Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States

Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and the Community Land Model version 5 (CLM5) to optimize the model representation of SOC over the conterminous United States. We firstly constrained parameters in CLM5 using observations of vertical profiles of SOC in both a batch mode (using all individual soil layers in one batch) and at individual sites (site-by-site). The estimated parameter values from the site-by-site data assimilation were then either randomly sampled (random-sampling) to generate continentally homogeneous (constant) parameter values or maximally preserved for their spatially heterogeneous distributions (varying parameter values to match the spatial patterns from the site-by-site data assimilation) so as to optimize spatial representation of SOC in CLM5 through a deep learning technique (neural networking) over the conterminous United States. Comparing modeled spatial distributions of SOC by CLM5 to observations yielded increasing predictive accuracy from default CLM5 settings (R2 = 0.32) to randomly sampled (0.36), one-batch estimated (0.43), and deep learning optimized (0.62) parameter values. While CLM5 with parameter values derived from random-sampling and one-batch methods substantially corrected the overestimated SOC storage by that with default model parameters, there were still considerable geographical biases. CLM5 with the spatially heterogeneous parameter values optimized from the neural networking method had the least estimation error and less geographical biases across the conterminous United States. Our study indicated that deep learning in combination with data assimilation can significantly improve the representation of SOC by complex land biogeochemical models.
Antiviral and Immunomodulatory Properties of Antimicrobial Peptides Produced by Human Keratinocytes

Keratinocytes, the main cells of the epidermis, are the first site of replication as well as the first line of defense against many viruses such as arboviruses, enteroviruses, herpes viruses, human papillomaviruses, or vaccinia virus. During viral replication, these cells can sense virus associated molecular patterns leading to the initiation of an innate immune response composed of pro-inflammatory cytokines, chemokines, and antimicrobial peptides. Human keratinocytes produce and secrete at least nine antimicrobial peptides: human cathelicidin LL-37, types 1–4 human β-defensins, S100 peptides such as psoriasin (S100A7), calprotectin (S100A8/9) and koebnerisin (S100A15), and RNase 7. These peptides can exert direct antiviral effects on the viral particle or its replication cycle, and indirect antiviral activity, by modulating the host immune response. The purpose of this review is to summarize current knowledge of antiviral and immunomodulatory properties of human keratinocyte antimicrobial peptides.
Fear the reaper: ungulate carcasses may generate an ephemeral landscape of fear for rodents

Animal carcasses provide an ephemeral pulse of nutrients for scavengers that use them. Carcass sites can increase species interactions and/or ephemeral, localized landscapes of fear for prey within the vicinity. Few studies have applied the landscape of fear to carcasses. Here, we use a mass die-off of reindeer caused by lightning in Norway to test whether rodents avoided larger scavengers (e.g. corvids and fox). We used the presence and abundance of faeces as a proxy for carcass use over the course of 2 years and found that rodents showed the strongest avoidance towards changes in raven abundance (β = −0.469, s.e. = 0.231, p-value = 0.0429), but not fox, presumably due to greater predation risk imposed by large droves of raven. Moreover, the emergence of rodent occurrence within the carcass area corresponded well with the disappearance of raven during the second year of the study. We suggest that carcasses have the potential to shape the landscape of fear for prey, but that the overall effects of carcasses on individual fitness and populations of species ultimately depend on the carcass regime, e.g. carcass size, count, and areal extent, frequency and the scavenger guild. We discuss conservation implications and how carcass provisioning and landscapes of fear could be potentially used to manage populations and ecosystems, but that there is a gap in understanding that must first be bridged.
Soil Microbial Community Response Differently to the Frequency and Strength of Freeze–Thaw Events in a Larix gmelinii Forest in the Daxing’an Mountains, China

Sustained climate warming increases the frequency and strength of soil freeze–thaw (FT) events, which strongly affect the properties of soil microbial communities. To explore the responses and mechanisms of the frequency and strength of freeze–thaw events on soil microbial communities, a lab-scale FT test was conducted on forest soil in permafrost region from the Daxing’an Mountains, China. The number of FT cycles (FTN) had a greater effect on microbial communities than FT temperature fluctuation (FTF). The FTN and FTF explained 20.9 and 10.8% of the variation in microbial community structure, respectively, and 22.9 and 11.6% of the variation in enzyme activities, respectively. The total and subgroup microbial biomass, the ratio of fungi to bacteria (F/B), and C- and N-hydrolyzing enzyme activities all decreased with an increase in FTN. Among microbial groups, arbuscular mycorrhizal fungi (AMF) were the most sensitive to FT events. Based on the changes of F/B and AMF, the reduction in soil carbon sequestration caused by frequent FT events can be explained from a perspective of microorganisms. Based on redundancy analysis and Mental Test, soil moisture, total organic carbon, and total nitrogen were the major factors affecting microorganisms in FT events. In the forest ecosystem, soil water and fertilizer were important factors to resist the damage of FT to microorganism, and sufficient water and fertilizer can lighten the damage of FT events to microorganisms. As a result of this study, the understanding of the responses of soil microorganisms to the variation in FT patterns caused by climate changes has increased, which will lead to better predictions of the effects of likely climate change on soil microorganisms.
HSI-II Gene Cluster of Pseudomonas syringae pv. tomato DC3000 Encodes a Functional Type VI Secretion System Required for Interbacterial Competition

The type VI secretion system (T6SS) is a widespread bacterial nanoweapon used for delivery of toxic proteins into cell targets and contributes to virulence, anti-inflammatory processes, and interbacterial competition. In the model phytopathogenic bacterium Pseudomonas syringae pv. tomato (Pst) DC3000, two T6SS gene clusters, HSI-I and HSI-II, were identified, but their functions remain unclear. We previously reported that hcp2, located in HSI-II, is involved in competition with enterobacteria and yeast. Here, we demonstrated that interbacterial competition of Pst DC3000 against several Gram-negative plant-associated bacteria requires mainly HSI-II activity. By means of a systematic approach using in-frame deletion mutants for each gene in the HSI-II cluster, we identified genes indispensable for Hcp2 expression, Hcp2 secretion and interbacterial competition ability. Deletion of PSPTO_5413 only affected growth in interbacterial competition assays but not Hcp2 secretion, which suggests that PSPTO_5413 might be a putative effector. Moreover, PSPTO_5424, encoding a putative σ54-dependent transcriptional regulator, positively regulated the expression of all three operons in HSI-II. Our discovery that the HSI-II gene cluster gives Pst DC3000 the ability to compete with other plant-associated bacteria could help in understanding a possible mechanism of how phytopathogenic bacteria maintain their ecological niches.
Comparative development and gametophyte morphology of the hart's-tongue fern, Asplenium scolopendrium L

Abstract Gametophyte morphology and development of the federally listed American hart’s tongue fern Asplenium scolopendrium var. americanum L. was examined and compared to the old world variety Asplenium scolopendrium var. scolopendrium. The North American variety is critically threatened and many populations in the Southeastern US have gone extinct while the number of plants in all northeastern populations is plummeting. The ultimate goal of this work was to compare gametophyte biology of these two varieties to understand better potential characters that may help separate plants in North America from those in the old world and generate a better picture of the biology of the North American populations. The two varieties differed significantly in gametophyte ontogeny, morphology, and propensity for sexual and asexual reproduction. Gametophytes of the American variety germinated earlier, grew significantly slower, and produced sporophytes much later (166 d vs. 119 d) than the old world variety. In addition, A. scolopendrium var. americanum produced copious gametophytic outgrowths that were capable of developing into functional, independent thalli while A. scolopendrium var. scolopendrium does not. The combined data set aids in understanding the origins of the American variety as its gametophyte biology is similar to several other taxa which now persist in climatically–moderated rockhouse habitats in eastern North America.
Advanced radiogasometric method for the determination of the rates of photorespiratory and respiratory decarboxylations of primary and stored photosynthates under steady‐state photosynthesis

An advanced radiogasometric method for the study of plant leaf CO 2 exchange is presented. The method enables determination of the rates of CO 2 fixation, photorespiration and respiration in the light under steady-state photosynthesis and discrimination between primary and stored photosynthates as substrates of photorespiratory and respiratory decarboxylations. The method is based on the analysis of the time curves of 14 CO2 evolution from labeled primary and stored photosynthates in leaves previously exposed to 14CO2. The molar rates of different decarboxylation reactions are calculated from the initial slopes of the curves taking into account the specific radioactivity of CO 2 fed to leaves and/or evolved from leaves. To estimate the contribution of primary and stored photosynthates, the measurements of 14 CO 2 evolution are performed after feeding plant leaves for different periods with 14 CO2. Photorespiration and respiration are distinguished on the basis of data obtained from measurements of 14 CO 2 evolution under normal (210 ml 1-1and low (15 ml l -l ) concentrations of oxygen. A principally new method for the determination of the rate of intracellular refixation of respiratory CO 2 has been developed. The method is based on the measurements of 1 4 CO 2 evolution from leaves into the medium of very high concentrations (30 ml I-1) of 12CO2, where the probability of refixation of 14 CO 2 evolved inside the cell is close to zero. The results obtained were comparable with the data derived from parallel refixation measurements by means of gasometric methods. As an example of application, the data on CO 2 exchange in leaves of two contrasting groups of C3-species, differing in the ability of starch accumulation, are presented.
Dietary oils modulate T-cell differentiation and IL-2 bioactivity of intestinal mucosal lymphocytes in chickens

The study was conducted to investigate the effects of dietary oils on the differentiation of intestinal lymphocytes and cytokines generation in chickens. One hundred and eighty chickens were assigned to three groups in one factorial design. Factor was dietary fat types (4.5% poultry oil, 4.5% corn oil or 4.5% fish oil). The proportion of CD3+CD4+ lymphocytes of intestinal mucosal lymphocytes (IMLs) in chickens fed fish-oil diets on 21 d and 42 d of age was significantly higher than those in chickens fed corn-oil and poultry-oil diets (P <0.01). And the proportion of intestinal CD3+CD8+ lymphocytes of chickens fed fish-oil diets was significantly lower than those of chickens fed corn-oil and poultry-oil diets (P <0.01) on 21 d and 42 d of age. Fish-oil consumption enhanced IL-2 secretion of IMLs stimulated with concanavalin A (ConA) (P <0.01), compared with poultry oil; but corn-oil consumption decreased IL-2 secretion than poultry-oil diets (P <0.01). Fish oil decreased the mRNA abundance of calcitonin gene-related peptide (cGRP) in intestinal mucosa of chickens on 21 d and 42 d of age compared to poultry oil (P <0.01). Corn oil increased the mRNA abundance of cGRP compared to poultry oil (P <0.01). Compared with group fed corn-oil or poultry-oil diets, IMLs had lower concentration of cAMP and lower bioactivity of adenylyl cyclase (AC) of chickens supplemented with fish oil on 21 d and 42 d of age. Meanwhile, corn oil increased cAMP level and AC bioactivity. Taken together, these data show a modulatory role for cGRP in the interaction between different type of oils and T-cell differentiation and IL-2 bioactivity of IMLs in chickens, which suggests that AC and cAMP signalling involved the intestinal mucosal immune responses.
The St. Petersburg Paradox: A Fresh Algorithmic Perspective

The St. Petersburg paradox is a centuries-old puzzle concerning a lottery with infinite expected payoff on which people are only willing to pay a small amount to play. Despite many attempts and several proposals, no generally-accepted resolution is yet at hand. In a recent paper, we show that this paradox can be understood in terms of the mind optimally using its limited computational resources (Nobandegani et al. 2019). Specifically, we show that the St. Petersburg paradox can be accounted for by a variant of normative expected-utility valuation which acknowledges cognitive limitations: sample-based expected utility (Nobandegani et al. 2018). SbEU provides a unified, algorithmic explanation of major experimental findings on this paradox. We conclude by discussing the implications of our work for algorithmically understanding human cognition and for developing human-like artificial intelligence.
Leakage-Robust Classifier via Mask-Enhanced Training (Student Abstract)

We synthetically add data leakage to well-known image datasets, which results in predictions of convolutional neural networks trained naively on these spoiled datasets becoming wildly inaccurate. We propose a method, dubbed Mask-Enhanced Training, that automatically identifies the possible leakage and makes the classifier robust. The method enables the model to focus on all features needed to solve the task, making its predictions on the original validation set accurate, even if the whole training dataset is spoiled with the leakage.
LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction

We present a system which allows a user to create event-event relation extractors on-demand with a small amount of effort. The system provides a suite of algorithms, flexible workflows, and a user interface (UI), to allow rapid customization of event-event relation extractors for new types and domains of interest. Experiments show that it enables users to create extractors for 6 types of causal and temporal relations, with less than 20 minutes of effort per type. Our system (source code, UI) is available at https://github.com/BBN-E/LearnIt. A demonstration video is available at https://vimeo.com/329950144.
Casein Kinase II Phosphorylation of E-cadherin Increases E-cadherin/β-Catenin Interaction and Strengthens Cell-Cell Adhesion*

β-Catenin, a member of the Armadillo repeat protein family, binds directly to the cytoplasmic domain of E-cadherin, linking it via α-catenin to the actin cytoskeleton. A 30-amino acid region within the cytoplasmic domain of E-cadherin, conserved among all classical cadherins, has been shown to be essential for β-catenin binding. This region harbors several putative casein kinase II (CKII) and glycogen synthase kinase-3β (GSK-3β) phosphorylation sites and is highly phosphorylated. Here we report that in vitro this region is indeed phosphorylated by CKII and GSK-3β, which results in an increased binding of β-catenin to E-cadherin. Additionally, in mouse NIH3T3 fibroblasts expression of E-cadherin with mutations in putative CKII sites resulted in reduced cell-cell contacts. Thus, phosphorylation of the E-cadherin cytoplasmic domain by CKII and GSK-3β appears to modulate the affinity between β-catenin and E-cadherin, ultimately modifying the strength of cell-cell adhesion.
CORAL-DMOEA: Correlation Alignment-Based Information Transfer for Dynamic Multi-Objective Optimization (Student Abstract)

One essential characteristic of dynamic multi-objective optimization problems is that Pareto-Optimal Front/Set (POF/POS) varies over time. Tracking the time-dependent POF/POS is a challenging problem. Since continuous environments are usually highly correlated, past information is critical for the next optimization process. In this paper, we integrate CORAL methodology into a dynamic multi-objective evolutionary algorithm, named CORAL-DMOEA. This approach employs CORAL to construct a transfer model which transfer past well-performed solutions to form an initial population for the next optimization process. Experimental results demonstrate that CORAL-DMOEA can effectively improve the quality of solutions and accelerate the evolution process.
Modeling Dynamic Behaviors within Population

The abundance of temporal data generated by mankind in recent years gives us the opportunity to better understand human behaviors along with the similarities and differences in groups of people. Better understanding of human behaviors could be very beneficial in choosing strategies, from group-level to society-level depending on the domain. This type of data could range from physiological data collected from sensors to activity patterns in social media. Identifying frequent behavioral patterns in sensor data could give more insight into the health of a community and provoke strategies towards improving it; By analyzing patterns of behaviors in social media, platform's attributes could be adjusted to the user's needs.This type of modeling introduces numerous challenges that varies depending on the data. The goal of my doctoral research is to introduce ways to better understand and capture human behavior by modeling individual's behaviors as time series and extracting interesting patterns within them.
'Watch the Flu': A Tweet Monitoring Tool for Epidemic Intelligence of Influenza in Australia

‘Watch The Flu’ is a tool that monitors tweets posted in Australia for symptoms of influenza. The tool is a unique combination of two areas of artificial intelligence: natural language processing and time series monitoring, in order to assist public health surveillance. Using a real-time data pipeline, it deploys a web-based dashboard for visual analysis, and sends out emails to a set of users when an outbreak is detected. We expect that the tool will assist public health experts with their decision-making for disease outbreaks, by providing them insights from social media.
Explainable Agency in Reinforcement Learning Agents

This thesis explores how reinforcement learning (RL) agents can provide explanations for their actions and behaviours. As humans, we build causal models to encode cause-effect relations of events and use these to explain why events happen. Taking inspiration from cognitive psychology and social science literature, I build causal explanation models and explanation dialogue models for RL agents. By mimicking human-like explanation models, these agents can provide explanations that are natural and intuitive to humans.
DICR: AI Assisted, Adaptive Platform for Contract Review

In the regular course of business, companies spend a lot of effort reading and interpreting documents, a highly manual process that involves tedious tasks, such as identifying dates and names or locating the presence or absence of certain clauses in a contract. Dealing with natural language is complex and further complicated by the fact that these documents come in various formats (scanned image, digital formats) and have different degrees of internal structure (spreadsheets, invoices, text documents). We present DICR, an end-to-end, modular, and trainable system that automates the mundane aspects of document review and allows humans to perform the validation. The system is able to speed up this work while increasing quality of information extracted, consistency, throughput, and decreasing time to decision. Extracted data can be fed into other downstream applications (from dashboards to Q&A and to report generation).
Meta-Learning on Graph with Curvature-Based Analysis (Student Abstract)

Learning latent representations in graphs is finding a mapping that embeds nodes or edges as data points in a low-dimensional vector space. This paper introduces a flexible framework to enhance existing methodologies that have difficulty capturing local proximity and global relationships at the same time. Our approach generates a virtual edge between non-adjacent nodes based on the Forman-Ricci curvature in network. By analyzing the network using topological information, global relationships structurally similar can easily be detected and successfully integrated with previous works.
A Unification-Based ID/LP Parsing Schema

In contemporary natural language formalisms like HPSG (Pollard and Sag 1994) the ID/LP format is used to separate the information on dominance from the one on linear precedence thereby allowing significant generalizations on word order. In this paper, we define unification ID/LP grammars. But as mentioned in Seiffert (1991) there are problems concerning the locality of the information determining LP acceptability during parsing. Since one is dealing with partially specified data, the information that is relevant to decide whether the local tree under construction is LP acceptable might be instantiated further during processing. In this paper we propose a modification of the Earley/Shieber algorithm on direct parsing of ID/LP grammars. We extend the items involved to include the relevant underspecified information using it in the completion steps to ensure the acceptability of the resulting structure. Following Sikkel (1993) we define it not as an algorithm, but as a parsing schema to allow the most abstract representation.
SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)

In recent years, there has been a substantial rise in the consumption of news via online platforms. The ease of publication and lack of editorial rigour in some of these platforms have further led to the proliferation of fake news. In this paper, we study the problem of detecting fake news on the FakeNewsNet repository, a collection of full length articles along with associated images. We present SpotFake+, a multimodal approach that leverages transfer learning to capture semantic and contextual information from the news articles and its associated images and achieves the better accuracy for fake news detection. To the best of our knowledge, this is the first work that performs a multimodal approach for fake news detection on a dataset that consists of full length articles. It outperforms the performance shown by both single modality and multiple-modality models. We also release the pretrained model for the benefit of the community.