Miracle-dz/UniFAR-FTUs huggingface dataset:
Xenoprotein engineering via synthetic libraries

Significance Combinatorial protein libraries—prepared via molecular biology-based approaches—are invaluable tools for protein engineering. The inclusion of noncanonical amino acids in such libraries is of considerable interest. However, at present no approach competes with chemical synthesis in terms of the variety and number of noncanonical amino acids that can be simultaneously incorporated into a protein molecule. Here, we describe selection from synthetic libraries as a strategy for protein engineering. The approach enables identification of small (∼30 aa), functional protein variants comprising a virtually unlimited variety of noncanonical amino acids. Increasing the throughput of synthetic library screening, which was achieved through this effort, is anticipated to improve the utility of synthetic libraries for identifying polypeptide-based ligands with de novo function. Chemical methods have enabled the total synthesis of protein molecules of ever-increasing size and complexity. However, methods to engineer synthetic proteins comprising noncanonical amino acids have not kept pace, even though this capability would be a distinct advantage of the total synthesis approach to protein science. In this work, we report a platform for protein engineering based on the screening of synthetic one-bead one-compound protein libraries. Screening throughput approaching that of cell surface display was achieved by a combination of magnetic bead enrichment, flow cytometry analysis of on-bead screens, and high-throughput MS/MS-based sequencing of identified active compounds. Direct screening of a synthetic protein library by these methods resulted in the de novo discovery of mirror-image miniprotein-based binders to a ∼150-kDa protein target, a task that would be difficult or impossible by other means.
BRN2, a POUerful driver of melanoma phenotype switching and metastasis

The POU domain family of transcription factors play a central role in embryogenesis and are highly expressed in neural crest cells and the developing brain. BRN2 is a class III POU domain protein that is a key mediator of neuroendocrine and melanocytic development and differentiation. While BRN2 is a central regulator in numerous developmental programs, it has also emerged as a major player in the biology of tumourigenesis. In melanoma, BRN2 has been implicated as one of the master regulators of the acquisition of invasive behaviour within the phenotype switching model of progression. As a mediator of melanoma cell phenotype switching, it coordinates the transition to a dedifferentiated, slow cycling and highly motile cell type. Its inverse expression relationship with MITF is believed to mediate tumour progression and metastasis within this model. Recent evidence has now outlined a potential epigenetic switching mechanism in melanoma cells driven by BRN2 expression that induces melanoma cell invasion. We summarize the role of BRN2 in tumour cell dissemination and metastasis in melanoma, while also examining it as a potential metastatic regulator in other tumour models.
High Dose and Delayed Treatment with Bile Acids Ineffective in RML Prion-Infected Mice

ABSTRACT Prion diseases are a group of neurodegenerative diseases associated with the misfolding of the cellular prion protein (PrPC) into the infectious form (PrPSc). There are currently no treatments for prion disease. Bile acids have the ability to protect hepatocytes from apoptosis and are neuroprotective in animal models of other protein-folding neurodegenerative diseases, including Huntington's, Parkinson's, and Alzheimer's disease. Importantly, bile acids are approved for clinical use in patients with cirrhosis and have recently been shown to be safe and possibly effective in pilot trials of patients with amyotrophic lateral sclerosis (ALS). We previously reported that the bile acid ursodeoxycholic acid (UDCA), given early in disease, prolonged incubation periods in male RML-infected mice. Here, we expand on this result to include tauro-ursodeoxycholic acid (TUDCA) treatment trials and delayed UDCA treatment. We demonstrate that despite a high dose of TUDCA given early in disease, there was no significant difference in incubation periods between treated and untreated cohorts, regardless of sex. In addition, delayed treatment with a high dose of UDCA resulted in a significant shortening of the average survival time for both male and female mice compared to their sex-matched controls, with evidence of increased BiP, a marker of apoptosis, in treated female mice. Our findings suggest that treatment with high-dose TUDCA provides no therapeutic benefit and that delayed treatment with high-dose UDCA is ineffective and could worsen outcomes.
Atrial Fibrillation Recurrence and Peri-Procedural Complication Rates in nMARQ vs. Conventional Ablation Techniques: A Systematic Review and Meta-Analysis

Background and Objectives: Atrial fibrillation is a common abnormal cardiac rhythm caused by disorganized electrical impulses. AF which is refractory to antiarrhythmic management is often treated with catheter ablation. Recently a novel ablation system (nMARQ) was introduced for PV isolation. However, there has not been a systematic review of its efficacy or safety compared to traditional ablation techniques. Therefore, we conducted this meta-analysis on the nMARQ ablation system. Methods: PubMed and EMBASE were searched up until 1st of September 2017 for articles on nMARQ. A total of 136 studies were found, and after screening, 12 studies were included in this meta-analysis. Results: Our meta-analysis shows that the use of nMARQ was associated with higher odds of AF non-recurrence (n = 1123, odds ratio = 6.79, 95% confidence interval 4.01–11.50; P < 0.05; I2 took a value of 83%). Moreover, the recurrence rate of AF using nMARQ was not significantly different from that of traditional ablation procedures (n = 158 vs. 196; OR = 0.97, 95% confidence interval:0.59–1.61). No significant difference in complication rates was observed between these groups (RR: 0.86; 95% CI: 0.37–1.99; P > 0.05). There were four reported mortalities in the nMARQ group compared to none in the conventional ablation group (relative risk: 1.58; 95% CI: 0.09–29.24; P > 0.05). Conclusions: AF recurrence rates are comparable between nMARQ and conventional ablation techniques. Although general complication rates are similar for both groups, the higher mortality with nMARQ suggests that conventional techniques should be used for resistant AF until improved safety profiles of nMARQ can be demonstrated.
How Does a Staggered Board Provision Affect Corporate Strategic Change?—Evidence from China’s Listed Companies

As China’s capital market has become more and more developed, listed companies have begun to establish some anti-takeover provisions to protect their controlling right. Existing studies have examined the consequences of the establishment of such provisions. However, few studies have explored how these provisions affect corporate strategic change. Based on agency theory and prospect theory, this paper proposes two channels through which one of the anti-takeover provisions, staggered board provision, impacts strategic change. Using the data of China’s listed companies which issue A-shares in Shenzhen and Shanghai stock exchanges from 2007 to 2014, these two channels are tested. We find that the existence of a staggered board provision negatively affects the extent of strategic change. In addition, if governance mechanisms restrict directors’ power, the relationship between staggered board provision and strategic change will be weakened, which supports the agency theory. If the listed company is faced with a more dynamic external environment, the relationship between staggered board provision and strategic change will be stronger, which supports the prospect theory. These results are robust after we use a different method to measure strategic change. Our conclusions not only enrich literature about strategic change and anti-takeover provisions, but also are helpful for improving corporate governance in China and other developing countries.
Capturing Dependencies among Labels and Features for Multiple Emotion Tagging of Multimedia Data

In this paper, we tackle the problem of emotion tagging of multimedia data by modeling the dependencies among multiple emotions in both the feature and label spaces. These dependencies, which carry crucial top-down and bottom-up evidence for improving multimedia affective content analysis, have not been thoroughly exploited yet. To this end, we propose two hierarchical models that independently and dependently learn the shared features and global semantic relationships among emotion labels to jointly tag multiple emotion labels of multimedia data. Efficient learning and inference algorithms of the proposed models are also developed. Experiments on three benchmark emotion databases demonstrate the superior performance of our methods to existing methods.
Landmark-Based Heuristics for Goal Recognition

Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.
Perceptron Linear Function Design with CMOS-Memristive Circuits

In the last decade, the interest to emulation of the functionality and structure of the human brain to solve the problems related to image processing and pattern recognition, especially using to Artificial Neural Network (ANN), has increased. Since the capability of ANN to compute at high-speed has been proven to be very useful for various computational problems. One of the simple ANN models is perceptron. Since the perceptron is the basic form of a neural network, the efficient implementation of analog activation functions is required. As various works introduce the design of sigmoid and tangent activation functions, the other activation functions remain an open research problem. This paper describes the design of the perception circuit with the linear activation function using operational amplifier and memristive crossbar. Additionally, the variation of performance with temperature, noise of the circuit is presented.
A hyperthermophilic phage decoration protein suggests common evolutionary origin with Herpesvirus Triplex proteins and an anti-CRISPR protein

Virus capsid proteins reproducibly self-assemble into regularly-shaped, stable shells that protect the viral genome from external environmental assaults, while maintaining the high internal pressure of the tightly packaged viral genome. To elucidate how capsids maintain stability under harsh conditions, we investigated the capsid components of a hyperthermophilic virus, phage P74-26. We determined the structure of a capsid protein gp87 and show that it has the same fold as trimeric decoration proteins that enhance the structural stability of capsids in many other phage, despite lacking significant sequence homology. We also find that gp87 is significantly more stable than its mesophilic homologs, reflecting the high temperature environment in which phage P74-26 thrives. Our analysis of the gp87 structure reveals that the core domain of the decoration protein is conserved in trimeric capsid components across numerous dsDNA viruses, including human pathogens such as Herpesviruses. Moreover, this core β-barrel domain is found in the anti-CRISPR protein AcrIIC1, which suggests a mechanism for the evolution of this broad spectrum Cas9 inhibitor. Our work illustrates the principles for increased stability of a thermophilic decoration protein, and extends the evolutionary reach of the core trimeric decoration protein fold.
Guidelines are endorsed by the Japanese Society of Medical Oncology ( JSMO ) Lynch syndrome

J. Balmaña1, F. Balaguer2, A. Cervantes3 & D. Arnold4, on behalf of the ESMO Guidelines Working Group* Department of Medical Oncology, Hospital Vall d’Hebron, Vall d’Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona; Department of Gastroenterology, Hospital Clínic, CIBERehd, IDIBAPS, University of Barcelona, Barcelona; Department of Hematology and Medical Oncology, INCLIVA, University of Valencia, Valencia, Spain; Department of Medical Oncology, Tumor Biology Clinic, Albert Ludwigs University, Freiburg, Germany;
Issues and promises of bevacizumab in prostate cancer treatment

ABSTRACT Introduction: There is general agreement that increased angiogenesis is an important factor in determining prostate cancer development and prognosis. Vascular Endothelial Growth Factor (VEGF) is thought to play a primary role in the molecular events that lead to prostate cancer progression, from androgen-dependency to castration-resistance until dissemination to the skeleton. Bevacizumab is a recombinant anti-VEGF monoclonal antibody that has exhibited clinical activity in different cancer types. Areas covered: In this review we summarize the data of clinical trials, investigating the effects of bevacizumab in prostate cancer patients. Until now, the drug has demonstrated anti-tumoral activity although with no improvements in overall survival (OS) and a wide range of alarming side effects in metastatic castration-resistant prostate cancer (mCRPC). Recently, promising results were achieved, using bevacizumab in combination with androgen deprivation therapy (ADT) in patients with recurrent prostate cancer after definitive local therapy. Expert opinion: The suboptimal efficacy of bevacizumab may relate to molecular events triggered during disease progression, such as redundancy of angiogenic factors or the interfering influence of androgens on angiogenic pathways. Further studies, using bevacizumab in combination with ADT and/or inhibitors of other key pathways on the subset of patients with low burden, hormone sensitive prostate cancer, need to be conducted.
Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions

Decision maker's preferences are often captured by some choice functions which are used to rank prospects. In this paper, we consider ambiguity in choice functions over a multi-attribute prospect space. Our main result is a robust preference model where the optimal decision is based on the worst-case choice function from an ambiguity set constructed through preference elicitation with pairwise comparisons of prospects. Differing from existing works in the area, our focus is on quasi-concave choice functions rather than concave functions and this enables us to cover a wide range of utility/risk preference problems including multi-attribute expected utility and $S$-shaped aspirational risk preferences. The robust choice function is increasing and quasi-concave but not necessarily translation invariant, a key property of monetary risk measures. We propose two approaches based respectively on the support functions and level functions of quasi-concave functions to develop tractable formulations of the maximin preference robust optimization model. The former gives rise to a mixed integer linear programming problem whereas the latter is equivalent to solving a sequence of convex risk minimization problems. To assess the effectiveness of the proposed robust preference optimization model and numerical schemes, we apply them to a security budget allocation problem and report some preliminary results from experiments.
Computational Methods for Computer Vision: Minimal Solvers and Convex Relaxations

Robust fitting of geometric models is a core problem in computer vision. The most common approach is to use a hypothesize-and-test framework, such as RANSAC. In these frameworks the model is estimated from as few measurements as possible, which minimizes the risk of selecting corrupted measurements. These estimation problems are called minimal problems, and they can often be formulated as systems of polynomial equations. In this thesis we present new methods for building so-called minimal solvers or polynomial solvers, which are specialized code for solving such systems. On several minimal problems we improve on the state-of-the-art both with respect to numerical stability and execution time.In many computer vision problems low rank matrices naturally occur. The rank can serve as a measure of model complexity and typically a low rank is desired. Optimization problems containing rank penalties or constraints are in general difficult. Recently convex relaxations, such as the nuclear norm, have been used to make these problems tractable. In this thesis we present new convex relaxations for rank-based optimization which avoid drawbacks of previous approaches and provide tighter relaxations. We evaluate our methods on a number of real and synthetic datasets and show state-of-the-art results. (Less)
The impact of surgical resection of the primary tumor on the development of synchronous colorectal liver metastasis: a systematic review

Abstract Background: In recent years different therapeutic strategies for synchronously liver metastasized colorectal cancer were described. Apart from the classical staged surgical approach, simultaneous and liver-first strategies are now commonly used. One theoretical drawback of the classical approach is, however, the stimulatory effect on liver metastases growth that may result from resection of the primary tumour. This systematic review, therefore, aims to investigate the current insights on the stimulatory effects of colorectal surgery on the growth of synchronous colorectal liver metastases in humans. Methods: The systematic review was conducted according to the PRISMA statement. A literature search was performed using PubMed and Embase. Articles investigating the effects of colorectal surgery on synchronous colorectal liver metastases were included. Primary endpoints were metastatic tumor volume, metabolic and proliferative activity and tumour vascularization. Results: Four articles meeting the selection criteria were found involving 200 patients. These studies investigate the effects of resection of the primary tumour on synchronous liver metastases using histological and radiological techniques. These papers support a possible stimulatory effect of resection of the primary tumor. Conclusions: Some limited evidence supports the hypothesis that colorectal surgery might stimulate the growth and development of synchronous colorectal liver metastases.
Multi-View Correlated Feature Learning by Uncovering Shared Component

Learning multiple heterogeneous features from different data sources is challenging. One research topic is how to exploit and utilize the correlations among various features across multiple views with the aim of improving the performance of learning tasks, such as classification. In this paper, we propose a new multi-view feature learning algorithm that simultaneously analyzes features from different views. Compared to most of the existing subspace learning methods that only focus on exploiting a shared latent subspace, our algorithm not only learns individual information in each view but also captures feature correlations among multiple views by learning a shared component. By assuming that such a component is shared by all views, we simultaneously exploit the shared component and individual information of each view in a batch mode. Since the objective function is non-smooth and difficult to solve, we propose an efficient iterative algorithm for optimization with guaranteed convergence. Extensive experiments are conducted on several benchmark datasets. The results demonstrate that our proposed algorithm performs better than all the compared multi-view learning algorithms.
Open Conformation of the Escherichia coli Periplasmic Murein Tripeptide Binding Protein, MppA, at High Resolution

Periplasmic ligand-binding proteins (PBPs) bind ligands with a high affinity and specificity. They undergo a large conformational change upon ligand binding, and they have a robust protein fold. These physical features have made them ideal candidates for use in protein engineering projects to develop novel biosensors and signaling molecules. The Escherichia coli MppA (murein peptide permease A) PBP binds the murein tripeptide, l-alanyl-γ-d-glutamyl-meso-diaminopimelate, (l-Ala-γ-d-Glu-meso-Dap), which contains both a D-amino acid and a gamma linkage between two of the amino acids. We have solved a high-resolution X-ray crystal structure of E. coli MppA at 1.5 Å resolution in the unliganded, open conformation. Now, structures are available for this member of the PBP protein family in both the liganded/closed form and the unliganded/open form.
Band Structures and Transport Properties of High-Performance Half-Heusler Thermoelectric Materials by First Principles

Half-Heusler (HH) compounds, with a valence electron count of 8 or 18, have gained popularity as promising high-temperature thermoelectric (TE) materials due to their excellent electrical properties, robust mechanical capabilities, and good high-temperature thermal stability. With the help of first-principles calculations, great progress has been made in half-Heusler thermoelectric materials. In this review, we summarize some representative theoretical work on band structures and transport properties of HH compounds. We introduce how basic band-structure calculations are used to investigate the atomic disorder in n-type MNiSb (M = Ti, Zr, Hf) compounds and guide the band engineering to enhance TE performance in p-type FeRSb (R = V, Nb) based systems. The calculations on electrical transport properties, especially the scattering time, and lattice thermal conductivities are also demonstrated. The outlook for future research directions of first-principles calculations on HH TE materials is also discussed.
Low-Cost Recurrent Neural Network Expected Performance Evaluation

Recurrent neural networks are strong dynamic systems, but they are very sensitive to their hyper-parameter configuration. Moreover, training properly a recurrent neural network is a tough task, therefore selecting an appropriate configuration is critical. There have been proposed varied strategies to tackle this issue, however most of them are still impractical because of the time/resources needed. In this study, we propose a low computational cost model to evaluate the expected performance of a given architecture based on the distribution of the error of random samples. We validate empirically our proposal using three use case.
Formalization of SPARQL over Federated RDF Data

Federated RDF Query Processing is concerned with querying a federation of RDF data sources where the queries are expressed using a declarative query language (typically, the RDF query language SPARQL) and the data sources are autonomous and heterogeneous. The current literature in this context assumes that the data and the data sources are semantically homogeneous, while heterogeneity occurs at the level of data formats and access protocols.