A novel synthetic chemistry approach to linkage-specific ubiquitin conjugation.

Ubiquitination is of great importance as the post-translational modification of proteins with ubiquitin, or ubiquitin chains, facilitates a number of vital cellular processes. Herein we present a facile method of preparing various ubiquitin conjugates under mild conditions using michael acceptors based on dibromo-maleimides and dibromo-pyridazinediones

MILCS: A mutual information learning classifier system

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM

Cooperation, collective action, and the archeology of large-scale societies

Archeologists investigating the emergence of large-scale societies in the past have renewed interest in examining the dynamics of cooperation as a means of understanding societal change and organizational variability within human groups over time. Unlike earlier approaches to these issues, which used models designated voluntaristic or managerial, contemporary research articulates more explicitly with frameworks for cooperation and collective action used in other fields, thereby facilitating empirical testing through better definition of the costs, benefits, and social mechanisms associated with success or failure in coordinated group action. Current scholarship is nevertheless bifurcated along lines of epistemology and scale, which is understandable but problematic for forging a broader, more transdisciplinary field of cooperation studies. Here, we point to some areas of potential overlap by reviewing archeological research that places the dynamics of social cooperation and competition in the foreground of the emergence of large-scale societies, which we define as those having larger populations, greater concentrations of political power, and higher degrees of social inequality. We focus on key issues involving the communal-resource management of subsistence and other economic goods, as well as the revenue flows that undergird political institutions. Drawing on archeological cases from across the globe, with greater detail from our area of expertise in Mesoamerica, we offer suggestions for strengthening analytical methods and generating more transdisciplinary research programs that address human societies across scalar and temporal spectra

NNMSM Type-II and III

We suggest two types of extension of the standard model, which are the so-called next to new minimal standard model (NNMSM) type-II and -III. They can achieve gauge coupling unification as well as suitable dark matter abundance, small neutrino masses, baryon asymmetry of the universe, inflation, and dark energy. The gauge coupling unification can be realized by introducing extra two or three new fields, and could explain the charge quantization. We also show that there are regions in which the vacuum stability, coupling perturbativity, and correct dark matter abundance can be realized with current experimental data at the same time.Comment: 20 pages, 5 figures, comments added. arXiv admin note: substantial text overlap with arXiv:1309.123

A network perspective on intermedia agenda-setting

In Communication Theory, intermedia agenda-setting refers to the influence that different news sources may have on each other, and how this subsequently affects the breadth of information that is presented to the public. Several studies have attempted to quantify the impact of intermedia agenda-setting in specific countries or contexts, but a large-scale, data-driven investigation is still lacking. Here, we operationalise intermedia agenda-setting by putting forward a methodology to infer networks of influence between different news sources on a given topic, and apply it on a large dataset of news articles published by globally and locally prominent news organisations in 2016. We find influence to be significantly topic-dependent, with the same news sources acting as agenda-setters (i.e., central nodes) with respect to certain topics and as followers (i.e., peripheral nodes) with respect to others. At the same time, we find that the influence networks associated to most topics exhibit small world properties, which we find to play a significant role towards the overall diversity of sentiment expressed about the topic by the news sources in the network. In particular, we find clustering and density of influence networks to act as competing forces in this respect, with the former increasing and the latter reducing diversit

Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings

Models based on the transformer architecture, such as BERT, have marked a crucial step for- ward in the field of Natural Language Pro- cessing. Importantly, they allow the creation of word embeddings that capture important semantic information about words in context. However, as single entities, these embeddings are difficult to interpret and the models used to create them have been described as opaque. Binder and colleagues proposed an intuitive embedding space where each dimension is based on one of 65 core semantic features. Un- fortunately, the space only exists for a small data-set of 535 words, limiting its uses. Pre- vious work (Utsumi, 2018, 2020; Turton et al., 2020) has shown that Binder features can be derived from static embeddings and success- fully extrapolated to a large new vocabulary. Taking the next step, this paper demonstrates that Binder features can be derived from the BERT embedding space. This provides two things; (1) semantic feature values derived from contextualised word embeddings and (2) insights into how semantic features are repre- sented across the different layers of the BERT model

Internal transport barriers in the National Spherical Torus Experiment

In the National Spherical Torus Experiment [M. Ono , Nucl. Fusion 41, 1435 (2001)], internal transport barriers (ITBs) are observed in reversed (negative) shear discharges where diffusivities for electron and ion thermal channels and momentum are reduced. While neutral beam heating can produce ITBs in both electron and ion channels, high harmonic fast wave heating can also produce electron ITBs (e-ITBs) under reversed magnetic shear conditions without momentum input. Interestingly, the location of the e-ITB does not necessarily match that of the ion ITB (i-ITB). The e-ITB location correlates best with the magnetic shear minima location determined by motional Stark effect constrained equilibria, whereas the i-ITB location better correlates with the location of maximum ExB shearing rate. Measured electron temperature gradients in the e-ITB can exceed critical gradients for the onset of electron thermal gradient microinstabilities calculated by linear gyrokinetic codes. A high-k microwave scattering diagnostic shows locally reduced density fluctuations at wave numbers characteristic of electron turbulence for discharges with strongly negative magnetic shear versus weakly negative or positive magnetic shear. Reductions in fluctuation amplitude are found to be correlated with the local value of magnetic shear. These results are consistent with nonlinear gyrokinetic simulations predicting a reduction in electron turbulence under negative magnetic shear conditions despite exceeding critical gradients.X1128sciescopu

In vitro and in vivo modification of Neisseria gonorrhoeae lipooligosaccharide epitope structure by sialylation.

After growth of gonococci in the presence of cytidine monophospho-N-acetyl-neuraminic acid (CMP-NANA), their 4.5-kD lipooligosaccharide (LOS) component was increased by approximately 400 daltons, whereas the LOS of strains lacking the 4.5-kD component were unaffected. Expression of mAb-defined epitopes on the 4.5-kD component was decreased on LOS of strains grown in CMP-NANA, and treatment of the LOS with neuraminidase reversed this affect. Gonococci incubated with human PMNs also had decreased expression of the 4.5-kD+ epitopes. A detergent extract of gonococci incorporated radiolabeled NANA in the LOS, suggesting the presence of a sialyltransferase in gonococci. Exogenous sialyltransferases also could use LOS as an acceptor

Artificial Intelligence and Machine Learning: A Perspective on Integrated Systems Opportunities and Challenges for Multi-Domain Operations

This paper provides a perspective on historical background, innovation and applications of Artificial Intelligence (AI) and Machine Learning (ML), data successes and systems challenges, national security interests, and mission opportunities for system problems. AI and ML today are used interchangeably, or together as AI/ML, and are ubiquitous among many industries and applications. The recent explosion, based on a confluence of new ML algorithms, large data sets, and fast and cheap computing, has demonstrated impressive results in classification and regression and used for prediction, and decision-making. Yet, AI/ML today lacks a precise definition, and as a technical discipline, it has grown beyond its origins in computer science. Even though there are impressive feats, primarily of ML, there still is much work needed in order to see the systems benefits of AI, such as perception, reasoning, planning, acting, learning, communicating, and abstraction. Recent national security interests in AI/ML have focused on problems including multidomain operations (MDO), and this has renewed the focus on a systems view of AI/ML. This paper will address the solutions for systems from an AI/ML perspective and that these solutions will draw from methods in AI and ML, as well as computational methods in control, estimation, communication, and information theory, as in the early days of cybernetics. Along with the focus on developing technology, this paper will also address the challenges of integrating these AI/ML systems for warfare