Skin Ageing and Cancer

Human matrix metalloproteinases (MMPs) belong to the M10 family of the MA clan of endopeptidases. They are ubiquitarian enzymes, structurally characterized by an active site where a Zn2+ atom, coordinated by three histidines, plays the catalytic role, assisted by a glutamic acid as a general base. Based on their structure and substrate specificity, they can be categorized into five main subgroups, namely (1) collagenases (MMP-1, MMP-8 and MMP-13); (2) gelatinases (MMP-2 and MMP-9); (3) stromelysins (MMP-3, MMP-10 and MMP-11); (4) matrilysins (MMP-7 and MMP-26) and (5) membrane-type (MT) MMPs (MMP-14, MMP-15, MMP-16, MMP-17, MMP-24 and MMP-25). MMPs can act on extracellular matrix (ECM) and non-ECM components affecting degradation and modulation of the ECM, growth-factor activation and cell-cell and cell-matrix signalling. In skin, MMPs are secreted by different cell types such as fibroblasts, keratinocytes, macrophages, endothelial cells, mast cells, and eosinophils. This chapter reviews the role of MMPs in maintaining skin homeostasis, skin ageing and skin cancer

A New Restriction on Low-Redundancy Restricted Array and Its Good Solutions

In array signal processing, a fundamental problem is to design a sensor array with low-redundancy and reduced mutual coupling, which are the main features to improve the performance of direction-of-arrival (DOA) estimation. For a $N$-sensor array with aperture $L$, it is called low-redundancy (LR) if the ratio $R=N(N-1)/(2L)$ is approaching the Leech's bound $1.217\leq R_{opt}\leq 1.674$ for $N\rightarrow\infty$; and the mutual coupling is often reduced by decreasing the numbers of sensor pairs with the first three smallest inter-spacings, denoted as $\omega(a)$ with $a\in\{1,2,3\}$. Many works have been done to construct large LRAs, whose spacing structures all coincide with a common pattern ${\mathbb D}=\{a_1,a_2,\ldots,a_{s_1},c^\ell,b_1,b_2,\ldots,b_{s_2}\}$ with the restriction $s_1+s_2=c-1$. Here $a_i,b_j,c$ denote the spacing between adjacent sensors, and $c$ is the largest one. The objective of this paper is to find some new arrays with lower redundancy ratio or lower mutual coupling compared with known arrays. In order to do this, we give a new restriction for ${\mathbb D}$ to be $s_1+s_2=c$ , and obtain 2 classes of $(4r+3)$-type arrays, 2 classes of $(4r+1)$-type arrays, and 1 class of $(4r)$-type arrays for any $N\geq18$. Here the $(4r+i)$-Type means that $c\equiv i\pmod4$. Notably, compared with known arrays with the same type, one of our new $(4r+1)$-type array and the new $(4r)$-type array all achieves the lowest mutual coupling, and their uDOFs are at most 4 less for any $N\geq18$; compared with SNA and MISC arrays, the new $(4r)$-type array has a significant reduction in both redundancy ratio and mutual coupling. We should emphasize that the new $(4r)$-type array in this paper is the first class of arrays achieving $R<1.5$ and $\omega(1)=1$ for any $N\geq18$

A Novel Floating High-Voltage Level Shifter with Pre-Storage Technique

This paper proposes a novel floating high-voltage level shifter (FHV-LS) with the pre-storage technique for high speed and low deviation in propagation delay. With this technology, the transmission paths from input to output are optimized, and thus the propagation delay of the proposed FHV-LS is reduced to as low as the sub-nanosecond scale. To further reduce the propagation delay, a pull-up network with regulated strength is introduced to reduce the fall time, which is a crucial part of the propagation delay. In addition, a pseudosymmetrical input pair is used to improve the symmetry of FHV-LS structurally to balance between the rising and falling propagation delays. Moreover, a start-up circuit is developed to initialize the output state of FHV-LS during the VDDH power up. The proposed FHV-LS is implemented using 0.3-µm HVCMOS technology. Post-layout simulation shows that the propagation delays and energy per transition of the proposed FHV-LS are 384 ps and 77.7 pJ @VH = 5 V, respectively. Finally, the 500-points Monte Carlo are performed to verify the performance and the stability

Natural antibiotic alternatives to boost animal immunity and disease resistance

Antibiotics have been used in the feed of livestock and poultry for growth promotion and disease prevention for many decades; however, in recent years, there has been an outcry for elimination of antibiotics in order to reduce the emergence and increase in drug-resistant bacteria. The Food and Drug Administration (FDA) has implemented a new guideline to phase out the use of medically important antibiotics in poultry, cattle, and swine as of January 2017. Therefore, new antibiotic alternative strategies are needed to ensure animal health and productivity. Modulating the synthesis of endogenous host defense peptides (HDPs) is being explored as a novel antibiotic-alternative approach to disease control and prevention. My study will focus on the evaluation of two natural dietary compounds, named butyrate and lactose, and if they will have a synergy in enhancing chicken HDP synthesis and barrier function genes far greater than when each compound is used individually. Butyrate was found to synergize with lactose to enhance the expressions of AvBD9 and the barrier function genes in chicken HD11 cells at the highest levels. The results also suggest that there is a beneficial role of using a combination of butyrate and lactose in enhancing gut health and possibly animal performance. The outcome of this research will help pave the way for the development of natural antibiotic alternatives to boost animal immunity and disease resistance for livestock and poultry use.Lew Wentz FoundationAnimal Scienc

QueryForm: A Simple Zero-shot Form Entity Query Framework

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes both the document schema and a specific entity type into a query, which is used to prompt a Transformer model to perform a single entity extraction task. Furthermore, we propose to leverage large-scale query-entity pairs generated from form-like webpages with weak HTML annotations to pre-train QueryForm. By unifying pre-training and fine-tuning into the same query-based framework, QueryForm enables models to learn from structured documents containing various entities and layouts, leading to better generalization to target document types without the need for target-specific training data. QueryForm sets new state-of-the-art average F1 score on both the XFUND (+4.6%~10.1%) and the Payment (+3.2%~9.5%) zero-shot benchmark, with a smaller model size and no additional image input.Comment: Accepted to Findings of ACL 202

A genome-wide screen identifies a single β-defensin gene cluster in the chicken: implications for the origin and evolution of mammalian defensins

BACKGROUND: Defensins comprise a large family of cationic antimicrobial peptides that are characterized by the presence of a conserved cysteine-rich defensin motif. Based on the spacing pattern of cysteines, these defensins are broadly divided into five groups, namely plant, invertebrate, α-, β-, and θ-defensins, with the last three groups being mostly found in mammalian species. However, the evolutionary relationships among these five groups of defensins remain controversial. RESULTS: Following a comprehensive screen, here we report that the chicken genome encodes a total of 13 different β-defensins but with no other groups of defensins being discovered. These chicken β-defensin genes, designated as Gallinacin 1–13, are clustered densely within a 86-Kb distance on the chromosome 3q3.5-q3.7. The deduced peptides vary from 63 to 104 amino acid residues in length sharing the characteristic defensin motif. Based on the tissue expression pattern, 13 β-defensin genes can be divided into two subgroups with Gallinacin 1–7 being predominantly expressed in bone marrow and the respiratory tract and the remaining genes being restricted to liver and the urogenital tract. Comparative analysis of the defensin clusters among chicken, mouse, and human suggested that vertebrate defensins have evolved from a single β-defensin-like gene, which has undergone rapid duplication, diversification, and translocation in various vertebrate lineages during evolution. CONCLUSIONS: We conclude that the chicken genome encodes only β-defensin sequences and that all mammalian defensins are evolved from a common β-defensin-like ancestor. The α-defensins arose from β-defensins by gene duplication, which may have occurred after the divergence of mammals from other vertebrates, and θ-defensins have arisen from α-defensins specific to the primate lineage. Further analysis of these defensins in different vertebrate lineages will shed light on the mechanisms of host defense and evolution of innate immunity

Combustion reactivity of ilmenite with coal volatiles under steam gasification atmosphere

学位記番号：理工博甲6

LMDX: Language Model-based Document Information Extraction and Localization

Large Language Models (LLM) have revolutionized Natural Language Processing (NLP), improving state-of-the-art on many existing tasks and exhibiting emergent capabilities. However, LLMs have not yet been successfully applied on semi-structured document information extraction, which is at the core of many document processing workflows and consists of extracting key entities from a visually rich document (VRD) given a predefined target schema. The main obstacles to LLM adoption in that task have been the absence of layout encoding within LLMs, critical for a high quality extraction, and the lack of a grounding mechanism ensuring the answer is not hallucinated. In this paper, we introduce Language Model-based Document Information Extraction and Localization (LMDX), a methodology to adapt arbitrary LLMs for document information extraction. LMDX can do extraction of singular, repeated, and hierarchical entities, both with and without training data, while providing grounding guarantees and localizing the entities within the document. In particular, we apply LMDX to the PaLM 2-S LLM and evaluate it on VRDU and CORD benchmarks, setting a new state-of-the-art and showing how LMDX enables the creation of high quality, data-efficient parsers