Single Factor Research Articles

Predicting rectal cancer tumor budding grading based on MRI and CT with multimodal deep transfer learning: A dual-center study

ObjectiveTo investigate the effectiveness of a multimodal deep learning model in predicting tumor budding (TB) grading in rectal cancer (RC) patients. Materials and methodsA retrospective analysis was conducted on 355 patients with rectal adenocarcinoma from two different hospitals. Among them, 289 patients from our institution were randomly divided into an internal training cohort (n = 202) and an internal validation cohort (n = 87) in a 7:3 ratio, while an additional 66 patients from another hospital constituted an external validation cohort. Various deep learning models were constructed and compared for their performance using T1CE and CT-enhanced images, and the optimal models were selected for the creation of a multimodal fusion model. Based on single and multiple factor logistic regression, clinical N staging and fecal occult blood were identified as independent risk factors and used to construct the clinical model. A decision-level fusion was employed to integrate these two models to create an ensemble model. The predictive performance of each model was evaluated using the area under the curve (AUC), DeLong's test, calibration curve, and decision curve analysis (DCA). Model visualization Gradient-weighted Class Activation Mapping (Grad-CAM) was performed for model interpretation. ResultsThe multimodal fusion model demonstrated superior performance compared to single-modal models, with AUC values of 0.869 (95% CI: 0.761–0.976) for the internal validation cohort and 0.848 (95% CI: 0.721–0.975) for the external validation cohort. N-stage and fecal occult blood were identified as clinically independent risk factors through single and multivariable logistic regression analysis. The final ensemble model exhibited the best performance, with AUC values of 0.898 (95% CI: 0.820–0.975) for the internal validation cohort and 0.868 (95% CI: 0.768–0.968) for the external validation cohort. ConclusionMultimodal deep learning models can effectively and non-invasively provide individualized predictions for TB grading in RC patients, offering valuable guidance for treatment selection and prognosis assessment.

Cell-fate conversion of intestinal cells in adult Drosophila midgut by depleting a single transcription factor

The manipulation of cell identity by reprograming holds immense potential in regenerative medicine, but is often limited by the inefficient acquisition of fully functional cells. This problem can potentially be resolved by better understanding the reprogramming process using in vivo genetic models, which are currently scarce. Here we report that both enterocytes (ECs) and enteroendocrine cells (EEs) in adult Drosophila midgut show a surprising degree of cell plasticity. Depleting the transcription factor Tramtrack in the differentiated ECs can initiate Prospero-mediated cell transdifferentiation, leading to EE-like cells. On the other hand, depletion of Prospero in the differentiated EEs can lead to the loss of EE-specific transcription programs and the gain of intestinal progenitor cell identity, allowing cell cycle re-entry or differentiation into ECs. We find that intestinal progenitor cells, ECs, and EEs have a similar chromatin accessibility profile, supporting the concept that cell plasticity is enabled by pre-existing chromatin accessibility with switchable transcription programs. Further genetic analysis with this system reveals that the NuRD chromatin remodeling complex, cell lineage confliction, and age act as barriers to EC-to-EE transdifferentiation. The establishment of this genetically tractable in vivo model should facilitate mechanistic investigation of cell plasticity at the molecular and genetic level.

Multi-factor based Regression Test Case Prioritization using Fuzzy Logic

The maintenance level activity generally done after the modification in the software to check whether it is functioning right or not is termed as regression testing. Test case prioritization, a key practice, involves strategically ordering test cases based on specific criteria to enhance the efficiency of fault detection within a condensed time frame. The fuzzy rule base serves as an alternative to the conventional crisp value set, offering a nuanced approach beyond binary outcomes (Yes or No). The primary objective of this research is to address critical factors often overlooked in existing literature on prioritization. Notably, prevalent approaches focus on singular factors during test case prioritization, highlighting the need for a comprehensive technique. To enhance the prioritization of test cases, there is a demand for a method that considers multi-factors or combinations thereof, ultimately increasing effectiveness. This paper introduces an innovative approach a multi-factors regression test-case prioritization technique utilizing fuzzy rules. The methodology aims to optimize the prioritization of test cases, striking a balance between effectiveness and time efficiency. Fuzzy rules are formulated to assess the effectiveness of a prioritized set of test cases in developing the proposed approach. A user-friendly tool has been developed to facilitate the application of this technique, allowing users to input relevant factors and subsequently prioritize test cases accordingly. Through extensive experiments using the developed tool, the effectiveness of the proposed approach has been validated. The results demonstrate that the priority lists of test cases generated for different projects, considering multi-factors, show greater promise compared to techniques relying solely on a single factor for prioritization.

Monte Carlo on manifolds in high dimensions

We introduce an efficient numerical implementation of a Markov Chain Monte Carlo method to sample a probability distribution on a manifold (introduced theoretically in Zappa, Holmes-Cerfon, Goodman (2018) [53]), where the manifold is defined by the level set of constraint functions, and the probability distribution may involve the pseudodeterminant of the Jacobian of the constraints, as arises in physical sampling problems. The algorithm is easy to implement and scales well to problems with thousands of dimensions and with complex sets of constraints provided their Jacobian retains sparsity. The algorithm uses direct linear algebra and requires a single matrix factorization per proposal point, which enhances its efficiency over previously proposed methods but becomes the computational bottleneck of the algorithm in high dimensions. We test the algorithm on several examples inspired by soft-matter physics and materials science to study its complexity and properties.

Effects of Geomorphic Spatial Differentiation on Vegetation Distribution Based on Remote Sensing and Geomorphic Regionalization

As the core area of human activities and economic development in the Xinjiang Autonomous Region, the hilly oasis zone of Xinjiang directly affects the regional sustainable development and stability of the ecosystem. Understanding the effects of different geomorphic types on vegetation distribution is crucial for maintaining vegetation growth and development, especially the improvement in the terrestrial ecological environment in arid areas under the background of climate change. However, there are few studies on the effect of spatial differences in detailed geomorphic types on vegetation distribution patterns. Therefore, this paper divides the Xinjiang hilly oasis zone into six geomorphologic level zones and innovatively investigates the influence of detailed geomorphologic types on the spatial distribution of vegetation and vegetation cover. Further, the area proportion of detailed landform types corresponding to different vegetation coverage in each geomorphic area was quantitatively calculated. Finally, the Geodetector method was used to detect the drivers of interactions between vegetation and the environment. The findings are shown as follows: (1) In the same climate zone, the spatial differentiation of landforms has a great influence on the vegetation distribution, manifesting as the significantly different vegetation distribution in different landform types. Grassland is the main vegetation type in the erosion and denudation of Nakayama; cultivated vegetation and meadows have a larger coverage in the alluvial flood plain and alluvial plain; and the distribution of vegetation in the Tianshan economic zone is characterized by obvious vertical zoning with the geomorphology. (2) The landform type and morphological types are the strongest driving factors for vegetation coverage with q values of 0.433 and 0.295, respectively, which effectually fill the gap caused by only using two terrain indicators, slope and elevation, to study the relationship between landforms and vegetation. (3) In addition, the improved nonlinear interaction resulting from the double factor of landform type and slope is 0.486, which has a stronger control on vegetation coverage than the single factor of landform type. These findings are conducive to enhancing the supply services of vegetation to the ecosystem in arid areas as well as providing important scientific guidance for the construction of ecological civilization and sustainable development in Xinjiang.

SafePass : Reinventing Digital Access with Visual Cryptography, Steganography, and Multi-Factor Authentication

Safe-Pass presents a user-friendly and secure solution for simplifying digital access. With a downloadable application that operates seamlessly across your devices, it eliminates the inconvenience of traditional passwords. The process begins with accessing the Master password app through a distinctive image-based authentication. Operating inconspicuously in the background, the app not only enhances the strength of your existing passwords but also manages and facilitates automatic logins. This system offers adaptable security options, enabling swift access through a single factor or heightened security through the combination of multiple factors. Addressing the persistent threat of phishing, wherein sensitive user information is compromised, we introduce an innovative approach leveraging Visual Cryptography and Steganography for enhanced online security. Our method involves the application of Visual Cryptography to confidential credentials, generating two shares. One share is stored on the server, while the other is concealed within a reCAPTCHA image or a user-defined image through Steganography. During login attempts, users provide their username along with the reCAPTCHA image or chosen image. Successful authentication grants access, while repeated failed attempts trigger email notifications. Master Login prioritizes user privacy, safeguarding passwords as individual and exclusive data. Data sharing or selling is never practiced, ensuring the confidentiality of user information.              

Influencing Factors as well as Implementation Path of Synergistic Development of Digitalization and Greening in Manufacturing Industry: Analysis from a Configuration Perspective

<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces’ manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.</span></p>

Multiple paths of green and low-carbon development in industrial parks: group analysis based on the TOE framework

The green and low-carbon development of industrial parks plays an important role in promoting industrial and technological agglomeration, How to optimise their green and low-carbon development pathways is an important management question to be answered, requiring the examination of multiple path based on a histological perspective. Based on the technology–organisation–environment framework, this study selected 29 state-level economic and technological development zones (ETDZs) in China as research objects, and explored multiple sets of paths using fuzzy set qualitative comparison and necessity-contribution analysis. The study found that: 1) four promotion paths can be adopted for green low-carbon development of the zones: endogenous drive, balanced promotion, core support drive and comprehensive drive; 2) single environmental factor does not constitute a bottleneck hindering the green low-carbon development of the zones, which should be combined with the actual situation of the zones, and the balanced enhancement of the effectiveness of green and low-carbon should be realised through measures such as industrial policy, environmental risk prevention, and performance assessment of green policies. Finally, the study proposes the following policy recommendations: industrial parks should focus on the key role of environmental risk prevention, apply a combination of measures in accordance with their own realities, and appropriate external pressure can help promote green and low-carbon development.

Word-of-mouth generated influences of different prepared dishes via online consumer purchases: Preliminary text-based research findings from "Jingdong Mall" flagship shops.

With the rapid development of China's prepared vegetable product market, consumer demand for prepared vegetable products is increasing. The study adopts a qualitative research method to construct a model of factors influencing the generation of consumer word-of-mouth (IWOM) in the online consumption context, taking the real consumer word-of-mouth of Jingdong's own flagship shop of prepared dishes as the object of the study. The model states that the objective factors that promote the generation of word of mouth include specific consumption context (emotionality, initiality, scarcity, convenience and process) and specific product attributes (richness, safety, accessibility and emotionality). Meanwhile subjective factors design consumer satisfaction with the product (satisfaction with a single attribute and satisfaction with the brand as a whole) and emotions (positive self-conscious emotions, high arousal positive emotions and medium arousal positive emotions). Objective factors may contribute directly to the generation of word-of-mouth, or they may further contribute to word-of-mouth generation through subjective consumer factors. In addition, IWOM is often generated not by a single factor, but by a combination of factors.

Traumatic dental injuries: Knowledge assessment of dentists in the Norwegian Public Dental Service of Vestland.

Most children and adolescents in Norway attend the Public Dental Service (PDS) where they are treated free-of-charge until the age of 19 years. Thus, general dentists employed in the PDS are the primary care providers for traumatic dental injuries (TDI) in young patients. This cross-sectional study assessed the knowledge of PDS general dentists on the acute management and follow-up of TDI and its socio-demographic and attitudinal covariates. All general dentists employed in the Vestland County PDS, Western Norway, (N = 170) received an online questionnaire. Socio-demographic and professional profiles of respondents as well as attitudinal indicators were queried. Clinical case scenarios on emergency treatment and further follow-ups of TDI were used to calculate a dental trauma knowledge score (DTKS; range: 0-21). Mann-Whitney U tests and Kruskal-Wallis tests determined differences between the demographic subgroups. Logistic regressions determined the potential of single factors in explaining the variability in dental trauma knowledge. The response rate was 46%. Most participants (60.5%) had not participated in a TDI course after graduation but would like such a course (84.2%). Mean DTKS was 13.82 (±2.6). Knowledge scores differed significantly between age groups (p = .014) and years since graduation (p = .0018). Younger dentists and recently graduated dentists scored highest. Dentists under the age of 30 years scored higher than 30-39- and 40-49-year-old dentists in these areas: emergency treatment of crown fractures with pulp exposure, identification of complications after avulsion, and management of severe intrusive luxation injury. Younger dentists had a higher theoretical knowledge of TDI. Continuing professional development among dentists in the Norwegian PDS is needed for emergency treatment and complication management after TDI.

Spatiotemporal Changes and Driving Analysis of Ecological Environmental Quality along the Qinghai–Tibet Railway Using Google Earth Engine—A Case Study Covering Xining to Jianghe Stations

The Qinghai–Tibet Railway is located in the most fragile and sensitive terrestrial ecosystem of the Qinghai–Tibet Plateau in China, and once the ecological environment is damaged, it is difficult to restore. This study, based on the Google Earth Engine platform, focuses on the section of the Qinghai–Tibet Railway from Xining to Jianghe. It utilizes Landsat series satellite imagery data from 1986 to 2020 to calculate the Remote Sensing Ecological Index (RSEI). This approach enables large-scale and long-term dynamic monitoring, analysis, and assessment of the ecological changes along the Qinghai–Tibet Railway corridor. The results indicate that (1) the average RSEI of the study area increased from 0.37 in 1986 to 0.53 in 2020, showing an overall trend of improvement. The ecological environment quality is mainly categorized as medium and good. (2) The quality of the ecological environment in the areas along the railway experienced fluctuations during different periods of railway construction and operation. From 1986 to 1994, after the first phase of the railway opened, the overall ecological environment showed a relative decline in quality. From 1994 to 2002, the ecological quality of 60% of the region saw slight improvements. During the extension construction of the second phase of the railway from 2002 to 2007, the regional ecology fluctuated again. However, from 2013 to 2020, during the operational period, a stable recovery trend was observed in the ecological environment. (3) The ecological environment in the study area is influenced by multiple factors. Different railway station areas exhibit strong spatial heterogeneity. The impact of single factors is significant, with the existence of spatial stratification and enhanced interactions among multiple factors. The strongest interactive effects are observed between land use types, the intensity of human activities, and temperature.

Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

A Smartphone-Gamified Virtual Reality Exposure Therapy Augmented With Biofeedback for Ailurophobia: Development and Evaluation Study.

To the best of our knowledge, no specialized research has been conducted to address ailurophobia (fear of cats) in Iran or globally. This has driven our project, along with the prevalence of ailurophobia and the absence of a gamified virtual reality exposure therapy (VRET) that incorporates affordable and easily accessible biofeedback (BF) tools. We hypothesize that a gamified VRET augmented with BF will yield more positive effects than a similar device lacking BF. This study primarily focuses on the development and preliminary evaluation of a smartphone-gamified VRET integrated with BF, targeting animal phobia, with a specific case study on ailurophobia. The secondary objectives are using affordable and readily available BF found in devices such as smart bands and smartwatches and creating a mobile virtual reality gamified app to improve patients' adherence to treatments while simultaneously enhancing the app's accessibility, scalability, and outreach. Evaluations encompassed 3 methods. First, we identified the tool's potential positive effects on phobia interventions, exploring 4 effects: intrinsic motivation, simulation of fearful situations, management of stressful circumstances without therapists' presence and mitigation of catastrophic thoughts, and preliminary effects on ailurophobia treatment. Participants were divided into BF and non-BF groups. Second, we gathered user preferences and opinions about the treatment. Third, we conducted heuristic evaluations using 44 heuristics from existing system usability scales assessing user interfaces, virtual reality platforms, and video games' playability. To interpret the data, mean scores; ANOVA, single factor; and ANOVA, 2-factor with replication were used. A total of 29 individuals were identified, of which 10 met the eligibility criteria or were accessible. The smartphone-gamified VRET augmented with BF exhibited better results on the identified effects compared with the non-BF version and contributed to normalizing encounters with cats. Moreover, 41 of the 44 heuristics achieved a percentage above 62%, indicating its potential as a therapeutic product and its ability to enhance patient adherence to treatments. Patient preferences on the treatment and its strengths and weaknesses were provided for further improvement. The tool has the potential to evolve into a comprehensive solution by incorporating various types of cats and their behaviors, simulating environments in which they are commonly found, and enhancing its appeal through an increased sense of adventure without inducing unrealistic fears. By adapting fear elements, the game can be tailored to treat various animal phobias. Phobia-focused games should avoid action and combat scenarios to prevent reinforcement of fear responses. After rigorous evaluation, further exploration is required to provide remote use beyond clinical settings.

Active tracking measures: Providing targets for youth alcohol use and health behavior interventions.

This study seeks to use examine the utility of recently published, developmentally informed measures of parental active tracking for work on youth health behaviors. Information on parental active tracking and health behaviors were collected in six independent survey studies (n's = 157-408). A series of empirical questions regarding parental active tracking were examined. The recently published measures of parental active tracking in high school and college were shown to have sufficient internal consistency, to be well described by single latent factors, to be empirically distinct from helicopter parenting, to be inconsistently linked with youth risk behaviors, and to demonstrate closer linkages with youth substance use when accounting for parental motivations for tracking. The measures of parental active tracking employed provide face valid, psychometrically sound, and empirically supported indices for use examining parental influences during adolescence and young adulthood.

Ecological Risk Assessment of Forest Landscapes in Lushan National Nature Reserve in Jiangxi Province, China

It is highly valuable to analyze and assess the landscape ecological risk of nature reserves to prevent and resolve ecological risks, as well as to effectively protect and maintain the sustainable development of nature reserves. Taking the forest landscape of the Lushan National Nature Reserve as its study object, this study performed grid processing for the nature reserve and classified forest landscape types using the Forest Resource Inventory Database in 2019. A landscape ecological index model was constructed to evaluate the ecological risk. Global and local Moran index values were used to reveal the autocorrelations for ecological risk. The geodetector method was used to comprehensively analyze the effects of natural and human factors on ecological risk. The results showed that, in general, the ecological risk level of the nature reserve was relatively low, as the proportion of the lowest-, lower-, and medium-risk areas to the total forestry land area accounted for 91.03%. The ecological risk ranking of each functional zone, from high to low, was in the order of the experimental zone, the buffer zone, and the core zone. The ecological risk levels of different forest landscape types were closely related to their area, spatial distribution, and succession stage, as well as human factors, such as the proximity to roads and settlements, etc. The forest landscape with the highest ecological risk was the Cunninghamia lanceolata (Lamb.) Hook. forest, and the forest landscape with the lowest ecological risk was other forestry land. Ecological risk had a positive spatial correlation and tended to be aggregated in space, demonstrating coupling with the proximity to roads and settlements. The ecological risk was affected by both human and natural factors, among which human factors played a dominant role. The proximity to roads and settlements, the relative humidity, and the temperature were the main driving factors. The interaction of pairwise factors had a stronger influence than that of single factors. Therefore, controlling the intensity of human activities and enhancing the coordination between humans and nature are beneficial for alleviating the ecological risks in the forest landscapes of nature reserves.

Global Sensitivity Analysis of Factors Influencing the Surface Temperature of Mold during Autoclave Processing.

During the process of forming carbon fiber reinforced plastics (CFRP) in an autoclave, deeply understanding the global sensitivity of factors influencing mold surface temperature is of paramount importance for optimizing large frame-type mold thermally and enhancing curing quality. In this study, the convective heat transfer coefficient (CHTC), the thickness of composite laminates (TCL), the thickness of mold facesheet (TMF), the mold material type (MMT), and the thickness of the auxiliary materials layer (TAL) have been quantitatively assessed for the effects on the mold surface temperature. This assessment was conducted by building the thermal-chemical curing model of composite laminates and utilizing the Sobol global sensitivity analysis (GSA) method. Additionally, the interactions among these factors were investigated to gain a comprehensive understanding of their combined effects. The results show that the sensitivity order of these factors is as follows: CHTC > MMT > TMF > TCL > TAL. Moreover, CHTC, MMT, and TMF are the main factors influencing mold surface temperature, as the sum of their first-order sensitivity indices accounts for over 97.3%. The influence of a single factor is more significant than that of the interaction between factors since the sum of the first-order sensitivity indices of the factors is more than 78.1%. This study will support the development of science-based guidelines for the thermal design of molds and associated heating equipment design.

The optimization of extraction process on Quzhou Fructus Aurantii flavonoids with natural deep eutectic solvents: A comparative study between orthogonal analysis and genetic neural network

Quzhou Fructus Aurantii (QFA) is the dried immature fruit of the Rutaceae plant Citrus changshanhuyou Y. B. Chang (CSHY). In this study, six flavonoids including narirutin, naringin, hesperidin, neohesperidin, nobiletin, tangeretin were obtained by the ultrasonic-assisted extraction. Subsequently, nine types of natural deep eutectic solvents (NaDESs) were systematically screened and evaluated by the extraction yields determined by high-performance liquid chromatography (HPLC). Based on the single factor experiment, the influences of critical parameters on the extraction rate were investigated. Finally, the extraction process was optimized by orthogonal analysis and genetic neural network (GNN), respectively. Besides, the yields and antioxidant capacities of the extracts derived from the two different optimized processes were measured and compared. Taken together, the results indicated that betaine-lactic acid used as an extractant showed significant advantages among the tested NaDESs. Compared with the optimization results of orthogonal experiments, better extraction yields and antioxidant abilities were produced by GNN. The optimal factors provided by GNN were as follows: ultrasonic power 100 %, ultrasonic temperature 76 °C, ultrasonic time 69 min, water content 36%, liquid-solid ratio 15:1 (mL: g), particle size 70 mesh. The optimum extraction process was simple, feasible, stable and efficient.

Single-Shot Factorization Approach to Bound States in Quantum Mechanics

Single-Shot Factorization Approach to Bound States in Quantum Mechanics

The Effect of Exercise-Induced Central Fatigue on Cervical Spine Joint Position Error, Strength, and Endurance.

Fatigue is common in sports, impairing performance and increasing injury risk, yet little is known regarding fatigue and concussion. Impaired neck neuromuscular function may contribute to concussion at baseline, where central fatigue may further impair neck function resulting in increased concussion risk. These effects may be magnified in athletes with a history of concussion. To determine the effect of exercise induced central fatigue on neck joint position error, strength, and endurance in healthy subjects and those with a history of concussion. The investigators hypothesized that EICF would have a negative effect on all variables. Healthy subjects were examined using a single factor, within-subjects repeated measures design. Concussion history subjects were examined using a single-subject design. Nineteen healthy subjects and five subjects with a history of concussion were recruited for the study. Cervical joint position error, muscle strength, and neck flexor endurance were tested before and after exercise induced fatigue. There was a significant increase in constant (p = 0.0027) and absolute joint position error (JPE) (p < 0.001); decrease in neck flexor endurance (p < 0.001); and decrease neck strength into cervical flexion (p = 0.01) in healthy subjects following fatigue. Among concussion history subjects, five demonstrated a significant increase in absolute and constant JPE (p < 0.05); four demonstrated a significant decrease in neck flexor endurance (p < 0.05); one in neck flexion muscle strength (p < 0.05); and three in neck extension and rotation muscle strength (p < 0.05) following fatigue. Cervical neuromuscular function deteriorated following fatigue in healthy subjects. Resulting impairments may affect force alterations in cervical control, potentially increasing concussion risk. Concussion history subjects descriptively demonstrated similar results, however further research should examine formal comparisons involving subjects with and without concussion history. 3b.

Combined high light and salt stress enhances accumulation of PsbS and zeaxanthin in Chlamydomonas reinhardtii.

The performance and acclimation strategies of Chlamydomonas reinhardtii under stress conditions are typically studied in response to single stress factors. Under natural conditions, however, organisms rarely face only one stressor at a time. Here, we investigated the impact of combined salt and high light stress on the photoprotective response of C. reinhardtii. Compared to the single stress factors, the combination of both stressors decreased the photosynthetic performance, while the activation of energy dissipation remained unaffected. However, the PsbS protein was strongly accumulated and the conversion of violaxanthin to zeaxanthin was enhanced. These results support an important photoprotective function of PsbS and zeaxanthin independently of energy dissipation under combined salt and high light stress in C. reinhardtii.