Miracle-dz/UniFAR-FTUs huggingface dataset:
A Reversible and Repeatable Thiol–Ene Bioconjugation for Dynamic Patterning of Signaling Proteins in Hydrogels

Biomolecule-functionalized hydrogels have emerged as valuable cell culture platforms to recapitulate the mechanical and biochemical properties of the extracellular niche. The typical strategy to functionalize hydrogels with biomolecules involves directly tethering them to the hydrogel backbone resulting in a static material. Thus, this approach fails to capture the dynamic changes in biomolecule composition that occur during biological processes or that may be required for regenerative medicine applications. Moreover, it also limits the scope of biomolecules to simple peptides, as signaling proteins generally have poor stability under cell culture conditions and lose their bioactivity over time. To that end, we sought to develop a bioconjugation reaction that would enable reversible and repeatable tethering of signaling proteins to hydrogels, so that spent protein could be released on-demand and replaced with fresh protein as needed. Specifically, we designed an allyl sulfide chain-transfer agent that enables a reversible, photomediated, thiol–ene bioconjugation of signaling proteins to hydrogels. Upon addition of a thiolated protein to the allyl sulfide moiety, the previously tethered protein is released, and the “ene” functionality is regenerated. Using this approach, we demonstrate that protein patterning can be achieved in hydrogels through a thiol–ene reaction, and the patterned protein can then be released through a subsequent thiol–ene reaction of a PEG thiol. Importantly, this process is repeatable through multiple iterations and proceeds at physiologically relevant signaling protein concentrations. Finally, we demonstrate that whole signaling proteins can be patterned and released in the presence of cells, and that cells respond to their presentation with spatial fidelity. Combined, these data represent the first example of a methodology that enables fully reversible and repeatable patterning and release of signaling proteins from hydrogels.
Action Detection from a Robot-Car Perspective

We present the new Road Event and Activity Detection (READ) dataset, designed and created from an autonomous vehicle perspective to take action detection challenges to autonomous driving. READ will give scholars in computer vision, smart cars and machine learning at large the opportunity to conduct research into exciting new problems such as understanding complex (road) activities, discerning the behaviour of sentient agents, and predicting both the label and the location of future actions and events, with the final goal of supporting autonomous decision making.
Genomic cfDNA Analysis of Aqueous Humor in Retinoblastoma Predicts Eye Salvage: The Surrogate Tumor Biopsy for Retinoblastoma

Tumor-derived cell-free DNA (cfDNA) has biomarker potential; therefore, this study aimed to identify cfDNA in the aqueous humor (AH) of retinoblastoma eyes and correlate somatic chromosomal copy-number alterations (SCNA) with clinical outcomes, specifically eye salvage. AH was extracted via paracentesis during intravitreal injection of chemotherapy or enucleation. Shallow whole-genome sequencing was performed using isolated cfDNA to assess for highly recurrent SCNAs in retinoblastoma including gain of 1q, 2p, 6p, loss of 13q, 16q, and focal MYCN amplification. Sixty-three clinical specimens of AH from 29 eyes of 26 patients were evaluated; 13 eyes were enucleated and 16 were salvaged (e.g., saved). The presence of detectable SCNAs was 92% in enucleated eyes versus 38% in salvaged eyes (P = 0.006). Gain of chromosome 6p was the most common SCNA found in 77% of enucleated eyes, compared with 25% of salvaged eyes (P = 0.0092), and associated with a 10-fold increased odds of enucleation (OR, 10; 95% CI, 1.8–55.6). The median amplitude of 6p gain was 1.47 in enucleated versus 1.07 in salvaged eyes (P = 0.001). The presence of AH SCNAs was correlated retrospectively with eye salvage. The probability of ocular salvage was higher in eyes without detectable SCNAs in the AH (P = 0.0028), specifically 6p gain. This is the first study to correlate clinical outcomes with SCNAs in the AH from retinoblastoma eyes, as such these findings indicate that 6p gain in the aqueous humor is a potential prognostic biomarker for poor clinical response to therapy. Implications: The correlation of clinical outcomes and SCNAs in the AH identified in the current study requires prospective studies to validate these finding before SCNAs, like 6p gain, can be used to predict clinical outcomes at diagnosis. Mol Cancer Res; 16(11); 1701–12. ©2018 AACR.
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation

We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT). Unlike the popular length normalization and coverage models, our model does not require training nor reranking the limited n-best outputs. Moreover, it is robust to large beam sizes, which is not well studied in previous work. On the Chinese-English and English-German translation tasks, our approach yields +0.4 1.5 BLEU improvements over the state-of-the-art baselines.
Game-Theoretic Approach to Malicious Controller Detection in Software Defined Networks

Software Defined Networking (SDN) enables programmability and flexibility in networks through the separation of control and data plane. SDN architecture poses new security threats to the network, especially in the control plane. A compromised controller can exhibit malicious behavior such as fraudulent rule installation while avoiding detection. Existing approaches deal with this issue by broadcasting every flow setup request to multiple controllers to check for forwarding rule consistency. This imposes heavy load on the control plane, leading to longer response time and increased cost. We propose a novel approach that can effectively detect a malicious controller without overloading the control plane. The proposed game-theoretic approach randomly selects switches to check for consistency of forwarding rules. We model the problem as a Stackelberg game and solve the corresponding optimization problem to obtain an effective randomization strategy. We also develop a heuristic algorithm to determine a set of actions for the defender to make the problem tractable. We consider load management at the controller and relative importance of switches while finding an optimal strategy for randomized checking. In comparison with three other heuristic approaches, our approach achieves up to 88% improvement in probability of detecting the malicious controller.
NovelPerspective: Identifying Point of View Characters

We present NovelPerspective: a tool to allow consumers to subset their digital literature, based on point of view (POV) character. Many novels have multiple main characters each with their own storyline running in parallel. A well-known example is George R. R. Martin’s novel: “A Game of Thrones”, and others from that series. Our tool detects the main character that each section is from the POV of, and allows the user to generate a new ebook with only those sections. This gives consumers new options in how they consume their media; allowing them to pursue the storylines sequentially, or skip chapters about characters they find boring. We present two heuristic-based baselines, and two machine learning based methods for the detection of the main character.
Improving OpenStack Networking: Advantages and Performance of Native SDN Integration

A key aspect that Telco operators must carefully consider when deploying Network Function Virtualization (NFV) solutions is the level of performance that cloud computing software platforms can guarantee in support of the offered network services. OpenStack is widely considered as one of the most relevant open-source frameworks that could accelerate the NFV adoption, also because the evolution of its networking components sees a progressive integration of SDN-based solutions that could significantly improve the performance of cloud-based connectivity services. In this paper, we discuss some of the most recent OpenStack innovations that enable a native SDN-like approach to firewalling functions in the data plane, as well as a native SDN-oriented control of the virtual network infrastructure. Then we present a detailed performance analysis of the aforementioned innovations at both the data and control/management plane, showing the potentials of native SDN adoption within OpenStack toward an integrated solution for production-level NFV deployments.
Robust Detection of Android UI Similarity

The similarity of the user interfaces (UIs) among Android apps is an important factor to indicate abnormal behaviors in Android apps. For example, phishing apps use UIs similar to those of their target apps to lure users to input sensitive information. As another example, repackaged apps preserve the UIs of the original apps while adding malicious code. In this paper, we propose a novel solution, GeminiScope, to robustly detect similar UIs among Android apps. Our approach analyzes the UI layouts of Android apps, extracts the fundamental features of the UIs' visual appearance, and rates the similarity among the UIs of apps. We evaluated GeminiScope using a few sets of apps from various sources. It reliably detected apps with similar UIs, showing the potential of using visual similarity as a unique feature to detect malicious apps.
Clustering Analysis of FDG-PET Imaging in Primary Progressive Aphasia

Background: Primary progressive aphasia (PPA) is a clinical syndrome characterized by the neurodegeneration of language brain systems. Three main clinical forms (non-fluent, semantic, and logopenic PPA) have been recognized, but applicability of the classification and the capacity to predict the underlying pathology is controversial. We aimed to study FDG-PET imaging data in a large consecutive case series of patients with PPA to cluster them into different subtypes according to regional brain metabolism. Methods: 122 FDG-PET imaging studies belonging to 91 PPA patients and 28 healthy controls were included. We developed a hierarchical agglomerative cluster analysis with Ward's linkage method, an unsupervised clustering algorithm. We conducted voxel-based brain mapping analysis to evaluate the patterns of hypometabolism of each identified cluster. Results: Cluster analysis confirmed the three current PPA variants, but the optimal number of clusters according to Davies-Bouldin index was 6 subtypes of PPA. This classification resulted from splitting non-fluent variant into three subtypes, while logopenic PPA was split into two subtypes. Voxel-brain mapping analysis displayed different patterns of hypometabolism for each PPA group. New subtypes also showed a different clinical course and were predictive of amyloid imaging results. Conclusion: Our study found that there are more than the three already recognized subtypes of PPA. These new subtypes were more predictive of clinical course and showed different neuroimaging patterns. Our results support the usefulness of FDG-PET in evaluating PPA, and the applicability of computational methods in the analysis of brain metabolism for improving the classification of neurodegenerative disorders.
Multi-Layer Depth of Field Rendering with Tiled Splatting

In this paper we present a scattering-based method to compute high quality depth of field in real time. Relying on multiple layers of scene data, our method naturally supports settings with partial occlusion, an important effect that is often disregarded by real time approaches. Using well-founded layer-reduction techniques and efficient mapping to the GPU, our approach out-performs established approaches with a similar high-quality feature set. Our proposed algorithm works by collecting a multi-layer image, which is then directly reduced to only keep hidden fragments close to discontinuities. Fragments are then further reduced by merging and then splatted to screen-space tiles. The per-tile information is then sorted and accumulated in order, yielding an overall approach that supports partial occlusion as well as properly ordered blending of the out-of-focus fragments.
Physically-based Muscles and Fibers Modeling from Superficial Patches

We propose a novel approach for the generation of volumetric muscle primitives and their associated fiber field, suitable for simulation in computer animation. Muscles are notoriously difficult to sculpt because of their complex shapes and fiber architecture, therefore often requiring trained artists to render anatomical details. Moreover, physics simulation requires these geometries to be modeled in an intersection-free rest state and to have a spatially-varying fiber field to support contraction with anisotropic material models. Inspired by the principles of computational design, we satisfy these requirements by generating muscle primitives automatically, complete with tendons and fiber fields, using physics based simulation of inflatable 3D patches which are user-defined on the external mesh of a character.
Minimizing the Risk of Perinatal Human Immunodeficiency Virus Transmission.

Importance There are approximately 284,500 adolescent and adult women living with human immunodeficiency virus (HIV) in the United States. It is estimated that approximately 8500 of these women give birth annually. While the rate of perinatal transmission in the United States has decreased by more than 90% since the early 1990s, potentially preventable HIV transmission events still occur and cause significant morbidity and mortality. Objective The aim of this review was to summarize the current data regarding perinatal HIV transmission timing and risk factors, current management recommendations, and implications of timing of transmission on patient management. Evidence Acquisition Literature review. Results This review reiterates that the risk of perinatal HIV transmission can be reduced to very low levels by following current recommendations for screening for HIV in all pregnant women and properly treating HIV-infected mothers, as well as using evidence-based labor management practices. Conclusions and Relevance Familiarity with the pathogenesis of HIV transmission is important for obstetric care providers to appropriately manage HIV-infected women in pregnancy, intrapartum, and the postpartum period.
Using Sensory Evaluation to Determine the Highest Acceptable Concentration of Mango Seed Extract as Antibacterial and Antioxidant Agent in Fresh-Cut Mango

Plant extracts have the potential to be used as food additives; however, their use have been limited by causing undesirable changes in the sensory attributes of foods. We characterized the mango seed extract as a preserving agent for fresh-cut mangoes. We established the maximum concentration of extract that, while increasing the antioxidant activity, and limiting microbial contamination of the fruit, did not negatively affect fruit sensory acceptability. The extract contained 277.4 g gallic acid equivalent (GAE)/kg dw (dry weight) of polyphenols and 143.7 g quercetin equivalent (QE)/kg dw of flavonoids. Antioxidant capacity values were 2034.1 and 4205.7 μmol Trolox equivalent (TE)/g against 2,2-diphenyl-1-picryl-hydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radicals, respectively. Chromatographic analysis revealed the presence of gallic and chlorogenic acids. The extract (16 g/L) inhibited the growth of Escherichia coli, Salmonella Typhimurium, Staphylococcus aureus and Listeria monocytogenes. The highest concentration with sensory acceptability was 6.25 g/L. At such concentration, the extract preserved fresh-cut fruits, increasing polyphenols (0.427 g GAE/kg fw (fresh weight)), flavonoid content (0.234 g QE/kg fw) and antioxidant activity (DPPH = 2.814 and ABTS = 0.551 mol TE/kg fw). It also reduced inoculated bacteria (range: 5.50 × 103 to 1.44 × 105 colony forming units (CFU)/g). These results showed the importance of considering consumer acceptability to determine the effective concentration of plant extracts as additives.
Goal Modelling for Security Problem Matching and Pattern Enforcement

This article describes how earlier detection of security problems and the implementation of solutions would be a cost-effective approach for developing secure software systems. Developing, gathering and sharing similar repeatable programming knowledge and solutions has led to the introduction of Patterns in the 90's. The same concept has been adopted to realise reoccurring security knowledge and hence security patterns. Detecting a security problem using the patterns in requirements models may lead to its early prevention. In this article, the authors have provided an overview of security patterns in the past two decades, followed by a summary of i*/Tropos goal modelling framework. Section 2 outlines model-driven development, meta-models and model transformation, within the context of requirements engineering. They have summarised security access control types, and formally described role-based access control (RBAC) in particular as a pattern that may occur in the stakeholder requirements models. Then the authors used the i* modelling language and some elements from its constructs - model-driven queries and transformations - to describe the pattern enforcement. This is applied to a number of requirements models within the literature, and the pattern-based transformation tool they designed has automated the detection and resolution of this security pattern in several goal-oriented stakeholder requirements. Finally, the article also reflects on a variety of existing applications and future work.
Exploiting Unbroken Surface Congruity for the Acceleration of Fragment Reassembly

Virtual reassembly problems are often encountered in the cultural heritage domain. The reassembly or "puzzling" problem is typically described as the process for the identiﬁcation of corresponding pieces within a part collection, followed by the clustering and pose estimation of multiple parts that result in a virtual representation of assembled objects. This work addresses this problem with an efﬁcient, user-guided computational approach. The proposed approach augments the typical reassembly pipeline with a smart culling step, where geometrically incompatible fragment combinations can be quickly rejected. After splitting each fragment into potentially fractured and intact facets, each intact facet is examined for prominent linear or curved structures and a heuristic test is employed to evaluate the plausibility of facet pairs, by comparing the number of feature curves associated with each facet, as well as the geometric texture of associated intact surfaces. This test excludes many pairwise combinations from the remaining part of the reassembly process, signiﬁcantly reducing overall time cost. For all facet pairs that pass the initial plausibility test, pairwise registration driven by enhanced simulated annealing is applied, followed by multi-part registration. The proposed reassembly approach is evaluated on real scanned data and our experiments demonstrate an increase in efﬁciency that ranges from 30% to more than 500% in some cases, depending on the number of culled combinations.
Does Fast Beat Thorough? Comparing RTAA* and LSS-LRTA

The RTAA* algorithm has been proposed as an alternative to the LSS-LRTA* algorithm for heuristic search under hard time constraints. It uses a cruder but faster learning procedure. Experimental results on pathfinding in mazes in the unknown terrain setting indicated that RTAA* was superior to LSS-LRTA*. In this paper, we attempt to replicate those results and we extend the analysis to additional domains. Our results suggest that RTAA* is superior to LSS-LRTA* only in situations where the heuristic is already relatively accurate, either inherently or because of large lookahead. Overall, neither algorithm seems superior to the other.
Radio Map Efficient Building Method Using Tensor Completion for WLAN Indoor Positioning System

Wireless local area network (WLAN) fingerprint-based indoor positioning system has a wide application prospect because it supplies good positioning performance without the requirement of additional hardware installations. However, huge labor and time are needed to establish the fingerprint database called radio map. To address this problem, we propose an efficient radio map building method using tensor completion. The cost of the proposed method is reduced by lessening the number of reference points (RPs) to be measured. Due to the strong correlation between data, estimating received signal strength (RSS) at unmeasured reference points can be formulated as a low rank tensor completion problem. And the issue can be transformed into a convex optimization problem by substituting rank operation with trace norm operation. We solve the problem by employing the alternating direction method of multipliers (ADMM) algorithm. The experiment results indicate that the proposed method can not only reduce the effort of radio map building remarkably, but also achieve high positioning accuracy.
Simplified Abugidas

An abugida is a writing system where the consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics. We investigate the feasibility of recovering the original text written in an abugida after omitting subordinate diacritics and merging consonant letters with similar phonetic values. This is crucial for developing more efficient input methods by reducing the complexity in abugidas. Four abugidas in the southern Brahmic family, i.e., Thai, Burmese, Khmer, and Lao, were studied using a newswire 20,000-sentence dataset. We compared the recovery performance of a support vector machine and an LSTM-based recurrent neural network, finding that the abugida graphemes could be recovered with 94% - 97% accuracy at the top-1 level and 98% - 99% at the top-4 level, even after omitting most diacritics (10 - 30 types) and merging the remaining 30 - 50 characters into 21 graphemes.
Solving the Synergistic Team Composition Problem

Co-operative learning is used to refer to learning procedures for heterogeneous teams in which individual and teamwork are organised to complete academic tasks. Key factors for team performance are competences, personality and team members' genders. Here, we present a computational model to form heterogeneous teams that incorporates those key factors. In addition, we propose efficient algorithms to partition a classroom into teams of even size and homogeneous performance. The first algorithm is based on an ILP formulation. For small problem instances this approach is appropriate. However, this is not the case for large problems, for which we propose a heuristic algorithm. We study the computational properties of both algorithms in the context of student teams.