Technion – Israel Institute of Technology

About: Technion – Israel Institute of Technology is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Nonlinear system. The organization has 31714 authors who have published 79377 publications receiving 2603976 citations. The organization is also known as: Technion Israel Institute of Technology & Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel.

IL-22–producing “T22” T cells account for upregulated IL-22 in atopic dermatitis despite reduced IL-17–producing TH17 T cells

Abstract: Background Psoriasis and atopic dermatitis (AD) are common inflammatory skin diseases. An upregulated T H 17/IL-23 pathway was demonstrated in psoriasis. Although potential involvement of T H 17 T cells in AD was suggested during acute disease, the role of these cells in chronic AD remains unclear. Objective To examine differences in IL-23/T H 17 signal between these diseases and establish relative frequencies of T-cell subsets in AD. Methods Skin biopsies and peripheral blood were collected from patients with chronic AD (n = 12) and psoriasis (n = 13). Relative frequencies of CD4 + and CD8 + T-cell subsets within these 2 compartments were examined by intracellular cytokine staining and flow cytometry. Results In peripheral blood, no significant difference was found in percentages of different T-cell subsets between these diseases. In contrast, psoriatic skin had significantly increased frequencies of T H 1 and T H 17 T cells compared with AD, whereas T H 2 T cells were significantly elevated in AD. Distinct IL-22–producing CD4 + and CD8 + T-cell populations were significantly increased in AD skin compared with psoriasis. IL-22 + CD8 + T-cell frequency correlated with AD disease severity. Conclusion Our data established that T cells could independently express IL-22 even with low expression levels of IL-17. This argues for a functional specialization of T cells such that "T17" and "T22" T-cells may drive different features of epidermal pathology in inflammatory skin diseases, including induction of antimicrobial peptides for "T17" T cells and epidermal hyperplasia for "T22" T-cells. Given the clinical correlation with disease severity, further characterization of "T22" T cells is warranted, and may have future therapeutic implications.

The stochastic approach for link-structure analysis (SALSA) and the TKC effect

Abstract: Today, when searching for information on the World Wide Web, one usually performs a query through a term-based search engine. These engines return, as the query's result, a list of Web sites whose contents match the query. For broad topic queries, such searches often result in a huge set of retrieved documents, many of which are irrelevant to the user. However, much information is contained in the link-structure of the World Wide Web. Information such as which pages are linked to others can be used to augment search algorithms. In this context, Jon Kleinberg introduced the notion of two distinct types of Web sites: hubs and authorities . Kleinberg argued that hubs and authorities exhibit a mutually reinforcing relationship : a good hub will point to many authorities, and a good authority will be pointed at by many hubs. In light of this, he devised an algorithm aimed at finding authoritative sites. We present SALSA, a new stochastic approach for link structure analysis, which examines random walks on graphs derived from the link structure. We show that both SALSA and Kleinberg's mutual reinforcement approach employ the same meta-algorithm. We then prove that SALSA is equivalent to a weighted in-degree analysis of the link-structure of World Wide Web subgraphs, making it computationally more efficient than the mutual reinforcement approach. We compare the results of applying SALSA to the results derived through Kleinberg's approach. These comparisons reveal a topological phenomenon called the TKC effect (Tightly Knit Community) which, in certain cases, prevents the mutual reinforcement approach from identifying meaningful authorities.

Three-Dimensional Face Recognition

Abstract: An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

Targeted resequencing in epileptic encephalopathies identifies de novo mutations in CHD2 and SYNGAP1

Abstract: Epileptic encephalopathies are a devastating group of epilepsies with poor prognosis, of which the majority are of unknown etiology. We perform targeted massively parallel resequencing of 19 known and 46 candidate genes for epileptic encephalopathy in 500 affected individuals (cases) to identify new genes involved and to investigate the phenotypic spectrum associated with mutations in known genes. Overall, we identified pathogenic mutations in 10% of our cohort. Six of the 46 candidate genes had 1 or more pathogenic variants, collectively accounting for 3% of our cohort. We show that de novo CHD2 and SYNGAP1 mutations are new causes of epileptic encephalopathies, accounting for 1.2% and 1% of cases, respectively. We also expand the phenotypic spectra explained by SCN1A, SCN2A and SCN8A mutations. To our knowledge, this is the largest cohort of cases with epileptic encephalopathies to undergo targeted resequencing. Implementation of this rapid and efficient method will change diagnosis and understanding of the molecular etiologies of these disorders.

Actively personalized vaccination trial for newly diagnosed glioblastoma

Abstract: Patients with glioblastoma currently do not sufficiently benefit from recent breakthroughs in cancer treatment that use checkpoint inhibitors1,2. For treatments using checkpoint inhibitors to be successful, a high mutational load and responses to neoepitopes are thought to be essential3. There is limited intratumoural infiltration of immune cells4 in glioblastoma and these tumours contain only 30–50 non-synonymous mutations5. Exploitation of the full repertoire of tumour antigens—that is, both unmutated antigens and neoepitopes—may offer more effective immunotherapies, especially for tumours with a low mutational load. Here, in the phase I trial GAPVAC-101 of the Glioma Actively Personalized Vaccine Consortium (GAPVAC), we integrated highly individualized vaccinations with both types of tumour antigens into standard care to optimally exploit the limited target space for patients with newly diagnosed glioblastoma. Fifteen patients with glioblastomas positive for human leukocyte antigen (HLA)-A*02:01 or HLA-A*24:02 were treated with a vaccine (APVAC1) derived from a premanufactured library of unmutated antigens followed by treatment with APVAC2, which preferentially targeted neoepitopes. Personalization was based on mutations and analyses of the transcriptomes and immunopeptidomes of the individual tumours. The GAPVAC approach was feasible and vaccines that had poly-ICLC (polyriboinosinic-polyribocytidylic acid-poly-l-lysine carboxymethylcellulose) and granulocyte–macrophage colony-stimulating factor as adjuvants displayed favourable safety and strong immunogenicity. Unmutated APVAC1 antigens elicited sustained responses of central memory CD8+ T cells. APVAC2 induced predominantly CD4+ T cell responses of T helper 1 type against predicted neoepitopes.