Miracle-dz/UniFAR-FTUs huggingface dataset:
Transforming growth factor‐β1 primes proliferating adult neural progenitor cells to electrophysiological functionality

The differentiation of adult neural progenitors (NPCs) into functional neurons is still a limiting factor in the neural stem cell field but mandatory for the potential use of NPCs in therapeutic approaches. Neuronal function requires the appropriate electrophysiological properties. Here, we demonstrate that priming of NPCs using transforming growth factor (TGF)‐β1 under conditions that usually favor NPCs' proliferation induces electrophysiological neuronal properties in adult NPCs. Gene chip array analyses revealed upregulation of voltage‐dependent ion channel subunits (Kcnd3, Scn1b, Cacng4, and Accn1), neurotransmitters, and synaptic proteins (Cadps, Snap25, Grik4, Gria3, Syngr3, and Gria4) as well as other neuronal proteins (doublecortin [DCX], Nrxn1, Sept8, and Als2cr3). Patch‐clamp analysis demonstrated that control‐treated cells expressed only voltage‐dependent K+‐channels of the delayed‐rectifier type and the A‐type channels. TGF‐β1‐treated cells possessed more negative resting potentials than nontreated cells owing to the presence of delayed‐rectifier and inward‐rectifier channels. Furthermore, TGF‐β1‐treated cells expressed voltage‐dependent, TTX‐sensitive Na+ channels, which showed increasing current density with TGF‐β1 treatment duration and voltage‐dependent (+)BayK8644‐sensitive L‐Type Ca2+ channels. In contrast to nontreated cells, TGF‐β1‐treated cells responded to current injections with action‐potentials in the current‐clamp mode. Furthermore, TGF‐β1‐treated cells responded to application of GABA with an increase in membrane conductance and showed spontaneous synaptic currents that were blocked by the GABA‐receptor antagonist picrotoxine. Only NPCs, which were treated with TGF‐β1, showed Na+ channel currents, action potentials, and GABAergic currents. In summary, stimulation of NPCs by TGF‐β1 fosters a functional neuronal phenotype, which will be of relevance for future cell replacement strategies in neurodegenerative diseases or acute CNS lesions. GLIA 2013;61:1767–1783
Brain Volumes and Longitudinal Cognitive Change: A Population-based Study

Objective: To investigate the association of brain volumes, white matter lesion (WML) volumes, and lacunes, with cognitive decline in a population-based cohort of nondemented persons. Methods: Within the Rotterdam Study, 3624 participants underwent brain magnetic resonance imaging. Cognition was evaluated at baseline (2005 to 2009) and at the follow-up visit (2011 to 2013). We used a test battery that tapped into domains of executive function, information processing speed, motor speed, and memory. The volumetric measures assessed were total brain volume, lobar (gray matter and white matter) volumes, and hippocampal volumes. We also studied the association of WML volumes and lacunes with cognitive decline using linear regression models. Results: Total brain volume was associated with decline in global cognition, information processing, and motor speed (P<0.001) in analyses controlled for demographic and vascular factors. Specifically, smaller frontal and parietal lobes were associated with decline in information processing and motor speed, and smaller temporal and parietal lobes were associated with decline in general cognition and motor speed (P<0.001 for all tests). Total WML volume was associated with decline in executive function. Lobar WML volume, hippocampal volume, and lacunes were not associated with cognitive decline. Conclusions: Lower brain volume is associated with subsequent cognitive decline. Although lower total brain volume was significantly associated with decline in global cognition, specific lobar volumes were associated with decline in certain cognitive domains.
Ontology matching for facilitating inventive design based on semantic similarity and case-based reasoning

Even if TRIZ is developing increasingly both in research and education, new users always encounter difficulties in their first attempts to practice it. In such situation, Altshuller's original contradiction matrix often appears as an "easy-to-begin-with" tool. However, while not being representative of what TRIZ really is, it continues to seduce new users, teachers and trainers. This article presents an innovative method for facilitating the use of the contradiction matrix, using a semantic similarity approach and case-based reasoning.
meGautz: A High Capacity, Fault-Tolerant and Traffic Isolated Modular Datacenter Network

The modular datacenter networks (MDCN) comprise inter- and intra-container networks. Although it simplifies the construction and maintenance of mega-datacenters, interconnecting hundreds of containers and supporting online data-intensive services is still challenging. In this paper, we present meGautz, which is the first inter-container network that isolates inter- and intra-container traffic, and it has the following advantages. First, meGautz offers uniform high capacity among servers in the different containers, and balances loads at the container, switch, and server levels. Second, it achieves traffic isolation and allocates bandwidth evenly. Therefore, even under an all-to-all traffic pattern, the inter- and intra-container networks can deal with their own flows without interfering with each other, and both can gain high throughput. meGautz hence improves the performance of both the entire MDCN and individual servers, for there is no performance loss caused by resource competition. Third, meGautz is the first to achieve as graceful performance degradation as computation and storage do. Results from theoretical analysis and experiments demonstrate that meGautz is a high-capacity, fault-tolerant, and traffic isolated inter-container network.
Mining entities and their values from semi-structured documents in business process outsourcing

One of the most commonly performed tasks in a business process operations is to convert heterogeneous documents in various formats like doc, xls, pdf into a structured data that is entered into a typical ERP system. For example, in a logistics based business process the objective is to identify the courier company, the delivery address, the booking address, the shipment and payment details from various types of documents. In this paper, we try to address the problem of entity extraction from the heterogenous data in an attempt to automate the manual task in financial domain. We have experimented with two supervised algorithms - CRFs and SVM for sequentially tagging the entities. We will present details of our experiments on some real-world data collected in a BPO organizations. We observe that CRF (81%,73% F1-measures) performed better than SVMhmm (80%,56% F1-measures) in logistics and accounts payables-based business process scenarios respectively. Also, we observe that structural in combination with the syntactic and domain knowledge features helped in getting better results.
GyrosFinger

Drones are widely used for various purposes such as delivery, aerial photography, and surveillance. Considering the increasing drone-related services, tracking the locations of drones can cause security threats such as escaping from drone surveillance, disturbing drone-related services, and capturing drones. For wirelessly monitoring the status of drones, telemetry is used, and this status information contains various data such as latitude and longitude, calibrated sensor outputs, and sensor offsets. Because most of the telemetry implementation supports neither authentication nor encryption, an attacker can obtain the status information of the drones by using an appropriate wireless communication device such as software-defined radio. While the attacker knows the locations of the drones from the status information, this information is not sufficient for tracking drones because the status information does not include any identity information that can bind the identity of the drone with its location. In this article, we propose a fingerprinting method for drones in motion for the binding of the identity of the drone with its location. Our fingerprinting method is based on the sensor outputs included in the status information, i.e., the offsets of micro-electro mechanical systems (MEMS) gyroscope, an essential sensor for maintaining the attitude of drones. We found that the offsets of MEMS gyroscopes are different from each other because of manufacturing mismatches, and the offsets of five drones obtained through their telemetry are distinguishable and constant during their flights. To evaluate the performance of our fingerprinting method on a larger scale, we collected the offsets from 70 stand-alone MEMS gyroscopes to generate fingerprints. Our experimental results show that, when using the offsets of three and two axes calculated from 128 samples of the raw outputs per axis as fingerprints, the F-scores of the proposed method reach 98.78% and 94.47%, respectively. The offsets collected after a month are also fingerprinted with F-scores of 96.58% and 78.45% under the same condition, respectively. The proposed fingerprinting method is effective, robust, and persistent. Additionally, unless the MEMS gyroscope is not replaced, our fingerprinting method can be used for drone tracking even when the target drones are flying.
A study of attention-based neural machine translation model on Indian languages

Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT). The use of LSTMs in machine translation has significantly improved the translation performance for longer sentences by being able to capture the context and long range correlations of the sentences in their hidden layers. The attention model based NMT system (Bahdanau et al., 2014) has become the state-of-the-art, performing equal or better than other statistical MT approaches. In this paper, we wish to study the performance of the attention-model based NMT system (Bahdanau et al., 2014) on the Indian language pair, Hindi and Bengali, and do an analysis on the types or errors that occur in case when the languages are morphologically rich and there is a scarcity of large parallel training corpus. We then carry out certain post-processing heuristic steps to improve the quality of the translated statements and suggest further measures that can be carried out.
Modeling variable length phoneme sequences — A step towards linguistic information for speech emotion recognition in wider world

Vocal gestures play an important role in emotion expression and can be used by speech based emotion recognition systems. This paper proposes the use of BLSTM neural networks to model salient variable length phoneme sequences, which in turn can represent relevant vocal gestures. Unlike existing techniques, the proposed approach is not restricted to modelling phoneme sequences of a fixed length and both salience and optimal modelling length of phoneme sequences are learnt from the training data. Three possible phoneme representations that can be modelled by BLSTMs are compared and experimental results suggest that sequences of Phone Log Likelihood Ratios are more representative of emotions when compared to sequences of phoneme labels represented as one — hot vectors. On the IEMOCAP database, the proposed approach achieves an Unweighted Average Recall (UAR) of 56.4%, an improvement of 6.5% in absolute terms over the previous approach of modelling fixed length phoneme sequences on a 4-class classification problem. The proposed linguistic system is complementary to acoustic features with a fused system leading to an absolute improvement of 5% to the UAR.
Optimizing effect of frying cycles on cooking oil properties using particle swarm optimization

In this paper, Particle swarm optimization (PSO) technique has been proposed to evaluate the quality of recycled cooking palm oil. This technique proved to be the most powerful and robust technique in the modern era. The sensitivity of the threat by used cooking oil on public health and the complex nature and laborious approaches of evaluating the oil will be overcome through the proposed algorithm. The findings in this study have revealed that the appropriate temperature in degree centigrade (°C), of grading cooking oil is higher than 200°C for both the TPC and FFA. The study also portrays none significant differences between PSO and GA in terms of optimum estimated value, fitness and temperature. The findings conclude that it is better to evaluate TPC and FFA of used oil at higher temperatures than at low temperatures.
Azadiradione ameliorates polyglutamine expansion disease in Drosophila by potentiating DNA binding activity of heat shock factor 1

Aggregation of proteins with the expansion of polyglutamine tracts in the brain underlies progressive genetic neurodegenerative diseases (NDs) like Huntington's disease and spinocerebellar ataxias (SCA). An insensitive cellular proteotoxic stress response to non-native protein oligomers is common in such conditions. Indeed, upregulation of heat shock factor 1 (HSF1) function and its target protein chaperone expression has shown promising results in animal models of NDs. Using an HSF1 sensitive cell based reporter screening, we have isolated azadiradione (AZD) from the methanolic extract of seeds of Azadirachta indica, a plant known for its multifarious medicinal properties. We show that AZD ameliorates toxicity due to protein aggregation in cell and fly models of polyglutamine expansion diseases to a great extent. All these effects are correlated with activation of HSF1 function and expression of its target protein chaperone genes. Notably, HSF1 activation by AZD is independent of cellular HSP90 or proteasome function. Furthermore, we show that AZD directly interacts with purified human HSF1 with high specificity, and facilitates binding of HSF1 to its recognition sequence with higher affinity. These unique findings qualify AZD as an ideal lead molecule for consideration for drug development against NDs that affect millions worldwide.
Parameter identification of bacterial growth bioprocesses using particle swarm optimization

This paper deals with the off-line parameters identification for a class of bacterial growth bioprocesses using particle swarm optimization (PSO) techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of a complex biotechnological system. The identification problem is formulated as a multi-modal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.
Ultra-high-throughput massive MIMO field-trial over radio computing architecture with peak spectrum efficiency of 79.82 bps/Hz

Massive multiple-input multiple-output (MIMO) has been considered as one of the key technologies in 5G communication, owing to its promising potential to increase the spectrum efficiency significantly. The capacity grows rapidly with the increasing number of antennas. However, in real-time environment, the more antennas are employed, the more computing resources are required, and this increase is even in a nonlinear progression. In this work, radio computing architecture (RCA) with multiple parallel general purpose processors (GPPs) and automatic distribution system (ADS) are utilized to overcome the strict real-time constraints and establish a highly flexible prototype platform for massive MIMO research. The feasibility and performance of the GPP-based prototype has been investigated through field trial. A collocated 64-RF-channel base station (BS) with each RF channel driving 3 antennas can simultaneously serve twelve 8-antenna user equipments (UEs). The maximum overall bandwidth is 200 MHz, and the maximum flow number is pre-defined as 24. In this field trial, a peak total user throughput of 11.29 Gbps and a cell spectrum efficiency of 79.82 bps/Hz are achieved. As far as we know, this is the world's highest throughput ever achieved in the sub 6 GHz band. Furthermore, apart from multi-user MIMO, single-user MIMO is also tested in this field trial.
A comprehensive model for the proliferation–quiescence decision in response to endogenous DNA damage in human cells

Significance Controlled transitions of human cells between proliferating and nonproliferating states are essential for normal development and tissue homeostasis. To understand how the decision to proliferate is made in response to positive input from growth factors and negative input from the DNA damage response, we have built a mathematical model of the underlying molecular network, based on data from live cell-imaging experiments. Our model suggests that two major cell-cycle transitions are crucial for decision making: the restriction point, which integrates pro- and antiproliferative signals, and the G1/S transition, which temporarily insulates cells from some aspects of the DNA damage response. Together, our model gives mechanistic insight into how cells maintain both sensitivity and robustness to external signals. Human cells that suffer mild DNA damage can enter a reversible state of growth arrest known as quiescence. This decision to temporarily exit the cell cycle is essential to prevent the propagation of mutations, and most cancer cells harbor defects in the underlying control system. Here we present a mechanistic mathematical model to study the proliferation–quiescence decision in nontransformed human cells. We show that two bistable switches, the restriction point (RP) and the G1/S transition, mediate this decision by integrating DNA damage and mitogen signals. In particular, our data suggest that the cyclin-dependent kinase inhibitor p21 (Cip1/Waf1), which is expressed in response to DNA damage, promotes quiescence by blocking positive feedback loops that facilitate G1 progression downstream of serum stimulation. Intriguingly, cells exploit bistability in the RP to convert graded p21 and mitogen signals into an all-or-nothing cell-cycle response. The same mechanism creates a window of opportunity where G1 cells that have passed the RP can revert to quiescence if exposed to DNA damage. We present experimental evidence that cells gradually lose this ability to revert to quiescence as they progress through G1 and that the onset of rapid p21 degradation at the G1/S transition prevents this response altogether, insulating S phase from mild, endogenous DNA damage. Thus, two bistable switches conspire in the early cell cycle to provide both sensitivity and robustness to external stimuli.
Detection of the number of coherent signals by the MDL principle

An approach is presented to the problem of detecting the number of sources impinging on a passive sensor array that is based on J. Rissanen's (1983) minimum description length (MDL) principle. The approach is applicable to any type of sources, including the case of sources which are fully correlated, referred to as the coherent signals case. Two slightly different detection criteria are derived, both requiring the estimation of the locations of the sources. The first is tailored to the detection problem per se, whereas the second is tailored to the combined detection/estimation problem. Consistency of the two criteria is proved and their performance is demonstrated by computer simulations. >
Decentralized Consensus for Edge-Centric Internet of Things: A Review, Taxonomy, and Research Issues

With the exponential rise in the number of devices, the Internet of Things (IoT) is geared toward edge-centric computing to offer high bandwidth, low latency, and improved connectivity. In contrast, legacy cloud-centric platforms offer deteriorated bandwidth and connectivity that affect the quality of service. Edge-centric Internet of Things-based technologies, such as fog and mist computing, offer distributed and decentralized solutions to resolve the drawbacks of cloud-centric models. However, to foster distributed edge-centric models, a decentralized consensus system is necessary to incentivize all participants to share their edge resources. This paper is motivated by the shortage of comprehensive reviews on decentralized consensus systems for edge-centric Internet of Things that elucidates myriad of consensus facets, such as data structure, scalable consensus ledgers, and transaction models. Decentralized consensus systems adopt either blockchain or blockchainless directed acyclic graph technologies, which serve as immutable public ledgers for transactions. This paper scrutinizes the pros and cons of state-of-the-art decentralized consensus systems. With an extensive literature review and categorization based on existing decentralized consensus systems, we propose a thematic taxonomy. The pivotal features and characteristics associated with existing decentralized consensus systems are analyzed via a comprehensive qualitative investigation. The commonalities and variances among these systems are analyzed using key criteria derived from the presented literature. Finally, several open research issues on decentralized consensus for edge-centric IoT are presented, which should be highlighted regarding centralization risk and deficiencies in blockchain/blockchainless solutions.
Liposomal Formulations for an Efficient Encapsulation of Epigallocatechin-3-Gallate: An In-Silico/Experimental Approach

As a part of research project aimed to optimize antioxidant delivery, here we studied the influence of both salts and lipid matrix composition on the interaction of epigallocatechin-3-gallate (EGCG) with bilayer leaflets. Thus, we combined in silico and experimental methods to study the ability of neutral and anionic vesicles to encapsulate EGCG in the presence of Ca2+ and Mg2+ divalent salts. Experimental and in silico results show a very high correlation, thus confirming the efficiency of the developed methodology. In particular, we found out that the presence of calcium ions hinders the insertion of EGCG in the liposome bilayer in both neutral and anionic systems. On the contrary, the presence of MgCl2 improves the insertion degree of EGCG molecules respect to the liposomes without divalent salts. The best and most efficient salt concentration is that corresponding to a 5:1 molar ratio between Mg2+ and EGCG, in both neutral and anionic vesicles. Concerning the lipid matrix composition, the anionic one results in better promotion of the catechin insertion within the bilayer since experimentally we achieved 100% EGCG encapsulation in the lipid carrier in the presence of a 5:1 molar ratio of magnesium. Thus, the combination of this anionic liposomal formulation with magnesium chloride, avoids time-consuming separation steps of unentrapped active principle and appears particularly suitable for EGCG delivery applications.
Manipulating fluorescence intensity with mechanical strains

In this paper we show that the fluorescence of rhodamine 6G (R6G) can be manipulated by applying mechanical strains to gold nanoparticles (GNs) sandwiched in graphene/GNs/polydimethylsiloxane (graphene/GNs/PDMS) structure. The fluorescence intensity of R6G on the graphene/GNs/PDMS sample shows a gradual increase with the mechanical strain. However, the fluorescence intensity of R6G on the graphene/PDMS structure without the GNs buried in between is almost unchanged under the action of the external mechanical strain. Experiment results indicate that the gap distance change between the GNs is the main cause of the fluorescence intensity increase and graphene, as a passivation layer, does not block the energy transfer from R6G to GNs. Compared with that tuning the gap distance between GNs by preparing various GNs samples, applying macroscopic mechanical strain on GNs is a simple way to manipulate the fluorescence intensity of a specific material and brings a new perspective for optoelectronic applications.