MXenes with ordered triatomic-layer borate polyanion terminations.

Surface terminations profoundly influence the intrinsic properties of MXenes, but existing terminations are limited to monoatomic layers or simple groups, showing disordered arrangements and inferior stability. Here we present the synthesis of MXenes with triatomic-layer borate polyanion terminations (OBO terminations) through a flux-assisted eutectic molten etching approach. During the synthesis, Lewis acidic salts act as the etching agent to obtain the MXene backbone, while borax generates BO2- species, which cap the MXene surface with an O-B-O configuration. In contrast to conventional chlorine/oxygen-terminated Nb2C with localized charge transport, OBO-terminated Nb2C features band transport described by the Drude model, exhibiting a 15-fold increase in electrical conductivity and a 10-fold improvement in charge mobility at the d.c. limit. This transition is attributed to surface ordering that effectively mitigates charge carrier backscattering and trapping. Additionally, OBO terminations provide Ti3C2 MXene with substantially enriched Li+-hosting sites and thereby a large charge-storage capacity of 420 mAh g-1. Our findings illustrate the potential of intricate termination configurations in MXenes and their applications for (opto)electronics and energy storage.

The role of long-distance mobile metabolites in the plant stress response and signaling.

Plants developed sophisticated mechanisms to perceive environmental stimuli and generate appropriate signals to maintain optimal growth and stress responses. A fascinating strategy employed by plants is the use of long-distance mobile signals which can trigger local and distant responses across the entire plant. Some metabolites play a central role as long-distance mobile signals allowing plants to communicate across tissues and mount robust stress responses. In this review, we summarize the current knowledge regarding the various long-distance mobile metabolites and their functions in stress response and signaling pathways. We also raise questions with respect to how we can identify new mobile metabolites and engineer them to improve plant health and resilience.

Stability Transition in Gap Expansion-Driven Interfacial Flow.

We investigate interfacial instability in a lifting Hele-Shaw cell by experiments and theory. We characterize the unexplored transition from stable to unstable patterns under a wide range of controlling parameters. Surprisingly, we find that the perturbation growth rate-based criterion for the onset of instability from linear stability theory is too strict by over 3 orders of magnitude. To reconcile this striking discrepancy, we propose a new criterion based on perturbation amplitude, which is in excellent agreement with the experimental results. We further show that the fingering pattern evolves to produce a hierarchical fluid structure and derive a theoretical equation to predict the fingering evolution.

Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer.

OBJECTIVES: Atypical cells (Atyp.C), as a new parameter determined by an automated urine analyzer, can be suspected of being malignant tumor cells. We evaluated the extent to which the Atyp.C can predict the existence of malignant tumor cells. METHODS: A total of 3,315 patients (1,751 in the training cohort and 1,564 in the testing cohort) were recruited and divided into five groups, namely, primary bladder cancer (BCa), recurrent BCa, post-treatment monitoring of BCa, other urological tumors, and controls. Urine Atyp. C, bacteria, white blood cell, and red blood cell were measured by a Sysmex UF-5000 analyzer. We compared the Atyp.C values across the different groups, sexes, and tumor stages. The diagnostic performance of Atyp.C alone and in combination with other parameters for detecting BCa was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The Atyp.C value of the primary BCa group was significantly higher than that in the other groups, except recurrent BCa group. The Atyp.C value was closely related to tumor staging. Atyp.C combined with bacteria had the highest diagnostic performance for primary BCa [training cohort AUC: 0.781 (95 % CI: 0.761-0.801); testing cohort AUC: 0.826 (95 % CI: 0.806-0.845)]. The AUC value of diagnosed recurrent BCa by Atyp.C plus bacteria for the training cohort was 0.784 (95 % CI: 0.762-0.804). CONCLUSIONS: Atyp.C was high in primary BCa patients and the combination of bacteria and Atyp.C showed high predictive value for primary BCa, suggesting that Atyp.C may be a useful objective indicator for the early detection of BCa.

Tubal mesosalpinx cysts combined with adnexal torsion in adolescents: a report of two cases and review of the literature.

BACKGROUND: Tubal mesosalpinx cysts are paratubal cysts, that account for approximately 10% of adnexal masses, and the presence of these cysts combined with adnexal torsion is a rare acute abdominal condition, with few cases reported in the literature. We reported two cases of adolescent tubal mesosalpinx cysts combined with adnexal torsion and reviewed the literature to help improve the diagnosis of the disease. CASE REPORTS: The first patient was an 11-year-old girl with left lower abdominal pain for 5 days and fever with nausea and vomiting for 3 days, who was found to have a cystic pelvic mass on preoperative imaging and was diagnosed intraoperatively and postoperatively on pathology as having a left tubal mesosalpinx cyst combined with adnexal torsion. The second patient was a 13-year-old girl with right lower abdominal pain for 16 h and a palpable mass in the lower and middle abdomen on examination, which was hard and tender to palpate. Preoperative imaging revealed a large cystic mass in the right adnexal region, and intraoperative and postoperative pathology revealed a right tubal mesosalpinx cyst combined with adnexal torsion. CONCLUSIONS: Tubal mesosalpinx cysts combined with adnexal torsion are rare causes of acute lower abdominal pain. Early diagnosis and timely surgery are necessary to ensure ovarian and tubal function. Accurate preoperative imaging diagnosis is challenging, and MRI is a beneficial supplement to ultrasound and CT examinations, providing more objective imaging information and reducing the incidence of adverse outcomes.

Redox-Bipolar Polyimide Two-Dimensional Covalent Organic Framework Cathodes for Durable Aluminium Batteries.

Emerging rechargeable aluminium batteries (RABs) offer a sustainable option for next-generation energy storage technologies with low cost and exemplary safety. However, the development of RABs is restricted by the limited availability of high-performance cathode materials. Herein, we report two polyimide two-dimensional covalent organic frameworks (2D-COFs) cathodes with redox-bipolar capability in RAB. The optimal 2D-COF electrode achieves a high specific capacity of 132 mAh g-1 . Notably, the electrode presents long-term cycling stability (with a negligible ≈0.0007 % capacity decay per cycle), outperforming early reported organic RAB cathodes. 2D-COFs integrate n-type imide and p-type triazine active centres into the periodic porous polymer skeleton. With multiple characterizations, we elucidate the unique Faradaic reaction of the 2D-COF electrode, which involves AlCl2+ and AlCl4 - dual-ions as charge carriers. This work paves the avenue toward novel organic cathodes in RABs.

Characterization of snakehead (Channa argus) interleukin-21: Involvement in immune defense against two pathogenic bacteria, in leukocyte proliferation, and in activation of JAK-STAT signaling pathway.

Interleukin-21 (IL-21), a crucial immune regulatory molecule, belongs to the common γ-chain family of type I cytokines, and exerts pleiotropic effects on multiple immune cell types in mammals. However, the characteristics and functions of fish IL-21 remain unclear. To further investigate the molecular mechanism of IL-21 in teleosts, we first cloned and identified the IL-21 gene (designated shIL-21) of the snakehead (Channa argus). The full-length open reading frame of shIL-21 is 438 bp in length, and encodes a predicted protein of 145 amino acid residues. A sequence analysis showed that shIL-21 has the typical structural characteristics of other IL-21 proteins, containing four α-helices and four conserved cysteine residues. In a phylogenetic analysis, shIL-21 clustered within a subgroup of IL-21 proteins from other teleost species and shared its closest evolutionary relationship with that of Lates calcarifer. The expression analysis showed that shIL-21 was ubiquitously expressed in all the healthy snakehead tissues tested, albeit at different levels. After infection with Nocardia seriolae or Aeromonas schubertii, the relative expression of shIL-21 was mainly upregulated in the head kidney and spleen in vivo. Similarly, after stimulation with the three pathogen analogues lipoteichoic acid, lipopolysaccharides, and polyinosinic-polycytidylic acid, the expression of shIL-21 was also induced in head kidney leukocytes in vitro. A recombinant shIL-21 protein was expressed and purified, and promoted the proliferation of head kidney leukocytes, induced the expression of genes encoding critical signaling molecules in the Janus kinase (JAK) and signal transducer and activator of transcription (STAT) pathway, including JAK1, JAK3, STAT1, and STAT3, and induced the expression of endogenous shIL-21 and genes encoding several key proinflammatory cytokines (tumor necrosis factor-α, interferon-γ, and IL-1ß). Taken together, these preliminary findings suggest that shIL-21 is involved in the immune defense against bacterial infection, in leukocyte proliferation, and in the activation of the JAK-STAT pathway. They thus extend the functional studies of IL-21 in teleosts.

Molecular identification and functional exploration of interleukin-20 in snakehead (Channa argus) involved in bacterial invasion and the proliferation of head kidney leukocytes.

As an inflammatory cytokine of the interleukin-20 (IL-20) subfamily, IL-20 has various functions in immune defenses, inflammatory diseases, tissue regeneration, cancer, and metabolism. Although the characteristics and functions of mammalian IL-20 have been clarified, those of fish IL-20 remain unclear. In this study, the IL-20 gene from the snakehead Channa argus (shIL-20) was cloned and functionally characterized. Similar to the IL-20 homologues of other species, the shIL-20 has a five exon/four intron structure in the coding region. The open reading frame of shIL-20 consists of 528 base pairs and encodes 175 amino acids (aa), including a signal peptide (aa 1-24) and a mature peptide (aa 25-175). The mature shIL-20 protein has six conserved cysteine residues, which occur in the IL-20 proteins of all species analyzed, and an additional cysteine residue (Cys-82) found only in the IL-20 proteins of several teleosts. The modeled tertiary structure of shIL-20 is similar with that of Homo sapiens IL-20. The shIL-20 was expressed constitutively in all the tissues analyzed, and its transcription was induced in the spleen and head kidney by Aeromonas schubertii and Nocardia seriolae in vivo and in head kidney leukocytes (HKLs) by lipoteichoic acid, lipopolysaccharide, and polyinosinic-polycytidylic acid in vitro. The recombinant shIL-20 protein induced the transcription of tumor necrosis factor α1 (TNF-α1), TNF-α2, IL-1ß, and endogenous shIL-20, and promoted the proliferation of HKLs. In conclusion, these findings demonstrate that shIL-20 participates in the immune response to bacterial invasion and promotes leukocyte proliferation, offering new insights into the functions of fish IL-20 during pathogen invasion.

Ionometallurgical Step-Electrodeposition of Zinc and Lead and its Application in a Cycling-Stable High-Voltage Zinc-Graphite Battery.

Ionometallurgy is a new development aiming at the sustainable low-temperature conversion of naturally occurring metal ores and minerals to their metals or valuable chemicals in ionic liquids (ILs) or deep eutectic solvents. The IL betainium bis((trifluoromethyl)sulfonyl)imide, [Hbet][NTf2 ], is especially suited for this process due to its redox-stability and specific-functionalization. The potentiostatic electrodeposition of zinc and lead starting directly from ZnO and PbO, which dissolve in [Hbet][NTf2 ] in high concentrations is reported. The initial reduction potentials of zinc(II) and lead(II) are about -1.5 and -1.0 V, respectively. The ionic conductivity of the solution of ZnO in [Hbet][NTf2 ] is measured and the effect of various temperatures and potentials on the morphology of the deposited material is explored. The IL proves to be stable under the chosen conditions. From IL-solutions, where ZnO, PbO, and MgO have been dissolved, metallic Zn and Pb are deposited under potentiostatic control either consecutively by step-electrodeposition or together in a co-electrodeposition. Using the method, Zn is also deposited on 3D copper foam and assembles into high-voltage zinc-graphite battery. It exhibits a working-voltage up to 2.7 V, an output midpoint discharge-voltage of up to 2.16 V, up to 98.6% capacity-retention after 150 cycles, and good rate performance.

n-type polyaniline hole-blocking layer for high-efficiency QDSC by one-pot electropolymerization and selective aprotic cation ([EMIM]+) doping.

In the field of clean solar-to-current devices, the photoelectron transfer process is essential for photovoltaic conversion in the typical n-i-p solar-cell structure. With regard to the oriented injection and ejection of photoelectrons, the development of hole-blocking layer (HBL) materials with a high electron transfer capability are exceedingly desirable. Profiting from the distortion of the p-π electron cloud attracted by a doped aprotic cation, a modified n-type polyaniline (PANI) as the HBL of a photoanode has been successfully fabricated through a facial one-pot square-wave potentiostatic electropolymerization method. In terms of flat-band potential, charge-carrier concentration and device impedance, the synthesized n-type polyaniline layer doped by aprotic ionic liquid (AIL; [EMIM] [EtSO4]) (AIL-PA layer) for quantum dot solar cells (QDSCs) directly facilitates the high electron carrying capacity as well as the electron transfer driving force. Furthermore, the n-type polyaniline layer doped by AIL ([EMIM] [EtSO4]) (AIL-PA layer) has a widely matching band gap for electron exportation and improved photovoltaic performance of CdSxSe1-x QDSCs: the power conversion efficiency is 10.5% and the J sc is 21.59 mA cm-2 for the device with an AIL-PA HBL. The electron diffusion length L D is 8.07 µm for the photoanode with AIL-PA I and 7.58 µm for the photoanode with AIL-PA II.

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology.

BACKGROUND: Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images. METHODS: In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions. RESULTS: The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. DISCUSSION: Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image. CONCLUSIONS: As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis.

BACKGROUND: The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. METHODS: This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. RESULTS: Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. DISCUSSION: Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. CONCLUSIONS: Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

Electronic Tongue Recognition with Feature Specificity Enhancement.

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.

Construction of TiO2 Nanotubes/C/MnO2 Composite Films as a Binder-Free Electrode for a High-Performance Supercapacitor.

Although titanium dioxide (TiO2) exhibits excellent promise in electrode materials for supercapacitors, its poor conductivity and low areal specific capacitance hamper further development. In this work, we have designed a clever way to deposit manganese dioxide (MnO2) in order to improve its electrochemical performance via a facile and typical hydrothermal method. In a hydrothermal process, carbon (C), which deposited via new gas thermal penetration, acts as a reducing agent, while a potassium permanganate (KMnO4) solution acts as an oxidant. In this way, MnO2, which has a high theoretical capacity, is generated on TiO2 nanotube arrays (denoted as TNTs) successfully. Remarkably, a TNTs/C/MnO2 film prepared at a hydrothermal temperature of 90 °C and 0.3 g of KMnO4 revealed a superior electrochemical property with 55 mF/cm2 areal capacitance at a scan rate of 5 mV/s, 23 times more enhanced than that of a TNTs/C film. Also, the energy density of a TNTs/C/MnO2 film reached 46.8 Wh/cm2 when the power density was 0.12 mW/cm2, and the energy density still remained at 22.4 Wh/cm2 at a high power density of 0.8 mW/cm2. After 1000 cycle tests, 93.2% capacitance was still retained, indicating excellent reversibility and cycle stability of TNTs/C/MnO2 electrode. This work opens up a facile path for efficient growth of electrode materials with high performance for energy storage devices.

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner.

BACKGROUND: The medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs. METHODS: In this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong. RESULTS: For CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved. DISCUSSION: Our semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples. CONCLUSIONS: Experimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

Muscle fatigue detection and treatment system driven by internet of things.

BACKGROUND: Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. METHODS: To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network. RESULTS: With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state. DISCUSSION: Environmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system. CONCLUSIONS: Experiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable.

Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System.

Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter the drift effect in more challenging situations in which the category information (labels) of the drifted samples is difficult or expensive to obtain. Thus, only a few of the drifted samples can be used for label querying. To solve this problem, we propose an innovative methodology based on Active Learning (AL) that selectively provides sample labels for drift correction. Moreover, we utilize a dynamic clustering process to balance the sample category for label querying. In the experimental section, we set up two E-nose drift scenarios-a long-term and a short-term scenario-to evaluate the performance of the proposed methodology. The results indicate that the proposed methodology is superior to the other state-of-art methods presented. Furthermore, the increasing tendencies of parameter sensitivity and accuracy are analyzed. In addition, the Label Efficiency Index (LEI) is adopted to measure the efficiency and labelling cost of the AL methods. The LEI values indicate that our proposed methodology exhibited better performance than the other presented AL methods in the online drift correction of E-noses.

Biotransformation and responses of antioxidant enzymes in hydroponically cultured soybean and pumpkin exposed to perfluorooctane sulfonamide (FOSA).

Perfluorooctane sulfonamide (FOSA) is an important perfluorooctane sulfonate (PFOS) precursor used for commercial applications. In order to investigate the transformation and responses of selected antioxidant and degradation enzymes of FOSA in the plants, in vivo exposure of soybean (Glycine max L. Merrill) and pumpkin (Cucurbita maxima L.) were conducted in the solution-plant microcosms. FOSA was readily taken up by soybean and pumpkin roots and translocated to shoots, and metabolized to PFOS, perfluorohexane sulfonate (PFHxS) and perfluorobutane sulfonate (PFBS). Although morphological and biomass effects were not visible, significant changes in oxidative stress response were observed except for thiobarbituric acid reactive substances (TBARS). Superoxide dismutase (SOD) and peroxidase (POD) activities were significantly increased by 19.2-30.8% and 19.2-20.7% in soybean (8-12 d) respectively, and increased by 39.2-92.8% and 21.1-37.6% in pumpkin (3-12 d) respectively, suggesting an activation of the antioxidant defense system in the plants exposed to FOSA. Glutathione-S-transferase (GST) activities were decreased in soybean (2-12 d) with 9.0-36.1% inhibition and increased in pumpkin (3-12 d) with 22.5-47.3% activation respectively; cytochrome P450 (CYP450) activities were increased markedly in soybean and pumpkin with 13.2-53.6% and 26.7-50.2% activation respectively, giving indirect evidences on the involvement of CYP450 and GST in degradation of FOSA in plants.

Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose.

Gas sensors are the key components of an electronic nose (E-nose) in violated odour analysis. Gas-sensor drift is a kind of physical change on a sensor surface once an E-nose works. The perturbation of gas-sensor responses caused by drift would deteriorate the performance of the E-nose system over time. In this study, we intend to explore a suitable approach to deal with the drift effect in an online situation. Considering that the conventional drift calibration is difficult to implement online, we use active learning (AL) to provide reliable labels for online instances. Common AL learning methods tend to select and label instances with low confidence or massive information. Although this action clarifies the ambiguity near the classification boundary, it is inadequate under the influence of gas-sensor drift. We still need the samples away from the classification plane to represent drift variations comprehensively in the entire data space. Thus, a novel drift counteraction method named AL on adaptive confidence rule (AL-ACR) is proposed to deal with online drift data dynamically. By contrast with conventional AL methods selecting instances near the classification boundary of a certain category, AL-ACR collects instances distributed evenly in different categories. This action implements on an adjustable rule according to the outputs of classifiers. Compared with other reference methods, we adopt two drift databases of E-noses to evaluate the performance of the proposed method. The experimental results indicate that the AL-ACR reaches higher accuracy than references on two E-nose databases, respectively. Furthermore, the impact of the labelling number is discussed to show the trend of performance for the AL-type methods. Additionally, we define the labelling efficiency index (LEI) to assess the contribution of certain labelling numerically. According to the results of LEI, we believe AL-ACR can achieve the best effect with the lowest cost among the AL-type methods in this work.

Marinagarivorans algicola gen. nov., sp. nov., isolated from marine algae.

Two novel agar-degrading, Gram-stain-negative, motile, heterotrophic, facultatively anaerobic and pale yellow-pigmented bacterial strains, designated Z1T and JL1, were isolated from marine algae Gelidium amansii (Lamouroux) and Gracilaria verrucosa, respectively. Growth of the isolates was optimal at 28-30 °C, pH 7.0-7.5 and with 2-3 % (w/v) NaCl. Both strains contained Q-8 as the sole respiratory quinone. The major cellular fatty acids in strain Z1T were C18 : 1ω7c, C16 : 0 and summed feature 3 (C16 : 1ω7c and/or iso-C15 : 0 2-OH). The predominant polar lipids in strain Z1T were phosphatidylethanolamine, phosphatidylglycerol and an aminolipid. The genomic DNA G+C content of both strains was 45.1 mol%. Strains Z1T and JL1 were closely related, with 99.9 % 16S rRNA gene sequence similarity. The average nucleotide identity (ANI) value between strains Z1T and JL1 was 99.3 %. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strains Z1T and JL1 form a distinct phyletic line within the class Gammaproteobacteria, with less than 92.3 % similarity to their closest relatives. Based on data from the current polyphasic study, the isolates are proposed to belong to a novel species of a new genus designated Marinagarivorans algicola gen. nov., sp. nov. The type strain of the type species is Z1T ( = ATCC BAA-2617T = CICC 10859T).