversible capacity of 1,023.0 mA h g-1 after 100 cycles at 100 mA g-1 and 501.1 mA h g-1 after 500 cycles at 1,000 mA g-1. The corresponding PIB delivers a high reversible capacity of 449.0 mA h g-1 at 100 mA g-1 after 150 cycles and stably cycled over 500 cycles at 1,000 mA g-1. This work provides a promising route to engineering organic electrodes.

betAS: intuitive analysis and visualization of differential alternative splicing using beta distributions.

Next-generation RNA sequencing allows alternative splicing (AS) quantification with unprecedented resolution, with the relative inclusion of an alternative sequence in transcripts being commonly quantified by the proportion of reads supporting it as percent spliced-in (PSI). However, PSI values do not incorporate information about precision, proportional to the respective AS events' read coverage. Beta distributions are suitable to quantify inclusion levels of alternative sequences, using reads supporting their inclusion and exclusion as surrogates for the two distribution shape parameters. Each such beta distribution has the PSI as its mean value and is narrower when the read coverage is higher, facilitating the interpretability of its precision when plotted. We herein introduce a computational pipeline, based on beta distributions accurately modeling PSI values and their precision, to quantitatively and visually compare AS between groups of samples. Our methodology includes a differential splicing significance metric that compromises the magnitude of intergroup differences, the estimation uncertainty in individual samples, and the intragroup variability, being therefore suitable for multiple-group comparisons. To make our approach accessible and clear to both noncomputational and computational biologists, we developed betAS, an interactive web app and user-friendly R package for visual and intuitive differential splicing analysis from read count data.

Commemorating local victims of past atrocities and far-right support over time.

Does public remembrance of past atrocities lead to decreased support for far-right parties today? Initiatives commemorating past atrocities aim to make visible the victims and crimes committed against them. This runs counter to revisionist actors who attempt to downplay or deny atrocities and victims. Memorials for victims might complicate such attempts and reduce support for revisionist actors. Yet, little empirical evidence exists on whether that happens. In this study, we examine whether exposure to local memorials that commemorate victims of atrocities reduces support for a revisionist far-right party. Our empirical case is the Stolpersteine ("stumbling stones") memorial in Berlin, Germany. It commemorates victims and survivors of Nazi persecution in front of their last freely chosen place of residence. We employ time-series cross-sectional analyses and a discontinuity design using a panel dataset that matches the location and date of placement of new Stolpersteine with the election results from seven elections (2013 to 2021) at the level of polling station areas. We find that, on average, the presence of Stolpersteine is associated with a 0.96%-point decrease in the far-right vote share in the following election. Our study suggests that local memorials that make past atrocities visible have implications for political behavior in the present.

Projections of human kinship for all countries.

Demographers have long attempted to project future changes in the size and composition of populations, but have ignored what these processes will mean for the size, composition, and age distribution of family networks. Kinship structures matter because family solidarity-a crucial source of informal care for millions of people around the world-is conditional on kin being alive. Here, we present innovative projections of biological kin for the 1950 to 2100 period and discuss what they imply for the availability of informal care. Overall, we project that the number of living kin for individuals will decline dramatically worldwide. While a 65-yo woman in 1950 could expect to have 41 living kin, a 65-yo woman in 2095 is projected to have just 25 [18.8 to 34.7] relatives (lower and upper 80% projection intervals). This represents a 38% [15 to 54] global decline. The composition of family networks is also expected to change, with the numbers of living grandparents and great-grandparents markedly increasing, and the numbers of cousins, nieces and nephews, and grandchildren declining. Family networks will age considerably, as we project a widening age gap between individuals and their kin due to lower and later fertility and longer lifespans. In Italy, for example, the average age of a grandmother of a 35-yo woman is expected to increase from 77.9 y in 1950 to 87.7 y [87.1 to 88.5] in 2095. The projected changes in kin supply will put pressure on the already stretched institutional systems of social support, as more individuals age with smaller and older family networks.

Directional electronic tuning of Ni nanoparticles by interfacial oxygen bridging of support for catalyzing alkaline hydrogen oxidation.

Metallic nickel (Ni) is a promising candidate to substitute Pt-based catalysts for hydrogen oxidation reaction (HOR), but huge challenges still exist in precise modulation of the electronic structure to boost the electrocatalytic performances. Herein, we present the use of single-layer Ti3C2Tx MXene to deliberately tailor the electronic structure of Ni nanoparticles via interfacial oxygen bridges, which affords Ni/Ti3C2Tx electrocatalyst with exceptional performances for HOR in an alkaline medium. Remarkably, it shows a high kinetic current of 16.39 mA cmdisk-2 at the overpotential of 50 mV for HOR [78 and 2.7 times higher than that of metallic Ni and Pt/C (20%), respectively], also with good durability and CO antipoisoning ability (1,000 ppm) that are not available for conventional Pt/C (20%) catalyst. The ultrahigh conductivity of single-layer Ti3C2Tx provides fast transmission of electrons for Ni nanoparticles, of which the uniform and small sizes endow them with high-density active sites. Further, the terminated -O/-OH functional groups on Ti3C2Tx directionally capture electrons from Ni nanoparticles via interfacial Ni-O bridges, leading to obvious electronic polarization. This could enhance the Nids-O2p interaction and weaken Nids-H1s interaction of Ni sites in Ni/Ti3C2Txenabling a suitable H-/OH-binding energy and thus enhancing the HOR activity.

The strong metal-support interactions induced electrocatalytic three-electron oxygen reduction to hydroxyl radicals for water treatment.

As a promising environmental remediation technology, the electro-Fenton (EF) process is mainly limited by the two rate-limiting steps, which are H2O2 generation and activation. The electrocatalytic three-electron oxygen reduction reaction (3e- ORR) can directly activate oxygen to hydroxyl radicals (•OH), which is expected to break through the rate-limiting steps of the EF process. However, limited success has been achieved in the design of 3e- ORR electrocatalysts. Herein, we propose Cu/CoSe2/C with the strong metal-support interactions to enhance the 3e- ORR process, exhibiting remarkable reactivity and stability for •OH generation. Both experiment and DFT calculation results reveal that CoSe2 is conducive to the generation of H2O2. Meanwhile, the metallic Cu can enhance the adsorption strength of *H2O2 intermediates and thus promotes the one-electron reduction to •OH. The Cu/CoSe2/C catalyst exhibits the electron-transfer number close to 3.0 during the ORR process, and exhibits the outstanding •OH generation performance, achieving a higher apparent rate constant (6.0 times faster) toward ciprofloxacin compared with its analogy without the SMSI effect. Our work represents that the SMSI effect endows Cu/CoSe2/C high activity and selectivity for •OH generation, providing a unique perspective for the design of a high-efficiency 3e- ORR catalyst.

Electrochemically active porous carbon nanospheres prepared by inhibition of pyrolytic condensation of polymers.

Porous carbon is a pivotal material for electrochemical applications. The manufacture of porous carbon has relied on chemical treatments (etching or template) that require processing in all areas of the carbon/carbon precursor. We present a unique approach to preparing porous carbon nanospheres by inhibiting the pyrolytic condensation of polymers. Specifically, the porous carbon nanospheres are obtained by coating a thin film of ZnO on polystyrene spheres. The porosity of the porous carbon nanospheres is controlled by the thickness of the ZnO shell, achieving a BET-specific area of 1,124 m2/g with a specific volume of 1.09 cm3/g. We confirm that under the support force by the ZnO shell, a hierarchical pore structure in which small mesopores are connected by large mesopores is formed and that the pore-associated sp3 defects are enriched. These features allow full utilization of the surface area of the carbon pores. The electrochemical capacitive performance of porous carbon nanospheres was evaluated, achieving a high capacitance of 389 F/g at 1 A/g, capacitance retention of 71% at a 20-fold increase in current density, and stability up to 30,000 cycles. In particular, we achieve a specific area-normalized capacitance of 34.6 µF/cm2, which overcomes the limitations of conventional carbon materials.

BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities.

RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.

2023 American Heart Association Focused Update on Adult Advanced Cardiovascular Life Support: An Update to the American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Cardiac arrest is common and deadly, affecting up to 700 000 people in the United States annually. Advanced cardiac life support measures are commonly used to improve outcomes. This "2023 American Heart Association Focused Update on Adult Advanced Cardiovascular Life Support" summarizes the most recent published evidence for and recommendations on the use of medications, temperature management, percutaneous coronary angiography, extracorporeal cardiopulmonary resuscitation, and seizure management in this population. We discuss the lack of data in recent cardiac arrest literature that limits our ability to evaluate diversity, equity, and inclusion in this population. Last, we consider how the cardiac arrest population may make up an important pool of organ donors for those awaiting organ transplantation.

Discovering Fragile Clades and Causal Sequences in Phylogenomics by Evolutionary Sparse Learning.

Phylogenomic analyses of long sequences, consisting of many genes and genomic segments, reconstruct organismal relationships with high statistical confidence. But, inferred relationships can be sensitive to excluding just a few sequences. Currently, there is no direct way to identify fragile relationships and the associated individual gene sequences in species. Here, we introduce novel metrics for gene-species sequence concordance and clade probability derived from evolutionary sparse learning models. We validated these metrics using fungi, plant, and animal phylogenomic datasets, highlighting the ability of the new metrics to pinpoint fragile clades and the sequences responsible. The new approach does not necessitate the investigation of alternative phylogenetic hypotheses, substitution models, or repeated data subset analyses. Our methodology offers a streamlined approach to evaluating major inferred clades and identifying sequences that may distort reconstructed phylogenies using large datasets.

Advancing Pharmacogenomics from Single-Gene to Preemptive Testing.

Pharmacogenomic testing can be an effective tool to enhance medication safety and efficacy. Pharmacogenomically actionable medications are widely used, and approximately 90-95% of individuals have an actionable genotype for at least one pharmacogene. For pharmacogenomic testing to have the greatest impact on medication safety and clinical care, genetic information should be made available at the time of prescribing (preemptive testing). However, the use of preemptive pharmacogenomic testing is associated with some logistical concerns, such as consistent reimbursement, processes for reporting preemptive results over an individual's lifetime, and result portability. Lessons can be learned from institutions that have implemented preemptive pharmacogenomic testing. In this review, we discuss the rationale and best practices for implementing pharmacogenomics preemptively.

A genome sequencing system for universal newborn screening, diagnosis, and precision medicine for severe genetic diseases.

Newborn screening (NBS) dramatically improves outcomes in severe childhood disorders by treatment before symptom onset. In many genetic diseases, however, outcomes remain poor because NBS has lagged behind drug development. Rapid whole-genome sequencing (rWGS) is attractive for comprehensive NBS because it concomitantly examines almost all genetic diseases and is gaining acceptance for genetic disease diagnosis in ill newborns. We describe prototypic methods for scalable, parentally consented, feedback-informed NBS and diagnosis of genetic diseases by rWGS and virtual, acute management guidance (NBS-rWGS). Using established criteria and the Delphi method, we reviewed 457 genetic diseases for NBS-rWGS, retaining 388 (85%) with effective treatments. Simulated NBS-rWGS in 454,707 UK Biobank subjects with 29,865 pathogenic or likely pathogenic variants associated with 388 disorders had a true negative rate (specificity) of 99.7% following root cause analysis. In 2,208 critically ill children with suspected genetic disorders and 2,168 of their parents, simulated NBS-rWGS for 388 disorders identified 104 (87%) of 119 diagnoses previously made by rWGS and 15 findings not previously reported (NBS-rWGS negative predictive value 99.6%, true positive rate [sensitivity] 88.8%). Retrospective NBS-rWGS diagnosed 15 children with disorders that had been undetected by conventional NBS. In 43 of the 104 children, had NBS-rWGS-based interventions been started on day of life 5, the Delphi consensus was that symptoms could have been avoided completely in seven critically ill children, mostly in 21, and partially in 13. We invite groups worldwide to refine these NBS-rWGS conditions and join us to prospectively examine clinical utility and cost effectiveness.

Functional support vector machine.

Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. In this work, we propose a novel method to integrate functional principal component analysis with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. We demonstrate the performance of our method through extensive simulation experiments and two real data applications: the classification of alcoholics using electroencephalography signals and the prediction of glucobrassicin concentration using near-infrared reflectance spectroscopy. Our methods especially have more advantages when the measurement errors in functional predictors are relatively large.

Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences.

N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms are time-consuming and labor-intensive, and most of the proposed methods focused on limited species types. To further understand the relevant biological mechanisms among different species with the same RNA modification, it is necessary to develop a computational scheme that can be applied to different species. To achieve this, we proposed an attention-based deep learning method, adaptive-m6A, which consists of convolutional neural network, bi-directional long short-term memory and an attention mechanism, to identify m6A sites in multiple species. In addition, three conventional machine learning (ML) methods, including support vector machine, random forest and logistic regression classifiers, were considered in this work. In addition to the performance of ML methods for multi-species prediction, the optimal performance of adaptive-m6A yielded an accuracy of 0.9832 and the area under the receiver operating characteristic curve of 0.98. Moreover, the motif analysis and cross-validation among different species were conducted to test the robustness of one model towards multiple species, which helped improve our understanding about the sequence characteristics and biological functions of RNA modifications in different species.

PROSE: phenotype-specific network signatures from individual proteomic samples.

Proteomic studies characterize the protein composition of complex biological samples. Despite recent advancements in mass spectrometry instrumentation and computational tools, low proteome coverage and interpretability remains a challenge. To address this, we developed Proteome Support Vector Enrichment (PROSE), a fast, scalable and lightweight pipeline for scoring proteins based on orthogonal gene co-expression network matrices. PROSE utilizes simple protein lists as input, generating a standard enrichment score for all proteins, including undetected ones. In our benchmark with 7 other candidate prioritization techniques, PROSE shows high accuracy in missing protein prediction, with scores correlating strongly to corresponding gene expression data. As a further proof-of-concept, we applied PROSE to a reanalysis of the Cancer Cell Line Encyclopedia proteomics dataset, where it captures key phenotypic features, including gene dependency. We lastly demonstrated its applicability on a breast cancer clinical dataset, showing clustering by annotated molecular subtype and identification of putative drivers of triple-negative breast cancer. PROSE is available as a user-friendly Python module from https://github.com/bwbio/PROSE.

Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection.

Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially determined and screened by modeling. As a result, this study created a new pipeline aimed at using protein sequence to predict protein crystallization propensity in the protein material production stage, purification stage and production of crystal stage. The newly created pipeline proposed a new feature selection method, which involves combining Chi-square (${\chi }^{2}$) and recursive feature elimination together with the 12 selected features, followed by a linear discriminant analysisfor dimensionality reduction and finally, a support vector machine algorithm with hyperparameter tuning and 10-fold cross-validation is used to train the model and test the results. This new pipeline has been tested on three different datasets, and the accuracy rates are higher than the existing pipelines. In conclusion, our model provides a new solution to predict multistage protein crystallization propensity which is a big challenge in computational biology.

methylClass: an R package to construct DNA methylation-based classification models.

DNA methylation profiling is a useful tool to increase the accuracy of a cancer diagnosis. However, a comprehensive R package specially for it is lacking. Hence, we developed the R package methylClass for methylation-based classification. Within it, we provide the eSVM (ensemble-based support vector machine) model to achieve much higher accuracy in methylation data classification than the popular random forest model and overcome the time-consuming problem of the traditional SVM. In addition, some novel feature selection methods are included in the package to improve the classification. Furthermore, because methylation data can be converted to other omics, such as copy number variation data, we also provide functions for multi-omics studies. The testing of this package on four datasets shows the accurate performance of our package, especially eSVM, which can be used in both methylation and multi-omics models and outperforms other methods in both cases. methylClass is available at: https://github.com/yuabrahamliu/methylClass.