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This paper mainly used database technology, machine learning, thermodynamic calculation, experimental verification, etc., on integrated computational materials engineering. The interaction between different alloying elements and the strengthening effect of precipitated phases are investigated mainly for martensitic ageing steels. Modelling and parameter optimization were performed by machine learning, and the highest prediction accuracy was 98.58%. Investigated the influence of composition fluctuation on performance and correlation tests to analyze the influence of elements from multiple perspectives. Furthermore, screen out the three-component composition process parameters with composition and performance with high contrast. Thermodynamic calculations studied the effect of alloying element content on the nano-precipitation phase, Laves phase, and austenite in the material. The heat treatment process parameters of the new steel grade were also developed based on the phase diagram. A new type of martensitic ageing steel was prepared by selected vacuum arc melting. The sample with the highest overall mechanical properties had a yield strength of 1887 MPa, a tensile strength of 1907 MPa, and a hardness of 58 HRC. The sample with the highest plasticity had an elongation of 7.8%. The machine learning process for the accelerated design of new ultra-high tensile steels was found to be generalizable and reliable.

The binder jetting (BJ) forming process is an additive manufacturing method based on discrete stacking and microdrop jetting technology. It has the advantages of low cost and high degree of automation; further, it has no special requirements pertaining to the shapes or structures of machining parts. However, at the present stage, all BJ processes involve hot curing, which leads to high molding and maintenance costs. To solve this problem, polyvinyl alcohol powder was used as the main material of the binder formed via the BJ process. The wetting of the powder bed by ink droplets was simulated, and the ink formula and printing process parameters were optimized. The final metal part was formed via defatting and sintering, resulting in a tensile strength of 323 MPa and density of 85.6%.

Balancing the benefits and risks of antimicrobials in health care requires an understanding of their effects on antimicrobial resistance at the population scale. Therefore, we aimed to investigate the association between the population antibiotics use and resistance rates and further identify their critical thresholds.Data for monthly consumption of six antibiotics (daily defined doses [DDDs]/1000 inpatient-days) and the number of cases caused by five common drug-resistant bacteria (occupied bed days [OBDs]/10,000 inpatient-days) from inpatients during 2009-2020 were retrieved from the electronic prescription system at Nanjing Drum Tower Hospital, a tertiary hospital in Jiangsu Province, China. Then, a nonlinear time series analysis method, named generalized additive models (GAM), was applied to analyze the pairwise relationships and thresholds of these antibiotic consumption and resistance.The incidence densities of carbapenem-resistant Acinetobacter baumannii (CRAB), carbapenem-resistant Klebsiella pneumoniae (CRKP), and aminoglycoside-resistant Pseudomonas aeruginosa were all strongly synchronized with recent hospital use of carbapenems and glycopeptides. Besides, the prevalence of carbapenem-resistant Escherichia coli was also highly connected the consumption of carbapenems and fluoroquinolones. To lessen resistance, we determined a threshold for carbapenem and glycopeptide usage, where the maximum consumption should not exceed 31.042 and 25.152 DDDs per 1000 OBDs, respectively; however, the thresholds of fluoroquinolones, third-generation cephalosporin, aminoglycosides, and β-lactams have not been identified.The inappropriate usage of carbapenems and glycopeptides was proved to drive the incidence of common drug-resistant bacteria in hospitals. Nonlinear time series analysis provided an efficient and simple way to determine the thresholds of these antibiotics, which could provide population-specific quantitative targets for antibiotic stewardship.

Exosomes are potential biomarkers, which play an important role in early diagnosis and prognosis prediction of cancer-related diseases. Nevertheless, direct quantification of exosomes in biological fluid, especially in point-of-care tests (POCTs), remains extremely challenging. Herein, we developed a sensitive and portable electrochemical biosensor in combination with smartphones for quantitative analysis of exosomes. The improved double-antibody sandwich method-based poly-enzyme signal amplification was adopted to detect exosomes. We could detect as low as 7.23 ng of CD63-positive exosomes in 5 μL of serum within 2 h. Importantly, we demonstrated that the biosensor worked well with microliter-level serum and cell culture supernatant. The biosensor holds great potential for the detection of CD-63-expressing exosomes in early diagnosis of prostate disease because CD63-positive exosomes were less detected from the prostate patient serum. Also, the biosensor was used to monitor the secretion of exosomes with the drug therapy, showing a close relationship between the secretion of exosomes and the concentration of cisplatin. The biosensing platform provides a novel way toward POCT for the diagnosis and prognosis prediction of prostate disease and other diseases via biomarker expression levels of exosomes.

The developed biosensor detection system can complete the detection of air samples by collecting exhaled breath condensate, greatly reducing the time to diagnose tuberculosis.

The immunosensor can complete the sputum detection directly within a few minutes, which greatly shortens the diagnosis time of TB.

Nowadays, superhydrophobicity has been the limelight of the academic community owing to the plethora of applications. In this work, the downhill motion of liquid drops and liquid marbles was investigated by two distinct particle-laden interfaces. Liquid drops and liquid marbles accelerate firstly and then move on the slope with a steady velocity which decreases with the liquid viscosity. The steady velocity of liquid marbles is lower than that of liquid drops while the maximum aspect ratio of liquid marbles is higher than that of liquid drops, due to different interface interactions. The particles on the solid substrate enhance the motion of liquid drops while those on the liquid surface reduce the surface tension of liquid marbles. Both liquid drops on non-stick solid surface and non-stick liquid marbles on solid substrate could provide theoretical basis for the precisely targeted transportation of small quantities of liquids by particle-laden interfaces, especially in chemical and biomedical applications.

Tuberculosis (TB) mostly spreads from person to person through Mycobacterium tuberculosis (MTB). However, the majority of conventional detection methods for MTB cannot satisfy the requirements for actual TB detection. As one of the most promising powerful platforms, a silicon nanowire field-effect transistor (SiNW-FET) biosensor shows good prospect in TB detection. In this study, an enhanced SiNW-FET biosensor was developed for the rapid and sensitive detection of MTB. The surface functional parameters of the biosensor were explored and optimized. The SiNW-FET biosensor has good sensitivity with a detection limit of 0.01 fg/mL toward protein. The current change value shows a linear upward trend with the increase in protein concentration in the range of 1 fg/mL to 100 μg/mL. One whole test cycle can be accomplished within only 30 s. More importantly, a good distinction was realized in the sputum without pretreatment between normal people and TB patients, which greatly shortened the TB detection time (only 2-5 min, considering the dilution of sputum). Compared with other methods, the SiNW-FET biosensor can detect MTB with a remarkably broad dynamic linear range in a shorter time.

The addition of Ti and its compounds particles has the potential to effectively enhance the fracture toughness of B4C ceramics. However, the influence of different Ti-based compounds on B4C ceramics has not been extensively studied. In this work, B4C–TiB2 multiphase ceramics were prepared by adding 5% Ti or its compounds (TiO2, TiB2 or TiC) in B4C and the effects of the additives on the B4C ceramics were studied. For this purpose, we adopted a sintering process carried out in situ in the absence of pressure at a temperature of 2250 °C for 1 h. It was evident from the results that the additives are capable of enhancing the sintering densification characteristics and the properties exhibited by B4C, as opposed to single-phase B4C ceramics. Additionally, different additives have varied effects on the microstructures and mechanical properties of B4C–TiB2 multiphase ceramics. The results of the study indicate that the TiC-added sample exhibits the most optimal mechanical properties, with 4.52 MPa m1/2 fracture toughness, 344.0 MPa flexural strength, 26.2 GPa hardness, and 516.0 GPa elastic modulus.

Silicon nanowire devices employed traditional MEMS technology has the advantages of low cost and easy to batch fabricated, which can be applied for high‐sensitivity biological and chemical species detection. In the preparation process, the device performance would be greatly influenced by the BOE corrosion process of silicon oxide outside silicon nanowires, which will define the performance of devices in subsequent test. In this paper, pH solution test was used to characterize the response sensitivity of silicon nanowires in different etching times, with the help of SEM and I–V characteristics before and after corrosion, it can be found that the thickness of silicon oxide layer enclosed nanowires was gradually reduced until completely etched in about five minutes to expose the silicon nanowires, which revealed that the silicon nanowires could start to sense and respond to external stimulus only when the silicon oxide was very thin even disappeared. The effect of corrosion could be equivalent to the influence of silicon oxide thickness on device performance. It provides an effective way to control the fabrication process of high performance devices.

In this study, boron carbide-metallic boride (B4C-MeBx, Me = Ti, Zr, Nb, Ta, or W) multiphase ceramics were fabricated via in situ pressureless sintering at 2250 °C for 1 h. The effects of transition metal carbides, namely, TiC, ZrC, NbC, TaC, and WC, on the phase composition, microstructure, and mechanical properties of the ceramics were investigated. The results showed that MeC could facilitate the sintering densification of B4C by distributing second-phase particles uniformly throughout the B4C. Additionally, the main phases observed were B4C and (Me, W)Bx (Me = Ti, Zr, Nb, or Ta) due to the doping of a small amount of WC during the ball milling process. As a result, the mechanical properties of B4C-MeBx showed significant improvements when compared with those of single-phase B4C ceramics. B4C–NbB2 ceramics were found to exhibit the best mechanical properties, with an elastic modulus of 393.0 GPa, a hardness of 28.7 GPa, a flexural strength of 368.0 MPa, and a fracture toughness of 6.94 MPa m1/2.

Rapid and comprehensive diagnostic methods are necessary for early identification and monitoring of SARS-CoV-2. Here, we have developed a universal and portable three-in-one biosensor linked to a smartphone for co-detection of SARS-CoV-2 viral RNA, antigen, and antibody. In combination with a smartphone, the online monitoring of SARS-CoV-2 virus-infected patients from infection to immunization could be intelligently achieved.

Traditional detection methods have shortcomings such as time-consumption and requirement of large instruments, which cannot meet the demands for on-site detection or analysis. Silicon nanowire-field-effect transistor (SiNW-FET) biosensors have the advantages of high speed, high sensitivity, strong specificity, and ease of integration. However, SiNW-FET biosensors also have some demerits: they are too sensitive, environmental factors such as light, temperature, and pH easily cause interference, and their performance uniformity needs to be calibrated in advance. In this work, we constructed a self-contained and integrated microfluidic nano-detection system containing a SiNW-FET biosensor for bio-detection and analysis. All analysis processes including liquid sample delivery, optical modulation, constant temperature control, signal amplification and data acquisition, and result display were automatically performed. In series tests including light-guided ones by analyzing various types of samples with an automatic sample injection mode, the system shows good stability and robustness. Its signal accuracy was verified using a commercial high-precision ammeter (

In this paper, we propose a new strategy for the sensitive and specific detections of dimethyl methylphosphonate (DMMP), as a simulant of nerve agent Sarin. Our silicon nanowire based sensor is modified with carboxyphenylboronic acid (CPBA) to demonstrate great efficiency. The response time is less than 100 seconds to recognize the DMMP, which is a key ability for on-site monitoring. The sensor exhibits a good reversibility during the repeated measurements between DMMP vapor and air. The sensor still performs well after being exposed in high concentration of DMMP vapor with excellent recovery for 3 times. The sensor is demonstrated to have a linear behavior in detecting DMMP within the concentration of 13~65 ppm in gaseous phase with the limit of detection (LOD) of 100 ppb. It is expected that the proposed sensor enables the testing system accurate, rapid, sensitive and reversible for on-site detection of nerve agents in the future.

Since the preparation of TNT is always proceeded in acid solution, it is very difficult to accurately detect it at the production stage. Developing a TNT sensor which can work in sulfuric acid solution will be very helpful. This paper presents a TNT sensor based on SiNWs-FET which can quickly detect TNT in sulfuric acid. The detection mechanism is based on the fact that TNT molecules can selectively bond to the amino group of nanowires, which act as grid elements and modulate the conductance of the silicon nanowires, leading to the variation of SiNWs-FET drain-source current. The SiNWs-FET devices were fabricated on a (111) silicon-on-insulator wafer using controllable silicon anisotropy etching and self-limiting oxidation. The silicon nanowires were treated with oxygen plasma and 3-aminopropyltriethoxysilane (APTES) to form hydroxyl and amino groups on the surface of the nanowires, respectively. The SiNWs-FET sensor demonstrated highly sensitive detection to TNT molecule in aqueous solution with 0.1% dimethyl sulfoxide (DMSO) and sulfuric acid solutions. These experimental results show that the SiNWs-FET sensor can work stably and reliably in some harsh environment, even in high concentration of sulfuric acid. The rapid response time also reflected the advantages of SiNWs-FET sensors in TNT fast detection. In addition, the SiNWs-FET sensor showed selectivity to TNT, due to the specific bind between amino groups and nitro groups.

Tuberculosis (TB) remains a public health problem that cannot be ignored. The portable and efficient detection of

Tuberculosis (TB) mostly spreads from person to person through

Silicon nanowires (SiNWs) field effect transistor (FET) sensor has been considered to be one of the most promising candidates in the low cost detection of biological and chemical molecules. However, because of silicon nanowires' size distribution, the responses to the same sample of different SiNWs FET sensors show obvious fluctuation. Such problem brings great obstacles to used SiNWs FET sensors in high-precision detection. Here, we proposed a new strategy to calibrate SiNWs FET sensors by using its photoresponse. The foundation of this method is the similar relationship between SiNWs FET sensor response to the target and its own response to the illumination. The validity of this method were confirmed through the results of experiments that calibrated SiNWs FET sensor can accurately get the concentration results of the target in serum samples from patients with breast cancer. This work represents an important step toward the using of SiNWs FET sensor for accurate quantitative biochemical sensing and is expected to open up exciting opportunities for widely clinical application in the future.

Theoretical study and software simulation on the sensitivity of silicon nanowires (SiNWs) field effect transistor (FET) sensors in terms of surface-to-volume ratio, depletion ratio, surface state and lattice quality are carried out. Generally, SiNWs-FET sensors with triangular cross-sections are more sensitive than sensors with circular or square cross-sections. Two main reasons are discussed in this article. Firstly, SiNWs-FET sensors with triangular cross-sections have the largest surface-to-volume ratio and depletion ratio which significantly enhance the sensors’ sensitivity. Secondly, the manufacturing processes of the electron beam lithography (EBL) and chemical vapor deposition (CVD) methods seriously affect the surface state and lattice quality, which eventually influence SiNWs-FET sensors’ sensitivity. In contrast, wet etching and thermal oxidation (WETO) create fewer surface defects and higher quality lattices. Furthermore, the software simulation confirms that SiNWs-FET sensors with triangular cross-sections have better sensitivity than the other two types of SiNWs-FET sensors under the same conditions, consistent with the theoretical analysis. The article fully proved that SiNWs-FET sensors fabricated by the WETO method produced the best sensitivity and it will be widely used in the future.

Biosensing interface based on screen-printed carbon electrodes (SPCE) has been widely used for electrochemical biosensors in the field of medical diagnostics, food safety, and environmental monitoring. Nevertheless, SPCE always has a rough surface, which is easy to result in the disorder of nucleic acid capture probes, the nonspecific adsorption of signaling probes, the steric hindrance of target binding, and decrease in the signal-to-noise ratio and sensitivity of biosensors. So far, it still remains extremely challenging to develop high-efficiency carbon-based biosensing interfaces, especially for DNA probe-based assembly and functionalization. In this paper, we first used a specific DNA framework, DNA tetrahedron to solve the defects of the carbon interface, improving the biosensing ability of SPCE. With covalent coupling, the DNA tetrahedron could be immobilized on the carbon surface. Biosensing probe sequences extending from the DNA tetrahedron can be changed for different target molecules. We demonstrated that the improved SPCE could be applied for the detection of a variety of bioactive molecules. Typically, we designed gap hybridization, aptamer "sandwich" and aptamer competition reduction strategy for the detection of miRNA-141, thrombin, and ATP, respectively. High signal-to-noise ratio, sensitivity, and specificity were obtained for all of these kinds. Especially, the DNA tetrahedron-modified SPCE can work well with serum samples. The carbon-based DNA framework nano-bio interface would expand the use of SPCE and make electrochemical biosensors more available and valuable in clinical diagnosis.

Aiming at the problem of solder joint opening between front wheel cover plate and auxiliary water tank mounting bracket assembly in the process of PAVE test of a vehicle body structure, this paper finds out the failure reason of solder joint through structural stress analysis, and investigates several types of vehicle, combines CAE simulation analysis and vehicle’s own characteristic structure, puts forward two improved design schemes, considers each factor synthetically, and chooses the development scheme. A scheme with better cost and development cycle. The validation of the improved scheme shows that it not only effectively solders the solder joint opening problem between the front wheel cover plate and the auxiliary water tank mounting bracket assembly, meets the vehicle PAVE road test target requirements and improves the vehicle body reliability, but also takes into account the structural weight, setting cost and other factors, which is conducive to the lightweight design of the car body in white, and proves the effectiveness of the improved scheme.

Epigenetic alterations hold great promise as biomarkers for early stage cancer diagnosis. Nevertheless, direct identification of rare methylated DNA in the genome remains challenging. Here, we report an ultrasensitive framework nucleic acid-based electrochemical sensor for quantitative and highly selective analysis of DNA methylation. Notably, we can detect 160 fg of methylated DNA in million-fold unmethylated DNA samples using this electrochemical methylation-specific polymerase chain reaction (E-MSP) method. The high sensitivity of E-MSP enables one-step detection of low-abundance methylation at two different genes in patient serum samples. By using a combination test with two methylation alterations, we achieve high accuracy and sensitivity for reliable differentiation of prostate cancer and benign prostate hypertrophy (BPH). This new method sheds new light on translational use in early cancer diagnosis and in monitoring patients' responses to therapeutic agents.

In this study, we investigated the temporal association between carbapenems usage and antimicrobial resistance among major Gram-negative bacteria, using the data of quarterly carbapenems consumptions and percentages of antibiotic resistance for Gram-negative bacteria from inpatients from 2013 to 2017 in a tertiary hospital from Jiangsu Province, China. First, carbapenems consumption showed an increasing trend in the past 5 years, accompanied with the rising rates of A. baumannii and P. aeruginosa resistance against imipenem. In A. baumannii, we identified correlations between carbapenems consumption and antimicrobial resistance against piperacillin/tazobactam, ceftazidime, ciprofloxacin and imipenem, respectively. Additionally, close correlations were observed between carbapenems consumption and antimicrobial resistance against ceftazidime and ciprofloxacin in E. coli. Our data indicated that a significant positive correlation between the usage of carbapenems and the rate of antimicrobial resistance among A. baumannii and E. coli, respectively. Carbapenems should be cautiously prescribed to prevent antimicrobial resistance outbreak in A. baumannii and E. coli.

The practical applications of ZrC are restricted by its low fracture toughness and resistance to sintering densification. This study reports the in-situ precipitation behavior of (Zr,W)C solid solution precipitates induced by Ti. The (Zr0.7W0.3)C solid solution powder was prepared via carbothermic reduction using pressureless sintering at 2200 °C for 1 h. Varying quantities of TiH2 were reacted with the (Zr0.7W0.3)C matrix, to precipitate the second phase in-situ, via hot-pressing sintering (50 MPa) at 1600 °C for 1 h. When the content of TiH2 was 10 mol%, W and W2C were the second phases precipitated. As the TiH2 concentration increased, the precipitates converted into W2C and the (Ti,Zr)C solid solution. Furthermore, nano-scale dot-like W precipitates gradually increased inside the crystal grains of the (Zr,W,Ti)C matrix. The optimized comprehensive mechanical properties of (Zr,W,Ti)C-based multiphase ceramics were obtained by adding 40 mol% TiH2; the relative density was 99.7%, the Vickers hardness was 24.68 GPa, and the fracture toughness was 5.76 MPa·m1/2. The precipitates (of varying type and concentration) exhibited distinct toughening effects relative to the (Zr,W)C solid solution.

Microplastics (MPs) are emergingly hazardous contaminants which ubiquitously exists in aquatic, terrestrial and atmospheric system, as well as in human system. Coal water slurry (CWS) is an important clean-coal-based liquid fuel with the characteristics of high-efficiency and low-pollution. This paper aims to implement the principles of clean production and prepare CWS by coblending coal particles and polyethylene terephthalate (PET) microplastics. It shows that the presence of PET particles significantly reduces the viscosity and increases the dispersion performance of CWS suspension. The apparent viscosity of CWS tends to decrease with the increase of PET content. Through the contact angle, surface morphology, and zeta potential analyses, particles with stronger hydrophobicity, more spherical shape and higher electronegativity, are more easily to form consistent and dispersed CWS suspensions. The extended DLVO theory demonstrates that the repulsion between solid particles becomes larger when PET particles are added to the slurry, which is beneficial to particle dispersion. In addition, the dispersant interfacial distribution differs between coal and PET particles, and significantly affect the rheological behavior and dispersion mechanism of CWS. This study proposes a potential approach to recycle the hazardous waste MPs and provides theoretical basis for enhancing CWS dispersion performance through coblending with PET microplastic particles.

Correction for ‘A smartphone-based three-in-one biosensor for co-detection of SARS-CoV-2 viral RNA, antigen and antibody’ by Yanzhi Dou et al., Chem. Commun., 2022, DOI: https://doi.org/10.1039/d2cc01297a.

Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel convolutional neural network (CNN)-based framework, ranking-CNN, for age estimation. Ranking-CNN contains a set of basic CNNs, each of which is trained with ordinal age labels. Then, their binary outputs are aggregated for the final age prediction. From a theoretical perspective, we obtain an approximation for the final ranking error, show that it is controlled by the maximum error produced among subranking problems, and thus find a new error bound, which provides helpful guidance for the training and analysis of deep rankers. Based on the new error bound, we theoretically give an explicit formula for the learning of ranking-CNN and demonstrate its convergence using the stochastic approximation method. Moreover, we rigorously prove that ranking-CNN, by considering ordinal relation between ages, is more likely to get smaller estimation errors when compared with multiclass classification approaches. Through extensive experiments, we show that ranking-CNN outperforms other state-of-the-art feature extractors and age estimators on benchmark datasets.

Previously, it was taken for granted that features learned for classification can also be used for ranking. However, ranking problems possess some distinctive properties, e.g., the ordinal class labels, which indicates the necessity of developing new feature learning procedures dedicated for ranking. In this article, the authors propose to use a convolutional neural network (CNN)-based framework, ranking-CNN, for learning and interpreting features to rank. As a case study, the authors propose to analyze, visualize and work to understand the deep aging patterns in human facial images using ranking-CNN. The authors develop a visualization method that can compare the facial appearance and track its changes at different ages through the mapping between 2D images and a 3D face template. The framework provides an innovative way to understand the human facial aging process.

Ultrasensitive biosensing technologies without gene amplification held great promise for direct detection of DNA. Herein we report a novel biosensing method, combining target recycling signal-amplification strategy and a homemade electrochemical device. Especially, the target recycling was achieved by a strand displacement process, no needing the help of any nucleases. In the presence of target DNA, the recycling system could be activated to generate a cascade of assembly steps with three hairpin DNA segments. Each recycling process were accompanied by a disassembly step that the last hairpin DNA segment displaces target DNA from the complex at the end of each circulation, freeing targets to activate the self-assembly of more trefoil DNA structures. This biosensing method could detect target DNA at aM level and can distinguish target DNA from interfering DNAs, demonstrating its high sensitivity and high selectivity. Importantly, the biosensing method could work well with serum samples.

Mycobacterium smegmatis MSMEG_6281, a peptidoglycan (PG) amidase, is essential in maintaining cell wall integrity. To address the potential roles during the MSMEG_6281-mediated biological process, we compared proteomes from wild-type M.smegmatis and MSMEG_6281 gene knockout strain (M.sm-ΔM_6281) using LC-MS/MS analysis. Peptide analysis revealed that 851 proteins were differentially produced with at least 1.2-fold changes, including some proteins involved in fatty acid metabolism such as acyl-CoA synthase, acyl-CoA dehydrogenase, MCE-family proteins, ATP-binding cassette (ABC) transporters, and MmpL4. Some proteins related to fatty acid degradation were enriched through protein-protein interaction analysis. Therefore, proteomic data showed that a lack of MSMEG_6281 affected fatty acid metabolism. Mycobacteria can produce diverse lipid molecules ranging from single fatty acids to highly complex mycolic acids, and mycobacterial surface-exposed lipids may impact biofilm formation. In this study, we also assessed the effects of MSMEG_6281 on biofilm phenotype using semi-quantitative and morphology analysis methods. These results found that M.sm-ΔM_6281 exhibited a delayed biofilm phenotype compared to that of the wild-type M.smegmatis, and the changes were recovered when PG amidase was rescued in a ΔM_6281::Rv3717 strain. Our results demonstrated that MSMEG_6281 impacts fatty acid metabolism and further interferes with biofilm formation. These results provide a clue to study the effects of PG amidase on mycobacterial pathogenicity.

This paper presents a Y type DNA structure-based signal amplification system combined with microarray fluorescent platform for trace amounts of heavy metals analysis, such as lead and mercury in environment. The results show LOD of 0.5 pM and 0.6 pM for Hg2+ and Pb2+; high discriminating ability from some interfering ions such as Mn2+, Cu2+ and Ca2+; the optimal incubation time was only 0.5 hour. Innovative points include: (1) with more robust, target recycling signal amplification method achieved ultrasensitive Hg2+ and Pb2+ detection comparable to enzyme-based method; (2) selected highly specific T-rich DNA aptamer sequences for Hg2+ and G-rich DNA aptamer sequences for Pb2+, and well-designed aptamer-locker sequences for highly selectivity; (3) high-throughput microarray platform with low-priced glass chips for its widely use in the future.

In this work, the addition of isoamyl alcohol and sec-octyl alcohol on the viscosity of coal water slurry (CWS) was investigated and comprehensively analyzed. The surface tension, contact angle, dispersant absorbed amount, the microstructure of the particles as well as surface functional groups of coal powder was impacted by the addition of alcohols in the CWS. The isoamyl alcohol and sec-octyl alcohol lowers the surface tension and also partially absorbs to the coal particle surface, which would reduce the dispersant absorbed amount of the coal surface. The results show that both the isoamyl alcohol and sec-octyl alcohol can effectively reduce the viscosity of CWS under the amount of organic alcohol below to its solubility, and sec-octyl alcohol exhibits preferable performance than isoamyl alcohol. Our results anticipate to provide a convenient way to reduce CWS viscosity and could also be a reference for CWS preparation with such wastewater containing alcohol compounds.

There is little available research on how different transition metal oxides influence the behavior of B4C-based ceramics, especially for Ta2O5 and Nb2O5. B4C-MeB2 (Me = Ti, Zr, Nb, and Ta) multiphase ceramic samples were prepared via in situ pressureless sintering at 2250 &deg, C, involving the mixing of B4C and MeOx powders, namely TiO2, ZrO2, Nb2O5, and Ta2O5. The phase constituents, microstructures, and mechanical properties of the samples were tested. The results indicated that different transition metal elements had different effects on the ceramic matrix, as verified through a comparative analysis. Additionally, the doped WC impurity during the ball milling process led to the production of (Me, W)B2 and W2B5, which brought about changes in morphology and performance. In this study, the Ta2O5-added sample exhibited the best performance, with elastic modulus, flexural strength, Vickers hardness, and fracture toughness values of 312.0 GPa, 16.3 GPa, 313.0 MPa, and 6.08 MPa&middot, m1/2, respectively. The comprehensive mechanical properties were better than the reported values when the mass fraction of the second phase was around five percent.

We have developed a fast, highly sensitive and low-cost biosensing system for the detection of clenbuterol (CLB), using a homemade mobile electrochemical device with an electric field-driven acceleration strategy. This system consists of an embedded circuit in smartphone for signal processing and a screen-printed carbon electrode (SPCE) modified with multi-walled carbon nanotubes (MWNTs) and goat anti mouse-immunoglobulin G (IgG) sensing layer (MWNTs-I-layer). CLB monoclonal antibody was assembled through its binding to the surface-confined antibody. Such modified electrodes were used for rapid and sensitive amperometric immunosensing detection of CLB. Horseradish peroxidase-coupled CLB (CLB–HRP) competed with free CLB in the samples to bind the monoclonal antibody. By using this mobile system, we could detect CLB ranging from 0.3 ng ⋅ mL − 1 to 100 ng ⋅ mL − 1 with the detection limit of 0.076 ng ⋅ mL − 1 . The whole competitive-type detection process was finished within 6 min. We expect this device can meet the requirements for field detection of various food security-related species.

DNA hydroxymethylation (5-hmC) is a kind of new epigenetic modification, which plays key roles in DNA demethylation, genomic reprogramming, and the gene expression in mammals. For further exploring the functions of 5-hmC, it is necessary to develop sensitive and selective methods for detecting 5-hmC. Herein, we developed a novel multiplexing electrochemical (MEC) biosensor for 5-hmC detection based on the glycosylation modification of 5-hmC and enzymatic signal amplification. The 5-hmC was first glycosylated by T4 β-glucosyltransferase and then oxidated by sodium periodate. The resulting glucosyl-modified 5-hmC (5-ghmC) was incubated with ARP-biotin and was bound to avidin-HRP. The 5-hmC can be detected at the subnanogram level. Finally, we performed 5-hmC detection for mouse tissue samples and cancer cell lines. The limit of detection of the MEC biosensor is 20 times lower than that of commercial kits based on optical meaurement. Also, the biosensor presented high detection specificity because the chemical reaction for 5-hmC modification can not happen at any other unhydroxymethylated nucleic acid bases. Importantly, benefited by its multiplexing capacity, the developed MEC biosensor showed excellent high efficiency, which was time-saving and cost less.

Heavy metals are the main factor of water pollution which seriously threaten residents and aquatic life. Here, we demonstrated a mercury ion (Hg2+) sensing device based on gold nanoparticles modified silicon nanowire array for highly sensitive, selective and stable detection of mercury ion. Decorated with gold nanoparticles to increase the number of the thiol groups serving as Hg2+ sensitive probe and improve performance, the silicon nanowire array based sensor had a large response electric signal in detecting low concentration mercury ion. Importantly, the devices exhibits excellent mercury ion sensing attributes in the range of 1 ng/l-10 μg l−1 with a fairly low detection limit of 0.06 pM. With similar accuracy to ICP-MS, we detected a know sample (1 μg l−1) and unknown sample in less than 1 min. Additionally, the low-cost fabricated process and label free analysis in river water sample make it a potential method in inspecting mercury in water quality.

Here, we demonstrate a real-time, ultrasensitive junctionless nanowire transistor (JNT) biosensor for breast tumor marker sensing. The proposed JNT biosensor with low-frequency noise, small subthreshold slope and high channel mobility has great potential to solve the key problem of poor signal-to-noise ratio or low sensitivity in current nanobiosensor. The JNT with CMOS-compatibility is made using a (111) silicon-on-insulator (SOi) wafer with a low-cost and top-down fabrication approach. The fabricated device shows excellent electrical properties and high sensitivity for recognizing breast tumor marker (the CEA protein) as low as 1fg/ml.

In transfer learning, one seeks to transfer related information from source tasks with sufficient data to help with the learning of target task with only limited data. In this paper, we propose a novel Coupled End-to-end Transfer Learning (CETL) framework, which mainly consists of two convolutional neural networks (source and target) that connect to a shared decoder. A novel loss function, the coupled loss, is used for CETL training. From a theoretical perspective, we demonstrate the rationale of the coupled loss by establishing a learning bound for CETL. Moreover, we introduce the generalized Fisher information to improve multi-task optimization in CETL. From a practical aspect, CETL provides a unified and highly flexible solution for various learning tasks such as domain adaption and knowledge distillation. Empirical result shows the superior performance of CETL on cross-domain and cross-task image classification.

Recent research shows that the aging patterns deeply learned from large-scale data lead to significant performance improvement on age estimation. However, the insight about why and how deep learning models achieved superior performance is inadequate. In this paper, we propose to analyze, visualize and understand the deep aging patterns. We first train a series of convolutional neural networks for age estimation, and then illustrate the learning outcomes using feature maps, activation histograms, and deconvolution. We also develop a visualization method that can compare the facial appearance and track its changes at different ages through the mapping between 2D images and a 3D face template. Our framework provides an innovative way to understand human facial aging process from a machine perspective.

Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel Convolutional Neural Network (CNN)-based framework, ranking-CNN, for age estimation. Ranking-CNN contains a series of basic CNNs, each of which is trained with ordinal age labels. Then, their binary outputs are aggregated for the final age prediction. We theoretically obtain a much tighter error bound for ranking-based age estimation. Moreover, we rigorously prove that ranking-CNN is more likely to get smaller estimation errors when compared with multi-class classification approaches. Through extensive experiments, we show that statistically, ranking-CNN significantly outperforms other state-of-the-art age estimation models on benchmark datasets.

This paper presents a low-cost and high-precision pH sensor using silicon nanowire (SiNW) array. With the help of traditional microfabrication technology, low-cost as well as highly controllable SiNW array was fabricated. After functionalized with APTES solution, SiNW array shows rapid and reliable response to pH value. With excellent linearity and repeatability, accuracy of the sensor can be achieved at ± 0.1pH. Further unknown pH value solutions are used to confirm the accuracy of the meter.