Miracle-dz/UniFAR-FTUs huggingface dataset:
The susceptibility analysis of Echinococcus multilocularis protoscoleces tubulin to mebendazole and RNA interference

Alveolar echinococcosis, caused by the larval (metacestode) stage of the tapeworm Echinococcus multilocularis, is a lethal parasitosis of the liver prevalent in the Northern Hemisphere. For chemotherapy the benzimidazole derivatives mebendazole and albendazole were introduced, which were found to disrupt the microtubules by inhibition of the polymerization of tubulin into microtubules, and β-tubulin was determined to be the drug target molecule. In the present study, we evaluated the chemosensitivity of E. multilocularis protoscoleces tubulin to mebendazole and RNA interference in vitro, and to explore whether the molecular level and ultrastructure of E. multilocularis protoscoleces microtubules change post-mebendazole and RNA interference. We identified that mebendazole is parasitostatic to E. multilocularis protoscoleces through suppression the tubulin expression and change the flame cell morphology in molecular level, besides RNA interference indicated that β 2 tubulin is probably one of the vital tubulin gene to form the flame cell and the protonephridial system tubules (collective tubes) of E. multilocularis protoscoleces. Molecular level and ultrastructure detection were performed by reverse transcription-PCR, western blotting and transmission electron microscope. The RNA interference would be probably as a parasitocidal method to disrupt the survival of PSCs, extend that the relevant tubulin maybe as potential target for drug development against AE.
TN-IDS for Network Layer Attacks in RPL based IoT Systems

* Abstract Routing protocol for Low power and lossy network (RPL) is a standardized optimal protocol for routing in Internet of Things (IoT). The constrained wireless sensor network in IoT is characterized by lack of processing speed, low power and low memory. Sometimes various network attacks enabling the RPL network aﬀect the network performance dismally. This leads to drastic variation in energy consumption at nodes and disturb the RPL network protocol structure. This leads to reduced processing speed and memory allocation in the network. We ﬁrst illustrate the attacks and their impact in RPL network by simulation. To detect such attacks, we propose an Intrusion Detection System (IDS) scheme for RPL network based on trust computation. Trust based Neighbor notiﬁcation IDS (TN-IDS) is a secure hierarchical distribution system which monitors the network intrusion and checks the performance of the network. The new TN-IDS system will track all nodes in the network and identify the malicious nodes. The activity list prepared by IDS indicates them to a sink node. This is achieved by introducing a distributed leader election algorithm to collect metrics related to the RPL network. Hence, the performance metrics of the RPL network together with TN-IDS module can identify the malicious node and isolate them.
WLAN Fingerprint Localization with Stable Access Point Selection and Deep LSTM

With the development of communication technologies, the demand for location-based services is growing rapidly. The presence of a large number of Wi-Fi network infrastructures in buildings makes Wi-Fi-based indoor positioning systems the most popular and practical means of providing location-based services in indoor environments. This paper proposes a machine learning indoor positioning method based on received signal strength. This algorithm considers the access point (AP) selection strategy to reduce the computational load and enhance noise robustness whereby improving the positioning accuracy. The local feature extraction method is used to extract powerful local features to further reduce the noise impact. We then employ the Long Short-Term Memory (LSTM) network to learn high-level representations for the extracted local features. The proposed method has been tested both in the simulation environment and the real environment. The experimental results show that the algorithm can greatly improve the accuracy and computational complexity of position prediction.
The Dilemma of contacttracing apps: Can this crucial technology be both effective and private?

IN JUNE, as the coronavirus swept across the United States, Paloma Beamer spent hours each day helping her university plan for a September reopening. Beamer, an associate professor of public health at the University of Arizona, was helping to test a mobile app that would notify users if they crossed paths with confirmed COVID-19 patients. • A number of such “contact tracing” apps have recently had their trials by fire, and many of the developers readily admit that the technology has not yet proven that it can slow the spread of the virus. But that caveat has not stopped national governments and local communities from using the apps. • “Right now, in Arizona, we're in the full-blown pandemic phase,” Beamer said, speaking in June, well before the new-case count had peaked. “And even manual contact tracing is very limited here—we need whatever tool we can get right now to curb our epidemic. ” • Traditionally, tracers would ask newly diagnosed patients to list the people they'd spent time with recently, then ask those people to provide contacts of their own. Such legwork has helped to control other infectious-disease outbreaks, such as syphilis in the United States and Ebola in West Africa. However, while these methods can extinguish the first spark or the last embers of an epidemic, they're no good in the wildfire stage, when the caseload expands exponentially.
Regulation of gene transcription by thyroid hormone receptor β agonists in clinical development for the treatment of non-alcoholic steatohepatitis (NASH)

Thyroid hormones are important modulators of metabolic activity in mammals and alter cholesterol and fatty acid levels through activation of the nuclear thyroid hormone receptor (THR). Currently, there are several THRβ agonists in clinical trials for the treatment of non-alcoholic steatohepatitis (NASH) that have demonstrated the potential to reduce liver fat and restore liver function. In this study, we tested three THRβ-agonism-based NASH treatment candidates, GC-1 (sobetirome), MGL-3196 (resmetirom), and VK2809, and compared their selectivity for THRβ and their ability to modulate the expression of genes specific to cholesterol and fatty acid biosynthesis and metabolism in vitro using human hepatic cells and in vivo using a rat model. Treatment with GC-1 upregulated the transcription of CPT1A in the human hepatocyte-derived Huh-7 cell line with a dose-response comparable to that of the native THR ligand, triiodothyronine (T3). VK2809A (active parent of VK2809), MGL-3196, and VK2809 were approximately 30-fold, 1,000-fold, and 2,000-fold less potent than T3, respectively. Additionally, these relative potencies were confirmed by quantification of other direct gene targets of THR, namely, ANGPTL4 and DIO1. In primary human hepatocytes, potencies were conserved for every compound except for VK2809, which showed significantly increased potency that was comparable to that of its active counterpart, VK2809A. In high-fat diet fed rats, a single dose of T3 significantly reduced total cholesterol levels and concurrently increased liver Dio1 and Me1 RNA expression. MGL-3196 treatment resulted in concentration-dependent decreases in total and low-density lipoprotein cholesterol with corresponding increases in liver gene expression, but the compound was significantly less potent than T3. In conclusion, we have implemented a strategy to rank the efficacy of THRβ agonists by quantifying changes in the transcription of genes that lead to metabolic alterations, an effect that is directly downstream of THR binding and activation.
On Matching Finger-Selfies Using Deep Scattering Networks

With the advancements in technology, smartphones’ capabilities have increased immensely. For instance, the smartphone cameras are being used for face and ocular biometric-based authentication. This research proposes finger-selfie based authentication mechanism, which uses a smartphone camera to acquire a selfie of a finger. In addition to personal device-level authentication, finger-selfies may also be matched with livescan fingerprints present in the legacy/national ID databases for remote or touchless authentication. We propose an algorithm which comprises segmentation, enhancement, Deep Scattering Network based feature extraction, and Random Decision Forest to authenticate finger-selfies. This paper also presents one of the largest finger-selfie database with over 19, 400 images. The images in the IIIT-D Smartphone Finger-selfie Database v2 are captured using multiple smartphones and include variations due to background, illumination, resolution, and sensors. Results and comparison with existing algorithms show the efficacy of the proposed algorithm which yields equal error rates in the range of 2.1 – 5.2% for different experimental protocols.
THE EFFICIENCY OF THREE METHODS OF SELECTION

THE practical value of a plant or animal is almost always affected by several traits. Hence deciding which are the most valuable individuals to select for parents of the next generation forces the breeder to consider several different characteristics. These are not likely all to be equally important or all to be independent of each other. There are many ways of selecting for several different things but these will not often be equally efficient. The most efficient method is that which results in the maximum genetic improvement per unit of time and effort expended. In planning some of their experimental work, the authors had occasion to compare the theoretical consequences of three fairly simple ways of selecting for net merit, considered as a compound of several different characteristics. While the results are not completely generalized, they are striking enough and seem to be of enough general validity to deserve presenting to plant and animal breeders. The three methods are designated as the "tandem" method, the "total score" method and the method of "independent culling levels. " The "tandem method" is to select for one trait at a time until that is improved ; then for a second trait; later for a third, etc.; until finally each has been improved to the desired level. The "total score method" is to select for all the traits simultaneously by using some index of net merit constructed by adding into one figure the credits and penalties given each animal according to the degree of its superiority or inferiority in each trait. In the method of "independent culling levels" a certain level of merit is established for each trait, and all individuals below that level are discarded, regardless of the superiority or inferiority of their other traits. In order to select most efficiently, the following things need to be known for each trait which influences practical value: (1) The amount by which each unit of variation in it actually raises or lowers an individual's practical value (relative economic value of the trait) ; (2) the extent to which observed variations in it represent differences in genetic constitution (its heritability) ; and (3) the genetic and environmental correlations of each trait with the others. The method of total score is most efficient, while the tandem method is the least efficient of the three. The reasons for that and the extent of it will appear in what follows.
High-Throughput/Low-Energy MTJ-Based True Random Number Generator Using a Multi-Voltage/Current Converter

This article introduces high-throughput/low-energy true random number generators (TRNGs) based on CMOS and three-terminal magnetic tunnel junction (MTJ) devices. MTJs are fast and probabilistic switching devices, which can be used as random number sources for TRNGs. However, as the switching probability is quite sensitive to the write current given to MTJs, precise closed-loop control is necessary. Thus, a high-complexity current control circuit is required, such as high precision digital-to-analog converters (DACs), occupying large area and causing large energy dissipation. In order to address the issue, we propose a multi-voltage/current (<inline-formula> <tex-math notation="LaTeX">$V/I$ </tex-math></inline-formula>) converter capable of multilevel coarse current switching and fine adjusting within each level. The fine adjusting can be done by DACs with fewer bits, resulting in much smaller size and energy dissipation than a conventional single-<inline-formula> <tex-math notation="LaTeX">$V/I$ </tex-math></inline-formula> converter. In addition, a multiple-writing scheme for three-terminal MTJs is proposed for increasing the throughput while maintaining the write power. The proposed TRNGs are designed using TSMC 65-nm CMOS and a three-terminal MTJ model that achieves a throughput of 333 Mb/s, an energy dissipation of 0.66 pJ/bit and an area of <inline-formula> <tex-math notation="LaTeX">$2040~\mu {\rm m^{2}}$ </tex-math></inline-formula>. This result exhibits a <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> throughput, a 93% energy reduction and an 86% area reduction in comparison with a conventional CMOS/MTJ-based TRNG.
Novel S-Band Metamaterial Extended Interaction Klystron

A miniaturized, high electronic efficiency, S-band metamaterial extended interaction klystron (MEIK) is proposed. The metamaterial-based extended interaction resonant cavities were designed, fabricated, and tested, the assembled and cold-tested results confirm the feasibility of miniaturization in MEIK. Furthermore, the particle-in-cell (PIC) simulation predicts that, typically, when the beam voltage and current are 30 kV and 3 A, respectively, the focusing magnetic field is 1000 G, a 3-cavity MEIK yields the output power of 56 kW at 2.453 GHz, with the saturated gain of 47 dB and the electronic efficiency of 62%, and, importantly, with the cavity diameter of its interaction structure being ~1/2 of a conventional extended interaction klystron. As a power amplifier, the emerging MEIK with its miniaturization and high electronic efficiency has potential applications such as in future accelerators and microwave heating systems.
A Multiplex Connectivity Map of Valence-Arousal Emotional Model

A high number of studies have already demonstrated an electroencephalography (EEG)-based emotion recognition system with moderate results. Emotions are classified into discrete and dimensional models. We focused on the latter that incorporates valence and arousal dimensions. The mainstream methodology is the extraction of univariate measures derived from EEG activity from various frequencies classifying trials into low/high valence and arousal levels. Here, we evaluated brain connectivity within and between brain frequencies under the multiplexity framework. We analyzed an EEG database called DEAP that contains EEG responses to video stimuli and users’ emotional self-assessments. We adopted a dynamic functional connectivity analysis under the notion of our dominant coupling model (DoCM). DoCM detects the dominant coupling mode per pair of EEG sensors, which can be either within frequencies coupling (intra) or between frequencies coupling (cross-frequency). DoCM revealed an integrated dynamic functional connectivity graph (IDFCG) that keeps both the strength and the preferred dominant coupling mode. We aimed to create a connectomic mapping of valence-arousal map via employing features derive from IDFCG. Our results outperformed previous findings succeeding to predict in a high accuracy participants’ ratings in valence and arousal dimensions based on a flexibility index of dominant coupling modes.
A Feature-Based Automatic LED Part Locating Method and the Inspection of Welding Quality of LED Chips

There are problems such as high error rate and low efficiency if the quality of LED chip welding is manually inspected. Digital image processing is used in this paper to inspect the quality of LED chip welding. Due to the limited field of view and insufficient resolution of the X-ray camera, multiple acquisitions shall be made, so there is inevitably overlap between images. Based on characteristics of the parts to be inspected, a feature-based automatic locating method is proposed. The copper rods whose gray level are the lowest is chosen as featured. At first, features are extracted through threshold segmentation. Then, the geometric relation-ship of the features is used to locate LED parts in the image and the overlap is removed. After the locating of LED parts, the LED chips on the part can be further located and inspected and inspected. The algorithm is efficient, the locating of 161 part can be finished in less then one second. The time for locating and inspection of the 161 part is about 12.4 seconds.
The ultraviolet offense: Germicidal UV lamps destroy vicious viruses. New tech might put them many more places without harming humans

WALK INTO THE CAMBRIDGE, ONT. , OFFICE of health-care equipment company PrescientX and you probably wouldn't suspect you're entering one of the most sanitary places in North America. • In this otherwise-ordinary Toronto-area office suite, you can disinfect your keys, phone, and other portables at the reception area's ultraviolet-sterilization stand. In cooler months, the air you breathe is cleansed of mold and bacteria in UV-sterilized heating units as well as blasted by UV fixtures in the office air ducts to eliminate viruses. In-room UV fixtures pointing at the ceiling disinfect the air, while other UV lights that turn on only when no one's in the room zap pathogens on desks, keyboards, and high-touch surfaces in bathrooms and work spaces. • The office, says PrescientX founder and CEO Barry Hunt, represents a possible future in which pandemics like COVID-19 are more commonplace — but in which germicidal ultraviolet light is one of the most potent weapons we have to face them down.
A Low-Power PAM4 Receiver With an Adaptive Variable-Gain Rectifier-Based Decoder

This article presents a low-power 1/4-rate four-level pulse amplitude modulation (PAM4) receiver with an adaptive variable-gain rectifier (AVGR)-based decoder in 28-nm CMOS technology. The PAM4 input signal is preconditioned by a continuous-time linear equalizer (CTLE) then sampled into four branches of decoders by 1/4-rate clocks. The proposed AVGR-based PAM4-to-nonreturn-to-zero (NRZ) decoder performs gain adaptation and amplitude rectification simultaneously for decoding the least significant bit (LSB). The linear sense amplifier in the AVGR is modified from a latch to achieve a high gain and low power. Compared with the full-rate receiver adopting a decoder consisting of three comparators, this design achieves a better power efficiency by employing a 1/4-rate topology and merging a variable-gain function into the decoder. Experimental results demonstrate that the receiver chip can receive and decode a 24-Gb/s 190-mVpp PAM4 signal at a BER of 10−11 and a bit efficiency of 1.38 pJ/bit.
A Broadband High-Gain Circularly Polarized Wide Beam Scanning Leaky-Wave Antenna

A new type of broadband circularly polarized leaky-wave antenna (LWA) with high-gain based on a corrugated substrate integrated waveguide (CSIW) structure is proposed and investigated. The CSIW structure that employs open-circuit stubs to replace metallic vias has the advantages of low-cost and easy fabrication as compared to conventional substrate integrated waveguides. Each unit-cell of the proposed LWA consists of two quarter-wavelength microstrip lines and a half-mode CSIW cell with three open-circuit stubs. Two M-shaped slots etched on the half-mode CSIW cell enables the generation of circularly polarized (CP) radiation. The full LWA, which consists of thirteen matched unit-cells cascaded along the direction of propagation enables continuous backward to forward beam scanning. The properties of the CSIW structure, including impedance matching and phase constant, are analyzed by Bloch impedance and dispersion simulations and the influence of the dimensions of the open-circuit stubs on the CP LWA performance is also investigated. A protype of the proposed LWA is fabricated and characterized. The measured results indicate that the proposed CSIW LWA has a high peak gain (9.3–12.5 dBi) throughout a large beam-scanning angle range from −28° to +25°, and the impedance bandwidth and 3-dB axial ratio (AR) bandwidth are over 40% covering the full $\textit {Ka}$ -band.
The Effect of Electric Field on Carrier Mobility in Organic Semiconductors Based on Exponential Type Density of States and Genuine Transport Energy

Carrier mobility plays a crucial role in organic semiconductors because it limits the efficiency of organic devices. Based on the exponential type density of states (EC DOS) and genuine transport energy, we introduce a model to describe the dependence of electric field and carrier mobility in organic semiconductors by using effective medium approximation. The theoretical results show that the carrier concentration is a key factor to influence the carrier mobility, which increases with the increasing carrier concentration. The Poole-Frenkel behavior is reproduced in our model. The calculated carrier mobility as functions of electric field at different temperatures are in good agreement with analytical data for 40’-(3,7-dimethyloctyloxy)-1,1’-biphenylene-2, 5-vinylene] (NRS-PPV).
Bidirectional Grid Fusion Network for Accurate Land Cover Classification of High-Resolution Remote Sensing Images

Land cover classification has achieved significant advances by employing deep convolutional network (ConvNet) based methods. Following the paradigm of learning deep models, land cover classification is modeled as semantic segmentation of very high resolution remote sensing images. In order to obtain accurate segmentation results, high-level categorical semantics and low-level spatial details should be effectively fused. To this end, we propose a novel bidirectional gird fusion network to aggregate the multilevel features across the ConvNet. Specifically, the proposed model is characterized by a bidirectional fusion architecture, which enriches diversity of feature interaction by encouraging bidirectional information flow. In this way, our model gains mutual benefits between top–down and bottom–up information flows. Moreover, a grid fusion architecture is then followed for further feature refinement in a dense and hierarchical fusion manner. Finally, effective feature upsampling is also critical for the multiple fusion operations. Consequently, a content-aware feature upsampling kernel is incorporated for further improvement. Our whole model consistently achieves significant improvement over state-of-the-art methods on two major datasets, ISPRS and GID.
Fault Location of Strip Steel Surface Quality Defects on Hot-Rolling Production Line Based on Information Fusion of Historical Cases and Process Data

Surface quality is the most important index to improve the overall quality of strip steel. In order to implement the fault location on the hot-rolling line with surface defects of strip steel, a fault tracing model based on information fusion of historical production cases and process data is proposed. For historical cases, the model determines the defect cause labels through text similarity calculation, and fuzzy semantic inference is used to obtain the probability distribution of defect causes on this basis; for the process data, the model uses L1 regularization method for feature selection, and XGBoost integration method is used to train the correlation model between process data and defects to determine the contribution of each feature in the data source. Finally, based on the D-S evidence theory, different rules are set to merge the two judgments to determine the probability of each source of failure on the hot-rolling production line. The model is applied to the real production environment of iron and steel enterprises, and it is verified that the proposed method can effectively assist experts in decision-making, which greatly improves the efficiency of tracing the source of faults on the hot-rolling production line.