Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing and Predicting Persistence of News Tweets

News is read and consumed differently based on its topic and timeliness to the reader. Some stories attract readers immediately after they are published, while others capture readership consistently over multiple days after their publication, regardless of their overall popularity. This paper studies this less explored facet of news story consumption, which we name persistence, operationalized as the time for a story to reach a certain percent of its total interest. In particular, we study persistence though a novel, publicly available data set of news tweets from 353 news outlets. We perform an extensive linguistic analysis of persistence in social media to uncover the underlying topical and stylistic cues that impact short-or long-term interest in a story. We train several models for predicting persistence that achieve predictive performance of up to 0.353 Spearman correlation when extrapolating to tweets from days unseen in training and retain significant predictive performance even on tweets from accounts unseen in training. The ability to predict news persistence can be useful in several practical applications that drive news and social media consumption including alerting, search ranking or recommendations.
“Dr LLM, what do I have?”: The Impact of User Beliefs and Prompt Formulation on Health Diagnoses

The strong capabilities of conversation-based large language models in healthcare applications are attracting an increasingly larger audience. However, the reliability of these models in consistently and accurately providing medical advice based on user-inputted symptoms is a critical concern. This study explores the sensitivity of LLMs to variations in user input, focusing specifically on how different symptom descriptions and prior users’ beliefs can potentially lead to different diagnoses. We test two GPT models with five different prompt templates to assess their ability of mentioning the true patient condition based on the symptoms. Our findings reveal a substantial sensitivity to input variations—especially when users have prior assumptions and beliefs—indicating potential inconsistencies in the diagnoses generated by these models.
An End-to-End Pipeline for Bibliography Extraction from Scientific Articles

We introduce a comprehensive end-to-end pipeline designed to extract complete bibliography section from English scientific articles in digital-born PDF format and further split them into individual citations. At the heart of our pipeline lies the utilization of Language-independent Layout Transformer (LiLT), a multimodal model that combines text and layout features to enhance the accuracy and robustness of bibliography extraction. By considering both text and visual structure, LiLT significantly improves the identification of bibliographic sections within scientific articles. To split the extracted full bibliography into individual citations, we employ a custom fine-tuned version of SciBERT, a Transformer-based model that excels at handling complex formatting variations common in scholarly bibliography. Having such end-to-end pipeline in-house allows us to bypass reliance on third-party black box tools, such as GROBID, offering greater control and transparency in the bibliography extraction process. Another highlight of our pipeline is its extensibility, as it can be seamlessly adapted to multilingual and image-based PDFs, hence allowing its utility across a wide range of scholarly content. When evaluated on an in-house dataset of digital-born English PDF articles published at Elsevier, we achieved an F1-score of 94.6%, a notable 3.1% improvement over GROBID, which is a well-regarded tool for bibliography parsing in the industry.
Style-sensitive Sentence Embeddings for Evaluating Similarity in Speech Style of Japanese Sentences by Contrastive Learning

Since dialogue systems are required to keep its speech style consistency, evaluating the similarity of speech styles is an important task. However, the Japanese language has a wide variety of speech styles, and also for each speech style, huge variety of vocabulary and word usage characteristics are observed, making it difﬁcult to evaluate the similarity of speech styles. This study proposes a speech style embedding model that produces a style-sensitive sentence embedding of Japanese sentences. The speech style embedding model is constructed by ﬁne-tuning a pre-trained BERT model. Here, sentence pairs with similar/dissimilar speech styles are automatically collected on a large scale using a sequence of sentences in web novels, with which con-strastive learning is performed for the training of the speech style embedding model. Using the Ward’s hierarchical clustering method, we also analyze the clusters of speech styles and the characteristic vocabulary/word usage of each speech style. Finally, we focus on the variation in speech styles of each person depending on the situation, and further analyze the variation in style-sensitive embeddings of each character in the novel.
Cycling reduces the entropy of neuronal activity in the human adult cortex

Electroencephalogram (EEG) data is often analyzed from a Brain Complexity (BC) perspective, having successfully been applied to study the brain in both health and disease. In this study, we employed recurrence entropy to quantify BC associated with the neurophysiology of movement by comparing BC in both resting state and cycling movement. We measured EEG in 24 healthy adults, and placed the electrodes on occipital, parietal, temporal and frontal sites, on both the right and left sides. EEG measurements were performed for cycling and resting states and for eyes closed and open. We then computed recurrence entropy for the acquired EEG series. Our results show that open eyes show larger entropy compared to closed eyes; the entropy is also larger for resting state, compared to cycling state for all analyzed brain regions. The decrease in neuronal complexity measured by the recurrence entropy could explain the neural mechanisms involved in how the cycling movements suppress the freezing of gate in patients with Parkinson’s disease due to the constant sensory feedback caused by cycling that is associated with entropy reduction.
Sequence, Structure and Functional space of Drosophila de novo proteins

During de novo emergence, new protein coding genes emerge from previously non-genic sequences. The de novo proteins they encode are dissimilar in composition and predicted biochemical properties to conserved proteins. However, many functional de novo proteins indeed exist. Both identification of functional de novo proteins and their structural characterisation are experimentally laborious. To identify functional and structured de novo proteins in silico, we applied recently developed machine learning based tools and refined the results for de novo proteins. We found that most de novo proteins are indeed different from conserved proteins both in their structure and sequence. However, some de novo proteins are predicted to adopt known protein folds, participate in cellular reactions, and to form biomolecular condensates. Apart from broadening our understanding of de novo protein evolution, our study also provides a large set of testable hypotheses for focused experimental studies on structure and function of de novo proteins in Drosophila.
Pyruvate metabolism dictates fibroblast sensitivity to GLS1 inhibition during fibrogenesis

Fibrosis is a chronic disease characterized by excessive extracellular matrix (ECM) production which leads to destruction of normal tissue architecture and disruption of organ function. Fibroblasts are key effector cells of this process and respond to a host of pro-fibrotic stimuli, including notably the pleiotropic cytokine, TGF-β1, which promotes fibroblast to myofibroblast differentiation. This is accompanied by the simultaneous rewiring of metabolic networks to meet the biosynthetic and bioenergetic needs of contractile and ECM-synthesizing cells, but the exact mechanisms involved remain poorly understood. In this study, we report that extracellular nutrient availability profoundly influences the TGF-β1 transcriptome of primary human lung fibroblasts (pHLFs) and the “biosynthesis of amino acids” emerges as a top enriched transcriptional module influenced by TGF-β1. We subsequently uncover a key role for pyruvate in influencing the pharmacological impact of glutaminase (GLS1) inhibition during TGF-β1-induced fibrogenesis. In pyruvate replete conditions which mimic the physiological concentration of pyruvate in human blood, GLS1 inhibition is ineffective in blocking TGF-β1-induced fibrogenesis, as pyruvate is able to be used as the substrate for glutamate and alanine production via glutamate dehydrogenase (GDH) and glutamic-pyruvic transaminase 2 (GPT2), respectively. We further show that dual targeting of either GPT2 or GDH in combination with GLS1-inhibition is required to fully block TGF-β1-induced collagen synthesis. These findings embolden a therapeutic strategy aimed at additional targeting of mitochondrial pyruvate metabolism in the presence of a glutaminolysis inhibitor in order to interfere with the pathological deposition of collagen in the setting of pulmonary fibrosis and potentially other fibrotic conditions.
Mutations in HUA2 restore flowering in the Arabidopsis trehalose 6-phosphate synthase1 (tps1) mutant

Plant growth and development are regulated by many factors, including carbohydrate availability and signaling. Trehalose 6-phosphate (T6P), which is synthesized by TREHALOSE-6-PHOSPHATE SYNTHASE 1 (TPS1), is positively correlated with and functions as a signal that informs the cell about the carbohydrate status. Mutations in TPS1 negatively affect the growth and development of Arabidopsis thaliana and complete loss-of-function alleles are embryo lethal, which can be overcome using inducible expression of TPS1 (GVG::TPS1) during embryogenesis. Using EMS mutagenesis in combination with genome re-sequencing we have identified several alleles in the floral regulator HUA2 that restore flowering and embryogenesis in tps1-2 GVG::TPS1. Genetic analyses using a HUA2 T-DNA insertion allele, hua2-4, confirmed this finding. RNA-seq analyses demonstrated that hua2-4 has widespread effects on the tps1-2 GVG::TPS1 transcriptome, including key genes and pathways involved in regulating flowering. Higher order mutants combining tps1-2 GVG::TPS1 and hua2-4 with alleles in the key flowering time regulators FLOWERING LOCUS T (FT), SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1), and FLOWERING LOCUS C (FLC) were constructed to analyze the role of HUA2 during floral transition in tps1-2 in more detail. Taken together, our findings demonstrate that loss of HUA2 can restore flowering and embryogenesis in tps1-2 GVG::TPS1 in part through activation of FT, with contributions of the upstream regulators SOC1 and FLC. Interestingly, we found that mutation of FLC is sufficient to induce flowering in tps1-2 GVG::TPS1. Furthermore, we observed that mutations in HUA2 modulate carbohydrate signaling and that this regulation might contribute to flowering in hua2-4 tps1-2 GVG::TPS1.
PHACTboost: A Phylogeny-aware Boosting Algorithm to Compute the Pathogenicity of Missense Mutations

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. The results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, meta-predictors, and deep learning-based approaches as well as state-of-the-art tools, AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 219 million missense variants over 20,191 proteins. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.
Systematic characterization of protein structural features of alternative splicing isoforms using AlphaFold 2

Alternative splicing is an important cellular process in eukaryotes, altering pre-mRNA to yield multiple protein isoforms from a single gene. However, our understanding of the impact of alternative splicing events on protein structures is currently constrained by a lack of sufficient protein structural data. To address this limitation, we employed AlphaFold 2, a cutting-edge protein structure prediction tool, to conduct a comprehensive analysis of alternative splicing for approximately 3,000 human genes, providing valuable insights into its impact on the protein structural. Our investigation employed state of the art high-performance computing infrastructure to systematically characterize structural features in alternatively spliced regions and identified changes in protein structure following alternative splicing events. Notably, we found that alternative splicing tends to alter the structure of residues primarily located in coils and beta-sheets. Our research highlighted a significant enrichment of loops and highly exposed residues within human alternatively spliced regions. Specifically, our examination of the Septin-9 protein revealed potential associations between loops and alternative splicing, providing insights into its evolutionary role. Furthermore, our analysis uncovered two missense mutations in the Tau protein that could influence alternative splicing, potentially contributing to the pathogenesis of Alzheimer’s disease. In summary, our work, through a thorough statistical analysis of extensive protein structural data, sheds new light on the intricate relationship between alternative splicing, evolution, and human disease.
Electrophysiological responses to syntactic and “morphological” structures: evidence from Mandarin Chinese

What are syntactic relations, and how does our brain infer them from a string of text characters? In the EEG study reported here we aimed to investigate these questions by contrasting syntactically separable compounds (zao4…fan3 → “rebel”) and non-separable compounds (chi2yi2 → “hesitate”) in Mandarin Chinese. Because both kinds of compounds have non-compositional meanings, their syntactic differences provide an elegant means for dissociating syntactic from semantic relations. Because non-separable compounds fit the traditional criteria for “wordhood”, this contrast also provides a means for asking whether syntactic and morphological relations are inferred in qualitatively different ways. We found that, relative to non-separable compounds, syntactically separable compounds elicited a left anterior negativity (LAN) effect between 275-400ms. Even though readers were always presented with the compounds in their unseparated form, our results suggest that the potentially separable compound forms drive the inference of a more complex set of underlying syntactic relations. In a companion manipulation in the same participants, we observed a qualitatively similar LAN effect when comparing non-separable compound nouns with simplex nouns. This finding raises doubts for the existence of a clear-cut distinction between “syntax” and “morphology”, at least in processing.
Effects of Controlling Parameters and Their Spatial Variability on Heat Flow in the Vadose Zone

While the use of geothermal energy is becoming more common, a key factor holding back the growth of geothermal energy production is the cost of exploration. Rapid shallow (two meter) temperature measurement is a relatively inexpensive exploration method that can be used to locate blind geothermal systems. This method has been successfully used to locate geothermal anomalies at two meters depth that are representative of a similar geothermal pattern at a greater depth (Coolbaugh et al., 2007). However, studies have shown that increased temperatures at two meters depth are not always analogous to increased temperature at a deeper depth (Olmsted and Ingebritsen, 1986). Therefore, a better understanding of the parameters that control heat flow to shallow depths is needed. This study uses numerical modeling to assess the effect of material heterogeneities on heat transport in the vadose zone. The TOUGH2 computer program is used to numerically simulate heat, water, and air transport through two-dimensional porous media. A 100 by 100 meter grid was created with a water table at the lower boundary held constant at 95°C and the average surface temperature at the upper boundary held constant at 20°C. Intrinsic permeability, porosity, residual liquid saturation and thermal conductivity of the porous media were systematically altered in different spatial patterns to determine the controlling factors affecting heat flow in the vadose zone. Results of this study indicate that individual changes in the parameters tested do not result in significant horizontal changes in temperature at shallow depths. Although these individual changes in the parameters tested are not enough to significantly change
20th USENIX

Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are engaged with a large set of graphs and embedding data on storage, which exhibits complex and irregular preprocessing. We propose a novel deep learning framework on large graphs, HolisticGNN , that provides an easy-to-use, near-storage inference infrastructure for fast, energy-efﬁcient GNN processing. To achieve the best end-to-end latency and high energy efﬁciency, HolisticGNN allows users to implement various GNN algorithms and directly executes them where the actual data exist in a holistic manner. It also enables RPC over PCIe such that the users can simply program GNNs through a graph semantic library without any knowledge of the underlying hardware or storage conﬁgurations. We fabricate HolisticGNN’s hardware RTL and implement its software on an FPGA-based computational SSD (CSSD). Our empirical evaluations show that the inference time of HolisticGNN outperforms GNN inference services us-ing high-performance modern GPUs by 7.1 × while reducing energy consumption by 33.2 × , on average.
Holistic Treatment of In ﬂ ammatory Disease for the Mobility Impaired

Background: Previous studies have demonstrated the positive impact of yoga and deep breathing exercises on overall physical wellness. The effects of deep breathing alone on metabolic rate, weight, and cholesterol management have not been studied well. Objective: This study assessed changes in C-Reactive Protein (CRP) following a 60-day intervention of a 12-minute deep breathing program. Methods: Sixty-six participants with a BMI >27 kg/m2 and 18 years - 70 years were enrolled in this study. Participants were assigned to either the control or intervention group in a single-blind manner. The intervention group followed the novel deep breathing program, while the control group did not modify their lifestyle or exercise. Anthropometric measurements and metabolic markers were evaluated and compared between the two groups after 60 days. Results: After 60 days, the control group exhibited a mean increase change in CRP of 3.9%. The intervention group showed a mean reduction of CRP by 25%. Conclusion: A guided daily 12-minute-deep breathing program can lead to reductions in in ﬂ ammation marker CRP even without additional lifestyle modi ﬁ cations or medication modi ﬁ cations. Further investigations are warranted to explore the effects of novel deep breathing programs on metabolic markers and elucidate the underlying mechanisms of CRP reduction.
Redistribution of pyrogenic carbon from hillslopes to stream corridors following a large montane wildfire

Pyrogenic carbon (PyC) constitutes a significant fraction of organic carbon in most soils. However, PyC soil stocks are generally smaller than what is expected from estimates of PyC produced from fire and decomposition losses, implying that other processes cause PyC loss from soils. Surface erosion has been previously suggested as one such process. To address this, following a large wildfire in the Rocky Mountains (CO, USA), we tracked PyC from the litter layer and soil, through eroded, suspended, and dissolved solids to alluvial deposits along riversides. We separated deposited sediment into high‐ and low‐density fractions to identify preferential forms of PyC transport and quantified PyC in all samples and density fractions using benzene polycarboxylic acid markers. A few months after the fire, PyC had yet to move vertically into the mineral soil and remained in the organic layer or had been transported off site by rainfall driven overland flow. During major storm events PyC was associated with suspended sediments in river water and later identified in low‐density riverbank deposits. Flows from an unusually long‐duration and high magnitude rainstorm either removed or buried the riverbank sediments approximately 1 year after their deposition. We conclude that PyC redistributes after wildfire in patterns that are consistent with erosion and deposition of low‐density sediments. A more complete understanding of PyC dynamics requires attention to the interaction of post fire precipitation patterns and geomorphological features that control surface erosion and deposition throughout the watershed.
Countering a Culture of Disengagement: The Role of Dialogical Self Theory in Teaching Podcasting in an Engineering Communication Class

A culture of student disengagement from concern with public welfare has been identified in engineering education, leading some educators to call for assignments and extra-curricular activities that promote student awareness of the role of engineering in society. This paper reviews a podcast assignment for engineering students, which asks them to create a short demo episode for a podcast called Engineering Moment about a topic addressing the social role of engineering. This paper proposes that the assignment fosters students’ self-reflexivity and supports a process of identity construction in relationship to others, countering the culture of disengagement. Ninety-two of the engineering majors who completed the podcast assignment took a voluntary anonymous survey that included open-ended questions. This paper discusses the assignment and the survey results in the context of dialogical self theory because the theory addresses identity development as a dynamic process within a social context. Overall, the survey results indicate that in the context of completing the podcast assignment, engineering students expressed valuing the ability to shift perspectives and they conveyed a deepened appreciation for the role of engineering in society. In this way, the podcast assignment offers particular value in the engineering classroom.