Prediction of Human Transcriptional Biomarkers for Severe Infection with SARS-CoV-2

Defining the human host factors associated with severe vs mild COVID-19 cases in infected individuals has become of increasing interest. Mining large numbers of public gene expression datasets is an effective way to identify genes that contribute to a given phenotype. Combining RNA-sequencing data with the associated clinical metadata describing disease severity can enable earlier identification of those patients who are at higher risk of developing severe COVID-19 disease. We consequently identified 356 public RNA-seq human transcriptome samples from the Gene Expression Omnibus database that had disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to quantify gene expression in each patient. This process involved using Salmon to map the reads to the reference transcriptomes, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then applied a machine learning algorithm to the read counts data to identify features that best differentiated samples based on COVID-19 severity phenotype. Ultimately, we produced a ranked list of genes based on their Gini importance values that includes GIMAP7 and S1PR2, which are associated with immunity and inflammation (respectively). We expect that these results can establish a groundwork foundation to improve the development of improved prognostics for severe COVID-19

Vulnerability of LTE to Hostile Interference

LTE is well on its way to becoming the primary cellular standard, due to its performance and low cost. Over the next decade we will become dependent on LTE, which is why we must ensure it is secure and available when we need it. Unfortunately, like any wireless technology, disruption through radio jamming is possible. This paper investigates the extent to which LTE is vulnerable to intentional jamming, by analyzing the components of the LTE downlink and uplink signals. The LTE physical layer consists of several physical channels and signals, most of which are vital to the operation of the link. By taking into account the density of these physical channels and signals with respect to the entire frame, as well as the modulation and coding schemes involved, we come up with a series of vulnerability metrics in the form of jammer to signal ratios. The ``weakest links'' of the LTE signals are then identified, and used to establish the overall vulnerability of LTE to hostile interference.Comment: 4 pages, see below for citation. M. Lichtman, J. Reed, M. Norton, T. Clancy, "Vulnerability of LTE to Hostile Interference'', IEEE Global Conference on Signal and Information Processing (GlobalSIP), Dec 201

Topology Driven Improvement of FDC Build Parameters

The likeliest failure origin for advanced ceramics parts, prepared by fused deposition, is a void from improper fill. Adequate filling of each cross-section is dependent upon the deposition toolpath. Cross-sectional spaces are conventionally filled with pre-defined parameters. We propose that adaptive build parameters will control variations in geometry and property of a part. Voids, overfilling, incomplete bonding and excess traversing can be suppressed by adjusting the fill parameters for cross-sectional areas. Improved build parameters and toolpath allows for faster build time and components ofj full density. Some implementations are discussed and presented.Mechanical Engineerin

Multiscale Modeling of Thermal Conductivity of Polymer/Carbon Nanocomposites

Molecular dynamics simulation was used to estimate the interfacial thermal (Kapitza) resistance between nanoparticles and amorphous and crystalline polymer matrices. Bulk thermal conductivities of the nanocomposites were then estimated using an established effective medium approach. To study functionalization, oligomeric ethylene-vinyl alcohol copolymers were chemically bonded to a single wall carbon nanotube. The results, in a poly(ethylene-vinyl acetate) matrix, are similar to those obtained previously for grafted linear hydrocarbon chains. To study the effect of noncovalent functionalization, two types of polyethylene matrices. -- aligned (extended-chain crystalline) vs. amorphous (random coils) were modeled. Both matrices produced the same interfacial thermal resistance values. Finally, functionalization of edges and faces of plate-like graphite nanoparticles was found to be only modestly effective in reducing the interfacial thermal resistance and improving the composite thermal conductivit

Crystal Structure of the Zorbamycin-Binding Protein ZbmA, the Primary Self-Resistance Element in Streptomyces flavoviridis ATCC21892

The bleomycins (BLMs), tallysomycins (TLMs), phleomycin, and zorbamycin (ZBM) are members of the BLM family of glycopeptide-derived antitumor antibiotics. The BLM-producing Streptomyces verticillus ATCC15003 and the TLM-producing Streptoalloteichus hindustanus E465-94 ATCC31158 both possess at least two self-resistance elements, an N-acetyltransferase and a binding protein. The N-acetyltransferase provides resistance by disrupting the metal-binding domain of the antibiotic that is required for activity, while the binding protein confers resistance by sequestering the metal-bound antibiotic and preventing drug activation via molecular oxygen. We recently established that the ZBM producer, Streptomyces flavoviridis ATCC21892, lacks the N-acetyltransferase resistance gene and that the ZBM-binding protein, ZbmA, is sufficient to confer resistance in the producing strain. To investigate the resistance mechanism attributed to ZbmA, we determined the crystal structures of apo and Cu(II)-ZBM-bound ZbmA at high resolutions of 1.90 and 1.65 Å, respectively. A comparison and contrast with other structurally characterized members of the BLM-binding protein family revealed key differences in the protein–ligand binding environment that fine-tunes the ability of ZbmA to sequester metal-bound ZBM and supports drug sequestration as the primary resistance mechanism in the producing organisms of the BLM family of antitumor antibiotics

The voice characterisation checklist: psychometric properties of a brief clinical assessment of voices as social agents

Aim: There is growing interest in tailoring psychological interventions for distressing voices and a need for reliable tools to assess phenomenological features which might influence treatment response. This study examines the reliability and internal consistency of the Voice Characterisation Checklist (VoCC), a novel 10-item tool which assesses degree of voice characterisation, identified as relevant to a new wave of relational approaches. Methods: The sample comprised participants experiencing distressing voices, recruited at baseline on the AVATAR2 trial between January 2021 and July 2022 (n = 170). Inter-rater reliability (IRR) and internal consistency analyses (Cronbach’s alpha) were conducted. Results: The majority of participants reported some degree of voice personification (94%) with high endorsement of voices as distinct auditory experiences (87%) with basic attributes of gender and age (82%). While most identified a voice intention (75%) and personality (76%), attribution of mental states (35%) to the voice (‘What are they thinking?’) and a known historical relationship (36%) were less common. The internal consistency of the VoCC was acceptable (10 items, α = 0.71). IRR analysis indicated acceptable to excellent reliability at the item-level for 9/10 items and moderate agreement between raters’ global (binary) classification of more vs. less highly characterised voices, κ = 0.549 (95% CI, 0.240–0.859), p < 0.05. Conclusion: The VoCC is a reliable and internally consistent tool for assessing voice characterisation and will be used to test whether voice characterisation moderates treatment outcome to AVATAR therapy. There is potential wider utility within clinical trials of other relational therapies as well as routine clinical practice

The Voice Characterisation Checklist:Psychometric Properties of a Brief Clinical Assessment of Voices as Social Agents

Aim: There is growing interest in tailoring psychological interventions for distressing voices and a need for reliable tools to assess phenomenological features which might influence treatment response. This study examines the reliability and internal consistency of the Voice Characterisation Checklist (VoCC), a novel 10-item tool which assesses degree of voice characterisation, identified as relevant to a new wave of relational approaches. Methods: The sample comprised participants experiencing distressing voices, recruited at baseline on the AVATAR2 trial between January 2021 and July 2022 (n = 170). Inter-rater reliability (IRR) and internal consistency analyses (Cronbach’s alpha) were conducted. Results: The majority of participants reported some degree of voice personification (94%) with high endorsement of voices as distinct auditory experiences (87%) with basic attributes of gender and age (82%). While most identified a voice intention (75%) and personality (76%), attribution of mental states (35%) to the voice (‘What are they thinking?’) and a known historical relationship (36%) were less common. The internal consistency of the VoCC was acceptable (10 items, α = 0.71). IRR analysis indicated acceptable to excellent reliability at the item-level for 9/10 items and moderate agreement between raters’ global (binary) classification of more vs. less highly characterised voices, κ = 0.549 (95% CI, 0.240–0.859), p &lt; 0.05. Conclusion: The VoCC is a reliable and internally consistent tool for assessing voice characterisation and will be used to test whether voice characterisation moderates treatment outcome to AVATAR therapy. There is potential wider utility within clinical trials of other relational therapies as well as routine clinical practise

Crystal structure of SgcJ, an NTF2-like superfamily protein involved in biosynthesis of the nine-membered enediyne antitumor antibiotic C-1027

Comparative analysis of the enediyne biosynthetic gene clusters revealed sets of conserved genes serving as outstanding candidates for the enediyne core. Here we report the crystal structures of SgcJ and its homologue NCS-Orf16, together with gene inactivation and site-directed mutagenesis studies, to gain insight into enediyne core biosynthesis. Gene inactivation in vivo establishes that SgcJ is required for C-1027 production in Streptomyces globisporus. SgcJ and NCS-Orf16 share a common structure with the nuclear transport factor 2-like superfamily of proteins, featuring a putative substrate binding or catalytic active site. Site-directed mutagenesis of the conserved residues lining this site allowed us to propose that SgcJ and its homologues may play a catalytic role in transforming the linear polyene intermediate, along with other enediyne polyketide synthase-associated enzymes, into an enzyme-sequestered enediyne core intermediate. These findings will help formulate hypotheses and design experiments to ascertain the function of SgcJ and its homologues in nine-membered enediyne core biosynthesis