Daily Fluctuations Research Articles

Daily fluctuations in adolescents' sleep predict next-day attention, sleepiness, and fatigue: an ecological momentary assessment study over 28 days.

Current understanding of the associations between adolescents' daily sleep and daytime alertness and fatigue under naturalistically occurring restricted (school) and unrestricted (vacation) sleep opportunities is limited. A convenience sample of adolescents (n = 205; 54.1% females, Mage ± SD = 16.9 ± 0.87 years) completed daily measures of sleep, alertness, and fatigue over 28 days (2 weeks during school, and the subsequent 2-week vacation). Actigraphy and sleep diary total sleep time (TST) and sleep efficiency (SE) were measured. Participants self-reported sleepiness and fatigue every morning and afternoon, and completed a tablet-based, 3.2-min psychomotor vigilance task (PVT) every afternoon. Cross-lagged multilevel models tested daily TST and SE as predictors of next-day subjective sleepiness/fatigue and PVT performance. Between- (i.e., differences between individuals) and within-person associations (i.e., whether nights with higher-than-individual's-average TST/SE, predict next-day outcomes) were tested simultaneously. Covariates included previous-day outcome, day of the week, study day (1-28), school/vacation, chronotype, and sociodemographic variables. Within-persons, higher-than-average TST and SE (both actigraphy and diary) predicted better next-day PVT performance (all p ≤ .006), and lower subjective sleepiness and fatigue the following morning and afternoon (all p ≤ .032). Between-persons, adolescents with higher overall diary SE had lower morning subjective sleepiness (p < .001) and fewer PVT false starts in the afternoon (p = 0.02). Nights with longer- and higher-than-average sleep efficiency (both actigraphy and diary) predicted better daytime alertness and fatigue, both when examined objectively via sustained attention and via self-report. These findings are relevant for understanding the significance of sleep for adolescents' day-to-day alertness levels and fatigue, particularly in the context of classroom learning and road safety.

Association Between Chronic Pain and Fatigue Severity with Weather and Air Pollution Among Females with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Weather and air quality conditions have been anecdotally reported to be related to symptom fluctuations in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), but this has never been empirically investigated. This exploratory study aims to examine the effects of weather and air quality on daily fluctuations of chronic pain and fatigue in women with ME/CFS. In an intensive longitudinal design, 58 participants with ME/CFS provided daily pain and fatigue ratings for an average of 61 days. Daily weather and air quality data were obtained from the National Oceanic and Atmospheric Administration and the US Environmental Protection Agency for the Birmingham, AL area. Linear mixed models revealed a significant relationship between days with more severe pain and worse Air Quality Indices (AQI, p &lt; 0.001), lower wind speeds (p = 0.009), greater particulate matter (p = 0.037), and lower carbon monoxide (p = 0.004), sulfur dioxide (p = 0.003), and ozone levels (p = 0.015). Greater fatigue was associated with more particulates (p = 0.023) and lower barometric pressure (p = 0.048). These results suggest that air quality and weather can have small effects on ME/CFS symptom severity.

Hair coat of the water deer &lt;I&gt;Hydropotes inermis&lt;/I&gt; (Cervidae, Artiodactyla) – a new species in the fauna of Russia

For the first time, the hair cover and hair microstructure of an adult male Hydropotes inermis from the Primorsky region of Russia were studied using light and scanning electron microscopy. The absence of abundant underfur, sparse wool and its weak layering are characteristic of this inhabitant of the temperate monsoon and tropical zones, which does not need an effective thermal protection.The alveolar and lattice medulla of the hair is similar to that of other deer, is well developed, and increases the heat-protective properties of the hair, since seasonal and daily fluctuations in temperature and humidity can be significant. The cuticle pattern is similar to that of deer tribes Alceini and Capreolini. The hair serves as reliable protection for the water deer from mechanical damage when moving through thickets of bushes and grasses along the banks of rivers and swamps in the biotopes that this deer inhabits, which is facilitated by the significant thickness and length of the hairs on the back, sides and thighs. The knotty nature of the hairs protects the deer’s skin from the sharp growths of aquatic and marsh plants.

Skin and hair of the Tibetan Hamster (&lt;i&gt;Urocricetus kamensis&lt;/i&gt;, Cricetidae, Rodentia): A comparative morphological analysis

For the first time, using light and scanning electron microscopy, the fine structure of the skin and its derivatives (glands, hairs, whiskers) was studied in adult male and female Tibetan hamsters (Urocricetus kamensis Satunin 1903), a unique species of the subfamily Cricetinae that lives only in the highlands of the Tibetan Plateau. A comparative morphological analysis of the skin, along with features of its similarity with the skin of other hamsters, revealed a number of characteristic features in the Tibetan hamster that contribute to the adaptation of this species to the harsh mountain climate conditions with sharp seasonal and daily temperature fluctuations: abundant subcutaneous fatty tissue, a special hair structure that provides effective heat protection due to a significant change in the volume of inert air in the fur: significant hair density, wavy arrangement of rows of hairs and the profile of the lower sections of the guard hairs. The presence of relatively long and thick guide hairs in the fur serves to protect the coat in rocky habitats. The array of specific skin glands is little compared to other Cricetinae; no mid-ventral and flank glands were found, this being unique for representatives of the subfamily, but this requires confirmation based on more abundant material.

Electrically Controlled Smart Window for Seasonally Adaptive Thermal Management in Buildings.

Smart windows offer a sustainable solution for energy-efficient buildings by adapting to various weather conditions. However, the challenge lies in achieving precise control over specific sunlight bands to effectively respond to complex weather changes and individual needs. Herein, a novel electrochromic smart window fabricated by integrating a self-assembled cellulose nanocrystals (CNCs) layer with an electrochromic poly(3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) (PEDOT:PSS) layer is reported, which exhibits high near-infrared transmittance, low solar reflectivity (26%), and infrared emissivity (20%) in winter, and low near-infrared transmittance, high solar reflectivity (91%), and infrared emissivity (≈94%) in summer. Interestingly, the material offers dynamic temperature control based on its photothermal properties, maintaining the internal temperature of buildings within a comfortable range of 20-25 °C from morning to night, particularly in winter with significant daily temperature fluctuations. Simulations show that the CNC-PED device demonstrates excellent energy-saving and CO2 emission reduction capacities across various global climate zones. This study presents a feasible pathway for constructing season-adaptive smart windows, making them suitable for energy-efficient buildings.

The effect of day-to-day temperature variability on agricultural productivity

Abstract With rising extreme weather events due to climate change, the impact on agricultural production has become increasingly severe. Yet, there has been a significant gap in research that assesses the influence of day-to-day temperature variability on agricultural productivity on a global scale. Our study addresses this gap by exploring the effects of day-to-day temperature variability and the change of rainfall patterns on agricultural productivity worldwide from 1961 to 2018. The results reveal that day-to-day temperature variability not only has a direct, negative impact on agricultural Total Factor Productivity (TFP), but also influences it by modulating the effects of monthly average temperatures and wet days. One unit increase of day-to-day temperature variability leads to a 2% decrease in TFP. Day-to-day temperature variability neutralizes the impact of monthly average temperature on TFP, while exacerbating the impact of wet days on TFP. Furthermore, extreme rainfall events result in a consistent negative marginal effect across all countries/seasons/rainfall intervals. This study also identifies differentiated impacts across countries with varying income levels. Low-income regions’ TFP demonstrates markedly significant sensitivities to both monthly average temperatures and daily temperature fluctuations, which means less resilient. Furthermore, the impacts of general and extreme rainfall are comparatively less pronounced in high-income countries, indicating higher resilience to climate variability in these regions and a relative vulnerability to extreme weather events in low-income regions. Our findings illuminate the intricate and multifaceted role that daily temperature variability plays in agricultural productivity, providing a theoretical basis for understanding the heterogeneous impacts of climate change on agriculture and contributing insights into the broader discourse on climate resilience and agricultural sustainability.

The relationship between daily blood glucose fluctuation and readmission in patients with acute coronary syndrome

Abstract Background Although the comprehensive diagnos and treatment of patients with acute coronary syndrome(ACS) has been greatly improved,the morbidity and mortality are still high. Previous literature has reported that about 70-75% of patients diagnosed with acute coronary syndrome have abnormal blood glucose metabolism1,and there existed gender differences. At present, most research results showed that elevated blood glucose is related to the prognosis of ACS. However, further intervention studies indicated that simply strengthen the reducement of blood glucose did not improve prognosis of these patients2. Glucose fluctuations, another dimension of blood glucose, reflect acute changes in blood glucose. However, there are few studies on the effect of blood glucose fluctuation on the prognosis of patients with ACS. Purpose This study aimed to explore the relationship between seven daily fluctuations of blood glucose and readmission in ACS patients. Methods In this study, a total of 664 patients admitted to our University from January 1, 2019 to December 31, 2019 were included and their blood glucose was monitored seven times a day during their hospitalization. The coefficient of variation of blood glucose (CV) was calculated from the results of seven daily blood glucose monitoring to represent the daily fluctuation of blood glucose. Univariate and multivariate regression analyses were performed to determine whether CV was an independent risk factor for re-hospitalization. In addition, the area under the curve (AUC) was used to evaluate the predictive efficacy of CV for outcome events. Finally, we performed a subgroup analysis based on gender and diabetes status to assess the association between CV and readmission in each subgroup. Results After a median follow-up of 21 months, the rate of readmission was 38.4%. Unstable angina pectoris (71.4%) and heart failure (16.4%) were the most common reasons for readmission. Patients with greater CV levels typically exhibited lower fasting blood glucose, higher post breakfast blood glucose, and higher admission blood glucose.The readmission rate increased with the increasing blood glucose variability after the cox regression analysis. Moreover, independent of glycated hemoglobin,CV could predict the risk of readmission in patients with ACS.When using 26.66% as the cutoff point of CV, the study population was divided into two groups and showed a significant differentiation for rehospitalization.In subgroup analysis, there was no significant association between seven daily blood glucose fluctuations and readmission in female and non-diabetes mellitus patients with acute coronary syndrome. Conclusions Besides some normal blood glucose index,seven daily fluctuations of blood glucose was associated with readmission in ACS patients. ACS patients who were male and combined with diabetes were more likely to be readmitted if their CV levels were greater.

Circadian heart rate fluctuations predict 21-year cardiovascular and all-cause mortality in type 2 and type 1 diabetes

Abstract Background Alterations in circadian heart rate (HR) fluctuations have been associated with cardiovascular events in the general population. This study aimed at defining their long-term prognostic value for cardiovascular and all-cause mortality in patients with diabetes. Methods We examined a cohort of 349 patients with type 2 or type 1 diabetes (52% women, age 57.1±11.9 years, BMI 29.4±5.9 kg/m2, HbA1c 8.6±2.1%, 81% type 2 diabetes) who underwent 24h ambulatory blood pressure and HR monitoring (ABPM) and assessment of diabetic microvascular complications. The median standard deviation (SD) value of ABPM-derived HR measurements was used to define patients with low daily HR fluctuations (low 24h-HR SD), while a &amp;lt;10% decline in average night-time vs day-time HR identified patients with blunted nocturnal HR dip (n=107, 31%). The clinical features and time-dependent prognostic impact of these conditions were evaluated over a 21-year observational follow-up. Results Low 24h-HR SD and blunted nocturnal HR dip were associated with an adverse cardiometabolic risk profile and high prevalence of cardiac autonomic neuropathy and nephropathy. After 6,251 person-years of follow-up (21.0 [14.0-21.0] years), a total of 136 (39%) deaths occurred, of which 100 (68%) of cardiovascular cause. After adjustment for potential confounders, the low 24h-HR SD group had a higher risk for both cardiovascular (hazard ratio 1.99, 95% CI 1.29–3.06, p=0.002) and all-cause mortality (hazard ratio 1.50, 95% CI 1.03–2.18, p=0.033), compared with high 24h-HR SD. Consistently, patients with blunted nocturnal HR dip had a higher adjusted risk for cardiovascular (1.61, 95% CI 1.07–2.43, p=0.023) and all-cause mortality (1.61, 95% CI 1.11–2.34, p=0.012), compared with those with preserved nocturnal HR dip. Conclusions Impaired circadian HR fluctuations are frequent in patients with long-standing diabetes and are associated with microvascular disease and increased long-term cardiovascular and all-cause mortality. Identifying these conditions via 24h-ABPM may provide a cost-effective risk stratification tool in this high-risk population.Kaplan-Meier curves

Enhancing thriving during COVID-19: The role of spirituality in fostering self-esteem among sojourners

The COVID-19 pandemic was particularly impactful on sojourners, absent from their families and unable to return home. This study explores how spirituality may impact self-esteem and how this may affect thriving during such a challenging time. Specifically, adopting a positive psychology framework, we expected higher/lower levels of spirituality would result in higher/lower levels of daily self-esteem, which in turn would result in higher/lower levels of daily thriving. We measured these daily fluctuations over a 9-day period by using a diary study. Results supported our hypothesized model. These findings extend our understanding of the role of spirituality during COVID-19 pandemic.

Forecasting Wholesale Electricity Market Prices Considering Bidding Conditions Using Price Sensitivity

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow.

Transition from endogenous to exogenous feeding in longfin yellowtail Seriola rivoliana larvae under simultaneous effects of daily temperature fluctuation and rotifer Brachionus rotundiformis enrichment.

Temperature and nutrition are suggested as the primary factors affecting larval survival during the transition from endogenous to exogenous feeding in fish. However, little is known about its simultaneous impact during this period. In this study, Seriola rivoliana eggs were subjected to a constant 24°C (CTE) and a daily temperature fluctuation (DTF) between 22.8 and 25.2°C until oil droplet exhaustion (5.5days after hatching). On the other hand, marine fish larvae mostly rely on live feed, with certain nutritional deficiencies such as poor long-chain fatty acids. Thus, rotifer Brachionus rotundiformis enrichment was simultaneously evaluated with temperature using three enrichment diets: Ori-green, S.presso, and a Domestic emulsion. For this purpose, the five experimental groups were established in triplicate using six 100-L tanks with three 10-L containers inside (18 experimental units in total). One hundred eggs were incubated, using a green water system, and 10 rotifers mL-1 were offered at mouth opening. After oil droplet exhaustion, survival was only affected by temperature (P < 0.01), being higher at DTF compared to CTE. At the same stage, Domestic emulsion resulted in bigger larvae than Ori-green. In a further assay at 3.7 DAH, the relative expression of the trypsin gene was higher at Domestic emulsion compared to S.presso and Ori-green. This study indicates that daily temperature fluctuation can improve larval performance and low levels of EPA and DHA in Domestic emulsion enriched rotifers were not critical for Seriola rivoliana at first feeding.

A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study’s overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos.

The Effect of Hive Type on Colony Homeostasis and Performance in the Honey Bee (Apis mellifera).

The colonies of honey bees are mostly sessile organisms. Consequently, the type of nest boxes that beekeepers provide to their bees should impact a colony's ability to maintain homeostasis, which is a key determinant of performance and fitness. Here, we used European honey bees (Apis mellifera) and provided them with two hive setups widely used and known as Langstroth and Warré. We compared colony performance in a Mediterranean climate for five months from late spring to early autumn, which covered the most active time of bees and included periods of heat and drought. We found that irrespective of hive type or season, honey bees kept hive temperature and humidity within a remarkably narrow range. Nevertheless, the hive type impacted the daily fluctuations in temperature and humidity. In Warré hives, where bees have more autonomy to build and maintain their combs, we found that bees were able to reduce daily fluctuations in temperature and humidity and kept both measures closer to the overall average. This increase in colony homeostasis found in Warré hives negatively correlated with other hive performance indicators, such as immunocompetence. We conclude that different hive types affect key areas, such as the central part of the colony with frames of developing brood or the queen, which are the most susceptible individuals. This implies that climatic changes resulting in extreme weather events are expected to impact colony performance and fitness, especially in non-managed honey bees that are limited by available nesting sites. For managed bees, adaptations to existing hive setups could be provided to help bees minimize the effects of abiotic stress.

Daily challenge-hindrance stress and work engagement in preschool teacher: the role of affect and mindfulness

BackgroundThe engagement of preschool teachers in their work is pivotal for maintaining teaching quality, ensuring teacher well-being, and fostering children’s development. Despite its significance, there is limited knowledge regarding the daily fluctuations in work engagement and the underlying factors influencing it. This study, guided by the Job Demands-Resources model and Affect Event Theory, utilized an experience sampling methodology to investigate the impact of challenge and hindrance stressors on daily work engagement, as well as the mediating role of affect and the moderating effect of mindfulness.MethodsUtilizing an experience sampling method, this study collected data from 220 preschool teachers in Shanghai over five consecutive workdays, conducting surveys once daily. Data analysis was performed using multilevel linear models.ResultsThe results from multilevel regression indicated that: (1) daily challenge stressors were positively related to work engagement, (2) daily hindrance stressors were negatively related to work engagement, (3) daily positive affect mediated the relationship between challenge stressors and work engagement, (4) daily negative affect mediated the relationship between hindrance stressors and work engagement, and (5) daily mindfulness played a crucial moderating role by alleviating the adverse effects of hindrance stressors on daily negative affect.ConclusionsThis study provides valuable insights into the daily experiences of preschool teachers and the factors that influence their work engagement. Understanding the impact of stressors, affect, and mindfulness on work engagement can inform the development of interventions and strategies to improve teacher well-being and work engagement.

Dynamic and Daily Partner-Specific Processes of Relationship Uncertainty and Enacted Relationship Talk

Guided by relational turbulence theory (RTT), this intensive longitudinal study examined how within-person daily fluctuations in relationship uncertainty corresponded with individuals’ decisions to engage in daily enacted relationship talk. Using a person-specific approach, this study also examined how individuals’ attachment insecurity predicted within-person differences in month-long processes predicted by RTT. College-aged dating partners ( N = 202, between-person) reported their attachment proclivities in a pre-test survey and subsequently reported on their relationship uncertainty and enacted relationship talk once per day over a period of 30 consecutive days ( N = 5,240, within-person). Results indicated that on days when individuals experienced elevated relationship uncertainty, they engaged in less relationship talk than they typically did. Additionally, we found that individuals with more volatility (intraindividual variability) and inertia (day-to-day carryover) in relationship uncertainty throughout the month enacted less relationship talk on average. Finally, results indicated that attachment insecurity predicted person-specific month-long processes consistent with RTT.

Using Short Time Series of Monofractal Synthetic Fluctuations to Estimate the Foreign Exchange Rate: The Case of the US Dollar and the Chilean Peso (USD–CLP)

Short time series are fundamental in the foreign exchange market due to their ability to provide real-time information, allowing traders to react quickly to market movements, thus optimizing profits and mitigating risks. Economic transactions show a strong connection to foreign currencies, making exchange rate prediction challenging. In this study, the exchange rate estimation between the US dollar (USD) and the Chilean peso (CLP) for a short period, from 2 August 2021 to 31 August 2022, is modeled using the nonlinear Schrödinger equation (NLSE) and calculated with the fourth-order Runge–Kutta method, respectively. Additionally, the daily fluctuations of the current exchange rate are characterized using the Hurst exponent, H, and later used to generate short synthetic fluctuations to predict the USD–CLP exchange rate. The results show that the USD–CLP exchange rate can be estimated with an error of less than 5%, while when using short synthetic fluctuations, the exchange rate shows an error of less than 10%.

Forecasting the Volatility of Tuna Fish Prices in North Sumatra using the ARCH Method in the Period January - April 2024

Tuna (Euthynnus affinis) is one of the most important fisheries commodities in Indonesia with significant economic value, especially in its contribution to fisheries export revenue. However, the price of tuna experiences significant fluctuations that can affect local and national economic stability. This study analyzes the daily price fluctuations of tuna in the North Sumatra market from January 1, 2024 to April 29, 2024 using a time series analysis approach. Daily price data were collected and analyzed to identify existing price patterns and volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) model was selected to address the heteroscedasticity in the data, which suggests that the volatility of tuna prices can be well predicted based on past price behavior. The analysis steps include identifying the optimal ARCH model using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), as well as testing parameter significance and normality assumptions to validate the model fit. The results show that the ARMA (1,0,0) model is the optimal one to model the price volatility of yellow tuna with the MAPE obtained of 2.382. compared to the ARMA-ARCH method with the MAPE value obtained of 2,747. Because it still contains heteroskedasticity effects, even though the results are good, the prediction results do not closely match the original data. The model is effective in improving price forecasting accuracy, which is important to support decision-making in risk management and economic planning in the fisheries sector. The findings contribute to understanding the dynamics of the yellowtail market and optimizing strategies for fisheries management.

Subsyndromes and symptom clusters: Multilevel factor analysis of behavioral and psychological symptoms of dementia with intensive longitudinal data.

Behavioral and psychological symptoms in dementia (BPSD) are dynamic phenomena with a high amount of intraindividual variability. We applied a multilevel framework to identify subsyndromes (between-person factors) that represent clinically relevant profiles of BPSD and identify symptom clusters (within-person factors) that represent contextually driven daily symptom experiences. This study used an intensive longitudinal design in which 68 co-residing family caregivers to persons living with dementia were recruited to proxy report on their care recipient's daily symptom experiences of 23 different BPSD for eight consecutive days (n=443 diaries). A multilevel exploratory/confirmatory factor analysis was used to account for nested data and separate within-person variances from between-level factor estimates. Exploratory factor analysis identified a 4-between 3-within factor structure based on fit statistics and clinical interpretability. This study offers major methodological and conceptual advancements for management of BPSD within Alzheimer's disease and related dementias by introducing two related but distinct concepts of subsyndromes and symptom clusters. Because behavioral and psychological symptoms of dementia (BPSD) are dynamic temporal phenomenon, this introduces measurement error into aggregate group-level estimates when trying to create subsyndromes. We propose a multilevel analysis to provide a more valid and reliable estimation by separating out variance due to within-person daily fluctuations. Using a multilevel exploratory factor analysis with intensive longitudinal data, we identified distinct and meaningful groups of BPSD. The four factors at the between-person level represented subsyndromes that are based on how BPSD co-occurred among persons with Alzheimer's disease (AD). These subsyndromes are clinically relevant because they share features of established clinical phenomena and may have similar neurobiological etiologies. We also found three within-person factors representing distinct symptom clusters. They are based on how BPSD clustered together on a given day for an individual with AD and related dementias. These clusters may have shared environmental triggers.

Adaptive vs. Conventional Deep Brain Stimulation: One-Year Subthalamic Recordings and Clinical Monitoring in a Patient with Parkinson's Disease.

Conventional DBS (cDBS) for Parkinson's disease uses constant, predefined stimulation parameters, while the currently available adaptive DBS (aDBS) provides the possibility of adjusting current amplitude with respect to subthalamic activity in the beta band (13-30 Hz). This preliminary study on one patient aims to describe how these two stimulation modes affect basal ganglia dynamics and, thus, behavior in the long term. We collected clinical data (UPDRS-III and -IV) and subthalamic recordings of one patient with Parkinson's disease treated for one year with aDBS, alternated with short intervals of cDBS. Moreover, after nine months, the patient discontinued all dopaminergic drugs while keeping aDBS. Clinical benefits of aDBS were superior to those of cDBS, both with and without medications. This improvement was paralleled by larger daily fluctuations of subthalamic beta activity. Moreover, with aDBS, subthalamic beta activity decreased during asleep with respect to awake hours, while it remained stable in cDBS. These preliminary data suggest that aDBS might be more effective than cDBS in preserving the functional role of daily beta fluctuations, thus leading to superior clinical benefit. Our results open new perspectives for a restorative brain network effect of aDBS as a more physiological, bidirectional, brain-computer interface.

Response of Native and Non-Native Subarctic Plant Species to Continuous Illumination by Natural and Artificial Light.

This study addressed the following questions: How does continuous lighting (CL) impact plant physiology, and photosynthetic and stress responses? Does the impact of CL depend on the source of the light and other environmental factors (natural vs. artificial)? Do responses to CL differ for native and non-native plant species in the subarctic region and, if differences exist, what physiological reasons might they be associated with them? Experiments were conducted with three plants native to the subarctic region (Geranium sylvaticum L., Geum rivale L., Potentilla erecta (L.) Raeusch.) and three non-native plant species (Geranium himalayense Klotzsch, Geum coccineum Sibth. and Sm., Potentilla atrosanguinea Loddiges ex D. Don) introduced in the Polar-Alpine Botanic Garden (KPABG, 67°38' N). The experimental groups included three species pairs exposed to (1) a natural 16 h photoperiod, (2) natural CL, (3) an artificial 16 h photoperiod and (4) artificial CL. In the natural environment, measurements of physiological and biochemical parameters were carried out at the peak of the polar day (at the end of June), when the plants were illuminated continuously, and in the second week of August, when the day length was about 16 h. Th experiments with artificial lighting were conducted in climate chambers where plants were exposed to 16 h or 24 h photoperiods for two weeks. Other parameters (light intensity, spectrum composition, temperature and air humidity) were held constant. The obtained results have shown that plants lack specific mechanisms of tolerance to CL. The protective responses are non-specific and induced by developing photo-oxidative stress. In climate chambers, under constant environmental conditions artificial CL causes leaf injuries due to oxidative stress, the main cause of which is circadian asynchrony. In nature, plants are not photodamaged during the polar day, as endogenous rhythms are maintained due to daily fluctuations of several environmental factors (light intensity, spectral distribution, temperature and air humidity). The obtained data show that among possible non-specific protective mechanisms, plants use flavonoids to neutralize the excess ROS generated under CL. In local subarctic plants, their photoprotective role is significantly higher than in non-native introduced plant species.