Intestinal Parasites Classification Using Deep Belief Networks

Currently, approximately $4$ billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical countries, leading to physical and mental disorders, and even death to children and immunodeficient individuals. Although subjected to high error rates, human visual inspection is still in charge of the vast majority of clinical diagnoses. In the past years, some works addressed intelligent computer-aided intestinal parasites classification, but they usually suffer from misclassification due to similarities between parasites and fecal impurities. In this paper, we introduce Deep Belief Networks to the context of automatic intestinal parasites classification. Experiments conducted over three datasets composed of eggs, larvae, and protozoa provided promising results, even considering unbalanced classes and also fecal impurities

The MoS2 Nanotubes with Defect-Controlled Electric Properties

We describe a two-step synthesis of pure multiwall MoS2 nanotubes with a high degree of homogeneity in size. The Mo6S4I6 nanowires grown directly from elements under temperature gradient conditions in hedgehog-like assemblies were used as precursor material. Transformation in argon-H2S/H2 mixture leads to the MoS2 nanotubes still grouped in hedgehog-like morphology. The described method enables a large-scale production of MoS2 nanotubes and their size control. X-ray diffraction, optical absorption and Raman spectroscopy, scanning electron microscopy with wave dispersive analysis, and transmission electron microscopy were used to characterize the starting Mo6S4I6 nanowires and the MoS2 nanotubes. The unit cell parameters of the Mo6S4I6 phase are proposed. Blue shift in optical absorbance and metallic behavior of MoS2 nanotubes in two-probe measurement are explained by a high defect concentration

Low Temperature Growth of In2O3and InN Nanocrystals on Si(111) via Chemical Vapour Deposition Based on the Sublimation of NH4Cl in In

Indium oxide (In2O3) nanocrystals (NCs) have been obtained via atmospheric pressure, chemical vapour deposition (APCVD) on Si(111) via the direct oxidation of In with Ar:10% O2at 1000 °C but also at temperatures as low as 500 °C by the sublimation of ammonium chloride (NH4Cl) which is incorporated into the In under a gas flow of nitrogen (N2). Similarly InN NCs have also been obtained using sublimation of NH4Cl in a gas flow of NH3. During oxidation of In under a flow of O2the transfer of In into the gas stream is inhibited by the formation of In2O3around the In powder which breaks up only at high temperatures, i.e.T > 900 °C, thereby releasing In into the gas stream which can then react with O2leading to a high yield formation of isolated 500 nm In2O3octahedrons but also chains of these nanostructures. No such NCs were obtained by direct oxidation forTG < 900 °C. The incorporation of NH4Cl in the In leads to the sublimation of NH4Cl into NH3and HCl at around 338 °C which in turn produces an efficient dispersion and transfer of the whole In into the gas stream of N2where it reacts with HCl forming primarily InCl. The latter adsorbs onto the Si(111) where it reacts with H2O and O2leading to the formation of In2O3nanopyramids on Si(111). The rest of the InCl is carried downstream, where it solidifies at lower temperatures, and rapidly breaks down into metallic In upon exposure to H2O in the air. Upon carrying out the reaction of In with NH4Cl at 600 °C under NH3as opposed to N2, we obtain InN nanoparticles on Si(111) with an average diameter of 300 nm

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Anti-depressant and anxiolytic like behaviors in PKCI/HINT1 knockout mice associated with elevated plasma corticosterone level

<p>Abstract</p> <p>Background</p> <p>Protein kinase C interacting protein (PKCI/HINT1) is a small protein belonging to the histidine triad (HIT) family proteins. Its brain immunoreactivity is located in neurons and neuronal processes. PKCI/HINT1 gene knockout (KO) mice display hyper-locomotion in response to D-amphetamine which is considered a positive symptom of schizophrenia in animal models. <it>Postmortem </it>studies identified PKCI/HINT1 as a candidate molecule for schizophrenia and bipolar disorder. We investigated the hypothesis that the PKCI/HINT1 gene may play an important role in regulating mood function in the CNS. We submitted PKCI/HINT1 KO mice and their wild type (WT) littermates to behavioral tests used to study anti-depressant, anxiety like behaviors, and goal-oriented behavior. Additionally, as many mood disorders coincide with modifications of hypothalamic-pituitary-adrenal (HPA) axis function, we assessed the HPA activity through measurement of plasma corticosterone levels.</p> <p>Results</p> <p>Compared to the WT controls, KO mice exhibited less immobility in the forced swim (FST) and the tail suspension (TST) tests. Activity in the TST tended to be attenuated by acute treatment with valproate at 300 mg/kg in KO mice. The PKCI/HINT1 KO mice presented less thigmotaxis in the Morris water maze and spent progressively more time in the lit compartment in the light/dark test. In a place navigation task, KO mice exhibited enhanced acquisition and retention. Furthermore, the afternoon basal plasma corticosterone level in PKCI/HINT1 KO mice was significantly higher than in the WT.</p> <p>Conclusion</p> <p>PKCI/HINT1 KO mice displayed a phenotype of behavioral and endocrine features which indicate changes of mood function, including anxiolytic-like and anti-depressant like behaviors, in conjunction with an elevated corticosterone level in plasma. These results suggest that the PKCI/HINT 1 gene could be important for the mood regulation function in the CNS.</p

In quest of a systematic framework for unifying and defining nanoscience

This article proposes a systematic framework for unifying and defining nanoscience based on historic first principles and step logic that led to a “central paradigm” (i.e., unifying framework) for traditional elemental/small-molecule chemistry. As such, a Nanomaterials classification roadmap is proposed, which divides all nanomatter into Category I: discrete, well-defined and Category II: statistical, undefined nanoparticles. We consider only Category I, well-defined nanoparticles which are >90% monodisperse as a function of Critical Nanoscale Design Parameters (CNDPs) defined according to: (a) size, (b) shape, (c) surface chemistry, (d) flexibility, and (e) elemental composition. Classified as either hard (H) (i.e., inorganic-based) or soft (S) (i.e., organic-based) categories, these nanoparticles were found to manifest pervasive atom mimicry features that included: (1) a dominance of zero-dimensional (0D) core–shell nanoarchitectures, (2) the ability to self-assemble or chemically bond as discrete, quantized nanounits, and (3) exhibited well-defined nanoscale valencies and stoichiometries reminiscent of atom-based elements. These discrete nanoparticle categories are referred to as hard or soft particle nanoelements. Many examples describing chemical bonding/assembly of these nanoelements have been reported in the literature. We refer to these hard:hard (H-n:H-n), soft:soft (S-n:S-n), or hard:soft (H-n:S-n) nanoelement combinations as nanocompounds. Due to their quantized features, many nanoelement and nanocompound categories are reported to exhibit well-defined nanoperiodic property patterns. These periodic property patterns are dependent on their quantized nanofeatures (CNDPs) and dramatically influence intrinsic physicochemical properties (i.e., melting points, reactivity/self-assembly, sterics, and nanoencapsulation), as well as important functional/performance properties (i.e., magnetic, photonic, electronic, and toxicologic properties). We propose this perspective as a modest first step toward more clearly defining synthetic nanochemistry as well as providing a systematic framework for unifying nanoscience. With further progress, one should anticipate the evolution of future nanoperiodic table(s) suitable for predicting important risk/benefit boundaries in the field of nanoscience

DNA Barcoding of Recently Diverged Species: Relative Performance of Matching Methods

Recently diverged species are challenging for identification, yet they are frequently of special interest scientifically as well as from a regulatory perspective. DNA barcoding has proven instrumental in species identification, especially in insects and vertebrates, but for the identification of recently diverged species it has been reported to be problematic in some cases. Problems are mostly due to incomplete lineage sorting or simply lack of a ‘barcode gap’ and probably related to large effective population size and/or low mutation rate. Our objective was to compare six methods in their ability to correctly identify recently diverged species with DNA barcodes: neighbor joining and parsimony (both tree-based), nearest neighbor and BLAST (similarity-based), and the diagnostic methods DNA-BAR, and BLOG. We analyzed simulated data assuming three different effective population sizes as well as three selected empirical data sets from published studies. Results show, as expected, that success rates are significantly lower for recently diverged species (∼75%) than for older species (∼97%) (P<0.00001). Similarity-based and diagnostic methods significantly outperform tree-based methods, when applied to simulated DNA barcode data (P<0.00001). The diagnostic method BLOG had highest correct query identification rate based on simulated (86.2%) as well as empirical data (93.1%), indicating that it is a consistently better method overall. Another advantage of BLOG is that it offers species-level information that can be used outside the realm of DNA barcoding, for instance in species description or molecular detection assays. Even though we can confirm that identification success based on DNA barcoding is generally high in our data, recently diverged species remain difficult to identify. Nevertheless, our results contribute to improved solutions for their accurate identification

Identification of IGF1, SLC4A4, WWOX, and SFMBT1 as Hypertension Susceptibility Genes in Han Chinese with a Genome-Wide Gene-Based Association Study

Hypertension is a complex disorder with high prevalence rates all over the world. We conducted the first genome-wide gene-based association scan for hypertension in a Han Chinese population. By analyzing genome-wide single-nucleotide-polymorphism data of 400 matched pairs of young-onset hypertensive patients and normotensive controls genotyped with the Illumina HumanHap550-Duo BeadChip, 100 susceptibility genes for hypertension were identified and also validated with permutation tests. Seventeen of the 100 genes exhibited differential allelic and expression distributions between patient and control groups. These genes provided a good molecular signature for classifying hypertensive patients and normotensive controls. Among the 17 genes, IGF1, SLC4A4, WWOX, and SFMBT1 were not only identified by our gene-based association scan and gene expression analysis but were also replicated by a gene-based association analysis of the Hong Kong Hypertension Study. Moreover, cis-acting expression quantitative trait loci associated with the differentially expressed genes were found and linked to hypertension. IGF1, which encodes insulin-like growth factor 1, is associated with cardiovascular disorders, metabolic syndrome, decreased body weight/size, and changes of insulin levels in mice. SLC4A4, which encodes the electrogenic sodium bicarbonate cotransporter 1, is associated with decreased body weight/size and abnormal ion homeostasis in mice. WWOX, which encodes the WW domain-containing protein, is related to hypoglycemia and hyperphosphatemia. SFMBT1, which encodes the scm-like with four MBT domains protein 1, is a novel hypertension gene. GRB14, TMEM56 and KIAA1797 exhibited highly significant differential allelic and expressed distributions between hypertensive patients and normotensive controls. GRB14 was also found relevant to blood pressure in a previous genetic association study in East Asian populations. TMEM56 and KIAA1797 may be specific to Taiwanese populations, because they were not validated by the two replication studies. Identification of these genes enriches the collection of hypertension susceptibility genes, thereby shedding light on the etiology of hypertension in Han Chinese populations