Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptome Analysis of Protein Kinase MoCK2, which Affects Acetyl-CoA Metabolism and Import of CK2-Interacting Mitochondrial Proteins into Mitochondria in the Rice Blast Fungus Magnaporthe oryzae

The protein kinase CK2 is highly expressed and essential for plants, animals, and fungi, affecting fatty acid-related metabolism. In addition, it directly affects the import of essential mitochondrial proteins into mitochondria. ABSTRACT The rice pathogen Magnaporthe oryzae causes severe losses to rice production. Previous studies have shown that the protein kinase MoCK2 is essential for pathogenesis, and this ubiquitous eukaryotic protein kinase might affect several processes in the fungus that are needed for infection. To better understand which cellular processes are affected by MoCK2 activity, we performed a detailed transcriptome sequencing analysis of deletions of the MoCK2 b1 and b2 components in relation to the background strain Ku80 and connected this analysis with the abundance of substrates for proteins in a previous pulldown of the essential CKa subunit of CK2 to estimate the effects on proteins directly interacting with CK2. The results showed that MoCK2 seriously affected carbohydrate metabolism, fatty acid metabolism, amino acid metabolism, and the related transporters and reduced acetyl-CoA production. CK2 phosphorylation can affect the folding of proteins and especially the effective formation of protein complexes by intrinsically disordered or mitochondrial import by destabilizing soluble alpha helices. The upregulated genes found in the pulldown of the b1 and b2 mutants indicate that proteins directly interacting with CK2 are compensatorily upregulated depending on their pulldown. A similar correlation was found for mitochondrial proteins. Taken together, the classes of proteins and the changes in regulation in the b1 and b2 mutants suggest that CK2 has a central role in mitochondrial metabolism, secondary metabolism, and reactive oxygen species (ROS) resistance, in addition to its previously suggested role in the formation of new ribosomes, all of which are processes central to efficient nonself responses as innate immunity. IMPORTANCE The protein kinase CK2 is highly expressed and essential for plants, animals, and fungi, affecting fatty acid-related metabolism. In addition, it directly affects the import of essential mitochondrial proteins into mitochondria. These effects mean that CK2 is essential for lipid metabolism and mitochondrial function and, as shown previously, is crucial for making new translation machinery proteins. Taken together, our new results combined with previously reported results indicate that CK2 is an essential protein necessary for the capacities to launch efficient innate immunity responses and withstand the negative effects of such responses necessary for general resistance against invading bacteria and viruses as well as to interact with plants, withstand plant immunity responses, and kill plant cells.
The Developmental Progression of Eight Opsin Spectral Signals Recorded from the Zebrafish Retinal Cone Layer Is Altered by the Timing and Cell Type Expression of Thyroxin Receptor β2 (trβ2) Gain-Of-Function Transgenes

Abstract Zebrafish retinal cone signals shift in spectral shape through larval, juvenile, and adult development as expression patterns of eight cone-opsin genes change. An algorithm extracting signal amplitudes for the component cone spectral types is developed and tested on two thyroxin receptor β2 (trβ2) gain-of-function lines crx:mYFP-2A-trβ2 and gnat2:mYFP-2A-trβ2, allowing correlation between opsin signaling and opsin immunoreactivity in lines with different developmental timing and cell-type expression of this red-opsin-promoting transgene. Both adult transgenics became complete, or nearly complete, “red-cone dichromats,” with disproportionately large long-wavelength-sensitive (LWS)1 opsin amplitudes as compared with controls, where LWS1 and LWS2 amplitudes were about equal, and significant signals from SWS1, SWS2, and Rh2 opsins were detected. But in transgenic larvae and juveniles of both lines it was LWS2 amplitudes that increased, with LWS1 cone signals rarely encountered. In gnat2:mYFP-2A-trβ2 embryos at 5 d postfertilization (dpf), red-opsin immunoreactive cone density doubled, but red-opsin amplitudes (LWS2) increased <10%, and green-opsin, blue-opsin, and UV-opsin signals were unchanged, despite co-expressed red opsins, and the finding that an sws1 UV-opsin reporter gene was shut down by the gnat2:mYFP-2A-trβ2 transgene. By contrast both LWS2 red-cone amplitudes and the density of red-cone immunoreactivity more than doubled in 5-dpf crx:mYFP-2A-trβ2 embryos, while UV-cone amplitudes were reduced 90%. Embryonic cones with trβ2 gain-of-function transgenes were morphologically distinct from control red, blue or UV cones, with wider inner segments and shorter axons than red cones, suggesting cone spectral specification, opsin immunoreactivity and shape are influenced by the abundance and developmental timing of trβ2 expression.
The efficacy of antiseptic agents against staphylococcal biofilm can be quantitatively assessed with the use of processed microscopic images

The staphylococcal biofilms are major causative factors of non-healing wound infections. Their treatment algorithms recommend the use of locally applied antiseptic agents to counteract the spread of infection. The efficacy of antiseptics against biofilm is assessed in vitro by a set of standard quantitative and semi-quantitative methods. The development of software for image processing allowed to obtain quantitative data also from microscopic images of biofilm dyed with propidium iodine and SYTO-9 reagents, differentiating dead cells from the live ones. In this work, the method of assessment of the impact of antiseptic agents on staphylococcal biofilm in vitro, based on biofilm’s processed images, was proposed and scrutinized with regard to clinically-relevant antiseptics. Taking into account the fact that in vitro results of the efficacy of antiseptic agents against staphylococcal biofilm are frequently applied to back up their use in hospitals and ambulatory units, our work should be considered an important tool providing reliable, quantitative data with this regard.
Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms

This book deals with the efficient numerical solution of challenging nonlinear problems in science and engineering, both in finite dimension (algebraic systems) and in infinite dimension (ordinary and partial differential equations). Its focus is on local and global Newton methods for direct problems or Gauss-Newton methods for inverse problems. The term 'affine invariance' means that the presented algorithms and their convergence analysis are invariant under one out of four subclasses of affine transformations of the problem to be solved. Compared to traditional textbooks, the distinguishing affine invariance approach leads to shorter theorems and proofs and permits the construction of fully adaptive algorithms. Lots of numerical illustrations, comparison tables, and exercises make the text useful in computational mathematics classes. At the same time, the book opens many directions for possible future research.
Bringing Plant Immunity to Light: A Genetically Encoded, Bioluminescent Reporter of Pattern Triggered Immunity in Nicotiana benthamiana

Plants rely on innate immune systems to defend against a wide variety of biotic attackers. Key components of innate immunity include cell-surface pattern recognition receptors (PRRs), which recognize pest/pathogen-associated molecular patterns (PAMPs). Unlike other classes of receptors which often have visible cell death immune outputs upon activation, PRRs generally lack rapid methods for assessing function. Here, we describe a genetically encoded bioluminescent reporter of immune activation by heterologously-expressed PRRs in the model organism Nicotiana benthamiana. We characterized N. benthamiana transcriptome changes in response to Agrobacterium tumefaciens (Agrobacterium) and subsequent PAMP treatment to identify PTI-associated marker genes, which were then used to generate promoter-luciferase fusion fungal bioluminescence pathway (FBP) constructs. A reporter construct termed pFBP_2xNbLYS1::LUZ allows for robust detection of PTI activation by heterologously expressed PRRs. Consistent with known PTI signaling pathways, activation by receptor-like protein (RLP) PRRs is dependent on the known adaptor of RLP PRRs, SOBIR1. This system minimizes the amount of labor, reagents, and time needed to assay function of PRRs and displays robust sensitivity at biologically relevant PAMP concentrations, making it ideal for high throughput screens. The tools described in this paper will be powerful for studying PRR function and investigations to characterize the structure-function of plant cell surface receptors.
Personalized and Explainable Course Recommendations for Students at Risk of Dropping out

This paper presents a course recommender system designed to support students who are struggling in their first semesters of university and who are at risk of dropping out. Considering the needs expressed by our students, we recommend a set of courses that have been passed by the majority of their nearest neighbors who have successfully graduated. We describe this recommender system, which is based on the explainable k -Nearest Neighbors al-gorithm, and evaluate the recommendations after the 1 st and the 2 nd semester using historical data. The evaluation reveals that the recommendations correspond to the actual courses passed by students who graduated, whereas the recommendations and actually passed courses differ for students who dropped out. The recommendations show to struggling students a different, ambitious, but hopefully feasible way through the study program. Furthermore, a dropout prediction confirms that students are less likely to drop out when they pass the courses recommended to them
Vertex partitioning problems: characterization, complexity and algorithms on partial K-trees

This thesis investigates the computational complexity of algorithmic problems defined on graphs. At the abstract level of the complexity spectrum we discriminate polynomial-time solvable problems from ${\cal NP}$-complete problems, while at the concrete level we improve on polynomial-time algorithms for generally hard problems restricted to tree-decomposable graphs. One contribution of this thesis is a precise characterization of vertex partitioning problems which include variants of domination, coloring and packing. An elaboration of this characterization is given for problems defined over vertex subsets and over maximal/minimal vertex subsets. We introduce several new graph parameters as vertex partition generalizations of classical parameters. The given characterizations provide a basis for a taxonomy of vertex partitioning problems, facilitating their common algorithmic treatment and allowing for their uniform complexity classification. We explore the computational complexity of two important types of problems within this taxonomy: vertex subset optimization problems and H-covering problems. The taxonomy is particularly useful in categorizing and analyzing the complexity of vertex subset problems, of which there are a great variety. Our investigation of the complexity of vertex subset problems uncovers several infinite classes of ${\cal NP}$-complete and of polynomial-time solvable problems. These results are contrasted and compared with the complexity of classical vertex subset problems. We also develop a methodology useful in analyzing the complexity of H-covering, a problem parameterized by a fixed graph H. As an illustration, we settle the complexity of the H-covering problem for any simple graph H on at most 6 vertices. We design efficient algorithms for H-covering problems by reduction to the 2-SAT problem and by reduction to factorization problems in regular graphs. Another contribution of this thesis is a methodology for the design of practical algorithms for generally ${\cal NP}$-hard problems restricted to partial k-trees. Based on very simple graph operations, we define a binary parse tree of partial k-trees that facilitates algorithm derivation. We account for dependency on the treewidth k in analysis of the computational complexity of the resulting algorithms. These contributions culminate in applying the partial k-tree algorithm methodology to the general class of vertex partitioning problems. The input graph in the resulting algorithms is assumed to be given with a width k tree-decomposition, and the answer is computed by a dynamic programming bottom-up traversal of its binary parse tree. We give the first algorithms for these problems with reasonable time complexity as a function of treewidth. For certain problems, mainly Graph Grundy Number, we give the first polynomial-time algorithms on partial k-trees.
Estimating the causal effects of Khan Academy Map Accelerator across demographic subgroups

As educational technology platforms become more and more commonplace in education, it is critical that these systems work well across a diverse range of student sub-groups. In this study, we estimated the effectiveness of MAP Accelerator; a large-scale, personalized, web-based, mathematics mastery learning platform. Our analysis placed a particular focus on students from historically under-resourced groups. Our sample comprised 181K students in grades 3-8 from 99 school districts across the United States, 52% of whom attended schools where the majority of the students are eligible for free or reduced price lunch (a proxy for high-poverty schools). Using a combination of system logs/platform telemetry data, standardized assessments, and publicly available school/district data, we estimated the causal effect of a year-long supplemental math intervention on students’ standardized mathematics growth outcomes via a quasi-experimental design with controls and a modification of the difference-in-differences framework. Students who used the platform as recommended (30+ min/wk) during the (COVID-19 disrupted) 2020-2021 school year had math growth scores 0.26 standard deviations higher on average than similar students who used the platform for < 15 min/week. Further, positive benefits of the platform were observed across all genders, race/ethnic groups, and school poverty levels, but were not as large for districts with greater than 20% English language learners. Further analysis revealed that these students were predominantly Hispanic, and tended to make less progress on fewer skills than their peers given the same amount of practice time. We discuss the implications of these findings, and potential avenues to ensure more equi-table outcomes for these students in the future.
Wildﬁres in the Siberian Arctic

: Wildﬁres are increasingly understood as an ecological driver within the entire Arctic biome. Arctic soils naturally store large quantities of C, as peat has formed throughout the Holocene. For the Siberian Arctic, we used observations from the MODIS remote sensing instrument to document changes in frequency, geographic extent, and seasonal timing of wildﬁres as well as vegetation productivity (GPP, NPP, EVI). We also used correlation and regression analysis to identify environmental factors of temperature, precipitation, and lightning occurrence associated with these changes. For the Siberian Arctic as a whole, we found that the decadal frequency of wildﬁre tripled from the 2001–2010 to the 2011–2020 periods. Increased decadal frequency was accompanied by the increased extent of the burnt area by a factor of 2.6. This increase in ﬁre frequency and extent was not uniform, with the greatest increase in western Siberia with no marked increase for the Siberian Far East. These changes were accompanied by the northward migration of the northern limit of wildﬁre occurrence and an increase in duration of the wildﬁre season. We found that annual ﬁre frequency and the extent of burnt areas were related to various combinations of seasonal air temperature, precipitation, ground moisture, and lightning frequency. After ﬁres, vegetation productivity rapidly recovered to pre-ﬁre levels. The northward spread of wildﬁre into the tundra will release carbon long-stored as peat. The enhanced vegetation productivity, rapid recovery of carbon ﬁxation for burnt areas and the northward migration of boreal forest tree species may offset that release and maintain the current status of the Siberian Arctic as a C sink. Increased wildﬁre and loss of permafrost may threaten ongoing settlement and industrialization, particularly for western Siberia.
Grade Prediction via Prior Grades and Text Mining on Course Descriptions: Course Outlines and Intended Learning Outcomes

Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from course content. In addition, we classify intended learning outcomes according to their higher-or lower-order thinking skills. A learning parameter is then formulated to model the impact of these cognitive levels (that are expected for each course) on student performance. These features are then embedded and represented as graphs. Past academic achievements are then fused with the above features for grade prediction. We validate the performance of the above approach via datasets corresponding to three engineering departments collected from a university. Results obtained highlight that the proposed technique generates meaningful feature representations and outperforms existing methods for grade prediction.
A Mathematical Theory of Plasticity Based on the Concept of Slip

Note presenting a theory of plasticity based on the concept of slip as proposed for the relationship between stress and strain for initially isotropic materials in the strain-hardening range. The theory is an extension to polyaxial stress conditions of the conventional uniaxial stress-strain relation, and time-dependent effects, such as creep and stress relaxation, are not considered.
Improving Peer Assessment with Graph Neural Networks

Peer assessment systems are emerging in many settings, such as peer grading in large (online) classes, peer review in conferences, peer art evaluation, etc. However, peer assessments might not be as accurate as expert evaluations, thus rendering these systems unreliable. The reliability of peer assessment systems is influenced by various factors such as assessment ability of peers, their strategic assessment behaviors, and the peer assessment setup (e.g., peer evaluating group work or individual work of others). In this work, we first model peer assessment as multi-relational weighted networks that can express a variety of peer assessment setups, and can also capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph neural network which can learn assessment patterns and user behaviors to more accurately predict expert evaluations. Our extensive experiments on real and synthetic datasets demonstrate the efficacy of our approach, which outperforms a variety of peer assessment methods.
Applying Transfer Learning using BERT-Based Models for Hate Speech Detection

Hateful and Offensive speech is rising along with social media. This issue has motivated researchers to devise novel approaches which perform better than the traditional algorithms. This paper presents the methods adopted by the BITS Pilani team for Subtask 1A of the Hate Speech and Offensive Content Identification in English and Indo-Aryan Language task proposed by the Forum of Information Retrieval Evaluation in 2021. We have used data augmentation to make the models generalize better. We have experimented with different feature extraction techniques along with machine learning algorithms. But, fine-tuning the pre-trained BERT-based models using transfer learning gave us the best results for all the given languages on the test set. We got the highest Macro-F1 of 0.7993 for the English Language, 0.7612 for the Hindi Language, and 0.8306 for the Marathi Language using the pre-trained BERT-based models.
Individual Fairness Evaluation for Automated Essay Scoring System

In Automated Essay Scoring (AES) systems, many previous works have studied group fairness using the demographic features of essay writers. However, individual fairness also plays an important role in fair evaluation and has not been yet explored. Initialized by Dwork et al. [10], the fundamental concept of individual fairness is “similar people should get similar treatment”. In the context of AES, individual fairness means that “similar essays should be treated sim-ilarly”. In this work, we propose a methodology to measure individual fairness in AES. The similarity of essays can be computed using the distance of the text representation of essays. We compare several text representations of essays, from the classical text features, such as BOW and TF-IDF, to the more recent deep-learning-based features, such as Sentence-BERT and LASER. We also show their performance against paraphrased essays to understand if they can maintain the ranking of similarities between the original and the paraphrased essays. Finally, we demonstrate how to evaluate the performance of automated scoring systems models with regard to individual fairness by counting the number of pairs of essays that satisfy the individual fairness equation and by observing the correlation of score difference with the distance of essays. Our analysis suggests that the Sentence-BERT, as the text representation of the essays, and Gradient Boosting, as the score prediction model, provide better results based on the proposed individual fairness evaluation methodology.
Employing Tree-based Algorithms to Predict Students' Self-Efficacy in PISA 2018

Self-efficacy is a critical psychological construct that has a substantial impact on students’ learning experience and global well-being. Thus, the early identification of low self-efficacious learners is an important task for educators and researchers. This study uses machine learning (ML) approaches to model the self-efficacy of over 520,000 students based on their test performance and responses to survey questions in the Programme for International Student Assessment (PISA) 2018. Two tree-based ensemble learning models (random forest and XGBoost) were built using 64 predictors and evaluated using nested cross-validation with a grid search method. The results showed that, although both algorithms predicted self-efficacy accurately, XGBoost slightly outperformed Random Forest (RF). The findings also revealed that students’ non-cognitive constructs such as meaning in life and the motivation for mastering tasks were the most important predictors. Theoretical contributions include the expansion of the body of literature on ML applications that predict students’ self-efficacy and the potential advancement of theoretical models of self-efficacy. Practical contributions include the applications of tree-based algorithms to identify low self-efficacious individuals at scale, in a large international assessment. Implications include the development of systems that use ML algorithms to detect low self-efficacious learners and provide support for early interventions.