Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland.

Modeling nitrate fate and transport in water sources is an essential component of predictive water quality management. Both mechanistic and data-driven models are currently in use. Mechanistic models, such as SWAT, simulate daily nitrate loads based on the results of simulating water flow. Data-driven models allow one to simulate nitrate loads and water flow independently. Performance of SWAT and deep learning model was evaluated in cases when deep learning model is used in (a) independent simulations of flow series and nitrate concentration series, and (b) in both flow rate and concentration simulations to obtain nitrate load values. The data were collected at the Tuckahoe Creek watershed in Maryland, United States. The data-driven deep learning model was built using long-short-term-memory (LSTM) and three-dimensional convolutional networks (3D Convolutional Networks) to simulate flow rate and nitrate concentration using weather data and imagery to derive leaf area index according to land use. Models were calibrated with data over training period 2014-2017 and validated with data over testing period. SWAT Nash-Sutcliffe efficiency (NSE) was 0.31 and 0.40 for flow rate and -0.26 and -0.18 for the nitrate load rate over training and testing periods, respectively. Three data-driven modeling scenarios were implemented: (1) using the observed flow rate and simulated nitrate concentration, (2) using the simulated flow rate and observed nitrate concentration, and (3) using the simulated flow rate and nitrate concentration. The deep learning model performed better than SWAT in all three scenarios with NSE from 0.49 to 0.58 for training and from 0.28 to 0.80 for testing periods with scenario 1 showing the best results. The difference in performance was most pronounced in fall and winter seasons. The deep learning modeling can be an efficient alternative to mechanistic watershed-scale water quality models provided the regular high-frequency data collection is implemented.

Development and validation of the self-consciousness type scale.

Introduction: Previous research has highlighted the duality of self-consciousness, which simultaneously plays adaptive and maladaptive roles. This study aims to develop a measure that categorically distinguishes between different types of self-consciousness styles based on the Regulatory Focus Theory (RFT) and examines their relationship with mental health-related indicators. Methods: Data were gathered through an online mental health survey conducted at a University Student Counseling Center in Seoul. The study involved exploratory factor analysis, confirmatory factor analysis, and reliability and validity analysis, which resulted in the development of a 14-question Self-Consciousness Type Scale (SCTS). Results: Both exploratory and confirmatory factor analyses validated the two-factor structure of the SCTS. The fit indices of the final model indicated a good fit, with high internal consistency for both sub-factors. Convergent and discriminant validity were confirmed through correlations between the sub-scales. Cluster analysis identified four distinct subtypes of self-consciousness styles: Growth-oriented, Defensive, Ambivalent, and Low-focus self-consciousness. Group difference analysis revealed significant differences in mental health-related variables among the subtypes, supporting the 2 × 2 model of prevention-focused and promotion-focused self-consciousness. Discussion: The findings support the SCTS as a valid measurement tool capable of distinguishing four distinct types of self-consciousness, aligning with the multidimensional model of self-consciousness. The study's limitations and implications were discussed based on the results, emphasizing the potential applications of the SCTS in mental health research and practice.

Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke.

Introduction: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity. Materials and methods: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand. Results: The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age. Conclusion: The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.

Enhancing microbial CO2 electrocatalysis for multicarbon reduction in a wet amine-based catholyte.

Microbial CO2 electroreduction (mCO2ER) offers a promising approach for producing high-value multicarbon reductants from CO2 by combining CO2 fixing microorganisms with conducting materials (i. e., cathodes). However, the solubility and availability of CO2 in an aqueous electrolyte pose significant limitations in this system. This study demonstrates the efficient production of long-chain multicarbon reductants, specifically carotenoids (~C40), within a wet amine-based catholyte medium during mCO2ER. Optimizing the concentration of the biocompatible CO2 absorbent, monoethanolamine (MEA), led to enhanced CO2 fixation in the electroautotroph bacteria. Molecular biological analyses revealed that MEA in the catholyte medium redirected the carbon flux towards carotenoid biosynthesis during mCO2ER. The faradaic efficiency of mCO2ER with MEA for carotenoid production was 4.5-fold higher than that of the control condition. These results suggest the mass transport bottleneck in bioelectrochemical systems could be effectively addressed by MEA-assissted mCO2ER, enabling highly efficient production of valuable products from CO2.

A Comparison of Virtual Reality to Traditional Simulation in Health Professions Education: A Systematic Review.

ABSTRACT: With the increasing availability of virtual reality (VR) and its lower overall costs of use, the objective of this review was to compare VR to traditional simulation in terms of learning outcomes. Studies were included if they met the following criteria: ( a ) research study (of any design), ( b ) focused on learners in health professions, and ( c ) compared VR with traditional simulation. Studies were excluded for the following reasons: ( a ) not a research study, ( b ) focused on learners outside health professions, ( c ) used screen-based or computer-based simulation, ( d ) used a task trainer, and ( e ) did not involve a comparison of VR to traditional simulation. The searches were run on November 11 and 12, 2021, in CINAHL via EBSCO, Ovid Embase, ERIC via EBSCO, IEEE Xplore, Ovid Medline, Ovid PsycINFO, Scopus, and Web of Science Core Collection. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the review. A team of researchers applied Kirkpatrick's Levels, Melnyk's Levels of Evidence, and Critical Appraisal Skills Programme guidelines to assess the level of evidence and look for bias. Fifteen studies were reviewed including 11 randomized controlled trials. The lead researcher synthesized the study results into 3 categories: (1) traditional simulation performed better, (2) VR performed better, and (3) comparable outcomes. There is insufficient evidence to endorse one form of simulation (VR or traditional) as more effective at this time. The body of evidence contained too few studies to draw meaningful conclusions to answer the guiding question. The studies covered a large range of modalities, learner groups, and healthcare topics, preventing a meta-analysis. Based on the literature and experience, we recommend that VR experiences be proctored, include debriefing, have a backup plan for cybersickness or myopia, and have time and costs documented. Use of VR is likely to expand; thus, research is needed to inform the best contexts and applications.

Development of dual strain microalgae cultivation system for the direct carbon dioxide utilization of power plant flue gas.

This study aims to propose a biological system that allows for direct utilization of flue gas for carbon dioxide capture and utilization by microalgae. The strain Chlorella sp. ABC-001 is employed for its high growth rate as well as lipid and carbohydrate content. Toxicity tests showed that cell growth was unaffected by NO, but the presence of SO2 showed critical damage on cell growth. Hence, an extremophile alga, Galdieria sulphuraria 5587.1 was applied to build a dual-strain cultivation system to mitigate the effect of SO2 toxicity and increase CO2 capture efficiency. All SO2 was removed by Galdieria culture and the system exhibited stable growth from a simulated flue gas stream containing CO2, NO and SO2. Combined CO2 biofixation rate of 793 mg/L/d and lipid productivity of 113 mg/L/d was achieved. The results showed that this new cultivation system is a promising alternative for reducing CO2 emissions from power plants.

Automatic classification of microplastics and natural organic matter mixtures using a deep learning model.

Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification.

Epigenetic Modulations for Prevention of Infectious Diseases in Shrimp Aquaculture.

Aquaculture assumes a pivotal role in meeting the escalating global food demand, and shrimp farming, in particular, holds a significant role in the global economy and food security, providing a rich source of nutrients for human consumption. Nonetheless, the industry faces formidable challenges, primarily attributed to disease outbreaks and the diminishing efficacy of conventional disease management approaches, such as antibiotic usage. Consequently, there is an urgent imperative to explore alternative strategies to ensure the sustainability of the industry. In this context, the field of epigenetics emerges as a promising avenue for combating infectious diseases in shrimp aquaculture. Epigenetic modulations entail chemical alterations in DNA and proteins, orchestrating gene expression patterns without modifying the underlying DNA sequence through DNA methylation, histone modifications, and non-coding RNA molecules. Utilizing epigenetic mechanisms presents an opportunity to enhance immune gene expression and bolster disease resistance in shrimp, thereby contributing to disease management strategies and optimizing shrimp health and productivity. Additionally, the concept of epigenetic inheritability in marine animals holds immense potential for the future of the shrimp farming industry. To this end, this comprehensive review thoroughly explores the dynamics of epigenetic modulations in shrimp aquaculture, with a particular emphasis on its pivotal role in disease management. It conveys the significance of harnessing advantageous epigenetic changes to ensure the long-term viability of shrimp farming while deliberating on the potential consequences of these interventions. Overall, this appraisal highlights the promising trajectory of epigenetic applications, propelling the field toward strengthening sustainability in shrimp aquaculture.

Improving Telenursing Skills Through Simulation-Based Education.

Background: Preparing graduate-level nurses to provide care via telehealth could both increase access to care and decrease healthcare costs. However, many nursing schools do not provide telehealth experiences, especially at the graduate level. Objective: The aim of this project is to incorporate telehealth simulations in the doctor of nursing practice (DNP) curriculum at the University of Miami School of Nursing and Health Studies. Methods: This quality improvement project used a posttest-only design. The Ottawa Model of Research Use guided the project. DNP students completed a peer-to-peer telehealth simulation scenario via Zoom. Results: DNP students demonstrated confidence with an average score of 2.875 out of 3. DNP students' teaching performance scores were 2.7 out of 3. DNP students demonstrated effective overall telehealth performance with a mean score of 13.74 out of 15. Conclusion: DNP students may benefit from increased opportunities to practice telehealth, including the use of peer-to-peer simulation, to be aptly prepared for contemporary nursing practice.

Simultaneous enhancement of lipid biosynthesis and solvent extraction of Chlorella using aminoclay nanoparticles.

Magnesium aminoclay nanoparticles (MgANs) exert opposing effects on photosynthetic microalgae by promoting carbon dioxide (CO2) uptake and inducing oxidative stress. This study explored the potential application of MgAN in the production of algal lipids under high CO2 concentrations. The impact of MgAN (0.05-1.0 g/L) on cell growth, lipid accumulation, and solvent extractability varied among three tested oleaginous Chlorella strains (N113, KR-1, and M082). Among them, only KR-1 exhibited significant improvement in both total lipid content (379.4 mg/g cell) and hexane lipid extraction efficiency (54.5%) in the presence of MgAN compared to those of controls (320.3 mg/g cell and 46.1%, respectively). This improvement was attributed to the increased biosynthesis of triacylglycerols and a thinner cell wall based on thin-layer chromatography and electronic microscopy, respectively. These findings suggest that using MgAN with robust algal strains can enhance the efficiency of cost-intensive extraction processes while simultaneously increasing the algal lipid content.

The Influence of Life Stress and Sleep Disturbance on White Matter Integrity.

OBJECTIVE: This study investigated whether sleep and stress mutually interact to induce changes in white matter integrity. METHODS: Diffusion tensor imaging (DTI) was conducted on 36 participants (male=22, female=14; mean age=38.33±12.78 years). Participants were divided into three groups depending on their sleep quality and stress levels: poor sleepers with stress, poor sleepers without stress, and good sleepers. Sleep quality and stress level were evaluated using the Pittsburgh Sleep Quality Index and the Life Experiences Survey, respectively. Fractional anisotropy (FA) values were calculated employing DTI tractography. RESULTS: After controlling for age and sex, poor sleepers with stress exhibited a lower FA of the left inferior cerebellar peduncle (ICP) than did poor sleepers without stress (t=2.81, p=0.02). Poor sleepers without stress showed a higher FA of the right middle longitudinal fasciculus (MdLF) than did good sleepers (t=3.35, p=0.006). CONCLUSION: The current study reports the effects of sleep, stress, and their interaction on the white matter integrities of the ICP and MdLF. ICP change seems to be associated with sleep disturbances related to stress, while MdLF change would be associated with sleep disturbances unrelated to stress.

A telehealth nursing intervention to improve the transition from the neonatal intensive care unit to home for infants & caregivers: Preliminary evaluation.

PURPOSE: The purpose of this nurse-led telehealth intervention was to support caregivers and infants during the difficult period of transition from the neonatal intensive care unit (NICU) to home. METHODS: The Baby Steps project was designed using quality improvement methodology, and was implemented in April 2020 at a stand-alone pediatric institution in South Florida. Using a nurse-led telehealth model, follow-up nursing care was provided in the home setting for two weeks after discharge. Any infant cared for in the NICU and discharged to a home setting in the state of Florida was eligible for services. Encounters included assessment, anticipatory guidance, connection with community resources, and general support. Caregiver satisfaction, unplanned emergency care use, and 30-day readmissions were assessed. RESULTS: Within the first 18 months of the program, a total of 378 infants were enrolled, and 74.6% received follow-up services in the home setting (n = 282). Caregivers reported high satisfaction with the program (100% strongly agree or agree). There was a 46% decrease in 30-day readmissions from baseline rates, and a substantial decrease in use of emergency care services within a month of discharge as compared to infants discharged during the same time period who did not receive services (30.9% vs.13.8%). DISCUSSION: This nurse-led intervention was found to be a feasible and highly satisfactory approach to improve NICU patient outcomes and support caregivers during transition from hospital to home. PRACTICE IMPLICATIONS: Nurses can provide post-discharge telehealth support, which not only improves caregiver satisfaction but also decreases readmissions and emergency care use among NICU patients.

Evidence of White Matter Integrity Changes in the Anterior Cingulum Among Shift Workers: A Cross-Sectional Study.

Introduction: We investigated the white matter integrity in shift and non-shift workers and its associations with sleep and activity. Methods: Diffusion tensor imaging (DTI) was performed on 61 shift workers and 31 non-shift workers. Their sleep and activity profiles were assessed using the Pittsburgh Sleep Quality Index (PSQI), sleep diaries, and actigraphy. Fractional anisotropy (FA) (a measure of white matter integrity) was calculated using DTI tractography. Results: Shift workers exhibited higher FA values in the bilateral anterior cingulum than did non-shift workers. An increased FA in the right anterior cingulum was correlated with poor sleep quality (ie, a high PSQI score) in shift workers. An increased FA in the right anterior cingulum was also correlated with higher actigraphic activity indices (the mesor and M10 indices) in shift workers. Discussion: The white matter integrity of the anterior cingulum was altered in shift workers, perhaps in association with sleep and activity disturbances.

Online learning performance and engagement during the COVID-19 pandemic: Application of the dual-continua model of mental health.

The COVID-19 pandemic has led to an abrupt transition from face-to-face learning to online learning, which has also affected the mental health of college students. In this study, we examined the relationship between students' adjustment to online learning and their mental health by using the Dual-Continua Model. The model assumes that mental disorder and mental well-being are related yet distinct factors of mental health. For this purpose, 2,933 college students completed an online survey around the beginning of the Fall semester of 2020 (N = 1,724) and the Spring semester of 2021 (N = 1,209). We assessed participants' mental well-being, mental disorders, and academic distress by means of the online survey. In addition, we incorporated grades and log data accumulated in the Learning Management System (LMS) as objective learning indicators of academic achievement and engagement in online learning. Results revealed that two dimensions of mental health (i.e., mental well-being and mental disorder) were independently associated with all objective and subjective online learning indicators. Specifically, languishing (i.e., low levels of mental well-being) was negatively associated with student engagement derived from LMS log data and academic achievement and was positively associated with self-reported academic distress even after we controlled for the effects of mental disorder. In addition, mental disorder was negatively related to student engagement and academic achievement and was positively related to academic distress even after we controlled for the effects of mental well-being. These results remained notable even when we controlled for the effects of sociodemographic variables. Our findings imply that applying the Dual-Continua Model contributes to a better understanding of the relationship between college students' mental health and their adaptation to online learning. We suggest that it is imperative to implement university-wide interventions that promote mental well-being and alleviate psychological symptoms for students' successful adjustment to online learning.

Gray Matter Volume of Thalamic Nuclei in Traumatized North Korean Refugees.

The current study investigated differences in the regional gray matter (GM) volume of specific thalamic nuclei between North Korean (NK) refugees and South Korean (SK) residents. It also investigated associations between thalamic GM volume changes and psychological symptoms. Psychological evaluations and magnetic resonance imaging were conducted on 50 traumatized NK refugees and 55 non-traumatized SK residents. The regional GM volume ratios in the bilateral thalami were calculated for all participants using voxel-based morphometry. NK refugees showed greater GM volume ratios in the right medial-posterior nuclei and left medial nuclei compared with SK residents. NK refugees also exhibited more depressive symptoms than SK residents. However, increased GM volume ratios in both right medial-posterior nuclei and left medial nuclei were correlated with fewer depressive symptoms in NK refugees, but not in SK residents. The findings indicate that traumatized NK refugees had increased GM volumes in the right medial-posterior nuclei and left medial nuclei, which were associated with fewer depressive symptoms. The enlarged specific thalamic nuclei presented among refugees in the current study might be associated with a neurobiological compensatory mechanism that prevents the development or progression of depression in refugees after repetitive traumatic experiences.

Enhanced CO2 electroconversion of Rhodobacter sphaeroides by cobalt-phosphate complex assisted water oxidation.

CO2 can be a next generation feedstock for electricity-driven bioproduction due to its abundance and availability. Microbial electrosynthesis (MES), a promising technique for CO2 electroconversion, provides an attractive route for the production of valuable products from CO2, but issues surrounding efficiency and reasonable productivity should be resolved. Improving the anode performance for water oxidation under neutral pH is one of the most important aspects to advance current MES. Here, we introduce cobalt-phosphate (Co-Pi) assisted water oxidation at the counter electrode (i.e., anode) to upgrade the MES performance at pH 7.0. We show that CO2 can be converted by photochemoautotrophic bacterium, Rhodobacter sphaeroides into organic acids and carotenoids in the MES reactor. Planktonic cells of R. sphareroides in the Co-Pi anode equipped MES reactor was ca. 1.5-fold higher than in the control condition (w/o Co-Pi). The faradaic efficiency of the Co-Pi anode equipped MES reactor was remarkably higher (58.3%) than that of the bare anode (27.8%). While the system can improve the CO2 electroconversion nonetheless there are some further optimizations are necessary.

Regulation of Reactive Oxygen Species Promotes Growth and Carotenoid Production Under Autotrophic Conditions in Rhodobacter sphaeroides.

Industrial demand for capture and utilization using microorganisms to reduce CO2, a major cause of global warming, is significantly increasing. Rhodobacter sphaeroides is a suitable strain for the process of converting CO2 into high-value materials because it can accept CO2 and has various metabolic pathways. However, it has been mainly studied for heterotrophic growth that uses sugars and organic acids as carbon sources, not autotrophic growth. Here, we report that the regulation of reactive oxygen species is critical for growth when using CO2 as a sole carbon source in R. sphaeroides. In general, the growth rate is much slower under autotrophic conditions compared to heterotrophic conditions. To improve this, we performed random mutagenesis using N-methyl-N'-nitro-N-nitrosoguanidine (NTG). As a result, we selected the YR-1 strain with a maximum specific growth rate (µ) 1.44 day-1 in the early growth phase, which has a 110% faster growth rate compared to the wild-type. Based on the transcriptome analysis, it was confirmed that the growth was more sensitive to reactive oxygen species under autotrophic conditions. In the YR-1 mutant, the endogenous contents of H2O2 levels and oxidative damage were reduced by 33.3 and 42.7% in the cells, respectively. Furthermore, we measured that concentrations of carotenoids, which are important antioxidants. The total carotenoid is produced 9.63 g/L in the YR-1 mutant, suggesting that the production is 1.7-fold higher than wild-type. Taken together, our observations indicate that controlling ROS promotes cell growth and carotenoid production under autotrophic conditions.

Improving hydrogen production by pH adjustment in pressurized gas fermentation.

Gas fermentation utilizes syngas converted from biomass or waste as feedstock. A bubble column reactor for pressurizing was designed to increase the mass transfer rate between gas and liquid, and reduce energy consumption by medium agitation. Thermococcus onnurineus, a hydrogenic CO-oxidizer, was cultured initially under ambient pressure with the initial inlet gas composition; 60% CO and 40% N2. The maximum H2 productivity was 363 mmol/l/h, without pH adjustment. When additional pressure was applied, the pH rapidly declined; this may be attributed to the increased CO2 solubility under pressure. By controlling pH, H2 productivity increased up to 450 mmol/l/h; which is comparable to the previously reported H2 productivity in a continuous stirred tank reactor. The results may suggest energy saving potentials of bubble column reactors in gas fermentation. This finding may be applied to other gas fermentation processes, as syngas itself contains CO2 and many microbial processes also release CO2.

Paradigm shift in algal biomass refinery and its challenges.

Microalgae have been studied and tested for over 70 years. However, biodiesel, the prime target of the algal industry, has suffered from low competitiveness and current steps toward banning the internal combustion engine all over the world. Meanwhile, interest in reducing CO2 emissions has grown as the world has witnessed disasters caused by global warming. In this situation, in order to maximize the benefits of the microalgal industry and surmount current limitations, new breakthroughs are being sought. First, drop-in fuel, mandatory for the aviation and maritime industries, has been discussed as a new product. Second, methods to secure stable and feasible outdoor cultivation focusing on CO2 sequestration were investigated. Lastly, the need for an integrated refinery process to simultaneously produce multiple products has been discussed. While the merits of microalgae industry remain valid, further investigations into these new frontiers would put algal industry at the core of future bio-based economy.