Miracle-dz/UniFAR-FTUs huggingface dataset:
ROR2 regulates cellular plasticity in pancreatic neoplasia and adenocarcinoma

Cellular plasticity is a hallmark of pancreatic ductal adenocarcinoma (PDAC) starting from the conversion of normal cells into precancerous lesions to the progression of carcinoma subtypes associated with aggressiveness and therapeutic response. We discovered that normal acinar cell differentiation, maintained by the transcription factor Pdx1, suppresses a broad gastric cell identity that is maintained in metaplasia, neoplasia, and the classical subtype of PDAC in mouse and human. We have identified the receptor tyrosine kinase Ror2 as marker of a gastric metaplasia (SPEM)-like identity in the pancreas. Ablation of Ror2 in a mouse model of pancreatic tumorigenesis promoted a switch to a gastric pit cell identity that largely persisted through progression to the classical subtype of PDAC. In both human and mouse pancreatic cancer, ROR2 activity continued to antagonize the gastric pit cell identity, strongly promoting an epithelial to mesenchymal transition, conferring resistance to KRAS inhibition, and vulnerability to AKT inhibition. Significance We discovered the receptor tyrosine kinase ROR2 as an important regulator of cellular identity in pancreatic precancerous lesions and pancreatic cancer. ROR2 drives an aggressive PDAC phenotype and confers resistance to Kras inhibitors, suggesting that targeting ROR2 will enhance sensitivity to this new generation of targeted therapies.
Investigation of using conductive asphalt concrete with carbon fiber additives in intelligent anti-icing systems

ABSTRACT In winter, ice on the roads causes traffic accidents and congestion. In recent years, various techniques have been developed to provide more effective, efficient, and environmentally friendly solutions to the problem of road icing. One of these is electrically conductive asphalt concrete (CAC). This study aims to develop an intelligent anti-icing system by increasing the conductivity and mechanical properties of asphalt concrete. Two different conductive asphalt series were produced. Series 1 uses a binder modified with 3% styrene butadiene styrene (SBS) block copolymer and Series 2 uses a binder modified with both 3% SBS and 14% carbon black (CB). In both series, 0.3% carbon fiber (CF) by weight was added to the dry mixture. Thermal dissipation status, ice melting performance, freezing, and electrical conductivity heating cycle tests were conducted. The long-term mechanical and conductivity performances of CAC specimens were assessed with pre-and post-cycle experiments. CAC, developed for its conductivity and mechanical properties, was integrated with the intelligent anti-icing system. A comparative cost analysis was also performed to assess conventional asphalt concrete and the CAC. The binder phase was found to contribute to the mixture conductivity. Such systems can be integrated with Intelligent Transportation Systems (ITS).
Transcriptional correlates of cocaine-associated learning in striatal ARC ensembles

Learned associations between the rewarding effects of drugs and the context in which they are experienced underlie context-induced relapse. Previous work demonstrates the importance of sparse neuronal populations – called neuronal ensembles – in associative learning and cocaine seeking, but it remains unknown whether the encoding vs. retrieval of cocaine-associated memories involves similar or distinct mechanisms of ensemble activation and reactivation in nucleus accumbens (NAc). We use ArcCreERT2 mice to establish that mostly distinct NAc ensembles are recruited by initial vs. repeated exposures to cocaine, which are then differentially reactivated and exert distinct effects during cocaine-related memory retrieval. Single-nuclei RNA-sequencing of these ensembles demonstrates predominant recruitment of D1 medium spiny neurons and identifies transcriptional properties that are selective to cocaine-recruited NAc neurons and could explain distinct excitability features. These findings fundamentally advance our understanding of how cocaine drives pathological memory formation during repeated exposures.
Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning

Integration of human feedback plays a key role in improving the learning capabilities of intelligent systems. This comparative study delves into the performance, robustness, and limitations of imitation learning compared to traditional reinforcement learning methods within these systems. Recognizing the value of human-in-the-loop feedback, we investigate the influence of expert guidance and suboptimal demonstrations on the learning process. Through extensive experimentation and evaluations conducted in a pre-existing simulation environment using the Unity platform, we meticulously analyze the effectiveness and limitations of these learning approaches. The insights gained from this study contribute to the advancement of human-centered artificial intelligence by highlighting the benefits and challenges associated with the incorporation of human feedback into the learning process. Ultimately, this research promotes the development of models that can effectively address complex real-world problems.
TriAD: Capturing Harmonics With 3D Convolutions

Thanks to advancements in deep learning (DL), automatic music transcription (AMT) systems recently outper-formed previous ones fully based on manual feature de-sign. Many of these highly capable DL models, however, are computationally expensive. Researchers are moving towards smaller models capable of maintaining state-of-the-art (SOTA) results by embedding musical knowledge in the network architecture. Existing approaches employ convolutional blocks specifically designed to capture the harmonic structure. These approaches, however, require either large kernels or multiple kernels, with each kernel aiming to capture a different harmonic. We present TriAD, a convolutional block that achieves an unequally distanced dilation over the frequency axis. This allows our method to capture multiple harmonics with a single yet small kernel. We compare TriAD with other methods of capturing harmonics, and we observe that our approach maintains SOTA results while reducing the number of parameters required. We also conduct an ablation study showing that our proposed method effectively relies on harmonic information.
A Review of Validity and Its Relationship to Music Information Research

Validity is the truth of an inference made from evidence and is a central concern in scientiﬁc work. Given the maturity of the domain of music information research (MIR), validity in our opinion should be discussed and considered much more than it has been so far. Puzzling MIR phenomena like adversarial attacks, horses, and performance glass ceilings become less mysterious through the lens of validity. In this paper, we review the subject of validity as presented in a key reference of causal inference: Shadish et al., Experimental and Quasi-experimental Designs for Generalised Causal Inference [1]. We discuss the four types of validity and threats to each one. We consider them in relationship to MIR experiments grounded with a practical demonstration using a typical MIR experiment.
Sequence-to-Sequence Network Training Methods for Automatic Guitar Transcription With Tokenized Outputs

We propose multiple methods for effectively training a sequence-to-sequence automatic guitar transcription model that uses tokenized music representation as an output. Our proposed method mainly consists of 1) a hybrid CTC-Attention model for sequence-to-sequence automatic guitar transcription that uses tokenized music representation, and 2) two data augmentation methods for training the model. Our proposed model is a generic encoder-decoder Transformer model but adopts multi-task learning with CTC from the encoder to speed up learning alignments be-tween the output tokens and acoustic features. Our proposed data augmentation methods scale up the amount of training data by 1) creating bar overlap when splitting an excerpt to be used for network input, and 2) by utilizing MIDI-only data to synthetically create audio-MIDI pair data. We conﬁrmed that 1) the proposed data augmentation methods were highly effective for training generic Trans-former models that generate tokenized outputs, 2) our proposed hybrid CTC-Attention model outperforms conventional methods that transcribe guitar performance with to-kens, and 3) the addition of multi-task learning with CTC in our proposed model is especially effective when there is an insufﬁcient amount of training data.
Monitoring Cyber Peer-Led Team Learning: A Multimodal Human-in-the-loop Approach

The recent adoption of generative artificial intelligence (AI) tools in education has transformed education and AI-assisted learning. However, researchers embracing applied machine learning (ML) in pedagogical settings continue to face challenges. Lack of publicly available multimodal datasets involving gesture and emotion recognition specifically in education is a bottleneck for building AI-enabled e-learning platforms. In the paper, we take a constructive stance at monitoring group behavior in cyber peer-led team learning (cPLTL) classes in Organic Chemistry using ML. Although past studies have attempted to quantify student engagement in e-learning, their use in the cPLTL use case for AI modeling has not yet been established. The hypothesis underlying our proposed framework is that online peer group behavior can be characterized by a human-in-the-loop model that relies on multiple input modalities. Thus, the aim is to identify behavioral patterns in head and facial movements that are augmented by lexical based sentiment and audio feature extraction. To combat the small data challenge, we propose a framework for the human-in-the-loop (HITL) system that actively learns the past group modalities. HITL strategies enable the algorithm to learn more efficiently from less data iteratively. The model will be implemented using active learning, measures of uncertainty, random sampling and entropy which are key in the design of the study. A qualitative comparison of sentiment modality with ChatGPT’s participant performance evaluation has been discussed. The study will increase the use of AI in tools that support educators in universities using pedagogies of active engagement in science, technology, engineering, and math.
Clustered Regularly Interspaced Short Palindromic Repeats-Cas system regulates Salmonella virulence

Objectives Investigating the type 1-E CRISPR-Cas-mediated regulation of Salmonella pathogenesis. Methods We assessed the pathogenicity of the wild-type and CRISPR-Cas knockout strains using infection models. The mechanisms were elucidated using antimicrobial assays and expression analysis. Results CRISPR-Cas knockout strains were defective in invasion and proliferation in intestinal epithelial cells and macrophages. However, proliferation defects were not observed in the Gp91phox-/- macrophages, suggesting the system’s role in antioxidant defence. The knockout strains show hampered colonization in in-vivo infection models, possibly due to increased sensitivity against innate immune barriers like antimicrobial peptides, complement proteins and oxidative stress. The expression studies of various virulence regulators: pmr genes, anti-oxidant genes, SPI-1 and SPI-2 encoded master regulators, and effectors showed repressed expression in the knockout strains. Some of these genes could be directly regulated by the CRISPR-spacers owing to partial complementarity between the sequences. Conclusion Overall, our study shows that the CRISPR-Cas system positively regulates Salmonella pathogenesis by regulating the expression of different virulence factors.
Salivary biomarkers as pioneering indicators for diagnosis and severity stratification of pediatric long COVID

Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), manifests as persistent and often debilitating symptoms enduring well beyond the initial COVID-19 infection. Presently, a specific diagnostic test or definitive biomarker set for confirming long COVID is lacking, relying instead on the protracted presence of symptoms post-acute infection. In this study, we examined 105 saliva samples (49 from children with long COVID and 56 controls), revealing significant alterations in salivary biomarkers. Pediatric long COVID exhibited increased oxidant biomarkers, decreased antioxidant, immune response, and stress-related biomarkers. Correlation analyses unveiled distinct patterns between biomarkers in long COVID and controls. Notably, a multivariate logistic regression pinpointed TOS, ADA2, total proteins, and AOPP as pivotal variables, culminating in a remarkably accurate predictive model distinguishing long COVID from controls. Furthermore, total proteins and ADA1 were instrumental in discerning between mild and severe long COVID symptoms. This research sheds light on the potential clinical utility of salivary biomarkers in diagnosing and categorizing the severity of pediatric long COVID. It also lays the groundwork for future investigations aimed at unraveling the prognostic value of these biomarkers in predicting the trajectory of long COVID in affected individuals.
Grouped Gaussian Processes for Solar Forecasting

We propose a Gaussian process regression network model where latent functions are arbitrarily coupled a priori. Driven by the problem of developing forecast methods for distributed solar and other renewable power generation, we propose coupled priors that exploit spatial dependencies in a scalable structure. We estimate short term forecast models for solar power at multiple distributed sites and ground wind speed at proximate weather stations. Our approach maintains or improves point-forecast accuracy relative to competing benchmarks. At the same time our approach significantly reduces forecast variance.
Improvement of VR science teaching materials that visualize invisible elements and Evaluation as a self-study use

Teaching materials using virtual reality (VR) technology are expected to further improve learners’ understanding. However, it is assumed that the instructor’s engineering knowledge may be insufficient to introduce these materials in the classroom, and that it will take time to educate the instructors. Therefore, we improved VR science teaching materials that visualize electric currents and magnetic fields for self-study at home
New Journal of Physics Spatial structure increases the waiting time for cancer

Cancer results from a sequence of genetic and epigenetic changes that lead to a variety of abnormal phenotypes including increased proliferation and survival of somatic cells and thus to a selective advantage of precancerous cells. The notion of cancer progression as an evolutionary process has been attracting increasing interest in recent years. A great deal of effort has been made to better understand and predict the progression to cancer using mathematical models; these mostly consider the evolution of a wellmixed cell population, even though pre-cancerous cells often evolve in highly structured epithelial tissues. In this study, we propose a novel model of cancer progression that considers a spatially structured cell population where clones expand via adaptive waves. This model is used to assess two different paradigms of asexual evolution that have been suggested to delineate the process of cancer progression. The standard scenario of periodic selection assumes that driver mutations are accumulated strictly sequentially over time. However, when the mutation supply is sufficiently high, clones may arise simultaneously on distinct genetic backgrounds, and clonal adaptation waves interfere with each other. We find that in the presence of clonal interference, spatial structure increases the waiting time for cancer, leads to a patchwork structure of non-uniformly sized clones and decreases the survival probability of virtually neutral (passenger) New Journal of Physics 13 (2011) 115014 1367-2630/11/115014+22$33.00 © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft
The Journal of Systems & Software

Apache Spark is one of the most popular big data frameworks that abstract the underlying distributed computation details. However, even though Spark provides various abstractions, developers may still encounter challenges related to the peculiarity of distributed computation and environment. To understand the challenges that developers encounter, and provide insight for future studies, in this paper, we conduct an empirical study on the questions that developers encounter. We manually analyze 1,000 randomly selected questions that we collected from Stack Overflow. We find that: 1) questions related to data processing (e.g., transforming data format) are the most common among the 11 types of questions that we uncovered. 2) Even though data processing questions are the most common ones, they require the least amount of time to receive an answer. Questions related to configuration and performance require the most time to receive an answer. 3) Most of the issues are caused by developers’ insufficient knowledge in API usages, data conversation across frameworks, and environment-related configurations. We also discuss the implication of our findings for researchers and practitioners. In summary, our work provides insights for future research directions and highlight the need for more software engineering research in this area.
Genomic stability in Cenostigma Tul., (Caesalpinioideae, Fabaceae): causes and consequences

The Pantropical Caesalpinia group includes 225 species distributed in 27 monophyletic genera, among which Cenostigma stands out by taxonomic and phylogenetic complexity. The genus includes trees and shrubs with interspecific hybridization and high diversity in north-eastern Brazil (Caatinga domain). Detailed cytogenomic characterizations have been performed only in C. microphyllum revealing enrichment of long terminal repeats (LTR) Ty3/gypsy transposable elements (TEs) and satellite DNA (satDNA) in the heterochromatin. Here, we aimed to perform a comparative analysis of seven Northeast Brazilian species of Cenostigma using cytogenomic and genomic approaches. The comparative genomic analysis revealed repeats stability with similar TE abundance, composition, and chromosomal localization in all species. On the other hand, satDNA were highly variable in abundance, in some cases species-specific. Cytogenomic data confirmed the karyotype stability with the TE elements Athila and Tekay enriching the proximal heterochromatin. Moreover, the satDNA CemiSat163 appeared to be exclusively located on acrocentric chromosomes of the analysed species. The genomic stability in Cenostigma may be related to their relatively recent age (~13.59 Mya), long-life cycle, and/ or similarity in ecological niche among this species. We propose that the genomic stability found in Cenostigma may facilitate the natural interspecific gene flow reported in sympatric species, complicating the interpretation of its systematics and evolution.
EngagedScholarship@CSU EngagedScholarship@CSU

This research assessed the effectiveness of a fomily-centered approach to aut-of hom e core in reunifying children with their families by comparing differential exit rates of children whose families received family-centered services with children whose families received routine child welfare services. The sample included 472 children who were in foster care from 1994 to 1996 in Missouri. Survival analysis was used to calculate the probability that a child would he reunified with his or her family at a particular time and to compare the differential exit rates for the children who experienced subsequent placement during the study period. The authors used Cox regression analysis to compare the likelihood of reunification between the two groups. Findings indicate that during the lotter part of the study, family-centered out-of-home care counties reunified children at a faster rate than comparison counties.
Identification of the Domain(s) in Protein Disulfide Isomerase Identification of the Domain(s) in Protein Disulfide Isomerase Required for Binding and Disassembly of the Cholera Holotoxin Required for Binding and Disassembly of the Cholera Holotoxin

Cholera, caused by the secretion of cholera toxin (CT) by Vibrio cholerae within the intestinal lumen, triggers massive secretory diarrhea which may lead to life-threatening dehydration. CT is an AB 5 -type protein toxin that is comprised of an enzymatically active A1 chain, an A2 linker, and a cell-binding B pentamer. Once secreted, the CT holotoxin moves from the cell surface to the endoplasmic reticulum (ER) of a host target cell. To cause intoxication, CTA1 must be displaced from CTA2/CTB 5 in the ER and is then transferred to the cytosol where it induces a diarrheal response by stimulating the efflux of chloride ions into the intestinal lumen. Protein disulfide isomerase (PDI), a resident ER oxidoreductase and chaperone, is involved in detaching CTA1 from the holotoxin. The PDI domain(s) that binds to CTA1 and precisely how this interaction is involved in CTA1 dissociation from the holotoxin are unknown. The goal of this project is to identify which domain(s) of PDI is responsible for binding to and dislodging CTA1 from the CT holotoxin. Through incorporation of ELISA, surface plasmon resonance (SPR), and Fourier transform infrared (FTIR) spectroscopy techniques in conjunction with a panel of purified PDI deletion constructs, this project aims to provide important molecular insight into a crucial interaction of the CT intoxication process.
Air Quality Modeling Using Real-time Urban Big Data

This study utilized real-time urban data from Seoul to assess the impact of transportation mode choice on air pollution metrics. EXtreme Gradient Boosting (XGBoost), an ensemble model, was employed for air quality analysis. Subsequently, SHapley Additive exPlanations (SHAP), one of eXplainable Artificial Intelligence (XAI) techniques, was used to understand the influence of urban factors on air quality. For spatial coverage, 50 locations with high traffic volume were selected, and the temporal coverage spanned from April 3, 2023, to April 30, 2023. Variables related to traffic, the environment, and weather were established as features, while Comprehensive Air-quality Index (CAI), PM2.5, and PM10 were determined as target variables. As a result, Root Mean Squared Error (RMSE) of the models predicting CAI, PM2.5, and PM10 were calculated as 0.57, 0.47, and 0.50. The study found that as the maximum number of pedestrians and the number of subway passengers alighting increased, the levels of CAI, PM2.5, and PM10 decreased. This indicates that the use of greener modes of transportation, such as walking and taking the subway, positively impacts air pollution reduction In addition, lower road traffic speeds were associated with higher PM2.5 levels, while increased road congestion correlated with higher PM10 levels. The observed increase in PM2.5 and PM10 levels in relation to the rise in passenger car traffic suggests that emissions from these vehicles are contributing to air pollution. Consequently, the study confirmed that traffic-related factors can influence air quality indicators, and that modifications to traffic volumes and modal splits can enhance air pollution control. This study provides a foundation for developing policies to improve air quality by quantifying and presenting various factors that impact air quality.