Cattle and other ruminants produce large quantities of methane (~110 million metric tonnes per annum), which is a potent greenhouse gas affecting global climate change. Methane (CH4) is a natural by-product of gastro-enteric microbial fermentation of feedstuffs in the rumen and contributes to 6% of total CH4 emissions from anthropogenic-related sources. The extent to which the host genome and rumen microbiome influence CH4 emission is not yet well known. This study confirms individual variation in CH4 production was influenced by individual host (cow) genotype, as well as the host’s rumen microbiome composition. Abundance of a small proportion of bacteria and archaea taxa were influenced to a limited extent by the host’s genotype and certain taxa were associated with CH4 emissions. However, the cumulative effect of all bacteria and archaea on CH4 production was 13%, the host genetics (heritability) was 21% and the two are largely independent. This study demonstrates variation in CH4 emission is likely not modulated through cow genetic effects on the rumen microbiome. Therefore, the rumen microbiome and cow genome could be targeted independently, by breeding low methane-emitting cows and in parallel, by investigating possible strategies that target changes in the rumen microbiome to reduce CH4 emissions in the cattle industry.

There has been an explosion of research into the physical and chemical properties of carbon-based nanomaterials, since the discovery of carbon nanotubes (CNTs) by Iijima in 1991. Carbon nanomaterials offer unique advantages in several areas, like high surface-volume ratio, high electrical conductivity, chemical stability and strong mechanical strength, and are thus frequently being incorporated into sensing elements. Carbon nanomaterial-based sensors generally have higher sensitivities and a lower detection limit than conventional ones. In this review, a brief history of glucose biosensors is firstly presented. The carbon nanotube and grapheme-based biosensors, are introduced in Sections 3 and 4, respectively, which cover synthesis methods, up-to-date sensing approaches and nonenzymatic hybrid sensors. Finally, we briefly outline the current status and future direction for carbon nanomaterials to be used in the sensing area.

In this study, from experiments and theoretical calculation, we reported that Ti 3 C 2 MXene can be applied as sensors for NH 3 detection at room temperature with high selectivity. Ti 3 C 2 MXene, a novel two-dimensional carbide, was prepared by etching off Al atoms from Ti 3 AlC 2 . The asprepared multilayer Ti 3 C 2 MXene powders were delaminated to a single layer by intercalation and ultrasonic dispersion. The colloidal suspension of single-layer Ti 3 C 2 -MXene was coated on the surface of ceramic tubes to construct sensors for gas detection. Thereafter, the sensors were used to detect various gases (CH 4 , H 2 S, H 2 O, NH 3 , NO, ethanol, methanol, and acetone) with a concentration of 500 ppm at room temperature. Ti 3 C 2 MXene-based sensors have high selectivity to NH 3 compared with other gases. The response to NH 3 was 6.13%, which was four times the second highest response (1.5% to ethanol gas). To understand the high selectivity, first-principles calculations were conducted to explore adsorption behaviors. From adsorption energy, adsorbed geometry, and charge transfer, it was confirmed that Ti 3 C 2 MXene theoretically has a high selectivity to NH 3 , compared with other gases in this experiment. Moreover, the response of the sensor to NH 3 increased almost linearly with NH 3 concentration from 10 to 700 ppm. The humidity tests and cycle tests of NH 3 showed that the Ti 3 C 2 MXenebased gas sensor has excellent performances for NH 3 detection at room temperature. KEYWORDS: two-dimensional materials, MXene, Ti 3 C 2 T x , room-temperature sensor, NH 3

Action Unit (AU) detection becomes essential for facial analysis. Many proposed approaches face challenging problems in dealing with the alignments of different face regions, in the effective fusion of temporal information, and in training a model for multiple AU labels. To better address these problems, we propose a deep learning framework for AU detection with region of interest (ROI) adaptation, integrated multi-label learning, and optimal LSTM-based temporal fusing. First, ROI cropping nets (ROI Nets) are designed to make sure specifically interested regions of faces are learned independently; each sub-region has a local convolutional neural network (CNN) -an ROI Net, whose convolutional filters will only be trained for the corresponding region. Second, multi-label learning is employed to integrate the outputs of those individual ROI cropping nets, which learns the inter-relationships of various AUs and acquires global features across sub-regions for AU detection. Finally, the optimal selection of multiple LSTM layers to form the best LSTM Net is carried out to best fuse temporal features, in order to make the AU prediction the most accurate. The proposed approach is evaluated on two popular AU detection datasets, BP4D and DISFA, outperforming the state of the art significantly, with an average improvement of around 13% on BP4D and 25% on DISFA, respectively.

In this paper, we propose a deep learning based approach for facial action unit detection by enhancing and cropping the regions of interest. The approach is implemented by adding two novel nets (layers): the enhancing layers and the cropping layers, to a pretrained CNN model. For the enhancing layers, we designed an attention map based on facial landmark features and applied it to a pretrained neural network to conduct enhanced learning (The E-Net). For the cropping layers, we crop facial regions around the detected landmarks and design convolutional layers to learn deeper features for each facial region (C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and Cropping (EAC) Net, which can learn both feature enhancing and region cropping functions. Our approach shows significant improvement in performance compared to the state-of-the-art methods applied to BP4D and DISFA AU datasets.

In this paper, we propose a deep learning based approach for facial action unit (AU) detection by enhancing and cropping regions of interest of face images. The approach is implemented by adding two novel nets (a.k.a. layers): the enhancing layers and the cropping layers, to a pretrained convolutional neural network (CNN) model. For the enhancing layers (noted as E-Net), we have designed an attention map based on facial landmark features and apply it to a pretrained neural network to conduct enhanced learning. For the cropping layers (noted as C-Net ), we crop facial regions around the detected landmarks and design individual convolutional layers to learn deeper features for each facial region. We then combine the E-Net and the C-Net to construct a so-called Enhancing and Cropping Net (EAC-Net), which can learn both features enhancing and region cropping functions effectively. The EAC-Net integrates three important elements, i.e., learning transfer, attention coding, and regions of interest processing, making our AU detection approach more efficient and more robust to facial position and orientation changes. Our approach shows a significant performance improvement over the state-of-the-art methods when tested on the BP4D and DISFA AU datasets. The EAC-Net with a slight modification also shows its potentials in estimating accurate AU intensities. We have also studied the performance of the proposed EAC-Net under two very challenging conditions: (1) faces with partial occlusion and (2) faces with large head pose variations. Experimental results show that (1) the EAC-Net learns facial AUs correlation effectively and predicts AUs reliably even with only half of a face being visible, especially for the lower half; (2) Our EAC-Net model also works well under very large head poses, which outperforms significantly a compared baseline approach. It further shows that the EAC-Net works much better without a face frontalization than with face frontalization through image warping as pre-processing, in terms of computational efficiency and AU detection accuracy.

A novel brush-like electrode based on carbon nanotube (CNT) nano-yarn fiber has been designed for electrochemical biosensor applications and its efficacy as an enzymatic glucose biosensor demonstrated. The CNT nano-yarn fiber was spun directly from a chemical-vapor-deposition (CVD) gas flow reaction using a mixture of ethanol and acetone as the carbon source and an iron nano-catalyst. The fiber, 28 μm in diameter, was made of bundles of double walled CNTs (DWNTs) concentrically compacted into multiple layers forming a nano-porous network structure. Cyclic voltammetry study revealed a superior electrocatalytic activity for CNT fiber compared to the traditional Pt-Ir coil electrode. The electrode end tip of the CNT fiber was freeze-fractured to obtain a unique brush-like nano-structure resembling a scale-down electrical 'flex', where glucose oxidase (GOx) enzyme was immobilized using glutaraldehyde crosslinking in the presence of bovine serum albumin (BSA). An outer epoxy-polyurethane (EPU) layer was used as semi-permeable membrane. The sensor function was tested against a standard reference electrode.The sensitivities, linear detection range and linearity for detecting glucose for the miniature CNT fiber electrode were better than that reported for a Pt-Ir coil electrode. Thermal annealing of the CNT fiber at 250 •C for 30 min prior to fabrication of the sensor resulted in a 7.5 fold increase in glucose sensitivity. The as-spun CNT fiber based glucose 2 biosensor was shown to be stable for up to 70 days. In addition, gold coating of the electrode connecting end of the CNT fiber resulted in extending the glucose detection limit to 25 μM. To conclude, superior efficiency of CNT fiber for glucose biosensing was demonstrated compared to a traditional Pt-Ir sensor.

Electrospinning is a simple, versatile technique for fabricating fibrous nanomaterials with the desirable features of extremely high porosities and large surface areas. Using emulsion electrospinning, polytetrafluoroethylene/polyethene oxide (PTFE/PEO) membranes were fabricated, followed by a sintering process to obtain pure PTFE fibrous membranes, which were further utilized against a polyamide 6 (PA6) membrane for vertical contact-mode triboelectric nanogenerators (TENGs). Electrostatic force microscopy (EFM) measurements of the sintered electrospun PTFE membranes revealed the presence of both positive and negative surface charges owing to the transfer of positive charge from PEO which was further corroborated by FTIR measurements. To enhance the ensuing triboelectric surface charge, a facile negative charge-injection process was carried out onto the electrospun (ES) PTFE subsequently. The fabricated TENG gave a stabilized peak-to-peak open-circuit voltage (V) of up to ∼900 V, a short-circuit current density (J) of ∼20 mA m, and a corresponding charge density of ∼149 μC m, which are ∼12, 14, and 11 times higher than the corresponding values prior to the ion-injection treatment. This increase in the surface charge density is caused by the inversion of positive surface charges with the simultaneous increase in the negative surface charge on the PTFE surface, which was confirmed by using EFM measurements. The negative charge injection led to an enhanced power output density of ∼9 W m with high stability as confirmed from the continuous operation of the ion-injected PTFE/PA6 TENG for 30 000 operation cycles, without any significant reduction in the output. The work thus introduces a relatively simple, cost-effective, and environmentally friendly technique for fabricating fibrous fluoropolymer polymer membranes with high thermal/chemical resistance in TENG field and a direct ion-injection method which is able to dramatically improve the surface negative charge density of the PTFE fibrous membranes.