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• Influence of salinity stress and serotonin was examined on Brassica carinata. • Application of serotonin improved plant growth, foliar functions, water use efficiency and nitrate reductase under salt stress conditions. • Upregulation of antioxidant enzymes evoked by salt stress situation. • Serotonin application fortified the tolerance power of the studied plant. Every year, high salinity reduces agricultural crop yields by a substantial amount due to osmotic and ionic impacts that prevent plants from absorbing the required water and promote the hyperaccumulation of ions, which enter the transpiration stream and injure plant cells. Thus, in general, the effects of salinity stress on plant physio-chemicals functions are well known, but little has been explored on how serotonin combats abiotic stress in Ethiopian mustard (Brassica carinata). In this study, the effect of serotonin (SR; 200 µmol L−1) on Ethiopian mustard under salt stress (SS; 0, 50, 100 and 150 mM) was examined. Results exhibited that all SS levels tested in this trial were able to offer a significant reduction of plant growth parameters, chlorophyll contents, gas exchange, water use efficiency, fv/fm and nitrate reductase activity (NR) and increased the antioxidants and proline contents. However, the foliar application of SR significantly ameliorated the influence of all SS levels by improving gas exchange parameters, chlorophyll contents, PSll photochemical efficiency, antioxidant activities, proline, NR, water use efficiency and growth parameters. Based on these results, it is recommended that SR application is useful to uplift the growth, yield and physio-chemical mechanisms of Ethiopian mustard. Furthermore, the SR application can be carried out on various traditional crops under different climate conditions for sustainable agriculture production. [Display omitted] [ABSTRACT FROM AUTHOR]

Objective: This paper used three feature selection methods on a Jordanian automobile drivers' dataset to identify the most significant features for stress prediction algorithm performance. The dataset contains "stress" and "no-stress" classes with 30 features, categorised into physiological and contextual subsets. Methods: Eighteen classifiers from six prediction algorithm categories were evaluated: Rule-based, Tree-based, Ensemble-based, Function-based, Naïve Bayes-based and Lazy-based. Three Feature Subset Selection (FSS) methods were used: Gain Ratio, Chi-square and feature separation. Eight evaluation measures included F 1, Accuracy, Specificity, Sensitivity, Kappa Statistics, Mean Absolute Error (MAE), Area Under Curve (AUC) and Precision Recall Curve Area (PRCA). Results: Among the classifiers, Lazy-based LocalKNN performed significantly well in F 1, Accuracy, Kappa and MAE. Naïve Bayes-based Bayesian Network excelled in other measures. The original dataset with all features yielded the best overall performance, followed by the physiological-only subset. Gain Ratio and Chi-square FSS methods also showed promising results, though not significant. Conclusion: Four physiological (EMG, EMG Amplitude, Heart rate, Respiration Amplitude) and seven contextual (time range of driving, gender, age, driving skills, general accidents, last year's accidents, stress frequency) features contributed to the best prediction outcomes. The study highlights the importance of proper feature selection and identifies optimal algorithms for specific measures. [ABSTRACT FROM AUTHOR]

Agriculture is grappling with water scarcity, leading to diminished crop yields, economic challenges, environmental degradation, and threats to food security. The future of agriculture hinges on the implementation of sustainable water resource management and adaptation strategies. Specifically, in arid regions, the adoption of water-efficient irrigation practices is crucial for fruit growers. This approach not only helps in conserving water but also reduces costs and ensures the vitality of orchards. The cultivation of fruit trees, especially in water-stressed areas, demands meticulous irrigation management for survival. Growers play a key role in identifying stress indicators that serve as crucial markers for monitoring tree health. Proactively addressing these indicators enables growers to maintain healthier trees, resulting in higher yields. Identifying and understanding fruit tree stress indicators play a pivotal role in enhancing orchard management practices, ultimately contributing to increased yields, cost reduction, and the promotion of sustainability. This review evaluates the effectiveness of stress indicators in gauging water stress levels. It delves into the multifaceted impacts of water scarcity on agriculture and underscores the recommendation for water-efficient irrigation practices to counteract fluctuations in water availability. In environments facing water scarcity, vigilant irrigation management coupled with the identification of stress indicators strengthens the resilience and productivity of fruit tree agriculture. This study contributes valuable insights to the ongoing discourse on sustainable horticulture in the face of a changing world. [ABSTRACT FROM AUTHOR]

This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model's R2 of 0.48 and MAE of 16.62. The extended model's feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers. [ABSTRACT FROM AUTHOR]

Fentanyl transdermal therapy is a suitable treatment for moderate-to-severe cancer-related pain. The inter-individual variability of the patients leads to different therapy responses. This study aims to determine the effect of physiological features on the achieved pain relief. Therefore, a set of virtual patients was developed by using Markov chain Monte Carlo (MCMC) based on actual patient data. The members of this virtual population differ by age, weight, gender, and height. Tailored digital twins were developed using these correlated, individualized parameters to propose a personalized therapy for each patient. It was shown that patients of different ages, weights, and gender have significantly different fentanyl blood uptake, plasma fentanyl concentration, pain relief, and ventilation rate. In the digital twins, we included the virtual patients' response to the treatment, namely, pain relief. Therefore, the digital twin was able to adjust the therapy in silico to have more efficient pain relief. By implementing digital-twin-assisted therapy, the average pain intensity decreased by 16% compared to conventional therapy. The median time without pain increased by 23 h over 72 h. Therefore, the digital twin can be successfully used in individual control of transdermal therapy to reach higher pain relief and maintain steady pain relief. (Created with BioRender.com) [ABSTRACT FROM AUTHOR]

The dromedary camel is mainly found in the tropics of Africa, the Middle East, and the Indian sub-content, where it is of great importance to nomadic and rural communities to provide high-quality animal protein in the form of milk and meat and as a means of transportation. In the Arabian Gulf region, the camel gained popularity and importance recently as a racing animal. The camel is well adapted to harsh environments characterized by inadequate water and vegetation, high ambient temperature, and rough terrain. Camels are working animals suited to their desert habitat and are a vital means of transport for humans and cargo, especially for rural farmers. There are three surviving species of camel. Camels originated in North America and eventually spread across Beringia to Asia. They survived in the Old World, and eventually, humans domesticated them and spread them globally. However, the literature on the physiological parameters of the camel is scanty. This review aims to provide a general overview of the physiological features of the camel and an update on the available studies of physiological and behavioural features of the camel and the effects of thermal environmental conditions on some physiological responses.

Breathing can be measured in a non-contact method using a thermal camera. The objective of this study investigates non-contact breathing measurements using thermal cameras, which have previously been limited to measuring the nostril only from the front where it is clearly visible. The previous method is challenging to use for other angles and frontal views, where the nostril is not well-represented. In this paper, we defined a new region called the breathing-associated-facial-region (BAFR) that reflects the physiological characteristics of breathing, and extract breathing signals from views of 45 and 90 degrees, including the frontal view where the nostril is not clearly visible. Experiments were conducted on fifteen healthy subjects in different views, including frontal with and without nostril, 45-degree, and 90-degree views. A thermal camera (A655sc model, FLIR systems) was used for non-contact measurement, and biopac (MP150, Biopac-systems-Inc) was used as a chest breathing reference. The results showed that the proposed algorithm could extract stable breathing signals at various angles and views, achieving an average breathing cycle accuracy of 90.9% when applied compared to 65.6% without proposed algorithm. The average correlation value increases from 0.587 to 0.885. The proposed algorithm can be monitored in a variety of environments and extract the BAFR at diverse angles and views.

The utilization of beneficial endophytic microorganisms presents a promising and innovative strategy for attaining environmental sustainability and fostering development. The majority of microbial bioagents are unsuitable for preparation in a suitable granular formula, and few are prepared in complicated formulas. In this work, Trichoderma viride was simply prepared in a marketable granular formula to manage Rhizoctonia solani and improve common bean growth. The GC-MS analysis showed several antimicrobial compounds in the fungal filtrate. T. viride was able to suppress the phytopathogenic R. solani in the laboratory. The formula had up to 6 months of shelf-life viability. Under greenhouse conditions, the formula improved plant resistance against R. solani. Moreover, the vegetative plant growth and physiological performance (peroxidase, polyphenol, total phenols, phenylalanine ammonia-lyase, and photosynthetic pigments) of the common bean showed obvious promotion. The formula reduced the disease incidence by 82.68% and increased the yield by 69.28%. This work may be considered a step in the right direction for producing simple bioactive products on a large scale. Moreover, the study's findings suggest that this method can be considered a novel approach to enhancing plant growth and protection, in addition to reducing costs, improving handling and application, and maintaining fungal viability for enhancing plant growth and protecting against fungal infections. [ABSTRACT FROM AUTHOR]

To predict whether a driver is fatigued, a fatigue prediction algorithm based on the fusion of improved optical flow features and microfeatures was proposed. By improving the Main Directional Mean Optical-flow (MDMO) algorithm to extract facial microfeatures, the spatial and temporal features of facial micro-expressions were obtained without loss of feature information. Simultaneously, a remote photoplethysmography-based physiological feature detection algorithm was applied to extract the facial region of interest of the driver. The algorithm improves the convenience and accuracy of heart rate variability signal extraction. Then, the physiological characteristics and micro-expression characteristics were input to the long short-term memory to obtain the corresponding timing characteristics. Finally, the two time-series features were fused to predict the fatigue state of the driver. The experiment used the CASME II video face database which contains a large number of spontaneous and dynamic micro-expressions, the self-built 20 face video database, and the actual detection items in a fatigue state for 1 min each. The experimental results show that the improved MDMO optical flow algorithm is accurate for micro-expression recognition, with a rate of 75.2%, which is 7.83% higher than that of the traditional optical flow algorithm. In the case of microfeature fusion, the accuracy of driver fatigue prediction is as high as 95.24%, showing acceptable results in the prediction of driver fatigue. [ABSTRACT FROM AUTHOR]

The adequate characterization of pain is critical in diagnosis and therapy selection, and currently is subjectively assessed by patient communication and self-evaluation. Thus, pain recognition and assessment have been a target of study in past years due to the importance of objective measurement. The goal of this work is the analysis of the electrocardiogram (ECG) under emotional contexts and reasoning on the physiological classification of pain under neutral and fear conditions. Using data from both contexts for pain classification, a balanced accuracy of up to 97.4% was obtained. Using an emotionally independent approach and using data from one emotional context to learn pain and data from the other to evaluate the models, a balanced accuracy of up to 97.7% was reached. These similar results seem to support that the physiological response to pain was maintained despite the different emotional contexts. Attempting a participant-independent approach for pain classification and using a leave-one-out cross-validation strategy, data from the fear context were used to train pain classification models, and data from the neutral context were used to evaluate the performance, achieving a balanced accuracy of up to 94.9%. Moreover, across the different learning strategies, Random Forest outperformed the remaining models. These results show the feasibility of identifying pain through physiological characteristics of the ECG response despite the presence of autonomic nervous system perturbations. [ABSTRACT FROM AUTHOR]

The dromedary camel is mainly found in the tropics of Africa, the Middle East, and the Indian sub-content, where it is of great importance to nomadic and rural communities to provide high-quality animal protein in the form of milk and meat and as a means of transportation. In the Arabian Gulf region, the camel gained popularity and importance recently as a racing animal. The camel is well adapted to harsh environments characterized by inadequate water and vegetation, high ambient temperature, and rough terrain. Camels are working animals suited to their desert habitat and are a vital means of transport for humans and cargo, especially for rural farmers. There are three surviving species of camel. Camels originated in North America and eventually spread across Beringia to Asia. They survived in the Old World, and eventually, humans domesticated them and spread them globally. However, the literature on the physiological parameters of the camel is scanty. This review aims to provide a general overview of the physiological features of the camel and an update on the available studies of physiological and behavioural features of the camel and the effects of thermal environmental conditions on some physiological responses. [ABSTRACT FROM AUTHOR]

Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence, this article presents an AIb-ased non-invasive and secured diagnosis for OLD using physiological and iris features. This research work implements different machine-learning-based techniques which classify various subjects, which are healthy and effective patients. The iris features include gray-level run-length matrix-based features, gray-level co-occurrence matrix, and statistical features. These features are extracted from iris images. Additionally, ten different classifiers and voting techniques, including hard and soft voting, are implemented and tested, and their performances are evaluated using several parameters, which are precision, accuracy, specificity, F-score, and sensitivity. Based on the statistical analysis, it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%. [ABSTRACT FROM AUTHOR]

Objective: Sleep apnea is a chronic respiratory disorder and its standard assessment requires full night in-laboratory polysomnography (PSG). However, PSG is expensive, time-consuming, and inconvenient. Thus, there is a need to monitor sleep apnea with more convenient wearable devices. The objective of this study was to implement deep learning algorithms to monitor sleep apnea severity based on respiratory movements that can be easily recorded over the trachea. Methods: Adult individuals referred to the sleep laboratory at the Toronto Rehabilitation Institute for overnight sleep studies were included (N=69). Simultaneously with the PSG, an accelerometer was attached to the participant's suprasternal notch to record tracheal respiratory movements. Twenty-one features were extracted from the tracheal movements and used in a deep learning classifier to detect respiratory events. The apnea hypopnea index (AHI) was estimated as the number of events per hour of sleep. Results: The F1 score of the event-by-event detection algorithm was between 12% and 71% for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived AHI (r=0.86, p

The article deals with the determinants of resistance to the combat stress of the military from the Ukrainian Armed Forces, which contribute to highly productive activity in the extreme space. The professional activity of many people takes place in extreme conditions ‒ especially of the military of the Armed Forces of Ukraine. Each stressful situation in a professional or other activity causes a complex of assessment processes, agreements, and settlement when a person interacts with stressors. Stress resistance can be distinguished as a personality trait that provides a harmonious relationship between all components of mental activity in an emotional situation and, thereby, contributes to the successful implementation of actions. There are three types of a person’s relationship to himself in a situation of stress caused by extreme conditions. The first is a primitive-selfish attitude toward oneself as a «victim». The second type can be called the objectively individualistic one. The third type, allows attitude to oneself as one of a number of people, most conducive to effective activity under stress. The level of self-esteem is complexly linked with stress. Individuals with fairly high self-esteem, as a rule, do not respond to emotionally severe or stressful stimuli. People with low self-esteem have a high level of fear or anxiety under the threat of stress and see themselves as having inadequate abilities to withstand it. The ability to cope with stress depends on how well a soldier is prepared for it, on the attitude to the daily requirements that are imposed by life. The ability to adapt is not only an individual, but also a person’s personality trait. Adaptive abilities depend on the psychological characteristics of the individual, which are determined by the possibility of adequate regulation of physiological states.

The scientific study of teamwork in the context of long-term spaceflight has uncovered a considerable amount of knowledge over the past 20 years. Although much is known about the underlying factors and processes of teamwork, much is left to be discovered for teams who operate in extreme isolation conditions during spaceflights. Thus, special considerations must be made to enhance teamwork and team well-being for long-term missions during which the team will live and work together. Being affected by both mental and physical stress during interactional context conversations might have a direct or indirect impact on team members' speech acoustics, facial expressions, lexical choices and their physiological responses. The purpose of this article is (a) to illustrate the relationship between the modalities of vocal-acoustic, language and physiological cues during stressful teammate conversations, (b) to delineate promising research paths to help further our insights into understanding the underlying mechanisms of high team cohesion during spaceflights, (c) to build upon our preliminary experimental results that were recently published, using a dyadic team corpus during the demanding operational task of "diffusing a bomb" and (d) to outline a list of parameters that should be considered and examined that would be useful in spaceflights for team-effectiveness research in similarly stressful conditions. Under this view, it is expected to take us one step towards building an extremely non-intrusive and relatively inexpensive set of measures deployed in ground analogs to assess complex and dynamic behavior of individuals. [ABSTRACT FROM AUTHOR]

Purpose: In this article, we develop a dynamic Bayesian network (DBN) model to measure 3D visual fatigue. As far as our information goes, this is the first adaptation of a DBN structure‐based probabilistic framework for inferring the 3D viewer's state of visual fatigue. Methods: Our measurement focuses on the interdependencies between each factor and the phenomena of visual fatigue in stereoscopy. Specifically, the implementation of DBN with using multiple features (e.g. contextual, contactless and contact physiological features) and dynamic factor provides a systematic scheme to evaluate 3D visual fatigue. Results: In contrast to measurement results between the mean opinion score (MOS) and Bayesian network model (with static Bayesian network and DBN), the visual fatigue in stereoscopy at time slice t is influenced by a dynamic factor (time slice t−1). In the presence of dynamic factors (time slice t−1), our proposed measuring scheme based on DBN is more comprehensive. Conclusion: (i) We cover more features for inferring the visual fatigue, more reliably and accurately; (ii) at different time slices, the dynamic factor features are significant for inferring the visual fatigue state of stereoscopy. [ABSTRACT FROM AUTHOR]

In Body Area Networks (BANs), bio-sensors can collect personal health information and cooperate with each other to provide intelligent health care services for medical users. Since personal health information is highly privacy-sensitive, the flourish of BANs still faces critical security challenges, especially secure communication between bio-sensors. In this paper, we propose a flexible and efficient authenticated key agreement scheme (PBAKA) to provide secure communication for BANs. Specifically, we employ a control unit (e.g., smart phone) to launch authentication based on physiological features collected from BANs, and integrate bilinear pairings to negotiate session keys for bio-sensors. Since physiological features can be collected from various kinds of bio-sensors in real time, PBAKA is flexible for adding new bio-sensors without pre-distributed keys. Meanwhile, PBAKA is computationally efficient by offloading authentication burden from resource-limited bio-sensors to the control unit. Security analysis demonstrates that PBAKA is provably secure under the decisional bilinear Diffie-Hellman assumption. Extensive experimental results validate efficient communication, computation and energy consumption of our scheme when compared with several existing solutions. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the impact of heat stress on physiological features, together with endogenous hormones and the transcription level of related genes, to estimate the heat resistance ability and stress injury mechanism of different dwarfing apple rootstocks. Among the six rootstocks, the rootstocks of native Shao series (SH series) showed better heat stress resistance than those of Budagovski 9 (B9), Cornell-Geneva 24 (CG24), and Malling 26 (M26) from abroad. Among SH series rootstocks, SH1 and SH6 showed higher heat stress resistance than SH40. M26 demonstrated the lowest adaption ability to heat stress, showing higher leaf conductivity and lower liquid water content (LWC) with the increase in temperature. Heat stress also resulted in the suppression of photosynthesis, which showed no significant restoration after 7-day recovery. It should be noted that although a higher temperature led to a lower LWC and photosynthetic efficiency (Pn) of CG24, there was no significant increase in leaf conductivity, and 7 days after the treatment, the Pn of CG24 recovered. The extremely high temperature tolerance of SH series rootstocks could be related to the greater osmotic adjustment (OA), which was reflected by smaller reductions in leaf relative water content (RWC) and higher turgor potentials and leaf gas exchange compared with the other rootstocks. Determination of hormones indicated multivariate regulation, and it is presumed that a relatively stable expression levels of functional genes under high-temperature stress is necessary for heat stress resistance of rootstocks.

The scientific study of teamwork in the context of long-term spaceflight has uncovered a considerable amount of knowledge over the past 20 years. Although much is known about the underlying factors and processes of teamwork, much is left to be discovered for teams who operate in extreme isolation conditions during spaceflights. Thus, special considerations must be made to enhance teamwork and team well-being for long-term missions during which the team will live and work together. Being affected by both mental and physical stress during interactional context conversations might have a direct or indirect impact on team members’ speech acoustics, facial expressions, lexical choices and their physiological responses. The purpose of this article is (a) to illustrate the relationship between the modalities of vocal-acoustic, language and physiological cues during stressful teammate conversations, (b) to delineate promising research paths to help further our insights into understanding the underlying mechanisms of high team cohesion during spaceflights, (c) to build upon our preliminary experimental results that were recently published, using a dyadic team corpus during the demanding operational task of “diffusing a bomb” and (d) to outline a list of parameters that should be considered and examined that would be useful in spaceflights for team-effectiveness research in similarly stressful conditions. Under this view, it is expected to take us one step towards building an extremely non-intrusive and relatively inexpensive set of measures deployed in ground analogs to assess complex and dynamic behavior of individuals.

Deception detection has received an increasing amount of attention in recent years, due to the significant growth of digital media, as well as increased ethical and security concerns. Earlier approaches to deception detection were mainly focused on law enforcement applications and relied on polygraph tests, which had proved to falsely accuse the innocent and free the guilty in multiple cases. In this paper, we explore a multimodal deception detection approach that relies on a novel data set of 149 multimodal recordings, and integrates multiple physiological, linguistic, and thermal features. We test the system on different domains, to measure its effectiveness and determine its limitations. We also perform feature analysis using a decision tree model, to gain insights into the features that are most effective in detecting deceit. Our experimental results indicate that our multimodal approach is a promising step toward creating a feasible, non-invasive, and fully automated deception detection system. [ABSTRACT FROM PUBLISHER]

The results of the indicators of physical development, psycho-physiological capacity and physical fitness of children. The study involved 52 children aged 1-2 years, 3-4 years 56 children, 56 children 4-5 years. It is established that in the age group of children 4-5 years time, a simple reaction to an auditory stimulus following simple reaction time to visual stimulus. It is recommended to use parenting techniques that are integrally stimulate all the sensory systems. Pointed out that the sound of children causes greater emotional response in comparison with the light signal. This reduces the latency time of reaction to an auditory stimulus.

The present study aims to determine the effect of different concentrations of EDTA (0 g EDTA/L, 0.1 g EDTA/L and 0.3 g EDTA/L) on fecundity, survival rate and biochemical responses of the adult female of Nile tilapia, Oreochromis niloticus reared in glass tanks. Results showed that, the fecundity increases with the increasing in the concentrations of EDTA compared with control. Survival rate are affected by high concentration of EDTA (0.3 g EDTA/L) and reached to 33.3 % at the end of third week. However, an addition of EDTA to aquaria containing adult females increased the biochemical parameters at high concentration compared with control and low concentration samples except total proteins showed deceasing to lowest level. Statistical analysis mentioned a significant difference between the low concentration of EDTA and both of high concentration and control in all parameters. Otherwise, total carbohydrates exhibit non significant differences between all of them. [ABSTRACT FROM AUTHOR]

The changes in physicochemical and physiological properties of modified soluble dietary fibre (mSDF) in the okara using enzymatic [cellulase/substrates ratio of 2.0-6.0% (w/w) at 50 °C for 90-150 min], chemical [water bath of 1-3 h, water bath temperature of 50-90 °C, Na2HPO4 concentration of 0.1-0.9% and sample/reagent radio (S/R) of 1:40-1:60 (w/v)] and physical (homogenised once or twice under the optimal cellulase treatment condition) treatments were evaluated. The mSDF yield with chemical treatment (57.16%) was significantly (P < 0.05) higher than that of physical treatment [homogenisation once (16.54%) or twice (42.02%)] in combination with cellulase treatment. All treatments improved swelling capacity of mSDF and promoted bile acid- and cholesterol-absorption capacity, but enzymatic and chemical treatments decreased the total reducing power of mSDF, except homogenisation-cellulase treatment (141.74 μM TE g-1). Therefore, homogenisation-cellulase treatment may be the appropriate method to improve the SDF proportion and ameliorate the functionality of okara. [ABSTRACT FROM AUTHOR]

Pure culture of magnetotactic bacteria with high magnetosome yield has been achieved for only a few strains. The major obstacles involve the nutritional requirements and culture conditions of the cells. To increase cell density and magnetosome production, it is necessary to elucidate the physiological characteristics of a particular strain during cell growth and develop an appropriate artificial control strategy.Large-scale culture of Magnetospirillum gryphiswaldense strain MSR-1 was successfully performed for 48 h in a 42-L autofermentor, and several key physiological parameters were measured in real time. Maximal values of cell density (OD565) (19.4) and cell yield (dry weight) (4.76 g/L) were attained at 40 h. The key time point for cell growth and magnetosome formation was found to be 18-20 h. At this point, cells entered the log phase of growth, the maximal values of Cmag (1.78), iron content (0.47% ), and magnetosome number (26±3 per cell) were observed, superoxide dismutase (SOD) activity began to decrease more rapidly, ATP content dropped to an extremely low level (0.17 fmol), and reducing power (NADH/NAD+ ratio) began to increase very rapidly. Excessive levels of dissolved oxygen (≥20 ppb) and lactic acid in the medium caused notable cytotoxic effects after 20 h. Artificial control measures for fermentation must be based on realistic cell physiological conditions. At the key time point (18-20 h), cell density is high and magnetosomes have matured. The process of magnetosome synthesis involves a high consumption of ATP and reducing power, and the cells require replenishment of nutrients prior to the 18-20 h time point. Culture conditions that effectively minimize dissolved oxygen accumulation, lactic acid content, and reducing power at this point will enhance magnetosome yield without obvious inhibition of cell growth.

On-body radio propagation in the 2.45-GHz industrial-scientific-medical frequency band (2.40–2.48 GHz) was investigated during three different activities: jogging, rowing and cycling. Four different links were examined, from the waist to the wrist, ankle, chest and back; it was observed that the channel behavior could be related to the repetitive nature of the activities. Furthermore, combinations of the wrist, ankle, and chest channels could potentially be used to identify the activity, while the waist-back channel shows little variation between activities (roughly 2 dB, compared to 6–15 dB for the other links). The results also show that dynamic on-body radio channels contain rich biomechanical information, such as motion pattern, heartbeat, and breathing process. It is demonstrated that physiological and kinetic features can, therefore, be extracted through some known signal-processing techniques. For example, the repetitive nature of the activities introduces harmonics into the signal via fading, which correspond to the speed of motion. In addition, the mechanical motion of the torso during respiration and cardiac activity also introduce harmonics due to changes in the path loss, albeit with low magnitudes. The detection of such signals is discussed. [ABSTRACT FROM PUBLISHER]

Abstract: Two fermentation methods, aerobic (generating solid compost) and anaerobic fermentation (generating fermented fluid), were adopted to treat the same amount of organic matter to generate solid compost and fermented fluid, respectively. These treatments were used to supplement replant soils in a pot, respectively, where Malus hupehensis Rehd. seedling was replanted. The pilot project was aimed at investigating the influence of organic matter applied to soil on the physiological features of the plant as well as its impact on soil properties. The following four treatments were adopted in the study: new cropping soil, replant soil, replant soil with solid compost, and replant soil with fermented fluid. The growth of seedlings in the four soils was monitored by biomass production and the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) in the plant roots. The activities of soil urease, soil invertase, and soil phosphatase were measured; and the bacterial, fungal, and actinomycetal contents in the soils were also measured. This study showed that the supplementation of organic matter in a fermented liquid form to replant soil significantly enhanced the biomass production of the seedlings compared to the solid compost form. The activities of SOD, POD, and CAT in the roots were increased by both forms of organic matter, with the solid compost form having a stronger effect than the fermented liquid form. Moreover, both forms increased the activities of soil urease, soil invertase, and soil phosphatase, with the fermented liquid form having a stronger impact than the solid compost form. The addition of organic matter in the form of fermented fluid to the replant soil obviously increased the bacteria content in the soil in comparison with the addition of organic matter in the form of solid compost. Compared to the solid compost, the fermented fluid led to a higher increase in the fungal and actinomycetal contents in the soil at the early stage but a lower increase at the late stage. When applied to replant soil, the fermented fluid form of organic matter was superior to the solid compost form of organic matter in preventing apple replant disease (ARD). [Copyright &y& Elsevier]

Tree species Asimina triloba (L.) Dunal, is native to North America. In the area of origin is cultivated, both as food species because the edible fruit, and as ornamental species. Ornamental value derives both from decorative flowers, that open in early spring, and because habitus species. The species is demanding from slightly acidic soils (pH 5.5 to 7.0) and well drained. Seedlings are susceptible to heatstroke and need areas of the sun, but since the second year, vegetate well in bright light conditions [27]. Optimum climate is temperate to subtropical one. The species exhibits unique quality traits for a temperate fruit that are similar to other fruit in the Annonaceae family, including cherimoya (Annona cherimola Mill.), sugar apple or sweetsop, (A. squamosa L.), soursop (A. muricata L.), custard apple (A. reticulata L.), and atemoya (A. squamosa X A. cherimola), all of which are tropical [2]. This study follows the behavior of the species, in particular conditions of the Baia Mare and its surroundings. In this area a few individuals were introduced, in order to diversigy the range of species of ornamental plants. In Baia Mare, topoclimate is specifically depression, sheltered by mountains, more atenuated as temperature and winds, than in surrounding areas. As a result of climatic conditions, chestnut Castanea sativa, grows in good conditions in Baia Mare. Instead, the area is heavily polluted, especially at ground level. Pollution by heavy metals is a historical being generated by the mining industry. The introduction and use of a new plant species into a new area involves: 1. easy to obtain seed; 2.- maintaining the crown shape habitus and and leaf shape and size, respectively; 3 - determination of optimal physiological parameters. Therefore have been performed, the following experimental determinations: 1. - germination of seed obtained in the particular conditions of the Baia Mare; 2. - some morphomtric characteristics of leaves, in the juveniles of the 1-2 years; 3. - the dynamic of photosynthesis intensity in these plants, during the day, during summer/autumn months. The study results are promising for acclimatization of the species Asimina triloba (L.) Dunal in particular conditions of Baia Mare. Morphological and physiological parametres tested are maintaned at similar to those of native habitat, described in the literature. [ABSTRACT FROM AUTHOR]

Abstract: The features used for speech recognition are expected to emphasize linguistic information while suppressing individual differences. For speaker recognition, in contrast, features should preserve individual information and attenuate the linguistic information at the same time. In most studies, however, identical acoustic features are used for the different missions of speaker and speech recognition. In this paper, we first investigated the relationships between the frequency components and the vocal tract based on speech production. We found that the individual information is encoded non-uniformly in different frequency bands of speech sound. Then we adopted statistical Fisher’s F-ratio and information-theoretic mutual information measurements to measure the dependencies between frequency components and individual characteristics based on a speaker recognition database (NTT-VR). From the analysis, we not only confirmed the finding of non-uniform distribution of individual information in different frequency bands from the speech production point of view, but also quantified their dependencies. Based on the quantification results, we proposed a new physiological feature which emphasizes individual information for text-independent speaker identification by using a non-uniform subband processing strategy to emphasize the physiological information involved in speech production. The new feature was combined with GMM speaker models and applied to the NTT-VR speaker recognition database. The speaker identification using proposed feature reduced the identification error rate 20.1% compared that with MFCC feature. The experimental results confirmed that emphasizing the features from highly individual-dependent frequency bands is valid for improving speaker recognition performance. [Copyright &y& Elsevier]

• Tetraploid carmine radish was produced by treating the diploid with 0.1% colchicine. • The tetraploid plants displayed "gigas" effect, including greater size of both vegetative and reproductive organs. • The physiological characterization demonstrated that tetraploid radish has improved antioxidant enzyme activity, soluble contents, and better quality. • GAs, JAs, CTKs, and SA are negatively, whereas ABA and IAA are positively associated with the delayed bolting and flowering in tetraploid. • Genes positively associated with flowering and bolting were expressed lower in tetraploid compared to diploid plants, at flowering and bolting stage. Polyploidization has been widely used in breeding novel verities in crops and vegetables, since the polyploid plants have many advantages and better performance than their relative diploids. As one of the most favorite diploid vegetable crops cultivated worldwide, there were very limited reports about the induction of radish (Raphunas sativas) tetraploid. Moreover, premature-bolting has become the major limitation factor impacting the radish production and quality. Previous studies suggested that phytohormones play essential roles in the regulation of bolting and flowering. However, the morphological, physiological and biochemical features, as well as the relationship between endogenous phytohormones, flowering genes and bolting in tetraploid radish have not been extensively reported. In this study, we used colchicine to successfully induce tetraploid carmine radish plants, evidenced by flow cytometric quantification of DNA content and the cytological observations of stomata numbers and chloroplasts in pollen mother cells. The tetraploid plants displayed "gigas" effect, including greater size of both vegetative and reproductive organs. Furthermore, the physiological characterization demonstrated that tetraploid radish has improved antioxidant enzyme activity, soluble contents and better quality. Importantly, quantification of endogenous phytohormone levels showed that multiple phytohormones, including GAs, JAs, CTKs, and SA are negatively, whereas ABA and IAA are positively associated with the delayed bolting and flowering in tetraploid, which are contrast to that in diploid plants. Moreover, qRT-PCR analysis suggested that the gene negatively associated with flowering and bolting, FLC1.1 , was highly expressed in tetraploid plants at flowering stage, whereas the expression levels of genes positively associated with flowering and bolting were much lower in tetraploid compared to diploid radish, at flowering and bolting stages. These data will provide evidences for breeding late-bolting radish variety through induction of tetraploids. [ABSTRACT FROM AUTHOR]

Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, for example, provide reasoning for the person's decision making or to support mental wellbeing (e.g., stress monitoring). Recently, beside of approaches based on audio, visual or text information, solutions relying on wearable sensors as observables, recording mainly physiological and inertial parameters, have received increasing attention. Wearable systems enable an ideal platform for long-term affect recognition applications due to their rich functionality and form factor, while providing valuable insights during everyday life through integrated sensors. However, existing literature surveys lack a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods and best practices of wearable affect and stress recognition. Following a summary of different psychological models, we detail the influence of affective states on the human physiology and the sensors commonly employed to measure physiological changes. Then, we outline lab protocols eliciting affective states and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain and review common approaches related to the preprocessing, feature extraction and classification steps. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field to conduct and evaluate user studies and develop wearable systems. [ABSTRACT FROM AUTHOR]