A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors.

Machine learning with deep neural networks (DNNs) is widely used for human activity recognition (HAR) to automatically learn features, identify and analyze activities, and to produce a consequential outcome in numerous applications. However, learning robust features requires an enormous number of labeled data. Therefore, implementing a DNN either requires creating a large dataset or needs to use the pre-trained models on different datasets. Multitask learning (MTL) is a machine learning paradigm where a model is trained to perform multiple tasks simultaneously, with the idea that sharing information between tasks can lead to improved performance on each individual task. This paper presents a novel MTL approach that employs combined training for human activities with different temporal scales of atomic and composite activities. Atomic activities are basic, indivisible actions that are readily identifiable and classifiable. Composite activities are complex actions that comprise a sequence or combination of atomic activities. The proposed MTL approach can help in addressing challenges related to recognizing and predicting both atomic and composite activities. It can also help in providing a solution to the data scarcity problem by simultaneously learning multiple related tasks so that knowledge from each task can be reused by the others. The proposed approach offers advantages like improved data efficiency, reduced overfitting due to shared representations, and fast learning through the use of auxiliary information. The proposed approach exploits the similarities and differences between multiple tasks so that these tasks can share the parameter structure, which improves model performance. The paper also figures out which tasks should be learned together and which tasks should be learned separately. If the tasks are properly selected, the shared structure of each task can help it learn more from other tasks.

Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation.

The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset-Sleep-EDFX-to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93-0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors.

The perception of hunger and satiety is of great importance to maintaining a healthy body weight and avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due to malnutrition. There are a number of disease patterns, characterized by a chronic loss of this perception. To our best knowledge, hunger and satiety cannot be classified using non-invasive measurements. Aiming to develop an objective classification system, this paper presents a multimodal sensory system using associated signal processing and pattern recognition methods for hunger and satiety detection based on non-invasive monitoring. We used an Empatica E4 smartwatch, a RespiBan wearable device, and JINS MEME smart glasses to capture physiological signals from five healthy normal weight subjects inactively sitting on a chair in a state of hunger and satiety. After pre-processing the signals, we compared different feature extraction approaches, either based on manual feature engineering or deep feature learning. Comparative experiments were carried out to determine the most appropriate sensor channel, device, and classifier to reliably discriminate between hunger and satiety states. Our experiments showed that the most discriminative features come from three specific sensor modalities: Electrodermal Activity (EDA), infrared Thermopile (Tmp), and Blood Volume Pulse (BVP).

AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature Review.

General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl's assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

Self-Nanoemulsifying Drug Delivery System (SNEDDS) for Improved Oral Bioavailability of Chlorpromazine: In Vitro and In Vivo Evaluation.

Background and Objectives: Lipid-based self-nanoemulsifying drug delivery systems (SNEDDS) have resurged the eminence of nanoemulsions by modest adjustments and offer many valuable opportunities in drug delivery. Chlorpromazine, an antipsychotic agent with poor aqueous solubility-with extensive first-pass metabolism-can be a suitable candidate for the development of SNEDDS. The current study was designed to develop triglyceride-based SNEDDS of chlorpromazine to achieve improved solubility, stability, and oral bioavailability. Materials and Methods: Fifteen SNEDDS formulations of each short, medium, and long chain, triglycerides were synthesized and characterized to achieve optimized formulation. The optimized formulation was characterized for several in vitro and in vivo parameters. Results: Particle size, zeta potential, and drug loading of the optimized SNEDDS (LCT14) were found to be 178 ± 16, -21.4, and 85.5%, respectively. Long chain triglyceride (LCT14) showed a 1.5-fold increased elimination half-life (p < 0.01), up to 6-fold increased oral bioavailability, and 1.7-fold decreased plasma clearance rate (p < 0.01) compared to a drug suspension. Conclusion: The findings suggest that SNEDDS based on long-chain triglycerides (LCT14) formulations seem to be a promising alternative for improving the oral bioavailability of chlorpromazine.

Corynebacterium glutamicum methionine sulfoxide reductase A uses both mycoredoxin and thioredoxin for regeneration and oxidative stress resistance.

Oxidation of methionine leads to the formation of the S and R diastereomers of methionine sulfoxide (MetO), which can be reversed by the actions of two structurally unrelated classes of methionine sulfoxide reductase (Msr), MsrA and MsrB, respectively. Although MsrAs have long been demonstrated in numerous bacteria, their physiological and biochemical functions remain largely unknown in Actinomycetes. Here, we report that a Corynebacterium glutamicum methionine sulfoxide reductase A (CgMsrA) that belongs to the 3-Cys family of MsrAs plays important roles in oxidative stress resistance. Deletion of the msrA gene in C. glutamicum resulted in decrease of cell viability, increase of ROS production, and increase of protein carbonylation levels under various stress conditions. The physiological roles of CgMsrA in resistance to oxidative stresses were corroborated by its induced expression under various stresses, regulated directly by the stress-responsive extracytoplasmic-function (ECF) sigma factor SigH. Activity assays performed with various regeneration pathways showed that CgMsrA can reduce MetO via both the thioredoxin/thioredoxin reductase (Trx/TrxR) and mycoredoxin 1/mycothione reductase/mycothiol (Mrx1/Mtr/MSH) pathways. Site-directed mutagenesis confirmed that Cys56 is the peroxidatic cysteine that is oxidized to sulfenic acid, while Cys204 and Cys213 are the resolving Cys residues that form an intramolecular disulfide bond. Mrx1 reduces the sulfenic acid intermediate via the formation of an S-mycothiolated MsrA intermediate (MsrA-SSM) which is then recycled by mycoredoxin and the second molecule of mycothiol, similarly to the glutathione/glutaredoxin/glutathione reductase (GSH/Grx/GR) system. However, Trx reduces the Cys204-Cys213 disulfide bond in CgMsrA produced during MetO reduction via the formation of a transient intermolecular disulfide bond between Trx and CgMsrA. While both the Trx/TrxR and Mrx1/Mtr/MSH pathways are operative in reducing CgMsrA under stress conditions in vivo, the Trx/TrxR pathway alone is sufficient to reduce CgMsrA under normal conditions. Based on these results, a catalytic model for the reduction of CgMsrA by Mrx1 and Trx is proposed.

Biosorption characteristics of Aspergillus fumigatus for the decolorization of triphenylmethane dye acid violet 49.

This study focuses on the possible use of Aspergillus fumigatus to remove acid violet 49 dye (AV49) from aqueous solution. In batch biosorption experiments, the highest biosorption efficiency was achieved at pH 3.0, with biosorbent dosage of 3.0 gL(-1) within about 30 min at 40 °C. The Langmuir and Freundlich models were able to describe the biosorption equilibrium of AV49 onto fungal biomass with maximum dye uptake capacity 136.98 mg g(-1). Biosorption followed a pseudo-second-order kinetic model with high correlation coefficients (R (2) > 0.99), and the biosorption rate constants increased with increasing temperature. Thermodynamic parameters indicated that the biosorption process was favorable, spontaneous, and endothermic in nature, with insignificant entropy changes. Fourier transform infrared spectroscopy strongly supported the presence of several functional groups responsible for dye-biosorbent interaction. Fungal biomass was regenerated with 0.1 M sodium hydroxide and could be reused a number of times without significant loss of biosorption activity. The effective decolorization of AV49 in simulated conditions indicated the potential use of biomass for the removal of color contaminants from wastewater.

Enhancing Corynebacterium glutamicum robustness by over-expressing a gene, mshA, for mycothiol glycosyltransferase.

Over-expression of the gene, mshA, coding for mycothiol glycosyl transferase improved the robustness of Corynebacterium glutamicum to various stresses. Intracellular mycothiol (MSH) content was increased by 114 % in WT(pXMJ19-mshA) compared to WT(pXMJ19). Survival rates increased by 44, 39, 90, 77, 131, 87, 52, 47, 57, 85 and 33 % as compared to WT(pXMJ19) under stress by H2O2 (40 mM), methylglyoxal (5.8 mM), erythromycin (0.08 mg ml(-1)), streptomycin (0.005 mg ml(-1)), Cd(2+) (0.01 mM), Mn(2+) (2 mM), formic acid (0.05 %), acetic acid (0.15 %), levulinic acid (0.25 %), furfural (7.2 mM), and ethanol (10 % v/v), respectively. Increased MSH content also decreased the concentration of reactive oxygen species in the presence of the above stresses. Our results may open a new avenue for enhancing robustness of industrial bacteria for production of commodity chemicals.

An Ultrasoft and Flexible PDMS-Based Balloon-Type Implantable Device for Controlled Drug Delivery.

Non-biodegradable implants have undergone extensive investigation as drug delivery devices to enable advanced healthcare toward personalized medicine. However, fibroblast encapsulation is one of the major challenges in all non-biodegradable implants, besides other challenges such as high initial burst, risk of membrane rupture, high onset time, non-conformal contact with tissues, and tissue damage. To tackle such challenges, we propose a novel ultrasoft and flexible balloon-type drug delivery device for unidirectional and long-term controlled release. The ultrasoft balloon-type device (USBD) was fabricated by using selective bonding between 2 polydimethylsiloxane (PDMS) membranes and injecting a fluid into the non-bonded area between them. The balloon acted as a reservoir containing a liquid drug, and at the same time, the membrane of the balloon itself acted as the pathway for release based on diffusion. The release was modulated by tuning the thickness and composition of the PDMS membrane. Regardless of the thickness and composition, all devices exhibited zero-order release behavior. The longest zero-order release and nearly zero-order release were achieved for 30 days and 58 days at a release rate of 1.16 µg/day and 1.68 µg/day, respectively. In vivo evaluation was performed for 35 days in living rats, where the USBD maintained zero-order and nearly zero-order release for 28 days and 35 days, respectively. Thanks to the employment of ultrasoft and flexible membranes and device design, the USBD could achieve minimal tissue damage and foreign body responses. It is expected that the proposed device may provide a novel approach for long-term drug delivery with new therapeutic modalities.

Symmetric Projection Attractor Reconstruction Analysis as a Method to Assess Seismocardiogram Quality in a Healthy Population.

Signal quality significantly affects the processing, analysis, and interpretation of biomedical signals. There are many procedures for assessing signal quality that use averaged numerical values, thresholding, analysis in the time or frequency domain, or nonlinear approaches. An interesting approach to the assessment of signal quality is using symmetric projection attractor reconstruction (SPAR) analysis, which transforms an entire signal into a two-dimensional plot that reflects the waveform morphology. In this study, we present an application of SPAR to evaluate the quality of seismocardiograms (SCG signals) from the CEBS database, a publicly available seismocardiogram signal database. Visual inspection of symmetric projection attractors suggests that high-quality (clean) seismocardiogram projections resemble six-pointed asterisks (*), and any deviation from this shape suggests the influence of noise and artifacts.Clinical relevance- SPAR analysis enables quick identification of noise and artifacts that can affect the reliability of the diagnosis of cardiovascular diseases based on SCG signals.

Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning.

Sleep is an important research area in nutritional medicine that plays a crucial role in human physical and mental health restoration. It can influence diet, metabolism, and hormone regulation, which can affect overall health and well-being. As an essential tool in the sleep study, the sleep stage classification provides a parsing of sleep architecture and a comprehensive understanding of sleep patterns to identify sleep disorders and facilitate the formulation of targeted sleep interventions. However, the class imbalance issue is typically salient in sleep datasets, which severely affects classification performances. To address this issue and to extract optimal multimodal features of EEG, EOG, and EMG that can improve the accuracy of sleep stage classification, a Borderline Synthetic Minority Oversampling Technique (B-SMOTE)-Based Supervised Convolutional Contrastive Learning (BST-SCCL) is proposed, which can avoid the risk of data mismatch between various sleep knowledge domains (varying health conditions and annotation rules) and strengthening learning characteristics of the N1 stage from the pair-wise segments comparison strategy. The lightweight residual network architecture with a novel truncated cross-entropy loss function is designed to accommodate multimodal time series and boost the training speed and performance stability. The proposed model has been validated on four well-known public sleep datasets (Sleep-EDF-20, Sleep-EDF-78, ISRUC-1, and ISRUC-3) and its superior performance (overall accuracy of 91.31-92.34%, MF1 of 88.21-90.08%, and Cohen's Kappa coefficient k of 0.87-0.89) has further demonstrated its effectiveness. It shows the great potential of contrastive learning for cross-domain knowledge interaction in precision medicine.

Characterization and applications of glutaminase free L-asparaginase from indigenous Bacillus halotolerans ASN9.

L-asparaginase (L-ASNase) is a versatile anticancer and acrylamide reduction enzyme predominantly used in medical and food industries. However, the high specificity of L-asparaginase formulations for glutamine, low thermostability, and blood clearance are the major disadvantages. Present study describes production, characterization, and applications of glutaminase free extracellular L-asparaginase from indigenous Bacillus halotolerans ASN9 isolated from soil sample. L-asparaginase production was optimized in M9 medium (containing 0.2% sucrose and 1% L-asparagine) that yielded maximum L-ASNase with a specific activity of 256 U mg-1 at pH 6 and 37°C. L-asparaginase was purified through acetone precipitation and Sephadex G-100 column, yielding 48.9 and 24% recovery, respectively. Enzyme kinetics revealed a Vmax of 466 mM min-1 and Km of 0.097 mM. Purified L-ASNase showed no activity against glutamine. The purified glutaminase free L-ASNase has a molecular mass of 60 kDa and an optimum specific activity of 3083 U mg-1 at pH 7 and 37°C. The enzyme retains its activity and stability over a wide range of pH and temperature, in the presence of selected protein inhibitors (SDS, ß-mercaptoethanol), CoCl2, KCl, and NaCl. The enzyme also exhibited antioxidant activity against DPPH radical (IC50 value 70.7 µg mL-1) and anticancer activity against U87 human malignant glioma (IC50 55 µg mL-1) and Huh7 human hepatocellular carcinoma (IC50 37 µg mL-1) cell lines. Normal human embryonic kidney cells (HEK293) had greater than 80% cell viability with purified L-ASNase indicating its least cytotoxicity against normal cells. The present work identified potent glutaminase free L-ASNase from B. halotolerans ASN9 that performs well in a wide range of environmental conditions indicating its suitability for various commercial applications.

Wearable-based human flow experience recognition enhanced by transfer learning methods using emotion data.

BACKGROUND: Flow experience is a specific positive and affective state that occurs when humans are completely absorbed in an activity and forget everything else. This state can lead to high performance, well-being, and productivity at work. Few studies have been conducted to determine the human flow experience using physiological wearable sensor devices. Other studies rely on self-reported data. METHODS: In this article, we use physiological data collected from 25 subjects with multimodal sensing devices, in particular the Empatica E4 wristband, the Emotiv Epoc X electroencephalography (EEG) headset, and the Biosignalplux RespiBAN - in arithmetic and reading tasks to automatically discriminate between flow and non-flow states using feature engineering and deep feature learning approaches. The most meaningful wearable device for flow detection is determined by comparing the performances of each device. We also investigate the connection between emotions and flow by testing transfer learning techniques involving an emotion recognition-related task on the source domain. RESULTS: The EEG sensor modalities yielded the best performances with an accuracy of 64.97%, and a macro Averaged F1 (AF1) score of 64.95%. An accuracy of 73.63% and an AF1 score of 72.70% were obtained after fusing all sensor modalities from all devices. Additionally, our proposed transfer learning approach using emotional arousal classification on the DEAP dataset led to an increase in performances with an accuracy of 75.10% and an AF1 score of 74.92%. CONCLUSION: The results of this study suggest that effective discrimination between flow and non-flow states is possible with multimodal sensor data. The success of transfer learning using the DEAP emotion dataset as a source domain indicates that emotions and flow are connected, and emotion recognition can be used as a latent task to enhance the performance of flow recognition.

The Symphony of Team Flow in Virtual Teams. Using Artificial Intelligence for Its Recognition and Promotion.

More and more teams are collaborating virtually across the globe, and the COVID-19 pandemic has further encouraged the dissemination of virtual teamwork. However, there are challenges for virtual teams - such as reduced informal communication - with implications for team effectiveness. Team flow is a concept with high potential for promoting team effectiveness, however its measurement and promotion are challenging. Traditional team flow measurements rely on self-report questionnaires that require interrupting the team process. Approaches in artificial intelligence, i.e., machine learning, offer methods to identify an algorithm based on behavioral and sensor data that is able to identify team flow and its dynamics over time without interrupting the process. Thus, in this article we present an approach to identify team flow in virtual teams, using machine learning methods. First of all, based on a literature review, we provide a model of team flow characteristics, composed of characteristics that are shared with individual flow and characteristics that are unique for team flow. It is argued that those characteristics that are unique for team flow are represented by the concept of collective communication. Based on that, we present physiological and behavioral correlates of team flow which are suitable - but not limited to - being assessed in virtual teams and which can be used as input data for a machine learning system to assess team flow in real time. Finally, we suggest interventions to support team flow that can be implemented in real time, in virtual environments and controlled by artificial intelligence. This article thus contributes to finding indicators and dynamics of team flow in virtual teams, to stimulate future research and to promote team effectiveness.

ZntR positively regulates T6SS4 expression in Yersinia pseudotuberculosis.

The type VI secretion system (T6SS) is a widespread and versatile protein secretion system found in most Gram-negative bacteria. Studies of T6SS have mainly focused on its role in virulence toward host cells and inter-bacterial interactions, but studies have also shown that T6SS4 in Yersinia pseudotuberculosis participates in the acquisition of zinc ions to alleviate the accumulation of hydroxyl radicals induced by multiple stressors. Here, by comparing the gene expression patterns of wild-type and zntR mutant Y. pseudotuberculosis cells using RNA-seq analysis, T6SS4 and 17 other biological processes were found to be regulated by ZntR. T6SS4 was positively regulated by ZntR in Y. pseudotuberculosis, and further investigation demonstrated that ZntR regulates T6SS4 by directly binding to its promoter region. T6SS4 expression is regulated by zinc via ZntR, which maintains intracellular zinc homeostasis and controls the concentration of reactive oxygen species to prevent bacterial death under oxidative stress. This study provides new insights into the regulation of T6SS4 by a zinc-dependent transcriptional regulator, and it provides a foundation for further investigation of the mechanism of zinc transport by T6SS.

Molecular characterization of a eukaryotic-like phenol hydroxylase from Corynebacterium glutamicum.

This study focuses on the genetic and biochemical characterization of phenol hydroxylase (Phe, NCgl2588) from Corynebacterium glutamicum that shares 31% identity in amino acids with phenol hydroxylase from yeast Trichosporon cutaneum but less similarity with that from bacteria. The phe deletion mutant significantly reduced its ability to grow with phenol as the sole carbon and energy source. Expression of the phe gene was strongly induced with phenol and also subject to the control of carbon catabolite repression (CCR). The molecular weight of purified Phe protein determined by gel filtration chromatography was 70 kDa, indicating that Phe exists as a monomer in the purification condition. However, Phe protein pre-incubated with phenol showed a molecular weight of 140 kDa, suggesting that Phe is likely active as a dimer. In addition to phenol, the Phe protein could utilize various other phenolic compounds as substrates. Site-directed mutagenesis revealed that D75, P261, R262, R269, C349 and C476 are key amino acid residues closely related to the enzyme activity of Phe.

Ohr Protects Corynebacterium glutamicum against Organic Hydroperoxide Induced Oxidative Stress.

Ohr, a bacterial protein encoded by the Organic Hydroperoxide Resistance (ohr) gene, plays a critical role in resistance to organic hydroperoxides. In the present study, we show that the Cys-based thiol-dependent Ohr of Corynebacterium glutamicum decomposes organic hydroperoxides more efficiently than hydrogen peroxide. Replacement of either of the two Cys residues of Ohr by a Ser residue resulted in drastic loss of activity. The electron donors supporting regeneration of the peroxidase activity of the oxidized Ohr of C. glutamicum were principally lipoylated proteins (LpdA and Lpd/SucB). A Δohr mutant exhibited significantly decreased resistance to organic hydroperoxides and marked accumulation of reactive oxygen species (ROS) in vivo; protein carbonylation was also enhanced notably. The resistance to hydrogen peroxide also decreased, but protein carbonylation did not rise to any great extent. Together, the results unequivocally show that Ohr is essential for mediation of organic hydroperoxide resistance by C. glutamicum.

Functional characterization of Corynebacterium glutamicum mycothiol S-conjugate amidase.

The present study focuses on the genetic and biochemical characterization of mycothiol S-conjugate amidase (Mca) of Corynebacterium glutamicum. Recombinant C. glutamicum Mca was heterologously expressed in Escherichia coli and purified to apparent homogeneity. The molecular weight of native Mca protein determined by gel filtration chromatography was 35 kDa, indicating that Mca exists as monomers in the purification condition. Mca showed amidase activity with mycothiol S-conjugate of monobromobimane (MSmB) in vivo while mca mutant lost the ability to cleave MSmB. In addition, Mca showed limited deacetylase activity with N-acetyl-D-glucosamine (GlcNAc) as substrate. Optimum pH for amidase activity was between 7.5 and 8.5, while the highest activity in the presence of Zn2+ confirmed Mca as a zinc metalloprotein. Amino acid residues conserved among Mca family members were located in C. glutamicum Mca and site-directed mutagenesis of these residues indicated that Asp14, Tyr137, His139 and Asp141 were important for activity. The mca deletion mutant showed decreased resistance to antibiotics, alkylating agents, oxidants and heavy metals, and these sensitive phenotypes were recovered in the complementary strain to a great extent. The physiological roles of Mca in resistance to various toxins were further supported by the induced expression of Mca in C. glutamicum under various stress conditions, directly under the control of the stress-responsive extracytoplasmic function-sigma (ECF-σ) factor SigH.

Genetic characterization of 4-cresol catabolism in Corynebacterium glutamicum.

Corynebacterium glutamicum uses 4-cresol as sole carbon source for growth. Protocatechuate 3,4-dioxygenase activity had been detected when C. glutamicum was grown with 4-cresol. In this work, we found that 4-cresol was catabolized via 4-hydroxybenzoate and protocatechuate as intermediate metabolites, and a genetic cluster called cre (designated for 4-cresol, creABCDEFGHIR, tagged as ncgl0521-ncgl0531 in NCBI) was identified. The cre gene cluster comprises of 11 genes, and six of them were experimentally confirmed to be involving in 4-cresol catabolism. The genes creD, creE, and creJ were involved in oxidation of 4-cresol into 4-hydroxybenzyl alcohol. The creD encoded a protein showing Mg(2+)-dependent phosphohydrolase activity. The genes creE, creF, creJ encoded a putative P450 system. The creG encoded a NAD(+)-dependent dehydrogenase and catalyzed 4-hydroxybenzyl alcohol to 4-hydroxybenzaldehyde. Two other genes creH and creI were involved in conversion of 4-hydroxybenzyl alcohol to 4-hydroxybenzoate, but their catalytic function is still unknown. Similar genetic clusters with high DNA sequence identity were identified in Arthrobacter and additional Corynebacterium species, suggesting that this genetic organization for 4-cresol catabolism might be more widely distributed in Gram-positive bacteria.