Miracle-dz/UniFAR-FTUs huggingface dataset:
T-DM1-resistant cells gain high invasive activity via EGFR and integrin cooperated pathways

ABSTRACT Ado-trastuzumab emtansine (Kadcyla®; T-DM1) is an antibody-drug conjugate developed to treat trastuzumab-resistant disease. Despite initial favorable outcomes, most patients eventually cease to respond due to developing acquired resistance to T-DM1. Currently, there is no targeted therapy to treat T-DM1-resistant disease. To explore novel therapeutic targets to improve therapeutic efficacy of T-DM1, we generated T-DM1-resistant cells using trastuzumab-resistant JIMT1 cells. We found that the loss of human epidermal growth factor receptor 2 confers T-DM1 resistance, which in turn activates a compensatory mechanism that increases epidermal growth factor receptor (EGFR) expression. Upregulation of EGFR increases the protein levels of α5β1 and αVβ3 integrins, resulting in enhanced motility and invasion of T-DM1-resistant cells. This study delineates previously unappreciated relationships between α5β1 and αVβ3 and suggests that specific integrins should be carefully selected as therapeutic targets to treat T-DM1-resistant disease. Specifically, silencing β1 integrin expression by siRNA in T-DM1-resistant cells destabilizes α5, but increases expression of αV, a critical integrin mediating the invasion and metastases in many different cancers. As a consequence, T-DM1-resistant cells gain metastatic potential and become more invasive. This finding is underscored by the fact that β1 integrin blockage induced by an inhibitory antibody, MAB 13, significantly increases invasion of T-DM1-resistant cells. However, the increased cell invasion induced by β1 integrin blockage can be significantly reduced by either EGFR inhibitor or specific siRNA against αV integrin. The discovery of functional cooperation between EGFR and αV integrin in regulating cell growth and invasion provides an opportunity to develop novel therapeutic strategy by dual-targeting EGFR and specific integrin to overcome T-DM1 resistance.
Attention Restoration Theory II: a systematic review to clarify attention processes affected by exposure to natural environments

ABSTRACT Attention Restoration Theory (ART) predicts exposure to natural environments may lead to improved cognitive performance through restoration of a limited cognitive resource, directed attention. A recent review by Ohly and colleagues (2016) uncovered substantial ambiguity surrounding details of directed attention and how cognitive restoration was tested. Therefore, an updated systematic review was conducted to identify relevant cognitive domains from which to describe elements of directed attention sensitive to the restoration effect. Forty-two articles that tested natural environments or stimuli against a suitable control, and included an objective measure of cognitive performance, had been published since July 2013. Articles were subjected to screening procedures and quality appraisal. Random effects meta-analyses were performed to calculate pooled effect sizes across 8 cognitive domains using data from 49 individual outcome measures. Results showed that working memory, cognitive flexibility, and to a less-reliable degree, attentional control, are improved after exposure to natural environments, with low to moderate effect sizes. Moderator analyses revealed that actual exposures to real environments may enhance the restoration effect within these three domains, relative to virtual exposures; however, this may also be due to differences in the typical lengths of exposure. The effect of a participants’ restoration potential, based upon diagnosis or fatigue-induction, was less clear. A new framework is presented to qualify the involvement of directed attention-related processes, using examples of tasks from the three cognitive domains found to be sensitive to the restoration effect. The review clarifies the description of cognitive processes sensitive to natural environments, using current evidence, while exploring aspects of protocol that appear influential to the strength of the restoration effect.
Coarsening Networks Based on Local Link Similarity for Community Detection

How can we detect the natural community structure in a social network mingled with anomalous links? The majority of existing methods almost universally ignore these anomalous links (either correct or incorrect), and treat them as ordinary links without taking their particularities into account. From a novel perspective, we focus on handling anomalous links for community detection, since the anomalous links derived from noise directly affect the accuracy of community detection and removing them makes it much easier to comprehend the network topology. Inspired by link prediction, an effective approach is proposed to coarsen networks which removes abnormal links by virtue of the local similarity index. Extensive experiments demonstrate that our strategy achieves higher accuracy than raw networks, especially when a network has high mixing of communities. For real-world networks, the improvement is up to 8.7%. In addition, fine-grained communities, usually revealing rich information in real-world networks, can be well detected.
An Improved Promoter Recognition Model Using Convolutional Neural Network

Gene expression is regulated by transcription and translation, and the promoter controls the start of transcription. Finding the exact location of a promoter is of great importance to life science. With the development of the Next-Generation Sequencing (NGS), more and more eukaryotic gene sequence data are available. Computational prediction of eukaryotic promoters has become one of the most challenging problems in sequence analysis. Many methods have been proposed, but the accuracy of prediction still needs to be improved. In this paper we use support vector machine (SVM) to verify that promoter elements are more important than non-elements for predicting promoters. With this factor in mind, we utilize convolution filters to compress non-elements information, and encode elements to emphasize their importance. A new prediction model is constructed based on neural networks. We applied a 10-fold cross validation test to validate the proposed model. We achieved 89.86% accuracy, 86.51% specificity and 89.64% sensitivity, which are better than the other three prediction methods (SVM, NNPP2.2 and CNN).
Charging utility maximization in wireless rechargeable sensor networks

Wireless energy transfer as a promising technology provides an alternative solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). In this paper, we study replenishing energy on sensors in a WRSN to shorten energy expiration durations of sensors, by employing a mobile wireless charger to replenish sensors dynamically. We first formulate a novel sensor recharging problem with an objective of maximizing the charging utility of sensors, subject to the total traveling distance of the mobile charger per tour and the charging time window of each to-be-charged sensor. Due to the NP-hardness of the problem, we then propose an approximation algorithm with quasi-polynomial time complexity. In spite of the guaranteed performance ratio of the approximate solution, its time complexity is prohibitively high and may not be feasible in practice. Instead, we devise a fast yet scalable heuristic for the problem in response to dynamic energy consumption of sensors in the network. Furthermore, we also consider the online version of the problem where sensor replenishment is scheduled at every fixed time interval. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are very promising.
Treatment of Unilateral PA by Adrenalectomy: Potential Reasons for Incomplete Biochemical Cure

Abstract The importance of an early diagnosis and appropriate management of patients with primary aldosteronism (PA) has become increasingly clear because of the adverse impact of the disorder on cardiovascular and cerebrovascular events and target organ damage. Adrenalectomy potentially cures patients with unilateral PA resulting in normalisation of blood pressure or significant clinical improvements in the majority of patients. Different criteria have been used to evaluate outcomes of unilateral adrenalectomy. Clinical remission (cure of hypertension) is observed in 6% to 86% of patients and clinical benefits from surgery are seen in the majority. Several factors have been identified that predict clinical success after surgery such as age, sex, anti-hypertensive medication dosage and known duration of hypertension. Biochemical remission of PA after unilateral adrenalectomy, characterised by the resolution of hyperaldosteronism and correction of pre-surgical hypokalaemia, is observed in 67% to 100% of patients with unilateral PA. In only a small proportion of patients, adrenalectomy fails to resolve hyperaldosteronism and inappropriate aldosterone production persists after surgery. In this review we discuss the potential reasons for failing to cure hyperaldosteronism after unilateral adrenalectomy for unilateral primary aldosteronism.
New Alpiniamides From Streptomyces sp. IB2014/011-12 Assembled by an Unusual Hybrid Non-ribosomal Peptide Synthetase Trans-AT Polyketide Synthase Enzyme

The environment of Lake Baikal is a well-known source of microbial diversity. The strain Streptomyces sp. IB2014/011-12, isolated from samples collected at Lake Baikal, was found to exhibit potent activity against Gram-positive bacteria. Here, we report isolation and characterization of linear polyketide alpiniamide A (1) and its new derivatives B–D (2–5). The structures of alpiniamides A–D were established and their relative configuration was determined by combination of partial Murata’s method and ROESY experiment. The absolute configuration of alpiniamide A was established through Mosher’s method. The gene cluster, responsible for the biosynthesis of alpiniamides (alp) has been identified by genome mining and gene deletion experiments. The successful expression of the cloned alp gene cluster in a heterologous host supports these findings. Analysis of the architecture of the alp gene cluster and the feeding of labeled precursors elucidated the alpiniamide biosynthetic pathway. The biosynthesis of alpiniamides is an example of a rather simple polyketide assembly line generating unusual chemical diversity through the combination of domain/module skipping and double bond migration events.
The Maximal Length of 2-Path in Random Critical Graphs

Copyright © 2018 Vonjy Rasendrahasina et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Given a graph, its 2-core is the maximal subgraph of G without vertices of degree 1. A 2-path in a connected graph is a simple path in its 2-core such that all vertices in the path have degree 2, except the endpoints which have degree ⩾ 3. Consider the Erdős-Rényi random graph G(n,M) built with n vertices andM edges uniformly randomly chosen from the set of ( 2 ) edges. Let ξn,M be the maximum 2-path length of G(n,M). In this paper, we determine that there exists a constant c(λ) such that E(ξn,(n/2)(1+λn−1/3)) ∼ c(λ)n1/3, for any real λ. This parameter is studied through the use of generating functions and complex analysis.
Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45–91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.
Downsampling Strategies are Crucial for Word Embedding Reliability

The reliability of word embeddings algorithms, i.e., their ability to provide consistent computational judgments of word similarity when trained repeatedly on the same data set, has recently raised concerns. We compared the effect of probabilistic and weighting as downsampling strategies. We found the latter to provide superior reliability while being competitive in accuracy when applied to singular value decomposition-based embeddings
An Ancestral Allele of Pyrroline-5-carboxylate synthase1 Promotes Proline Accumulation and Drought Adaptation in Cultivated Barley1[OPEN]

Allelic variation of Pyrroline-5-carboxylate synthase1 underlies sequence divergence across the promoter in the cultivated and wild barley which modulates its drought-inducible transcriptional activity. Water scarcity is a critical threat to global crop production. Here, we used the natural diversity of barley (Hordeum vulgare) to dissect the genetic control of proline (Pro) mediated drought stress adaptation. Genetic mapping and positional cloning of a major drought-inducible quantitative trait locus (QPro.S42-1H) revealed unique allelic variation in pyrroline-5-carboxylate synthase (P5cs1) between the cultivated cultivar Scarlett (ssp. vulgare) and the wild barley accession ISR42-8 (ssp. spontaneum). The putative causative mutations were located in the promoter of P5cs1 across the DNA binding motifs for abscisic acid-responsive element binding transcription factors. Introgression line (IL) S42IL-143 carrying the wild allele of P5cs1 showed significant up-regulation of P5cs1 expression compared to Scarlett, which was consistent with variation in Pro accumulation under drought. Next, we transiently expressed promoter::reporter constructs of ISR42-8 and Scarlett alleles in Arabidopsis (Arabidopsis thaliana) mesophyll protoplasts. GUS expression analysis showed a significantly higher activation of the ISR42-8 promoter compared to Scarlett upon abscisic acid treatment. Notably, the ISR42-8 promoter activity was impaired in protoplasts isolated from the loss-of-function abf1abf2abf3abf4 quadruple mutant. A series of phenotypic evaluations demonstrated that S42IL-143 maintained leaf water content and photosynthetic activity longer than Scarlett under drought. These findings suggest that the ancestral variant of P5cs1 has the potential for drought tolerance and understanding drought physiology of barley and related crops.
Improving the Effectiveness of SQL Learning Practice: A Data-Driven Approach

Most engineering courses include fundamental practice activities to be performed by students in computer labs. During lab sessions, students work on solving exercises with the help of teaching assistants, who often have a hard time for guaranteeing a timely, optimized, and "democratic" support to everybody. This paper presents a learning environment to improve the experience of the lab sessions participants, both the students and the teaching assistants. In particular, the environment was designed, implemented, and experimented in the context of a database course. The application designed to support the learning environment stores all the events occurring during a SQL practice lab, i.e., task progression, query submissions, error feedback, assistance requests and interventions, and it provides information useful both for use on-the-fly and for later analysis. Thanks to the analysis of these data, the application dynamically provides teaching assistants with a graphical interface highlighting where assistance is most needed, by considering different factors such as the progression rate, the percentage of correct solutions, and the difficulties in solving the current exercise. Furthermore, the stored data allow teachers later on to analyze and to interpret the behavior of the students during the lab, and to have insights on their main mistakes and misconceptions. After describing the environment, the interfaces, and the approaches used to identify the students' teams that need timely assistance, the paper presents the results of different analyses performed using the collected data, to help the teacher better understand students' educational needs.
Recirculation Surfaces for Flow Visualization

We present a formal approach to the visual analysis of recirculation in flows by introducing recirculation surfaces for 3D unsteady flow fields. Recirculation surfaces are the loci where massless particle integration returns to its starting point after some variable, finite integration. We give a rigorous definition of recirculation surfaces as 2-manifolds embedded in 5D space and study their properties. Based on this we construct an algorithm for their extraction, which searches for intersections of a recirculation surface with lines defined in 3D. This reduces the problem to a repeated search for critical points in 3D vector fields. We provide a uniform sampling of the search space paired with a surface reconstruction and visualize results. This way, we present the first algorithm for a comprehensive feature extraction in the 5D flow map of a 3D flow. The problem of finding isolated closed orbits in steady vector fields occurs as a special case of recirculation surfaces. This includes isolated closed orbits with saddle behavior. We show recirculation surfaces for a number of artificial and real flow data sets.
Generation and characterization of a high affinity anti-human FcRn antibody, rozanolixizumab, and the effects of different molecular formats on the reduction of plasma IgG concentration

ABSTRACT Rozanolixizumab (UCB7665), a humanized high-affinity anti-human neonatal Fc receptor (FcRn) monoclonal antibody (IgG4P), has been developed to reduce pathogenic IgG in autoimmune and alloimmune diseases. We document the antibody isolation and compare rozanolixizumab with the same variable region expressed in various mono-, bi- and trivalent formats. We report activity data for rozanolixizumab and the different molecular formats in human cells, FcRn-transgenic mice, and cynomolgus monkeys. Rozanolixizumab, considered the most effective molecular format, dose-dependently and selectively reduced plasma IgG concentrations in an FcRn-transgenic mouse model (no effect on albumin). Intravenous (IV) rozanolixizumab dosing in cynomolgus monkeys demonstrated non-linear pharmacokinetics indicative of target-mediated drug disposition; single IV rozanolixizumab doses (30 mg/kg) in cynomolgus monkeys reduced plasma IgG concentration by 69% by Day 7 post-administration. Daily IV administration of rozanolixizumab (initial 30 mg/kg loading dose; 5 mg/kg daily thereafter) reduced plasma IgG concentrations in all cynomolgus monkeys, with low concentrations maintained throughout the treatment period (42 days). In a 13-week toxicology study in cynomolgus monkeys, supra-pharmacological subcutaneous and IV doses of rozanolixizumab (≤ 150 mg/kg every 3 days) were well tolerated, inducing sustained (but reversible) reductions in IgG concentrations by up to 85%, with no adverse events observed. We have demonstrated accelerated natural catabolism of IgG through inhibition of IgG:FcRn interactions in mice and cynomolgus monkeys. Inhibition of FcRn with rozanolixizumab may provide a novel therapeutic approach to reduce pathogenic IgG in human autoimmune disease. Rozanolixizumab is being investigated in patients with immune thrombocytopenia (NCT02718716) and myasthenia gravis (NCT03052751).
A novel method to generate dynamic boundary conditions for airway CFD by mapping upper airway movement with non‐rigid registration of dynamic and static MRI

Computational fluid dynamics (CFD) simulations of airflow in the human airways have the potential to provide a great deal of information that can aid clinicians in case management and surgical decision making, such as airway resistance, energy expenditure, airflow distribution, heat and moisture transfer, and particle deposition, as well as the change in each of these due to surgical interventions. However, the clinical relevance of CFD simulations has been limited to date, as previous models either did not incorporate neuromuscular motion or any motion at all. Many common airway pathologies, such as obstructive sleep apnea (OSA) and tracheomalacia, involve large movements of the structures surrounding the airway, such as the tongue and soft palate. Airway wall motion may be due to many factors including neuromuscular motion, internal aerodynamic forces, and external forces such as gravity. Therefore, to realistically model these airway diseases, a method is required to derive the airway wall motion, whatever the cause, and apply it as a boundary condition to CFD simulations.
A Wisdom Search Engine

Human message can be classified into data, information, knowledge and wisdom. It is accepted by many that wisdom is at a higher level of human "knowledge". Presently, people may use a search engine as their first port of call in searching answers to all sorts of questions, including wisdoms. In this sense, broadly speaking, knowledge can be searched, for example, in the Internet. Commonly-used search engines normally supply search results in data, information and knowledge. The proposed search engine aims at supplying wisdom to users. To be more precise, this is a special "search" engine in that wisdoms are searched/calculated/generated.
Efficient Discovery of Traveling Companion from Evolving Trajectory Data Stream

Trajectory data stream contain an enormous amount of data about spatial and temporal information of moving objects. Discovering useful pattern from moving objects can convey valuable knowledge to a variety applications such as transportation management, military surveillance, and weather forecasting by analyzing animal movement behaviors. In many real applications, trajectory data keep coming into the database or server for immediate analysis. Most moving objects' pattern discovery approaches analyze the data by re-computing from scratch. Now, existing some group pattern approaches incrementally illuminate this problem. However, there was still high computational time complexity to the efficient and accurate discovery of traveling together moving objects(i.e., traveling companion) from evolving data streams. In this paper, we propose micro-group based clustering algorithm over evolving data stream to reduce computational time complexity. Experiments of this proposed system will conduct on real taxi trajectory data and synthetic data.