Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibitory Role of the Mud Crab Short Neuropeptide F in Vitellogenesis and Oocyte Maturation via Autocrine/Paracrine Signaling

Neuropeptides, in addition to their classical role in the nervous system, act on intraovarian factors to regulate reproductive functions in vertebrates. However, this function of neuropeptides has not been characterized in crustaceans. Short neuropeptide F (sNPF), a highly conserved invertebrate neuropeptide, has been reported to be involved in feeding, metabolism, and in differentiation processes including reproduction. Although sNPF and its receptor (sNPFR) have been detected in the ovary in different species, ovarian colocalization of sNPF/sNPFR has not been investigated. In this study, we identified Scylla paramamosain (mud crab) sNPF (Sp-sNPF) as an endogenous ligand for the S. paramamosain orphan G protein-coupled receptor NPY2R in mammalian cell line HEK293T. We designated this receptor as Sp-sNPFR. RNA in situ hybridization in pre-vitellogenic ovary and reverse transcription-PCR on isolated denuded oocytes and follicle layers showed that Sp-sNPF was exclusively localized to the follicle cells, whereas Sp-sNPFR was detected in both follicle cells and oocytes. We also found that Sp-sNPF partly suppressed spontaneous maturation of denuded oocytes and caused intracellular cAMP accumulation and Ca2+ mobilization. Moreover, injection of synthetic Sp-sNPF peptides inhibited the expression of vitellogenin and vitellogenin receptor genes in vivo. These combined results suggest for the first time that Sp-sNPF may have inhibitory functions in vitellogenesis and oocyte maturation possibly via the autocrine/paracrine pathway.
Comparison of treatment effects between four premolar extraction and total arch distalization using the modified C-palatal plate

Objective The purpose of this study was to compare the skeletal, dental, and soft-tissue treatment effects of nonextraction therapy using the modified C-palatal plate (MCPP) to those of premolar extraction (PE) treatment in adult patients with Class II malocclusion. Methods Pretreatment and posttreatment lateral cephalographs of 40 adult patients with Class II malocclusion were retrospectively analyzed. The MCPP group comprised 20 patients treated with total arch distalization of the maxillary arch while the PE group comprised 20 patients treated with four PE. Fifty-eight linear and angular measurements were analyzed to assess the changes before and after treatment. Descriptive statistics, paired t-test, and multivariate analysis of variance were performed to evaluate the treatment effects within and between the two groups. Results The MCPP group presented 3.4 mm of retraction, 1.0 mm of extrusion, and 7.3° lingual inclination of the maxillary central incisor. In comparison, the PE group displayed greater amount of maxillary central incisor retraction and retroclination, mandibular incisor retraction, and upper lip retraction (5.3 mm, 14.8°, 5.1 mm, and 2.0 mm, respectively; p < 0.001 for all). In addition, the MCPP group showed 4.0 mm of distalization and 1.3 mm of intrusion with 2.9° distal tipping of the maxillary first molars. Conclusions These findings suggest the MCPP is an effective distalization appliance in the maxillary arch. The amount of incisor retraction, however, was significantly higher in the PE group. Therefore, four PE may be recommended when greater improvement of incisor position and soft-tissue profile is required.
Augusto: Exploiting Popular Functionalities for the Generation of Semantic GUI Tests with Oracles

Testing software applications by interacting with their graphical user interface (GUI) is an expensive and complex process. Current automatic test case generation techniques implement explorative approaches that, although producing useful test cases, have a limited capability of covering semantically relevant interactions, thus frequently missing important testing scenarios. These techniques typically interact with the available widgets following the structure of the GUI, without any guess about the functions that are executed. In this paper we propose Augusto, a test case generation technique that exploits a built-in knowledge of the semantics associated with popular and well-known functionalities, such as CRUD operations, to automatically generate effective test cases with automated functional oracles. Empirical results indicate that Augusto can reveal faults that cannot be revealed with state of the art techniques.
Dynamics and Prediction of Clicks on News from Twitter

Social networks are a major gateway to access news content. It is estimated that a third of all web visits originate on social media, and about half of users rely on those to keep up-to-date with world events. Strangely, no model has been proposed and validated to study how to reproduce and interpolate clicks created by social media. Here we study news posted on Twitter, leveraging public information as well as private data from a popular online publisher. We propose and validate a simple two-step model of information diffusion that can be easily interpreted and applied using only public information to determine current and future clicks.
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures

The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.
Adaptive coding for dynamic sensory inference

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally-relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we derive adaptive sensory encoding schemes that dynamically navigate this tradeoff between coding cost and inference error. We find that optimal encodings increase the fidelity of the neural response following a change in the stimulus distribution, whereas stimuli originating from a known distribution can be encoded with low fidelity at little cost. We predict dynamical signatures of an encoding scheme designed to preserve only those stimulus details that are relevant for inference, and we demonstrate how known phenomena, such as burst coding and the perception of metamers, can be understood as hallmarks of adaptive encoding for accurate inference.
A library for constraint consistent learning

This paper introduces the first, open source software library for Constraint Consistent Learning (CCL). It implements a family of data-driven methods that are capable of (i) learning state-independent and -dependent constraints, (ii) decomposing the behaviour of redundant systems into task- and null-space parts, and (iii) uncovering the underlying null space control policy. It is a tool to analyse and decompose many everyday tasks, such as wiping, reaching and drawing. The library also includes several tutorials that demonstrate its use with both simulated and real world data in a systematic way. This paper documents the methods contained within the library, including the implementations of said methods in tutorials and associated helper methods. The software is made freely available to the community, to enable code reuse and allow users to gain in-depth experience in statistical learning in this area. GRAPHICAL ABSTRACT
Comparison of Aggressive Surgical Treatment and Palliative Treatment in Elderly Patients with Poor-Grade Intracranial Aneurysmal Subarachnoid Hemorrhage

Objective To compare the current treatment approach in elderly patients with poor-grade aneurysmal subarachnoid hemorrhage (SAH) and identify the independent predictors of the outcome after aggressive surgical treatment. Method This prospective, multicenter cohort study included 104 poor-grade aneurysmal SAH elderly patients, 60 years or older, treated in our institution from October 2010 to March 2013. Patients were grouped according to three treatment arms. Neurological outcome was assessed using the Glasgow Outcome Scale (GOS) at baseline and at a 12-month follow-up. Univariate and multivariate analysis were performed using the following factors: sex, age, smoking history, breathing ability, alcohol consumption, cerebral hernia, aneurysm location, aneurysm diameter, WFNS grade, CT Fisher grade, treatment approach, and the timing of the aneurysm surgery. Results At the 12-month follow-up, patients in the coiling group and clipping group had better prognosis than patients in the palliative treatment group. Univariate analysis confirmed that the treatment approach, WFNS grade, CT Fisher grade, and age are critical factors for neurological outcomes in poor-grade SAH. Multivariate analysis indicated that WFNS grade V, CT Fisher grades 3–5, and palliative treatment were independent predictors of poor prognoses. Conclusion Aggressive surgical treatment improves the prognoses in poor-grade aneurysm elderly patients with SAH. Elderly Patients of WFNS grade IV and CT Fisher grades 1-2 are more likely to have a better outcome.
To Post or Not to Post: Using Online Trends to Predict Popularity of Offline Content

Predicting the popularity of online content has attracted much attention in the past few years. In news rooms, for instance, journalists and editors are keen to know, as soon as possible, the articles that will bring the most traffic into their website. In this paper, we propose a new approach for predicting the popularity of news articles before they go online. Our approach complements existing content-based methods, and is based on a number of observations regarding article similarity and topicality. First, the popularity of a new article is correlated with the popularity of similar articles of recent publication. Second, the popularity of the new article is related to the recent historical popularity of its main topic. Based on these observations, we use time series forecasting to predict the number of visits an article will receive. Our experiments, conducted on a real data collection of articles in an international news website, demonstrate the effectiveness and efficiency of the proposed method.
Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions

Smoothing is an essential tool in many NLP tasks, therefore numerous techniques have been developed for this purpose in the past. One of the most widely used smoothing methods are the Kneser-Ney smoothing (KNS) and its variants, including the Modified Kneser-Ney smoothing (MKNS), which are widely considered to be among the best smoothing methods available. Although when creating the original KNS the intention of the authors was to develop such a smoothing method that preserves the marginal distributions of the original model, this property was not maintained when developing the MKNS. In this article I would like to overcome this and propose such a refined version of the MKNS that preserves these marginal distributions while keeping the advantages of both previous versions. Beside its advantageous properties, this novel smoothing method is shown to achieve about the same results as the MKNS in a standard language modelling task.
The Design Space of 3D Printable Interactivity

The capabilities of 3D printers are rapidly progressing towards fabrication of fully interactive products. For designers to reason about the best way to achieve their interaction design goals, it is helpful to not only know what exists in the literature, but to also understand the design space of options. Such an understanding can help in comparison, analysis, selection of suitable technology, and also in the generation of new ideas. In this article, we survey the state of the art in 3D printing fully functional sensors and actuators to support explicit interaction techniques. We classify and organize the surveyed works around the following parameters: mechanism, designed affordances, interaction primitives, and output modality. The design space is presented in the form of a multidimensional matrix known as a Zwicky box. Using the tables, we can make observations about the existing literature and also identify gaps in this design space. Many such gaps can potentially lead to exciting new research or engineering opportunities.
Recent advancements in esophageal cancer treatment in Japan

The 11th edition of the Japanese Classification of Esophageal Cancer (EC) was published in 2017. Some correction was made in the depth of tumor invasion to be consistent with the TNM classification by the Union for International Cancer Control (UICC). With regard to surgery, short‐term safety and long‐term effectiveness under thoracotomy/video‐assisted thoracoscopic surgery are expected to be proven by the Japan Clinical Oncology Group (JCOG)1409 study. Results of nutritional management and countermeasures for adverse events not only during the perioperative period but also during EC chemotherapy were reported. From now on, the pursuit of low invasiveness and radicality is desired. Esophageal surgery is also expected to be safe at all institutions. To determine the optimal modality of preoperative treatment and a novel chemo(radio)therapy regimen for patients with distant metastasis, the results of the ongoing JCOG1109 and 0807 studies are being released. The effect of the addition of molecular targeted drugs on chemotherapy and concurrent chemoradiation has not yet improved overall survival. Immune checkpoint inhibitor drugs could offer a potential new treatment approach for patients with treatment‐refractory advanced squamous cell carcinoma (SCC). The Cancer Genome Atlas Research Network reported the results of a comprehensive genome analysis and molecular analysis of SCC and adenocarcinoma of the esophagus. Further differentiation of SCC and adenocarcinoma by molecular characterization analysis may be useful for the development of clinical trials and targeted drug therapies as precision medicine. The era of ultimate minimally invasive surgery and personalized treatment has begun. Large, prospective studies will be required to confirm the value of these advancements.
Post-Turing tissue pattern formation: Advent of mechanochemistry

Chemical and mechanical pattern formation is fundamental during embryogenesis and tissue development. Yet, the underlying molecular and cellular mechanisms are still elusive in many cases. Most current theories assume that tissue development is driven by chemical processes: either as a sequence of chemical patterns each depending on the previous one, or by patterns spontaneously arising from specific chemical interactions (such as “Turing-patterns”). Within both theories, mechanical patterns are usually regarded as passive by-products of chemical pre-patters. However, several experiments question these theories, and an increasing number of studies shows that tissue mechanics can actively influence chemical patterns during development. In this study, we thus focus on the interplay between chemical and mechanical processes during tissue development. On one hand, based on recent experimental data, we develop new mechanochemical simulation models of evolving tissues, in which the full 3D representation of the tissue appears to be critical for obtaining a realistic mechanochemical behaviour. The presented modelling approach is flexible and numerically studied using state of the art finite element methods. Thus, it may serve as a basis to combine simulations with new experimental methods in tissue development. On the other hand, we apply the developed approach and demonstrate that even simple interactions between tissue mechanics and chemistry spontaneously lead to robust and complex mechanochemical patterns. Especially, we demonstrate that the main contradictions arising in the framework of purely chemical theories are naturally and automatically resolved using the mechanochemical patterning theory.
Me, Myself and I: Are Looking for a Balance between Personalization and Privacy

Websites are more than ever tailoring themselves to their customers, gathering and using the information they are providing in order to offer a differentiated product. Most people are aware of their browser's history and cookies, but with the rise of single-login, geolocation and online profiles, the boundaries are getting blurrier. Companies are collecting data at an exponential rate, jeopardizing their clients' privacy. And, so far, people are making it easy to collect their data since they are so willingly disclosing it online. In addition, the rise of social networks makes the need for privacy protection more crucial than ever. But technology brings new choices, new risks, and new opportunities. In particular, privacy-protection concerns should not hamper the benefits of a society of sharing. Thus, a delicate balance must be reached between these apparently conflicting requirements.
Everybody's Got To Be Somewhere

The key to any nameless representation of syntax is how it indicates the variables we choose to use and thus, implicitly, those we discard. Standard de Bruijn representations delay discarding maximally till the leaves of terms where one is chosen from the variables in scope at the expense of the rest. Consequently, introducing new but unused variables requires term traversal. This paper introduces a nameless 'co-de-Bruijn' representation which makes the opposite canonical choice, delaying discarding minimally, as near as possible to the root. It is literate Agda: dependent types make it a practical joy to express and be driven by strong intrinsic invariants which ensure that scope is aggressively whittled down to just the support of each subterm, in which every remaining variable occurs somewhere. The construction is generic, delivering a universe of syntaxes with higher-order metavariables, for which the appropriate notion of substitution is hereditary. The implementation of simultaneous substitution exploits tight scope control to avoid busywork and shift terms without traversal. Surprisingly, it is also intrinsically terminating, by structural recursion alone.
A paradigm for the cooperation of objects belonging to different IoTs

The Internet of Things (IoT) is currently considered the new frontier of the Internet. One of the most effective ways to investigate and implement IoT is based on the use of the social network paradigm. In the last years, social network researchers have introduced new models capable of capturing the growing complexity of this scenario. One of the most known of them is the Social Internetworking System, which models a scenario comprising several related social networks. In this paper, we investigate the possibility of applying the ideas characterizing the Social Internetworking System to IoT and we propose a new paradigm capable of modelling this scenario and of favoring the cooperation of objects belonging to different IoTs. Furthermore, in order to give an idea of both the potentialities and the complexity of this new paradigm, we illustrate in more detail one of the most interesting issues regarding it, namely the redefinition of the betweenness centrality measure.
Assessing single-objective performance convergence and time complexity for refactoring detection

The automatic detection of refactoring recommendations has been tackled in prior optimization studies involving bad code smells, semantic coherence and importance of classes; however, such studies informally addressed formalisms to standardize and replicate refactoring models. We propose to assess the refactoring detection by means of performance convergence and time complexity. Since the reported approaches are difficult to reproduce, we employ an Artificial Refactoring Generation (ARGen) as a formal and naive computational solution for the Refactoring Detection Problem. ARGen is able to detect massive refactoring's sets in feasible areas of the search space. We used a refactoring formalization to adapt search techniques (Hill Climbing, Simulated Annealing and Hybrid Adaptive Evolutionary Algorithm) that assess the performance and complexity on three open software systems. Combinatorial techniques are limited in solving the Refactoring Detection Problem due to the relevance of developers' criteria (human factor) when designing reconstructions. Without performance convergence and time complexity analysis, a software empirical analysis that utilizes search techniques is incomplete.
RF Bandaid

This paper presents a passive wireless RF sensor platform (RFSP), with only analog components, that harvests energy from an RF source and reflects data as a direct subcarrier modulation, thus making it battery free. A fully-analog architecture results in an ultra-low power device (under 200 μW) with a low component count, reducing the physical footprint. We envision such a platform to enable medical sensing systems that fit on a small bandaid like flexible structure, require no-battery, or charging and are able to provide continuous physiological monitoring. To realize this vision, we have developed and optimized a novel RF architecture that 1) directly maps sensor output to frequency modulation and transmits it to a remote receiver processing unit (RPU). This direct frequency mapping allows all further digitization and computation to be moved to the RPU -- reducing power and size requirements on the RFSP; 2) harvests energy from the carrier signal transmitted by a simple continuous wave transmitter, thereby requiring no batteries or supercap; and 3) uses backscatter to communicate with the RPU enabling ultra-low power requirements. The total power consumption of our prototype device leveraging this architecture was measured to be between 35 μW and 160 μW. We demonstrate that the RFSP can harvest sufficient power, sense, and communicate continuously without necessity for energy storage at a distance of 4 m from a transmitter emitting a 915 MHz continuous wave at 26 dBm (0.39 W). Prior backscatter systems typically have power budgets of 1 mW and require energy storage (battery or supercap), RFSP's sub 200 μW power consumption provides a significant improvement and longer range for a given TX power. To demonstrate applicability to real-world health sensing and the flexibility to adapt to different sensors, this paper presents results from breathing, heart rate, temperature, and sound sensing applications.