Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks

Deep learning-based and lately Transformer-based language models have been dominating the studies of natural language processing in the last years. Thanks to their accurate and fast fine-tuning characteristics, they have outperformed traditional machine learning-based approaches and achieved state-of-the-art results for many challenging natural language understanding (NLU) problems. Recent studies showed that the Transformer-based models such as BERT, which is Bidirectional Encoder Representations from Transformers, have reached impressive achievements on many tasks. Moreover, thanks to their transfer learning capacity, these architectures allow us to transfer pre-built models and fine-tune them to specific NLU tasks such as question answering. In this study, we provide a Transformer-based model and a baseline benchmark for the Turkish Language. We successfully fine-tuned a Turkish BERT model, namely BERTurk that is trained with base settings, to many downstream tasks and evaluated with a the Turkish Benchmark dataset. We showed that our studies significantly outperformed other existing baseline approaches for Named-Entity Recognition, Sentiment Analysis, Question Answering and Text Classification in Turkish Language. We publicly released these four fine-tuned models and resources in reproducibility and with the view of supporting other Turkish researchers and applications.
Identifying and characterizing superspreaders of low-credibility content on Twitter

The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content -- so-called superspreaders -- are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection

Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing. However, the misuse of GANs for generating deceptive images, such as face replacement, raises significant security concerns, which have gained widespread attention. Therefore, it is urgent to develop effective detection methods to distinguish between real and fake images. Current research centers around the application of transfer learning. Nevertheless, it encounters challenges such as knowledge forgetting from the original dataset and inadequate performance when dealing with imbalanced data during training. To alleviate this issue, this paper introduces a novel GAN-generated image detection algorithm called X-Transfer, which enhances transfer learning by utilizing two neural networks that employ interleaved parallel gradient transmission. In addition, we combine AUC loss and cross-entropy loss to improve the model's performance. We carry out comprehensive experiments on multiple facial image datasets. The results show that our model outperforms the general transferring approach, and the best metric achieves 99.04%, which is increased by approximately 10%. Furthermore, we demonstrate excellent performance on non-face datasets, validating its generality and broader application prospects.
Bayesian Learning in Mean Field Games

We consider a mean-field game model where the cost functions depend on a fixed parameter, called \textit{state}, which is unknown to players. Players learn about the state from a a stream of private signals they receive throughout the game. We derive a mean field system satisfied by the equilibrium payoff of the game and prove existence of a solution under standard regularity assumptions. Additionally, we establish the uniqueness of the solution when the cost function satisfies the monotonicity assumption of Lasry and Lions at each state.
The impact of coarsening an exposure on partial identifiability in instrumental variable settings

In instrumental variable (IV) settings, such as in imperfect randomized trials and observational studies with Mendelian randomization, one may encounter a continuous exposure, the causal effect of which is not of true interest. Instead, scientific interest may lie in a coarsened version of this exposure. Although there is a lengthy literature on the impact of coarsening of an exposure with several works focusing specifically on IV settings, all methods proposed in this literature require parametric assumptions. Instead, just as in the standard IV setting, one can consider partial identification via bounds making no parametric assumptions. This was first pointed out in Alexander Balke's PhD dissertation. We extend and clarify his work and derive novel bounds in several settings, including for a three-level IV, which will most likely be the case in Mendelian randomization. We demonstrate our findings in two real data examples, a randomized trial for peanut allergy in infants and a Mendelian randomization setting investigating the effect of homocysteine on cardiovascular disease.
Time to Eat - A Personalized Circadian Eating Schedule Leads to Weight Loss Without Calorie Restriction: A Randomized Controlled Trial

Objective: Many weight loss strategies are based on the restriction of calories or certain foods. Here, we tested a weight loss intervention based solely on increasing the regularity of meals to allow the circadian system to optimally prepare food metabolism for these times. Participants & Methods: In a two-group, single center randomized-controlled single-blind study (pre-registration DRKS00021419) with participants aged 18-65 years and BMI [≥] 22 kg/m2, we used a smartphone application to identify the times at which each participant eats particularly frequently and asked participants of the experimental group to restrict their meals to only these times for six weeks. Control participants received sham treatment. Primary outcome was self-reported body weight/BMI and secondary outcome the well-being of participants. Results: Of 148 participants entering the study, 121 were randomized and of these 100 (control: 33, experimental: 67) finished the study. Our results show that the more regular the meals of participants of the experimental group became, the more weight/BMI they lost, averaging 2.62 kg (0.87 kg/m2); p < 0.0001 (BMI: p < 0.0001) compared to an insignificant weight loss of 0.56 kg (0.20 kg/m2) in the control group; p = 0.0918 (BMI: p = 0.0658). Strikingly, weight loss was not related to changes in self-reported calories, food composition, and other food-related factors. Additionally, physical and mental well-being improved significantly. Conclusion: In summary, increasing the regularity of meals causes participants to lose excess body weight and improves overall well-being.
Calibrating Segmentation Networks with Margin-based Label Smoothing

Despite the undeniable progress in visual recognition tasks fueled by deep neural networks, there exists recent evidence showing that these models are poorly calibrated, resulting in over-confident predictions. The standard practices of minimizing the cross entropy loss during training promote the predicted softmax probabilities to match the one-hot label assignments. Nevertheless, this yields a pre-softmax activation of the correct class that is significantly larger than the remaining activations, which exacerbates the miscalibration problem. Recent observations from the classification literature suggest that loss functions that embed implicit or explicit maximization of the entropy of predictions yield state-of-the-art calibration performances. Despite these findings, the impact of these losses in the relevant task of calibrating medical image segmentation networks remains unexplored. In this work, we provide a unifying constrained-optimization perspective of current state-of-the-art calibration losses. Specifically, these losses could be viewed as approximations of a linear penalty (or a Lagrangian term) imposing equality constraints on logit distances. This points to an important limitation of such underlying equality constraints, whose ensuing gradients constantly push towards a non-informative solution, which might prevent from reaching the best compromise between the discriminative performance and calibration of the model during gradient-based optimization. Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances. Comprehensive experiments on a variety of public medical image segmentation benchmarks demonstrate that our method sets novel state-of-the-art results on these tasks in terms of network calibration, whereas the discriminative performance is also improved.
Comparative transcriptome database for Camellia sinensis reveals genes important for flavonoid synthesis in tea plants

Tea, as one of the most popular beverages in the world, possesses a plethora of secondary metabolites that are beneficial to human health. Tea plants (Camellia sinensis) exhibit rich genetic diversity, where different cultivars can vary significantly in terms of yield, adaptability, morphology, and composition of secondary metabolites. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify valuable functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 cultivars of Camellia sinensis were assembled and combined with the coding sequences of 13 cultivars of Camellia sinensis to study the differences and similarities of gene expression and biological functions among cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that provides user-friendly tools and interactive visualizations that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform cross-cultivar comparison of co-expressed gene neighborhoods, clusters, and tissue-specific gene expression. We show that the members of the chalcone synthase (CHS) gene family, important for flavonoid synthesis, exhibit the highest expression variability, specific expression in leaves and buds, and significant modulation by methyl jasmonate (MeJA) treatment. By using comparative co-expression tools of TeaNekT, we identified multiple conserved genes involved in flavonoid biosynthesis among cultivars that have not been previously studied, warranting further research.
The integrated stress response/eIF2a pathway controls cytokine production in tissue-resident memory CD4+ T cells

Tissue-resident memory T (Trm) cells are a specialized T cell population that resides in tissues and can play both a protective and pathogenic role. The mechanism that enables Trm cells to provide a rapid protective response while restricting their function in homeostasis remains unclear. Here, we show that human and mouse CD4+ Trm cells exist in a poised state, characterized by storage of proinflammatory type-1 and type-3 cytokine mRNAs without protein production. In steady-state conditions, cytokine mRNA translation in Trm cells is suppressed by the integrated stress response (ISR)/eIF2α pathway, whereas Trm-cell activation under inflammatory conditions results in eIF2α dephosphorylation, leading to derepression and rapid translation of the cytokine mRNAs stored in stress granules. Pharmacological inhibition of eIF2α dephosphorylation resulted in reduced cytokine production from Trm cells, and ameliorated autoimmune kidney disease in mice. Consistent with these results, the ISR pathway in Trm cells was downregulated in patients with immune-mediated diseases of the kidney and the intestine. Our results identify ISR/eIF2α-mediated control of cytokine mRNA translation as an underlying mechanism that restricts Trm cell activity in homeostasis but also promotes rapid response upon local infection or autoimmune reaction.
Early-stage cancer results in a multiplicative increase in cell-free DNA originating from healthy tissue

Sources of elevated cell-free DNA (cfDNA) in early-stage cancer patients are poorly understood. Leveraging a rich dataset of cfDNA in healthy individuals and cancer patients, we find that a large majority of cfDNA in cancer patients originates from non-tumor sources, and that the presence of early-stage cancer results in a multiplicative increase in the concentration of cfDNA originating from healthy tissue. The magnitude of the multiplicative increase is cancer-type specific and ranges from a ∼1.3-fold increase in lung cancer, to a ∼12-fold increase in patients with liver cancer. As cfDNA is cleared in the liver, the large increase in patients with liver cancer may imply that the systemic increase in cfDNA levels in the presence of cancer is due to slower clearance rate rather than higher cell turnover.
Density dependence shapes life-history trade-offs in a food limited ungulate population

Quantifying trade-offs within populations is an important goal in life-history and evolutionary theory. However, most studies focusing on life-history variation assume trade-offs to be static. In this paper, we provide a framework for understanding life-history variation at different densities while enabling us to reveal trade-offs that are often masked due to individual heterogeneity. We use published individual-based data from a population of Soay sheep and find density dependence strongly shapes life-history trade-offs and the distribution of lifetime reproductive success. We find trade-offs between juvenile survival and growth structures life-history variation for Soay sheep at all densities, but the intensity of the trade-offs increases with population density. At carrying capacity (K), a new trade-off appears between reproduction and juvenile survival. In addition, we find the distribution of LRS to be highly constrained at K, with mothers of prime adult sizes (∼ 25kg) contributing the most to reproduction. We also find the effects of density-dependence on demographic measures such as net reproductive rate, stable size distribution, and average vital rate functions. Our results suggest high density limits the diversity of individual life history strategies and has implications for better understanding the evolution of reproductive tactics via density-dependent selection and management of ungulates in food limited environments.
Evidence for interacting but decoupled controls of decisions and movements in non-human primates

Many recent studies indicate that control of decisions and actions is integrated during interactive behavior. Among these, several carried out in humans and monkeys conclude that there is a co-regulation of choices and movements. Another perspective, based on human data only, proposes a decoupled control of decision duration and movement speed, allowing for instance to trade decision duration for movement duration when time pressure increases. Crucially, it is not currently known whether this ability to flexibly dissociate decision duration from movement speed is specific to humans, whether it can vary depending on the context in which a task is performed, and whether it is stable over time. These are important questions to address, especially to rely on monkey electrophysiology to infer the neural mechanisms of decision-action coordination in humans. To do so, we trained two macaque monkeys in a perceptual decision-making task and analyzed data collected over multiple behavioral sessions. Our findings reveal a strong and complex relationship between decision duration and movement vigor. Decision duration and action duration can co-vary but also “compensate” each other. Such integrated but decoupled control of decisions and actions aligns with recent studies in humans, validating the monkey model in electrophysiology as a means of inferring neural mechanisms in humans. Crucially, we demonstrate for the first time that this control can evolve with experience, in an adapted manner. Together, the present findings contribute to deepening our understanding of the integrated control of decisions and actions during interactive behavior. New & noteworthy The mechanism by which the integrated control of decisions and actions occurs, coupled or interactive but decoupled, is debated. In the present study, we show in monkeys that decisions and actions influence each other in a decoupled way. For the first time, we also demonstrate that this control can evolve depending the subject’s experience, allowing to trade movement time for decision time and limit the temporal discounting of reward value.
The interplay between dormant mutated cells and tumor promotion by chronic tissue damage in determining cancer risk

While the causal role of mutagenic carcinogens in tumor development is well established, the relative contribution of environmental tumor promoting factors, wounding, and chronic inflammation is still unclear. Recent sequencing studies have suggested that most environmental carcinogens act as promoters rather than through mechanisms that involve direct induction of point mutations, but whether cancer risk factors such as obesity, chronic inflammation, wounding, or tumor promoters contribute directly or indirectly to mutation burden, or induce novel signatures, has not been investigated. Here, we present WGS analysis of over 100 mouse skin tumors to compare the effects of exposure to mutagens, the tumor promoter TPA, chronic wounding, obesity, or chemotherapy, on mutational burden and cancer risk. All tumors initiated by the carcinogen Dimethylbenzanthracene (DMBA) show a very strong A>T mutational signature (SBS.DMBA) attributable to a single exposure to this carcinogen. The number of SBS.DMBA mutations also showed a strong correlation with the “clock” signature SBS5, suggesting that one treatment with this mutagen can induce mutational signatures attributed to endogenous processes. No specific signatures could be attributed to obesity, high fat diet, wounding, or TPA. Cells carrying thousands of mutations persist over very long periods without inducing tumors or causing pathological changes but can give rise to tumors after short term exposure to TPA. Furthermore, normal cell turnover and proliferation during fetal and adult growth, is not sufficient for promotion, but tissue damage followed by regenerative proliferation seems to be required for tumor development. We conclude that tumor promoters, chronic inflammation, wounding, and obesity do not contribute significantly to tumor mutational burden, and that the rate-limiting determinant of tumor growth is exposure to a tumor promoter rather than the nature or number of genomic point mutations. These data are highly relevant to the recent demonstration of persistent oncogenic mutations in histologically normal human tissues during ageing.
Predicting social experience from dyadic interaction dynamics: the BallGame, a novel paradigm to study social engagement

Theories of embodied cognition suggest that a shared environment and ongoing sensorimotor interaction are central for interpersonal learning and engagement. To investigate the embodied, distributed and hence dynamically unfolding nature of social cognitive capacities, we present a novel laboratory-based coordination task: the BallGame. Our paradigm requires continuous sensing and acting between two players who jointly steer a virtual ball around obstacles towards as many targets as possible. By analysing highly resolved measures of movement coordination and gaming behaviour, game-concurrent experience ratings, semi-structured interviews, and personality questionnaires, we reveal contributions from different levels of observation on social experience. In particular, successful coordination (number of targets collected) and intermittent periods of high versus low movement coordination (variability of relation) emerged as prominent predictors of social experience. Importantly, having the same (but incomplete) view on the game environment strengthened interpersonal coordination, whereas complementary views enhanced engagement and tended to generate more complex interactive behaviour. Overall, we find evidence for a critical balance between similarity and synchrony on the one hand, and variability and difference on the other, for successful engagement in social interactions. Finally, following participant reports, we highlight how interpersonal experience emerges from specific histories of coordination that are closely related to the interaction context in both space and time.
Polarity-JaM: An image analysis toolbox for cell polarity, junction and morphology quantification

Cell polarity involves the asymmetric distribution of cellular components such as signaling molecules and organelles within a cell, asymmetries of a cell’s shape as well as contacts with neighbouring cells. Gradients and mechanical forces often act as global cues that bias cell polarity and orientation, and polarity is coordinated by communication between adjacent cells. Advances in fluorescence microscopy combined with deep learning algorithms for image segmentation open up a wealth of possibilities to understand cell polarity behaviour in health and disease. We have therefore developed the open-source package Polarity-JaM, which offers versatile methods for performing reproducible exploratory image analysis. Multi-channel single cell segmentation is performed using a flexible and user-friendly interface to state-of-the-art deep learning algorithms. Interpretable single-cell features are automatically extracted, including cell and organelle orientation, cell-cell contact morphology, signaling molecule gradients, as well as collective orientation, tissue-wide size and shape variation. Circular statistics of cell polarity, including polarity indices, confidence intervals, and circular correlation analysis, can be computed using our web application. We have developed data graphs for comprehensive visualisation of key statistical measures and suggest the use of an adapted polarity index when the expected polarisation direction or the direction of a global cue is known a priori. The focus of our analysis is on fluorescence image data from endothelial cells (ECs) and their polarisation behaviour. ECs line the inside of blood vessels and are essential for vessel formation and repair, as well as for various cardiovascular diseases, cancer, and inflammation. However, the general architecture of the software will allow it to be applied to other cell types and image modalities. The Polarity-JaM package integrates these analyses in a reproducible manner with a level of documentation that allows the user to analyse image data accurately and efficiently. The software suite is documented at https://polarityjam.readthedocs. io and the app runs online at www.polarityjam. com.