Miracle-dz/UniFAR-FTUs huggingface dataset:
Antidepressant effect of recombinant NT4-NAP/AAV on social isolated mice through intranasal route

The purpose of the present study was to observe the depression-like behavior induced by social isolation; detect the antidepressant effect of a recombinant adeno-associated virus (AAV) expressing NAP on social isolation mice by intranasal delivery. After construction of NT4-NAP/AAV, expression of NAP was confirmed in vitro. 3-week-old C57/BL mice were bred individually in cages as social isolation-rearing. Six weeks later, the first subset of mice underwent behavioral tests and western blot; the second was for enzyme-linked immunosorbent assay. NT4-NAP/AAV was delivered quaque die by nasal administration for consecutive 10 days before behavioral test. Several depression-like behaviors were observed in social isolation mice, including decreased relative sucrose preference, longer immobility time in forced swimming test, lower plasma corticosterone and decreased brain-derived neurotrophic factor in hippocampus. Thus, social isolation procedure appears to be an animal model of depression with good face and construct validity. What's more, the antidepressant effect in social isolation-rearing mice was observed after intranasal administration of NT4-NAP/AAV, suggesting that this might be a promising therapeutic strategy for depressive disorder.
Two-Sided Arnoldi and Nonsymmetric Lanczos Algorithms

We introduce new two-sided Arnoldi recursions and use them to define a model reduction procedure for large, linear, time-invariant, multi-input/multi-output differential algebraic systems. We prove that this procedure has desirable moment matching properties. We define a corresponding model reduction procedure which is based on a band nonsymmetric Lanczos recursion and prove that if the deflation is exact and there are no breakdowns in the recursions, then these two model reduction procedures generate identical reduced-order systems. We prove similar equivalences for corresponding eigenelement procedures. We concentrate on the theoretical properties of the new algorithms.
Fabrication and Characterization of Hydroxyapatite Nanocomposites Based on Bacterial Cellulose with Surface Modification

In this study, biomimetic synthesis of hydroxyapatite (HAp) nanocomposites based on bacterial cellulose (BC) with sodium alginate (SA) surface modification was studied. The results showed that SA treatment promoted the apatite nucleation ability of BC in SBF solution. The FE-SEM images demonstrated that HAp nanoparticles were successfully formed in the three dimensional nanofiber network of BC. FTIR spectra indicated that the obtained HAp crystals were partially substituted with carbonate, which were very similar with natural bones. It was also found that both of crystallite size and crystallinity of HAp crystals observed from XRD pattern were very low. The resultant nanocomposites consisted of nano-scale HAp crystals and SA treated BC nanofibrous have great potential application in bone tissue engineering.
Adaptive Response and Tolerance to Acetic Acid in Saccharomyces cerevisiae and Zygosaccharomyces bailii: A Physiological Genomics Perspective

Acetic acid is an important microbial growth inhibitor in the food industry; it is used as a preservative in foods and beverages and is produced during normal yeast metabolism in biotechnological processes. Acetic acid is also a major inhibitory compound present in lignocellulosic hydrolysates affecting the use of this promising carbon source for sustainable bioprocesses. Although the molecular mechanisms underlying Saccharomyces cerevisiae response and adaptation to acetic acid have been studied for years, only recently they have been examined in more detail in Zygosaccharomyces bailii. However, due to its remarkable tolerance to acetic acid and other weak acids this yeast species is a major threat in the spoilage of acidic foods and beverages and considered as an interesting alternative cell factory in Biotechnology. This review paper emphasizes genome-wide strategies that are providing global insights into the molecular targets, signaling pathways and mechanisms behind S. cerevisiae and Z. bailii tolerance to acetic acid, and extends this information to other weak acids whenever relevant. Such comprehensive perspective and the knowledge gathered in these two yeast species allowed the identification of candidate molecular targets, either for the design of effective strategies to overcome yeast spoilage in acidic foods and beverages, or for the rational genome engineering to construct more robust industrial strains. Examples of successful applications are provided.
Attitude-Rate Measurement and Control Integration Using Magnetically Suspended Control and Sensitive Gyroscopes

To achieve the high-accuracy and high-bandwidth attitude-rate measurement and control integration of spacecraft, a novel method based on a magnetically suspended control and sensitive gyroscope (MSCSG) configuration is proposed in this paper. MSCSGs are employed as both actuators and attitude-rate sensors. First, the structure and principle of a MSCSG is given and its modeling is established. Second, the attitude-rate measurement method based on a four-cross configuration is proposed. The corresponding attitude decoupling control law of spacecraft and the steering law of the MSCSGs have been further designed. Compared with the traditional attitude-rate measurement methods, the proposed one entirely preserves the inertial coupling moment items and cross-coupling moment items in the resolution of attitude rate by making full use of the dynamic information of the configuration. Thus, the measurement precision can be greatly improved, and the traditional tension between the accuracy and bandwidth can be well alleviated. The proposed method also enlarges greatly the attitude control bandwidth by the microgimbal moment, providing a new method for high-frequency vibration suppression. Semiphysical simulation results demonstrate the correctness and superiority of the proposed method.
Strain-induced bi-thermoelectricity in tapered carbon nanotubes

We show that carbon-based nanostructured materials are a novel testbed for controlling thermoelectricity and have the potential to underpin the development of new cost-effective environmentally-friendly thermoelectric materials. In single-molecule junctions, it is known that transport resonances associated with the discrete molecular levels play a key role in the thermoelectric performance, but such resonances have not been exploited in carbon nanotubes (CNTs). Here we study junctions formed from tapered CNTs and demonstrate that such structures possess transport resonances near the Fermi level, whose energetic location can be varied by applying strain, resulting in an ability to tune the sign of their Seebeck coefficient. These results reveal that tapered CNTs form a new class of bi-thermoelectric materials, exhibiting both positive and negative thermopower. This ability to change the sign of the Seebeck coefficient allows the thermovoltage in carbon-based thermoelectric devices to be boosted by placing CNTs with alternating-sign Seebeck coefficients in tandem.
Human action recognition using wearable sensors and neural networks

Accurate recognition of daily activities could be useful in many fields including health, sports, childcare, and homes for the elderly, etc. In this paper, we propose a human action recognition method using data acquired from wearable sensors and learned using a Neural Network. The data collected from the sensors is processed for features using the Akamatsu transform. The Akamatsu Transform is a signal processing technique that given point, P(i) in a signal, N data points before and after the selected point are used to derive the integral and differential transforms, The Akamatsu Integration is an average of the N data points while the differential is the difference between the integral and the original value. Recently, wearable sensors are emerging as an indispensable method to recognize human actions.
Right ventricular pressure elevated in one-kidney, one clip Goldblatt hypertensive rats

ABSTRACT Both renal and respiratory diseases are common with high mortality rate around the world. This study was the first to compare effects of two kidneys, one clip (2K1C) and one-kidney, one clip (1K1C) Goldblatt hypertension on right ventricular pressure during normal condition and mechanical ventilation with hypoxia gas. Male Sprague–Dawley rats were subjected to control, 2K1C, or 1K1C groups. Twenty-eight days after the first surgery, animals were anesthetized, and femoral artery and vein, and right ventricle cannulated. Systemic arterial pressure and right ventricular systolic pressures (RVSP) were recorded during ventilation the animals with normoxic or hypoxic gas. RVSP in the 1K1C group was significantly more than the control and 2K1C groups during baseline conditions and ventilation the animals with hypoxic gas. Administration of antioxidant Trolox increased RVSP in the 1K1C and control groups compared with their baselines. Furthermore, there was no alteration in RVSP during hypoxia in the presence of Trolox. This study indicated that RVSP only increased after 28 days induction of 1K1C but not 2K1C model. In addition, it seems that the response to hypoxic gas and antioxidants in 1K1C is more than 2K1C. These data also suggest that effects of 1K1C may partially be related to reactive oxygen species (ROS) pathways.
Optimizing Cementitious Content in Concrete Mixtures for Required Performance

This research investigated the effects of changing the cementitious content required at a given water-to-cement ratio (w/c) on workability, strength, and durability of a concrete mixture. An experimental program was conducted in which 64 concrete mixtures with w/c ranging between 0.35 and 0.50, cementitious content ranging from 400 to 700 per cubic yard (pcy), and containing four different supplementary cementitious material (SCM) combinations were tested. The fine-aggregate to total-aggregate ratio was fixed at 0.42 and the void content of combined aggregates was held constant for all the mixtures. Fresh (i.e., slump, unit weight, air content, and setting time) and hardened properties (i.e., compressive strength, chloride penetrability, and air permeability) were determined. The hypothesis behind this study is that when other parameters are kept constant, concrete properties such as strength, chloride penetration, and air permeability will not be improved significantly by increasing the cement after a minimum cement content is used. The study found that about 1.5 times more paste is required than voids between the aggregates to obtain a minimum workability. Below this value, water-reducing admixtures are of no benefit. Increasing paste thereafter increased workability. In addition, for a given w/c, increasing cementitious content does not significantly improve compressive strength once the critical minimum has been provided. The critical value is about twice the voids content of the aggregate system. Finally, for a given w/c, increasing paste content increases chloride penetrability and air permeability.
Biomass behaviour in a conventional activated sludge system treating olive mill wastewater

ABSTRACT The current work aims to study the biomass behaviour in a continuous mode activated sludge system (ASS) treating olive mill wastewater (OMWW) through an increasing OMWW food to microorganism ration (F/M). To this end, the biomass growth, the specific oxygen uptake rate (SOUR), microbial characterization, sludge volume index (SVI) as well as COD and phenolic compounds removal efficiencies were examined over time. Results showed a successful growth of the biomass that reached 6.79 gTSS l−1 and 5.42 gVSS l−1. Its viability, its adaptability, and its good physiological activity were confirmed by the obtained result of SOUR with an average of 9.95 mgO2 gVSS−1h−1, as well as aerobic microbial population characterization in terms of aerobic revivable bacteria at 22°C and 37°C, Pseudomonas sp. , mould and yeast and total fungi. The concentration of these strains characterized by their ability to degrade effectively COD and phenolic compounds increased significantly (p < .05) over time. This demonstrated a great promptness in response to the increasing OMWW mass ratio. For all treatment steps, removal efficiencies were high and reached 95% of COD and 93% of phenolic compounds, also the flocs settleability shown by SVI measurement was optimal.
Mining Precise-Positioning Episode Rules from Event Sequences

Episode Rule Mining is a popular framework for discovering sequential rules from event sequential data. However, traditional episode rule mining methods only tell that the consequent event is likely to happen within a given time intervals after the occurrence of the antecedent events. As a result, they cannot satisfy the requirement of many time sensitive applications, such as program security trading due to the lack of fine-grained response time. In this study, we come up with the concept of fixed-gap episode to address this problem. A fixed-gap episode consists of an ordered set of events where the elapsed time between any two consecutive events is a constant. Based on this concept, we formulate the problem of mining precise-positioning episode rules in which the occurrence time of each event in the consequent is clearly specified. In addition, we develop a triebased data structure to mine such precise-positioning episode rules with several pruning strategies incorporated for improving the performance as well as reducing memory consumption. Experimental results on real datasets show the superiority of our proposed algorithms.
Before we knew it: an empirical study of zero-day attacks in the real world

Little is known about the duration and prevalence of zero-day attacks, which exploit vulnerabilities that have not been disclosed publicly. Knowledge of new vulnerabilities gives cyber criminals a free pass to attack any target of their choosing, while remaining undetected. Unfortunately, these serious threats are difficult to analyze, because, in general, data is not available until after an attack is discovered. Moreover, zero-day attacks are rare events that are unlikely to be observed in honeypots or in lab experiments. In this paper, we describe a method for automatically identifying zero-day attacks from field-gathered data that records when benign and malicious binaries are downloaded on 11 million real hosts around the world. Searching this data set for malicious files that exploit known vulnerabilities indicates which files appeared on the Internet before the corresponding vulnerabilities were disclosed. We identify 18 vulnerabilities exploited before disclosure, of which 11 were not previously known to have been employed in zero-day attacks. We also find that a typical zero-day attack lasts 312 days on average and that, after vulnerabilities are disclosed publicly, the volume of attacks exploiting them increases by up to 5 orders of magnitude.
Implementation of a ramp generator with Schmitt trigger circuit for PWM modulator applications

A ramp (sawtooth wave) generator consisting of the voltage-current converter circuit, PMOS two-stage operational amplifier, current mirror and Schmitt trigger circuit is presented for the application of PWM modulator in this paper. The PWM modulator is one the PWM control core circuit of the switching power converters or the supply modulator of a wideband wireless transmitter. This paper proposes a ramp generator with Schmitt trigger circuit replace the conventional ramp circuit by use of two hysteresis comparators and one S-R latch circuit. The ramp generator with Schmitt trigger circuit can application to low power management integrated circuits, not only can reduce the area of the chip, more able to reduce the power consumption of the chip. This proposed ramp generator for PWM modulator circuit is design and implemented using the TSMC 0.35 μm 2P4M process technology. Based on measured results, the chip size is 671.99 × 586 μm2 with power dissipation about 2.48 mW, and the chip supply voltage can from 1.9 V to 3.3 V. The ramp voltage which linearly rise from 0 V to 2.82 V, and the output frequency is about 500 KHz and 700 KHz stably when the chip supply voltage is fixed at 3.3 V and 2.0 V respectively.
Genetic algorithms for lifetime elongation of clustered WSN

Energy-efficiency has been always a concern in wireless sensor network. Since sensor clustering was proposed as a technique for energy-efficient routing in hierarchical wireless sensor networks, many algorithms were proposed for the formation of clusters, the selection of cluster heads, and routing data within and between clusters. The use of Genetic algorithm is proposed to obtain the optimal number of clusters as well as the position of each cluster head during the network's lifetime. In this paper, we propose an algorithm based on the use of genetic algorithms to improve the network lifetime, especially in sensor networks that can tolerate the loss of some nodes without affecting the functionality of the whole network. Simulation results show that the proposed algorithm almost 1.5 times the network's lifetime.
A Novel Intrusion Detection Algorithm: An AODV Routing Protocol Case Study

Mobile ad-hoc network (MANET) is a collection of movable nodes capable of self-routing, constraining energy and decentralized handling of nodes. It faces many challenges due to uncertainty of network topology i.e. security and congestion. In this paper we propose a novel algorithm for intrusion detection against attacks such as probing, Denial-of-service (DoS), vampire and User-To-Root (U2R) in a MANET environment. The attack detection has been carried out using a profile (behavior) analysis and a confusion matrix (True positives, True negatives, False positives, False negatives). The performance of a standard Ad-hoc On-Demand Distance Vector (AODV) routing protocol has been reported for all 4 types of attack in a network simulator-2(ns-2) environment. To the best of authors' knowledge, this is the first paper reporting a novel intrusion detection algorithm using behavior analysis for an AODV protocol in a MANET environment.
Monosodium Urate Crystals Generate Nuclease-Resistant Neutrophil Extracellular Traps via a Distinct Molecular Pathway

Neutrophil extracellular traps (NETs) and the cell death associated with it (NETosis) have been implicated in numerous diseases. Mechanistic studies of NETosis have typically relied on nonphysiological stimuli, such as PMA. The human disease of gout is caused by monosodium urate (MSU) crystals. We observed that DNA consistent with NETs is present in fluid from acutely inflamed joints of gout patients. NETs also coat the crystals found in uninflamed tophi of chronic gout patients. We developed a quantitative, live cell imaging assay, which measures the key features of NETosis, namely, cell death and chromatin decondensation. We show that MSU and other physiologically relevant crystals induce NETosis through a molecular pathway that is distinct from PMA and Candida hyphae. Crystals interact with lysosomes to induce NADPH oxidase-independent cell death, with postmortem chromatin decondensation mediated by neutrophil elastase. The resulting MSU-induced NETs are enriched for actin and are resistant to serum and DNase degradation. These findings demonstrate a distinct physiological NETosis pathway in response to MSU crystals, which coats MSU crystals in DNA that persists in tissues as gouty tophi.
A cloud native solution for dynamic auto scaling of MME in LTE

Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%.
TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection

In this paper, we introduce TR-BREATH, a time-reversal (TR)-based contact-free breathing monitoring system. It is capable of breathing detection and multiperson breathing rate estimation within a short period of time using off-the-shelf WiFi devices. The proposed system exploits the channel state information (CSI) to capture the miniature variations in the environment caused by breathing. To magnify the CSI variations, TR-BREATH projects CSIs into the TR resonating strength (TRRS) feature space and analyzes the TRRS by the Root-MUSIC and affinity propagation algorithms. Extensive experiment results indoor demonstrate a perfect detection rate of breathing. With only 10 s of measurement, a mean accuracy of <inline-formula><tex-math notation="LaTeX">$99\%$</tex-math></inline-formula> can be obtained for single-person breathing rate estimation under the non-line-of-sight (NLOS) scenario. Furthermore, it achieves a mean accuracy of <inline-formula><tex-math notation="LaTeX">$98.65\%$</tex-math></inline-formula> in breathing rate estimation for a dozen people under the line-of-sight scenario and a mean accuracy of <inline-formula> <tex-math notation="LaTeX">$98.07\%$</tex-math></inline-formula> in breathing rate estimation of nine people under the NLOS scenario, both with 63 s of measurement. Moreover, TR-BREATH can estimate the number of people with an error around 1. We also demonstrate that TR-BREATH is robust against packet loss and motions. With the prevailing of WiFi, TR-BREATH can be applied for in-home and real-time breathing monitoring.
Multiscale High-Level Feature Fusion for Histopathological Image Classification

Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It can gain better representation for the histopathological image than only using coding network. The main process is that training a deep convolutional neural network is to extract high-level feature and fuse two convolutional layers' high-level feature as multiscale high-level feature. In order to gain better performance and high efficiency, we would employ sparse autoencoder (SAE) and principal components analysis (PCA) to reduce the dimensionality of multiscale high-level feature. We evaluate the proposed method on a real histopathological image dataset. Our results suggest that the proposed method is effective and outperforms the coding network.