Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of a 14-3-3 Binding Sequence in the Common β Chain of the Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF), Interleukin-3 (IL-3), and IL-5 Receptors That Is Serine-Phosphorylated by GM-CSF

The common b chain (bc) of the granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-3 (IL-3), and IL-5 receptors is the major signaling subunit of these receptors coupling ligand binding to multiple biological activities. It is thought that these multiple functions arise as a consequence of the recruitment of specific signaling molecules to tyrosine-phosphorylated residues in the cytoplasmic domain of bc. However, the contribution of serine phosphorylation in bc to the recruitment of signaling molecules is not known. We show here the identification of a phosphoserine motif in the cytoplasmic domain of bc that interacts with the adaptor protein 14-3-3z. Coimmunoprecipitation and pull-down experiments with a glutathione S-transferase (GST):14-3-3z fusion protein showed that 14-3-3 directly associates with bc but not the GM-CSF receptor a chain. C-terminal truncation mutants of bc further showed that a region between amino acids 544 and 626 in bc was required for its association with 14-3-3z. This region contains the sequence 582 HSRSLP 587 , which closely resembles the RSXSXP (where S is phosphorylated) consensus 14-3-3 binding site identified in a number of signaling molecules, including Raf-1. Significantly, substitution of 582HSRSLP587 for EFAAAA completely abolished interaction of bc with GST‐14-3-3z. Furthermore, the interaction of bc with GST‐14-3-3 was greatly reduced in the presence of a peptide containing the 14-3-3 binding site, but only when 585Ser was phosphorylated. Direct binding experiments showed that the peptide containing phosphorylated 585Ser bound 14-3-3z with an affinity of 150 nmol/L. To study the regulation of 585S phosphorylation in vivo, we raised antibodies that specifically recognized 585Ser-phosphorylated bc. Using these antibodies, we showed that GM-CSF stimulation strongly upregulated 585Ser phosphorylation in M1 myeloid leukemic cells. The proximity of the SHC-binding site (577Tyr) to the 14-3-3‐binding site (582HSRSLP587) and their conservation between mouse, rat, and human bc but not in other cytokine receptors suggest that they form a distinct motif that may subserve specialized functions associated with the GM-CSF, IL-3, and IL-5 receptors. r 1999 by The American Society of Hematology.
Colonization history in Fennoscandian rodents

Fennoscandia probably constitutes one of the best places on earth to study faunal history. During the height of the most recent glacial period Fennoscandia was completely covered with ice. Thus, the majority of extant species must originate from ancestors who survived the latest glaciation in non-glaciated areas outside Fennoscandia. Moreover, the geography and geological history of Fennoscandia suggests that post-glacial recolonization by land mammals must have been restricted to specific routes in time and space. Phylogeographic surveys of mitochondrial DNA (mtDNA) variation in Fennoscandian rodents have demonstrated that glacial history and patterns of post-glacial colonization have played a major role in shaping present day patterns of genetic differentiation within species. Thus, the genetic imprints of historical demographic conditions and vicariant geographic events have been retained within species and can be used to infer the history of populations. The field vole (Microtus agrestis) is used to illustrate these data and processes. Comparisons are made with phylogeographic surveys of the bank vole (Clethrionomys glareolus), the eastern house mouse (Mus musculus) and the wood lemming (Myopus schisticolor) as well as a few other species for which less extensive studies have been performed. The main patterns of post-glacial colonization of Fennoscandia by rodents are described. The effects of timing and patterns of colonization on contemporary population genetic structure and levels of genetic variation are discussed. Specifically, the effects of hybridization and introgression as well as founder events and bottlenecks are explored.
A meta-analysis of psychoeducational programs for coronary heart disease patients.

In a meta-analysis of 37 studies, the effects of psychoeducational (health education and stress management) programs for coronary heart disease patients were examined. The results suggest that these programs yielded a 34% reduction in cardiac mortality; a 29% reduction in recurrence of myocardial infarction (MI); and significant (p < .025) positive effects on blood pressure, cholesterol, body weight, smoking behavior, physical exercise, and eating habits. No effects of psychoeducational programs were found in regard to coronary bypass surgery, anxiety, or depression. The results also suggest that cardiac rehabilitation programs that were successful on proximal targets (systolic blood pressure, smoking behavior, physical exercise, emotional distress) were more effective on distal targets (cardiac mortality and MI recurrences) than programs without success on proximal targets.
Cloning and Expression Analysis of GhERF8 Gene in Gossypium hirsutum L.

The ethylene-responsive element-binding factor(ERF) gene family is one of the largest transcription factor gene families in plants.In this study,a novel ERF gene named GhERF8(GenBank: JN656957) was isolated from our laboratory's leaf cDNA library for Gossypium hirsutum L.The gene encodes a putative of 265 amino-acid protein with a conserved AP2 domain.To understand the function of GhERF8,real-time quantitative PCR(RT-PCR) was used to evaluate the relative expression of GhERF8 at different developmental stages of leaves and tissues.The results showed that GhERF8 could express in all tissues,but expression levels differed.The expression of GhERF8 was the highest in the later stage of leaf maturation.During leaf senescence,GhERF8 expression and chlorophyll content were inversely correlated,indicating that GhERF8 could be related to leaf senescence.Under ethephon and jasmonic acid(JA) treatment,the expression of GhERF8 was up-regulated in leaves,but there was no significant change in expression of GhERF8 under abscisic acid(ABA) treatment.These results indicated that GhERF8 could participate in the signal transduction of ethylene and JA,but not in the ABA-dependent pathway.
Social Identity: Context, Commitment, Content

Foreword: John C Turner (Australia National University). 1. Social Identity Theory: Where Are We Now? : John C Turner. 2. An Introduction to the Contributors: Naomi Ellemers, Russell Spears and Bertjan Doosje. 3. Types Of Identity Threats And Their Self Esteem Consequences: Nyla Branscombe (University of Kansas), Naomi Ellemers, Russell Spears and Bertjan Doosje. 4. Dealing With Deprivation: The Role of Social Identity Salience: Heather Smith (Berkeley) and Russell Spears. 5. Commitment and the Context of Intergroup Perceptions: Russell Spears, Naomi Ellemers and Bertjan Doosje. 6. Identity Needs and Contextual Restrictions: Naomi Ellemers, Manuela Barreto (Free University, Amsterdam) and Russell Spears. 7. Social Identity, Social Norms and Social Influence: Tom Postmes (University of Amsterdam), Russell Spears and Martin Lea (University of Manchester). 8. Linguistic Communication Biases in Intra and Intergroup Contexts: Russell Spears, Daniel Wigboldus (Free University, Amsterdam) and Gun Semin (Free University, Amsterdam). 9. Group Commitment and Intergroup Behavior: Bertjan Doosje, Naomi Ellemers and Russell Spears. 10. Affect, Commitment and Collective Effort: Naomi Ellemers, Jaap Ouwerkerk (Free University, Amsterdam) and Dick de Gilder (University of Amsterdam). 11. Group Distinctiveness and Intergroup Discrimination: Russell Spears, Tony Manstead (University of Amsterdam) and Jolanda Jetten (University of Amsterdam).
Long-term follow up of single-incision laparoscopic cholecystectomy compared to conventional laparoscopic cholecystectomy

Purpose: Conventional Laparoscopic Cholecystectomy (CLC) is the “gold standard” approach for patients with gallstones. Single-incision Laparoscopic Cholecystectomy (SILC) was an alternative technique, purportedly offering several postoperative benefits over CLC. Studies comparing short-term postoperative outcomes of SILC versus CLC have yielded conflicting results. Our paper aims to compare the long-term postoperative outcomes of patients undergoing SILC and CLC with a minimum follow up of seven years.
Neural Mechanisms of Reward Seeking Behavior and Cognitive Control in Individuals With Internet Addiction

Internet addiction(IA) as a behavioral addiction is currently becoming a serious mental health issue around the globe. According to the neurobiological model of brain development, exploring the neural mechanisms of reward seeking behavior and cognitive control in internet addicts may help in developing treatments for individuals with IA. Behavioral research has indicated that IA is commonly associated with enhanced reward sensitivity and decreased inhibitory control. Additionally, research focused on the neural mechanisms of IA indicates that deficits in the reward or cognitive control systems might be a high risk factor for addictive behaviors.Compared with substance addictions, IA, as a kind of psychological addiction, has a specific reward mechanism.While previous research has deepened the understanding of the psychological and neural mechanisms in IA, there still exist many issues surrounding diagnosis and treatment of IA: the screening criteria are not scientific; the classification is ambiguous; the effect of intervention and treatment is controversial; causal research is scarce; and research paradigms are flawed. Substantial future research is needed to explore, fully understand, and treat IA.
RECONSTRUCTION OF SMOOTH SURFACE BY USING CUBIC BEZIER TRIANGULAR PATCH IN GUI

A smooth surface reconstruction by using the non-uniform distribution of the data points constitutes a major challenge in the technique involving reconstructing surfaces in the field of reverse engineering. In this study, the Delaunay triangulation process was selected as the method was not involve the deletion of the sample data points and could preserve the original surface topology. The study also focused on developing a smooth surface using two processes, i.e., the derivative estimation and the surface interpolation using the cubic Bezier triangular patch. We estimated the 2nd order partial derivatives by applying the least square minimization technique wherein the process was seen to decrease the errors between the estimated and the actual values. Thereafter, we interpolated the surface with the help of the cubic Bezier triangular patch. In this study, rather than using the results generated by multiple subplots in the MATLAB programming (MATLAB, R2015), the result was represented by using the MATLAB Graphical User Interface (GUI) function for the development of an interface for all the algorithms. Our results show a comparison of the interpolating surface generated by 6 test functions.
A Convolutional Neural Network based self-learning approach for classifying neurodegenerative states from EEG signals in dementia

In this paper, a novel deep learning based approach is proposed for the automatic classification of Electroencephalographic (EEG) signals of subjects diagnosed with the dementia of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Specifically, a custom Convolutional Neural Network (CNN) is designed to receive as input AD/MCI/HC EEG segments (epochs) of the same temporal width, and perform 2-way classification tasks: AD vs. HC, AD vs. MCI, MCI vs. HC. Our proposed architecture, termed EEG-CNN, is shown to exhibit remarkable abilities to self-learn relevant features directly from the EEG traces, avoiding the need for hand-crafted feature extraction engineering. Comparative experimental results demonstrate the promising performance of EEG-CNN, which is based on an analysis of the EEG time series only, reporting accuracies of 85.78 ± 2.18%, 69.03 ± 1.33%, 85.34 ± 1.86% in AD vs. HC, AD vs. MCI and MCI vs. HC classifications, respectively.
Multiple-source Domain Adaptation in Rule-based Neural Network

Domain adaptation uses the previously acquired knowledge (source domain) to support predicted tasks in the current domain without sufficient labeled data (target domain). Although many methods have been developed in domain adaptation, one issue hasn’t been solved: how to implement knowledge transfer when more than one source domain is available. In this paper we present a neural network-based method which extracts domain knowledge in the form of rules to facilitate knowledge transfer, merge rules from all source domains and further select related rules for target domain and clip redundant rules. The method presented is validated on datasets that simulate the multi-source scenario and the experimental results verify the superiority of our method in handling multi-source domain adaptation problems.
Goal Analytics via Model Checking for Autonomous Systems

Analogous to planning via model checking, we propose to apply model checking on goal analytics of self-regulated agents in autonomous systems. Our project involves investigating how to integrate goal reasoning techniques into the Process Analysis Toolkit (PAT) model checking framework and evaluating which model checking algorithms are efﬁcient for complex and concurrent systems with non-determinism. This paper presents the initial research progress on a new Australian Defence Science and Technology (DST) Group funded project on goal analysis for autonomous systems.
Early Medieval Berkshire

This survey covers the administrative county of Ber kshire as it existed between 1974 and 1998. The county boundaries in that period diff ered significantly from those of the ‘historic’ county that existed for over a thous and years. The most significant changes occurred in 1974 with the loss of the north -western area of the county to Oxfordshire and the acquisition of Slough, Eton, Da tchet, Horton and Wraysbury from Buckinghamshire. Other changes to the exterior boundaries included a series of alterations in 1895 which saw the acquisition of Co ombe from Hampshire and an exchange of lands with Wilshire on the south-wester n boundary. In 1911 Caversham was transferred from Oxfordshire (Hunter 1995, 11-1 2; Durrant 1998, 6-7). At the time of writing ‘new’ Berkshire is split into six u nitary local authorities, with the county remaining as a functioning administrative un it o ly for ambulance drivers, firemen, strategic planners and archivists. The cou nty is currently served by two local authority archaeology services and hence two Sites and Monuments Records, one for West Berkshire and another for the five unitaries i n the east of the county (Reading, Wokingham, Bracknell Forest, Royal Borough of Winds or and Maidenhead, and Slough)
EDNet: A Mesoscale Eddy Detection Network with Multi-Modal Data

Mesoscale eddies play an important role in the transportation and distribution of energy, material and heat in the global ocean. Therefore, mesoscale eddy detection has been researched for a long time. At present, several deep learning models have been proposed for mesoscale eddy detection. However, most of these methods only use single-modal data, while ignoring data of other modals closely related to mesoscale eddy detection. In this paper, we introduce a multi-modal mesoscale eddy dataset, consisting of the satellite data in three modals, i.e., sea surface height (SSH), sea surface temperature (SST) and velocity of flow. Furthermore, we propose an EDNet (Eddy Detection Network), which contains four modules, i.e., multi-modal data fusion module, deep fusion module, region proposal module and head module. We use multi-modal data fusion module to fuse multi-modal data, use deep fusion module to learn the feature representations of the fused multi-modal data and use the region proposal module to generate region proposals containing the mesoscale eddies. There are two branches in the head module, one for classifying and locating the mesoscale eddies, while the other for providing pixel-level instance segmentation of the mesoscale eddies. The experimental results show that EDNet based on multi-modal data fusion significantly improves the accuracy of mesoscale eddy detection over previous approaches.
A General Framework for Continual Learning of Compositional Structures

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and reuse them in novel combinations for solving different problems. To date, research into compositional learning has largely proceeded separately from work on continual learning. We present a general-purpose framework for continual learning of compositional structures, which separates the learning process into two broad stages: learning how to combine existing components to assimilate a novel problem, and learning how to adapt existing components to accommodate the new problem. This separation trades off the stability required to remember how to solve earlier tasks and the ﬂexi-bility required to solve new tasks.
Chapter V. Maltreatment, Negative Expressivity, and the Development of Type D Attachments from 12 to 24 Months of Age

In this longitudinal follow-up study of maltreated and demographically comparable nonmaltreated children, the issue of whether child negative expressivity was related to the formation of an Insecure-Disorganized/ Disoriented, Type D, attachment pattern was examined. In addition, the stability of the Type D pattern and its influence on children's negative expressivity over time was investigated. Developing a secure attachment predisposes children toward social and emotional well-being (Ainsworth & Bowlby, 1991; Carlson & Sroufe, 1995). Central to attachment theory is the idea that secure attachments result from infants receiving consistent, sensitive, responsive, and contingent care from their attachment figures, and not as a result of a constitutionally based child predisposition toward security (Ainsworth & Bowlby, 1991; Sroufe, 1985). Indeed, sensitive, empathic parenting repeatedly has been found to predict the development of secure attachments among infants (e.g., Ainsworth, Blehar, Waters, & Wall, 1978; Belsky & Isabella, 1988; De Wolff & van IJzendoorn, 1997). Research also has supported the notion that infant temperament variables such as difficulty and distress proneness have not consistently been found to exert a direct influence on the development of secure attachments (Goldsmith & Alansky, 1987; Sroufe, 1985).
BLOOD BIOMARKER ANALYSIS FOR THE HIGH-PERFORMANCE ATHLETE

sleep loss, travel, and psychological stress. • Two synergistic approaches to blood biomarker analysis in sport, profiling and monitoring, offer an opportunity to gain insight into an athlete’s nutritional and physiological status. These approaches may help to avoid overtraining, injury and illness when combined with other contextual information. • Common problems that can be identified via blood test data include: poor vitamin D and iron status, low energy availability, persistent inflammation, persistent oxidative stress, and decreased hormonal drive. • Blood test data are easily compromised by poor pre-analytic approaches, for example, drawing blood following exercise or a meal. In order for data to be valid and reliable, a range of pre-analytic considerations must be adhered to. • Clinical diagnostic normative ranges have limited application in sport. Sport specific and athlete adaptive ranges, however, enable the identification of meaningful within-athlete changes that can inform nutrition and recovery strategies. BLOOD BIOMARKER ANALYSIS FOR THE HIGH-PERFORMANCE ATHLETE
GuideSQL: Utilizing Tables to Guide the Prediction of Columns for Text-to-SQL Generation

Text-to-SQL is a task of synthesizing SQL queries from utterances. Most existing approaches of text-to-SQL rarely utilize tables to guide the prediction of SQL query. We present a novel approach called GuideSQL which predicts tables first and uses a pruning algorithm for removing the columns which don’t belong to the predicted tables to avoid errors caused by misprediction of table-column dependencies. For reducing the prediction errors of tables, we use the top-K predicted tables to generate SQL queries and employ a string-matching algorithm to get the most reasonable one. Futhermore, a type linking mechanism is utilized to augment the relevance between utterances and schemas. On the challenging text-to-SQL benchmark SParC, we use previous query attention to get context-dependent information of SQL queries. GuideSQL obtains 36.3% question matching accuracy and 19.5% interaction matching accuracy on the dev set. With BERT augmentation, GuideSQL achieves 49.2% question matching accuracy and 31.6% interaction matching accuracy on the dev set, outperforms the previous state-of-the-art model by 2% question matching accuracy and 2.1% interaction matching accuracy.
Behavioral Finance: Factors Influencing Angel Investor Due Diligence

Angel investors invest in high-risk projects and provide an important source of early-stage entrepreneurial financing. When considering an investment, an important decision is how much time to spend on due diligence – deeper investigation into the person, business, and/or industry under consideration. As angels act individually, different from institutional investors with professional advisors, behavioral factors may particularly influence angels’ decisions. Such factors are considered in behavioral finance, which is based on the paradigm that markets are not always rational and are driven by other influencing factors such as overconfidence and risk aversion. To provide insight into factors affecting angel investors’ time spent on due diligence, we examine the relationships three factors: an angel’s investing experience, an angel’s assessment of the firm’s top management team (TMT), and the firm’s stage of development.
Deep Learning based fully automatic efficient Burn Severity Estimators for better Burn Diagnosis

Each year, burn injuries lead to several deaths and lifelong disabilities for many others. Timely provided appropriate diagnosis and treatment can reduce sufferings for many, however automated burns diagnosis techniques are still under exploration. Laser Doppler Imaging (LDI) has been found as promising for burns depth assessment, but high costs, delays and portability issues limit its usage in developing automated burns diagnosis methods. The visual images based automated approaches for burn diagnosis have been limitedly explored. This research presents a deep learning based novel approach for burn severity assessment and a new labeled dataset of burn images with varying burn severity that would be made publically available in order to facilitate and advance research for burn severity estimation. As skin characteristics vary across different body regions so will be the burn impact, so we propose customized burn severity estimators (specific to body parts) instead of having a single generic burn severity estimator for the whole human body. Extensive experiments were conducted to evaluate the performance of the proposed approach with different network settings, obtaining competitive results to state-of-the-art methods, despite each customized estimator using a smaller set of images compared to generic one. Also, the experiments suggest that the deep learning based customized estimators perform better than handcrafted features based methods for burns diagnosis.