Miracle-dz/UniFAR-FTUs huggingface dataset:
Creating successful and sustainable educational administrative internship experiences

Purpose With increase requirements and a more clinical approach for administrator internships in principal preparation programs, universities are taking a look at their internships and how they are implemented. The purpose of this paper is to propose a structure and framework incorporating promising practices from recent literature and explores how to successfully implement new standards and state requirements. Additionally, a framework for practice is included as a foundation for a clinical internship program that applies theory and promising practices. Design/methodology/approach Internships are a critical aspect of the principal preparation program and are crucial to preparing students to fulfill their future leadership roles. This brief reviewed the related literature, which revealed the lack of quality in various educational leadership programs and posited that the internship is an essential element to principal preparation. Key considerations to successful internships, and why they are necessary to overall candidate preparation were also presented. Additionally, a sample program was described that included aspects of both performance and accountability and served as a framework for new and revised internships. Findings Although the internship experience is but one part of a larger curricular sequence, it has a central role in the preparation of future school administrators. School administrator preparation programs have an obligation to allow participants to fully experience the breadth and depth of issues and environments that they will encounter. A structure and framework is needed that incorporates promising practices from recent literature, and explores how to successfully implement new standards and state requirements for a clinical internship program applying current theory and promising practices. Originality/value Creating and maintaining effective internship experiences is an ever-evolving process that requires dedicated faculty and resources. Learning through internships is a central element of future principal candidates’ overall success. Authentic practice and experience in real school settings ensures that future educational leaders receive the best preparation that universities and faculty can provide.
MATTE: anti-noise module alignment for phenotype-gene-related analysis

Purpose: Although many transcriptome analysis methods find fundamental interactions or markers of some phenotypes, preservation of module or network is still a challenge. Methods: The study developed a method to directly compare the transcriptome data of phenotypes and present the differences modularly, called Module Alignment of TranscripTomE(MATTE). Results: MATTE performs better under high noise than differential co-expression(DC) clustering in the simulation experiments but still detects differential expres-sion(DE) and DC genes. After subsequent annotation of cell types in single-cell data, MATTE obtained the best scores in both supervised and unsupervised learning, i. e. MATTE found meaningful markers. Finally, we apply MATTE in analyzing the transcriptome of Breast Cancer(BRCA). We have found five BRCA subtypes, and the characteristic of one subtype is detected in the form of a module network. Conclusion: MATTE can find meaningful genes and modules, thus facilitating the downstream analysis task to obtain insight into biology. ME of modules whose SNR is higher than 0.5 is used as the samples’ feature in sample clustering. A with PCC
Mapping Structural Drivers of Insulin Analogs Using Molecular Dynamics and Free Energy Calculations at Insulin Receptor

A century on from the discovery of insulin, a complete understanding of insulin interactions with the insulin receptor (IR) at atomic level remains elusive. In this work, we have leveraged recent advancements in structural biology that have resulted in multiple high-resolution structures of the insulin-IR complex. As a first step, we employed molecular dynamics (MD) simulations to unravel atomic insights into the interactions between insulin-IR complexes in order to better understand ligand recognition at the receptor. The MD simulations were followed up with free energy perturbation (FEP) calculations to discriminate between and elucidate the drivers for ligand association for various natural and man-made insulin analogs. As an example, these calculations were utilized to understand the molecular mechanisms that characterized the loss-of-function seen in disease-associated insulin mutations seen in different populations. Further, multiple man-made insulin analogs spanning a range of potencies, mutations, and sequence lengths were studied using FEP and a comprehensive molecular level map of potency determinants were established. ∼85% of FEP calculations captured the direction of shift of potency, and in ∼53% of cases the predictions were within 1 kcal/mol of experiment. The impressive accuracy of FEP in recapitulating functional profiles across such a span of insulin analogs and potency profiles provided clear evidence of its utility in computational mutagenesis. In addition to the impressive accuracy, the ability of FEP to provide a dissected understanding of protein residue, solvent and solvent-mediated contributions to binding energy clearly establishes its utility in the design of novel insulins and peptides in general.
scMinerva: a GCN-featured Interpretable Framework for Single-cell Multi-omics Integration with Random Walk on Heterogeneous Graph

: The development of single-cell multi-omics technologies profiles DNA, mRNA, and proteins at a single-cell resolution. To meet the demand, we present scMinerva for single-cell multi-omics integration utilizing graph convolutional networks and a new random walk strategy, which outperforms existing methods on various datasets. Our method is especially robust on high-noise more-omics data and is lightweight concerning speed and memory. scMinerva can effectively perform downstream tasks, such as biomarker detection and cell differentiation analysis. We extensively interpret the robustness of scMinerva by analyzing components’ occurrence frequency in walks during training at omics level, cell-type level, and single-cell level. by the FNN we trained upon sci-CAR. Similarly, we generate two other ADT matrices from simulated scRNA-seq and scATAC-seq. These two models are trained with scRNA-seq, scATAC-seq, and ADT matrices from GSE156478 and we utilize PCA to make the dimension of scRNA-seq and scATAC-seq consistent with sci-CAR so that we can generate a set of data. We developed four sets of data, with five classes and sample number 2k, 5k, 10k, and 30k, respectively. The simulated RNA, ATAC, ADT from RNA and ADT from ATAC data are of feature number 815, 2613, 227 and 227 respectively.
PACeR: a bioinformatic pipeline for the analysis of chimeric RNA-seq data

MicroRNAs (miRNAs) are small non-coding RNAs that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets which results in inhibition of translation and often destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA-target interactions by cross-linking and ligation of miRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high throughput sequencing of the chimeric RNAs. Despite the strength of these direct chimeric miRNA:target profiling approaches, standardized pipelines for analyzing the resulting chimeric RNA sequencing data are not readily available. Here we present PACeR, a robust bioinformatic pipeline for the analysis of chimeric RNA sequencing data. PACeR consists of two parts, each of which are optimized for the distinctive characteristics of chimeric RNA sequencing reads: first, read processing and alignment and second, peak calling and motif analysis. We apply PACeR to chimeric RNA sequencing data generated in our lab as well as a published benchmark dataset. PACeR has minimal computational power requirements and contains extensive annotation to broaden accessibility for processing chimeric RNA sequencing data and enable insights to be gained about the targets of small non-coding RNAs in regulating diverse biological systems.
PAM binding ensures orientational integration during Cas4-Cas1-Cas2 mediated CRISPR adaptation

Adaptation in CRISPR-Cas systems immunizes bacteria and archaea against mobile genetic elements. In many DNA-targeting systems, the Cas4-Cas1-Cas2 complex is required for selection and processing of DNA segments containing PAM sequences, prior to integration of these “prespacer” substrates as spacers in the CRISPR array. We determined cryo-EM structures of the Cas4-Cas1-Cas2 adaptation complex from the type I-C system that encodes standalone Cas1 and Cas4 proteins. The structures reveal how Cas4 specifically reads out bases within the PAM sequence and how interactions with both Cas1 and Cas2 activate Cas4 endonuclease activity. The Cas4-PAM interaction ensures tight binding between the adaptation complex and the prespacer, significantly enhancing integration of the non-PAM end into the CRISPR array and ensuring correct spacer orientation. Corroborated with our biochemical results, Cas4-Cas1-Cas2 structures with substrates representing various stages of CRISPR adaptation reveal a temporally resolved mechanism for maturation and integration of functional spacers into the CRISPR array.
Undermatching Is a Consequence of Policy Compression

The matching law describes the tendency of agents to match the ratio of choices allocated to the ratio of rewards received when choosing among multiple options (Herrnstein, 1961). Perfect matching, however, is infrequently observed. Instead, agents tend to undermatch or bias choices toward the poorer option. Overmatching, or the tendency to bias choices toward the richer option, is rarely observed. Despite the ubiquity of undermatching, it has received an inadequate normative justification. Here, we assume agents not only seek to maximize reward, but also seek to minimize cognitive cost, which we formalize as policy complexity (the mutual information between actions and states of the environment). Policy complexity measures the extent to which the policy of an agent is state dependent. Our theory states that capacity-constrained agents (i.e., agents that must compress their policies to reduce complexity) can only undermatch or perfectly match, but not overmatch, consistent with the empirical evidence. Moreover, using mouse behavioral data (male), we validate a novel prediction about which task conditions exaggerate undermatching. Finally, in patients with Parkinson's disease (male and female), we argue that a reduction in undermatching with higher dopamine levels is consistent with an increased policy complexity. SIGNIFICANCE STATEMENT The matching law describes the tendency of agents to match the ratio of choices allocated to different options to the ratio of reward received. For example, if option a yields twice as much reward as option b, matching states that agents will choose option a twice as much. However, agents typically undermatch: they choose the poorer option more frequently than expected. Here, we assume that agents seek to simultaneously maximize reward and minimize the complexity of their action policies. We show that this theory explains when and why undermatching occurs. Neurally, we show that policy complexity, and by extension undermatching, is controlled by tonic dopamine, consistent with other evidence that dopamine plays an important role in cognitive resource allocation.
Postural analysis reveals persistent vigilance in paper wasps after conspecific challenge

Vigilant animals detect and respond to threats in the environment, often changing posture and movement patterns. In social animals vigilance is modulated not only by predators but also by threatening conspecifics. Precisely how social interactions alter vigilance behavior over time is not well understood. We report persistent effects of a simulated social challenge on the vigilance behavior of wild northern paper wasp foundresses, Polistes fuscatus. During the founding phase of the colony cycle conspecific wasps may usurp nests from the resident foundress, representing a severe threat. Using postural tracking, we found that after simulated intrusions wasps displayed increased vigilance during the minutes after the threat was removed. Sustained vigilance elicited after social threat manifested as increased movement, greater bilateral wing extension, and reduced antennal separation. However, no postural changes were observed after a control stimulus presentation. By rapidly adjusting individual vigilance behavior after fending off a conspecific intruder, paper wasp foundresses invest in surveillance of potential social threats, even when such threats are no longer immediately present. The prolonged state of vigilance observed here is relevant to plasticity of recognition processes as a result of conspecific threats.
The evolution of manipulative cheating

A social cheat is typically assumed to be an individual that does not perform a cooperative behaviour, or performs less of it, but can still exploit the cooperative behaviour of others. However, empirical data suggests that cheating can be more subtle, involving evolutionary arms races over the ability to both exploit and resist exploitation. These complications have not been captured by evolutionary theory, which lags behind empirical studies in this area. We bridge this gap with a mixture of game-theoretical models and individual-based simulations, examining what conditions favour more elaborate patterns of cheating. We found that as well as adjusting their own behaviour, individuals can be selected to manipulate the behaviour of others, which we term ‘manipulative cheating’. Further, we found that manipulative cheating can lead to dynamic oscillations (arms races), between selfishness, manipulation, and suppression of manipulation. Our results can help explain both variation in the level of cheating, and genetic variation in the extent to which individuals can be exploited by cheats.
Resource competition predicts assembly of in vitro gut bacterial communities

Members of microbial communities interact via a plethora of mechanisms, including resource competition, cross-feeding, and pH modulation. However, the relative contributions of these mechanisms to community dynamics remain uncharacterized. Here, we develop a framework to distinguish the effects of resource competition from other interaction mechanisms by integrating data from growth measurements in spent media, synthetic community assembly, and metabolomics with consumer-resource models. When applied to human gut commensals, our framework revealed that resource competition alone could explain most pairwise interactions. The resource-competition landscape inferred from metabolomic profiles of individual species predicted assembly compositions, demonstrating that resource competition is a dominant driver of in vitro community assembly. Moreover, the identification and incorporation of interactions other than resource competition, including pH-mediated effects and cross-feeding, improved model predictions. Our work provides an experimental and modeling framework to characterize and quantify interspecies interactions in vitro that should advance mechanistically principled engineering of microbial communities.
Doublecortin‐like kinase 1 is a therapeutic target in squamous cell carcinoma

Doublecortin like kinase 1 (DCLK1) plays a crucial role in several cancers including colon and pancreatic adenocarcinomas. However, its role in squamous cell carcinoma (SCC) remains unknown. To this end, we examined DCLK1 expression in head and neck SCC (HNSCC) and anal SCC (ASCC). We found that DCLK1 is elevated in patient SCC tissue, which correlated with cancer progression and poorer overall survival. Furthermore, DCLK1 expression is significantly elevated in human papilloma virus negative HNSCC, which are typically aggressive with poor responses to therapy. To understand the role of DCLK1 in tumorigenesis, we used specific shRNA to suppress DCLK1 expression. This significantly reduced tumor growth, spheroid formation, and migration of HNSCC cancer cells. To further the translational relevance of our studies, we sought to identify a selective DCLK1 inhibitor. Current attempts to target DCLK1 using pharmacologic approaches have relied on nonspecific suppression of DCLK1 kinase activity. Here, we demonstrate that DiFiD (3,5‐bis [2,4‐difluorobenzylidene]−4‐piperidone) binds to DCLK1 with high selectivity. Moreover, DiFiD mediated suppression of DCLK1 led to G2/M arrest and apoptosis and significantly suppressed tumor growth of HNSCC xenografts and ASCC patient derived xenografts, supporting that DCLK1 is critical for SCC growth.
Parallel adaptation to lower altitudes is associated with enhanced plasticity in Heliosperma pusillum (Caryophyllaceae)

High levels of phenotypic plasticity are thought to be inherently costly in stable or extreme environments, but enhanced plasticity may evolve as a response to novel environments and foster adaptation. Heliosperma pusillum forms pubescent montane and glabrous alpine ecotypes that diverged recurrently and polytopically (parallel evolution). The specific montane and alpine localities are characterized by distinct temperature conditions, available moisture and light. To disentangle the relative contribution of constitutive versus plastic gene expression to altitudinal divergence, we analyze the transcriptomic profiles of two parallely evolved ecotype pairs, grown in reciprocal transplantations at native altitudinal sites. In both ecotype pairs, only a minor proportion of genes appear constitutively differentially expressed between the ecotypes regardless of the growing environment. Both derived, montane populations bear comparatively higher plasticity of gene expression than the alpine populations that can be considered in this system as ‘ancestor-proxies’. Genes that change expression plastically and constitutively underlie similar ecologically relevant pathways, related to response to drought and trichome formation. Other relevant processes, such as photosynthesis, seem to rely mainly on plastic changes. The enhanced plasticity consistently observed in the montane ecotype likely evolved as a response to the newly colonized niche. Our findings confirm that directional changes in gene expression plasticity can shape initial stages of phenotypic evolution, likely fostering adaptation to novel environments. Significance Statement Understanding the importance of phenotypic plasticity for fast adaptation to stress is very timely for breeding and current environmental challenges. Our study of an alpine plant in the carnation family evidences an increased level of expression plasticity in early stages of adaptation to hotter and drier habitats.
Effects of source sample amount on biodiversity surveys of bacteria, fungi, and nematodes in soil ecosystems

Bacteria, fungi, and nematodes are major components of soil ecosystems, playing pivotal roles in belowground material cycles and biological community processes. A number of studies have recently uncovered the diversity and community structure of those organisms in various types of soil ecosystems based on DNA metabarcoding (amplicon sequencing). However, because each of the most previous studies examined only one or two of the three organismal groups, it remains an important challenge to reveal the entire picture of soil community structure. We herein examined how we could standardize DNA extraction protocols for simultaneous DNA metabarcoding of bacteria, fungi, and nematodes. Specifically, in an Illumina sequencing analysis of forest and farmland soil samples, we performed DNA extraction at five levels of soil-amount settings (0.5 g, 2 g, 5 g, 10 g, and 20 g). We then found that DNA extraction with the 0.5-g soil setting, which had been applied as default in many commercial DNA extraction kits, could lead to underestimation of α-diversity in nematode community. The detected family-level diversity of nematodes, in particular, increased with increasing soil amounts in DNA extraction. Our analyses also indicated that dissimilarity (β-diversity) of community structure among replicate samples were higher in fungi and nematodes than in bacteria, suggesting difference in basic levels of spatial heterogeneity among the three organismal groups. These findings provide platforms for optimizing DNA metabarcoding protocols commonly applicable to major groups of organisms in soil ecosystems.
The energy landscape reshaped by strain-specific mutations underlies the long-range epistasis in NS1 evolution of influenza A virus

The mechanisms underlying how individual mutations affect the protein energy landscape are crucial for understanding how proteins evolve. However, predicting mutational effects remains challenging because of epistasis—the nonadditive interactions between mutations. Here, we investigate the biophysical mechanism of strain-specific epistasis in the nonstructural protein 1 (NS1) of the influenza A virus (IAV). To understand the molecular basis of epistasis, we conducted comprehensive analyses of four NS1s of IAV strains that emerged between 1918 and 2004. We find that strain-specific mutations of NS1s are near-neutral with respect to the association with the p85β subunit of PI3K. However, the individual residues on the p85β-binding interface show long-range epistatic interactions with strain-specific mutations. We reveal that strain-specific mutations reshaped the energy landscape of NS1, leading to long-range epistasis. Our findings offer a high-resolution mechanism of how near-neutral mutations silently alter the biophysical energy landscapes, resulting in diverse background effects during molecular evolution.
On predicting heterogeneity in nanoparticle dosage

Nanoparticles are increasingly employed as a vehicle for the targeted delivery of therapeutics to specific cell types. However, much remains to be discovered about the fundamental biology that dictates the interactions between nanoparticles and cells. Accordingly, few nanoparticle-based targeted therapeutics have succeeded in clinical trials. One element that hinders our understanding of nanoparticle-cell interactions is the presence of heterogeneity in nanoparticle dosage data obtained from standard experiments. It is difficult to distinguish between heterogeneity that arises from stochasticity in nanoparticle behaviour, and that which arises from heterogeneity in the cell population. Mathematical investigations have revealed that both sources of heterogeneity contribute meaningfully to the heterogeneity in nanoparticle dosage. However, these investigations have relied on simplified models of nanoparticle internalisation. Here we present a stochastic mathematical model of nanoparticle internalisation that incorporates hitherto neglected biological phenomena such as multistage internalisation, cell division, asymmetric nanoparticle inheritance and nanoparticle saturation. Critically, our model provides information about nanoparticle dosage at an individual cell level. We perform model simulations to examine the influence of specific biological phenomena on the heterogeneity in nanoparticle dosage. Under certain modelling assumptions, we derive analytic approximations of the nanoparticle dosage distribution. We demonstrate that the analytic approximations are accurate, and show that nanoparticle dosage can be described by a Poisson mixture distribution with rate parameters that are a function of Beta-distributed random variables. We discuss the implications of the analytic results with respect to parameter estimation and model identifiability from standard experimental data. Finally, we highlight extensions and directions for future research. 2010 MSC 92B05