The Bet v 1 fold: an ancient, versatile scaffold for binding of large, hydrophobic ligands

<p>Abstract</p> <p>Background</p> <p>The major birch pollen allergen, Bet v 1, is a member of the ubiquitous PR-10 family of plant pathogenesis-related proteins. In recent years, a number of diverse plant proteins with low sequence similarity to Bet v 1 was identified. In addition, determination of the Bet v 1 structure revealed the existence of a large superfamily of structurally related proteins. In this study, we aimed to identify and classify all Bet v 1-related structures from the Protein Data Bank and all Bet v 1-related sequences from the Uniprot database.</p> <p>Results</p> <p>Structural comparisons of representative members of already known protein families structurally related to Bet v 1 with all entries of the Protein Data Bank yielded 47 structures with non-identical sequences. They were classified into eleven families, five of which were newly identified and not included in the Structural Classification of Proteins database release 1.71. The taxonomic distribution of these families extracted from the Pfam protein family database showed that members of the polyketide cyclase family and the activator of Hsp90 ATPase homologue 1 family were distributed among all three superkingdoms, while members of some bacterial families were confined to a small number of species. Comparison of ligand binding activities of Bet v 1-like superfamily members revealed that their functions were related to binding and metabolism of large, hydrophobic compounds such as lipids, hormones, and antibiotics. Phylogenetic relationships within the Bet v 1 family, defined as the group of proteins with significant sequence similarity to Bet v 1, were determined by aligning 264 Bet v 1-related sequences. A distance-based phylogenetic tree yielded a classification into 11 subfamilies, nine exclusively containing plant sequences and two subfamilies of bacterial proteins. Plant sequences included the pathogenesis-related proteins 10, the major latex proteins/ripening-related proteins subfamily, and polyketide cyclase-like sequences.</p> <p>Conclusion</p> <p>The ubiquitous distribution of Bet v 1-related proteins among all superkingdoms suggests that a Bet v 1-like protein was already present in the last universal common ancestor. During evolution, this protein diversified into numerous families with low sequence similarity but with a common fold that succeeded as a versatile scaffold for binding of bulky ligands.</p

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models.

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on (i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and (ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi–Sugeno fuzzy systems (termed as PLS-fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, (i) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and (ii) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (self-adaptive forecast models with increased flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic) chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-fuzzy forecast models (showing mostly lower errors than achieved by other model architectures) as well as in terms of Pareto fronts with individuals (solutions) whose fitness was close to the optimal values of three most important target QCs (being used for supervision): flatness, void events and RMSEs of the chips. Suggestions could thus be provided to experts/operators how to best change process values and associated machining parameters at the injection molding process in order to achieve significantly higher product quality for the final chips at the end of the bonding process

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach

Fish-derived low molecular weight components modify bronchial epithelial barrier properties and release of pro-inflammatory cytokines

The prevalence of fish allergy among fish-processing workers is higher than in the general population, possibly due to sensitization via inhalation and higher exposure. However, the response of the bronchial epithelium to fish allergens has never been explored. Parvalbumins (PVs) from bony fish are major sensitizers in fish allergy, while cartilaginous fish and their PVs are considered less allergenic. Increasing evidence demonstrates that components other than proteins from the allergen source, such as low molecular weight components smaller than 3 kDa (LMC) from pollen, may act as adjuvants during allergic sensitization. We investigated the response of bronchial epithelial cells to PVs and to LMC from Atlantic cod, a bony fish, and gummy shark, a cartilaginous fish. Polarized monolayers of the bronchial epithelial cell line 16HBE14owere stimulated apically with fish PVs and/-or the corresponding fish LMC. Barrier integrity, transport of PVs across the monolayers and release of mediators were monitored. Intact PVs from both the bony and the cartilaginous fish were rapidly internalized by the cells and transported to the basolateral side of the monolayers. The PVs did not disrupt the epithelial barrier integrity nor did they modify the release of proinflammatory cytokines. In contrast, LMC from both fish species modified the physical and immunological properties of the epithelial barrier and the responses differed between bony and cartilaginous fish. While the barrier integrity was lowered by cod LMC 24 h after cell stimulation, it was increased by up to 2.3-fold by shark LMC. Furthermore, LMC from both fish species increased basolateral and apical release of IL 6 and IL-8, while CCL2 release was increased by cod but not by shark LMC. In summary, our study demonstrated the rapid transport of PVs across the epithelium which may result in their availability to antigen presenting cells required for allergic sensitization. Moreover, different cell responses to LMC derived from bony versus cartilaginous fish were observed, which may play a role in different allergenic potentials of these two fish classes

WHO/IUIS Allergen Nomenclature: Providing a common language

A systematic nomenclature for allergens originated in the early 1980s, when few protein allergens had been described. A group of scientists led by Dr. David G. Marsh developed a nomenclature based on the Linnaean taxonomy, and further established the World Health Organization/International Union of Immunological Societies (WHO/IUIS) Allergen Nomenclature Sub-Committee in 1986. Its stated aim was to standardize the names given to the antigens (allergens) that caused IgE-mediated allergies in humans. The Sub-Committee first published a revised list of allergen names in 1986, which continued to grow with rare publications until 1994. Between 1994 and 2007 the database was a text table online, then converted to a more readily updated website. The allergen list became the Allergen Nomenclature database (www.allergen.org), which currently includes approximately 880 proteins from a wide variety of sources. The Sub-Committee includes experts on clinical and molecular allergology. They review submissions of allergen candidates, using evidence-based criteria developed by the Sub-Committee. The review process assesses the biochemical analysis and the proof of allergenicity submitted, and aims to assign allergen names prior to publication. The Sub-Committee maintains and revises the database, and addresses continuous challenges as new “omics” technologies provide increasing data about potential new allergens. Most journals publishing information on new allergens require an official allergen name, which involves submission of confidential data to the WHO/IUIS Allergen Nomenclature Sub-Committee, sufficient to demonstrate binding of IgE from allergic subjects to the purified protein

A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production.

We describe a general strategy for optimizing the quality of products of industrial batch processes that comprise multiple production stages. We focus on the particularities of applying this strategy in the field of micro-fluidic chip production. Our approach is based on three interacting components: (i) a new hybrid design of experiments (DoE) strategy that combines expert- and distribution-based space exploration with model-based uncertainty criteria to obtain a representative set of initial samples (i.e., settings of essential machining process parameters), (ii) construction of linear and non-linear predictive mappings from these samples to describe the relation between machining process parameters and resulting quality control (QC) values and (iii) incorporation of these mappings as surrogate fitness estimators into a multi-objective optimization process to discover settings that outperform those routinely used by operators. These optimized settings lead to final products with better quality and/or higher functionality for the clients. The optimization module employs a co-evolutionary strategy we developed that is able to deliver better Pareto non-dominated solutions than the renowned NSGA-II multi-objective solver. We applied our proposed high-level surrogate-based multi-objective strategy both in a single/late-stage optimization scenario and in a more challenging multi-stage scenario, yielding final optimization results that improved parameter settings and thus product quality compared to standard expert-based production process parameterizations