Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression and Function of Alpha 3 and Beta 2 Neuronal Nicotinic Acetylcholine Receptor Subunits in HEK-293 Cells

EXPRESSION OF ALPHA3 BETA2 NEURONAL NICOTINIC ACETYLCHOLINE RECEPTOR SUBUNITS IN HEK-293 CELLS Nathan W. Steinhafel Department of Physiology and Developmental Biology Master of Science Single-cell real-time quantitative RT-PCR was used to characterize the mRNA expression of rat neuronal nicotinic acetylcholine receptor (nAChR) subunits α3 and β2 in CA1 hippocampus stratum radiatum and stratum oriens interneurons. α3β2 co-expression was detected in 43% of interneurons analyzed. The nAChR subtype α3β2 was transiently expressed in cells derived from the human embryonic kidney cell line 293 at mRNA levels found in the CA1. The functional properties of α3β2 in HEK-293 cells were characterized by whole-cell patch clamping using acetylcholine (ACh) as an agonist. The kinetics of α3β2 channels were further analyzed by altering the level of α3 DNA transfected into HEK-293 cells. Varying the α3 concentration by more than 100,000 fold did not significantly alter the majority of the kinetics; the 10%-90% rise-time was the main characteristic found to be significantly different. A decrease in α3 concentration illustrated a significant increase in rise time. This and future studies will further our understanding of the extensive role neuronal nAChRs play in modulating hippocampal activity and consequently influencing cognition and memory.
Bizygomatic Width and its Association with Social and Personality Traits in Males

This study will examine how differences in measurements between bizygomatic and bigonial arches of the human face can provide relevant information regarding personality and abilities of the subject. Sample: 82 males. Group A: 55 subject s with th bizygomatic arch in contraction. Group B: 27 subjects without the bizygomatic arch in contraction. Subjects performed self-asse ssment tests in the following areas: ability incorporating themselves into the group, alexithymia and emotional expressiveness. Group A howed greater self-sufficiency and independence than group B, greater difficulty in describing and verbally expressing their emotions , l ss ability to express themselves emotionally through other forms of communication and reduced willingness to cooperate philanthrop ica ly. Results suggest a strong relationship between morphological structure of the bizygomatic arch and certain psychological charact eristi s.
Differences in Reward Allocation Biases Cultural Construction of Success and Epistemic Motives Moderate American-Chinese

When the relative contribution of the self and the group to a group success is unclear, Americans tend to exhibit a self-serving bias (rewarding the self more than what the self deserves), whereas the Chinese tend to exhibit an other-serving bias (rewarding the group more than the group deserves). In a study comparing the reward allocation biases of Americans and Chinese in different group outcome conditions, the authors showed that the abovementioned cultural difference is found (a) only for culturally congruent success experience (attaining approach goals for Americans and avoidance goals for Chinese) and (b) among individuals who are motivated by the need for cognitive closure to exhibit culturally typical responses. This finding has important implications for understanding the dynamic nature of cultural influences on social behaviors.
A comprehensive survey on handover management for vehicular ad hoc network based on 5G mobile networks technology

Vehicular ad hoc network (VANET) is a style of high‐mobility mobile ad hoc network where the mobile nodes are vehicles traveling across a road or a street. VANET was proposed to provide safety, comfortability, reliability, and security of vehicles driving. One of the serious challenges is handover (HO) problem that occurs when a vehicle moves from one network area covered by a roadside unit (RSU) to another network area covered by a different RSU. The fast mobility of vehicles and the limited coverage area of the RSU (without overlapping signals) should be considered in providing a seamless HO. This paper presents a comprehensive survey on HO management for VANET based on the new features of fifth‐generation (5G) mobile network technologies. The 5G mobile network technology provides enhanced characteristics that will fit the requirements of real‐time video streaming and assist to design an effective solution for the HO problem. In addition, this paper presents the reasons for HO failures and the limitations of available HO failure solutions. Furthermore, it highlights the potential proposed solutions that will tackle the HO failures. This survey will open up the gate of developing an optimal solution based on 5G for HO problem on VANET that might be used in vehicles manufacturing.
Tracking Your Mind's Eye during Recollection: Decoding the Long-Term Recall of Short Audiovisual Clips

Unlike familiarity, recollection involves the ability to reconstruct mentally previous events that results in a strong sense of reliving. According to the reinstatement hypothesis, this specific feature emerges from the reactivation of cortical patterns involved during information exposure. Over time, the retrieval of specific details becomes more difficult, and memories become increasingly supported by familiarity judgments. The multiple trace theory (MTT) explains the gradual loss of episodic details by a transformation in the memory representation, a view that is not shared by the standard consolidation model. In this study, we tested the MTT in light of the reinstatement hypothesis. The temporal dynamics of mental imagery from long-term memory were investigated and tracked over the passage of time. Participant EEG activity was recorded during the recall of short audiovisual clips that had been watched 3 weeks, 1 day, or a few hours beforehand. The recall of the audiovisual clips was assessed using a Remember/Know/New procedure, and snapshots of clips were used as recall cues. The decoding matrices obtained from the multivariate pattern analyses revealed sustained patterns that occurred at long latencies (>500 msec poststimulus onset) that faded away over the retention intervals and that emerged from the same neural processes. Overall, our data provide further evidence toward the MTT and give new insights into the exploration of our “mind's eye. ”
Cathepsin L secretion by host and neoplastic cells potentiates invasion

The presence of macrophages within breast tumors correlates with metastatic potential. These tumor-associated macrophages often take on a pro-tumorigenic (M2-like) phenotype resulting in the secretion of growth factors and proteases, including the lysosomal protease cathepsin L. Since cathepsin L also is frequently secreted by breast cancer cells and contributes to tumor invasion, metastasis, and angiogenesis, we hypothesized that secretion of cathepsin L by both tumor-associated macrophages and neoplastic cells would facilitate the metastatic phenotype. Our results showed that the novel cathepsin L/K inhibitors KGP94 and KGP207 could inhibit in vitro M2 macrophage invasion and reduce the macrophage-stimulated invasion of 4T1 murine breast cancer cells. KGP94 and KGP207 treatment also reduced the expression of several M2-associated markers, suggesting that cathepsin L activity may be important for IL-4-driven M0 to M2 differentiation. In addition, cathepsin L shRNA knockdown studies revealed that cathepsin L from both the tumor cell and the macrophage population is important for tumor cell invasion. Thus our data suggest that tumor cells and macrophages may both contribute to the cathepsin L-driven metastatic phenotype of breast cancer. Taken together, these studies highlight the importance of cathepsin L in macrophage functions and suggest that cathepsin inhibition strategies may be therapeutically beneficial by impairing the progression of tumors with high infiltration of M2 macrophages.
Multiple-Rate Channel Codes in GF(pn2)

A code C(n, k, d) defined over GF(q) is conventionally designed to encode a k-symbol user data into a codeword of length n, resulting in a fixed-rate coding. This paper proposes a coding procedure to derive a multiple-rate code from existing channel codes defined over a composite field GF(q). Formally, by viewing a symbol of GF(q) as an n-tuple over the base field GF(q), the proposed coding scheme employs children codes C1(n, 1), C2(n, 2), . , Cn(n, n) defined over GF(q) to encode user messages of arbitrary lengths and incorporates a variable-rate feature. In sequel, unlike the conventional block codes of length n, the derived multiple-rate code of fixed blocklength n (over GF(q)) can be used to encode and decode user messages m (over GF(q)) of arbitrary lengths |m| = k, k+1, . , kn, thereby supporting a range of information rates inclusive of the code rates 1/n, 2/n, . , (k−1)/n, in addition to the existing code rate k/n. The proposed multiple-rate coding scheme is also equipped with a decoding strategy, wherein the identification of children encoded user messages of variable length are carried out through a simple procedure using orthogonal projectors.
Constrained de Bruijn Codes and their Applications

A sequence s = (s1,⋯,sn) is called a (b, h)-constrained de Bruijn sequence if all substrings of length h starting within b consecutive positions are distinct. A set of (b, h)-constrained de Bruijn sequences is called a (b, h)-constrained de Bruijn code. A (b, h)-constrained de Bruijn sequence was constructed and used as a component of a code correcting multiple limited-shift-errors in racetrack memories. In this work, we show that a (b, h)-constrained de Bruijn code can correct deletions and sticky-insertions and also can determine the locations of these errors in an ℓ-symbol read channel. We also show that it is possible to use sequences from a (b, h)-constrained de Bruijn code to construct a code correcting shift-errors in racetrack memories. As a consequence, we improve the rates on previous known codes.It is shown in this work that a (b, h)-constrained de Bruijn code is a constrained code avoiding a set of specific patterns. Finally, we present some techniques to compute the maximum asymptotic rate and find some efficient encoding/decoding algorithms for (b, h)-constrained de Bruijn codes.
Flow-Based Network Slicing: Mapping the Future Mobile Radio Access Networks

Nowadays mobile networks are asked to support different applications and services characterized by very specific Quality of Service (QoS) requirements. With this aim in mind, deploying network slices with particular resource allocation policies on a per-service basis becomes extremely relevant. In this regard, we introduce a solution able to dynamically partition the underlying physical infrastructure of a mobile radio access network into multiple logical slices with distinctive service-level agreements. We leverage Software-Defined Networking principles to provide fine-grained flow identification and sophisticated QoS management policies on a generic architecture supporting 4G and 5G networks with the objective of mapping the path towards the future mobile networks. The experimental evaluation of the deployed prototype on a real-world testbed has demonstrated the slicing capabilities of the system while ensuring full performance and functional isolation. We release the entire implementation under a permissive APACHE 2.0 license for academic use.
Urdu Text Classification: A comparative study using machine learning techniques

In the last decade, online content has entered a stage where news related organizations are reluctant to invest in offline operations due to excessive aberrations in content distributions. However, the proliferation of digital data in an unstructured or rather disordered form particularly for languages like Urdu has complicated the easy access to information. Consequently, the paper addresses the peculiarities of Urdu text classification of news origin. For this, the performance of the three classifiers such as Decision Tree (J48), Support Vector Machine (SVM) and k-nearest neighbor (KNN) was measured on the classification of Urdu text using WEKA (Waikato Environment Knowledge Analysis) tool. The assessment was carried out on a relatively large collection of Urdu text having over 16,678 documents containing mainly news articles from The Daily Roshni, an Urdu newspaper. Additionally, TF-IDF weighting scheme was used for feature selection and extraction of data. The Urdu text classification using SVM classifier performed quite better with promising accuracy and superior efficiency when compared to the other two classifiers. For this study, the dataset was formulated as per TRC (Text Retrieval Conference) community standard.
Low Power IoT Network Sensors Optimization for Smart Cities Applications

Nowadays, Low Power Wide Area Networks (LPWAN) assumes an important role on the evolution and growing of wireless network sensors. The actual technologies based on Internet of Things (IoT) allow sensor connectivity to Internet. This feature enables real time acquisition of several physical data important for smart cities monitoring. Depending on the adopted wireless communication standard, sensor information is transmitted according to several radio communication conditions, namely binary rate, bandwidth and duty cycle policy. This paper presents a wireless sensor network design based on power consumption optimization. The proposed optimization focus on the number of end devices (sensors), distance of each end device to the gateway and the radio configuration parameters. Two end devices distribution around the gateway are analyzed for prior network power consumption optimization. The obtained results are supported on two different IoT devices.
Computation and management of weighted activation vectors in support to fMRI analysis of clinical subjects

ABSTRACT In the present work, we investigate the usefulness of a new representation of the results obtained by fMRI data analysis, named weighted activation vector (WAV), built based on statistical parametric mapping. A software package for the generation and management of WAVs is illustrated. It is designed to support single-subject, multi-temporal and collective brain tumour studies. As seen in our experimental context, the combined use of WAVs and statistical parametric maps (SPMs) improves the quality of medical decisions before and after neurosurgical practice. Clustering techniques applied to WAVs can be efficiently analysed and optimised in an attempt to discover relevant properties of collective data.
Impact of Network Control System Performance on Efficiency of Large Corporate Network

Complex and heterogeneous network with different physical principles of data exchange is learned. The impact of efficiency and correct choice of parameters of network control system on successful operation of network information system as object of control is researched. It was shown that common criterion of network system efficiency is the functional of its parameters and state, which may be described by stochastic Markov processes. The evaluations of number of possible states of network as complex information system are given. The states of full performance, slow degradation of parameters, and full failure of system are singed. The discrete process of transition of system from one state to another one is described by Markov chain.
DISC: A Novel Distributed On-Demand Clustering Protocol for Internet of Multimedia Things

Internet of Multimedia Things (IoMT) are receiving significant attention due to a wide variety of applications, e.g., wildlife habitat monitoring, but they are often highly resource constrained. Compared to Internet of Things, preserving battery power of nodes, and maximizing the lifespan of IoMT are more critical and challenging as sensed data are mostly image/video instead of simple scalar. Recent studies have shown that clustering is an efficient solution to reduce energy consumption. In clusters, the role of each node changes to reduce energy consumption, thereby, prolonging lifespan. In this paper, we address the lifespan maximization problem in IoMT by designing a clustering protocol where clusters are formed dynamically. Specifically, we analyze and solve an optimization problem aiming to maximize the lifespan by reducing the energy consumption among cluster heads. Based on the analysis, we propose a novel DIStributed on-demand Clustering (DISC) protocol. Our cluster head election procedure is not periodic, but adaptive, based on the dynamism of the occurrence of events. This on-demand execution of DISC aims to significantly reduce computation and message overheads. We validate the performance of DISC through extensive experiments. Experimental results show that DISC is 25% more energy balanced and achieves 32% more lifespan as compared to two state-of-the-art solutions.
MobiAmbulance: Optimal Scheduling of Emergency Vehicles in Catastrophic Situations

With recent experience in multiple large-scale disasters, it has been widely confirmed that the severity of a disaster is greatly dependent on the effectiveness of ambulance dispatching during disaster phase. However, previous base station (i.e., temporary or permanent hospital) based ambulance redeployment methods and dynamic ambulance scheduling methods cannot handle the ambulance dispatching problem in catastrophic situations. In this paper, we present MobiAmbulance: a human Mobility based Ambulance dispatching system that aims to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delay of the fulfilled requests. We studied a state-scale human mobility dataset and found that the change of vehicle flow rate can be utilized to determine the connection status between road segments, and the distribution of people in catastrophic situations is drastically different from that in normal situations. Then, we develop a method to determine the road network connection status and the set of road segments that can still be driven through by ambulances after disaster. Based on the updated road network graph, we develop an ambulance dispatching method based on weighted driving route to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delays of the fulfilled requests. Our trace-driven experiments demonstrate the superior performance of MobiAmbulance over other comparison methods.
Overlapping Multi-Bandit Best Arm Identification

In the multi-armed bandit literature, the multibandit best-arm identification problem consists of determining each best arm in a number of disjoint groups of arms, with as few total arm pulls as possible. In this paper, we introduce a variant of the multi-bandit problem with overlapping groups, and present two algorithms for this problem based on successive elimination and lower/upper confidence bounds (LUCB). We bound the number of total arm pulls required for high-probability best-arm identification in every group, and we complement these bounds with a near-matching algorithm-independent lower bound.
Home Energy Management for a AC/DC Microgrid Using Model Predictive Control

This paper presents a Model Predictive Control (MPC) approach to Home Energy Management (HEM) of microgrid operating with AC/DC bus. The proposed optimization model consists of renewable energy sources, two different storage devices and loads commonly found in the household of Brazilian consumers. In particular, we present a formulation that includes the cost of the electricity, the aging of the components, and the operational constraints. The MPC thecniques allow to satisfy user demand, maximizing the economical benefit of the microgrid, minimizing the electricity purchased of the power grid, as well as extend the lifespan of each storage device, fulfilling the different system constraints contributing to the Demand Side Management (DSM). In order to verify the dynamics of the proposed model, results of numerical simulations will be presented.
Distributed Matrix Multiplication with MDS Array BP-XOR Codes for Scaling Clusters

This study presents a novel coded computation technique for distributed matrix-matrix product computation at a massive scale that outperforms well known previous strategies in terms of total execution time. Our method achieves this performance by distributing the encoding operation over the cluster (slave) nodes at the expense of increased master-slave communication. The product computation is performed using MDS array Belief Propagation (BP)-decodable codes based on pure XOR operations. In addition, our scheme is configurable and suited for modern compute node architectures equipped with multiple processing units organized in a hierarchical manner. Assuming the number of backup nodes being sublinear in the size of the product, we shall demonstrate that the proposed scheme achieves order-optimal computation from an end-to-end latency perspective while ensuring acceptable communication requirements that can be addressed by today’s high speed network link infrastructures.
A Transactive Energy Management Framework for Regional Network of Microgrids

To maximize the microgrids profit in presence of renewable energy resources (RERs), this paper employs the transactive energy (TE) technique for optimal scheduling of the distributed energy resources (DERs) throughout the system. Microgrids can exchange energy with the main grid not only to meet their demand but also to make a profit by active participation in energy and reserve provision process. In this regard, TE can be applied as an effective solution for energy supply management and dynamic balancing between the microgrids and the power grid. In this paper, the participation of five grid-connected commercial microgrids in day-ahead (DA) market is considered. The applicability and the satisfactory performance of the proposed approach are tested and validated on a 10-bus IEEE test system. Simulation results indicate that maximum profit can be achieved for the microgrids with optimal scheduling of the DERs based on the TE approach.
Taksim: A Constrained Graph Partitioning Framework for Procedural Content Generation

We present Taksim, an Answer Set Programming (ASP) framework for generating content in games through constrained graph partitioning. We illustrate its expressivity by implementing logical constraints that are relevant to generating the spaces of game levels. Furthermore, we present a case study for creating game levels from a given Mission Graph. Finally, we propose key concepts that make constrained graph partitioning, coupled with ASP, an asset for Procedural Content Generation.
Multi-Agent non-Overlapping Pathfinding with Monte-Carlo Tree Search

In this work, we propose a novel implementation of Monte-Carlo Tree Search (MCTS) algorithm to solve a multiagent pathfinding (MAPF) problem. We employ an optimization of MCTS with low time-complexity and acceptable reliability to approach the MAPF problems with no time constraint. To examine the efficiency and performance of the proposed approach, the NumberLink problem as a MAPF is investigated. We show that the addressed problem could be characterized as multi-agent pathfinding problem with no overlapping paths for the agents. Furthermore, we define this problem to be a simplified and special case of Multi-commodity flow problem (MCFP). Our MCTS solution utilizes a modified search-tree structure to efficiently solve the problem based on a 2-dimensional search space which performs in quadratic time complexity (O(m4) where input size is m2) and linear memory complexity (O(m2)). To evaluate our algorithm, we investigate the efficiency of the proposed solution for the well-known Flow Free puzzle. Our implementation solves a large 40 × 40 Numberlink puzzle in 21 minutes. To the best of our knowledge, there is no other efficient solution for this puzzle where the size of the problem is considerably large.