Prospects of searching for composite resonances at the LHC and beyond

Composite Higgs models predict the existence of resonances. We study in detail the collider phenomenology of both the vector and fermionic resonances, including the possibility of both of them being light and within the reach of the LHC. We present current constraints from di-boson, di-lepton resonance searches and top partner pair searches on a set of simplified benchmark models based on the minimal coset $SO(5)/SO(4)$, and make projections for the reach of the HL-LHC. We find that the cascade decay channels for the vector resonances into top partners, or vice versa, can play an important role in the phenomenology of the models. We present a conservative estimate for their reach by using the same-sign di-lepton final states. As a simple extrapolation of our work, we also present the projected reach at the 27 TeV HE-LHC and a 100 TeV $pp$ collider.Comment: 61 pages, 13 figures; accepted version of JHE

Information Filtering on Coupled Social Networks

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm based on the coupled social networks, which considers the effects of both social influence and personalized preference. Experimental results on two real datasets, \emph{Epinions} and \emph{Friendfeed}, show that hybrid pattern can not only provide more accurate recommendations, but also can enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding structure and function of coupled social networks

Comparative functional genomics and the bovine macrophage response to strains of the Mycobacterium genus

Mycobacterial infections are major causes of morbidity and mortality in cattle and are also potential zoonotic agents with implications for human health. Despite the implementation of comprehensive animal surveillance programs, many mycobacterial diseases have remained recalcitrant to eradication in several industrialized countries. Two major mycobacterial pathogens of cattle are Mycobacterium bovis and Mycobacterium avium subspecies paratuberculosis (MAP), the causative agents of bovine tuberculosis (BTB) and Johne's disease (JD), respectively. BTB is a chronic, granulomatous disease of the respiratory tract that is spread via aerosol transmission, while JD is a chronic granulomatous disease of the intestines that is transmitted via the fecal-oral route. Although these diseases exhibit differential tissue tropism and distinct complex etiologies, both M. bovis and MAP infect, reside, and replicate in host macrophages - the key host innate immune cell that encounters mycobacterial pathogens after initial exposure and mediates the subsequent immune response. The persistence of M. bovis and MAP in macrophages relies on a diverse series of immunomodulatory mechanisms, including the inhibition of phagosome maturation and apoptosis, generation of cytokine-induced necrosis enabling dissemination of infection through the host, local pathology, and ultimately shedding of the pathogen. Here, we review the bovine macrophage response to infection with M. bovis and MAP. In particular, we describe how recent advances in functional genomics are shedding light on the host macrophage-pathogen interactions that underlie different mycobacterial diseases. To illustrate this, we present new analyses of previously published bovine macrophage transcriptomics data following in vitro infection with virulent M. bovis, the attenuated vaccine strain M. bovis BCG, and MAP, and discuss our findings with respect to the differing etiologies of BTB and JD

RNA-seq transcriptional profiling of peripheral blood leukocytes from cattle infected with Mycobacterium bovis

Bovine tuberculosis, caused by infection with Mycobacterium bovis, is a major endemic disease affecting cattle populations worldwide, despite the implementation of stringent surveillance and control programs in many countries. The development of high-throughput functional genomics technologies, including gene expression microarrays and RNA-sequencing (RNA-seq), has enabled detailed analysis of the host transcriptome to M. bovis infection, particularly at the macrophage and peripheral blood level. In the present study, we have analyzed the peripheral blood leukocyte (PBL) transcriptome of eight natural M. bovis-infected and eight age- and sex-matched non-infected control Holstein-Friesian animals using RNA-seq. In addition, we compared gene expression profiles generated using RNA-seq with those previously generated using the high-density Affymetrix(®) GeneChip(®) Bovine Genome Array platform from the same PBL-extracted RNA. A total of 3,250 differentially expressed (DE) annotated genes were detected in the M. bovis-infected samples relative to the controls (adjusted P-value ≤0.05), with the number of genes displaying decreased relative expression (1,671) exceeding those with increased relative expression (1,579). Ingenuity(®) Systems Pathway Analysis (IPA) of all DE genes revealed enrichment for genes with immune function. Notably, transcriptional suppression was observed among several of the top-ranking canonical pathways including Leukocyte Extravasation Signaling. Comparative platform analysis demonstrated that RNA-seq detected a larger number of annotated DE genes (3,250) relative to the microarray (1,398), of which 917 genes were common to both technologies and displayed the same direction of expression. Finally, we show that RNA-seq had an increased dynamic range compared to the microarray for estimating differential gene expression

Body composition, IGF1 status, and physical functionality in nonagenarians: implications for osteosarcopenia

OBJECTIVES: Body composition alterations occur during aging. The purpose of the present analysis was to explore the functional consequences of the overlap of sarcopenia and osteoporosis, and the potential role of insulin-like growth factor 1 (IGF1) in their development in the oldest old. SETTING AND PARTICIPANTS: Eighty-seven nonagenarians from the Louisiana Healthy Aging Study were included. MEASURES: The definition of sarcopenia was based on appendicular lean mass (ALM). Osteoporosis was diagnosed based on bone mineral density (BMD) T score. Four phenotypes were compared: (1) healthy body composition, that is, nonosteoporotic nonsarcopenic (CO, control group), (2) osteoporotic (O, low BMD T score), (3) sarcopenic (S, low ALM), and (4) osteosarcopenic (OS, low BMD T score and low ALM). Sex- and age-specific IGF1-Standard Deviation Scores (SDS) were calculated. The Continuous Scale-Physical Functional Performance (CS-PFP) test was performed. RESULTS: In OS men, IGF1-SDS values (-0.61 ±0.37 vs -0.04 ± 0.52, P = .02) were lower than those in CO males (control group), whereas IGF1-SDS were similar in the 4 body composition phenotypes in women. In men only, ALM was positively associated with IGF1-SDS values (P = .01) independent of age and C-reactive protein concentration. Regarding bone health, we found no association between IGF1-SDS values and BMD. IGF1-SDS was not associated with functional performance (CS-PFP) in men and women. CONCLUSIONS/IMPLICATIONS: IGF1 sensitivity in skeletal muscle and bone may differ by sex in the oldest old. IGF1 status did not appear to affect physical functionality. Determinants and clinical and functional characteristics of osteosarcopenia need to be further investigated in order to define conclusive diagnostic criteria

Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification

Deep learning algorithms have become an essential component in the field of cognitive radio, especially playing a pivotal role in automatic modulation classification. However, Deep learning also present risks and vulnerabilities. Despite their outstanding classification performance, they exhibit fragility when confronted with meticulously crafted adversarial examples, posing potential risks to the reliability of modulation recognition results. Addressing this issue, this letter pioneers the development of an intelligent modulation classification framework based on conformal theory, named the Conformal Shield, aimed at detecting the presence of adversarial examples in unknown signals and assessing the reliability of recognition results. Utilizing conformal mapping from statistical learning theory, introduces a custom-designed Inconsistency Soft-solution Set, enabling multiple validity assessments of the recognition outcomes. Experimental results demonstrate that the Conformal Shield maintains robust detection performance against a variety of typical adversarial sample attacks in the received signals under different perturbation-to-signal power ratio conditions