Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflectance and fluorescence hyperspectral elastic image registration

Science and Technology International (STI) presents a novel multi-modal elastic image registration approach for a new hyperspectral medical imaging modality. STI's HyperSpectral Diagnostic Imaging (HSDI) cervical instrument is used for the early detection of uterine cervical cancer. A Computer-Aided-Diagnostic (CAD) system is being developed to aid the physician with the diagnosis of pre-cancerous and cancerous tissue regions. The CAD system uses the fusion of multiple data sources to optimize its performance. The key enabling technology for the data fusion is image registration. The difficulty lies in the image registration of fluorescence and reflectance hyperspectral data due to the occurrence of soft tissue movement and the limited resemblance of these types of imagery. The presented approach is based on embedding a reflectance image in the fluorescence hyperspectral imagery. Having a reflectance image in both data sets resolves the resemblance problem and thereby enables the use of elastic image registration algorithms required to compensate for soft tissue movements. Several methods of embedding the reflectance image in the fluorescence hyperspectral imagery are described. Initial experiments with human subject data are presented where a reflectance image is embedded in the fluorescence hyperspectral imagery.
Rapid Isolation of Mitochondrial DNA-Depleted Mammalian Cells by Ethidium Bromide and Dideoxycytidine Treatments

Mitochondrial DNA (mtDNA)-depleted () cells are often used as mtDNA recipients to study the interaction between the nucleus and mitochondria in mammalian cells. Therefore, it is crucial to obtain mtDNA-depleted cells with many different nuclear backgrounds for the study. Here, we demonstrate a rapid and reliable method to isolate mammalian mtDNA-depleted cells involving treatment with the antimitochondrial agents ethidium bromide (EtBr) and 2`,3`-dideoxycytidine (ddC). After a short exposure to EtBr or ddC, followed by rapid clonal isolation, we were able to generate viable mtDNA-depleted cells from mouse and human cells and were able to successfully repopulate them with exogenous mitochondria from platelets isolated from mouse and human blood samples. These mtDNA-depleted cells can be used to characterize the nuclear mitochondrial interactions and to study mtDNA-associated defects in mammalian cells. Our method of isolating mtDNA-depleted cells is practical and applicable to a variety of cell types.
Performance Analysis of MIMO–OFDM System with Relaying Techniques in Rayleigh Fading Channel

—A relay network is a wide class of network configuration which is often used in wireless networks, where the source node and destination node cannot communicate directly and are interlinked with the help of some nodes. The different methods by which the relay networks are implemented in the system are known as relaying techniques. In this paper, we have presented the spectral efficiency and BER performance analysis of the MIMO-OFDM wireless system with different relaying techniques in Rayleigh fading channel. The simulated results for different modulation schemes are presented. The obtained results have shown that the hybrid relaying technique provides better spectral efficiency and bit error rate (BER) performance as compared to the other relaying techniques.
Interactive comment on “ A timescale analysis of the NH temperature response tovolcanic and solar forcing in the past millenium ” by S . L . Weber

The manuscript presents an analysis of several records of the Northern Hemisphere mean temperature, taken from empirical reconstructions and from a number of simulations with the climate model of intermediate complexity EC-Bilt. The main objective of the manuscript is to relate variations of f solar and volcanic forcing in the past millennium with simulated and reconstructed Northern Hemisphere temperatures at different time scales. This analysis should therefore yield an estimation of the sensitivity of the Northern Hemisphere temperature to external radiative forcing. The main conclusions are that: the sensitivities derived from the reconstructions and from the simulations are consistent with one another; that the sensitivity to volcanic forcing is smaller than for solar forcing, due to the short-term character of volcanic eruptions; and that the millennial trend in the solar forcing and the trend in the reconstructions are not quite compatible with the sensitivities derived at shorter timescales.
Is producing a private label counterproductive for a branded manufacturer

Branded food manufacturers vindicate the use of excess production capacities (idle otherwise) to justify their production of retailers' brands. We study the distributor and food manufacturer's private label strategy for production within a framework featuring endogenous store brand quality, bargaining power, possible differences in production technology and potential capacity constraint for the branded manufacturer. According to the structure of capacity constraint (applying to both products or private label only), the retailer may prefer to choose an independent firm whereas he selected the branded manufacturer when unconstrained. The conclusions of our article thus partially confirm branded manufacturers' thinking: they may produce store brands when they are not capacity constrained
Differential rates of change in consonant and vowel systems

Consonants and vowels are processed differently and they seem to have distinct neural representations (Caramazza et al. 2000). Böe et al. (2017) insist that vowel-like systems must be inferred to the last common ancestor of Baboons and humans, 25mya. Unlike vowels, however, consonants appear to be a later innovation in the communication systems of Hominids. Primates, including chimpanzees and orangutans, employ a repertoire of voiceless calls (so-called raspberries), which show homology with voiceless consonants (Lameira et al. 2014). During the course of human evolution, smaller orofacial cavities, increased neuro-cognitive abilities, and more precise motor control of the articulators led to greater phonetic variation, particularly among consonants, which have become phonologized in many ways in different language families. In comparison to vowels, there are over three times as many consonant phonemes in the world’s languages. Their number and diversity ranges greatly, from 6 in Rotokas to over 90 in ! Xu (Maddieson 1984); compare vowel systems which range in size from 2 to 14. Why are there are so many more consonants in the world’s languages? The answer to this question is complex, with factors involving a need for increased number of lexical contrasts in order to accommodate a growing vocabulary throughout the evolution of language, and the greater possibility for consonants rather than vowels to increase the number of contrastive sounds in a language through secondary articulations. Two strands of evidence support this conclusion. First, comparing a database of proto-language reconstructions (Marsico et al, accepted; n=100) with modern languages in UPSID (Maddieson 1984), Marsico (1999) notes an increase in the number of consonants in modern 322
Transducerless position and velocity estimation in induction and salient AC machines

This paper presents a viable transducerless rotor position and velocity estimation scheme for PWM inverter driven induction, synchronous, and reluctance machines with the capability of providing robust and accurate dynamic estimation independent of operating point, including zero and very high speeds, light and heavy loading. The injection of a balanced 3 phase high frequency signal (500 to 2 kHz) generated by the inverter, followed by appropriate signal demodulation and processing combined with a closed-loop observer, enable the tracking of rotor magnetic saliencies from the machine terminals. Although rotor magnetic saliency is inherent within reluctance machines, and most synchronous machines, saliency in the induction machine is introduced via a modulation of the rotor slot leakage with minimal detrimental effects on the machine performance. Experimental verification for the induction machine is included. <<ETX>>
Nonlinear Volterra-Weyl transforms

It is well known that nonlinear time-invariant filtering may be viewed as a nonlinear superposition of time-shifted versions of the input signal, that is described as a time invariant Volterra convolution. Nonlinear superposition of time- and frequency shifted versions of the input signal is called Volterra-Weyl convolution. In the present paper, we associate with each orthogonal transform (Legandre, Hermite, Laguerre, Walsh, Haar, Gabor, fractional Fourier, wavelet, etc.) a family of generalized shift operators. Using them we construct a nonlinear superposition of generalized time-shifted versions of the input signal. We call such a superposition a generalized Volterra-Weyl convolution (VWC). Particular cases of the VWC are nonlinear Gabor and Zak transformations, generalized higher-order Wigner distribution and ambiguity functions.
Geometric morphometrics in herpetology: modern tools for enhancing the study of morphological variation in amphibians and reptiles

The use of geometric morphometrics for studying phenotypic variation in amphibians and reptiles has visibly increased in the last decade. These modern tools provide a robust statistical framework to study organismal shape while preserving the geometric properties of the studied structures and thus improve our capacity for investigating patterns of morphological variation, and understanding their ecological and historical causes. Their application in herpetology has shed new light to the remarkable diversity observed among extant and extinct amphibians and reptiles. Here I first briefly consider the historical emergence of geometric morphometric methods, trying to provide a practical guide for herpetologists interested in implementing these tools to their investigation. I then review the wide array of published studies using geometric morphometrics to investigate morphological patterns in amphibians and reptiles. Across different investigation fields, an emergent pattern is the existence of general similarities, but also profound differences, among members of higher taxonomic groups. Size-shape allometry is a common pattern in many groups, but remarkable variation of allometric trajectories exists among closely related taxa. Sexual dimorphism has been extensively studied in reptiles, but less so in amphibians, while the contrary is true for phenotypic plasticity. The use of geometric morphometrics has allowed the detection of potentially adaptive shape patterns and the investigation of their causes. Finally, these methods have been invaluable in the study of fossils and have provided a better understanding of the paleobiology of extinct taxa.
Radial subsampling for fast cost function computation in intensity-based 3D image registration

Image registration is always a trade-off between accuracy and speed. Looking towards clinical scenarios the time for bringing two or more images into registration should be around a few seconds only. We present a new scheme for subsampling 3D-image data to allow for efficient computation of cost functions in intensity-based image registration. Starting from an arbitrary center point voxels are sampled along scan lines which do radially extend from the center point. We analyzed the characteristics of different cost functions computed on the sub-sampled data and compared them to known cost functions with respect to local optima. Results show the cost functions are smooth and give high peaks at the expected optima. Furthermore we investigated capture range of cost functions computed under the new subsampling scheme. Capture range was remarkably better for the new scheme compared to metrics using all voxels or different subsampling schemes and high registration accuracy was achieved as well. The most important result is the improvement in terms of speed making this scheme very interesting for clinical scenarios. We conclude using the new subsampling scheme intensity-based 3D image registration can be performed much faster than using other approaches while maintaining high accuracy. A variety of different extensions of the new approach is conceivable, e.g. non-regular distribution of the scan lines or not to let the scan lines start from a center point only, but from the surface of an organ model for example.
Multiple sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness

This paper presents a methodology for segmenting PD- and T2-weighted brain magnetic resonance (MR) images of multiple sclerosis (MS) patients into white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and MS lesions. For a given vectorial image (with PD- and T2-weighted components) to be segmented, we perform first intensity inhomogeneity correction and standardization prior to segmentation. Absolute fuzzy connectedness and certain morphological operations are utilized to generate the brain intracranial mask. The optimum thresholding method is applied to the product image (the image in which voxel values represent T2 value x PD value) to automatically recognize potential MS lesion sites. Then, the recently developed technique -- vectorial scale-based relative fuzzy connectedness -- is utilized to segment all voxels within the brain intracranial mask into WM, GM, CSF, and MS lesion regions. The number of segmented lesions and the volume of each lesion are finally output as well as the volume of other tissue regions. The method has been tested on 10 clinical brain MRI data sets of MS patients. An accuracy of better than 96% has been achieved. The preliminary results indicate that its performance is better than that of the k-nearest neighbors (kNN) method.
Evaluation of a robust fiducial tracking algorithm for image-guided radiosurgery

Fiducial tracking is a widely used method in image guided procedures such as image guided radiosurgery and radiotherapy. Our group has developed a new fiducial identification algorithm, concurrent Viterbi with association (CVA) algorithm, based on a modified Hidden Markov Model (HMM), and reported our initial results previously. In this paper, we present an extensive performance evaluation of this novel algorithm using phantom testing and clinical images acquired during patient treatment. For a common three-fiducial case, the algorithm execution time is less than two seconds. Testing with a collection of images from more than 35 patient treatments, with a total of more than 10000 image pairs, we find that the success rate of the new algorithm is better than 99%. In the tracking test using a phantom, the phantom is moved to a variety of positions with translations up to 8 mm and rotations up to 4 degree. The new algorithm correctly tracks the phantom motion, with an average translation error of less than 0.5 mm and rotation error less than 0.5 degrees. These results demonstrate that the new algorithm is very efficient, robust, easy to use, and capable of tracking fiducials in a large region of interest (ROI) at a very high success rate with high accuracy.
Analysis and Classification of Microelectrode Recordings in Deep Brain Stimulation Surgery

In late years neurosurgery has had an increased acceptance in scientific community for the treatment of Parkinson's Disease due to the good outcomes of Deep Brain Stimulation (DBS) clinical procedure. Of utmost importance for its success is the precise location of the very small target region in the brain. In the current study we research the most effective methods for the automated classification of the spontaneous activity of this target and develop a decision support software application in Matlab to accurately identify the optimum location for the implant of chronic stimulation electrode directly from spontaneous local neural activity. We show it is possible to estimate the human labeling of DBS microelectrode recordings, as belonging to the Subthalamic Nucleus region, with an average accuracy of at least 92.8%, using a Multinomial Logistic Classifier, trained with the first 25 principal components of the extracted features, from a dataset composed of 1000 signals from 4 patients only. This system has the potential to improve performance of the medical teams and reduce DBS surgery morbidity indicators. Index Terms – Deep Brain Stimulation; DBS; targeting automation; Parkinson’s Disease; MER; biomedical signal processing; pattern recognition;
Novel Verification Method for Timing Optimization Based on DPSO

Timing optimization for logic circuits is one of the key steps in logic synthesis. Extant research data are mainly proposed based on various intelligence algorithms. Hence, they are neither comparable with timing optimization data collected by the mainstream electronic design automation (EDA) tool nor able to verify the superiority of intelligence algorithms to the EDA tool in terms of optimization ability. To address these shortcomings, a novel verification method is proposed in this study. First, a discrete particle swarm optimization (DPSO) algorithm was applied to optimize the timing of the mixed polarity Reed-Muller (MPRM) logic circuit. Second, the Design Compiler (DC) algorithm was used to optimize the timing of the same MPRM logic circuit through special settings and constraints. Finally, the timing optimization results of the two algorithms were compared based on MCNC benchmark circuits. The timing optimization results obtained using DPSO are compared with those obtained from DC, and DPSO demonstrates an average reduction of 9.7% in the timing delays of critical paths for a number of MCNC benchmark circuits. The proposed verification method directly ascertains whether the intelligence algorithm has a better timing optimization ability than DC.
Consensus on multi-time Scale digraphs

This paper studies the consensus problem in multi-agent network where each agent possesses a local clock that may be misaligned with other agents; we refer to such a network as a Multi-Time Scale (MTS) network. Our first result shows that the consensus on a MTS network is guaranteed when the product of each agent’s local clock skew and in-degree is less than one. The corresponding consensus value depends on both agents’ local clocks and initial states. When considering that the local clocks are corrupted by noise, we propose a stochastic approximation type protocol to ensure consensus in the mean-square and almost-sure sense. We claim that a stochastic MTS network is more tolerant to the local clocks. Furthermore, we design an algorithm based on local-time dependent weights to guarantee average-consensus on a general MTS network. Simulations illustrate our findings.
Short Text Clustering; Challenges & Solutions: A Literature Review

Clustering has been an interesting research area due to its usability. It is an unsupervised learning technique which is used in many fields such as machine learning, data mining, pattern recognition, image analysis and bioinformatics etc. Also, Clustering helps in finding targeted patterns and structures directly from very large data sets with little or none of the background knowledge. Researchers are working on it by using various clustering methods/algorithms like K-means, C-means, Fuzzy C-means etc. to make it useful and have obtained meaningful results. In Short Text Clustering, clustering is performed using short text data like tweets, facebook messages, various news feeds etc. Short text as its name suggests is a text that contains only a few words; for instance, the length of a short text in Twitter is less than 140 words; Search engine queries are mostly short texts. However, it may prove very helpful in extracting meaningful information if this large unorganized data may be grouped on the basis of some similarity. But, the major problem in clustering short text is its sparse feature vector. ‘Feature Vector’ is the key element in clustering technique. So, the major solution proposed by researchers is to expand short text data as a long text using various concepts. In this paper, we have discussed various challenges in short text clustering, concepts used to overcome these challenges and also we have discussed the other possible solutions which may further improve clustering results.
Antifungal Potency of Foeniculum vulgare Seed Extract

Foeniculum vulgare belongs to family Apiaceae and is also called by the name of fennel. Agar disc diffusion method was used to determine the antifungal activity of its seed extracts in water and methanol against a number of pathogenic fungi. Methanolic extract of fennel seeds was less effective against test fungi as compared to its aqueous extract. However in case of Candida species, both types of extracts were found to be effective.
Molecular epidemiology of verotoxigenic Escherichia coli O157:H7

Verotoxigenic E. coli (VTEC) O157:H7 is a zoonotic disease agent with asymptomatic ruminants such as cattle and sheep as reservoir hosts. Infection in humans can cause bloody diarrhoea with occasionally fatal complications such as haemolytic-uraemic syndrome (HUS), particularly in children. In this thesis, the genetic variation among representative sets of O157:H7 isolates from Swedish cattle, sheep and human clinical cases is studied to find markers to predict the relative pathogenicity of different genotypes, and to improve methods for tracing spread of the bacteria. The results show that the same types occur in cattle, sheep and human patients infected within the country. However, certain types are common among ruminants while extremely rare among patient isolates, while others are overrepresented among patients. Better molecular tracing tools together with interventions targeted against highly pathogenic types of O157:H7 will allow more efficient and cost-effective efforts to reduce the burden of disease caused by VTEC O157:H7.
Networked Control Systems Secured by Quantum Key Distribution

Cyber-security has become vital for modern networked control systems (NCS). In this paper, we propose that the emerging technology of quantum key distribution (QKD) can be applied to enhance the privacy and security of NCS up to an unbreakable level. QKD can continuously distribute random secret keys with much higher privacy between communication parties, and thus enable the one-time pad encryption that cannot be truly applied in classical networks. We show that the resulting overall security of NCS can be essentially improved, and present a composable definition of security based on the analysis of the key generation and management processes. Moreover, because the security is mainly determined by quantum keys rather than the complexity of encryption algorithms, the control performance can be improved as well by reducing the time delay using simpler algorithms. These advantages are demonstrated by the example of a remotely controlled servo system, showing that the introduction of QKD to NCS can simultaneously improve the security and performance by using the simplest encryption algorithm XOR. Furthermore, we propose a novel Kalman-filter embedded communication protocol that can more efficiently use the raw keys generated by QKD.