Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting REV-ERBα for therapeutic purposes: promises and challenges

REV-ERBα (NR1D1) is a circadian clock component that functions as a transcriptional repressor. Due to its role in direct modulation of metabolic genes, REV-ERBα is regarded as an integrator of cell metabolism with circadian clock. Accordingly, REV-ERBα is first proposed as a drug target for treating sleep disorders and metabolic syndromes (e.g., dyslipidaemia, hyperglycaemia and obesity). Recent years of studies uncover a rather broad role of REV-ERBα in pathological conditions including local inflammatory diseases, heart failure and cancers. Moreover, REV-ERBα is involved in regulation of circadian drug metabolism that has implications in chronopharmacology. In the meantime, recent years have witnessed discovery of an array of new REV-ERBα ligands most of which have pharmacological activities in vivo. In this article, we review the regulatory role of REV-ERBα in various types of diseases and discuss the underlying mechanisms. We also describe the newly discovered ligands and the old ones together with their targeting potential. Despite well-established pharmacological effects of REV-ERBα ligands in animals (preclinical studies), no progress has been made regarding their translation to clinical trials. This implies certain challenges associated with drug development of REV-ERBα ligands. In particular, we discuss the potential challenges related to drug safety (or adverse effects) and bioavailability. For new drug development, it is advocated that REV-ERBα should be targeted to treat local diseases and a targeting drug should be locally distributed, avoiding the adverse effects on other tissues.
Activity Profiling and Phenotypes of Physical Activity and Sleep

Physical inactivity and obesity contribute signiﬁcantly to several health problems and have been a topic of research amongst the public health research community. Objective measurements of physical behaviour using body-worn sensors have radically uplifted the method of collecting physical behaviour data from a large population and opened the possibility of analysing these objective recordings using machine learning methods to gain useful insights such as identifying diﬀerent physical activities and the duration of time spent in each activity, among others. We aim to address the theme of physical activity from both the public health and computer science perspective by ﬁrst determining the best strategy to increase the accuracy of human activity classiﬁcation from body-worn sensor data collected in cohort studies using the same data collection protocol as HUNT4 1 and strategically using objectively measured physical activity data to identify clusters of diﬀerent physical behaviour phenotypes such that intra-cluster similarity is maximized. Identifying physical behaviour phenotypes can allow us in the future to design tailored interventions for users to self-manage their daily physical activity routines. Additionally, we consider the compositional nature of the physical behaviour data we have and plan to employ suitable methodologies which allow us to model the compositional co-dependency between physical behaviours and sleep.
Multiple metastases from myxoid liposarcoma (MLS) for which Whole Body MRI (WB-MRI) was useful for their detection

Liposarcoma is one of the most common soft-tissue sarcomas in adults, with peak incidence between 40 and 60 years old [1-4]. MLS with round cell components have a tendency to metastasize to unusual sites including soft-tissue, retroperitoneum, mediastinum, chest wall, lymph node and so on [1]. It is well known that MRI is a highly sensitive method to detect abnormal bone marrow [1-6]. FDG-PET measures glucose uptake into the tumor itself. These characteristics allow PET to detect small volumes of disease before a secondary cortical bone reaction appears [1]. However, it is also well known that in low grade soft-tissue sarcomas, specificity and sensitivity are limited [3-5]. WBMRI including DWI has recently been widely used in the oncologic field for detecting metastatic lesions [7-12]. This case report is that of the multiple extrapulmonary metastases of MLS of the left thigh, which were detected by the WB-MRI including DWI. Also, DWI could evaluate the chemotherapeutic effect.
Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters

In this paper, we propose an efficient Least Squares Support Vector Machine (LS-SVM) training algorithm, which incorporates sparserepresentationanddictionarylearning.First,weformalizetheLS-SVMtrainingasasparserepresentationprocess.Second, kernelparametersareadjustedbyoptimizingtheiraveragecoherence.Assuch,theproposedalgorithmaddressesthetraining problemviageneratingthesparsesolutionandoptimizingkernelparameterssimultaneously.Experimentalresultsdemonstrate thattheproposedalgorithmiscapableofachievingcompetitiveperformancecomparedtostate-of-the-artapproaches. © 2020 The Authors . Published by Atlantis Press SARL. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
CONVEXITY PROPERTIES FOR A NEW INTEGRAL OPERATOR

For some classes of analytic functions f , g and h in the open unit disk U , we define a new integral operator Hn,α(z) = ∫ z 0 ∏n i=1 ( fi(t) hi(t) g′ i(t) )αi dt and we study convexity properties of this general integral operator. 2010 Mathematics Subject Classification: 30C45.
Fodor and demonstratives in LOT & ( Fodor y los demostrativos en el lenguaje del pensamiento )

In this paper, we consider a range of puzzles for demonstratives in the language of thought we had raised in our last philosophical conversation we had with Jerry Fodor. We argue against the Kaplan-inspired indexing solution Fodor proposed to us, but offer a Fodor-friendly account of the demonstratives in the language of thought in its stead, building on our account of demonstrative pronouns
HexMage - Encounter Balancing in Hex Arena

: Procedural content generation (PCG) is mostly examined in the context of map/environment creation, rather than generating the actual game characters. The goal of this thesis is to design a turn-based RPG-like game with perfect information for which we can generate balanced encounters. The game consists of a hex-based arena in which two teams fight. Each team consists of a few player controller characters with unique abilities. We generate the attributes of these abilities in order to make the encounter balanced. We will also build an AI that can be used to automatically play-test the PCG algorithm. The goal is to generate an equally strong, but different opponent.
When Is a MAX Not the MAX? How News Resolves Information Uncertainty

A well-known asset pricing anomaly, the “MAX” effect, measured by the maximum daily return in the past month, depicts stocks’ lottery-like features and investor gambling behaviour. Using the comprehensive stock-level Dow Jones (DJNS) news database between 1979 and 2016, we consider in a empirical setting how the presence of news reports affects these lottery-type stocks. We find an augmented negative relationship between MAX stocks without news and expected returns, whereby MAX with news coverage generates return momentum. The differing future return relationships between MAX stocks with and without news appears to be best explained by information uncertainty mitigation upon news arrival. Overall, our findings suggest that news plays a role in resolving information uncertainty in the stock market.
Underwater Image Enhancement by Cubic Interpolation and Histogram Distribution Prior

Images captured under water are usually degraded due to the effects of absorption and scattering. Degraded underwater images show some limitations when they are used for display and analysis. For example, underwater images with low contrast and color cast decrease the accuracy rate of underwater object detection and marine biology recognition. To overcome those limitations, a systematic underwater image enhancement method, which includes an underwater image dehazing algorithm and a contrast enhancement algorithm, is proposed. Built on a minimum information loss principle, an effective underwater image dehazing algorithm is proposed to restore the visibility, color, and natural appearance of underwater images. In this project , a novel self-similarity-based method for descattering and super resolution (SR)of underwater images is proposed based on ambient light and transmission map estimation . A simple yet effective contrast enhancement algorithm is proposed based on a kind of histogram distribution prior, which increases the contrast and brightness of underwater images. The proposed method can yield two versions of enhanced output. One version with relatively genuine color and natural appearance is suitable for display. The other version with high contrast and brightness can be used for extracting more valuable information and unveiling more details. MATLAB simulation tool will be used to evaluate existing and proposed system performance
Modeling and verification of early age thermal stress in second lining concrete of NATM tunnels

The objective of this research is to establish proper modeling of FEM simulation for early thermal stress in second lining concrete of NATM tunnels. The proposed model was verified by field measurements. Stresses at the crown and at the sides close to the invert of the second lining were investigated using the established FEM model. First, a simulation scheme was proposed considering thermal behaviors in detail and was sufficiently verified using tunnel monitoring data obtained from a previous study. It was observed that accurate modeling was indispensable for the variation of air temperature and for thermal properties of waterproofing membrane to obtain dependable temperature simulation results. Second, appropriate modeling for several kinds of joints and important inputs related to early thermal stress such as effective Young’s modulus, autogenous shrinkage development, and setting of hardening time were proposed. Simulation results of structural strain and stress were verified by measurement data in a recently constructed tunnel. Slight difference between simulation results and measurement might be partially due to the material model idealizations and due to the structural imperfections in actual structures. From the stress distributions extracted from the simulation results, it was observed that the crown cracks might be non-penetrating cracks and the cracks close to invert might be penetrating cracks.
Towards a Robot Path Planner for Dangerzones and Desirezones

This paper discusses the problem of planning a robot path in a workplace that contains a set of obstacles , a set of dangerzones , and a set of desirezones . The path cannot collide with the obstacles, should avoid the dangerzones, and should go through the desirezones. In order to plan such a path, we introduce the notion of fuzzy probabilistic roadmap ( fuzzy PRM ) as an extension of the traditional PRM. Each edge in a fuzzy PRM is assigned a possibility degree to represent the possibility that it is feasible. We also introduce an algorithm for computing a fuzzy PRM that uses lazy collision checking; that is, the collision of edges in the fuzzy roadmap with obstacles is delayed until a candidate path has been computed.
Parameterizing Human Speech Generation

In modern days, the synthesis of human images and videos is, arguably, one of the most popular topics in the Data Science community. The synthesis of human speech is less trendy but is deeply bounded to the mentioned topic. Since the publication of WaveNet paper in 2016, the State-of-the-Art transited from parametric and concatenative systems to the use of deep learning models. Each significant paper on the topic mentions the way to parameterize the output audio with different voices and sentiments, though parameterizing is not the major focus of those works. Most of the time-proven solutions require re-training of models for speech synthesis of unknown to the model voice. In my master’s project, I aim to implement a competitive text-to-speech solution, enhance parameterization abilities, and improve the performance of current models.
Representing Temporal Dependencies in Human Activity Recognition

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier.
Validation of Revised Chinese Version of PD-CRS in Parkinson's Disease Patients

There is a high prevalence of mild cognitive impairment (MCI) and dementia in Parkinson's disease (PD) patients, but a Chinese version of cognitive rating scale that is specific and sensitive to PD patients is still lacking. The aims of this study are to test the reliability and validity of a Chinese version of Parkinson's disease-cognitive rating scale (PD-CRS), establish cutoff scores for diagnosis of Parkinson's disease dementia (PDD) and PD with mild cognitive impairment (PD-MCI), explore cognitive profiles of PD-MCI and PDD, and find cognitive deficits suggesting a transition from PD-MCI to PDD. PD-CRS was revised based on the culture background of Chinese people. Ninety-two PD patients were recruited in three PD centers and were classified into PD with normal cognitive function (PD-NC), PD-MCI, and PDD subgroups according to the cognitive rating scale (CDR). Those PD patients underwent PD-CRS blind assessment by a separate neurologist. The PD-CRS showed a high internal consistency (Cronbach's Alpha = 0.840). Intraclass Correlation coefficient (ICC) of test-retest reliability reached 0.906 (95% CI 0.860–0.935, p < 0.001). ICC of inter-rater reliability was 0.899 (95% CI 0.848–0.933, p < 0.001). PD-CRS had fair concurrent validity with MDRS (ICC = 0.731, 95% CI 0.602–0.816). All the frontal-subcortical items showed significant decrease in PD-MCI compared with the PD-NC group (p ≤ 0.001), but the instrument cortical items did not (confrontation naming p=0.717, copying a clock p=0.620). All the frontal-subcortical and instrumental-cortical functions showed significant decline in PDD compared with the PD-NC group (p ≤ 0.001). The cutoff value for diagnosis of PD-MCI is 80.5 with the sensitivity of 75.7% and the specificity of 75.0%, and for diagnosis of PDD is 73.5 with the sensitivity of 89.2% and the specificity of 98.9%. Revised Chinese version of PD-CRS is a reliable, acceptable, valid, and useful neuropsychological battery for assessing cognition in PD patients.
Privacy Saving Multi-Keyword Ranking Search Anonymous ID on the Cloud Data that has been encrypted

 Abstract — Cloud computing market is growing rapidly in the recent years. It provides on-demand, high scalability computing resources with high availability and reliability. However, security and privacy of user’s data is considered as one of the biggest obstacles. Cloud computing provide great economic savings. Actually protecting data privacy, sensitive data must to be encrypted before outsourcing to untrusted cloud. So enabling encrypted cloud data searching is very important. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). For achieving solution for above problem we choose the “coordinate matching,” i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. In order to provide more security to the data on the cloud server, the allocation of the anonymous ID to the user is done. To improve the search experience data retrieval services, further expansion of the two methods will be made to support multiple search semantics.
A More Robust Text Based CAPTCHA For Security in Web Applications

: Nowadays, in web application mapping, developers use Captcha tools for preventing several security attacks. To improve the capability of a character image to hinder many of breaking attacks, we propose a text-based Captcha algorithm in which each challenge will be associated with main features in order to enhance the Captcha security. Different and new features at each challenge will be generated randomly. The randomness is used to reduce the chance of predicting the next challenge, thus, such automated breaking-Captcha techniques will be unable to use these features as attack vectors when they are extracted from the text Captcha. The main intent of this work is to provide an effective and efficient method to generate a robust text-based Captcha that thwarts many of breaking attacks and at the same time provides reliable and usable way that can distinguish between clients and computer.
Secure Protocol for MANET based on Pseudonomity

— Adhoc networks that require strong privacy need private routing, which is still a very big challenge. For this sake, so many methodologies have been proposed to address the privacy requirement of adhoc networks. Though, no single methodology can support unlinkability, anonymity in a full-fledged manner. In this work, we concentrate on pseudonomity which ensures high degree of anonymity and unlinkability. This in turn provides strong privacy. Thus, the user is protected against attackers. Experimental results show that our proposed method shows a remarkable degree of privacy when compared to the existing methodologies.
Security Evaluation of Pattern Classifiers in Adversarial Environments

— Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Several works have addressed the problem of designing robust classifiers against these threats, although mainly focusing on specific applications and kinds of attacks. In this paper, we address one of the main open issues: evaluating at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. We propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature.
Molecular domino reactor built by automated modular synthesis for cancer treatment

Rationale: A cascade, or domino, reaction consists of two, or more, consecutive reactions such that subsequent reactions occur only if some chemical functionality has first been established in the prior step. However, while construction of predesigned and desired molecular domino reactors in a tailored manner is a valuable endeavor, it is still challenging. Methods: To address this challenge, we herein report an aptamer-based photodynamic domino reactor built through automated modular synthesis. The engineering of this reactor takes advantage of the well-established solid-phase synthesis platform to incorporate a photosensitizer into G-quadruplex/ hemin DNAzyme at the molecular level. Results: As a proof of concept, our photodynamic domino reactor, termed AS1411/hemin- pyrochlorophyll A, achieves in vivo photodynamic domino reaction for efficient cancer treatment by using a high concentration of hydrogen peroxide (H2O2) in the tumor microenvironment (TME) to produce O2, followed by consecutive generation of singlet oxygen (1O2) using the pre-produced O2. More specifically, phosphoramidite PA (pyrochlorophyll A) is coupled to aptamer AS1411 to form AS1411-PA ApDC able to simultaneously perform in vivo targeted imaging and photodynamic therapy (PDT). The insertion of hemin into the AS1411 G-quadruplex was demonstrated to alleviate tumor hypoxia by decomposition of H2O2 to produce O2. This was followed by the generation of 1O2 by PA to trigger cascading amplified PDT. Conclusion: Therefore, this study provides a general strategy for building an aptamer-based molecular domino reactor through automated modular synthesis. By proof of concept, we further demonstrate a novel method of achieving enhanced PDT, as well as alleviating TME hypoxia at the molecular level.
Studies on the Impact of Growing Transgenic Cotton on Soil Health in Major Bt Cotton Growing Areas of Tamil Nadu, India

There is a persistent environmental concern that transgenic Bt-crops have indirect undesirable effect to natural and agroecosystem function. We investigated the effect of Bt-cotton (with Cry 1 Ac gene) on soil biology in Bt cotton growing soils of Perambalur district, Tamil Nadu under rainfed scenario. Soil samples randomly from ten Bt cotton growing fields were selected in each of the taluks of Perambalur district of Tamil Nadu region, India, where Bt-cotton has been growing at least for ten continuous years and side by side non-Bt cotton grown soils were also collected to compare the extent of adverse effect of Bt toxin, if any. Samples were analyzed for various soil biological indicators like microbial population, microbial respiration, Microbial Biomass Carbon (MBC), Microbial Biomass Nitrogen (MBN), and soil Dehydrogenase (DHA) activities. The soil biological indicators like microbial population, soil respiration, DHA, MBC and MBN were found to be comparitively higher in Btgrown soils than their non Bt counter parts over a period of 10 years.
Effect of saffron on mouse embryo development

Saffron is widely used as a food additive, as an important ingredient of Arabic coffee and as an herbal medicine. The aim of this study was to evaluate the effect of high and low doses of aqueous saffron extract on mice embryos development. Pregnant mice were divided into three groups of fifteen animals each. Group 1 received 10ml/kg body weight double distilled water as control, group 2 was treated with 100 mg saffron / kg body weight and group 3 was treated with 2.5 mg saffron / kg body weight. Doses were administered for 5 days during the first and second weeks of gestation and for four days during the third week of gestation. Embryos were extracted on day 14, 18 of gestation and day 1 neonates. Whole body weight, whole body length, tail length, half head circumference and eye dimensions of the embryos and neonates were recorded. Congenital malformations of all groups were studied. Both treatments caused embryonic growth parameters to be significantly less than the controls. Congenital malformations were seen in treated embryos and neonates such as subcutaneous bleeding and head malformations. It was concluded that oral administration of both doses of saffron might cause intrauterine growth retardation and congenital malformations to mouse embryos. (Fatma Al-Qudsi and Amal Ayedh. Effect of saffron on mouse embryo development. J Am Sci 2012;8(12):1554- 1568). (ISSN: 1545-1003). http://www.jofamericanscience. org. 207