Academic literature on the topic 'Human functional fingerprint'

Two pathologies commonly associated with gut microbiota dysbiosis are type 2 diabetes and cardiovascular diseases. Since diet and medication are two important causes of microbiome fingerprint modifications, new complementary and alternative methods can include wild edible mushrooms, which serve as functional products, given their properties in modulating the microbial pattern at the colon level. A disturbance in microbial balance translates into the occurrence of degenerative dysfunctions that are also associated with other pathologies, such as obesity, colon cancer. The metagenomic study has enabled the identification of some competitive microbiological and biochemical biomarkers which allow the development of innovative strategies in controling microbial disbalance from human gut. Thus, the aim of this review was to present the significant findings related to human microbiome modulation via the prebiotic effects of wild edible mushrooms as a complementary strategy to modern treatment. Certain mushroom species have been approached and their effects on the microbiota fingerprint of two major target groups are highlighted.

The evolution of the alkaline phosphatase (AP) gene family has lead to the existence in humans of one tissue-nonspecific (TNAP) and three tissue-specific isozymes,i.e.intestinal (IAP), germ cell (GCAP), and placental AP (PLAP). To define the structural differences between these isozymes, we have built models of the TNAP, IAP, and GCAP molecules based on the 1.8-Å structure of PLAP (1) and have performed a comparative structural analysis. We have examined the monomer-monomer interface as this area is crucial for protein stability and enzymatic activity. We found that the interface allows the formation of heterodimers among IAP, GCAP, and PLAP but not between TNAP with any of the three tissue-specific isozymes. Secondly, the active site cleft was mapped into three regions,i.e.the active site itself, the roof of the cleft, and the floor of the cleft. This analysis led to a structural fingerprint of the active site of each AP isozyme that suggests a diversification in substrate specificity for this isozyme family.

The flatworm Taenia solium causes human and pig cysticercosis. When cysticerci are established in the human central nervous system, they cause neurocysticercosis, a potentially fatal disease. Neurocysticercosis is a persisting public health problem in rural regions of Mexico and other developing countries of Latin America, Asia, and Africa, where the infection is endemic. The great variability observed in the phenotypic and genotypic traits of cysticerci result in a great heterogeneity in the patterns of molecules secreted by them within their host. This work is aimed to identify and characterize cysticercal secretion proteins of T. solium cysticerci obtained from 5 naturally infected pigs from Guerrero, Mexico, using 2D-PAGE proteomic analysis. The isoelectric point (IP) and molecular weight (MW) of the spots were identified using the software ImageMaster 2D Platinum v.7.0. Since most secreted proteins are impossible to identify by mass spectrometry (MS) due to their low concentration in the sample, a novel strategy to predict their sequence was applied. In total, 108 conserved and 186 differential proteins were identified in five cysticercus cultures. Interestingly, we predicted the sequence of 14 proteins that were common in four out of five cysticercus cultures, which could be used to design vaccines or diagnostic methods for neurocysticercosis. A functional characterization of all sequences was performed using the algorithms SecretomeP, SignalP, and BlastKOALA. We found a possible link between signal transduction pathways in parasite cells and human cancer due to deregulation in signal transduction pathways. Bioinformatics analysis also demonstrated that the parasite release proteins by an exosome-like mechanism, which could be of biological interest.

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common “hybrid” connectivity patterns that represent the connectivity fingerprint of a subject. We tested this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracts two main task-sensitive hybrid traits. The first trait encompasses the within and between connections of dorsal attentional and visual areas, as well as frontoparietal circuits. The second trait mainly encompasses the connectivity between visual, attentional, default mode network (DMN), and subcortical network. Overall, these findings confirm the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.

Functional MRI studies report insular activations across a wide range of tasks involving affective, sensory, and motor processing, but also during tasks of high-level perception, attention, and control. Although insular cortical activations are often reported in the literature, the diverse functional roles of this region are still not well understood. We used a meta-analytic approach to analyze the coactivation profiles of insular subdivisions—dorsal anterior, ventral anterior, and posterior insula—across fMRI studies in terms of multiple task domains including emotion, memory, attention, and reasoning. We found extensive coactivation of each insular subdivision, with substantial overlap between coactivation partners for each subdivision. Functional fingerprint analyses revealed that all subdivisions cooperated with a functionally diverse set of regions. Graph-theoretical analyses revealed that the dorsal anterior insula was a highly “central” structure in the coactivation network. Furthermore, analysis of the studies that activate the insular cortex itself showed that the right dorsal anterior insula was a particularly “diverse” structure in that it was likely to be active across multiple task domains. These results highlight the nuanced functional profiles of insular subdivisions and are consistent with recent work suggesting that the dorsal anterior insula can be considered a critical functional hub in the human brain.

Glycosylation is a complex post-translational modification that conveys functional diversity to glycoconjugates. Cell surface glycosylation mediates several biological activities such as induction of the intracellular signaling pathway and pathogen recognition. Red blood cell (RBC) membrane N-glycans determine blood type and influence cell lifespan. Although several proteomic studies have been carried out, the glycosylation of RBC membrane proteins has not been systematically investigated. This work aims at exploring the human RBC N-glycome by high-sensitivity MALDI-MS techniques to outline a fingerprint of RBC N-glycans. To this purpose, the MALDI-TOF spectra of healthy subjects harboring different blood groups were acquired. Results showed the predominant occurrence of neutral and sialylated complex N-glycans with bisected N-acetylglucosamine and core- and/or antennary fucosylation. In the higher mass region, these species presented with multiple N-acetyllactosamine repeating units. Amongst the detected glycoforms, the presence of glycans bearing ABO(H) antigens allowed us to define a distinctive spectrum for each blood group. For the first time, advanced glycomic techniques have been applied to a comprehensive exploration of human RBC N-glycosylation, providing a new tool for the early detection of distinct glycome changes associated with disease conditions as well as for understanding the molecular recognition of pathogens.

The biodiversity of microorganisms is maintained by intricate nets of interactions between competing species. Impaired functionality of human microbiomes correlates with their reduced biodiversity originating from aseptic environmental conditions and antibiotic use. Microbiomes of wild animals are free of these selective pressures. Microbiota provides a protecting shield from invasion by pathogens in the wild, outcompeting their growth in specific ecological niches. We applied ultrahigh-throughput microfluidic technologies for functional profiling of microbiomes of wild animals, including the skin beetle, Siberian lynx, common raccoon dog, and East Siberian brown bear. Single-cell screening of the most efficient killers of the common human pathogen Staphylococcus aureus resulted in repeated isolation of Bacillus pumilus strains. While isolated strains had different phenotypes, all of them displayed a similar set of biosynthetic gene clusters (BGCs) encoding antibiotic amicoumacin, siderophore bacillibactin, and putative analogs of antimicrobials including bacilysin, surfactin, desferrioxamine, and class IId cyclical bacteriocin. Amicoumacin A (Ami) was identified as a major antibacterial metabolite of these strains mediating their antagonistic activity. Genome mining indicates that Ami BGCs with this architecture subdivide into three distinct families, characteristic of the B. pumilus, B. subtilis, and Paenibacillus species. While Ami itself displays mediocre activity against the majority of Gram-negative bacteria, isolated B. pumilus strains efficiently inhibit the growth of both Gram-positive S. aureus and Gram-negative E. coli in coculture. We believe that the expanded antagonistic activity spectrum of Ami-producing B. pumilus can be attributed to the metabolomic profile predetermined by their biosynthetic fingerprint. Ultrahigh-throughput isolation of natural probiotic strains from wild animal microbiomes, as well as their metabolic reprogramming, opens up a new avenue for pathogen control and microbiome remodeling in the food industry, agriculture, and healthcare.

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

DNA fingerprints are not prints of fingers. So why the name? The “fingerprint” label, of course, conveys far more than some pattern of swirls, whorls, and arches on the skin. As celebrated in detective lore, fingerprints are emblems of uniqueness. DNA can thus form a “fingerprint” by establishing personal identity. Genes are often characterized as “information.” Thus, DNA “codes for” an organism’s unique traits. In terms of uniqueness and developmental causality, then, genes seem to underlie human identity. Yet with deeper reflection, one might find this commonplace association spurious and misleading: another sacred bovine. Ironically, perhaps, DNA fingerprinting reveals very little about an individual’s DNA, or genome. The technique does not exhaustively profile every form of every gene, as many imagine. Nor does it even sequence the DNA. Rather, it focuses on a rather incidental feature of chromosome structure: differences in noncoding sections of DNA. There, short “nonsense” segments are repeated. The number of repeats, however, varies widely among individuals. Thus, they are convenient markers, or indicators, for identifying a particular organism—or a potential criminal suspect. Each person’s DNA may well be unique, but only a small and physiologically insignificant fragment of it is needed to identify a particular individual. Other biological features function as identifiers, as well. Forensic scientists have long relied on fingerprints and “mug shots,” both introduced into criminology by Charles Darwin’s cousin Francis Galton. They also use hair, skin tone, blood and tissue type, and voice sonograms. Some high-tech security systems—including ones adopted by US immigration—use eye scans. These record the unique pattern of the eye’s iris. (Blood vessel patterns on the retina work as well.) In all these cases, the aim is unambiguous identification. What matters is diagnostically unique properties. So these particular features are effective indicators. At the same time, their functional role is trivial. They are biologically insignificant. They hardly profile someone’s sense of self. Nor do they fully characterize who they are (personally, culturally, or even biologically). Identification and identity are distinct. A unique feature is not necessarily an important feature.

This research aims to examine the scientific study related to fingerprint patterns and brains lobes. Generally, this method is used to find and develop the inborn potential and personality especially of children. Every person is having inborn potential and personality, which will help us to analyze strength and weakness. The present work is based only on the analysis and used as a reference for scientific research in the field of Galtian and statistical study conducted based on the fingerprint processing. Human brain is divided into two parts, left hemispheres and right hemispheres. Fingers of right hand represent the functions of left brain and fingers of left hand represent the functions of right brain. Human brain is divided into 10 lobes and each lobe is related with each finger. Each lobe represents different intelligences. A detailed analysis of the fingerprint would help the researchers to find the inborn talents. It will provide them the most appropriate learning habits from young age and improve learning ability effectively. The vital factor of an individual’s intelligence is determined by neural network connection of brain cells. Cognitive science is the scientific study that will help you to know about yourself.

Kinase-targeted drug design is challenging. It requires designing inhibitors that can bind to specific kinases, when all kinase catalytic domains share a common folding scaffold that binds ATP. Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery. This begins with deciphering the kinase-ligand characteristics, analyzing the structure–activity relationships and prioritizing the desired drug molecules across the whole kinome. Currently, there are more than 300 kinases with released PDB structures, which provides a substantial structural basis to gain these necessary insights. Here, we review in silico structure-based methods – notably, a function-site interaction fingerprint approach used in exploring the complete human kinome. In silico methods can be explored synergistically with multiple cell-based or protein-based assay platforms such as KINOMEscan. We conclude with new drug discovery opportunities associated with kinase signaling networks and using machine/deep learning techniques broadly referred to as structural biomedical data science.

Mud, a cohesive material, consists of water, clay minerals, sand, silt and small quantities of organic matter (i.e., biopolymers). Amongst the different mud layers formed by human or natural activities, the fluid mud layer found on top of all the others is quite important from navigational point of view in ports and waterways. Rheological properties of fluid mud layers play an important role in navigation through fluid mud and in fluid mud transport. However, the rheological properties of mud are known to vary as a function of sampling location within a port, sampling depth and sampling location across the globe. Therefore, this variability in rheological fingerprint of mud requires a detailed and systematic analysis. This chapter presents two different sampling techniques and the measured rheological properties of mud, obtained from laboratory experiments. The six protocols used to measure the yield stresses are detailed and compared. Furthermore, the empirical or semi-empirical models that are commonly used to fit rheological experimental data of such systems are presented. The influence of different factors such as density and organic matter content on the rheological behavior of mud is discussed. The fluidic yield stress of mud samples was observed to vary from 0.2 Pa to 500 Pa as a function of density and organic matter content.

In this work, analytical models available from contact mechanics theory having a proven record in mechanical engineering were used to develop a model predicting the friction behavior of human skin. A multi-scale contact model was developed in which the contact parameters are calculated at three levels, each level characterized by its elastic behavior and geometry. For a product part in contact with the so-called hairy skin the skin topography can be described as being composed of spherical contacts, whereas for the finger in contact with a product surface the fingerprint ridges are modeled as annulus shaped line contacts. Sliding friction was measured in vivo between the skin and different surface textures produced using ultra-short pulsed laser technology. The results observed during in vivo experiments are very well explained by the developed model, which predicts the friction as a function of product geometry, asperity geometry and normal load.

Tactile sensor for detecting slip is essential to grasp an object safely in a robot hand with a complicated structure and various functions. In this paper, sensors with three patterns in different forms are designed to sense slip moment through a resistance change. The slip sensors developed in this paper have flexible and stretchable characteristics to be used on the robot hand surface. For realizing these characteristics, acrylonitrile-butadiene rubber (NBR) is used as a substrate of the sensor. Graphene material is employed bacause of suitable for flexible substrate as an electrode. An imprint process to produce a micro-unit sensor structure is newly developed. As a result, sensors can have an extremely small structure like human fingerprint. Experiments for performance of the developed sensors are conducted. In addition, a stretchable and fatigue tests are conducted to verify the performance of sensors with a flexible characteristic like the human skin.

Over the last decades, biometrics has become an important way for human identification in many areas, since it can avoid frauds and increase the security of individuals in society. Nowadays, most popular biometric systems are based on fingerprint and face features. Despite the great development observed in Biometrics, an important challenge lasts, which is the automatic people identification in low-resolution videos captured in unconstrained scenarios, at a distance, in a covert and noninvasive way, with little or none subject cooperation. In these cases, gait biometrics can be the only choice. The goal of this work is to propose a new method for gait recognition using information extracted from 2D poses estimated over video sequences. For 2D pose estimation, our method uses OpenPose, an open-source robust pose estimator, capable of real-time multi-person detection and pose estimation with high accuracy and a good computational performance. In order to assess the new proposed method, we used two public gait datasets, CASIA Gait Dataset-A and CASIA Gait Dataset-B. Both datasets have videos of a number of people walking in different directions and conditions. In our new method, the classification is carried out by a 1-NN classifier. The best results were obtained by using the chi-square distance function, which obtained 95.00% of rank-1 recognition rate on CASIA Gait Dataset-A and 94.22% of rank-1 recognition rate on CASIA Gait Dataset-B, which are comparable to state-of-the-art results.