Heavy Color-Octet Particles at the LHC

Many new-physics models, especially those with a color-triplet top-quark partner, contain a heavy color-octet state. The "naturalness" argument for a light Higgs boson requires that the color-octet state be not much heavier than a TeV, and thus it can be pair-produced with large cross sections at high-energy hadron colliders. It may decay preferentially to a top quark plus a top-partner, which subsequently decays to a top quark plus a color-singlet state. This singlet can serve as a WIMP dark-matter candidate. Such decay chains lead to a spectacular signal of four top quarks plus missing energy. We pursue a general categorization of the color-octet states and their decay products according to their spin and gauge quantum numbers. We review the current bounds on the new states at the LHC and study the expected discovery reach at the 8-TeV and 14-TeV runs. We also present the production rates at a future 100-TeV hadron collider, where the cross sections will be many orders of magnitude greater than at the 14-TeV LHC. Furthermore, we explore the extent to which one can determine the color octet's mass, spin, and chiral couplings. Finally, we propose a test to determine whether the fermionic color octet is a Majorana particle.Comment: 20 pages, 9 figures; journal versio

Enzymatic Cross-Linking of Dynamic Thiol-Norbornene Click Hydrogels

Enzyme-mediated in situ forming hydrogels are attractive for many biomedical applications because gelation afforded by enzymatic reactions can be readily controlled not only by tuning macromer compositions, but also by adjusting enzyme kinetics. For example, horseradish peroxidase (HRP) has been used extensively for in situ cross-linking of macromers containing hydroxyl-phenol groups. The use of HRP to initiate thiol-allylether polymerization has also been reported, yet no prior study has demonstrated enzymatic initiation of thiol-norbornene gelation. In this study, we discovered that HRP can generate the thiyl radicals needed for initiating thiol-norbornene hydrogelation, which has only been demonstrated previously using photopolymerization. Enzymatic thiol-norbornene gelation not only overcomes light attenuation issue commonly observed in photopolymerized hydrogels, but also preserves modularity of the cross-linking. In particular, we prepared modular hydrogels from two sets of norbornene-modified macromers, 8-arm poly(ethylene glycol)-norbornene (PEG8NB) and gelatin-norbornene (GelNB). Bis-cysteine-containing peptides or PEG-tetra-thiol (PEG4SH) was used as a cross-linker for forming enzymatically and orthogonally polymerized hydrogel. For HRP-initiated PEG-peptide hydrogel cross-linking, gelation efficiency was significantly improved via adding tyrosine residues on the peptide cross-linkers. Interestingly, these additional tyrosine residues did not form permanent dityrosine cross-links following HRP-induced gelation. As a result, they remained available for tyrosinase-mediated secondary cross-linking, which dynamically increased hydrogel stiffness. In addition to material characterizations, we also found that both PEG- and gelatin-based hydrogels exhibited excellent cytocompatibility for dynamic 3D cell culture. The enzymatic thiol-norbornene gelation scheme presented here offers a new cross-linking mechanism for preparing modularly and dynamically cross-linked hydrogels

Improving gelation efficiency and cytocompatibility of visible light polymerized thiol-norbornene hydrogels via addition of soluble tyrosine

Hydrogels immobilized with biomimetic peptides have been used widely for tissue engineering and drug delivery applications. Photopolymerization has been among the most commonly used techniques to fabricate peptide-immobilized hydrogels as it offers rapid and robust peptide immobilization within a crosslinked hydrogel network. Both chain-growth and step-growth photopolymerizations can be used to immobilize peptides within covalently crosslinked hydrogels. A previously developed visible light mediated step-growth thiol-norbornene gelation scheme has demonstrated efficient crosslinking of hydrogels composed of an inert poly(ethylene glycol)-norbornene (PEGNB) macromer and a small molecular weight bis-thiol linker, such as dithiothreitol (DTT). Compared with conventional visible light mediated chain-polymerizations where multiple initiator components are required, step-growth photopolymerized thiol-norbornene hydrogels are more cytocompatible for the in situ encapsulation of radical sensitive cells (e.g., pancreatic β-cells). This contribution explored visible light based crosslinking of various bis-cysteine containing peptides with macromer 8-arm PEGNB to form biomimetic hydrogels suitable for in situ cell encapsulation. It was found that the addition of soluble tyrosine during polymerization not only significantly accelerated gelation, but also improved the crosslinking efficiency of PEG-peptide hydrogels as evidenced by a decreased gel point and enhanced gel modulus. In addition, soluble tyrosine drastically enhanced the cytocompatibility of the resulting PEG-peptide hydrogels, as demonstrated by in situ encapsulation and culture of pancreatic MIN6 β-cells. This visible light based thiol-norbornene crosslinking mechanism provides an attractive gelation method for preparing cytocompatible PEG-peptide hydrogels for tissue engineering applications

Attention Allocation for Human Multi-Robot Control: Cognitive Analysis based on Behavior Data and Hidden States

Human multi-robot interaction exploits both the human operator’s high-level decision-making skills and the robotic agents’ vigorous computing and motion abilities. While controlling multi-robot teams, an operator’s attention must constantly shift between individual robots to maintain sufficient situation awareness. To conserve an operator’s attentional resources, a robot with self reflect capability on its abnormal status can help an operator focus her attention on emergent tasks rather than unneeded routine checks. With the proposing self-reflect aids, the human-robot interaction becomes a queuing framework, where the robots act as the clients to request for interaction and an operator acts as the server to respond these job requests. This paper examined two types of queuing schemes, the self-paced Open-queue identifying all robots’ normal/abnormal conditions, whereas the forced-paced shortest-job-first (SJF) queue showing a single robot’s request at one time by following the SJF approach. As a robot may miscarry its experienced failures in various situations, the effects of imperfect automation were also investigated in this paper. The results suggest that the SJF attentional scheduling approach can provide stable performance in both primary (locate potential targets) and secondary (resolve robots’ failures) tasks, regardless of the system’s reliability levels. However, the conventional results (e.g., number of targets marked) only present little information about users’ underlying cognitive strategies and may fail to reflect the user’s true intent. As understanding users’ intentions is critical to providing appropriate cognitive aids to enhance task performance, a Hidden Markov Model (HMM) is used to examine operators’ underlying cognitive intent and identify the unobservable cognitive states. The HMM results demonstrate fundamental differences among the queuing mechanisms and reliability conditions. The findings suggest that HMM can be helpful in investigating the use of human cognitive resources under multitasking environments

Effects of dietary fatty acid composition on lipid metabolism and body fat accumulation in ovariectomized rats

BACKGROUND: Obesity is a state of excess energy storage resulting in body fat accumulation, and postmenopausal obesity is a rising issue. In this study using ovariectomized (OVX) rats, we mimicked low estrogen levels in a postmenopausal state in order to investigate the effects of different amounts and types of dietary fatty acids on body fat accumulation and body lipid metabolism. METHODS: At 9 weeks of age, rats ( RESULTS: After OVX, compared to the S group, the C group showed significantly higher body weight, and insulin and leptin levels. Compared to the C group, the H group had lower hepatic triglyceride level and FAS enzyme activity, and higher hepatic ACO and CPT-1 gene expressions and enzyme activities. CONCLUSIONS: An OVX leads to severe weight gain and lipid metabolism abnormalities, while according to previous studies, high fat diet may worsen the situation. However, during our experiment, we discovered that the experimental oil mixture with 60% MUFAs and P/S = 5 may ameliorate these imbalances

Anti-Bladder-Tumor Effect of Baicalein from Scutellaria baicalensis Georgi and Its Application In Vivo

Some phytochemicals with the characteristics of cytotoxicity and/or antimetastasis have generated intense interest among the anticancer studies. In this study, a natural flavonoid baicalein was evaluated in bladder cancer in vitro and in vivo. Baicalein inhibits 5637 cell proliferation. It arrests cells in G1 phase at 100 μM and in S phase below 75 μM. The protein expression of cyclin B1 and cyclin D1 is reduced by baicalein. Baicalein-induced p-ERK plays a minor role in cyclin B1 reduction. Baicalein-inhibited p65NF-κB results in reduction of cell growth. Baicalein-induced pGSK(ser9) has a little effect in increasing cyclin B1/D1 expression instead. The translation inhibitor cycloheximide blocks baicalein-reduced cyclin B1, suggesting that the reduction is caused by protein synthesis inhibition. On the other hand, neither cycloheximide nor proteasome inhibitor MG132 completely blocks baicalein-reduced cyclin D1, suggesting that baicalein reduces cyclin D1 through protein synthesis inhibition and proteasomal degradation activation. In addition, baicalein also inhibits cell invasion by inhibiting MMP-2 and MMP-9 mRNA expression and activity. In mouse orthotopic bladder tumor model, baicalein slightly reduces tumor size but with some hepatic toxicity. In summary, these results demonstrate the anti-bladder-tumor properties of the natural compound baicalein which shows a slight anti-bladder-tumor effect in vivo

GenEpi: gene-based epistasis discovery using machine learning.

BackgroundGenome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).ResultsIn this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.ConclusionsThe results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future

An Analysis System for Integrating High-Throughput Transcript Abundance Data with Metabolic Pathways in Green Algae

As the most important non-vascular plants, algae have many research applications, including high species diversity, biofuel sources, adsorption of heavy metals and, following processing, health supplements. With the increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes, an integrated resource for retrieving gene expression data and metabolic pathway is essential for functional analysis and systems biology in algae. However, gene expression profiles and biological pathways are displayed separately in current resources, and making it impossible to search current databases directly to identify the cellular response mechanisms. Therefore, this work develops a novel AlgaePath database to retrieve gene expression profiles efficiently under various conditions in numerous metabolic pathways. AlgaePath, a web-based database, integrates gene information, biological pathways, and next-generation sequencing (NGS) datasets in Chlamydomonasreinhardtii and Neodesmus sp. UTEX 2219-4. Users can identify gene expression profiles and pathway information by using five query pages (i.e. Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-Expression Analysis). The gene expression data of 45 and 4 samples can be obtained directly on pathway maps in C. reinhardtii and Neodesmus sp. UTEX 2219-4, respectively. Genes that are differentially expressed between two conditions can be identified in Folds Search. Furthermore, the Gene Group Analysis of AlgaePath includes pathway enrichment analysis, and can easily compare the gene expression profiles of functionally related genes in a map. Finally, Co-Expression Analysis provides co-expressed transcripts of a target gene. The analysis results provide a valuable reference for designing further experiments and elucidating critical mechanisms from high-throughput data. More than an effective interface to clarify the transcript response mechanisms in different metabolic pathways under various conditions, AlgaePath is also a data mining system to identify critical mechanisms based on high-throughput sequencing