pwc-archive/papers-with-abstracts huggingface dataset:
MigraR: an open-source, R-based application for analysis and quantification of cell migration parameters
Authors: Nirbhaya Shajia, Florbela Nunes, M. Ines Rocha, Elsa Ferreira Gomes, Helena Castro
Date: 2023-03-12 00:00:00
Tasks: Cell Tracking

Background and objective: Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. Methods: This report describes MigraR, an intuitive graphical user interface executed from the RStudioTM (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. Results: MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. Conclusions: Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced.
A primer on correlation-based dimension reduction methods for multi-omics analysis
Authors: Tim Downing, Nicos Angelopoulos
Date: 2023-03-13 00:00:00
Tasks: Dimensionality Reduction, Experimental Design, Navigate

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will guide researchers navigate the emerging methods for multi-omics and help them integrate diverse omic datasets appropriately and embrace the opportunity of population multi-omics.
Molecular characterization of wild Pleurotus ostreatus (MW457626) and evaluation of $β$-glucans polysaccharide activities
Authors: Ghazwan Qasim Hasan, Shimal Younis Abdulhadi
Date: 2023-03-10 00:00:00

Pleurotus ostreatus is a common cultivated edible mushroom worldwide. The fruiting bodies of P. ostreatus is a rich source of a $\beta$-glucans polysaccharide. The current study aimed to investigate the effectiveness of $\beta$-glucans as a natural polysaccharide produced by P. ostreatus as an antioxidant, antimicrobial, and anticancer. The molecular identification of P. ostreatus isolate was confirmed by Internal Transcribed Spacer (ITS) sequence. The sequence alignment and phylogenetic evolutionary relationship of studied ITS sequence were performed against some deposited sequences in GenBank. The analysis of high-performance liquid chromatography (HPLC) as well as the result of fourier transform infrared spectroscopy (FTIR) has confirmed the presence of $\beta$-glucans polysaccharide in the tested samples. The percentage of antioxidant activity of $\beta$-glucans showed a gradual increase from 8.59% to 12.36, 18.56, 23.69, 44.66 and 80.36% at the concentrations of 31.2, 64.4, 125, 250, 500, and 800 $\mu$g/ml, respectively. In addition, all concentrations of $\beta$-glucans showed higher antioxidant activities when compared with standard antioxidant (Vitamin C). The highest antimicrobial activity of $\beta$-glucans polysaccharide was against P. aeruginosa with a zone of inhibition (45 mm), while the lowest activity was against S. aureus (13 mm) both at 100 mg/mL. The percentage of growth-inhibiting of MCF-7 a humanbreast cancer cell line and normal WRL-68 cell line affected by $\beta$-glucans were determined by 3-(4,5)-dimethylthiazol (-z-y1)-3,5-di-phenytetrazoliumromide (MTT assay).
Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents
Authors: Emmanouil Giannakakis, Sina Khajehabdollahi, Anna Levina
Date: 2023-03-12 00:00:00
Tasks: Form

The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem. We hypothesize that the emergence and exact form of learning behaviors is naturally connected with the statistics of environmental fluctuations and tasks an organism needs to solve. Here, we study how different aspects of simulated environments shape an evolved synaptic plasticity rule in static and moving artificial agents. We demonstrate that environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity. Interestingly, the form of the emerging plasticity rule is additionally determined by the details of the task the artificial organisms are aiming to solve. Moreover, we show that co-evolution between static connectivity and interacting plasticity mechanisms in distinct sub-networks changes the function and form of the emerging plasticity rules in embodied agents performing a foraging task.
Semi-Quantitative Analysis and Serological Evidence of Hepatitis A Virus IgG Antibody among children in Rumuewhor, Emuoha, Rivers State, Nigeria
Authors: Iheanyi Omezuruike Okonko, Chisom Chimbundum Adim, Blessing Jachinma Okonko, Edith Ijeego Mba
Date: 2023-03-11 00:00:00

Hepatitis A virus (HAV) infection has been greatly reduced in most developed countries through the use of vaccine and improved hygienic conditions. However, the magnitude of the problem is underestimated and there are no well-established Hepatitis A virus prevention and control strategies in Nigeria. The aim of this study was to determine the prevalence of Hepatitis A virus infection among children aged 2 to 9 years in Rumuewhor, Emuoha LGA, Rivers State, Nigeria. Blood samples were collected from the 89 children enrolled in this study, and analyzed for the presence of HAV IgG antibodies using ELISA techniques. Of the 89 participants, 22 (24.7%) tested positive for HAV IgG antibodies, while 67 (75.3%) were negative. The children within the ages of 4 to 6 years had the highest seropositivity rate (33.3%) while those less than 4 years had the least seropositivity rate (22.4%). The prevalence rate ratio of the males to females was 1:1.3. There was no significant difference (p between IgG seropositivity and age groups and gender. However, there was a statistical association of IgG seropositivity rates with respect to immunization. The seroprevalence rate recorded in this study was significant, indicating that the virus is endemic in this study area. Proper awareness, health education and vaccination are imperative to controlling and preventing HAV infection in Rumuewhor, Emuoha, Rivers State, Nigeria.
The balance between contamination and predation determine species existence in prey-predator dynamics with contaminated and uncontaminated prey
Authors: Amit Samadder, Arnab Chattopadhyay, Sabyasachi Bhattacharya
Date: 2023-03-13 00:00:00

In freshwater ecosystems, aquatic insects that ontogenetically shift their habitat from aquatic to terrestrial play vital roles as prey subsidies that move nutrients and energy from aquatic to terrestrial food webs. As a result, these subsidies negatively affect alternative terrestrial prey by enhancing predator density. However, these aquatic insects can also transport contamination to the terrestrial community that is primarily produced in aquatic ecosystems. Which can reduce insectivore abundance and biomass, lower insectivore reproductive success, and increase predation pressure on alternative prey with consequences for aquatic and terrestrial food webs. Motivated by this, here we consider a prey-predator model where the predator consumes contaminated and uncontaminated prey together. We find that, at a high level of contamination, the vulnerability of contaminated prey and predator is determined by predation preference. More specifically, a very low predation preference towards contaminated prey ensures predator persistence, whereas a low, high preference excludes the predator from the system. Interestingly, either contaminated prey or the predator exist at intermediate predation preference due to bi-stability. Furthermore, when there is no contamination in one of the prey, the other prey can not coexist due to apparent competition for a specific range of predation preferences. However, when sufficient contamination exists in one prey, alternative uncontaminated prey coexists. With this, contamination also stabilizes and destabilizes the three species dynamics. Our result also indicates that if the intensity of the contamination in predator reproduction is low, then contaminated prey is more susceptible to the contamination.
Towards a quantitative theory of tolerance
Authors: Thierry Mora, Aleksandra M. Walczak
Date: 2023-03-13 00:00:00
Tasks: Decision Making

A cornerstone of the classical view of tolerance is the elimination of self-reactive T cells during negative selection in the thymus. However, high-throughput T-cell receptor sequencing data has so far failed to detect substantial signatures of negative selection in the observed repertoires. In addition, quantitative estimates as well as recent experiments suggest that the elimination of self-reactive T cells is at best incomplete. We discuss several recent theoretical ideas that can explain tolerance while being consistent with these observations, including collective decision making through quorum sensing, and sensitivity to change through dynamic tuning and adaptation. We propose that a unified quantitative theory of tolerance should combine these elements to explain the plasticity of the immune system and its robustness to autoimmunity.
A generative model to synthetize spatio-temporal dynamics of biomolecules in cells
Authors: Lisa Balsollier, Frédéric Lavancier, Jean Salamero, Charles Kervrann
Date: 2023-03-13 00:00:00

Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This approach is very flexible and allows us to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g. Brownian) to another (e.g. a directed motion), along with the appearance over time of new trajectories and their death after some lifetime, all of these features possibly depending on the current spatial configuration of all existing particles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. Based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.
A high yield method for protoplast isolation and ease detection of rol B and C genes in the hairy roots of cauliflflower (Brassica oleracea L.) inoculated with Agrobacterium rhizogenes
Authors: Qutaiba Shuaib Al-Nema, Ghazwan Qasim Hasan, Omar Abdulazeez Alhamd
Date: 2023-03-10 00:00:00

Protoplasts represent a unique experimental system for the circulation and formation of genetically modified plants. Here, protoplasts were isolated from genetically modified hairy root tissues of Brassica oleracea L. induced by the Agrobacterium rhizogenes strain (ATCC13332). The concentration of enzyme solutions utilized for protoplast isolation was 1.5 % Cellulase YC and 0.1 % Pectolyase Y23 in 13% mannitol solution, which resulted in high efficiency of isolation within 8 hours, in which the protoplast yield was 2 x 104 cells ml-1 and the percentage of viability was 72%. Each protoplast has one nucleus with a nucleation of 48%. A polymerase chain reaction (PCR) assay verified the presence of rol B and rol C genes in hairy root tissues by detaching a single bundle of DNA replication from these roots using a specific pair of primers. The current study demonstrated that A. rhizogenes strain (ATCC13332) is a vector for the incorporation of T-DNA genes into cauliflower plants, as well as the success of the hairy roots retention of rol B and rol C genes transferred to it.
Reconciling rough volatility with jumps
Authors: Eduardo Abi Jaber, Nathan De Carvalho
Date: 2023-03-13 00:00:00

We reconcile rough volatility models and jump models using a class of reversionary Heston models with fast mean reversions and large vol-of-vols. Starting from hyper-rough Heston models with a Hurst index $H \in (-1/2,1/2)$, we derive a Markovian approximating class of one dimensional reversionary Heston-type models. Such proxies encode a trade-off between an exploding vol-of-vol and a fast mean-reversion speed controlled by a reversionary time-scale $\epsilon>0$ and an unconstrained parameter $H \in \mathbb R$. Sending $\epsilon$ to 0 yields convergence of the reversionary Heston model towards different explicit asymptotic regimes based on the value of the parameter H. In particular, for $H \leq -1/2$, the reversionary Heston model converges to a class of L\'evy jump processes of Normal Inverse Gaussian type. Numerical illustrations show that the reversionary Heston model is capable of generating at-the-money skews similar to the ones generated by rough, hyper-rough and jump models.
Uniform Pessimistic Risk and its Optimal Portfolio
Authors: Sungchul Hong, Jong-June Jeon
Date: 2023-03-02 00:00:00
Tasks: quantile regression, scoring rule

The optimal allocation of assets has been widely discussed with the theoretical analysis of risk measures, and pessimism is one of the most attractive approaches beyond the conventional optimal portfolio model. The $\alpha$-risk plays a crucial role in deriving a broad class of pessimistic optimal portfolios. However, estimating an optimal portfolio assessed by a pessimistic risk is still challenging due to the absence of a computationally tractable model. In this study, we propose an integral of $\alpha$-risk called the \textit{uniform pessimistic risk} and the computational algorithm to obtain an optimal portfolio based on the risk. Further, we investigate the theoretical properties of the proposed risk in view of three different approaches: multiple quantile regression, the proper scoring rule, and distributionally robust optimization. Real data analysis of three stock datasets (S\&P500, CSI500, KOSPI200) demonstrates the usefulness of the proposed risk and portfolio model.
Probabilistic Overview of Probabilities of Default for Low Default Portfolios by K. Pluto and D. Tasche
Authors: Andrius Grigutis
Date: 2023-02-04 00:00:00

This article gives a probabilistic overview of the widely used method of default probability estimation proposed by K. Pluto and D. Tasche. There are listed detailed assumptions and derivation of the inequality where the probability of default is involved under the influence of systematic factor. The author anticipates adding more clarity, especially for early career analysts or scholars, regarding the assumption of borrowers' independence, conditional independence and interaction between the probability distributions such as binomial, beta, normal and others. There is also shown the relation between the probability of default and the joint distribution of $\sqrt{\varrho}X-\sqrt{1-\varrho}Y$, where $X$, including but not limiting, is the standard normal, $Y$ admits, including but not limiting, the beta-normal distribution and $X,\,Y$ are independent.
PN-OWL: A Two Stage Algorithm to Learn Fuzzy Concept Inclusions from OWL Ontologies
Authors: Franco Alberto Cardillo, Franca Debole, Umberto Straccia
Date: 2023-03-01 00:00:00

OWL ontologies are a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given a target class T of an OWL ontology, with a focus on ontologies with real- and boolean-valued data properties, we address the problem of learning graded fuzzy concept inclusion axioms with the aim of describing enough conditions for being an individual classified as instance of the class T. To do so, we present PN-OWL that is a two-stage learning algorithm made of a P-stage and an N-stage. Roughly, in the P-stage the algorithm tries to cover as many positive examples as possible (increase recall), without compromising too much precision, while in the N-stage, the algorithm tries to rule out as many false positives, covered by the P-stage, as possible. PN-OWL then aggregates the fuzzy inclusion axioms learnt at the P-stage and the N-stage by combining them via aggregation functions to allow for a final decision whether an individual is instance of T or not. We also illustrate its effectiveness by means of an experimentation. An interesting feature is that fuzzy datatypes are built automatically, the learnt fuzzy concept inclusions can be represented directly into Fuzzy OWL 2 and, thus, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T or not.
An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis
Authors: Akshat Shukla, Chaarvi Bansal, Sushrut Badhe, Mukul Ranjan, Rohitash Chandra
Date: 2023-02-28 00:00:00
Tasks: Translation

Google Translate has been prominent for language translation; however, limited work has been done in evaluating the quality of translation when compared to human experts. Sanskrit one of the oldest written languages in the world. In 2022, the Sanskrit language was added to the Google Translate engine. Sanskrit is known as the mother of languages such as Hindi and an ancient source of the Indo-European group of languages. Sanskrit is the original language for sacred Hindu texts such as the Bhagavad Gita. In this study, we present a framework that evaluates the Google Translate for Sanskrit using the Bhagavad Gita. We first publish a translation of the Bhagavad Gita in Sanskrit using Google Translate. Our framework then compares Google Translate version of Bhagavad Gita with expert translations using sentiment and semantic analysis via BERT-based language models. Our results indicate that in terms of sentiment and semantic analysis, there is low level of similarity in selected verses of Google Translate when compared to expert translations. In the qualitative evaluation, we find that Google translate is unsuitable for translation of certain Sanskrit words and phrases due to its poetic nature, contextual significance, metaphor and imagery. The mistranslations are not surprising since the Bhagavad Gita is known as a difficult text not only to translate, but also to interpret since it relies on contextual, philosophical and historical information. Our framework lays the foundation for automatic evaluation of other languages by Google Translate
The Science of Detecting LLM-Generated Texts
Authors: Ruixiang Tang, Yu-Neng Chuang, Xia Hu
Date: 2023-02-04 00:00:00
Tasks: LLM-generated Text Detection, Misinformation, Text Detection, Text Generation

The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans. However, this has also sparked concerns about the potential misuse of such texts, such as spreading misinformation and causing disruptions in the education system. Although many detection approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. This survey aims to provide an overview of existing LLM-generated text detection techniques and enhance the control and regulation of language generation models. Furthermore, we emphasize crucial considerations for future research, including the development of comprehensive evaluation metrics and the threat posed by open-source LLMs, to drive progress in the area of LLM-generated text detection.
Deep Color Mismatch Correction In Stereoscopic 3D Images
Authors: Simone Croci, Cagri Ozcinar, Emin Zerman, Roman Dudek, Sebastian Knorr, Aljosa Smolic
Date: 2021-06-01 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2021 6
Tasks: Color Mismatch Correction, Depth Estimation

Color mismatch in stereoscopic 3D (S3D) images can create visual discomfort and affect the performance of S3D image processing algorithms, e.g., for depth estimation. In this paper, we propose a new deep learning-based solution for the problem of color mismatch correction. The proposed solution consists of a multi-task convolutional neural network, where color correction is the primary task and correspondence estimation is the secondary task. For the training and evaluation of the proposed network, a new S3D image dataset with color mismatch was created. Based on this dataset, experiments were conducted showing the effectiveness of our solution.
Exploiting Pairwise Mutual Information for Knowledge-Grounded Dialogue
Authors: Bo Zhang, Jian Wang, Hongfei Lin, Hui Ma, Bo Xu
Date: 2022-03-22 00:00:00
Proceeding: IEEE/ACM Transactions on Audio, Speech, and Language Processing 2022 3
Tasks: Dialogue Generation

External document knowledge is helpful for dialogue systems to generate high-quality responses. Although several knowledge-grounded dialogue models have been designed, external knowledge cannot be comprehensively exploited due to the complex relationships among dialogue context, knowledge, and responses. To this end, we propose a novel transformer-based model, named TransIKG, which incorporates external document knowledge for dialogue generation. TransIKG comprises a two-step integration mechanism, including correlation integration and overall integration. Correlation integration is designed to fully exploit the pairwise mutual information among dialogue context, knowledge, and responses, while overall integration adopts an integration gate to capture global information. Furthermore, we utilize the positional information of dialogue turns to better represent the dialogue context and enhance the generalization ability of our model on out-of-domain documents. Finally, we propose a novel knowledge-aware pointer network to generate knowledge-enhanced response tokens. Experimental results on two benchmark datasets demonstrate that our model outperforms state-of-the-art models on both open-domain and domain-specific dialogues.