Miracle-dz/UniFAR-FTUs huggingface dataset:
Oligonucleotide‐induced alternative splicing of serotonin 2C receptor reduces food intake

The serotonin 2C receptor regulates food uptake, and its activity is regulated by alternative pre‐mRNA splicing. Alternative exon skipping is predicted to generate a truncated receptor protein isoform, whose existence was confirmed with a new antiserum. The truncated receptor sequesters the full‐length receptor in intracellular membranes. We developed an oligonucleotide that promotes exon inclusion, which increases the ratio of the full‐length to truncated receptor protein. Decreasing the amount of truncated receptor results in the accumulation of full‐length, constitutively active receptor at the cell surface. After injection into the third ventricle of mice, the oligonucleotide accumulates in the arcuate nucleus, where it changes alternative splicing of the serotonin 2C receptor and increases pro‐opiomelanocortin expression. Oligonucleotide injection reduced food intake in both wild‐type and ob/ob mice. Unexpectedly, the oligonucleotide crossed the blood–brain barrier and its systemic delivery reduced food intake in wild‐type mice. The physiological effect of the oligonucleotide suggests that a truncated splice variant regulates the activity of the serotonin 2C receptor, indicating that therapies aimed to change pre‐mRNA processing could be useful to treat hyperphagia, characteristic for disorders like Prader–Willi syndrome.
The Allocation of Inventory Risk in a Supply Chain: Push, Pull, and Advance - Purchase Discount Contracts

While every firm in a supply chain bears supply risk (the cost of insufficient supply), some firms may, even with wholesale price contracts, completely avoid inventory risk (the cost of unsold inventory). With a push contract there is a single wholesale price and the retailer, by ordering his entire supply before the selling season, bears all of the supply chain's inventory risk. A pull contract also has a single wholesale price, but the supplier bears the supply chain's inventory risk because only the supplier holds inventory while the retailer replenishes as needed during the season. (Examples include Vendor Managed Inventory with consignment and drop shipping.) An advance-purchase discount has two wholesale prices: a discounted price for inventory purchased before the season, and a regular price for replenishments during the selling season. Advance-purchase discounts allow for intermediate allocations of inventory risk: The retailer bears the risk on inventory ordered before the season while the supplier bears the risk on any production in excess of that amount. This research studies how the allocation of inventory risk (via these three types of wholesale price contracts) impacts supply chain efficiency (the ratio of the supply chain's profit to its maximum profit). It is found that the efficiency of a single wholesale price contract is considerably higher than previously thought as long as firms consider both push and pull contracts. In other words, the literature has exaggerated the value of implementing coordinating contracts (i.e., contracts that achieve 100% efficiency, such as buy-backs or revenue sharing) because coordinating contracts are compared against an inappropriate benchmark (often just a push contract). Furthermore, if firms also consider advance-purchase discounts, which are also simple to administer, then the coordination of the supply chain and the arbitrary allocation of its profit is possible. Several limitations of advance-purchase discounts are discussed.
Infective Arthritis: Bacterial 23S rRNA Gene Sequencing as a Supplementary Diagnostic Method

Consecutively collected synovial fluids were examined for presence of bacterial DNA (a 700-bp fragment of the bacterial 23S rRNA gene) followed by DNA sequencing of amplicons, and by conventional bacteriological methods. One or more microorganisms were identified in 22 of the 227 synovial fluids (9,7%) originating from 17 patients. Sixteen of the patients had clinical signs of arthritis. For 11 patients molecular and conventional bacterial examinations were in agreement. Staphylococcus aureus, Streptococcus dysgalactiae subspecies equisimilis and Streptococcus pneumoniae, were detected in synovial fluids from 6, 2 and 2 patients, respectively. In 3 patients only 23S rRNA analysis was positive; 2 synovial fluids contained S. dysgalactiae subspecies equisimilis and 1 S. pneumoniae). The present study indicates a significant contribution by PCR with subsequent DNA sequencing of the 23S rRNA gene analysis in recognizing and identification of microorganisms from synovial fluids.
A Lane Detection Method for Lane Departure Warning System

The vision based Lane Departure Warning System (LDWS)is a effective way to prevent Single Vehicle Road Departure accident. In practice, a variety of complex noise make it very difficult to detect lanes fast and accurately, so to establish a set of image processing method which can give results fast and accurately in the non-ideal conditions is the primary work. In this paper, a lane detection method for Lane Departure Warning System is proposed. The Canny algorithm is determined as the edge detection method according to the experiment comparison, and Hough transform is selected as the efficient way to detect the beeline. To meet the real-time requirement, the region of interest(ROI)is defined to reduce noise for rising accurately, and to enhance the processing speed. At last, experiment results indicate that this lane detection method can extract lane information from road images acquired efficiently and accurately.
An Efficient Application Mapping Approach for the Co-Optimization of Reliability, Energy, and Performance in Reconfigurable NoC Architectures

In this paper, an efficient application mapping approach is proposed for the co-optimization of reliability, communication energy, and performance (CoREP) in network-on-chip (NoC)-based reconfigurable architectures. A cost model for the CoREP is developed to evaluate the overall cost of a mapping. In this model, communication energy and latency (as a measure of performance) are first considered in energy latency product (ELP), and then ELP is co-optimized with reliability by a weight parameter that defines the optimization priority. Both transient and intermittent errors in NoC are modeled in CoREP. Based on CoREP, a mapping approach, referred to as priority and ratio oriented branch and bound (PRBB), is proposed to derive the best mapping by enumerating all the candidate mappings organized in a search tree. Two techniques, branch node priority recognition and partial cost ratio utilization, are adopted to improve the search efficiency. Experimental results show that the proposed approach achieves significant improvements in reliability, energy, and performance. Compared with the state-of-the-art methods in the same scope, the proposed approach has the following distinctive advantages: 1) CoREP is highly flexible to address various NoC topologies and routing algorithms while others are limited to some specific topologies and/or routing algorithms; 2) general quantitative evaluation for reliability, energy, and performance are made, respectively, before being integrated into unified cost model in general context while other similar models only touch upon two of them; and 3) CoREP-based PRBB attains a competitive processing speed, which is faster than other mapping approaches.
Unsupervised extraction of semantic relations using discourse cues

This paper presents a knowledge base containing triples involving pairs of verbs associated with semantic or discourse relations. The relations in these triples are marked by discourse connectors between two adjacent instances of the verbs in the triple in the large French corpus, frWaC. We detail several measures that evaluate the relevance of the triples and the strength of their association. We use manual annotations to evaluate our method, and also study the coverage of our resource with respect to the discourse annotated corpus Annodis. Our positive results show the potential impact of our resource for discourse analysis tasks as well as other semantically oriented tasks like temporal and causal information extraction
DRiVE-ing to the Internet: Dynamic Radio for IP services in Vehicular Environments

The European Union funded DRiVE project (Dynamic Radio for IP Services in Vehicular Environments) aims at enabling spectrum-efficient high-quality wireless IP communication in a heterogeneous multi-radio environment to deliver in-vehicle multimedia services. Key issues are spectrum efficiency and dynamic spectrum allocation within different radio networks, the IPv6 based network infrastructure with inter-working of cellular and broadcast radio networks, as well as adaptive services for the vehicular environment with a high degree of mobility. The paper gives a general overview of the DRiVE project and focuses on the network architecture for IP over multi-radio access networks and traffic control aspects within the emerging "DRiVE" network.
Development of a Haptic Fishing Robot

In this paper, a haptic fishing robot, Fishbot, for virtual fishing system is presented, to simulate the motion of fish. XY table is controlled by position servo drivers with variable torque constraint to move Fishbot, and a wheel axis is controlled by a torque servo driver. Finally, our Fishbot detects the end point of a fishing pole with stereo camera to recognize the fishing pole of a user, and thus it can interface with a virtual reality system.
A locally-adaptive approach for image gamma correction

This paper proposes a new adaptive gamma correction method. The method estimates gamma values in different regions of an image and transforms individual pixels of the image following estimated gamma values. In this method we initially made a database of two feature vectors: pixel intensity histograms and dispersion vs. location distributions, from nine standard images with different gamma conditions. After computing these feature vectors for each region of the input image, the most appropriate gamma value is found from the database using a K-Nearest Neighbor technique. One of the advantages of the proposed method is that the gamma value for each pixel of image is modified separately. Experimental results demonstrate that the proposed method performs better in estimating gamma values and improving image quality.
Decomposing Cross-Country Differences in Skills: Evidence from Four Nordic Countries

ABSTRACT This paper performs multivariate analysis of skill differences in four Nordic countries as assessed by the Organisation for Economic Co-operation and Development’s Programme for the International Assessment of Adult Competencies survey of adults aged 16–65. The differences in average skills between Finland and each of the three Scandinavian countries are decomposed into a component that is due to different skill levels in subgroups of the population and a component that is due to differences in the composition of subgroups. The decompositions show that the high Finnish average skill level compared to the three Scandinavian countries can be attributed to the low share of immigrants in Finland and to high scores among Finns with high school and less than high school education. The Finnish average score is pulled substantially downwards as a consequence of the low numeracy skill level among older Finns, which is consistent with an increase in the quantity or quality of Finnish education over time, relative to the other three Nordic countries.
Exploitation of dynamic and partial hardware reconfiguration for on-line power/performance optimization

This paper presents the results from research work done in the field of reconfigurable architectures and systems. Dynamic and partial reconfiguration has mainly been investigated as a way to configure functionalities in hardware on-demand, controlled either by the user or by the system itself. This paper presents work that was aimed at applying hardware reconfiguration even for run-time adaptation of functional implementation in order to enable self-optimization of power and performance according to the run-time specific requirements of the application.
Influence maximization for Big Data through entropy ranking and min-cut

As Big Data becomes prevalent, the traditional models from Data Mining or Data Analysis, although very efficient, lack the speed necessary to process problems with data sets in the range of million samples. Therefore, the need for designing more efficient and faster algorithms for these new types of problems. Specifically, from the field of social network analysis, we have the influence maximization problem. This is a problem with many possible applications in advertising, marketing, social studies, etc, where we have representations of influences by large scale graphs. Even though, the optimal solution of this problem, the minimum set of graph nodes which can influence a maximum set of nodes, is a NP-Hard problem, it is possible to devise an approximated solution to the problem. In this paper, we have proposed a novel algorithm for influence maximization analysis. This algorithm consist in two phases: the first one is an entropy based node ranking where entropy ranking is used to determine node importance in a directed weighted influence graph. The second phase computes the minimum cut using a novel metric. To test the propose algorithm, experiments were performed in several popular data sets to evaluate performance and the seed quality over the influences.
Restrictification of function arguments

Pointer aliasing still hinders compiler optimizations, in spite of years of research on pointer disambiguation. Because the automatic disambiguation of pointers is a difficult endeavor, several programming languages offer programmers mechanisms to distinguish memory references, such as the “restrict” keyword in C. However, the use of such mechanisms is prone to human mistakes. In this paper we present a suite of automatic techniques that mitigate this problem. We have designed, implemented and tested three different ways to disambiguate pointers passed as arguments of functions. Our techniques combine static analyses to infer symbolic bounds of memory regions and code versioning. We generate a clone for each function whose arguments we can disambiguate and optimize it assuming the absence of aliasing among formal parameters. At runtime, we use the results of the symbolic interval tests to decide which version of a function we should call: the original one or the “restricted” clone, whenever we can prove that no aliasing can occur at runtime. An implementation of our restrictification methods in LLVM shows that we can vectorize up to 63% more operations than what could be accomplished using the -O3 optimization level of said compiler. When applying the optimization on OpenCV benchmarks, we have observed speedups as great as 40%.
Subgraphs in random networks.

Understanding the subgraph distribution in random networks is important for modeling complex systems. In classic Erdos networks, which exhibit a Poissonian degree distribution, the number of appearances of a subgraph G with n nodes and g edges scales with network size as approximately N(n-g). However, many natural networks have a non-Poissonian degree distribution. Here we present approximate equations for the average number of subgraphs in an ensemble of random sparse directed networks, characterized by an arbitrary degree sequence. We find scaling rules for the commonly occurring case of directed scale-free networks, in which the outgoing degree distribution scales as P(k) approximately k(-gamma). Considering the power exponent of the degree distribution, gamma, as a control parameter, we show that random networks exhibit transitions between three regimes. In each regime, the subgraph number of appearances follows a different scaling law, approximately Nalpha, where alpha=n-g+s-1 for gamma<2, alpha=n-g+s+1-gamma for 2gamma(c), where s is the maximal outdegree in the subgraph, and gamma(c)=s+1. We find that certain subgraphs appear much more frequently than in Erdos networks. These results are in very good agreement with numerical simulations. This has implications for detecting network motifs, subgraphs that occur in natural networks significantly more than in their randomized counterparts.
Post-Processing of Discovered Association Rules Using Ontologies

In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. In this paper we propose a new approach to prune and filter discovered rules. Using Domain Ontologies, we strengthen the integration of user knowledge in the post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task. On the one hand, we represent user domain knowledge using a Domain Ontology over database. On the other hand, a novel technique is suggested to prune and to filter discovered rules. The proposed framework was applied successfully over the client database provided by Nantes Habitat.
IMITATIVE LEARNING OF COMBAT BEHAVIOURS IN FIRST-PERSON COMPUTER GAMES

Modern, commercial computer games rely primarily on AI techniques that were developed several decades ago, and until recently there has been little impetus to change this. Despite the fact that the computer-controlled agents in such games often possess abilities far in advance of the limits imposed on human participants, competent players are capable of easily beating their artificial opponents suggesting that approaches based on the analysis and imitation of human play may produce superior agents, both in terms of performance and believability. The very fact that games provide the ability to quickly and easily generate vast quantities of raw, objective human behavioural data presents many fascinating opportunities to the AI community; opportunities which, with few exceptions, have not yet been suitably explored. Through our work in the field of imitation learning, we therefore investigate how best to utilise the low-level data accrued from recorded game sessions in the creation of intelligent, convincingly human game agents. In previous contributions, we have described models capable of imitating goal-oriented strategic navigation (Gorman & Humphrys 2005) and of reproducing characteristically human movement in first-person shooter games (Gorman, Thurau, Bauckhage & Humphrys 2006a); we have also outlined a comprehensive approach to the evaluation of agent believability (Gorman et al 2006b). Here, we present an approach to the imitation of combat behaviours in such environments. We first describe the extraction and processing of relevant feature vectors from the game session using our custom-built QASE API (Gorman, Fredriksson & Humphrys 2005). We then outline a neural-network based model designed to learn the aiming and context-sensitive weapon handling exhibited by the human players. Finally, we describe an experiment to demonstrate the efficacy of this approach; some observations and future directions for our work close this contribution.
The Relation of Erythropoietin Towards Hemoglobin and Hematocrit in Varying Degrees of Renal Insufficiency

Introduction: Hypoxia is a basic stimulant in production of erythropoietin (EPO). The primary function of erythrocytes is the transport of oxygen to tissues. Erythropoietin stimulates erythropoiesis which leads to increased production of erythrocytes- their total mass. This increases the capacity of the blood to carry oxygen, reduces the hypoxic stimulus and provides a negative feedback of stopping EPO production. The aim of this study was to establish a quantitative relationship between the concentration of erythropoietin, hemoglobin and hematocrit in different values of renal insufficiency. Material and methods: The survey was conducted on 562 subjects divided into two groups: with and without renal insufficiency. EPO, hemoglobin, hematocrit, serum creatinine and additional parameters iron, vitamin B12, and folic acid were determined by using immunochemical and spectrophotometric methods and glomerular filtration rate (GFR) was calculated as well. Results: EPO values (median) grow to the first degree of renal insufficiency, as compared to EPO values of healthy subjects, this increase is statistically significant, p=0.002. With further deterioration of renal function the values of EPO between all pathological groups are decreasing, and this decrease is statistically significant between first and second degree of renal insufficiency (RI) p<0.001. In the group of healthy subjects EPO is correlated rho = -0.532, p <0.0005 with hematocrit. The correlations are negative and strong and can be predicted by regression line (EP0 = 41.375- Hct * .649; EPO = 61.41–Hb * 0.355). In the group of subjects with the first degree of renal insufficiency EPO is in correlation with hematocrit rho=-0.574, p<0, 0005. It is also correlated with hemoglobin rho=-0.580, p< 0.0005. The correlation is negative (EP0= 42.168- Hct * 0.678). In the group of subjects with the third degree of renal insufficiency EPO is in correlation with hemoglobin rho=0.257, p=0.028. The correlation is medium strong and positive. In the group of subjects with third and fourth degree of renal insufficiency EPO is not in correlation with hemoglobin and hematocrit p>0.05. Conclusion: Renal dysfunction, depending on the level of RI effects differently on the biosynthesis of EPO in a diseased kidney, and consequently it also has a different effect on biosynthesis of HB in bone marrow and its content in the blood.
CIXB-1: combined input-one-cell-crosspoint buffered switch

Buffered crossbars have been considered as an alternative for non-buffered crossbars to improve switching throughput. The drawback of a buffered crossbar is the memory amount that is proportional to the square of the number of ports (O(N/sup 2/)). This is not the main limitation when the buffer size is kept to a minimum size such that implementation is feasible. For a small buffer size, the number of ports of a switch module is not limited by the memory amount but by the pin count. We propose a novel architecture: a combined input-one-cell-crosspoint buffer crossbar (CIXB-1) with virtual output queues (VOQs) at the inputs and round-robin arbitration. We show that the proposed architecture can provide 100% throughput under uniform traffic. A CIXB-1 offers several advantages for a feasible implementation such as scalability and timing relaxation. With the currently available memory technology, a one-cell-crosspoint buffered switch is feasible for a 32/spl times/32 fabric module.
The Joint Effects of Academic and Behavioral Interventions on Behavioral Outcomes

Latino English Language Learners (ELLs) are at high risk for academic and social-behavioral problems. Prevention research suggests that a combination of behavioral and academic interventions is the most effective at addressing the diverse needs of this population. A single subject multiple baseline research design was employed to examine the joint effects of behavioral and academic interventions with kindergarten ELLs identified as at risk for behavioral and academic problems. Specifically, this study aimed to examine the effects of combining these interventions on behavioral outcomes [i.e., off-task, disruptive, and/or problem behaviors). Participants included four (3 Males and 1 Female) Spanish speaking ELLs exhibiting similar disruptive behaviors maintained by attention and poor reading skills. Participants received adapted evidence based interventions for behavior (First Steps to Success) and reading (Reading Mastery) to target both at-risk areas. Results showed that a functional relationship between the combined interventions and improved behavioral outcomes was obtained. This study provides evidence that a combined approach to intervention targeting both behavior and academics is useful in both (a) decreasing problem behavior and (b) increase on-task behavior for ELL students.