Miracle-dz/UniFAR-FTUs huggingface dataset:
Signal Processing for Single Biomolecule Identification Using Nanopores: A Review

Nanopore sensors provide an innovative platform for rapid and label-free biomolecule detection. The analytical information extracted from resistive pulse sensing (RPS) measurement, though powerful has several limitations. To address these challenges, various signal conditioning algorithms have been researched which include efficient clustering and classification of data, applying probabilistic models to reduce information overlap and also leveraging transient analysis of ionic current instead of the steady state RPS measurement. Additionally, as the current is typically in few picoampere regime with high transition rates, its faithful measurement requires precision instrumentation like a patch clamp amplifier with low noise and high bandwidth. However, for practical deployment of such systems, it is necessary to develop portable electronic interface. Significant efforts have been directed globally to develop on-chip customized integrated circuits with low noise, high bandwidth amplifiers. Thus, it is absolutely timely and pertinent to conduct a review on the various signal processing schemes and electronic interfaces for single biomolecule identification using nanopores which will enable to comprehend the current status for its deployment as a commercial platform for genomics and ultrasensitive detection. To the best of our knowledge, the data extraction methodologies and electronic interface schemes for nanopores have not yet been summarized. In this paper, a comprehensive review emphasizing the signal processing algorithms for DNA sequencing and protein identification in complex analyte using functionalized and non-functionalized nanopores has been presented along with the on-chip low noise electronic interfaces required for field deployable measurement.
Regulation of Ventral Tegmental Area Dopamine Neuron Activity by Feeding-related Hypothalamic Neuropeptides

The prevalence of obesity has doubled worldwide since the 1980s, and having a high body mass index contributes to more deaths worldwide than being underweight. Over the past 20 years, consumption of calorie-dense foods has increased, and this is considered one of the major causes of the rapid rise in obesity. Thus, understanding the neural control of food intake is important for the development of new and effective treatments of obesity. Two important brain regions that regulate food intake are the hypothalamus and the mesocorticolimbic dopamine system. The hypothalamus is essential for the homeostatic control of feeding and body weight, while the mesocorticolimbic dopamine system, also known as the reward system, is the primary circuit for reward and motivated behavior. The reward system also regulates food intake and food reward, and there is increasing evidence that hypothalamic feeding-related neuropeptides alter dopamine neuron activity to affect feeding. Nevertheless, how these neuropeptides interact with the reward system to regulate feeding is not fully understood. For example, centrally delivered neurotensin and neuropeptide-Y (NPY) increase dopamine release in the nucleus accumbens, but cause opposite effects on food reward. In addition, injection of the hypothalamic neuropeptides neurotensin, NPY, or alpha-melanocyte-stimulating hormone (-MSH) into the ventral tegmental area (VTA), where reward-related dopamine neurons are located, alters multiple aspects of feeding, but how these neuropeptides interact with the reward system to alter feeding at both the circuit and cellular levels is not fully understood. In these studies, I have used whole cell patch-clamp electrophysiology in acute brain slices from mice to examine how neurotensin, -MSH, and NPY affect VTA dopamine neuron activity. I have demonstrated that these neuropeptides use multiple mechanisms to alter VTA dopamine neuron activity, including both preand post-synaptic mechanisms. Neurotensin and -MSH increased dopamine neuron activity, while NPY had both excitatory and inhibitory effects on dopamine neuron activity. Overall, these studies provide an important advancement in our understanding of the different mechanisms utilized by hypothalamic neuropeptides to alter VTA dopamine neuron activity and how hypothalamic neuropeptides interact with the mesocorticolimbic dopamine system to control food intake and food reward. INDEX WORDS: Dopamine, Ventral tegmental area, Neurotensin, Alpha-melanocyte stimulating hormone, Neuropeptide-Y, MC3R, GIRK REGULATION OF VENTRAL TEGMENTAL AREA DOPAMINE NEURON ACTIVITY BY FEEDING-RELATED HYPOTHALAMIC NEUROPEPTIDES
A Compact Beamsteering Metasurface Lens Array Antenna With Low-Cost Phased Array

A metasurface (MTS) lens array (MLA) fed by a phased array with less phase shifters (PSs) is proposed for compact low-cost beamsteering applications. By dividing a single-large-aperture lens into <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> small-aperture lens elements with the focus-to-diameter ratio of a lens antenna unchanged, the overall thickness of the proposed antenna is reduced by <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> times. The beamsteering is achieved in two steps. First, the main beam direction of MLA antenna is switched over a large angular step by shifting the feeding antennas beneath each lens element. Then, the switched beams are fine steered by a low-cost <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-element phased array. Theoretical analysis using array theory is performed to work out a general design method with discussion on the taper and spillover effect of feed-power pattern on the lens array. Based on the proposed method, a three-lens linear MLA fed by a phased array is designed to operate at 10 GHz. The proposed antenna achieves a 3 dB beamwidth coverage range of ±30° with a beam crossing level higher than −3 dB and a gain tolerance of 1.6 dB with a maximum gain of 19.1 dBi. The presented antenna can be used to achieve volumetric beamsteering performance directly. The proposed design features the merits of higher gain, lower cost, simpler feeding network, less PSs, and lower profile compared with conventional full phased arrays and single-aperture lens antennas.
Facial Depression Recognition by Deep Joint Label Distribution and Metric Learning

While existing prediction models built on popular deep architectures have shown promising results in facial depression recognition, they still lack sufficient discriminative power due to the issues of 1) limited amount of labeled depression data for deep representation learning and, 2) large variation in facial expression across different persons of the same depression score and the subtle difference in facial expression across different depression levels. In this article, we formulate the facial depression recognition as a label distribution learning (LDL) problem, and propose a deep joint label distribution and metric learning (DJ-LDML) method to address these issues. In DJ-LDML, LDL exploits label relevance inherent in depression data to implicitly increase the amount of training data associated with each depression level without actually enlarging the dataset, while deep metric learning (DML) aims at learning a deep ordinal embedding with a specifically designed label-aware histogram loss, allowing semantics similarity between video sequences (described by ordinal labels) to be preserved for discriminative feature learning. The two learning modules in our DJ-LDML work collaboratively to enhance the representation ability and discriminative power of the deeply learned spatiotemporal feature, leading to improved depression prediction. We empirically evaluate our method on two benchmark datasets and the results demonstrate the effectiveness of our formulation.
Lung cancer classification using feed forward back propagation neural network for CT images

Manual computation of lung cancer is a time taking process. In the medical industry, software aided detection (SAD) aims to optimise the classification process. This paper proposes lung cancer detection for computed tomography (CT) images. It uses speed up robust feature (SURF) for feature extraction, genetic algorithm (GA) for feature optimisation and feed forward back propagation (FFBP), neural network (NN) for classification. The training mechanism utilises 200 cancerous images and the proposed method results in 96% classification accuracy and 94.7% sensitivity. This paper also discusses the possible future modifications in the presented work.
Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution

Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.
Sequence analysis of 12S rRNA and 16S rRNA mitochondrial genes in Iranian Afshari sheep

Phylogenetic relationships and genetic variation in Iranian Afshari sheep breed were analyzed using 12S rRNA and 16S rRNA gene sequences. The genomic DNA was isolated by salting out method and amplified 12S rRNA and 16S rRNA genes using PCR method. PCR amplification of 12S and 16S rRNA generated PCR amplicons at 859 and 1053 bp lengths, respectively. Sequence analysis was performed using BioEdit software. Phylogenetic tree was constructed using MEGA software. Phylogenetic analysis of haplotype in the combination with the sheep from GenBank showed that Iranian Afshari sheep made a close to the Australian sheep cluster. This study was found informative for establishing relationships between breeds from different parts of the world. This study may facilitate the future researchers and breeders for better understanding the genetic interactions and breed differentiation for devising future breeding and conservation strategies to preserve the rich animal genetic reservoir of the country.
Preservation of Methanogenic Cultures to Enhance Anaerobic Digestion

PRESERVATION OF METHANOGENIC CULTURES TO ENHANCE ANAEROBIC DIGESTION Ujwal H. Bhattad, M.E. Marquette University, 2012 The use of anaerobic biotechnology is increasing as a sustainable process to treat various organic wastes. Methanogens convert organic COD into CH4 and play the key role to drive thermodynamically unfavorable biochemical fermentation reactions and keep the digestion process steady and efficient. Progressive understanding of anaerobic microbiology with digester functionality may help to develop efficient, customized methanogenic cultures to enhance anaerobic bioprocesses. Preservation of methanogenic cultures via drying would be a cost-effective option for research and practical applications. However, preservation of methanogenic cultures is challenging due to methanogen sensitivity to O2 toxicity and drying, and very limited work is reported on their preservation. The work described herein involves preservation and subsequent storage of various methanogenic cultures in oxic conditions as well as applications to improve performance of anaerobic digesters and standardize laboratory testing. Five methanogenic cultures were customized under different growth conditions. The cultures were preserved using freezeand heat-drying, and subsequently stored for short and long periods in the presence of air. Their activity was then assayed by measuring specific methanogenic activity. The influences of growth conditions and protective agent addition were investigated to improve methanogenic activity after preservation. Clone library and qPCR techniques were used to identify and quantify methanogenic communities before and after drying. The usefulness of preserved cultures was examined to bioaugment transiently upset anaerobic digesters and as seed inocula for a standard laboratory test, the biochemical methane potential (BMP) assay. The effect of bioaugmentation was correlated with methanogenic community structure using the DGGE molecular fingerprinting technique. All customized methanogenic cultures were significantly active even after handling, drying and subsequent storage in the presence of air, suggesting methanogenic culture preservation and storage in air is feasible. Freeze-dried cultures maintained higher methanogenic activity than heat-dried cultures. The culture developed in the presence of limited O2 exhibited higher methanogenic activity than cultures developed in strict anaerobic conditions regardless of the drying method employed. Glucose as a protective agent resulted in higher methanogenic activity, more so in freeze drying than heat drying. Some methanogenic community members were found to be more tolerant to drying stress than others. Dried methanogenic cultures were found to be viable options to use as a bioaugment to improve treatment efficiency of anaerobic digesters after toxic upset and for the BMP assay.
An assemblage of discrimination and social preferences

Labor market opportunities and wages may be unfair for various reasons, and how workers respond to different types of unfairness can have major economic consequences. Using an online labor platform, where workers engage in an individual task for a piece-rate wage, we investigate the causal effect of neutral and gender-discriminatory unfair chances on labor supply. We randomize workers into treatments where we control relative pay and chances to receive a low or a high wage. Chances can be fair, unfair based on an unspecified source, or unfair based on gender discrimination. Unequal pay reduces labor supply of low-wage workers, irrespective of whether the low wage is the result of fair or unfair chances. Importantly, the source of unfair chances matters. When a low wage is the result of gender-discriminatory chances, workers matched with a high-wage worker substantially reduce their labor supply compared to the case of equal low wages (−22%). This decrease is twice as large as those induced by low wages due to fair chances or unfair chances coming from an unspecified source. In addition, exploratory analysis suggests that in response to unequal pay, low-wage male workers reduce labor supply irrespective of the source of inequality, whereas low-wage female workers reduce labor supply only if unequal pay is due to gender-discriminatory chances. Our results concerning gender discrimination indicate a new reason for the lower labor supply of women, which is a prominent explanation for the gender gap in earnings. This chapter is co-authored with Kristof Bosmans and Arno Riedl.
High-Performance Photovoltaic Constant Power Generation Control With Rapid Maximum Power Point Estimation

Photovoltaic (PV) constant power generation (CPG) control is regarded as an advanced active power control by limiting the maximum feed-in power in order to avoid the adverse impacts of high-penetration PV systems such as overloading and overvoltage. Conventional CPG strategies show obvious limitations in terms of the dynamics response, oscillations during the steady state, and the estimation speed of limited power points (LPPs). In this article, a novel CPG control with the rapid approach to LPPs is proposed with the estimation of maximum power point (MPP), which guarantees fast converging speed in response to rapid changes in environmental conditions and low power oscillations for the steady state. Specifically, the proposed control allocates the operating point at the left-hand side of the MPP, which can not only avoid the instability issue but also simplify the MPP and LPP estimation according to the single-diode PV equivalent model. Furthermore, based on the estimated available power $P_{\rm avi}$, the proposed CPG control contributes a direct solution in approaching the region around LPP or MPP, which improves the converging speed under environmental-changing conditions. Thus, regardless of the changes in irradiance or temperature, high-performance and reliable operation are ensured by switching the maximum power point tracking mode and CPG mode smoothly. The proposed control can be directly applied for the existing PV system and no additional hardware devices such as irradiance sensors are required, which is cost effective. A systematic comparison of the proposed control with other advanced CPG strategies was made under various scenarios and the advantages of the proposed control were validated experimentally.
Excitation-Wavelength Dependence of Photoinduced Charge Injection at the Semiconductor-Dye Interface: Evidence for Electron Transfer from Vibrationally Hot Excited States

Heterodyad systems composed of a redox photosensitizer adsorbed on the surface of a wide band gap semiconductor were designed in a way that the ν'=0 energy level of the electronically excited state of the dye lies below the bottom of the conduction band of the solid. Under these conditions, the quantum yield of the charge injection from the sensitizer into the conduction band of the solid was found to depend upon the excitation photon energy. This observation provides an evidence that interfacial charge transfer is occurring prior to nuclear relaxation of the sensitizer's excited state. It allows the use of a simplified kinetic model and offers an easy experimental path to the determination of the electronic coupling that controls the rate of the ultrafast injection process.
Equation predicts non-Darcy flow coefficient

An equation has been derived for obtaining a non-Darcy flow coefficient that facilitates gas well remedial treatment calculations and production performance evaluations. For high-capacity gas wells, pressure drop near the well bore cannot be estimated from the Darcy equation in which the pressure gradient is proportional to the flow velocity (laminar flow). To describe the additional pressure drop in the transition of the laminar-turbulent flow regime, Forchheimer proposed an equation in which the non-Darcy term is proportional to the flow velocity squared. To determine the coefficient of proportionality, the non-Darcy flow coefficient, [beta], empirical data or semitheoretical equations are used. As a result of non-Darcy flow, estimates of the coefficient from build-up pressure test values of the apparent skin become rate dependent. For this reason, the nature of the skin, formation (true) skin or rate dependent skin, is important for designing remedial treatments or for evaluating the productivity of a perforated gas well. A semitheoretical equation is proposed in this article for relating the non-Darcy flow coefficient, [beta] (cm[sup [minus]1]), to effective gas permeability, k[sub g] (Darcies), porosity, [phi], and irreducible water saturation, S[sub w].
Assessment of the individual risk of fatal injury to coal mine workers during collapses

purpose. To develop an effective model for assessing occupational risk due to rock caving in the country’s coal mines. Methodology. A mathematical model based on the maximum­likelihood method is presented, which makes it possible to assess the probability of a rockfall. The use of Bayes theorem for assessing the individual risk of fatal injuries of coal mine workers is justified. The complex method for effective control of mountain pressure is illustrated by application of the developed methodol­ ogy of computer modeling of geomechanical processes, instrumental and geophysical methods for protection and maintenance of mine workings at development of a coal seam of the Barentsburg field. findings. The work demonstrates the relationship between the key statistical indicator that affects the accident rate and the value of professional risk. A key statistical indicator, the value of which is determined using the multifunctional systems of safety, is substantiated. originality. The development efficiency of the multifunctional safety system installation in coal mines to monitor the condition of the rock mass is analyzed. practical value. The model developed by the authors will make it possible to determine the predicted value of the individual risk of fatal injury to personnel during rock collapse more accurately compared to the existing methods.
Cognitive Analysis in Social Networks for Viral Marketing

Viral marketing is the modern version of the old “word-of-mouth” advertising, where companies choose a restricted number of persons, considered “influential,” recommending them products or services that will be in turn iteratively suggested. In this article, we propose cognitive models and algorithms for marketing applications through online social networks, considered as a graph database, and define the concept of influence graph leveraging particular user behavioral patterns, by querying the initial heterogeneous graph network. We also model the diffusion across the network, without any preliminary information, as a combinatorial multiarmed bandit problem, for the selection of most influential users. We have used the YELP social network as a case study for our approach, showing how it is possible to generate an influence graph considering several kinds of relevant paths (mainly considering reviews to the same firms) by which a user can influence other ones. Several experiments have been carried out and discussed, putting into evidence the effectiveness and efficacy of the proposed methods for influence maximization with respect to other approaches of state of the art.