Non-homology-based prediction of gene functions in maize (\u3ci\u3eZea mays\u3c/i\u3e ssp. \u3ci\u3emays\u3c/i\u3e)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions.As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations

Synergic effect within n-type inorganic–p-type organic nano-hybrids in gas sensors

This paper describes the exploration of a synergic effect within n-type inorganic–p-type organic nanohybrids in gas sensors. One-dimensional (1D) n-type SnO2–p-type PPy composite nanofibers were prepared by combining the electrospinning and polymerization techniques, and taken as models to explore the synergic effect during the sensing measurement. Outstanding sensing performances, such as large responses and low detection limits (20 ppb for ammonia) were obtained. A plausible mechanism for the synergic effect was established by introducing p–n junction theory to the systems. Moreover, interfacial metal (Ag) nanoparticles were introduced into the n-type SnO2–p-type PPy nano-hybrids to further supplement and verify our theory. The generality of this mechanism was further verified using TiO2–PPy and TiO2–Au–PPy nano-hybrids. We believe that our results can construct a powerful platform to better understand the relationship between the microstructures and their gas sensing performances

A fast humidity sensor based on Li⁺-doped SnO₂ one-dimensional porous nanofibers

One-dimensional SnO₂- and Li⁺-doped SnO₂ porous nanofibers were easily fabricated via electrospinning and a subsequent calcination procedure for ultrafast humidity sensing. Different Li dopant concentrations were introduced to investigate the dopant\u27s role in sensing performance. The response properties were studied under different relative humidity levels by both statistic and dynamic tests. The best response was obtained with respect to the optimal doping of Li⁺ into SnO₂ porous nanofibers with a maximum 15 times higher response than that of pristine SnO₂ porous nanofibers, at a relative humidity level of 85%. Most importantly, the ultrafast response and recovery time within 1 s was also obtained with the 1.0 wt % doping of Li⁺ into SnO₂ porous nanofibers at 5 V and at room temperature, benefiting from the co-contributions of Li-doping and the one-dimensional porous structure. This work provides an effective method of developing ultrafast sensors for practical applications-especially fast breathing sensors

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function pre- diction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random- forest-based prediction consistently provided the most accurate gene function predic- tion. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations

Electrospun Nanofibers Hybrid Wrinkled Micropyramidal Architectures for Elastic Self-Powered Tactile and Motion Sensors

Conformable, sensitive, long-lasting, external power supplies-free multifunctional electronics are highly desired for personal healthcare monitoring and artificial intelligence. Herein, we report a series of stretchable, skin-like, self-powered tactile and motion sensors based on single-electrode mode triboelectric nanogenerators. The triboelectric sensors were composed of ultraelastic polyacrylamide (PAAm)/(polyvinyl pyrrolidone) PVP/(calcium chloride) CaCl2 conductive hydrogels and surface-modified silicon rubber thin films. The significant enhancement of electrospun polyvinylidene fluoride (PVDF) nanofiber-modified hierarchically wrinkled micropyramidal architectures for the friction layer was studied. The mechanism of the enhanced output performance of the electrospun PVDF nanofibers and the single-side/double-side wrinkled micropyramidal architectures-based sensors has been discussed in detail. The as-prepared devices exhibited excellent sensitivity of a maximum of 20.1 V/N (or 8.03 V/kPa) as tactile sensors to recognize a wide range of forces from 0.1 N to 30 N at low frequencies. In addition, multiple human motion monitoring was demonstrated, such as knee, finger, wrist, and neck movement and voice recognition. This work shows great potential for skin-like epidermal electronics in long-term medical monitoring and intelligent robot applications

A Bilayer Skin-Inspired Hydrogel with Strong Bonding Interface

Conductive hydrogels are widely used in sports monitoring, healthcare, energy storage, and other fields, due to their excellent physical and chemical properties. However, synthesizing a hydrogel with synergistically good mechanical and electrical properties is still challenging. Current fabrication strategies are mainly focused on the polymerization of hydrogels with a single component, with less emphasis on combining and matching different conductive hydrogels. Inspired by the gradient modulus structures of the human skin, we propose a bilayer structure of conductive hydrogels, composed of a spray-coated poly(3,4-dihydrothieno-1,4-dioxin): poly(styrene sulfonate) (PEDOT:PSS) as the bonding interface, a relatively low modulus hydrogel on the top, and a relatively high modulus hydrogel on the bottom. The spray-coated PEDOT:PSS constructs an interlocking interface between the top and bottom hydrogels. Compared to the single layer counterparts, both the mechanical and electrical properties were significantly improved. The as-prepared hydrogel showed outstanding stretchability (1763.85 ± 161.66%), quite high toughness (9.27 ± 0.49 MJ/m3), good tensile strength (0.92 ± 0.08 MPa), and decent elastic modulus (69.16 ± 8.02 kPa). A stretchable strain sensor based on the proposed hydrogel shows good conductivity (1.76 S/m), high sensitivity (a maximum gauge factor of 18.14), and a wide response range (0–1869%). Benefitting from the modulus matching between the two layers of the hydrogels, the interfacial interlocking network, and the patch effect of the PEDOT:PSS, the strain sensor exhibits excellent interface robustness with stable performance (>12,500 cycles). These results indicate that the proposed bilayer conductive hydrogel is a promising material for stretchable electronics, soft robots, and next-generation wearables

Ultrasensitive hydrogen sensor based on Pd0-loaded SnO2 electrospun nanofibers at room temperature

Pd0-loaded SnO2 nanofibers have been successfully synthesized with different loaded levels via electrospinning process, sintering technology, and in situ reduction. This simple strategy could be expected to extend for the fabrication of similar metal?oxide loaded nanofibers using different precursors. The morphological and structural characteristics of the resultant product were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectra (XPS). To demonstrate the usage of such Pd0-loaded SnO2 nanomaterial, a chemical gas sensor has been fabricated and investigated for H2 detection. The sensing performances versus Pd0-loaded levels have been investigated in detail. An ultralow limit of detection (20 ppb), high response, fast response and recovery, and selectivity have been obtained on the basis of the sensors operating at room temperature. The combination of SnO2 crystal structure and catalytic activity of Pd0-loaded gives a very attractive sensing behavior for applications as real-time monitoring gas sensors

Robust Conductive Hydrogels with Ultrafast Self-Recovery and Nearly Zero Response Hysteresis for Epidermal Sensors

Robust conductive hydrogels are in great demand for the practical applications of smart soft robots, epidermal electronics, and human–machine interactions. We successfully prepared nanoparticles enhanced polyacrylamide/hydroxypropyl guar gum/acryloyl-grafted chitosan quaternary ammonium salt/calcium ions/SiO2 nanoparticles (PHC/Ca2+/SiO2 NPs) conductive hydrogels. Owing to the stable chemical and physical hybrid crosslinking networks and reversible non-covalent interactions, the PHC/Ca2+/SiO2 NPs conductive hydrogel showed good conductivity (~3.39 S/m), excellent toughness (6.71 MJ/m3), high stretchability (2256%), fast self-recovery (80% within 10 s, and 100% within 30 s), and good fatigue resistance. The maximum gauge factor as high as 66.99 was obtained, with a wide detectable strain range (from 0.25% to 500% strain), the fast response (25.00 ms) and recovery time (86.12 ms), excellent negligible response hysteresis, and good response stability. The applications of monitoring the human’s body movements were demonstrated, such as wrist bending and pulse tracking

A Stretchable Alternating Current Electroluminescent Fiber

Flexible, stretchable electroluminescent fibers are of significance to meet the escalating requirements of increasing complexity and multifunctionality of smart electronics. We report a stretchable alternating current electroluminescent (ACEL) fiber by a low-cost and all solution-processed scalable process. The ACEL fiber provides high stretchability, decent light-emitting performance, with excellent stability and nearly zero hysteresis. It can be stretched up to 80% strain. Our ACEL fiber device maintained a stable luminance for over 6000 stretch-release cycles at 50% strain. The mechanical stretchability and optical stability of our ACEL fiber device provides new possibilities towards next-generation stretchable displays, electronic textiles, advanced biomedical imaging and lighting, conformable visual readouts in arbitrary shapes, and novel health-monitoring devices