nils-herrmann/clean_scirepeval huggingface dataset:
Current Approaches and Applications in Avian Genome Editing
Venue: International journal of molecular sciences
Year: 2020

Advances in genome editing technologies and sequencing of animal genomes enable researchers to generate genome edited (GE) livestock as valuable animal models that benefit biological researches and biomedical and agricultural industries. As birds are an important species in biology and agriculture, their genome editing has gained significant interest and is mainly performed by using a primordial germ cell (PGC) mediated method because pronuclear injection is not practical in the avian species. In this method, PGCs can be isolated, cultured, genetically edited in vitro, and injected into a recipient embryo to produce GE offspring. Recently, a couple of GE quail have been generated by using the newly developed adenovirus mediated method. Without technically required in vitro procedures of the PGC mediated method, direct injection of adenovirus into the avian blastoderm in the freshly laid eggs resulted in the production of germ line chimera and GE offspring. As more approaches are available in avian genome editing, avian research in various fields will progress rapidly. In this review, we describe the development of avian genome editing and scientific and industrial applications of GE avian species.
Microarray breast cancer data classification using machine learning methods
Venue: 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)
Year: 2018

In this study We used microarray breast cancer data for classification of the patients using machine learning methods. First, 8 different machine learning algorithms are applied to the data, without applying any feature selection methods. Then two different feature selection methods are applied. The results of the classifications are compared with each other and with the results of the first case. The methods applied are SVM, KNN, MLP, Decision Trees, Random Forest, Logistic Regression, Adaboost and Gradient Boosting Machines. After applying the two different feature selection methods with the best 50 features are applied, SVM gave the best results. MLP is applied using different number of layers and neurons to examine the effect of the number of layers and neurons on the classification accuracy. It is determined that the increase in the number of layers sometimes decreased, sometimes didn't change the accuracy.
The Geography of Crowdfunding
Year: 2010

Perhaps the most striking feature of "crowdfunding" is the broad geographic dispersion of investors in small, early stage projects. This contrasts with existing theories that predict entrepreneurs and investors will be co located due to distance sensitive costs. We examine a crowdfunding setting that connects artist entrepreneurs with investors over the internet for financing musical projects. The average distance between artists and investors is about 3,000 miles, suggesting a reduced role for spatial proximity. Still, distance does play a role. Within a single round of financing, local investors invest relatively early, and they appear less responsive to decisions by other investors. We show this geography effect is driven by investors who likely have a personal connection with the artist entrepreneur "family and friends" Although the online platform seems to eliminate most distance related economic frictions such as monitoring progress, providing input, and gathering information, it does not eliminate social related frictions.
Construction of Sports Lottery Security Management System
Year: 2015

The purpose of this study was to analyze construction of China sports lottery security management system which can provide references for sports lottery issuing. In the last two decades,China sports lottery has been developed very quickly,but security management system hasn't been constructed. Without such a system,it is very hard to widely manage and efficiently control national and regional sports lottery security. Based on the comparative analysis and logical analysis,this paper defined that the security management system of sports lottery is a kind of planed,coordinated,communicated and controlled system for sport lottery's hardware and software safety. The hardware and software include organization,brand,employee,fund,goods,technology,data,and place. The system includes the elements of subject,object and support. Using process management as a longitudinal axis and function management as a transverse axis,the security management system can be constructed,which is strategic,systematic,multi leveled and dynamic. This system have many functional applications such as identifying the security and risk elements,managing the security and risk elements according to different classifications,assessing the level of security,warning and controlling the security events. This well designed system can wholly manage and control sports lottery security.
Grant Free Non Orthogonal Multiple Access: A Key Enabler for 6G IoT
Venue: ArXiv
Year: 2020

The proliferating number of devices with short payloads as well as low power budget has already driven researchers away from classical grant based access schemes that are notorious for their large signalling overhead as well as power consuming retransmissions. Instead, light weight random access protocols have been re investigated and their throughput has been improved in orders of magnitude with sophisticated yet still low complex transceiver algorithms. In fact, grant free access has been identified as a key medium access control technique for providing massive connectivity in machine type communications in cellular networks. In this paper, we show that grant free access combined with non orthogonal transmission schemes is a promising solution for 6G Internet of Things (IoT) We present novel and promising results for deep learning (DL) based techniques for joint user detection and decoding. Then, we propose a multi layered model for GF NOMA for power efficient communications. We also discuss resource allocation issues to enable the co existence of GF NOMA with other orthogonal or even grant based schemes. Finally, we conclude with proposed research directions for medium access towards enabling 6G IoT.
Oriented Response Networks
Venue: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017

Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global image rotations remains limited. In this paper, we propose Active Rotating Filters (ARFs) that actively rotate during convolution and produce feature maps with location and orientation explicitly encoded. An ARF acts as a virtual filter bank containing the filter itself and its multiple unmaterialised rotated versions. During back propagation, an ARF is collectively updated using errors from all its rotated versions. DCNNs using ARFs, referred to as Oriented Response Networks (ORNs) can produce within class rotation invariant deep features while maintaining inter class discrimination for classification tasks. The oriented response produced by ORNs can also be used for image and object orientation estimation tasks. Over multiple state of the art DCNN architectures, such as VGG, ResNet, and STN, we consistently observe that replacing regular filters with the proposed ARFs leads to significant reduction in the number of network parameters and improvement in classification performance. We report the best results on several commonly used benchmarks.
Chemical composition of essential oils of garlic Allium sativum L.
Year: 2015

Six varieties of garlic grown in different geographical locations in Gujarat, India were analysed for their proximate composition including volatile oil content. Twenty nine compounds were identified in the volatile oils by gas chromatography and gas chromatography mass spectrometry, among which 15 were new compounds hitherto not reported to be components of garlic. The relative concentrations of 13 major components in the volatile oils of all six varieties are also presented.
Truss based Community Search: a Truss equivalence Based Indexing Approach
Venue: Proc. VLDB Endow.
Year: 2017

We consider the community search problem defined upon a large graph G: given a query vertex q in G, to find as output all the densely connected subgraphs of G, each of which contains the query v. As an online, query dependent variant of the well known community detection problem, community search enables personalized community discovery that has found widely varying applications in real world, large scale graphs. In this paper, we study the community search problem in the truss based model aimed at discovering all dense and cohesive k truss communities to which the query vertex q belongs. We introduce a novel equivalence relation, k truss equivalence, to model the intrinsic density and cohesiveness of edges in k truss communities. Consequently, all the edges of G can be partitioned to a series of k truss equivalence classes that constitute a space efficient, truss preserving index structure, EquiTruss. Community search can be henceforth addressed directly upon EquiTruss without repeated, time demanding accesses to the original graph, G, which proves to be theoretically optimal. In addition, EquiTruss can be efficiently updated in a dynamic fashion when G evolves with edge insertion and deletion. Experimental studies in real world, large scale graphs validate the efficiency and effectiveness of EquiTruss, which has achieved at least an order of magnitude speedup in community search over the state of the art method, TCP Index.
Deep Natural Language Processing for Search and Recommender Systems
Venue: KDD
Year: 2019

Search and recommender systems share many fundamental components including language understanding, retrieval and ranking, and language generation. Building powerful search and recommender systems requires processing natural language effectively and efficiently. Recent rapid growth of deep learning technologies has presented both opportunities and challenges in this area. This tutorial offers an overview of deep learning based natural language processing (NLP) for search and recommender systems from an industry perspective. It first introduces deep learning based NLP technologies, including language understanding and language generation. Then it details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and language generation. Applications in LinkedIn production systems are presented. The tutorial concludes with discussion of future trend.
Stability of Vitamins in Pharmaceutical Preparations A Review
Year: 2020

Vitamins are one of the few groups of food constituents in which it is possible to demonstrate quantitatively deterioration in content over a period. The rate of this reduction in vitamin content is influenced by a number of factors. The stability of the more commonly used vitamins in vitamin concentrates and multivitamin preparations for oral administration has been studied in a variety of basic aqueous solvent mixtures of practical significance under normal storage conditions. The overage required for each vitamin under various conditions and in different formulations has been indicated in order to determine the excess of each vitamin necessary to maintain label declarations for normal storage periods. In general, many physical and chemical factors can have a negative effect on stability of vitamins. Water soluble vitamins are prone to degradation in solutions, particularly when exposed to light. In the present study, the interaction of vitamins in all conventional formulations is discussed.
Perceived cognitive deficits and depressive symptoms in patients with multiple sclerosis: Perceived stress and sleep quality as mediators.
Venue: Multiple sclerosis and related disorders
Year: 2018

Multiple Sclerosis (MS) an autoimmune disorder marked by inflammation of the central nervous system, is associated with a myriad of symptoms. Individuals with MS are more likely to experience depressive symptoms, perhaps due to perceived cognitive impairments. Thus, we aimed to explore perceived stress and sleep deficits as potential mediators of the association between perceived cognitive deficits and depressive symptoms. We recruited a sample of 77 MS participants from an outpatient, university based MS clinic in the United States. Participants ranged in age between 30 and 75 years old (M 51.12; SD 9.6) with more females than males (83% female; n 64) Participants completed the Perceived Deficits Questionnaire, the Pittsburgh Sleep Quality Index, the Perceived Stress Scale, and the Center for Epidemiological Studies Depression Scale Revised. Correlation analyses and mediation analyses were conducted with bootstrapping technique. Statistical analyses revealed that higher levels of perceived cognitive deficits were associated with lower quality of sleep, more perceived stress, and higher levels of depressive symptoms. Additionally, both perceived stress and sleep quality served as a significant mediator in the perceived cognitive impairments and depressive symptoms linkage. Our novel findings demonstrate the importance of underlying mechanisms (e.g. sleep quality and perceived stress) in the conceptualization of MS. Perceived stress and sleep quality are potentially modifiable factors, perhaps serving as a target for future treatment, to buffer risk of MS patients developing depression.
Deep convolutional neural networks for predicting leukemia related transcription factor binding sites from DNA sequence data
Year: 2020

Transcription factors are proteins that could bind to specific DNA sequences so as to regulate gene expressions. Currently, identification of transcription factor binding sites locating in DNA sequences is very important for building regulatory model in biological systems and identifying pathogenic variations. Traditional machine learning methods have been successfully used for biological prediction problems based on DNA or protein sequences, but they all need to manually extract numerical features, which is not only tedious, but also would ignore effective information of first order sequences. In this paper, based on the principle of deep learning (DL) we constructed prediction model for transcription factor binding sites only from DNA original base sequences. Here, a DL method based on convolutional neural network (CNN) and long short term memory (LSTM) were proposed to investigate four leukemia categories from the perspective of transcription factor binding sites using four large non redundant datasets for acute, chronic, myeloid and lymphatic leukemia, respectively. Compared with three widely used machine learning methods of artificial neural network (ANN) support vector machine (SVM) and random forest (RF) our DL method exhibits significant superiority in terms of prediction performance, since the prediction accuracy of three machine learning models either based on sequence feature or k mer feature extraction are all lower than that of DL model. The available DL models for four leukemia categories gives an average prediction accuracy of 75% based only on sequence segments with 101 bases, which indicates that the DL based method is promising with unique advantages over the traditional machine learning methods. But focusing on leukemia related transcription factor binding site prediction, further improvements would be implemented such as optimizing base segment length and CNN architecture, in order to improve the current prediction accuracy.
Shielding behaviors of some polymer and plastic materials for gamma rays
Year: 2015

Gamma rays interaction parameters with six polymer and plastic materials have been computed and investigated for their shielding behaviours in the experimental energy range 10 1400 keV. Low mass density and ability to transform into thin sheets of H containing plastic and polymer materials generate the curiosity for present study. Gamma Ray Interaction Coefficients (GRIC) toolkit: a computer program designed in MS Excel, 2007 for the investigation. The toolkit has the capability to compute different parameters linked to shielding properties of the sample material such that; mass attenuation coefficients m r mass energy absorption coefficients m en r equivalent atomic number Z eq KERMA (kinetic energy released per unit mass) Polyvinyl Chloride (PVC) seems to be the best shield for gamma radiations in energy range 10 110 keV. The interaction parameters for selected samples have been computed and provided in the extended energy range 0.015 15 MeV.
Deep Fusion Feature Learning Network for MI EEG Classification
Venue: IEEE Access
Year: 2018

Brain computer interfaces (BCIs) are used to provide a direct communication between the human brain and the external devices, such as wheelchairs and intelligent apparatus, by interpreting the electroencephalograph (EEG) signals. Recently, motor imagery EEG (MI EEG) has become an active research field where a subject's active intent can be detected. The accurate decoding of MI EEG signals is essential for effective BCI systems but also very challenging due to the lack of informative correlation between the signals and the brain activities. To improve the precision performance of a BCI system, accurate feature discrimination from input signals and proper classification are necessary. However, the traditional deep learning scheme is failed to generate spatio temporal representation simultaneously and capture the dynamic correlation for an MI EEG sequence. To address this problem, we propose a long short term memory network combined with a spatial convolutional network that concurrently learns spatial information and temporal correlations from raw MI EEG signals. In addition, spectral representations of EEG signals are obtained via a discrete wavelet transformation decomposition. In order to achieve even higher learning rates and less demanding initialization, we employ a batch normalization method before training and recognition. Various experiments have been performed to evaluate the performance of the proposed deep learning architectures. Results indicate a high level of accuracy over both the public data set and the local data set. Our method can also serve as a useful and robust model for multi task classification and subject independent movement class decoder across many different methods.
Minorities Under Attack in Pakistan
Year: 2020

Religious minorities were bound to be treated as second class citizens in Pakistan as the country was conceived as the homeland of Muslims from the British Raj and became an Islamic Republic. Hindus, Christians, and others were confined to separate electorates for quite some time and on several occasions. A new minority, the Ahmadis, emerged in 1974 when the 1973 Constitution was amended for defining who a Muslim is. All these groups are victims of discrimination, including ghettoization. They are particularly affected by the anti blasphemy law.
Chronic stress and antidepressant treatment alter purine metabolism and beta oxidation within mouse brain and serum
Venue: Scientific reports
Year: 2020

Major depressive disorder (MDD) is a complex condition with unclear pathophysiology. Molecular disruptions within limbic brain regions and the periphery contribute to depression symptomatology and a more complete understanding the diversity of molecular changes that occur in these tissues may guide the development of more efficacious antidepressant treatments. Here, we utilized a mouse chronic social stress model for the study of MDD and performed metabolomic, lipidomic, and proteomic profiling on serum plus several brain regions (ventral hippocampus, nucleus accumbens, and medial prefrontal cortex) of susceptible, resilient, and unstressed control mice. To identify how commonly used tricyclic antidepressants impact the molecular composition in these tissues, we treated stress exposed mice with imipramine and repeated our multi OMIC analyses. Proteomic analysis identified three serum proteins reduced in susceptible animals; lipidomic analysis detected differences in lipid species between resilient and susceptible animals in serum and brain; and metabolomic analysis revealed dysfunction of purine metabolism, beta oxidation, and antioxidants, which were differentially associated with stress susceptibility vs resilience by brain region. Antidepressant treatment ameliorated stress induced behavioral abnormalities and affected key metabolites within outlined networks, most dramatically in the ventral hippocampus. This work presents a resource for chronic social stress induced, tissue specific changes in proteins, lipids, and metabolites and illuminates how molecular dysfunctions contribute to individual differences in stress sensitivity.
Indigenous Cultural Tourism at the Grampians: Benchmarking Visitor Satisfaction at Brambuk The National Parks and Cultural Centre
Year: 2009

The conduct of customer and visitor satisfaction studies is the key to understanding if individual expectation and satisfaction levels are being adequately met in both the competitive and the public goods markets. In leisure markets generally and especially those involving free public goods, data collected from visitors is skewed towards satisfaction and the reporting of aggregates can miss the underlying discontent or signs of dissatisfaction.This study attempts to benchmark visitor satisfaction against criteria relating to organisational objectives and their operational performance or implementation. The focus of the study is to develop a visitor value management approach and a market based performance evaluation model. This approach is objective based and encourages the collection of visitor data that is centered around operational objectives that relate to various activities and programs.The organisation performance is judged by visitor responses and measured against benchmarks set by management, for the organisation's activities and programs. This study reports on a visitor satisfaction study and identifies the extent to which service quality standards have been reached. The study identifies operational objectives that may be in need of reconsideration and renewal and program performance that may be in need for improvements and enhancement. The overall levels of satisfaction experienced by visitors was very high, as is generally expected in leisure markets and especially in relation to public goods or free services. Some areas of concern for visitors were the ease with which visitors could get information and advice; the signage and labelling; the knowledge and accessibility of staff; and the opening hours. The overall visitor awareness of the Centre (pre visit) was poor. For people visiting the area for the first time only 41.4% of domestic and 43.6% of overseas visitors were aware of the Centre prior to their visit to the destination. Word of mouth was by far the most important and the most influential in the decision to visit, with 27.8% of all visitors citing 'friends and relatives' as their major source of information. The second ranked on the 'influence scale' and second most popular source were the 'Roadside Signs' with 24.5% of the visitors identifying this as a sou ce of information and influence on their decision to visit. For Overseas visitors, the most influential sources of information were 'Travel Agents' (40% 'Guide Book' (21.5% 'Friends Relatives' (15.4% and 'Tourist Information Centre' (7.7% Other sources, including the 'Internet' were only marginally utilised. While overall satisfaction experienced was high, a range of operational, service quality and service delivery aspects were identified as needing attention and improvement.