Allele and genotype frequency for milk beta-casein in dairy cattle in the northern region of Tocantins State, Brazil

At present, there is a concern about the quality of milk and diseases related to its consumption, as it can generate discomfort and allergic reactions in some individuals due to its protein components. Thus, the present study was developed to identify the allele and genotype frequencies of genes for β casein, A1 and A2, in dairy herds in the region of Araguaína-TO, Brazil. Genetic material from 421 animals (crossbred dairy cattle in lactation) was used. All animals were numbered for identification, and DNA samples were extracted from hair bulbs. Samples for two markers from the polymorphic regions were characterized and confirmed by real time PCR using the ABI Prism® 7500 Sequence Detection System (Applied Biosystems). Allele and genotype frequencies were determined using the TaqMan™ detection system, where the primer and probe release different fluorescence signals for each allele of the polymorphism. The sampled herd showed frequencies of 28.27% for the A1 allele and 71.73% for the A2 allele. Genotype frequencies were 52.96% (223/421) for A2A2; 37.53% (158/421) for the A1A2 genotype; and 9.50% (40/421) for the A1A1 genotype. The frequency of the A1 allele for β-casein in dairy herds from the northern region of Tocantins was low and is per the results of previous studies. Although the A2A2 genotype of β-casein had a high relative frequency, the A1A2 genotype is still rather frequent, warranting greater selection pressure.

Design of a High-Sensitivity Dimeric G-Quadruplex/Hemin DNAzyme Biosensor for Norovirus Detection

G-quadruplexes can bind with hemin to form peroxidase-like DNAzymes that are widely used in the design of biosensors. However, the catalytic activity of G-quadruplex/hemin DNAzyme is relatively low compared with natural peroxidase, which hampers its sensitivity and, thus, its application in the detection of nucleic acids. In this study, we developed a high-sensitivity biosensor targeting norovirus nucleic acids through rationally introducing a dimeric G-quadruplex structure into the DNAzyme. In this strategy, two separate molecular beacons each having a G-quadruplex-forming sequence embedded in the stem structure are brought together through hybridization with a target DNA strand, and thus forms a three-way junction architecture and allows a dimeric G-quadruplex to form, which, upon binding with hemin, has a synergistic enhancement of catalytic activities. This provides a high-sensitivity colorimetric readout by the catalyzing H2O2-mediated oxidation of 2,2′-azino-bis(3-ethylbenzothiazoline -6-sulfonic acid) diammonium salt (ABTS). Up to 10 nM of target DNA can be detected through colorimetric observation with the naked eye using our strategy. Hence, our approach provides a non-amplifying, non-labeling, simple-operating, cost-effective colorimetric biosensing method for target nucleic acids, such as norovirus-conserved sequence detection, and highlights the further implication of higher-order multimerized G-quadruplex structures in the design of high-sensitivity biosensors.

Tractor-trailer-train braking time sequence detection based on monocular vision

The tractor-trailer-train at the braking process prone to braking instability caused by asynchronous braking between the shafts. With respect to the lack of intelligent detection of Braking Time Sequence (BTS), a non-contact dynamic detection scheme of intelligent vehicle BTS is proposed. Based on the monocular vision principle, the edge markers of tractor-trailer train tires are identified, and the tire slip rate is solved. The noise reduction of the collected image is processed. The marker area is obtained by Blob analysis. This region at the image to be matched is identified by the template matching algorithm based on contour. The camera is calibrated by Zhang’s calibration method. In order to verify the effectiveness of the detection scheme, the real vehicle test was carried out. The test results show that the error of slip rate solution is below 4.2%.

Mammalian octopus cells are direction selective to frequency sweeps by synaptic sequence detection

Octopus cells are remarkable projection neurons of the mammalian cochlear nucleus, with extremely fast membranes and wide frequency tuning. They are considered prime examples of coincidence detectors but are poorly characterized in vivo. We discover that octopus cells are selective to frequency sweep direction, a feature that is absent in their auditory nerve inputs. In vivo intracellular recordings reveal that direction selectivity does not derive from cross-channel coincidence detection but hinges on the amplitudes and activation sequence of auditory nerve inputs tuned to clusters of hotspot frequencies. A simple biophysical model of octopus cells excited with real nerve spike trains recreates direction selectivity through interaction of intrinsic membrane conductances with activation sequence of clustered inputs. We conclude that octopus cells are sequence detectors, sensitive to temporal patterns across cochlear frequency channels. The detection of sequences rather than coincidences is a much simpler but powerful operation to extract temporal information.

Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration (Brain-CODE) data platform, each involving MRI data from multiple research institutes, are used to build and test our model. The preliminary results show that a random forest model can be trained to accurately identify MRI sequence types, and to recognize MRI scans that do not belong to any of the known sequence types. Therefore the proposed approach can be used to automate processing of MRI data that involves a large number of variations in sequence names, and to help standardize sequence naming in ongoing data collections. This study highlights the potential of the machine learning approaches in helping manage health data.

Longitudinal Secretion of Paramyxovirus RNA in the Urine of Straw-Coloured Fruit Bats (Eidolon helvum)

The straw-coloured fruit bat (Eidolon helvum) is widespread in sub-Saharan Africa and is widely hunted for bushmeat. It is known to harbour a range of paramyxoviruses, including rubuloviruses and henipaviruses, but the zoonotic potential of these is unknown. We previously found a diversity of paramyxoviruses within a small, captive colony of E. helvum after it had been closed to contact with other bats for 5 years. In this study, we used under-roost urine collection to further investigate the paramyxovirus diversity and ecology in this colony, which had been closed to the outside for 10 years at the time of sampling. By sampling urine weekly throughout an entire year, we investigated possible seasonal patterns of shedding of virus or viral RNA. Using a generic paramyxovirus L-gene PCR, we detected eight distinct paramyxovirus RNA sequences. Six distinct sequences were detected using a Henipavirus-specific PCR that targeted a different region of the L-gene. Sequence detection had a bi-annual pattern, with the greatest peak in July, although different RNA sequences appeared to have different shedding patterns. No significant associations were detected between sequence detection and birthing season, environmental temperature or humidity, and no signs of illness were detected in any of the bats in the colony during the period of sample collection.

A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity

Since the first half of the twentieth century, numerous studies have been conducted on how the visual cortex encodes basic image features. One of the hallmarks of basic feature extraction is the phenomenon of orientation selectivity, of which the underlying neuronal-level computational mechanisms remain partially unclear despite being intensively investigated. In this work we present a reduced visual system model (RVSM) of the first level of scene analysis, involving the retina, the lateral geniculate nucleus and the primary visual cortex (V1), showing orientation selectivity. The detection core of the RVSM is the neuromorphic spike-decoding structure MNSD, which is able to learn and recognize parallel spike sequences and considerably resembles the neuronal microcircuits of V1 in both topology and operation. This structure is equipped with plasticity of intrinsic excitability to embed recent findings about V1 operation. The RVSM, which embeds 81 groups of MNSD arranged in 4 oriented columns, is tested using sets of rotated Gabor patches as input. Finally, synthetic visual evoked activity generated by the RVSM is compared with real neurophysiological signal from V1 area: (1) postsynaptic activity of human subjects obtained by magnetoencephalography and (2) spiking activity of macaques obtained by multi-tetrode arrays. The system is implemented using the NEST simulator. The results attest to a good level of resemblance between the model response and real neurophysiological recordings. As the RVSM is available online, and the model parameters can be customized by the user, we propose it as a tool to elucidate the computational mechanisms underlying orientation selectivity.