Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical Sensor Based on Oxidation of 2,8‐Dihydroxyadenine to Monitor DNA Damage in Calf Thymus DNA

A novel and reliable direct electrochemical method has been established to monitor DNA damage in acid hydrolyzed calf thymus DNA, based on the determination of 2,8-dihydroxyadenine (2,8-DHA). A single-wall carbon nanotubes (SWCNT) modified edge plane pyrolytic graphite electrode (EPPGE) has been used as a sensor to monitor the DNA damage. 2,8-DHA the main in vivo adenine oxidation product undergoes oxidation at ∼395 mV at SWCNT modified EPPGE using square wave voltammetry (SWV). The sensor exhibits potent and persistent electron-mediating behavior. A well-defined oxidation peak for the oxidation of 2,8-DHA was observed at modified electrode with lowering of peak potential and increase in peak current as compared to bare EPPGE. At optimal experimental conditions, the catalytic oxidative peak current was responsive with the 2,8-DHA concentrations ranging from 0.05 nM to 100 nM. The detection limit was 3.8×10−11 M and limit of quantification was 1.27×10−10 M. The modified electrode exhibited high stability and reproducibility.
Effect of haloperidol on the numeric density of neurons and nuclear height in the rat hippocampus: A stereological and histopathological study

In recent studies, some neuroleptics have shown neurotoxic activities. Clinical and experimental studies have been carried out to investigate the effects of different neuroleptic drugs considered to affect the central nervous system. The aim of this study was to evaluate neurotoxic effects of halperidol on hippocampal neurons. The drug was given in daily doses of either 1 or 3 mg/kg for 6 weeks to adult male guinea pigs. After treatment, all animals were anaesthetized via short inhalation of ether, and then were fixed by a mixture of 2% glutaraldehyde and +2% paraformaldehyde in 0.1 M phosphate buffer. Brains were removed from the cranium and stored in the same fixative overnight. On the following day, the CA1 region of the hippocampus was dissected out. After embedding in araldite resin and obtaining semi-thin sections, the tissues were stained with toluidine blue. The physical dissector was used for measurements of nuclear height and numerical density of neurons and the sections were also evaluated histopathologically. The numerical density of neurons and nuclear height in the hippocampus for the low-dose (1 mg/kg) and high-dose (3 mg/kg) experimental groups were 12.4 mm3 and 3.6 μm and 7.14 mm3 and 3.56 μm, respectively. In contrast, the control group had a neuronal numerical density of 16.55 mm −3 and a nuclear height of 4.09 μm. There was a significant difference in both the mean density of neurons and the mean height of nuclei between haloperidol-treated and control groups (p < 0.05). There was also a statistical difference in the mean density of neurons (but not in nuclear height) when comparing the dosage of haloperidol (p < 0.05). These findings suggest that haloperidol treatment may lead to a loss of neurons as well as a decrease in the height of nuclei in the hippocampus.
Music Generation and Emotion Estimation from EEG Signals for Inducing Affective States

Although emotion induction using music has been studied, the emotions felt by listening to it vary among individuals. In order to provide personalized emotion induction, it is necessary to predict an individual's emotions and select appropriate music. Therefore, we propose a feedback system that generates music from the continuous value of emotion estimated from electroencephalogram (EEG). In this paper, we describe a music generator and a method of emotion estimation from EEG to construct a feedback system. First, we generated music by calculating parameters from the valence and arousal values of the desired emotion. Our generated music was evaluated by crowdworkers. The median of the correlation coefficients between the input of the music generator and the emotions felt by the crowdworkers were valence r=0.60 and arousal r=0.76. Next, we recorded EEG when listening to music and estimated emotions from them. We compared three regression models: linear regression and convolutional neural network (with/without transfer learning). We obtained the lowest RMSE (valence: 0.1807, arousal: 0.1945) between the actual and estimated emotional values with a convolutional neural network with transfer learning.
Trends & Methods in Chatbot Evaluation

Chatbots are computer programs aiming to replicate human conversational abilities through voice exchanges, textual dialogues, or both. They are becoming increasingly pervasive in many domains like customer support, e-coaching or entertainment. Yet, there is no standardised way of measuring the quality of such virtual agents. Instead, multiple individuals and groups have established their own standards either specifically for their chatbot project or have taken some inspiration from other groups. In this paper, we make a review of current techniques and trends in chatbot evaluation. We examine chatbot evaluation methodologies and assess them according to the ISO 9214 concepts of usability: Effectiveness, Efficiency and Satisfaction. We then analyse the methods used in the literature from 2016 to 2020 and compare their results. We identify a clear trend towards evaluating the efficiency of chatbots in many recent papers, which we link to the growing popularity of task-based chatbots that are currently being deployed in many business contexts.
On Camera Pose Estimation for 3D Scene Reconstruction

Three-Dimensional (3D) scene reconstruction using depth cameras is ubiquitous in Augmented Reality, Robotics, and Medical Imaging. Although many gradient-based highly computational reconstruction methods have been proposed in the literature, there is hardly any attempt at using meta-heuristic optimization techniques like the Genetic Algorithm (GA) for performing global camera pose estimation in a scene reconstruction framework. In this paper, we develop a 3D scene reconstruction framework that uses a combination of local image features and outlier removal technique (RANSAC) for performing local camera pose estimation. Further, we formulate a GA based global camera pose estimation approach. The 3D model is represented using an efficient and salable voxel-based representation. The paper presents the influence of various parameters on the local and global camera pose estimation techniques and the prescription for best-suited parameter values to achieve near-optimal performances.
"Was It You Who Stole 500 Rubles?" - The Multimodal Deception Detection

Automatic deception detection is a challenging issue since human behaviors are too complex to establish any standard behavioral signs that would explicitly indicate that a person is lying. Furthermore, it is difficult to collect naturalistic datasets for supervised learning as both external and self-annotation may be unreliable for deception annotation. For these purposes, we collected the TRuLie dataset that consists of synchronously recorded videos (34 hours in total) and data received from contact photoplethysmography (PPG) and hardware eye-tracker of ninety three subjects who tried to feign innocence during interrogation after they committed mock crimes. Thus, we had multimodal fragments with lie (n=3380) and truth (n=6444). We trained an end-to-end convolutional neural network (CNN) on this dataset to predict lie and truth from audio and video, and also built classifiers on combined features extracted from video, audio, PPG, eye-tracker, and predictions from CNN. The best classifier (LightGBM) showed a mean balanced accuracy of 0.64 and an F1-score of 0.76 on a 5-fold cross-validation.
Link prediction techniques to handle tax evasion

Circular trading of goods is a carefully designed scam ubiquitous among fraudulent business dealers all around the world. Dealers involved in this scheme create an artificial trading network by issuing doctored sales-invoices amongst themselves without any movement of goods. In practice, it is observed that almost all cases of circular trade involve two or three dealers. Here, we work towards predicting circular trade involving three dealers. For the same, we built four different classification models consisting of feature variables tailored for predicting any plausible circular trade amongst three dealers. In particular, the logistic regression model gave the best performance among all the four different models with a prediction accuracy of 80%. Interestingly, we observe that a feature variable formed by using the personalised PageRank technique significantly improves the model over the state of the art link prediction variables. Predicting a future circular trade from a huge network of sales-transactions data is of significant importance to the tax enforcement officers. In addition to automating the process of detecting circular trading, which is manually impossible, this model helps them to target on a set of plausible evaders and take appropriate preventive measures. This model have been developed for the Commercial Taxes Department, Government of Telangana, India, using their first two quarter’s tax returns dataset.
An Epidemic Model of social and political campaigns on Twitter

In this paper, we have studied the propagation dynamics of social and political events on Twitter with the help of two epidemiological models SIR and SIRI, which are originally used to model diseases. In OSNs, the posting about some specific campaigns is analogous to people who are getting affected in a disease outbreak, and abandonment of any news is similar to recovery from the disease [1, 4]. Here, our objective is to model and compare the rise and fall [3] of two most popular social campaigns which occurred in India in past 12 months, namely scrapping of Article 370 and passing of CAA-NRC Bill with the help of parameters of epidemiological models, estimated from Markov Chain Monte Carlo Simulations.
Fusion of Intrinsic & Extrinsic Sentential Traits for Text Coherence Assessment

In this paper, we investigate the problem of text coherence modeling with a new perspective of learning better distributed sentence representation by incorporating document dependent and document independent features of a sentence. To this end, we propose a data-driven end-to-end novel neural coherence model that captures text coherence by exploiting the semantic and distributional aspects of the sentences in a document. The network is able to capture various document independent and document dependent features of a sentence which is imperative in assessing text coherence. Experiments on the standard Sentence Ordering task indicate that our proposed model shows significant performance gain of 1.7 percent in terms of accuracy score compared with the state-of-the-art baselines.
Transforming Rehabilitation to Virtually Supported Care - The vCare Project

vCare is designing personalized rehabilitation programs that will lead to better continuity of care and a better quality of life for patients with stroke, heart failure, Parkinson's disease or ischemic heart disease. It's goal is to provide a holistic approach for transferring rehabilitation pathways from stationary rehabilitation to the patient's home. VCare persues two novel approaches in the field. First, it combines persuasive system design (PSD) with a health psychological model (IMB) which are implemented into a software system to motivate the user. Second, it integrates personalized rehabilitation paths and a virtual coach with graphical representation to support the rehabilitation process. The coach is based on patients' personalized care pathways. It engages with patients so that they meet their individual care plans. This encourages compliance with the patients' rehabilitation programs.
It's Not What They Play, It's What You Hear: Understanding Perceived vs. Induced Emotions in Hindustani Classical Music

Music is an efficient medium to elicit and convey emotions. The comparison between perceived and induced emotions from western music has been widely studied. However, this relationship has not been studied from the perspective of Hindustani classical music. In this work, we explore the relationship between perceived and induced emotions with Hindustani classical music as our stimuli. We observe that there is little to no correlation between them, however, audio features help in distinguishing the increase or decrease in induced emotion quality. We also introduce a novel dataset which contains induced valence and arousal annotations for 18 Hindustani classical music songs. Furthermore, we propose a latent space representation based approach, that leads to a relative increase in F1 Score of 32.2% for arousal and 34.5% for valence classification, as compared to feature-based approaches for Hindustani classical music.
Bayesian Generative Adversarial Nets with Dropout Inference

Generative adversarial networks are one of the most popular approaches to generate new data from complex high-dimensional data distributions. They have revolutionized the area of generative models by creating quality samples that highly resemble the true data distribution. However, these samples often cover only few high density areas of the true data distribution. As some of the modes are missing in the generated data, this issue is referred to as mode collapse. Bayesian GANs (BGANs) can address this to a great extend by considering Bayesian learning principles. Instead of learning point estimates of parameters in the network, BGANs learn a probability distribution over these parameters and make use of the posterior distribution over parameters to make prediction. As these models are huge neural networks, analytical inference is not feasible due to the intractable likelihood and evidence terms. Hence, BGANs perform an approximate inference based on stochastic gradient Hamiltonian Monte Carlo (SGHMC) sampling which is computationally expensive and displays convergence problems. We propose a simple and effective Bayesian GAN model based on Monte Carlo dropout based inference (BDGAN). We establish theoretical connection between variational inference in Bayesian GANs and Monte Carlo dropout in GANs. The effectiveness of the proposed model in overcoming mode collapse is demonstrated on various synthetic and real-world data sets. Additionally, we analyse the training time and memory usage to show case the proposed method’s advantages over Bayesian GAN.
Exploring State-of-the-Art Nearest Neighbor (NN) Search Techniques

Finding nearest neighbors (NN) is a fundamental operation in many diverse domains such as databases, machine learning, data mining, information retrieval, multimedia retrieval, etc. Due to the data deluge and the application of nearest neighbor queries in many applications where fast performance is necessary, efficient index structures are required to speed up finding nearest neighbors. Different application domains have different data characteristics and, therefore, require different types of indexing techniques. While the internal indexing and searching mechanism is generally hidden from the top-level application, it is beneficial for a data scientist to understand these fundamental operations and choose a correct indexing technique to improve the performance of the overall end-to-end workflow. Choosing the correct searching mechanism to solve a nearest neighbor query can be a daunting task, however. A wrong choice can potentially lead to low accuracy, slower execution time, or in the worst case, both. The objective of this tutorial is to present the audience with the knowledge to choose the correct index structure for specific applications. We present the state-of-the-art Nearest Neighbor (NN) indexing techniques for different data characteristics. We also present the effect, in terms of time and accuracy, of choosing the wrong index structure for different application needs. We conclude the tutorial with a discussion on the future challenges in the Nearest Neighbor search domain.
Robot Assisted Diagnosis for Autism in Children

The complexity of the diagnostic procedure coupled with the absence of medical markers poses a variety of challenges in the early detection of autism in children. The diagnosis relies on the observation of behavioural symptoms and parent reporting of the developmental history of the child. It requires years of experience and training for identifying these symptoms which could be often misjudged as other mental conditions with similar symptomology. In this work, we propose a robot-assisted diagnostic protocol for the early detection of autism leveraging the onboard sensors of Socially Assistive Robots (SAR) and the vast potential of machine learning techniques. The robot assistant's role will be two-fold: (a) supporting the expert in administering the diagnostic procedure and (b) evaluating the behavioural symptoms and reaching a reliable diagnosis. Even though similar studies have been done in the Global North, the challenges posed by the social and cultural diversity of the Indian population is varied and needs to be explored further.
Measuring and Fostering Engagement with Mental Health e-Coaches

Mental health e-coaches and technology-delivered services are showing considerable benefits to foster mental health literacy, monitor symptoms, favour self-management of different mental health conditions and scaffold positive behaviours. However, adherence to these systems is usually low and generally declines over time. There exists a recent body of work addressing engagement with mental health technology with the aim to understand the factors that influence sustained use and inform the design of systems that are able to generate sufficient engagement to attain their expected results. This paper explores the different facets of engagement in mental health e-coaches, including aspects related to the estimation of system use from log data, effective engagement, user experience, motivation, incentives, user expectations, peer support and the specific challenges of technologies addressed to mental health.
Multimodal Self-Assessed Personality Prediction in the Wild

Personality is an essential human attribute, and plays an important role for personalization of face-to-face services that improves sales and customer satisfaction in a variety of different business domains. Most studies addressing personality prediction to date built a prediction model with user data gathered via online services (e.g., SNS and mobile phone services). On the other hand, predicting the personality traits of customers without an online service log (e.g., first-time visitors and customers who only use a physical store) is challenging. In this paper, we present a multimodal approach to predict the personality of customers about whom only visual data collected using an in-store surveillance cameras are available. Our approach extracts simple gait features and projects them into pseudo online service log features to gain predictive power. Through an evaluation using the data collected by real mobile retailers, our approach improved prediction accuracy compared with a method that directly predicts personality from the visual data.
Editorial 2021

Molecular Ecology continues to be one of the most influential journals in ecology and evolution. It ranks second in overall impact among ecology or evolution journals as measured by Google Scholar's h5-index, which is the h-index for articles published over the past five years. Molecular Ecology also ranks highly among ecology and evolutionary biology journals in the h5-median (third in both disciplines), which is the median number of citations for the articles that make up a journal's h5-index. In terms of Impact Factor (IF), Molecular Ecology ranks 7th out of 50 journals in evolutionary biology according to Clarivate. In our view, the h5-index and h5-median offer more accurate and statistically sound measures of a journal's quality and influence than IFs, which are means and therefore highly sensitive to the inclusion of a handful of very highly cited papers such as computer programs or methods papers. Also, Impact Factors are based on citations over a two-year window only, whereas the h5-index and h5-median measure impact over five years and thus are less prone to yearly fluctuations.
Temporal stimulus segmentation by reinforcement learning in populations of spiking neurons

Learning to detect, identify or select stimuli is an essential requirement of many behavioral tasks. In real life situations, relevant and non-relevant stimuli are often embedded in a continuous sensory stream, presumably represented by different segments of neural activity. Here, we introduce a neural circuit model that can learn to identify action-relevant stimuli embedded in a spatio-temporal stream of spike trains, while learning to ignore stimuli that are not behaviorally relevant. The model uses a biologically plausible plasticity rule and learns from the reinforcement of correct decisions taken at the right time. Learning is fully online; it is successful for a wide spectrum of stimulus-encoding strategies; it scales well with population size; and can segment cortical spike patterns recorded from behaving animals. Altogether, these results provide a biologically plausible theory of reinforcement learning in the absence of prior information on the relevance and timing of input stimuli.
Mediator of DNA damage checkpoint 1 (MDC1) is a novel estrogen receptor co-regulator in invasive lobular carcinoma of the breast

Invasive lobular carcinoma (ILC) is the most common histological subtype of breast cancer, and nearly all ILC tumors express estrogen receptor alpha (ER). However, clinical and laboratory data suggest ILC are strongly estrogen-driven but not equally sensitive to anti-estrogen therapies. We hypothesized that ILC-specific ER transcriptional co-regulators mediate ER functions in ILC and anti-estrogen resistance, and profiled ER-associated proteins by mass spectrometry. Three ER+ ILC cell lines, MDA MB 134VI, SUM44PE, and BCK4, were compared to published data from ER+ invasive ductal carcinoma (IDC) cell lines, and we examined whether siRNA knockdown of identified proteins suppressed ER-driven proliferation in ILC cells. This approach found mediator of DNA damage checkpoint 1 (MDC1), a key tumor suppressor in DNA damage response (DDR), as a putative novel ER co-regulator in ILC. We confirmed ER:MDC1 interaction was specific to ILC cell lines versus IDC cells, and found MDC1 knockdown suppressed ILC cell proliferation and suppressed tamoxifen resistance in MDA MB 134VI. Using RNA-sequencing, we found that in ILC cells, MDC1 knockdown broadly dysregulates the estrogen-driven ER transcriptome, with ER:MDC1 target genes enriched for hormone-response-elements in their promoter regions. Importantly, our data are inconsistent with MDC1 regulating ER via MDC1 DDR and tumor suppressor functions, but instead suggest a novel oncogenic role for MDC1 in mediating ER transcriptional activity as a co-regulator. Supporting this, in breast tumor tissue microarrays MDC1 protein was frequently low or absent in IDC or ER-ILC, but MDC1 loss is rare in ER+ ILC. ER:MDC1 interaction and MDC1 co-regulator functions may underlie cell type-specific ER functions in ILC, and serve as important biomarkers and therapeutic targets to overcome anti-estrogen resistance in ILC.