Miracle-dz/UniFAR-FTUs huggingface dataset:
Dust in the Emission-Line Gas of the Seyfert 2 Galaxy Markarian 3

On presente les resultats des modeles de la photoionisation du gaz a raies d'emission de Mrk 3. On examine l'influence de la poussiere sur le transfert du rayonnement, tout particulierement les raies de resonance UV, ainsi que la contribution de la poussiere chaude au continuum ih de la galaxie
Novel banana lectin CAR-T cells to target pancreatic tumors and tumor-associated stroma

Background Cell therapies for solid tumors are thwarted by the hostile tumor microenvironment (TME) and by heterogeneous expression of tumor target antigens. We address both limitations with a novel class of chimeric antigen receptors based on plant lectins, which recognize the aberrant sugar residues that are a ‘hallmark’ of both malignant and associated stromal cells. We have expressed in T cells a modified lectin from banana, H84T BanLec, attached to a chimeric antigen receptor (H84T-CAR) that recognizes high-mannose (asparagine residue with five to nine mannoses). Here, we tested the efficacy of our novel H84T CAR in models of pancreatic ductal adenocarcinoma (PDAC), intractable tumors with aberrant glycosylation and characterized by desmoplastic stroma largely contributed by pancreatic stellate cells (PSCs). Methods We transduced human T cells with a second-generation retroviral construct expressing the H84T BanLec chimeric receptor, measured T-cell expansion, characterized T-cell phenotype, and tested their efficacy against PDAC tumor cells lines by flow cytometry quantification. In three-dimensional (3D) spheroid models, we measured H84T CAR T-cell disruption of PSC architecture, and T-cell infiltration by live imaging. We tested the activity of H84T CAR T cells against tumor xenografts derived from three PDAC cell lines. Antitumor activity was quantified by caliper measurement and bioluminescence signal and used anti-human vimentin to measure residual PSCs. Results H84T BanLec CAR was successfully transduced and expressed by T cells which had robust expansion and retained central memory phenotype in both CD4 and CD8 compartments. H84T CAR T cells targeted and eliminated PDAC tumor cell lines. They also disrupted PSC architecture in 3D models in vitro and reduced total tumor and stroma cells in mixed co-cultures. H84T CAR T cells exhibited improved T-cell infiltration in multicellular spheroids and had potent antitumor effects in the xenograft models. We observed no adverse effects against normal tissues. Conclusions T cells expressing H84T CAR target malignant cells and their stroma in PDAC tumor models. The incorporation of glycan-targeting lectins within CARs thus extends their activity to include both malignant cells and their supporting stromal cells, disrupting the TME that otherwise diminishes the activity of cellular therapies against solid tumors.
Epitope tag-specific differences in the detection of COSA-1 marked crossover sites in C. elegans spermatocytes

Nascent crossover sites in C. elegans meiocytes can be cytologically detected using epitope-tagged versions of the pro-crossover protein COSA-1. In spermatocytes, differences exist between cytologically-detected and genetically-detected double crossover rates. Here, we examine nascent crossovers using both GFP- and OLLAS-tagged COSA-1. Similar to previous work, we find that most late pachytene spermatocytes display 5 COSA-1 foci, indicating one crossover per autosome bivalent. However, we detected more nuclei with >5 COSA-1 foci using OLLAS::COSA-1, reflecting some bivalents having 2 COSA-1 foci. These results demonstrate tag-specific differences in the detection of COSA-1 marked nascent crossovers in spermatocytes.
Transformer-based deep learning for predicting protein properties in the life sciences

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model—the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.
A Longitudinal Study of Relationships Between Vocational Graduates’ Career Adaptability, Career Decision-Making Self-Efficacy, Vocational Identity Clarity, and Life Satisfaction

Career construction theory proposes that high career adaptability leads to positive adaptation outcomes during career transition. However, the specific pathways of how this happens remain underexplored. Drawing on the career construction model of adaptation, we hypothesized that career decision-making self-efficacy mediates the link of career adaptability with vocational identity clarity and life satisfaction as two measures of adaptation outcomes. We conducted a three-wave survey with an initial sample of 3126 Czech upper-secondary vocational graduates transitioning from vocational school to the labor market. Structural equation modeling revealed that career decision-making self-efficacy fully mediated the relationship between career adaptability before graduation and vocational identity clarity 20 months later. In contrast, the mediation effect of career decision-making self-efficacy on the relationship between career adaptability and life satisfaction was not supported. Additionally, in contrast to the previous literature, career adaptability was not directly related to vocational identity clarity and life satisfaction. Nevertheless, our findings demonstrated a positive long-term association of career adaptability with adaptation outcomes within the working life domain. Practical implications and future directions are discussed.
Optimal fractional-order PID control design for time-delayed multi-input multi-output seismic-excited structural system

The application of the fractional-order PID (FOPID) controller is recently becoming a topic of research interest for vibration control of structures. Some researchers have successfully implemented the FOPID controller in a single-input single-output (SISO) control structural system subjected to earthquake excitations. However, there is a lack of research that focuses on its application in multi-input multi-output (MIMO) control systems to implement it in seismic-excited structures. In this case, the cross-coupling of the process channels in the MIMO control structural system may result in a complex design process of controllers so that each loop is independently designed. From an operational point of view, the time delay and saturation limit of the actuators are other challenges that significantly affect the performance and robustness of the controller so that ignoring them in the design process may lead to unrealistic results. According to the challenges, the present study proposed an optimal fractional-order PID control design approach for structural control systems subjected to earthquake excitation. Gases Brownian motion optimization (GBMO) algorithm is utilized for optimal tuning of the controller parameters. Considering six real earthquakes and seven performance indices, the performance of the proposed controller, implemented on a ten-story building equipped with an active tendon system (ATS), is compared with those provided by the classical PID controller. Simulation results indicate that the proposed FOPID controller is more efficient than the PID in both terms of seismic performance and robustness against time-delay effects. The proposed FOPID controller can maintain suitable seismic performance in small time delays, while a significant performance loss is observed for the PID controller.
Connection and Caring Through a Therapeutic Juvenile Corrections Model: Staff and Youth Resident Perceptions of Structural and Interpersonal Dimensions

This mixed-method exploratory inductive study examined incarcerated youths’ and staff members’ perceptions of a new community-focused therapeutic model in a large youth prison. Via 18 focus groups (N = 141) and facility-wide surveys (N = 248), both youth and staff shared perceptions of specific structural components of the model designed to change their relationship to one another, such as consistent staffing, higher staff-resident ratios, and program features designed to enhance rapport. Both groups also provided rich descriptions of the altered interpersonal dynamics related to connection and caring, two of the five C’s of Positive Youth Development (PYD), that were facilitated through those structural changes. Findings suggest the model’s intentional redefinition of resident-staff relationships directly contributed to meaningful resident and staff experiences. Perceptions of those relationships—rarely explored in the extant literature—were examined and illustrated through focus group data. This study illuminates the subjective experiences of both groups as they put the model into practice and reveals key insights about therapeutic correctional programs based on PYD in secure facilities that have important implications for juvenile correctional theory, research, practice, and policy.
Reporting Monte Carlo Studies in Structural Equation Modeling

In structural equation modeling, Monte Carlo simulations have been used increasingly over the last two decades, as an inventory from the journal Structural Equation Modeling illustrates. Reaching out to a broad audience, this article provides guidelines for reporting Monte Carlo studies in that field. The framework of discourse is set by a number of steps to be taken in such research, matching outlines of experimental design by Paxton, Curran, Bollen, Kirby, and Chen (2001) and Skrondal (2000). Throughout the article, reference is made to exemplary publications and, occasionally, to imperfect reporting.
Bottom-up and Top-down Object Inference Networks for Image Captioning

A bottom-up and top-down attention mechanism has led to the revolutionizing of image captioning techniques, which enables object-level attention for multi-step reasoning over all the detected objects. However, when humans describe an image, they often apply their own subjective experience to focus on only a few salient objects that are worthy of mention, rather than all objects in this image. The focused objects are further allocated in linguistic order, yielding the “object sequence of interest” to compose an enriched description. In this work, we present the Bottom-up and Top-down Object inference Network (BTO-Net), which novelly exploits the object sequence of interest as top-down signals to guide image captioning. Technically, conditioned on the bottom-up signals (all detected objects), an LSTM-based object inference module is first learned to produce the object sequence of interest, which acts as the top-down prior to mimic the subjective experience of humans. Next, both of the bottom-up and top-down signals are dynamically integrated via an attention mechanism for sentence generation. Furthermore, to prevent the cacophony of intermixed cross-modal signals, a contrastive learning-based objective is involved to restrict the interaction between bottom-up and top-down signals, and thus leads to reliable and explainable cross-modal reasoning. Our BTO-Net obtains competitive performances on the COCO benchmark, in particular, 134.1% CIDEr on the COCO Karpathy test split. Source code is available at https://github.com/YehLi/BTO-Net.
My AI Wants to Know if This Will Be on the Exam: Testing OpenAI’s Codex on CS2 Programming Exercises

The introduction of OpenAI Codex sparked a surge of interest in the impact of generative AI models on computing education practices. Codex is also the underlying model for GitHub Copilot, a plugin which makes AI-generated code accessible to students through auto-completion in popular code editors. Research in this area, particularly on the educational implications, is nascent and has focused almost exclusively on introductory programming (or CS1) questions. Very recent work has shown that Codex performs considerably better on typical CS1 exam questions than most students. It is not clear, however, what Codex’s limits are with regard to more complex programming assignments and exams. In this paper, we present results detailing how Codex performs on more advanced CS2 (data structures and algorithms) exam questions taken from past exams. We compare these results to those of students who took the same exams under normal conditions, demonstrating that Codex outscores most students. We consider the implications of such tools for the future of undergraduate computing education.
Wall Pressure and Shear Stress Spectra from Direct Simulations of Channel Flow

Wall pressure and shear stress spectra from direct numerical simulations of turbulent plane channel flow are presented in this paper. Simulations have been carried out at a series of Reynolds numbers up to Re? = 1440, which corresponds to Re = 6:92 x 10(4) based on channel width and centerline velocity. Single-point and two-point statistics for velocity, pressure, and their derivatives have been collected, including velocity moments up to fourth order. § The results have been used to study the Reynolds number dependence of wall pressure and shear stress spectra. It is found that the point spectrum of wall pressure collapses for Re? 360 under a mixed scaling for frequencies lower than the peak frequency of the frequency-weighted spectrum, and under viscous scaling for frequencies higher than the peak. Point spectra of wall shear stress components are found to collapse for Re? 360 under viscous scaling. The normalized mean square wall pressure increases linearly with the logarithm of Reynolds number. The rms wall shear stresses also increase with Reynolds number over the present range, but suggest some leveling off at high Reynolds number.
Genome-wide analysis of annexin gene family in Schrenkiella parvula and Eutrema salsugineum suggests their roles in salt stress response

Annexins (Anns) play an important role in plant development, growth and responses to various stresses. Although Ann genes have been characterized in some plants, their role in adaptation mechanisms and tolerance to environmental stresses have not been studied in extremophile plants. In this study, Ann genes in Schrenkiella parvula and Eutrema salsugineum were identified using a genome-wide method and phylogenetic relationships, subcellular distribution, gene structures, conserved residues and motifs and also promoter prediction have been studied through bioinformatics analysis. We identified ten and eight encoding putative Ann genes in S. parvula and E. salsugineum genome respectively, which were divided into six subfamilies according to phylogenetic relationships. By observing conservation in gene structures and protein motifs we found that the majority of Ann members in two extremophile plants are similar. Furthermore, promoter analysis revealed a greater number of GATA, Dof, bHLH and NAC transcription factor binding sites, as well as ABRE, ABRE3a, ABRE4, MYB and Myc cis-acting elements in compare to Arabidopsis thaliana. To gain additional insight into the putative roles of candidate Ann genes, the expression of SpAnn1, SpAnn2 and SpAnn6 in S. parvula was studied in response to salt stress, which indicated that their expression level in shoot increased. Similarly, salt stress induced expression of EsAnn1, 5 and 7, in roots and EsAnn1, 2 and 5 in leaves of E. salsugineum. Our comparative analysis implies that both halophytes have different regulatory mechanisms compared to A. thaliana and suggest SpAnn2 gene play important roles in mediating salt stress.
Spinal cord injury in mice amplifies anxiety: A novel light-heat conflict test exposes increased salience of anxiety over heat

Spinal cord injury (SCI) predisposes individuals to anxiety and chronic pain. Anxiety- and pain-like behavior after SCI can be tested in rodents, yet commonly used tests assess one variable and may not replicate effects of SCI or sex differences seen in humans. Thus, novel preclinical tests should be optimized to better evaluate behaviors relating to anxiety and pain. Here, we use our newly developed conflict test – the Thermal Increments Dark-Light (TIDAL) test – to explore how SCI affects anxiety- vs. pain-like behavior, and whether sex affects post-SCI behavior. The TIDAL conflict test consists of two plates connected by a walkway; one plate remains illuminated and at an isothermic temperature, whereas the other plate is dark but is heated incrementally to aversive temperatures. Control mice are tested with both plates illuminated (thermal place preference). Female and male mice received moderate T9 contusion SCI or remained uninjured. At 7 days post-operative (dpo), mice with SCI increased dark plate preference throughout the TIDAL conflict test compared to uninjured mice. SCI increased dark plate preference for both sexes, although female (vs. male) mice remained on the heated-dark plate to higher temperatures. Mice with SCI that repeated TIDAL at 7 and 21 dpo showed reduced preference for the dark-heated plate at 21 dpo. Overall, in female and male mice, SCI enhances the salience of anxiety (vs. heat sensitivity). The TIDAL conflict test meets a need for preclinical anxiety- and pain-related tests that recapitulate the human condition; thus, future rodent behavioral studies should incorporate TIDAL or other conflict tests to help understand and treat neurologic disorders.
Aqueous spice extracts as alternative antimycotics to control highly drug resistant extensive biofilm forming clinical isolates of Candida albicans

Candida albicans forms biofilm by associating with biotic and abiotic surfaces. Biofilm formation by C. albicans is relevant and significant as the organisms residing within, gain resistance to conventional antimycotics and are therefore difficult to treat. This study targeted the potential of spice-based antimycotics to control C. albicans biofilms. Ten clinical isolates of C. albicans along with a standard culture MTCC-3017 (ATCC-90028) were screened for their biofilm-forming ability. C. albicans M-207 and C. albicans S-470 were identified as high biofilm formers by point inoculation on TSA medium as they formed a lawn within 16 h and exhibited resistance to fluconazole and caspofungin at 25 mcg and 8 mcg respectively. Aqueous and organic spice extracts were screened for their antimycotic activity against C. albicans M-207 and S-470 by agar and disc diffusion and Zone of Inhibition was observed. Minimal Inhibitory Concentration was determined based on growth absorbance and cell viability measurements. The whole aqueous extract of garlic inhibited biofilms of C. albicans M-207, whereas whole aqueous extracts of garlic, clove, and Indian gooseberry were effective in controlling C. albicans S-470 biofilm within 12 h of incubation. The presence of allicin, ellagic acid, and gallic acid as dominant compounds in the aqueous extracts of garlic, clove, and Indian gooseberry respectively was determined by High-Performance Thin Layer Chromatography and Liquid Chromatography-Mass Spectrometry. The morphology of C. albicans biofilm at different growth periods was also determined through bright field microscopy, phase contrast microscopy, and fluorescence microscopy. The results of this study indicated that the alternate approach in controlling high biofilm-forming, multi-drug resistant clinical isolates of C. albicans M-207 and S-470 using whole aqueous extracts of garlic, clove, and Indian gooseberry is a safe, potential, and cost-effective one that can benefit the health care needs with additional effective therapeutics to treat biofilm infections.
Corynebacterium diphtheriae causes keratinocyte-intrinsic ribotoxic stress and NLRP1 inflammasome activation in a model of cutaneous diphtheria

NLRP1 is an innate immune sensor protein that activates inflammasome-driven pyroptotic cell death. Recent work demonstrates that human NLRP1 has evolved to sense viral infections. Whether and how human NLRP1 responds to other infectious agents is unclear. Here, and in a companion manuscript, we report that human NLRP1, as an integral component of the ribotoxic stress response (RSR), is activated by bacterial exotoxins that target human ribosome elongation factors EEF1 and EEF2, including Diphtheria Toxin (DT) from Corynebacterium diphtheriae, exotoxin A from Pseudomonas aeruginosa and sidI from Legionella pneumophila. In human keratinocytes, DT activates RSR kinases ZAKα, p38 and JNKs, upregulates a set of signature RSR transcripts and triggers rapid NLRP1-dependent pyroptosis. Mechanistically, these processes require 1) DtxR-mediated de-repression of DT production in the bacteria, as well as 2) diphthamide synthesis and 3) ZAKα/p38-driven NLRP1 phosphorylation in the host. In 3D human skin cultures, Corynebacterium diphtheriae infection disrupts barrier function and induces IL-1 driven inflammation. Pharmacologic inhibition of p38 and ZAKα suppresses keratinocyte pyroptosis and rescues barrier integrity of Corynebacterium diphtheriae-treated organotypic skin. In summary, these findings implicate RSR and the NLRP1 inflammasome in antibacterial innate immunity and might explain certain aspects of diphtheria pathogenesis. KEY POINTS EEF1/EEF2-targeting bacterial exotoxins activate the human NLRP1 inflammasome. DT+ve toxigenic Corynebacterium diphtheriae induces ZAKα-driven RSR and NLRP1-driven pyroptosis in human keratinocytes. Identification of transcripts that are induced by multiple RSR agents across multiple cell types. p38 and ZAKα inhibition rescues epidermal integrity by limiting pyroptosis in 3D skin mode of cutaneous diphtheria.
Change in Monarch Butterfly Winter Abundance Over the Past Decade: A Red List Perspective

1. Assessing invertebrate species for the IUCN Red List under Criterion A requires fitting an appropriate statistical model to available abundance data and calculating a ten-year change (TYC) estimate from predicted abundances. When the rate of change has not been constant across the entire time series, models that accommodate variable change rates are strongly recommended. 2. The monarch butterfly (Danaus plexippus) was recently added to the IUCN Red List (A2ab Endangered) based on analysis of data on winter abundances in Mexico and the western USA between 1993 and 2020. TYC estimates in the monarch assessment came from models that assumed constant change rates. We conducted a Bayesian analysis of the same data using models that accommodated variable change rates and used those models to compute TYC estimates. 3. Our results suggested that monarch population change rates have not been constant. The analysis yielded a model averaged TYC estimate of +5.23%, which was not statistically distinguishable from 0% and was considerably different from values of −46% and −72% in the assessment. The Bayesian posterior probability of a TYC value below −30% (A2ab Vulnerable) was 0.15 and that of a TYC value below −50% (A2ab Endangered) was 0.03. 4. We suggest that a more thorough analysis of recent overwintering abundances will lead to an improved IUCN assessment for monarch butterflies. We recommend that other researchers evaluating monarch conservation status consider using models with variable change rates, as models with constant change rates may not accurately predict the trajectory of monarch abundances into the future.