Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal exometabolites are regulated by essential microbial metabolisms in the oligotrophic ocean

Predictions of how the biogeochemical reservoir of marine dissolved organic matter (DOM) will respond to future ocean changes are limited by bulk characterizations that mask a complex network of thousands of individual microbe-molecule interactions. The marine microbial community regulates the transformation and fate of organic carbon by utilizing DOM molecules as the products and reactants of their diverse enzymatic functions. Linking these molecules with the microbial taxa responsible for their utilization requires characterization of both microbial activity and the resulting metabolic footprint. Here we present a time series of the seasonal dynamics of both the exometabolome and the bacterioplankton community at the Bermuda Atlantic Time series Study (BATS) site and show that metabolic functions are greater predictors of DOM composition than microbial taxonomy. Putative exometabolite identifications (gonyol, glucose 6-sulfate, succinate, and trehalose) indicate that at least a portion of the exometabolome contains rapidly remineralized, labile molecules. We hypothesize that observations of seasonal accumulation of these labile molecules result from decoupled enrichments of microbial production and consumption enzymes. Critically, we found the composition of seasonal DOM features was more stable inter-annually than the microbial community structure. By estimating redundancy in the BATS metagenomes of metabolisms responsible for cycling these molecules, we propose a paradigm whereby microbial metabolisms that are essential, either to all or to a subset of marine microbes, determine DOM composition. The molecular-level characterization of DOM achieved herein greatly enhances possibilities for connecting the mechanisms behind the DOM-microbe network that cycle Earth’s largest reservoir of organic carbon. Significance statement Marine dissolved organic matter (DOM) is a major carbon reservoir that serves as a critical control on Earth’s climate. Marine DOM production and removal is largely regulated by microbial activity, but a mechanistic understanding of these processes is lacking. By analyzing individual molecules of DOM across a three-year ocean time series with ultrahigh-resolution mass spectrometry, we found the composition of DOM molecules is more stable inter-annually than the microbial community. We suggest that while the identity of marine microbes may change, essential metabolisms within microbial communities are maintained and thus result in less variability of the DOM composition. Therefore, comprehensive and mechanistic knowledge of metabolic functions and pathways will be critical to predicting the response of marine DOM to future climate-driven changes.
Emotion induction modulates neural dynamics during ideational originality

Emotions remarkably impact our creative minds; nevertheless, a comprehensive mapping of their underlying neural mechanisms remains elusive. Therefore, we explored the influence of induced emotional states on ideational originality and its associated neural dynamics. Participants were randomly presented with three short videos with sad, neutral, and happy content. After each video, ideational originality was evaluated using the alternate uses task (AUT). Ideational originality was significantly higher after induction of the happy state than the neutral state; in contrast, there was a nonsignificant difference between the sad and neutral states. Associated neural dynamics were assessed through EEG time-frequency (TF) power and phase-amplitude coupling (PAC) analysis. Our findings suggest that emotional states elicit distinct TF and PAC profiles associated with ideational originality. Relative to baseline, gamma activity was enhanced after the neutral induction and more enhanced after the induction of a happy state but reduced after the induction of a sad state in 2-4 seconds after starting the task. Our PAC findings suggest that the attention system may be silent after the induction of a happy emotional state to load rich materials into working memory (WM) and active in the sad state to maintain these materials in WM. Highlight Ideational originality was significantly higher after the induction of a happy state than in a neutral state. Emotional states elicited distinct EEG time-frequency and phase-amplitude coupling profiles associated with ideational originality. Relative to baseline, gamma activity was enhanced in the neutral state and more robust in a happy state but reduced in a sad state 2-4 seconds after starting AUT. Enhancing ideational originality requires the induction of emotional states to suppress overlearned associations and strengthen weaker coupling associations, which is the case after the induction of a happy emotional state.
Investigating the mode of action for wasting produced by tetrachlorodibenzo-p-dioxin (TCDD) in rats using transcriptomics: Evidence for roles of AHR and ARNT in circadian cycling

Single, high doses of TCDD in rats caused wasting, a progressive loss of 30 to 50% body weight and death within several weeks. To identify pathway perturbations at or near doses causing wasting, we examined differentially gene expression (DGE) and pathway enrichment in centrilobular (CL) and periportal (PP) regions of female rat livers following 6 dose levels of TCDD – 0, 3, 22, 100, 300, and 1000 ng/kg/day, 5 days/week for 4 weeks. At the higher doses, rats lost weight, had increased liver/body weight ratios and nearly complete cessation of liver cell proliferation, signs consistent with wasting. DGE curves were left shifted for the CL versus the PP regions. Canonical Phase I and Phase II genes were maximally increased at lower doses and remained elevated at all doses. At lower doses, < 22 ng/kg/day in the CL and < 100 ng/kg/day, upregulated genes showed transcription factor (TF) enrichment for AHR and ARNT. At the mid- and hi-dose doses, there was a large number of downregulated genes and pathway enrichment for DEGs showed downregulation of many cellular metabolism processes including those for steroids, fatty acid metabolism, pyruvate metabolism and citric acid cycle. There was significant TF enrichment of the hi-dose downregulated genes for RXR, ESR1, LXR, PPARalpha. At the highest dose, there was also pathway enrichment with upregulated genes for extracellular matrix organization, collagen formation, hemostasis and innate immune system. TCDD demonstrates most of its effects through binding the aryl hydrocarbon receptor (AHR) while the downregulation of metabolism genes at higher TCDD doses is known to be independent of AHR binding to DREs. Based on our results with DEG, we provide a hypothesis for wasting in which high doses of TCDD shifts circadian processes away from the resting state leading to greatly reduced synthesis of steroids and complex lipids needed for cell growth and producing gene expression signals consistent with an epithelial-to-mesenchymal transition in hepatocytes.
Real-time control of a hearing instrument with EEG-based attention decoding

Restoring normal speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly but hearing instrument technology cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of a neuro-steered hearing instrument that selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present a real-time brain-computer interface (BCI) system implementing this concept. Our system combines a stimulus-response model based on canonical correlation analysis (CCA) for real-time EEG attention decoding with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. In this paper, we provide an overview of the system and its various components and discuss prospects and limitations of the technology. We illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.
Identification of RACK1A as a component of the auxin-ethylene crosstalk regulating apical hook development in Arabidopsis thaliana

Apical hook development is an ideal model for studying differential growth in plants, and is controlled by complex hormonal crosstalk, with auxin and ethylene being the major players. Here, we identified a bioactive small molecule that decelerates apical hook opening in Arabidopsis thaliana. Our genetic studies suggest that this molecule enhances or maintains the auxin maximum found in the inner hook side and requires certain auxin and ethylene signaling components to modulate apical hook opening. Using biochemical approaches, we then revealed the WD40 repeat scaffold protein RECEPTOR FOR ACTIVATED C KINASE 1A (RACK1A) as a direct target of this compound. We present data in support of RACK1A playing a positive role in apical hook opening by negatively regulating the differential auxin response gradient across the hook via specific auxin and ethylene signaling mechanisms and thereby adjusting differential cell growth, an essential process for organ structure and function in plants. We have thus identified a role for RACK1A and auxin-ethylene crosstalk in negatively regulating differential cell growth to promote apical hook opening. Significance Statement Differential growth, or the growth of cells at different rates across tissues, is essential for providing shape and structure during plant development. The apical hook is a transient structure formed by differential cell growth across the hypocotyl tip in dark-grown seedlings, which protects the underlying tissues, and which opens during seedling development. We identified a small molecule that decelerates hook opening and discovered that it targets the protein RECEPTOR FOR ACTIVATED C KINASE 1A (RACK1A). We then showed that RACK1A promotes apical hook opening at the level of crosstalk between the plant hormones auxin and ethylene, by adjusting differential cell growth. Our work paves the way to a better understanding of how plants regulate and adapt their growth during development.
Causal Brain Network Evaluates Surgical Outcomes in Patients with Drug-Resistant Epilepsy: A Retrospective Comparative Study

Network neuroscience has greatly facilitated epilepsy studies; meanwhile, drug-resistant epilepsy (DRE) is increasingly recognized as a brain network disorder. Unfortunately, surgical success rates in patients with DRE are still very limited, varying 30% ∼70%. At present, there is almost no systematic exploration of intracranial electrophysiological brain network closely related to surgical outcomes, and it is not clear which brain network methodologies can effectively promote DRE precision medicine. In this retrospective comparative study, we included multicenter datasets, containing electrocorticogram (ECoG) data from 17 DRE patients with 55 seizures. Ictal ECoG within clinically-annotated epileptogenic zone (EZ) and non-epileptogenic zone (NEZ) were separately computed using six different algorithms to construct causal brain networks. All the brain network results were divided into two groups, successful and failed surgery. Statistical results based on the Mann-Whitney-U-test show that: causal connectivity of α-frequency band (8 ∼ 13 Hz) in EZ calculated by convergent cross mapping (CCM) gains the most significant differences between the surgical success and failure groups, with a P value of 7.85e-08 and Cohen’s d effect size of 0.77. CCM-defined EZ brain network can also distinguish the successful and failed surgeries considering clinical covariates (clinical centers, DRE types) with P < 0. 001 . Based on the brain network features, machine learning models are established to predict the surgical outcomes. Among them, SVM classifier with Gaussian kernel function and Bayesian Optimization demonstrates the highest average accuracy of 84.48% through 5-fold cross validation, further indicating that the CCM-defined EZ brain network is a reliable biomarker for predicting DRE’s surgical outcomes.
In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics

The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior. In order to model this response, we devised a multiscale agent-based approach using Simuscale software. Within each agent (cell) of our model, we introduced a gene regulatory network (GRN) based upon a piecewise deterministic Markov process (PDMP) formalism. Cell fate – differentiation, proliferation, death – was coupled to the state of the GRN through rule-based mechanisms. Cells interact in a 3D computational domain and signal to each other via cell-cell contacts, influencing the GRN behavior. Results show the ability of the model to correctly capture both population behaviour and molecular time-dependent evolution. We examined the impact of several parameters on molecular and population dynamics, and demonstrated the add-on value of using a multiscale approach by showing that a higher degradation rate for the protein controlling cell death induces a later peak in the response.
Major Uses of Land in the United States, 1997

The United States has a total land area of nearly 2.3 billion acres. Major uses in 1997 were forest-use land, 642 million acres (28 percent); grassland pasture and range, 580 million (26 percent); cropland, 455 million (20 percent); miscellaneous other uses, 300 million (13 percent); and special uses, 286 million acres (13 percent). Cropland acreage decreased from 1992 to 1997, but by only 1 percent (5 million acres). Grassland pasture and range decreased 2 percent (11 million acres). Special uses (primarily parks and wildlife areas) increased by 5 million acres, continuing an almost uninterrupted upward trend since 1945. Miscellaneous other uses increased 17 million acres, mostly due to expanding urban area. Rural residential area (a new category) was estimated to be 73 million acres in 1997. Urban and rural residential area, together, accounted for about 46 percent of miscellaneous other uses. Private land ownership was 60 percent of the total land area; Federal ownership was 29 percent; and State, other public, and Indian trust lands, together, accounted for the remaining 11 percent of the land area.
Floating seed dispersal in open channel flow with emergent vegetation

In this paper, we investigate the effects of stem density and flow velocity on interaction processes between two types of floating particles and rigid emergent vegetation in open channel flow. A new definition of the probability that a floating particle interacts with a stem is proposed and validated, and particle–stem interaction processes (including collision and capture mechanisms) are further explored, including a collision model proposed and validated to predict the probability of collision with a correction parameter. We also establish an appropriate length scale to analyse the particle–stem mechanism (for the vegetation characteristics and arrangement used in this study). A more accurate statistical model based on Gamma function is proposed to describe floating particle dispersal from unvegetated section to vegetated section, which is verified compared with exponential model, and two empirical formulas are proposed to apply the Gamma distribution to the particle dispersal in natural systems. We also propose a dimensionless parameter to evaluate the attraction capacity of the floating particle attached to the stem based on analysing forces acting on the particle. Phenomena observed in these experiments are presented for future analysis and research.
Information Services

Information Services provides a variety of campuswide services: • E-mail accounts and passwords • Wireless and wired networking • Calendaring • Telephones and voicemail • Cable TV • Technology service desk, a help desk for questions about accounts, software, wireless, and more Information Services also operates the administrative software for finance, student records, human resources management, and payroll, as well as the servers and storage that run all of these services. The Information Services technology service desk (tech desk) supports and aids university-affiliated students and members of the faculty and staff with their computing needs. Tech desk services include the following: • Software troubleshooting • Account support • First-level Ethernet and wireless network support • Education in personal computing best practices IT Website The UO information technology (IT) website includes the following: • Software downloads • Step-by-step instructions for common technology-related tasks • IT-related event announcements
Active Generalized Category Discovery

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently faces intractable problems, including imbalanced classification performance and inconsistent confidence between old and new classes, especially in the low-labeling regime. Hence, some annotations of new classes are deemed necessary. However, labeling new classes is extremely costly. To address this issue, we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem, we devise an adaptive sampling strategy, which jointly considers novelty, informativeness and diversity to adaptively select novel samples with proper uncertainty. However, owing to the varied orderings of label indices caused by the clustering of novel classes, the queried labels are not directly applicable to subsequent training. To overcome this issue, we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier, thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCD
Energy barriers for boundary nucleation in a two-well model without gauge invariance

We study energy scaling laws for a simplified, singularly perturbed, double-well nucleation problem confined in a half-space, in the absence of gauge invariance and for an inclusion of fixed volume. Motivated by models for boundary nucleation of a single-phase martensite inside a parental phase of austenite, our main focus in this nonlocal isoperimetric problem is how the relationship between the rank-1 direction and the orientation of the half-space influences the energy scaling with respect to the fixed volume of the inclusion. Up to prefactors depending on this relative orientation, the scaling laws coincide with the corresponding ones for bulk nucleation \cite{knupfer2011minimal} for all rank-1 directions, \textit{but} the ones normal to the confining hyperplane, where the scaling is as in a three-well problem in full space, resulting in a lower energy barrier \cite{Tribuzio-Rueland_1}.
Towards Robustness Analysis of E-Commerce Ranking System

Information retrieval (IR) is a pivotal component in various applications. Recent advances in machine learning (ML) have enabled the integration of ML algorithms into IR, particularly in ranking systems. While there is a plethora of research on the robustness of ML-based ranking systems, these studies largely neglect commercial e-commerce systems and fail to establish a connection between real-world and manipulated query relevance. In this paper, we present the first systematic measurement study on the robustness of e-commerce ranking systems. We define robustness as the consistency of ranking outcomes for semantically identical queries. To quantitatively analyze robustness, we propose a novel metric that considers both ranking position and item-specific information that are absent in existing metrics. Our large-scale measurement study with real-world data from e-commerce retailers reveals an open opportunity to measure and improve robustness since semantically identical queries often yield inconsistent ranking results. Based on our observations, we propose several solution directions to enhance robustness, such as the use of Large Language Models. Note that the issue of robustness discussed herein does not constitute an error or oversight. Rather, in scenarios where there exists a vast array of choices, it is feasible to present a multitude of products in various permutations, all of which could be equally appealing. However, this extensive selection may lead to customer confusion. As e-commerce retailers use various techniques to improve the quality of search results, we hope that this research offers valuable guidance for measuring the robustness of the ranking systems.
Electrical transport signatures of metallic surface state formation in the strongly-correlated insulator FeSb2

We present local and nonlocal electrical transport measurements of the correlated insulator FeSb$_2$. By employing wiring configurations that delineate between bulk- and surface-dominated conduction, we reveal the formation of a metallic surface state in FeSb$_2$ for temperatures $\lessapprox 5$~K. This result is corroborated by an angular rotation study of this material's magnetotransport, which also shows signatures of the transition from bulk- to surface-dominated conduction over the same temperature interval as the local/nonlocal transport divergence. Notable similarities with the topological Kondo insulator candidate SmB$_6$ are discussed.
Yi: Open Foundation Models by 01.AI

We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models. Our base models achieve strong performance on a wide range of benchmarks like MMLU, and our finetuned chat models deliver strong human preference rate on major evaluation platforms like AlpacaEval and Chatbot Arena. Building upon our scalable super-computing infrastructure and the classical transformer architecture, we attribute the performance of Yi models primarily to its data quality resulting from our data-engineering efforts. For pretraining, we construct 3.1 trillion tokens of English and Chinese corpora using a cascaded data deduplication and quality filtering pipeline. For finetuning, we polish a small scale (less than 10K) instruction dataset over multiple iterations such that every single instance has been verified directly by our machine learning engineers. For vision-language, we combine the chat language model with a vision transformer encoder and train the model to align visual representations to the semantic space of the language model. We further extend the context length to 200K through lightweight continual pretraining and demonstrate strong needle-in-a-haystack retrieval performance. We show that extending the depth of the pretrained checkpoint through continual pretraining further improves performance. We believe that given our current results, continuing to scale up model parameters using thoroughly optimized data will lead to even stronger frontier models.