Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Jurassic mass extinction: A global long-term event: Geol-ogy

The end-Pliensbachian extinction event (187 Ma) has been interpreted either as one of 10 global periodically recurring mass extinctions of the past 250 m.y. or as a minor localized European event. Elevated levels of family extinction spanned five ammonite zones during the late Pliensbachian and the early Toarcian, an interval of ∼7.5 m.y. , and were distributed unequally in the Boreal, Tethyan, and Austral realms. Detailed sampling of invertebrate macrofaunas through complete expanded sequences in northwest Europe shows that most species extinctions occurred in the early Toarcian, following a regional anoxic event. The Early Jurassic mass-extinction event took place over a long time scale, and it was global in extent.
Developing a Competency‐based Curriculum in Basic and Clinical Pharmacology – A Delphi Study among Physicians

A new curriculum is planned for the medical school at Lund University, Sweden. Pharmacology, in a broad sense, has been identified as a subject that needs to be strengthened based on needs in the healthcare system. The aim was to identify the competencies in basic and clinical pharmacology that a newly qualified physician needs. Using a modified three‐round Delphi technique, 31 physicians were invited to list necessary competencies (round 1). After content analysis, these panel members classified the list by importance on two occasions (rounds 2 and 3) using a 4‐point scale (4 = necessary, 3 = desirable, 2 = useful, 1 = not necessary). Competencies with the highest ranks based on necessity were retained. Thirty physicians accepted the invitation and 25 (83%) of them completed all three rounds. Round 1 resulted in 258 suggestions, which were subsequently reduced to 95 competencies. Of these 95 competencies, 40 were considered necessary by at least 75% of the panel members. The degree of consensus increased between round 2 and round 3. Using a modified Delphi technique, we identified 40 competencies that could be transferred to learning outcomes for a new curriculum in basic and clinical pharmacology at medical school.
Infection-experienced HSPCs protect against infections by generating neutrophils with enhanced bactericidal activity

Hematopoietic stem and progenitor cells (HSPCs) respond to infection by proliferating and generating in-demand neutrophils through a process called emergency granulopoiesis (EG). Recently, infection-induced changes in HPSCs have also been shown to underpin the longevity of trained immunity, where they generate innate immune cells with enhanced responses to subsequent microbial threats. Using larval zebrafish to live image neutrophils and HSPCs we show that infection-experienced HSPCs generate neutrophils with enhanced bactericidal functions. Transcriptomic analysis of EG neutrophils uncovered a previously unknown function for mitochondrial reactive oxygen species in elevating neutrophil bactericidal activity. We also reveal that driving expression of zebrafish C/EBPβ within infection-naïve HSPCs is sufficient to generate neutrophils with similarly enhanced bactericidal capacity. Our work suggests that this demand-adapted source of neutrophils contributes to trained immunity by providing enhanced protection towards subsequent infections. Manipulating demand-driven granulopoiesis may provide a therapeutic strategy to boost neutrophil function and treat infectious disease.
Genome-Wide Analysis and Expression Profiling of Trehalose-6-Phosphate Phosphatase (TPP) in Punica granatum in Response to Abscisic-Acid-Mediated Drought Stress

Trehalose, a nonreducing disaccharide, has been linked to plant growth and development as well as stress response. The enzyme trehalose-6-phosphate phosphatase (TPP) plays a crucial role in the production of trehalose in higher plants. This study identified a total of seven TPP family genes within the pomegranate species (PgTPP1–PgTPP7). Three subgroups of the seven PgTPPs were identified through phylogenetic analysis. The gene length, coding sequence (CD) length, and chromosomal location of the PgTPP genes were studied. In addition, the PgTPP proteins’ length, isoelectric point (Ip), grand average of hydropathicity (GRAVY), conserved domains, conserved motifs, synteny, and phylogenetic relationships with Arabidopsis and tomato TPP proteins were examined. The cis-acting elements in the promoter region and the expression of the PgTPP genes under abscisic acid (ABA)-mediated drought stress as well as the differences in expression in the root, flower, and leaf tissues were also assessed. The PgTPP2 and PgTPP5 genes are involved in the response to abscisic-acid-mediated drought stress, as shown by drought-mediated stress transcriptomes. The PgTPP1 and PgTPP2 genes were expressed only in floral tissue and roots, respectively. The remaining PgTPPs did not exhibit any significant alterations in gene expression in roots, flowers, or leaves. The current study has the potential to provide a comprehensive understanding of the biological characteristics of PgTPP proteins in various developmental processes and their role in the pomegranate plant’s response to different stressors. However, further research is required to explore their precise biological role. Hence, conducting a comprehensive functional validation study on PgTPPs could contribute to the development of stress-resistant agricultural cultivars.
SARS-CoV-2 nucleocapsid protein inhibits the PKR-mediated integrated stress response through RNA-binding domain N2b

The nucleocapsid protein N of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enwraps and condenses the viral genome for packaging but is also an antagonist of the innate antiviral defense. It suppresses the integrated stress response (ISR), purportedly by interacting with stress granule (SG) assembly factors G3BP1 and 2, and inhibits type I interferon responses. To elucidate its mode of action, we systematically deleted and over-expressed distinct regions and domains. We show that N via domain N2b blocks PKR-mediated ISR activation, as measured by suppression of ISR-induced translational arrest and SG formation. N2b mutations that prevent dsRNA binding abrogate these activities also when introduced in the intact N protein. Substitutions reported to block post-translation modifications of N or its interaction with G3BP1/2 did not have a detectable additive effect. In an encephalomyocarditis virus-based infection model, N2b - but not a derivative defective in RNA binding—prevented PKR activation, inhibited β-interferon expression and promoted virus replication. Apparently, SARS-CoV-2 N inhibits innate immunity by sequestering dsRNA to prevent activation of PKR and RIG-I-like receptors. Similar observations were made for the N protein of human coronavirus 229E, suggesting that this may be a general trait conserved among members of other orthocoronavirus (sub)genera.
Robust Backdoor Attacks on Object Detection in Real World

Deep learning models are widely deployed in many applications, such as object detection in various security fields. However, these models are vulnerable to backdoor attacks. Most backdoor attacks were intensively studied on classified models, but little on object detection. Previous works mainly focused on the backdoor attack in the digital world, but neglect the real world. Especially, the backdoor attack's effect in the real world will be easily influenced by physical factors like distance and illumination. In this paper, we proposed a variable-size backdoor trigger to adapt to the different sizes of attacked objects, overcoming the disturbance caused by the distance between the viewing point and attacked object. In addition, we proposed a backdoor training named malicious adversarial training, enabling the backdoor object detector to learn the feature of the trigger with physical noise. The experiment results show this robust backdoor attack (RBA) could enhance the attack success rate in the real world.
A performance characteristic curve for model evaluation: the application in information diffusion prediction

The information diffusion prediction on social networks aims to predict future recipients of a message, with practical applications in marketing and social media. While different prediction models all claim to perform well, general frameworks for performance evaluation remain limited. Here, we aim to identify a performance characteristic curve for a model, which captures its performance on tasks of different complexity. We propose a metric based on information entropy to quantify the randomness in diffusion data, then identify a scaling pattern between the randomness and the prediction accuracy of the model. Data points in the patterns by different sequence lengths, system sizes, and randomness all collapse into a single curve, capturing a model's inherent capability of making correct predictions against increased uncertainty. Given that this curve has such important properties that it can be used to evaluate the model, we define it as the performance characteristic curve of the model. The validity of the curve is tested by three prediction models in the same family, reaching conclusions in line with existing studies. Also, the curve is successfully applied to evaluate two distinct models from the literature. Our work reveals a pattern underlying the data randomness and prediction accuracy. The performance characteristic curve provides a new way to systematically evaluate models' performance, and sheds light on future studies on other frameworks for model evaluation.
Joint Demosaicing and Denoising with Double Deep Image Priors

Demosaicing and denoising of RAW images are crucial steps in the processing pipeline of modern digital cameras. As only a third of the color information required to produce a digital image is captured by the camera sensor, the process of demosaicing is inherently ill-posed. The presence of noise further exacerbates this problem. Performing these two steps sequentially may distort the content of the captured RAW images and accumulate errors from one step to another. Recent deep neural-network-based approaches have shown the effectiveness of joint demosaicing and denoising to mitigate such challenges. However, these methods typically require a large number of training samples and do not generalize well to different types and intensities of noise. In this paper, we propose a novel joint demosaicing and denoising method, dubbed JDD-DoubleDIP, which operates directly on a single RAW image without requiring any training data. We validate the effectiveness of our method on two popular datasets -- Kodak and McMaster -- with various noises and noise intensities. The experimental results show that our method consistently outperforms other compared methods in terms of PSNR, SSIM, and qualitative visual perception.
Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions

How to properly set the privacy parameter in differential privacy (DP) has been an open question in DP research since it was first proposed in 2006. In this work, we demonstrate the ability of influence functions to offer insight into how a specific privacy parameter value will affect a model's test loss in the randomized response-based local DP setting. Our proposed method allows a data curator to select the privacy parameter best aligned with their allowed privacy-utility trade-off without requiring heavy computation such as extensive model retraining and data privatization. We consider multiple common randomization scenarios, such as performing randomized response over the features, and/or over the labels, as well as the more complex case of applying a class-dependent label noise correction method to offset the noise incurred by randomization. Further, we provide a detailed discussion over the computational complexity of our proposed approach inclusive of an empirical analysis. Through empirical evaluations we show that for both binary and multi-class settings, influence functions are able to approximate the true change in test loss that occurs when randomized response is applied over features and/or labels with small mean absolute error, especially in cases where noise correction methods are applied.
Total-effect Test May Erroneously Reject So-called"Full"or"Complete"Mediation

The procedure for establishing mediation, i.e., determining that an independent variable X affects a dependent variable Y through some mediator M, has been under debate. The classic causal steps require that a"total effect"be significant, now also known as statistically acknowledged. It has been shown that the total-effect test can erroneously reject competitive mediation and is superfluous for establishing complementary mediation. Little is known about the last type, indirect-only mediation, aka"full"or"complete"mediation, in which the indirect (ab) path passes the statistical partition test while the direct-and-remainder (d) path fails. This study 1) provides proof that the total-effect test can erroneously reject indirect-only mediation, including both sub-types, assuming least square estimation (LSE) F-test or Sobel test; 2) provides a simulation to duplicate the mathematical proofs and extend the conclusion to LAD-Z test; 3) provides two real-data examples, one for each sub-type, to illustrate the mathematical conclusion; 4) in view of the mathematical findings, proposes to revisit concepts, theories, and techniques of mediation analysis and other causal dissection analyses, and showcase a more comprehensive alternative, process-and-product analysis (PAPA).
The Impact of Recommendation Systems on Opinion Dynamics: Microscopic Versus Macroscopic Effects

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users' confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system.
Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks

Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing. However, their performance in speech processing remains limited due to the lack of an effective auditory front-end. To address this limitation, we introduce Spiking-LEAF, a learnable auditory front-end meticulously designed for SNN-based speech processing. Spiking-LEAF combines a learnable filter bank with a novel two-compartment spiking neuron model called IHC-LIF. The IHC-LIF neurons draw inspiration from the structure of inner hair cells (IHC) and they leverage segregated dendritic and somatic compartments to effectively capture multi-scale temporal dynamics of speech signals. Additionally, the IHC-LIF neurons incorporate the lateral feedback mechanism along with spike regularization loss to enhance spike encoding efficiency. On keyword spotting and speaker identification tasks, the proposed Spiking-LEAF outperforms both SOTA spiking auditory front-ends and conventional real-valued acoustic features in terms of classification accuracy, noise robustness, and encoding efficiency.
How Much Freedom Does An Effectiveness Metric Really Have?

It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into. In fact that second freedom is, to a considerable degree, illusory. That is because if one SERP in a pair has been given a certain score by a metric, fundamental ordering constraints in many cases then dictate that the score for the second SERP must be either not less than, or not greater than, the score assigned to the first SERP. We refer to these fixed relationships as innate pairwise SERP orderings. Our first goal in this work is to describe and defend those pairwise SERP relationship constraints, and tabulate their relative occurrence via both exhaustive and empirical experimentation. We then consider how to employ such innate pairwise relationships in IR experiments, leading to a proposal for a new measurement paradigm. Specifically, we argue that tables of results in which many different metrics are listed for champion versus challenger system comparisons should be avoided; and that instead a single metric be argued for in principled terms, with any relationships identified by that metric then reinforced via an assessment of the innate relationship as to whether other metrics are likely to yield the same system‐versus‐system outcome.
Performance Metrics for Probabilistic Ordinal Classifiers

Ordinal classification models assign higher penalties to predictions further away from the true class. As a result, they are appropriate for relevant diagnostic tasks like disease progression prediction or medical image grading. The consensus for assessing their categorical predictions dictates the use of distance-sensitive metrics like the Quadratic-Weighted Kappa score or the Expected Cost. However, there has been little discussion regarding how to measure performance of probabilistic predictions for ordinal classifiers. In conventional classification, common measures for probabilistic predictions are Proper Scoring Rules (PSR) like the Brier score, or Calibration Errors like the ECE, yet these are not optimal choices for ordinal classification. A PSR named Ranked Probability Score (RPS), widely popular in the forecasting field, is more suitable for this task, but it has received no attention in the image analysis community. This paper advocates the use of the RPS for image grading tasks. In addition, we demonstrate a counter-intuitive and questionable behavior of this score, and propose a simple fix for it. Comprehensive experiments on four large-scale biomedical image grading problems over three different datasets show that the RPS is a more suitable performance metric for probabilistic ordinal predictions. Code to reproduce our experiments can be found at https://github.com/agaldran/prob_ord_metrics .
Effect of the F\"orster Interaction and the Pulsed Pumping on the Quantum Correlations of a Two Quantum Dot-Microcavity System in the Strong Coupling Regime

The quantum correlations of a system of two quantum dots with F\"oster interaction ({\Gamma}) in a microcavity with strongly coupled dissipation and a single mode of the electromagnetic field and driven by a laser pulse were studied theoretically, using the formalism of the master equation in Lindbland form. The energy eigenvalues of the system were studied as a function of detuning for the first and second excitation varieties. Concurrence (CC), formation entanglement (EoF),mutual information (I) and quantum discord (Q) are studied as a function of time considering different values of F\"oster coupling, varying the pump times of the simulated laser pulse and pulse intensity. We found a discrepancy between EoF and CC as entanglement quantifiers, noting that concurrence reaches much higher values than EoF; so concurrence can indicate results that are well above the EoF. The presence of the F\"oster interaction favors that the quantum discord is the dominant correlation in the system, which indicates that the system maintains quantum correlations even when the entanglement of the system has disappeared, but that it is affected by the increase in the laser pump time
Integrated BATF transcriptional network regulates suppressive intratumoral regulatory T cells

Human regulatory T cells (Tregs) are crucial regulators of tissue repair, autoimmune diseases, and cancer. However, it is challenging to inhibit the suppressive function of Tregs for cancer therapy without affecting immune homeostasis. Identifying pathways that may distinguish tumor-restricted Tregs is important, yet the transcriptional programs that control intratumoral Treg gene expression, and that are distinct from Tregs in healthy tissues, remain largely unknown. We profiled single-cell transcriptomes of CD4+ T cells in tumors and peripheral blood from patients with head and neck squamous cell carcinomas (HNSCC) and those in nontumor tonsil tissues and peripheral blood from healthy donors. We identified a subpopulation of activated Tregs expressing multiple tumor necrosis factor receptor (TNFR) genes (TNFR+ Tregs) that is highly enriched in the tumor microenvironment (TME) compared with nontumor tissue and the periphery. TNFR+ Tregs are associated with worse prognosis in HNSCC and across multiple solid tumor types. Mechanistically, the transcription factor BATF is a central component of a gene regulatory network that governs key aspects of TNFR+ Tregs. CRISPR-Cas9–mediated BATF knockout in human activated Tregs in conjunction with bulk RNA sequencing, immunophenotyping, and in vitro functional assays corroborated the central role of BATF in limiting excessive activation and promoting the survival of human activated Tregs. Last, we identified a suite of surface molecules reflective of the BATF-driven transcriptional network on intratumoral Tregs in patients with HNSCC. These findings uncover a primary transcriptional regulator of highly suppressive intratumoral Tregs, highlighting potential opportunities for therapeutic intervention in cancer without affecting immune homeostasis. Description A distinct subpopulation of suppressive intratumoral regulatory T cells correlated with poor prognosis across multiple solid tumors. Editor’s summary Blocking the suppressive activity of CD4+ regulatory T cells (Tregs) to reinvigorate the immune system against tumors comes with substantial risks of adverse effects due to their critical role in immune tolerance. Shan et al. identified a subpopulation of Tregs enriched in the tumor microenvironment specifically by comparing CD4+ T cells from healthy individuals with those from patients with head and neck squamous cell carcinomas. Characterized by gene expression programs and suppressor function under the control of the transcription factor BATF, these distinct Tregs were associated with poorer outcomes across a variety of cancers. Thus, this work indicates that it may be possible to inhibit tumor-infiltrating Tregs by targeting the distinct gene regulatory networks that control their suppressor function without impairing immune homeostasis in general. —Sarah H. Ross