Miracle-dz/UniFAR-FTUs huggingface dataset:
Glioblastoma with Both Oligodendroglioma and Primitive Neuroectodermal Tumor-Like Components in a Case with 9-Year Survival

Introduction Glioblastoma multiforme (GBM), the most common primary malignant brain tumor in adults, is characterized by extensive heterogeneity in its clinicopathological presentation. A primary brain tumor with both astrocytic differentiation and neuronal immunophenotype features is rare. Here, we report a long-term survival patient who presented this rare form of GBM in the disease course. Presentation of Case A 23-year-old woman, presenting with rapidly progressive headache and right-side weakness, was diagnosed with brain tumor over the left basal ganglion. She underwent the first craniectomy for tumor removal, and histopathology revealed classic GBM. Tumor recurrence occurred 8 years later. Another gross total resection was performed and pathology revealed GBM with the oligodendroglioma component (GBM-O). Due to disease progression, she received debulking surgery the following year. The third pathology revealed glioblastoma with primitive neuroectodermal tumor-like component (GBM-PNET). Discussion GBM-PNETs are collision tumors with both neuronal and glial components. They are rare, and a few case reports have suggested that these tumors are associated with favorable outcomes but a higher risk of cerebrospinal fluid dissemination. Conclusion We report a patient who developed the distinct pathologic variants of classic GBM, GBM-O, and GBM-PNET, throughout the disease course. Young age, aggressive surgical resection, and pathologic and genetic features may have contributed to the long-term survival of the patient.
Overview on the Mycotoxins Incidence in Serbia in the Period 2004–2016

With an average annual production of 6.9 M tonnes and 2.5 M tonnes of maize and wheat respectively, Serbia is one of the main grain producers and exporters in Europe. Cereals are also the major staple food in Serbian diet. In view of the high cereal consumption, for human and animal nutrition, the presence of mycotoxins entails a high public health risk of chronic exposure to mycotoxins. This study provides an overview of the incidence of predominant mycotoxins, mainly in cereal and dairy products, in Serbia, in the 2004–2016, using data reported in the scientific literature. The study demonstrated that the total prevalence of aflatoxins was 62.9% (n = 12,517) with 26.2% of the samples exceeding the EU limits during this period. Results obtained for T-2/HT-2 (n = 523), deoxynivalenol (n = 2907), fumonisins (n = 998), zearalenone (n = 689) and ochratoxin A (n = 740) indicated the prevalence of 45.5%, 42.9%, 63.3%, 39.3% and 28.1%, respectively. For these mycotoxins, the EU limits were less frequently exceeded. Comprehensive collection and analysis of all accessible information reviewed in this paper showed moderate incidence and prevalence of mycotoxins in Serbia, with an exception of the 2012 drought year and the 2014 flood year.
Getting in and Staying Alive: Role for Coronin 1 in the Survival of Pathogenic Mycobacteria and Naïve T Cells

There are many different pathogenic stimuli that are able to activate the immune system, ranging from microbes that include bacteria, viruses, fungi, and parasites to host-derived triggers such as autoantigens that can induce autoimmunity as well as neoantigens involved in tumorigenesis. One of the key interactions shaping immunity toward these triggers involves the encounter of antigen-processing and -presenting cells such as macrophages and dendritic cells with T cells, resulting in immune responses that are highly selective for the antigenic trigger. Research over the past few years has implicated members of the coronin protein family, in particular coronin 1, in responses against several pathogenic triggers. While coronin 1 was initially described as a host factor allowing the intracellular survival of the pathogen Mycobacterium tuberculosis, subsequent work showed it to be a crucial factor for naïve T cell homeostasis. The activity of coronin 1 in allowing the intracellular survival of pathogenic mycobacteria is relatively well characterized, involving the activation of the Ca2+/calcineurin pathway, while coronin 1’s role in modulating naïve T cell homeostasis remains more enigmatic. In this mini review, we discuss the knowledge on the role for coronin 1 in immune cell functioning and provide a number of potential scenarios via which coronin 1 may be able to regulate naïve T cell homeostasis.
Video Game Navigation: A Classification System for Navigational Acts

Navigation in video games has been a vastly neglected topic in game studies. In this paper a classification system for navigational acts has been developed through theoretical work as well as the analysis of multiple games. The result is an exclusive five-step classification system. Moreover, the development showed that navigational acts are highly dependent on the environment in which they occur. The system is a first step towards a deeper understanding of how the player navigates the gameworld, instead of what she navigates.
Draft Genome Sequences of 256 Salmonella enterica subsp. enterica Serovar Enteritidis Strains Isolated from Humans, Food, Chickens, and Farm Environments in Brazil

ABSTRACT Salmonella enterica subsp. enterica serovar Enteritidis emerged in the late 1980s as the most isolated Salmonella serovar worldwide. Here, we report the draft genomes of 256 S. Enteritidis strains isolated from humans, food, chickens, and farm environments in Brazil. These draft genomes will help enhance our understanding of this serovar in Brazil.
Combination therapy: A feasibility strategy for CAR-T cell therapy in the treatment of solid tumors

Chimeric antigen receptor (CAR) T-cell therapies have been demonstrated to have durable and potentially curative therapeutic efficacies in patients with hematological malignancies. Currently, multiple clinical trials in CAR-T cell therapy have been evaluated for the treatment of patients with solid malignancies, but have had less marked therapeutic effects when the agents are used as monotherapies. When summarizing relevant studies, the present study found that combination therapy strategies for solid tumors based on CAR-T cell therapies might be more effective. This review will focus on various aspects of treating solid tumors with CAR-T cell therapy: i) The therapeutic efficacy of CAR-T cell monotherapy, ii) the feasibility of the CAR-T cell therapy in conjunction with chemotherapy, iii) the feasibility of CAR-T cell therapy with radiotherapy, iv) the feasibility of CAR-T cell therapy with chemoradiotherapy, and v) the feasibility of the combination of CAR-T cell therapy with other strategies.
Antimicrobial Activity of Fruit Latexes from Ten Laticiferous Plants

In the present investigation antibacterial activity of latexes from ten Indian plant species Spondias dulcis (Amra), Diospyros melanoxylon (Tendu), Terminalia bellirica (Wahera), Ficus glomerata (Gular), Phyllanthus emblica (Awla), Thevetia neriifolia (Kaner), Carica papaya (Papita), Calotropis procera (Ak), Ficus benghalensis (Bargad), Artocarpus heterophyllus (Kathal) collected from Gorakhpur, North India were determined in various in vitro systems. MIC (minimum inhibitory concentration) and MBC (minimum bactericidal concentration) values were determined in growth inhibitory bioassays by using different increasing concentrations of various latex extracts. Latex samples were diluted by using serial micro dilution method up to 10−10 method with Luria broth culture medium. These values were obtained significantly 2 3 times lower than that of broad spectrum antibiotic drugs. Besides this, inhibition zone diameters were measured in agar disc diffusion assay. A known volume i.e. 0.1 20 μg/μl of each latex were coated on separate sterile filter paper discs (Whatman No. 1) measuring 6 mm in size. Latex fractions registered significantly higher growth inhibition than that of broad spectrum antimicrobial drugs. Present study indicates the potential use of shows that both latex and its components and latex as are valuable source of medicinal products/active principles that can be used for treatment of life threatening infectious diseases. Because of higher inhibitory and cidal potential obtained in latexes than the synthetic drugs these that could lead to become efficient phytomedicines mainly to have and develop as complete drug formulations against to control infectious microbes.
Direct inversion from partial-boundary data in electrical impedance tomography

In electrical impedance tomography (EIT) one wants to image the conductivity distribution of a body from current and voltage measurements carried out on its boundary. In this paper we consider the underlying mathematical model, the inverse conductivity problem, in two dimensions and under the realistic assumption that only a part of the boundary is accessible to measurements. In this framework our data are modeled as a partial Neumann-to-Dirichlet map (ND map). We compare this data to the full-boundary ND map and prove that the error depends linearly on the size of the missing part of the boundary. The same linear dependence is further proved for the difference of the reconstructed conductivities—from partial and full boundary data. The reconstruction is based on a truncated and linearized D-bar method. Auxiliary results include an extrapolation method to estimate the full-boundary data from the measured one, an approximation of the complex geometrical optics solutions computed directly from the ND map as well as an approximate scattering transform for reconstructing the conductivity. Numerical verification of the convergence results and reconstructions are presented for simulated test cases.
Automatic Gradient Boosting

Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference. This is achieved by making machine learning applications parameter-free, i.e. only a dataset is provided while the complete model selection and model building process is handled internally through (often meta) optimization. Projects like Auto-WEKA and auto-sklearn aim to solve the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem resulting in huge configuration spaces. However, for most real-world applications, the optimization over only a few different key learning algorithms can not only be sufficient, but also potentially beneficial. The latter becomes apparent when one considers that models have to be validated, explained, deployed and maintained. Here, less complex model are often preferred, for validation or efficiency reasons, or even a strict requirement. Automatic gradient boosting simplifies this idea one step further, using only gradient boosting as a single learning algorithm in combination with model-based hyperparameter tuning, threshold optimization and encoding of categorical features. We introduce this general framework as well as a concrete implementation called autoxgboost. It is compared to current AutoML projects on 16 datasets and despite its simplicity is able to achieve comparable results on about half of the datasets as well as performing best on two.
A Cluster-based Approach of Smartphone Camera Fingerprint for User Profiles Resolution within Social Network

In the last decades, Social Networks (SNs) have deeply changed interactions and habits of the users that are also prone to create more than one profile on the same SN. On the flip side, fake profiles (i.e., impersonating profiles), have become a considerable problem in digital investigations. In this paper, we propose a method for user profiles resolution through a cluster-based approach of the smartphone fingerprints extracted from the images being posted on SNs. The proposed method is thus able to detect fake profiles. To evaluate our approach, we use a real dataset of 1,500 images from 10 different smartphone devices and Facebook and WhatsApp platforms. The results show that the average of sensitivity and specificity for user profiles resolution is about 98%.
Co-KV: A Collaborative Key-Value Store Using Near-Data Processing to Improve Compaction for the LSM-tree

Log-structured merge tree (LSM-tree) based key-value stores are widely employed in large-scale storage systems. In the compaction of the key-value store, SSTables are merged with overlapping key ranges and sorted for data queries. This, however, incurs write amplification and thus degrades system performance, especially under update-intensive workloads. Current optimization focuses mostly on the reduction of the overload of compaction in the host, but rarely makes full use of computation in the device. To address these issues, we propose Co-KV, a Collaborative Key-Value store between the host and a near-data processing ( i.e., NDP) model based SSD to improve compaction. Co-KV offers three benefits: (1) reducing write amplification by a compaction offloading scheme between host and device; (2) relieving the overload of compaction in the host and leveraging computation in the SSD based on the NDP model; and (3) improving the performance of LSM-tree based key-value stores under update-intensive workloads. Extensive db_bench experiment show that Co-KV largely achieves a 2.0x overall throughput improvement, and a write amplification reduction by up to 36.0% over the state-of-the-art LevelDB. Under YCSB workloads, Co-KV increases the throughput by 1.7x - 2.4x while decreases the write amplification and average latency by up to 30.0% and 43.0%, respectively.
Exploring Adaptive Strategies for Providing Learning Activities

Research shows that Worked Examples (WE) and Erroneous Examples (ErrEx) provide learning benefits, particularly when presented alternatively with problems to solve. We previously proposed an adaptive strategy for selecting WE, ErrEx, and Problem Solving (PS) adaptively based on the student's problem-solving score and found that the adaptive strategy was beneficial for students in comparison to learning from a fixed sequence of alternating WE/PS pairs and ErrEx/PS pairs [1]. Students who received learning activities adaptively achieved the same learning outcomes as their peers in a fixed condition, but with fewer learning activities [2]. In this paper, we investigate a different adaptive strategy, which provides WEs and ErrExs to novices, and ErrEx and PS to advanced students. We found that the original adaptive strategy [2] is more effective than the new adaptive strategy. Furthermore, both novices and advanced students who learned with the original adaptive strategy demonstrated better performance on the post-test.
Genetic optimisation of BCI systems for identifying games related cognitive states

This paper aims to show a glimpse of the potential benefits of using genetic algorithms (GA's) in EEG data analysis. The system attempts machine-learning based classification of a mental state reached during competitive gameplay and an idle state. EEG activity from a large number of experienced players of League of Legends has been recorded during both idle periods and during a competitive, ranked game. A proven, industry-tested GA is used in order to optimize aspects of the brain-computer interface (BCI) system, by evolving both electrode and feature sets. Alongside a rather high cross validation base classification accuracy, statistically significant improvements have been obtained by applying the GA in selecting both electrode subset and features. The run time improvements are evident as well, although even further improvements are possible. This not only allows major improvements in both processing power and data acquisition requirements, but it is a step further in the attempt of bringing both BCI technology closer to consumer products, and cognitive state identification as a means of improving gaming experience, as well as other aspects of everyday life.
Difficult features of combinatorial optimization problems and the tunable w-model benchmark problem for simulating them

The first event of the Black-Box Discrete Optimization Benchmarking (BB-DOB) workshop series aims to establish a set of example problems for benchmarking black-box optimization algorithms for discrete or combinatorial domains. In this paper, we 1) discuss important features that should be embodied by these benchmark functions and 2) present the W-Model problem which exhibits them. The W-Model follows a layered approach, where each layer can either be omitted or introduce a different characteristic feature such as neutrality via redundancy, ruggedness and deceptiveness, epistasis, and multi-objectivity, in a tunable way. The model problem is defined over bit string representations, which allows for extracting some of its layers and stacking them on top of existing problems that use this representation, such as OneMax, the Maximum Satisfiability or the Set Covering tasks, and the NK landscape. The ruggedness and deceptiveness layer can be stacked on top of any problem with integer-valued objectives. We put the W-Model into the context of related model problems targeting ruggedness, neutrality, and epistasis. We then present the results of a series of experiments to further substantiate the utility of the W-Model and to give an idea about suitable configurations of it that could be included in the BB-DOB benchmark suite.
On Improving Data Skew Resilience In Main-memory Hash Joins

Main memory hash joins are an important category of in-memory joins. However, the performance of these joins can be hindered by dataset skew, shuffling, and load balancing. We conducted a comprehensive study on the effects of dataset skew on four hash join algorithms. We show that hash joins are acutely affected by dataset skew, and the performance gets worse with shuffled data. To address these issues, we propose non-partitioning hash joins using two different hash tables. First, we use a separate chaining hash table that is based on an existing implementation that we have modified. This version outperforms the original implementation on skewed datasets by up to three orders of magnitude. Second, we propose a novel hash table for hash joins, called Maple hash table. We demonstrate that this hash table is better suited to skewed and/or shuffled datasets. Moreover, this approach further improves performance by up to 17.3×.
Personalizing Persuasive Technologies: Do Gender and Age Affect Susceptibility to Persuasive Strategies?

Personalizing Persuasive Technologies (PTs) increase their effectiveness at motivating desired behavioral change. However, most existing efforts towards personalizing PTs and developing personalization models were focused on people from the western countries. In this work, we focused on African audience to investigate how individual's responsiveness to three persuasive strategies (Reward, Social Learning, and Social Comparison) varies by Gender and Age group via a large-scale study of 712 participants. The results of a RM-ANOVA show significant differences in responsiveness to the strategies across the gender and age groups. Females are more responsive to the Reward and Social Learning strategies while males are more responsive to the social comparison strategy. People who are under 25 years are more likely to be persuaded by the Reward and social Learning than participants above 35 years who are more responsive to the Social Comparison strategy. The results will inform PT designers on the appropriate strategy to employ to personalize PTs to individual users based on their Age and Gender.
Perceptual Validation for the Generation of Expressive Movements from End-Effector Trajectories

Endowing animated virtual characters with emotionally expressive behaviors is paramount to improving the quality of the interactions between humans and virtual characters. Full-body motion, in particular, with its subtle kinematic variations, represents an effective way of conveying emotionally expressive content. However, before synthesizing expressive full-body movements, it is necessary to identify and understand what qualities of human motion are salient to the perception of emotions and how these qualities can be exploited to generate novel and equally expressive full-body movements. Based on previous studies, we argue that it is possible to perceive and generate expressive full-body movements from a limited set of joint trajectories, including end-effector trajectories and additional constraints such as pelvis and elbow trajectories. Hence, these selected trajectories define a significant and reduced motion space, which is adequate for the characterization of the expressive qualities of human motion and that is both suitable for the analysis and generation of emotionally expressive full-body movements. The purpose and main contribution of this work is the methodological framework we defined and used to assess the validity and applicability of the selected trajectories for the perception and generation of expressive full-body movements. This framework consists of the creation of a motion capture database of expressive theatrical movements, the development of a motion synthesis system based on trajectories re-played or re-sampled and inverse kinematics, and two perceptual studies.
Training Neural Networks Using Features Replay

Training a neural network using backpropagation algorithm requires passing error gradients sequentially through the network. The backward locking prevents us from updating network layers in parallel and fully leveraging the computing resources. Recently, there are several works trying to decouple and parallelize the backpropagation algorithm. However, all of them suffer from severe accuracy loss or memory explosion when the neural network is deep. To address these challenging issues, we propose a novel parallel-objective formulation for the objective function of the neural network. After that, we introduce features replay algorithm and prove that it is guaranteed to converge to critical points for the non-convex problem under certain conditions. Finally, we apply our method to training deep convolutional neural networks, and the experimental results show that the proposed method achieves {faster} convergence, {lower} memory consumption, and {better} generalization error than compared methods.
Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful

Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models. Our empirical research analysis on state-of-the-art generative models discern their ability to generalize concepts across classes. In the process, we introduce Infinite World, an evaluable, scalable, multi-modal, light-weight dataset and Zero-Shot Intelligence Metric ZSI. The proposed tests condenses human-level spatial and numerical reasoning tasks to its simplistic geometric forms. The dataset is scalable to a theoretical limit of infinity, in numerical features of the generated geometric figures, image size and in quantity. We systematically analyze state-of-the-art model's internal consistency, identify their bottlenecks and propose a pro-active optimization method for few-shot and zero-shot learning.