Miracle-dz/UniFAR-FTUs huggingface dataset:
Specific ADAM10 inhibitors localize in exosome-like vesicles released by Hodgkin lymphoma and stromal cells and prevent sheddase activity carried to bystander cells

ABSTRACT Shedding of ADAM10 substrates, like TNFα, MICA or CD30, is reported to affect both anti-tumor immune response and antibody-drug-conjugate (ADC)-based immunotherapy. Soluble forms of these molecules and ADAM10 can be carried and spread in the microenvironment by exosomes released by tumor cells. We reported new ADAM10 inhibitors able to prevent MICA shedding in Hodgkin lymphoma (HL), leading to recognition of HL cells by cytotoxic lymphocytes. In this paper, we show that the mature bioactive form of ADAM10 is released in exosome-like vesicles (ExoV) by HL cells and lymph node mesenchymal stromal cells (MSC). We demonstrate that ADAM10 inhibitors are released in ExoV by MSC or HL cells, endocytosed by bystander cells and localized in the endolysosomal compartment in HL MSC. ExoV released by HL cells can enhance MICA shedding by MSC, while ExoV from MSC induce TNFα or CD30 shedding by HL cells. Of note, ADAM10 sheddase activity carried by ExoV is prevented with the ADAM10 inhibitors LT4 and CAM29, pretreating either the ExoV-producing or the ExoV-receiving cells. In particular, both inhibitors reduce CD30 shedding maintaining the anti-tumor effects of the ADC Brentuximab-Vedotin or the anti-CD30 Iratumumab on HL cells. Thus, spreading of ADAM10 activity due to ExoV can result in the release of cytokines, like TNFα, a lymphoma growth factor, or soluble molecules, like sMICA or sCD30, that potentially interfere with host immune surveillance or immunotherapy. ADAM10 blockers can interfere with this process, allowing the development of anti-lymphoma immune response and/or efficient ADC-based or human antibody-based immunotherapy.
Automated Mouse Organ Segmentation: A Deep Learning Based Solution

The analysis of animal cross section images, such as cross sections of laboratory mice, is critical in assessing the effect of experimental drugs such as the biodistribution of candidate compounds in preclinical drug development stage. Tissue distribution of radiolabeled candidate therapeutic compounds can be quantified using techniques like Quantitative Whole-Body Autoradiography (QWBA). QWBA relies, among other aspects, on the accurate segmentation or identification of key organs of interest in the animal cross section image - such as the brain, spine, heart, liver and others. Currently, organs are identified manually in such mouse cross section images - a process that is labor intensive, time consuming and not robust, which leads to requiring a larger number of laboratory animals used. We present a deep learning based organ segmentation solution to this problem, using which we can achieve automated organ segmentation with high precision (dice coefficient in the 0.83-0.95 range depending on organ) for the key organs of interest.
Offloading Massive Data Onto Passenger Vehicles: Topology Simplification and Traffic Assignment

Offloading is a promising technique for alleviating the ever-growing traffic load from infrastructure-based networks such as the Internet. Offloading consists of using alternative methods of transmission as a cost-effective solution for network operators to extend their transport capacity. In this paper, we advocate the use of conventional vehicles equipped with storage devices as data carriers whilst being driven for daily routine journeys. The road network can be turned into a large-capacity transmission system to offload bulk transfers of delay-tolerant data from the Internet. One of the challenges we address is assigning data to flows of vehicles while coping with the complexity of the road network. We propose an embedding algorithm that computes an offloading overlay where each logical link spans over multiple stretches of road from the underlying road infrastructure. We then formulate the data transfer assignment problem as a novel linear programming model we solve to determine the optimal logical paths matching the performance requirements of a data transfer. We evaluate our road traffic allocation scheme using actual road traffic counts in France. The numerical results show that 20% of vehicles in circulation in France equipped with only one Terabyte of storage can offload Petabyte transfers in a week.
Experiments with POS Tagging Code-mixed Indian Social Media Text

This paper presents Centre for Development of Advanced Computing Mumbai's (CDACM) submission to the NLP Tools Contest on Part-Of-Speech (POS) Tagging For Code-mixed Indian Social Media Text (POSCMISMT) 2015 (collocated with ICON 2015). We submitted results for Hindi (hi), Bengali (bn), and Telugu (te) languages mixed with English (en). In this paper, we have described our approaches to the POS tagging techniques, we exploited for this task. Machine learning has been used to POS tag the mixed language text. For POS tagging, distributed representations of words in vector space (word2vec) for feature extraction and Log-linear models have been tried. We report our work on all three languages hi, bn, and te mixed with en.
Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications, such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Furthermore, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Extensive simulation results based on accurate ray-tracing, show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead. Compared with traditional beamforming solutions, the results show that the proposed deep learning-based strategy attains higher rates, especially in high-mobility large-array regimes.
Physical Layer Authentication Enhancement Using Maximum SNR Ratio Based Cooperative AF Relaying

Physical layer authentication techniques developed in conventional macrocell wireless networks face challenges when applied in the future fifth-generation (5G) wireless communications, due to the deployment of dense small cells in a hierarchical network architecture. In this paper, we propose a novel physical layer authentication scheme by exploiting the advantages of amplify-and-forward (AF) cooperative relaying, which can increase the coverage and convergence of the heterogeneous networks. The essence of the proposed scheme is to select the best relay among multiple AF relays for cooperation between legitimate transmitter and intended receiver in the presence of a spoofer. To achieve this goal, two best relay selection schemes are developed by maximizing the signal-to-noise ratio (SNR) of the legitimate link to the spoofing link at the destination and relays, respectively. In the sequel, we derive closed-form expressions for the outage probabilities of the effective SNR ratios at the destination. With the help of the best relay, a new test statistic is developed for making an authentication decision, based on normalized channel difference between adjacent end-to-end channel estimates at the destination. The performance of the proposed authentication scheme is compared with that in a direct transmission in terms of outage and spoofing detection.
Semantic Interoperability in Heterogeneous IoT Infrastructure for Healthcare

Interoperability remains a significant burden to the developers of Internet of Things’ Systems. This is due to the fact that the IoT devices are highly heterogeneous in terms of underlying communication protocols, data formats, and technologies. Secondly due to lack of worldwide acceptable standards, interoperability tools remain limited. In this paper, we proposed an IoT based Semantic Interoperability Model (IoT-SIM) to provide Semantic Interoperability among heterogeneous IoT devices in healthcare domain. Physicians communicate their patients with heterogeneous IoT devices to monitor their current health status. Information between physician and patient is semantically annotated and communicated in a meaningful way. A lightweight model for semantic annotation of data using heterogeneous devices in IoT is proposed to provide annotations for data. Resource Description Framework (RDF) is a semantic web framework that is used to relate things using triples to make it semantically meaningful. RDF annotated patients’ data has made it semantically interoperable. SPARQL query is used to extract records from RDF graph. For simulation of system, we used Tableau, Gruff-6.2. 0, and Mysql tools.
Developing the Scale of Factors Increasing Study Motivation

The purposes of this study is to develop a scale of factors that increase high school students’ motivation for studying, and investigate this scale’s psychometric properties. 234 high school students took part in the study (119 females [50.9%] and 115 males [49.1%]). In order to evaluate validity of the developed scale, the Life Satisfaction Scale was used. The data were evaluated using statistical techniques such as exploratory and confirmatory factor analysis, and validity analysis. In the study, a four-dimensional scale containing dimensions such as “lessons to be studied is suitable for the students’ interests and skills”, “being in a positive working environment”, “satisfaction of expectations from the teacher” and “being in a positive physical and emotional state” was developed. The scale was named “Motivational factors that increase studying. ” The scale was found to be reliable and valid. Further studies may be conducted on the scale in the future.
The Impact of Thin-Capitalization Rules on Multinationals' Financing and Investment Decisions

This paper analyzes the role of Thin-Capitalization rules for capital structure choice and investment decisions of multinationals. A theoretical analysis shows that the imposition of such rules tends to affect not only the leverage and the level of investment but also their tax-sensitivity. An empirical investigation of leverage and investment reported for affiliates of German multinationals in 24 countries in the period between 1996 and 2004 offers some support for the theoretical predictions. While Thin-Capitalization rules are found to be effective in restricting debt finance, investment is found to be more sensitive to taxes if debt finance is restricted.
On Estimating Edit Distance: Alignment, Dimension Reduction, and Embeddings

Edit distance is a fundamental measure of distance between strings and has been widely studied in computer science. While the problem of estimating edit distance has been studied extensively, the equally important question of actually producing an alignment (i.e., the sequence of edits) has received far less attention. Somewhat surprisingly, we show that any algorithm to estimate edit distance can be used in a black-box fashion to produce an approximate alignment of strings, with modest loss in approximation factor and small loss in run time. Plugging in the result of Andoni, Krauthgamer, and Onak, we obtain an alignment that is a $(\log n)^{O(1/\varepsilon^2)}$ approximation in time $\tilde{O}(n^{1 + \varepsilon})$. Closely related to the study of approximation algorithms is the study of metric embeddings for edit distance. We show that min-hash techniques can be useful in designing edit distance embeddings through three results: (1) An embedding from Ulam distance (edit distance over permutations) to Hamming space that matches the best known distortion of $O(\log n)$ and also implicitly encodes a sequence of edits between the strings; (2) In the case where the edit distance between the input strings is known to have an upper bound $K$, we show that embeddings of edit distance into Hamming space with distortion $f(n)$ can be modified in a black-box fashion to give distortion $O(f(\operatorname{poly}(K)))$ for a class of periodic-free strings; (3) A randomized dimension-reduction map with contraction $c$ and asymptotically optimal expected distortion $O(c)$, improving on the previous $\tilde{O}(c^{1 + 2 / \log \log \log n})$ distortion result of Batu, Ergun, and Sahinalp.
Novel Prediction Techniques Based on Clusterwise Linear Regression

In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into $k$ clusters, such that linear regressions fitted to each of the clusters minimize overall mean squared error on the whole data. The main obstacle preventing to use found regression models for prediction on the unseen test points is the absence of a reasonable way to obtain CLR cluster labels when the values of target variable are unknown. In this paper we propose two novel approaches on how to solve this problem. The first approach, predictive CLR builds a separate classification model to predict test CLR labels. The second approach, constrained CLR utilizes a set of user-specified constraints that enforce certain points to go to the same clusters. Assuming the constraint values are known for the test points, they can be directly used to assign CLR labels. We evaluate these two approaches on three UCI ML datasets as well as on a large corpus of health insurance claims. We show that both of the proposed algorithms significantly improve over the known CLR-based regression methods. Moreover, predictive CLR consistently outperforms linear regression and random forest, and shows comparable performance to support vector regression on UCI ML datasets. The constrained CLR approach achieves the best performance on the health insurance dataset, while enjoying only $\approx 20$ times increased computational time over linear regression.
Dynamical characteristics of the FSO transmission capacity in the presence of Rician turbulence

In this paper temporal behavior of the free-space optics (FSO) channel capacity under the influence of Rician turbulence is considered. Expressions for the Cumulative Distribution Function (CDF), Probability Density Function (PDF), and the Level Crossing Rate (LCR) of the transmitted FSO signal exposed to Rician turbulence are derived, and numerical results graphically presented.
Representing the Unknown – Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation

Even though motion planning for automated vehicles has been extensively discussed for more than two decades, it is still a highly active field of research with a variety of different approaches having been published in the recent years. When considering the market introduction of SAE Level 3+ vehicles, the topic of motion planning will most likely be subject to even more detailed discussions between safety and user acceptance. This paper shall discuss parameters of the motion planning problem and requirements to an environment model. The focus is put on the representation of different types of uncertainty at the example of sensor occlusion, arguing the importance of a well-defined interface between decision making and trajectory generation.
A Membrane-Bound NAC-Like Transcription Factor OsNTL5 Represses the Flowering in Oryza sativa

In spite of short-day (SD) nature, rice (Oryza sativa) shares a conserved photoperiodic network for flowering control with long-day plants like Arabidopsis thaliana. Flowering or heading is an important agronomic trait in rice. NAC transcription factors (TFs) are well-conserved and one of the largest families of plant TFs. However, their function in flowering or heading time is not well-known yet. A preferential expression of a membrane-bound NAC-like TF OsNTL5 in developing leaves and panicles of rice indicated to us its putative role in flowering. To examine its function, three independent constructs was generated, one with a deletion in the C terminus membrane-spanning domain (OsNTL5∆C), OsNTL5∆C fused with the SRDX transcriptional repressor motif and OsNTL5∆C used with the VP16 activation domain under the Ubiquitin promoter to produce the overexpressing lines OsNTL5∆C, OsNTL5∆C-SRDX, and OsNTL5∆C-VP, respectively in rice. The OsNTL5∆C-VP line showed an early-flowering phenotype. In contrast to this, the plants with OsNTL5∆C and OsNTL5∆C-SRDX showed a very strong late-flowering phenotype, suggesting that OsNTL5 suppresses flowering as a transcriptional repressor. The protein subcellular localization assay suggested that N-terminal part of the OsNTL5 is localized to the nucleus after the protein is cleaved from its membrane-spanning domain at the C-terminal end and functions as a TF. Expression of flowering genes responsible for day length signals such as Early Heading Date 1 (Ehd1), Heading Date 3a (Hd3a), and Rice Flowering Locus T1 (RFT1) was significantly changed in the overexpression lines of OsNTL5∆C-VP, OsNTL5∆C, and OsNTL5∆C-SRDX as analyzed by Quantitative Real-time PCR. ChIP-qPCR and rice protoplasts assays indicate that OsNTL5 directly binds to the promoter of Ehd1 and negatively regulates the expression of Ehd1, which shows antagonistic photoperiodic expression patterns of OsNTL5 in a 24-h SD cycle. Hence in conclusion, the NAC-like TF OsNTL5 functions as a transcriptional repressor to suppress flowering in rice as an upstream factor of Ehd1.
Device-Specific Linux Kernel Optimization for Android Smartphones

To make smartphones more powerful, researchers have proposed many techniques to improve the performance of mobile systems and applications. As the most popular mobile operating system, Android is based on the Linux kernel. Therefore optimizing kernel performance can potentially accelerate Android smartphones.In this paper, we propose a compiler-based approach to constructing device-specific optimized Linux kernels for Android smartphones. By utilizing runtime feedback from the device, we can instruct the compiler to perform profile-guided optimization (PGO) and produce a Linux kernel image optimized specifically for the device, which can be shipped together with the device when it is manufactured, or released later in an update of the whole system. To the best of our knowledge, this paper is the first work that successfully applies PGO to the Linux kernel on Android smartphones to gain performance improvement.We conduct experiments on three different Android smartphones, namely Nexus 5X, Nexus 6 and Nexus 5. Results show that system performance improves by 11.1%, 4.1% and 9.3% on average, respectively. Specifically, we measure performance improvements of key Android system components such as multithreading and task scheduling, Binder, and storage and file systems.
Semantic and fuzzy aspects of opinion mining

In recent years the amount of opinions and reviews available on the web has grown tremendously and therefore the role of sentiment analysis is even more crucial than before. Given the very ambiguous and imprecise nature of sentiments and of their expressions, this study focuses on the review of semantic and fuzzy aspects of opinion mining. Opinions are fuzzy in nature and dealing with the semantic part of the expressed sentiments possesses many challenges and require effective techniques to properly extract and summarize people's views. Previous studies have often reflected that the use of fuzzy logic techniques have proved to be beneficial in mining reviews. This paper presents a review covering the semantic and Fuzzybased logic techniques and methods in sentiment analysis and challenges appear in the field.
The inverse problem of determining several coefficients in a nonlinear Lotka–Volterra system

In this paper, we prove a uniqueness result in the inverse problem of determining several non-constant coefficients of a system of two parabolic equations, which corresponds to a Lotka–Volterra competition model. Our result gives a sufficient condition for the uniqueness of the determination of four coefficients of the system. This sufficient condition only involves pointwise measurements of the solution (u, v) of the system and of the spatial derivative ∂u/∂x or ∂v/∂x of one component at a single point x0, during a time interval (0, ε). Our results are illustrated by numerical computations.
High Levels of Copy Number Variation of Ampliconic Genes across Major Human Y Haplogroups

Abstract Because of its highly repetitive nature, the human male-specific Y chromosome remains understudied. It is important to investigate variation on the Y chromosome to understand its evolution and contribution to phenotypic variation, including infertility. Approximately 20% of the human Y chromosome consists of ampliconic regions which include nine multi-copy gene families. These gene families are expressed exclusively in testes and usually implicated in spermatogenesis. Here, to gain a better understanding of the role of the Y chromosome in human evolution and in determining sexually dimorphic traits, we studied ampliconic gene copy number variation in 100 males representing ten major Y haplogroups world-wide. Copy number was estimated with droplet digital PCR. In contrast to low nucleotide diversity observed on the Y in previous studies, here we show that ampliconic gene copy number diversity is very high. A total of 98 copy-number-based haplotypes were observed among 100 individuals, and haplotypes were sometimes shared by males from very different haplogroups, suggesting homoplasies. The resulting haplotypes did not cluster according to major Y haplogroups. Overall, only two gene families (RBMY and TSPY) showed significant differences in copy number among major Y haplogroups, and the haplogroup of a male could not be predicted based on his ampliconic gene copy numbers. Finally, we did not find significant correlations either between copy number variation and individual’s height, or between the former and facial masculinity/femininity. Our results suggest rapid evolution of ampliconic gene copy numbers on the human Y, and we discuss its causes.