What Works for Small Apparel Manufacturing: Issues Affecting Reshoring

This study is the first step in a National Science Foundation supported investigation of evolving innovative US supply chain linkages that support development of small apparel manufacturing centers across a southwestern state. We provide a case study of a small US apparel manufacturing business organized in 2014 to meet the needs of local and regional industry, while creating sustainable, dignified jobs. The company defined itself as, \u27a social enterprise with a sewing machine at its heart\u27. Researchers conducted in-depth interviews with the owner and founder of the manufacturing firm. Overall lessons learned that could advance emerging small manufacturing businesses include the importance of brand creation and building an organizational structure with cohesive alignment. Further research is needed, but there is the indication that the ability to collaborate or network with other similar small manufacturing centers could play an important role in meeting large orders or providing training for workers

The Image 1994: Soaring To The Top

Rowan College of New Jersey yearbook for the Class of 1994; 184 pages. Contents: Soaring to the Top p. 4, Seniors p. 17, Graduation p. 93, Faculty and Administration p. 98, Athletics p. 113, Organizations p. 150, Yearbook Staff p. 174.https://rdw.rowan.edu/yearbooks/1037/thumbnail.jp

Risk perceptions about personal Internet-of-Things: Research directions from a multi-panel Delphi study

Internet-of-Things (IoT) research has primarily focused on identifying IoT devices\u27 organizational risks with little attention to consumer perceptions about IoT device risks. The purpose of this study is to understand consumer risk perceptions for personal IoT devices and translate these perceptions into guidance for future research directions. We conduct a sequential, mixed-methods study using multi-panel Delphi and thematic analysis techniques to understand consumer risk perceptions. The results identify four themes focused on data exposure and user experiences within IoT devices. Our thematic analysis also identified several emerging risks associated with the evolution of IoT device functionality and its potential positioning as a resource for malicious actors to conduct security attacks

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Lessons learned for surveillance system strengthening through capacity building and partnership engagement in post-Ebola Guinea, 2015–2019

The 2014–2016 Ebola outbreak in Guinea revealed systematic weaknesses in the existing disease surveillance system, which contributed to delayed detection, underreporting of cases, widespread transmission in Guinea and cross-border transmission to neighboring Sierra Leone and Liberia, leading to the largest Ebola epidemic ever recorded. Efforts to understand the epidemic's scale and distribution were hindered by problems with data completeness, accuracy, and reliability. In 2017, recognizing the importance and usefulness of surveillance data in making evidence-based decisions for the control of epidemic-prone diseases, the Guinean Ministry of Health (MoH) included surveillance strengthening as a priority activity in their post-Ebola transition plan and requested the support of partners to attain its objectives. The U.S. Centers for Disease Control and Prevention (US CDC) and four of its implementing partners—International Medical Corps, the International Organization for Migration, RTI International, and the World Health Organization—worked in collaboration with the Government of Guinea to strengthen the country's surveillance capacity, in alignment with the Global Health Security Agenda and International Health Regulations 2005 objectives for surveillance and reporting. This paper describes the main surveillance activities supported by US CDC and its partners between 2015 and 2019 and provides information on the strategies used and the impact of activities. It also discusses lessons learned for building sustainable capacity and infrastructure for disease surveillance and reporting in similar resource-limited settings

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies