Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of General Event Types and Contributing Factors in Patient Safety System Data

Patient safety event reporting systems (PSRS) aim to identify safety hazards by encouraging hospital staff to report adverse events, errors, and potential errors in the healthcare system. PSRS collect different information regarding a safety event such as contributing factors and general event types. Users select contributing factors and general event types of a safety incident from predefined lists; therefore, most frequent general event types and contributing factors are commonly recorded together. Contributing factors are often related to the leading cause of an unsafe condition; therefore, identifying contributing factors can help with detecting and mitigating safety issues. In this study, we identified the general event types and contributing factors which were frequently recorded together. We calculated point-wise mutual information (PMI) between each pair to find the association between a contributing factor and a general event type. Our analysis demonstrated strong association between general event type and contributing factors which were most frequently recorded together. Fall and lost/impaired balance (PMI=15.12), skin/tissue and friction/shear (PMI=14.32), and blood bank and patient/sample incorrectly identified (PMI=13.78) were the three pairs of general event type and contributing factors which presented strong association.
Machine Learning Data Pipeline for the Democratization of AI

The use of artificial intelligence continues to increase. In healthcare, there has been a recent increase in AI applications to real-time individual patient clinical care, as opposed to population-based research or quality improvement efforts. However, the expertise to evaluate and implement these solutions is limited and often congregates in academic medical centers, creating barriers to adoption for smaller community and rural centers. Lowering the barrier to entry for innovative tools can help address disparities in patient outcomes due to access and other urban/rural contributors. We describe a strategy for evaluating commercially available machine learning models to disseminate lessons learned from developing, validating, and implementing machine learning-based models in clinical care in radiation therapy. In addition, we share an end-to-end data pipeline as open-source code with the tools necessary to identify, extract, organize, and process the data for use in machine-learning applications. We illustrate the application of this data pipeline to the use of brachytherapy to treat female cervical cancer patients. The example will show how we used the proposed pipeline to extract 708 potential participants and applied the developed methods and visualizations to clean the data providing 144 study participants for inclusion in our study. Finally, we discuss the anticipated challenges in implementing machine learning models in commercially available FDA-approved devices and suggest solutions using discrete tools built in different programming languages.
Exploring Language Anxiety Among Turkish Heritage Language Learners in Germany

Summary Heritage language speakers may feel anxiety about using their heritage language in different settings due to monolingual ideologies, family attitudes, language proficiency, and many other factors. However, the experience of those enrolling in heritage language classes has yet to be known in detail. This study examined heritage language anxiety (HLA) toward speaking skills among Turkish heritage language learners (HLLs). A descriptive design was used to seek the HLA level and its relations with various variables such as the home language, communicative language with friends, self-perceived proficiency, age, gender, grade level, birthplaces of children and parents, age of acquisition (AoA), book-reading and movie-watching languages. Three hundred and three school-age bilingual Turkish children in Germany participated in the study. The results demonstrated that the HLA level of the target group was low. In addition, in-class HLA was significantly higher than out-of-class. Moreover, HLA levels significantly differed in terms of the father's birthplace, the communicative language with friends, self-perceived proficiency, and the movie-watching language. Despite evident differences regarding the book-reading language and birthplace of the mother, these were not statistically significant. Also, no relationship was observed between HLA and some variables: Age, grade level, and AoA. This initial study attempted to comprehend the complex patterns behind the language anxiety concept in the context of Turkish immigrants in Germany and heritage language education (HLE). In conclusion, interaction via heritage language (HL) and access to HLE may contribute to reducing HLA. However, paradoxically, while HLE may alleviate general HLA, it may cause more in-class anxiety due to sociocultural and pedagogical factors.
Stabilization of a new memory-type thermoelastic Timoshenko system

This paper investigates the asymptotic stability of a new coupling to a thermoelastic Timoshenko-type system with memory. The memory term acts on the shear force in the evolution equations. We establish the well-posedness result for the system. Furthermore, we prove a general decay result for the solution energy. Interestingly, our stability result is obtained without imposing the condition of equal-wave speed.
RHIC Proton Beam Lifetime Increase with 10- and 12-pole Correctors

The RHIC beam lifetime in polarized proton operation is dominated by the beam-beam effect, parameter modulations, and nonlinear magnet errors in the interaction region magnets. Sextupole and skew sextupole errors have been corrected deterministically for a number of years based on tune shift measurements with orbit bumps in the triplets. During the most recent polarized proton run 10- and 12- pole correctors were set through an iterative procedure, and used for the first time operationally in one of the beams. We report on the procedure to set these high-order multipole correctors and estimate their effect on the integrated luminosity.
Loss of Lamin A leads to the nuclear translocation of AGO2 and compromised RNA interference

In mammals, RNA interference (RNAi) was historically studied as a cytoplasmic event; however, in the last decade, a growing number of reports convincingly show the nuclear localization of the Argonaute (AGO) proteins. Nevertheless, the extent of nuclear RNAi and its implication in biological mechanisms remain to be elucidated. We found that reduced Lamin A levels significantly induce nuclear influx of AGO2 in SHSY5Y neuroblastoma and A375 melanoma cancer cell lines, which normally have no nuclear AGO2. Lamin A KO manifested a more pronounced effect in SHSY5Y cells compared to A375 cells, evident by changes in cell morphology, increased cell proliferation, and oncogenic miRNA expression. Furthermore, in SHSY5Y cells, AGO fPAR-CLIP in Lamin A KO cells revealed significantly reduced activity of RNAi. Further exploration of the nuclear AGO interactome by mass spectrometry indicated that AGO2 is in complex with FAM120A, an RNA-binding protein and known interactor of AGO2. By performing FAM120A fPAR-CLIP, we discovered that FAM120A co-binds AGO targets and that this competition reduces the activity of RNAi. Therefore, loss of Lamin A triggers nuclear AGO2 translocation, RNAi impairment, and selective upregulation of oncogenic miRNAs, facilitating cancer cell proliferation.
Shared Gene Targets of the ATF4 and p53 Transcriptional Networks

The master tumor suppressor p53 regulates multiple cell fate decisions, like cell cycle arrest and apoptosis, via transcriptional control of a broad gene network. Dysfunction in the p53 network is common in cancer, often through mutations that inactivate p53 or other members of the pathway. Induction of tumor-specific cell death by restoration of p53 activity without off-target effects has gained significant interest in the field. In this study, we explore the gene regulatory mechanisms underlying a putative anti-cancer strategy involving stimulation of the p53-independent Integrated Stress Response (ISR). Our data demonstrate the p53 and ISR pathways converge to independently regulate common metabolic and pro-apoptotic genes. We investigated the architecture of multiple gene regulatory elements bound by p53 and the ISR effector ATF4 controlling this shared regulation. We identified additional key transcription factors that control basal and stress-induced regulation of these shared p53 and ATF4 target genes. Thus, our results provide significant new molecular and genetic insight into gene regulatory networks and transcription factors that are the target of numerous antitumor therapies.
Top-down Proteomics of 10,000 Single Brain Cells

We introduce single-cell Proteoform imaging Mass Spectrometry (scPiMS), which realizes the benefit of direct analysis of intact proteins to process 10,836 single cells from the rat hippocampus. This new platform addresses the throughput bottleneck for single cell proteomics using mass spectrometry, boosting cell processing rates by >20-fold in the field. We identified 169 proteoforms <70 kDa from single brain cells and classified 2758 of them as neurons, astrocytes or microglia cell types.
High-resolution Dynamic Human Brain Neural Activity Recording Using 3T MRI

Despite extensive research over decades, a non-invasive technique capable of capturing neural activities in the human brain with high spatiotemporal resolution is still lacking. The recently proposed direct imaging of neuronal activity (DIANA) using ultrahigh-field magnetic resonance imaging (MRI) has shown promise, but the translation from anesthetized mice to awake humans poses a significant challenge. Here we present Time Resolved ImaGing of Global Electroneurophysiological Record (TRIGGER), a novel technique that enables the direct detection of neural activity in the awake human brain using 3T MRI. In 18 participants, visual responses were captured at 5-mm spatial resolution and 1.4-ms temporal resolution. Importantly, the delay in stimulus presentation reliably corresponded to the latency of neural responses on a millisecond scale. Furthermore, when stimuli were presented to one visual field, the responses in two hemispheres exhibited the expected time difference. This non-invasive mapping approach holds the potential to elucidate neural dynamics underlying human brain function and disorders.
A mutation-mediated host shift drives Avian paramyxovirus type 1 evolution

PPMV-1, an antigenic variant of APMV-1, associated with specific pigeon host species. However, its evolutionary strategy and underlying drivers of host specificity remain unknown. In this study, we collect the outbreak data on a global scale to investigate its evolutionary dynamics, and provide an evidence-supported analysis the host shift of PPMV-1 from chickens to pigeons, and this shift is driven by the P protein. Our data indicated that the viruses in the United States and China have undergone convergent evolution. We find that three mutations of P protein, especially R163G, can significantly affect the adaptation of APMV-1 in pigeons. Mechanistically, sensor LSm14A inhibits the replication APMV-1 in DF-1 cells, and R163G substitutionon P protein increase LSm14A degradation. We propose the host shift drive the evolution of PPMV-1 and the underlying mechanism, offering new insights into the adaptive evolutionary process of the virus.
BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets and increasing reproducibility. A further void exists in standardized quantitative EEG (qEEG) and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) qEEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for performing these three methods on an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs via the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible. SUMMARY The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It offers three main protocols: heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. It should aid researchers and clinicians in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.
One-shot learning and robust recall with BTSP, a biological synaptic plasticity rule

Recent experimental studies in the awake brain have identified a rule for synaptic plasticity that is instrumental for the instantaneous creation of memory traces in area CA1 of the mammalian brain: Behavioral Time scale Synaptic Plasticity (BTSP). This one-shot learning rule differs in essential aspects from previously considered plasticity mechanisms. We introduce a transparent model for the core function of BTSP, and establish a theory that enables a principled understanding of the system of memory traces that it creates. Our theoretical predictions and numerical simulations show that BTSP is able to create a functionally powerful content-addressable memory without the need for high-resolution synaptic weights. Furthermore, it reproduces the repulsion effect of human memory, whereby traces for similar memory items are pulled apart to enable differential downstream processing. Altogether, our results create a link between synaptic plasticity in area CA1 of the hippocampus and its network function. They also provide a blueprint for implementing content-addressable memory with on-chip learning capability in highly energy-efficient crossbar arrays of memristors.
Forecasting fish recruitment using machine learning methods: A case study of arabesque greenling

Fish recruitment prediction is a central topic in fisheries science. We use a machine learning method for predicting the recruitment of Arabesque greenling in Hokkaido, Japan. Biological, fisheries-related, and environmental factors were included in the predictive models as feature variables. A gradient boosting model (GBM) showed better predictive performance compared with a linear regression model (LRM) and a random forest model (RFM) in terms of relative bias and relative root mean square error for recruitment prediction in the last 5 years. The most influential feature for GBM was spawning stock biomass in the last year, followed by the fishing rate for older fish. The sea surface temperature (SST) was a very weak predictor in the GBM but was the most influential feature in the LRM. The difference among models suggests the importance of nonlinearity and variable interactions. Machine learning methods will greatly contribute to fish recruitment forecast and thereby sustainable fisheries management.
Machine Learning Classification of Alzheimer’s Disease Pathology Reveals Diffuse Amyloid as a Major Predictor of Cognitive Impairment in Human Hippocampal Subregions

Analyzing Alzheimer’s disease (AD) pathology within anatomical subregions is a significant challenge, often carried out by pathologists using a standardized, semi-quantitative approach. To augment traditional methods, a high-throughput, high-resolution pipeline was created to classify the distribution of AD pathology within hippocampal subregions. USC ADRC post-mortem tissue sections from 51 patients were stained with 4G8 for amyloid, Gallyas for neurofibrillary tangles (NFTs) and Iba1 for microglia. Machine learning (ML) techniques were utilized to identify and classify amyloid pathology (dense, diffuse and APP (amyloid precursor protein)), NFTs, neuritic plaques and microglia. These classifications were overlaid within manually segmented regions (aligned with the Allen Human Brain Atlas) to create detailed pathology maps. Cases were separated into low, intermediate, or high AD stages. Further data extraction enabled quantification of plaque size and pathology density alongside ApoE genotype, sex, and cognitive status. Our findings revealed that the increase in pathology burden across AD stages was driven mainly by diffuse amyloid. The pre and para-subiculum had the highest levels of diffuse amyloid while NFTs were highest in the A36 region in high AD cases. Moreover, different pathology types had distinct trajectories across disease stages. In a subset of AD cases, microglia were elevated in intermediate and high compared to low AD. Microglia also correlated with amyloid pathology in the Dentate Gyrus. The size of dense plaques, which may represent microglial function, was lower in ApoE4 carriers. In addition, individuals with memory impairment had higher levels of both dense and diffuse amyloid. Taken together, our findings integrating ML classification approaches with anatomical segmentation maps provide new insights on the complexity of disease pathology in AD progression. Specifically, we identified diffuse amyloid pathology as being a major driver of AD in our cohort, regions of interest and microglial responses that might advance AD diagnosis and treatment.
Cell age drives asynchronous transcriptome aging

Organs age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse organs and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis, and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.
Ribosome clustering and surface layer reorganization in the microsporidian host-invasion apparatus

During host cell invasion, microsporidian spores translocate their entire cytoplasmic content through a thin, hollow superstructure known as the polar tube. To achieve this, the polar tube transitions from a compact spring-like state inside the environmental spore to a long needle-like tube capable of long-range sporoplasm delivery. The unique mechanical properties of the building blocks of the polar tube allow for an explosive transition from compact to extended state and support the rapid cargo translocation process. The molecular and structural factors enabling this ultrafast process and the structural changes during cargo delivery are unknown. Here, we employ light microscopy and in situ cryo-electron tomography to visualize multiple ultrastructural states of the polar tube, allowing us to evaluate the kinetics of its germination and characterize the underlying morphological transitions. We describe a cargo-filled state with a unique ordered arrangement of microsporidian ribosomes, which cluster along the thin tube wall, and an empty post-translocation state with a reduced diameter but a thicker wall. Together with a proteomic analysis of endogenously affinity-purified polar tubes, our work provides comprehensive data on the infection apparatus of microsporidia and demonstrates that ribosomes are efficiently transported through polar tubes in a spiral-like parallel arrangement.
A NanoDam toolkit for tissue-specific transcription factor profiling in C. elegans

During development of multicellular organisms, cells must execute precise molecular decisions to achieve cell fate specification and differentiation. These decisions are orchestrated by networks of transcription factors (TFs) which act to regulate gene expression of specific cohorts of genes to ultimately confer identity. Depending on the cellular context, TF expression can vary dramatically both spatially and temporally. These differences in expression patterns can result in tissue-specific differences in TF binding to downstream targets. To identify targets on a tissue-specific basis, Targeted DamID (TaDa) has been recently introduced to generate TF binding profiles in various models including C. elegans. However, TaDa suffers from portability such that a new promoter-TF fusion transgene must be constructed for every new experimental condition of interest. Here, we adapt NanoDam for usage in C. elegans, which relies on endogenous TF-GFP knock-ins, a plethora of which have already been generated by the community. We report that NanoDam single copy transgenes consisting of lowly expressed, tissue-specific GFP nanobody-Dam fusions, when combined with endogenous GFP-tagged alleles of TFs, results in robust, tissue-specific profiling. Using an endogenous GFP-tagged allele of EGL-43/EVI1, we performed NanoDam profiling of two disparate tissue types, the anchor cell (AC) and dopaminergic neurons, and identify targets unique to each and shared by both cell types. We also identify two GATA TFs, ELT-6 and EGL-18, as novel regulators of AC invasion. Together, we demonstrate that NanoDam is capable of profiling endogenous GFP-tagged TFs to identify novel downstream targets in specific cell types of C. elegans.
A fungal RNA-dependent RNA polymerase is a novel player in plant infection and cross-kingdom RNA interference

Small RNAs act as fungal pathogen effectors that silence host target genes to promote infection, a virulence mechanism termed cross-kingdom RNA interference (RNAi). The essential pathogen factors of cross-kingdom small RNA production are largely unknown. We here characterized the RNA-dependent RNA polymerase (RDR)1 in the fungal plant pathogen Botrytis cinerea that is required for pathogenicity and cross-kingdom RNAi. B. cinerea bcrdr1 knockout (ko) mutants exhibited reduced pathogenicity and loss of cross-kingdom small RNAs. We developed a novel “switch-on” GFP reporter to study cross-kingdom RNAi in real-time within the living plant tissue which highlighted that bcrdr1 ko mutants were compromised in cross-kingdom RNAi. Moreover, blocking seven pathogen cross-kingdom small RNAs by expressing a short-tandem target mimic RNA in transgenic Arabidopsis thaliana led to reduced infection levels of the fungal pathogen B. cinerea and the oomycete pathogen Hyaloperonospora arabidopsidis. These results demonstrate that cross-kingdom RNAi is significant to promote host infection and making pathogen small RNAs an effective target for crop protection.