Miracle-dz/UniFAR-FTUs huggingface dataset:
Carry-Forward Effect: Early scaffolding learning processes

Multimodal data enables us to capture the cognitive and affective states of students to provide a holistic understanding of learning processes in a wide variety of contexts. With the use of sensing technology, we can capture learners’ states in near real-time and support learning. Moreover, multimodal data allows us to obtain early-predictions of learning performance, and support learning in a timely manner. In this contribution, we utilize the notion of “carry forward effect”, an inferential and predictive modelling approach that utilizes multimodal data measurements detrimental to learning performance to provide timely feedback suggestions. carry forward effect can provide a way to prioritize conflicting feedback suggestions in a multimodal data based scaffolding tool. We showcase the empirical proof of carry forward effect with the use of two different learning scenarios: debugging and game-based learning.
An improved null steering digital beamformer based on multi-group time delays for echo separation in HRWS SAR

ABSTRACT Digital beamforming with null steering in elevation can be used to separate echoes corresponding to different sub-pulses, but the pulse extension loss will decrease performance on echo separation. To resolve this problem, an improved null steering beamformer based on multi-group time delays is proposed. In the proposed beamformer, the phase weighting matrix is multiplied and the multi-group time delays are used to improve the coherence between echoes from different channels. Then, the modified phase weighing matrix for null steering is introduced to further improve the capacity of echo separation. Compared with the conventional null steering beamformer, the multi-group time delays can provide the more accurate time delay compensation for better echo separation. Simulation results on both point and distributed targets validate the proposed beamformer.
Optical spectra for a cholesteric elastomer doped with metallic nanospheres with an inversion defect

We are considering the propagation of circularly polarized light normally impinging on a cholesteric elastomer slab doped with metallic spheres. The slab is composed of two layers: the first layer is a right-handed chiral structure, and the second one is a helix with left-handedness; however, the pitch of the chiral structure remains constant. We performed a theoretical model to find numerically, reflection and transmission spectra for circularly polarized light impinging on the slab. We considered the effects of mechanical strains along the helical axis since the force exerted on the structure can tilt the mesogens in the slab along the helical axis. The Photonic Band-Gap(PBG) of the elastomer slab becomes narrowed because of the strain; there we also observe high reflectance (hyper-reflectivity) that depends on the filling factor of the silver doping and on whether the defect is located at a position that contains complete periods of the structure. Additionally, the spectra display reflection bands for both right- and left-polarized light. We expressed the PBG edges in terms of material parameters; this allows us to select the region of the electromagnetic spectrum for the reflection bands. This medium exhibits a hyper-reflective photonic band when the pitches of both right- and left-handed helical layers are identical.
Faster Algorithms for Computing the Hairpin Completion Distance and Minimum Ancestor

Hairpin completion is an operation on formal languages that has been inspired by hairpin formation in DNA biochemistry and has many applications especially in DNA computing. Consider s to be a string over the alphabet { A, C, G, T } such that a prefix/suffix of it matches the reversed complement of a substring of s . Then, in a hairpin completion operation the reversed complement of this prefix/suffix is added to the start/end of s forming a new string. In this paper we study two problems related to the hairpin completion. The first problem asks the minimum number of hairpin operations necessary to transform one string into another, number that is called the hairpin completion distance . For this problem we show an algorithm of running time O ( n 2 ), where n is the maximum length of the two strings. Our algorithm improves on the algorithm of Manea (TCS 2010), that has running time O ( n 2 log n ). In the minimum distance common hairpin completion ancestor problem we want to find, for two input strings x and y , a string w that minimizes the sum of the hairpin completion distances to x and y . Similarly, we present an algorithm with running time O ( n 2 ) that improves by a O (log n ) factor the algorithm of Manea (TCS 2010).
Trade-Off and Synergy Relationships and Spatial Bundle Analysis of Ecosystem Services in the Qilian Mountains

Significant heterogeneity has been observed among different ecosystem services (ES). Understanding the trade-offs and synergies among ES and delineating ecological functional zones is crucial for formulating regional management policies that improve human well-being and sustainably develop and maintain ecosystems. In this study, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and Carnegie–Ames–Stanford Approach (CASA) models to evaluate the spatial distribution patterns of nine ES (food supply, raw material supply, water resource supply, water connotation, climate regulation, soil conservation, water purification, habitat quality, and entertainment tourism) in the Qilian Mountains from 2000 to 2018. We also investigated the trade-offs and synergistic relationships among ES through Spearman correlation analysis, identified ES hotspots through exploratory spatial data analysis, and identified ES bundles (ESB) using K-means clustering. Our results revealed that water purification and habitat quality remained relatively stable, while food supply, raw material supply, water resource supply, water conservation, climate regulation, soil conservation, and entertainment tourism increased by 1038.83 Yuan·ha−1, 448.21 Yuan·ha−1, 55.45 mm, 7.80 mm, 0.60 tc·ha−1, 40.01 t·ha−1 and 4.82, respectively. High-value areas for water resource supply were mainly concentrated in the high-altitude mountainous area, whereas high-value areas for soil conservation were found in the western and eastern parts of the study area. The low-value areas of water purification were primarily located in the east, while the remaining six services were highly distributed in the east and were less common in the west. Correlation analysis showed that water resource supply, water conservation, and soil conservation exhibited a synergistic relationship in the Qilian Mountains. Moreover, food supply, raw material supply, climate regulation, habitat quality, and entertainment tourism showed synergistic relationships. However, there were trade-offs between food supply and water purification as well as water resource supply, and habitat quality showed a tradeoff with water resource supply, water conservation, and soil conservation. We identified four ESB. The food supply bundle consisted mainly of farmland ecosystems, while the windbreak and sand fixation and ecological coordination bundles were dominant in the Qilian Mountains. Notably, the area of the water conservation bundle increased significantly. Our comprehensive findings on ES and ESB can provide a theoretical foundation for the formulation of ecological management policies and the sustainable development of ecosystems in the Qilian Mountains.
Assessing Transferability of Remote Sensing Pasture Estimates Using Multiple Machine Learning Algorithms and Evaluation Structures

Both the vastness of pasturelands and the value they contain—e.g., food security, ecosystem services—have resulted in increased scientific and industry efforts to remotely monitor them via satellite imagery and machine learning (ML). However, the transferability of these models is uncertain, as modelers commonly train and test on site-specific or homogenized—i.e., randomly partitioned—datasets and choose complex ML algorithms with increased potential to overfit a limited dataset. In this study, we evaluated the accuracy and transferability of remote sensing pasture models, using multiple ML algorithms and evaluation structures. Specifically, we predicted pasture above-ground biomass and nitrogen concentration from Sentinel-2 imagery. The implemented ML algorithms include principal components regression (PCR), partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine regression (SVR), and a gradient boosting model (GBM). The evaluation structures were determined using levels of spatial and temporal dissimilarity to partition the train and test datasets. Our results demonstrated a general decline in accuracy as evaluation structures increase in spatiotemporal dissimilarity. In addition, the more simplistic algorithms—PCR, PLSR, and LASSO—out-performed the more complex models RF, SVR, and GBM for the prediction of dissimilar evaluation structures. We conclude that multi-spectral satellite and pasture physiological variable datasets, such as the one presented in this study, contain spatiotemporal internal dependence, which makes the generalization of predictive models to new localities challenging, especially for complex ML algorithms. Further studies on this topic should include the assessment of model transferability by using dissimilar evaluation structures, and we expect generalization to improve for larger and denser datasets.
Overlooked Candida glabrata petites are echinocandin tolerant, induce host inflammatory responses, and display poor in vivo fitness

Small colony variants (SCVs) are relatively common among some bacterial species and are associated with poor prognosis and recalcitrant infections. Similarly, Candida glabrata – a major intracellular fungal pathogen – produces small and slow-growing respiratory-deficient colonies, termed “petite. ” Despite reports of clinical petite C. glabrata strains, our understanding of petite behavior in the host remains obscure. Moreover, controversies exist regarding in-host petite fitness and its clinical relevance. Herein, we employed whole-genome sequencing (WGS), dual-RNAseq, and extensive ex vivo and in vivo studies to fill this knowledge gap. WGS identified multiple petite-specific mutations in nuclear and mitochondrially-encoded genes. Consistent with dual-RNAseq data, petite C. glabrata cells did not replicate inside host macrophages and were outcompeted by their non-petite parents in macrophages and in gut colonization and systemic infection mouse models. The intracellular petites showed hallmarks of drug tolerance and were relatively insensitive to the fungicidal activity of echinocandin drugs. Petite-infected macrophages exhibited a pro-inflammatory and type I IFN-skewed transcriptional program. Interrogation of international C. glabrata blood isolates (n=1000) showed that petite prevalence varies by country, albeit at an overall low prevalence (0–3.5%). Collectively, our study sheds new light on the genetic basis, drug susceptibility, clinical prevalence, and host-pathogen responses of a clinically overlooked phenotype in a major fungal pathogen. Importance Candida glabrata is a major fungal pathogen, which is able to lose mitochondria and form small and slow-growing colonies, called “petite”. This attenuated growth rate has created controversies and questioned the clinical importance of petiteness. Herein, we have employed multiple omicstechnologies and in vivo mouse models to critically assess the clinical importance of petite phenotype. Our WGS identifies multiple genes potentially underpinning petite phenotype. Interestingly, petite C. glabrata cells engulfed by macrophages are dormant and therefore are not killed by the frontline antifungal drugs. Interestingly, macrophages infected with petite cells mount distinct transcriptomic responses. Consistent with our ex-vivo observations, mitochondrial-proficient parental strains outcompete petites during systemic and gut colonization. Retrospective examination of C. glabrata isolates identified petite prevalence a rare entity, can significantly vary from country to country. Collectively, our study overcomes the existing controversies and provides novel insights regarding the clinical relevance of petite C. glabrata isolates.
Humanized yeast to study the role of human ECE-1 isoforms in apoptosis in congenital heart disease

Endothelin convert enzyme-1(ECE-1) plays a significant role in cardiovascular development including four isoforms with unclear function. Therefore, we are interested in studying the function of ECE-1 isoforms through mitochondria due to the high correlation between congenital heart disease (CHD) and apoptosis. Since the expression of human Bax and Bcl-xL in budding yeast (Saccharomyces cerevisiae) results in similar effects in mammalian cells, a yeast system was generated for mimicking human Bax-induced apoptosis and the expression of human ECE-1 isoforms was involved. The correlation between Bax-induced growth defect and the candidates of apoptosis via mitochondrial pathway was preliminarily investigated in this system. Furthermore, the phenotypes of ECE-1 isoforms have been identified through yeast growth defect. Individual ECE-1 isoform does not affect yeast growth but act as enhancers for the Bax-induced growth defect. ECE-1c is the strongest enhancer that affect the expression of candidates of outer membrane translocases. This study indicates that ECE-1 might play an important role in inducing apoptosis and we speculate these findings are possible to provide new perspectives with clarifying the mechanism of CHD.
Regional pulmonary perfusion, blood volume, and their relationship change during early ARDS in an experimental study

Regional pulmonary perfusion (Q) has been investigated using blood volume (Fb) imaging as an easier-to-measure surrogate. However, it is unclear if changing pulmonary conditions could affect their relationship. We hypothesized that vascular changes in early acute respiratory distress syndrome (ARDS) affect Q and Fb differently. Five sheep were anesthetized and received protective mechanical ventilation for 20 hours while endotoxin was continuously infused. Using dynamic 18F-FDG and 13NN Positron Emission Tomography (PET), regional Fb and Q were analysed in 30 regions of interest (ROIs) and normalized by tissue content (Fbn and Qn, respectively). After 20 hours, the animals’ lung injury showed characteristics of early ARDS, including gas exchange and lung mechanics. PET images of Fbn and Qn showed substantial differences between baseline and lung injury. Lung injury caused a significant change in the Fbn-Qn relationship compared to baseline (p<0.001). The best models at baseline and lung injury were Fbn=0.32+0.690Qn and Fbn=1.684Qn–0.538Qn2, respectively. Early ARDS changed the relationship between Fb and Q from linear to curvilinear. Effects of endotoxin exposure on the vasoactive blood flow regulation were most likely the key factor for this change limiting the quantitative accuracy of Fb imaging as a surrogate for regional Q.
Serine ubiquitination of p62 regulates Nrf2 dependent redox homeostasis

The KEAP1–Nrf2 axis is essential for the cellular response against metabolic and oxidative stress. KEAP1 is an adaptor protein of Cullin-3 ubiquitin ligase that controls the cellular levels of Nrf2, a critical transcription factor of several cytoprotective genes. Oxidative stress, defective autophagy and pathogenic infections activate Nrf2 signaling through phosphorylation of the adaptor protein p62, which competes with Nrf2 for binding to KEAP1. Here we show that phosphoribosyl-linked serine ubiquitination of p62 catalyzed by SidE effectors of Legionella pneumophila controls Nrf2 signaling and cell metabolism upon Legionella infection. Serine ubiquitination of p62 sterically blocks its binding to KEAP1, resulting in Nrf2 ubiquitination and degradation. This reduces Nrf2-dependent antioxidant synthesis in the early phase of infection. Levels of serine ubiquitinated p62 diminish in the later stage of infection allowing the expression of Nrf2-target genes; resulting in a differential regulation of the host metabolome and proteome in a Nrf2 dependent manner.
Multiple beta cell-independent mechanisms drive hypoglycemia in Timothy syndrome

The canonical G406R gain of function mutation that reduces inactivation and increases Ca2+ influx through the CACNA1C-encoded CaV1.2 voltage gated Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by invariant Long QT syndrome and consequent life-threatening arrhythmias. Severe episodic hypoglycemia, which exacerbates arrhythmia risk, is among the myriad non-cardiac TS pathologies that are poorly characterized. While hypoglycemia is thought to result from increased Ca2+ influx through CaV1.2 channels in pancreatic beta cells and consequent hyperinsulinism, this mechanism has never been demonstrated due to a lack of informative animal models, thus hampering development of preventive strategies. We generated a CaV1.2 G406R knockin mouse model that recapitulates key TS features including hypoglycemia. Unexpectedly, these mice did not show hyperactive beta cells or hyperinsulinism in the setting of normal intrinsic beta cell function, suggesting dysregulated glucose homeostasis. We discovered multiple alternative contributors to hypoglycemia, including perturbed counterregulatory hormone responses with defects in glucagon secretion and abnormal hypothalamic glucose sensing. Together, these data provide new insights into physiological contributions of the broadly expressed CaV1.2 channel and reveal integrated consequences of the mutant channel that underlie the life-threatening events in TS. Brief Summary Gain of function mutant CaV1.2 channels drive hypoglycemia through adverse effects on counterregulatory hormones and central nervous system glucose sensing
High-accuracy meets high-throughput for microbiome profiling with near full-length 16S rRNA amplicon sequencing on the Nanopore platform

Amplicon sequencing of small subunit (SSU) rRNA genes is a foundational method for studying microbial communities within various environmental, human, and engineered ecosystems. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification. The low-cost Oxford Nanopore Technologies (ONT) platform is capable of sequencing full-length SSU rRNA genes, but the lower raw-read accuracies of previous ONT sequencing chemistries have limited accurate taxonomic classification and de novo generation of operational taxonomic units (OTUs) and amplicon sequence variants (ASVs). Here, we examine the potential for Nanopore sequencing with newer (R10.4+) chemistry to provide high-throughput and high-accuracy full-length 16S rRNA gene amplicon sequencing. We present a sequencing workflow utilizing unique molecular identifiers (UMIs) for error-correction of SSU rRNA (e.g. 16S rRNA) gene amplicons, termed ssUMI. Using two synthetic microbial community standards, the ssUMI workflow generated consensus sequences with 99.99% mean accuracy using a minimum UMI subread coverage threshold of 3x, and was capable of generating error-free ASVs and 97% OTUs with no false-positives. Non-corrected Nanopore reads generated error-free 97% OTUs but with reduced detection sensitivity, and also generated false-positive ASVs. We showcase the cost-competitive and high-throughput scalability of the ssUMI workflow by sequencing 90 time-series samples from seven different wastewater matrices, generating ASVs that were tightly clustered based on sample matrix type. This work demonstrates that highly accurate full-length 16S rRNA gene amplicon sequencing on Nanopore is possible, paving the way to more accessible microbiome science.
Context-aware geometric deep learning for protein sequence design

Protein design and engineering are evolving at an unprecedented pace leveraging the advances of deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. This new concept is anticipated to improve the design versatility for engineering proteins with desired functions.
The T766M-EGFR lung cancer mutation promotes tumor growth by exploiting newfound mechanisms assembling ligand-free EGFR oligomer structures

Epidermal growth factor receptor (EGFR) is central to cell growth in physiology and pathophysiologies, including non-small cell lung cancer (NSCLC). EGFR has been successfully targeted with tyrosine kinase inhibitor generations, but the missense secondary T766M mutation is a common cause of resistance. Overcoming this therapeutic challenge has been hindered by poor understanding of how T766M dysregulates EGFR function leading to tumor progression. Here we show that T766M amplifies tumor growth in vivo by exploiting newly discovered oligomer assembly mechanisms employed by wild type (WT)-EGFR to maintain ligand-independent basal phosphorylation. These mechanisms, also shared by drug-resistant exon 20 EGFR insertions, reveal tumor growth promoting functions for hitherto orphan transmembrane and kinase interfaces and for the ectodomain tethered conformation of EGFR. Placing our findings into the context of a ligand-free oligomer structure model, we provide a framework for future drug discovery directed at tackling EGFR mutations in cancer by disabling oligomer-assembling interactions.
Proteins containing 6-crossing knot types and their folding pathways

Studying complex protein knots can provide new insights into potential knot folding mechanisms and other fundamental aspects of why and how proteins knot. This paper presents results of a systematic analysis of the 3D structure of proteins with 6-crossings knots predicted by the artificial intelligence program AlphaFold 2. Furthermore, using a coarse-grained native based model, we found that three representative proteins can self tie to a 63 knot, the most complex knot found in a protein thus far. Because it is not a twist knot, the 63 knot cannot be folded via a simple mechanism involving the threading of a single loop. Based on successful trajectories for each protein, we determined that the 63 knot is formed after folding a significant part of the protein backbone to the native conformation. Moreover, we found that there are two distinct knotting mechanisms, which are described here. Also, building on a loop flipping theory developed earlier, we present two new theories of protein folding involving the creation and threading of two loops, and explain how our theories can describe the successful folding trajectories for each of the three representative 63-knotted proteins.