Daylight driven vertical migration of mesozooplankton in the Arabian sea and the Bay of Bengal.

Diurnal vertical migration (DVM) of mesozooplankton in the Deep Scattering Layer (DSL) of the Indian seas is poorly studied. This cyclical vertical migration substantially controls the carbon sequestration in the ocean. The present research is a comprehensive examination to analyse the factors affecting the DVM pattern of the zooplankton community in the Arabian Sea (AS) and the Bay of Bengal (BoB). Echo sounder profiling was conducted at shallow depths (∼10-400m) of the AS (January 2023) and BoB (March 2023) with a period of 24 h to monitor the DVM pattern of the DSL. Vertical migration in both basins showcased the notable influence of the spatio-temporal contrast in the occurrence of daybreak, with the day (descend) and night (ascend) cycle of the DSL. Delayed descent was observed in the AS contrary to BoB, owing to the delayed day break in the AS relative to BoB. Intensity and temporal pattern of the incoming solar radiation were correlated with the DVM whereas diurnal variation of sea surface temperature was observed to be contrasting. The preliminary analysis is indicative of the diversified community structure of the zooplankton community in these basins resulting from the vertical migration. Furthermore, it is conclusive that the surface residence time of the zooplankton is distinct and is affirmed based on daybreak and light intensity particular for each basin. Since daybreak vary with the geolocation, sole dependence on a particular time for migration study can be erroneous, which is highlighted in the present study.

Heavy metals in centralized drinking water sources of the Yangtze River: A comprehensive study from a basin-wide perspective.

Water quality in the Yangtze River Basin (YRB) has received considerable attention because it supplies water to 400 million people. However, the trends, sources, and risks associated with heavy metals (HMs) in water of centralized drinking water sources (CDWSs) in the YRB region are not well understood due to the lack of high-frequency, large-scale monitoring data. Moreover, research on the factors affecting the transportation of HMs in natural water are limited, all of which significantly reduce the effectiveness of CDWSs management. Therefore, this study utilized data on 11 HMs and water quality from 114 CDWSs, covering 71 prefecture-level cities (PLC) in 15 provinces (cities), to map unprecedented geospatial distribution of HMs in the YRB region and examine their concentrations in relation to water chemistry parameters. The findings revealed that the frequency of detection (FOD) of 11 HMs ranged from 28.59% (Hg) to 99.64% (Ba). The mean concentrations are ranked as follows: Ba (40.775 µg/L) > B (21.866 µg/L) > Zn (5.133 µg/L) > V (2.668 µg/L) > Cu (2.049 µg/L) > As (1.989 µg/L) > Mo (1.505 µg/L) > Ni (1.108 µg/L) > Sb (0.613 µg/L) > Pb (0.553 µg/L) > Hg (0.002 µg/L). Concentrations of Zn, As, Hg, Pb, Mo, Sb, Ni, and Ba exhibited decreasing trends from 2018 to 2022. Human activities, including industrial and agricultural production, have led to higher pollution levels in the midstream and downstream of the river than in its upstream. Additionally, the high concentrations of Ba and B are influenced by natural geological factors. Anion concentrations and nutrient levels, play a significant role in the transport of HMs in water. Probabilistic health risk assessment indicates that As, Ba, and Sb pose a potential carcinogenic risk. Additionally, non-carcinogenic risk to children under extreme conditions should also be considered.

Variability of ambient air ammonia in urban Europe (Finland, France, Italy, Spain, and the UK).

This study addressed the scarcity of NH3 measurements in urban Europe and the diverse monitoring protocols, hindering direct data comparison. Sixty-nine datasets from Finland, France, Italy, Spain, and the UK across various site types, including industrial (IND, 8), traffic (TR, 12), urban (UB, 22), suburban (SUB, 12), and regional background (RB, 15), are analyzed to this study. Among these, 26 sites provided 5, or more, years of data for time series analysis. Despite varied protocols, necessitating future harmonization, the average NH3 concentration across sites reached 8.0 ± 8.9 µg/m3. Excluding farming/agricultural hotspots (FAHs), IND and TR sites had the highest concentrations (4.7 ± 3.2 and 4.5 ± 1.0 µg/m3), followed by UB, SUB, and RB sites (3.3 ± 1.5, 2.7 ± 1.3, and 1.0 ± 0.3 µg/m3, respectively) indicating that industrial, traffic, and other urban sources were primary contributors to NH3 outside FAH regions. When referring exclusively to the FAHs, concentrations ranged from 10.0 ± 2.3 to 15.6 ± 17.2 µg/m3, with the highest concentrations being reached in RB sites close to the farming and agricultural sources, and that, on average for FAHs there is a decreasing NH3 concentration gradient towards the city. Time trends showed that over half of the sites (18/26) observed statistically significant trends. Approximately 50 % of UB and TR sites showed a decreasing trend, while 30 % an increasing one. Meta-analysis revealed a small insignificant decreasing trend for non-FAH RB sites. In FAHs, there was a significant upward trend at a rate of 3.51[0.45,6.57]%/yr. Seasonal patterns of NH3 concentrations varied, with urban areas experiencing fluctuations influenced by surrounding emissions, particularly in FAHs. Diel variation showed differing patterns at urban monitoring sites, all with higher daytime concentrations, but with variations in peak times depending on major emission sources and meteorological patterns. These results offer valuable insights into the spatio-temporal patterns of gas-phase NH3 concentrations in urban Europe, contributing to future efforts in benchmarking NH3 pollution control in urban areas.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 µg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

Dynamics of Gut Bacteria Across Different Zooplankton Genera in the Baltic Sea.

In aquatic ecosystems, zooplankton-associated bacteria potentially have a great impact on the structure of ecosystems and trophic networks by providing various metabolic pathways and altering the ecological niche of host species. To understand the composition and drivers of zooplankton gut microbiota, we investigated the associated microbial communities of four zooplankton genera from different seasons in the Baltic Sea using the 16S rRNA gene. Among the 143 ASVs (amplified sequence variants) observed belonging to heterotrophic bacteria, 28 ASVs were shared across all zooplankton hosts over the season, and these shared core ASVs represented more than 25% and up to 60% of relative abundance in zooplankton hosts but were present at low relative abundance in the filtered water. Zooplankton host identity had stronger effects on bacterial composition than seasonal variation, with the composition of gut bacterial communities showing host-specific clustering patterns. Although bacterial compositions and dominating core bacteria were different between zooplankton hosts, higher gut bacteria diversity and more bacteria contributing to the temporal variation were found in Temora and Pseudocalanus, compared to Acartia and Synchaeta. Diet diatom and filamentous cyanobacteria negatively correlated with gut bacteria diversity, but the difference in diet composition did not explain the dissimilarity of gut bacteria composition, suggesting a general effect of diet on the inner conditions in the zooplankton gut. Synchaeta maintained high stability of gut bacterial communities with unexpectedly low bacteria-bacteria interactions as compared to the copepods, indicating host-specific regulation traits. Our results suggest that the patterns of gut bacteria dynamics are host-specific and the variability of gut bacteria is not only related to host taxonomy but also related to host behavior and life history traits.

Temporal gut microbiota variability and association with dietary patterns: From the one-year observational Diet, Cancer, and Health - Next Generations MAX study.

BACKGROUND: Knowledge about the variability of gut microbiota within an individual over time is important to allow meaningful investigations of the gut microbiota in relation to diet and health outcomes in observational studies. Plant-based dietary patterns have been associated with a lower risk of morbidity and mortality and may alter gut microbiota in a favorable direction. OBJECTIVES: To assess the gut microbiota variability during one year and investigate the association between adherence to diet indexes and the gut microbiota in a Danish population. METHODS: Four hundred forty-four participants were included in the Diet, Cancer, and Health - Next Generations MAX study (DCH-NG MAX). Stool samples collected up to three times during a year were analyzed by 16S ribosomal ribonucleic acid gene sequencing. Diet was obtained by 24-hour dietary recalls. Intraclass correlation coefficient (ICC) was calculated to assess temporal microbial variability based on 214 individuals. Diet indexes (Nordic, Mediterranean, and plant-based diets) and food groups thereof were associated with gut microbiota using linear regression analyses. RESULTS: We found that 91 out of 234 genera had an ICC >0.5. We identified three subgroups dominated by Bacteroides, Prevotella 9, and Ruminococcaceae and adherence to diet indexes differed between subgroups. Higher adherence to diet indexes was associated with the relative abundance of 22 genera. Across diet indexes, higher intakes of fruit, vegetables, whole grains/cereals, and nuts were most frequently associated with these genera. CONCLUSIONS: In the DCH-NG MAX study, 39% of the genera had an ICC >0.5 over one year, suggesting that these genera could be studied with health outcomes in prospective analyses with acceptable precision. Adherence to the Nordic, Mediterranean, and plant-based diets differed between bacterial subgroups and was associated with a higher abundance of genera with fiber-degrading properties. Fruits, vegetables, whole grains/cereals, and nuts were frequently associated with these genera.

Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study.

BACKGROUND: Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS: Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS: Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS: Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.

The thermal journey of macroalgae: Four decades of temperature-induced changes in the southeastern Bay of Biscay.

Global warming is triggering significant shifts in temperate macroalgal communities worldwide, favoring small, warm-affinity species over large canopy-forming, cold-affinity species. The Cantabrian Sea, a region acutely impacted by climate change, is also witnessing this shift. This study delved into the impacts of increasing sea surface temperature on the subtidal macroalgal communities in the southeastern Bay of Biscay over the last four decades, by using data from the years 1982, 2007, 2014, and 2020. We found that temperature has shaped the community structure, with warm-affinity species steadily displacing their cold-affinity counterparts. Notably, new communities exhibited a profusion of smaller algal species, explaining the observed increased biodiversity within the area. In the last period investigated (2014-2020), we observed a partial recovery of the communities, coinciding with cooler sea surface temperatures. Shallow algal communities were more reactive to temperature variations than deeper communities, possibly associated with higher exposure to increased temperatures. Our study offered insights into the intricate relationship between the changes in ocean temperature and algal species in the southeastern Bay of Biscay, shedding light on the ongoing ecological shifts in this region.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit.

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Plantar intrinsic foot muscle activity and its relationship with postural sway during tiptoe standing in ballet dancers and non-dancers.

BACKGROUND: Minimizing postural sway during tiptoe standing is essential for ballet dancers. Investigation of the activity of the plantar intrinsic foot muscles (PIFMs) may provide insight into postural sway in dancers. Herein, we compared PIFM activity during tiptoe standing between dancers and non-dancers and examined its relationship with postural sway. METHODS: We enrolled 14 female ballet dancers and 13 female non-dancers. Electromyography (EMG) amplitudes of 64 channels of PIFMs and center of pressure (COP) data were recorded during bipedal tiptoe standing tasks performed with ankle plantarflexion angles of 20°, 40°, and 60° (dancers only). The EMG amplitudes were normalized to those during the maximum voluntary contraction, and the muscle activity level and its coefficient of variation over time (EMG-CVtime) during the task were assessed. Standard deviations in the anteroposterior and mediolateral directions, velocity, and area were calculated from the COP data. RESULTS: Most COP and EMG variables were significantly lower in dancers than in non-dancers in both the 20° and 40° tasks (p < 0.05). Significant correlations were found between most combinations of the COP and EMG variables in both the 20° and 40° tasks in the whole cohort (r = 0.468-0.807, p ≤ 0.014). In the 60° task in dancers, COP velocity was strongly correlated with EMG-CVtime (r = 0.700, p = 0.005). CONCLUSION: These results provide novel evidence that the PIFMs do not require high activity, but rather that its low, steady activity is the key, to achieve less postural sway during bipedal tiptoe standing in dancers.

Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems.

A tenet of ecology is that temporal variability in ecological structure and processes tends to decrease with increasing spatial scales (from locales to regions) and levels of biological organization (from populations to communities). However, patterns in temporal variability across trophic levels and the mechanisms that produce them remain poorly understood. Here we analyzed the abundance time series of spatially structured communities (i.e., metacommunities) spanning basal resources to top predators from 355 freshwater sites across three continents. Specifically, we used a hierarchical partitioning method to disentangle the propagation of temporal variability in abundance across spatial scales and trophic levels. We then used structural equation modeling to determine if the strength and direction of relationships between temporal variability, synchrony, biodiversity, and environmental and spatial settings depended on trophic level and spatial scale. We found that temporal variability in abundance decreased from producers to tertiary consumers but did so mainly at the local scale. Species population synchrony within sites increased with trophic level, whereas synchrony among communities decreased. At the local scale, temporal variability in precipitation and species diversity were associated with population variability (linear partial coefficient, ß = 0.23) and population synchrony (ß = -0.39) similarly across trophic levels, respectively. At the regional scale, community synchrony was not related to climatic or spatial predictors, but the strength of relationships between metacommunity variability and community synchrony decreased systematically from top predators (ß = 0.73) to secondary consumers (ß = 0.54), to primary consumers (ß = 0.30) to producers (ß = 0). Our results suggest that mobile predators may often stabilize metacommunities by buffering variability that originates at the base of food webs. This finding illustrates that the trophic structure of metacommunities, which integrates variation in organismal body size and its correlates, should be considered when investigating ecological stability in natural systems. More broadly, our work advances the notion that temporal stability is an emergent property of ecosystems that may be threatened in complex ways by biodiversity loss and habitat fragmentation.

Methane ebullition fluxes and temperature sensitivity in a shallow lake.

Inland waters are important sources of atmospheric methane (CH4), with a major contribution from the CH4 ebullition pathway. However, there is still a lack of CH4 ebullition flux (eFCH4) and their temperature sensitivity (Q10) in shallow lakes, which might lead to large uncertainties in CH4 emission response from aquatic to climate and environmental change. Herein, the magnitude and regulatory of two CH4 pathways (ebullition and diffusion) were studied in subtropical Lake Chaohu, China, using the real-time portable greenhouse gas (GHG) analyzer-floating chamber method at 18 sites over four seasons. eFCH4 (12.06 ± 4.10 nmol m-2 s-1) was the dominant contributing pathway (73.0 %) to the two CH4 emission pathways in Lake Chaohu. The whole-lake mass balance calculation demonstrated that 56.6 % of the CH4 emitted from the sediment escaped through the ebullition pathway. eFCH4 was significantly higher in the western (WL: 16.54 ± 22.22 nmol m-2 s-1) and eastern lake zones (EL: 11.89 ± 15.43 nmol m-2 s-1) than in the middle lake zone (ML: 8.86 ± 13.78 nmol m-2 s-1; p < 0.05) and were significantly higher in the nearshore lake zone (NL: 15.94 ± 19.58 nmol m-2 s-1) than in the pelagic lake zone (PL: 6.64 ± 12.37 nmol m-2 s-1; p < 0.05). eFCH4 was significantly higher in summer (32.12 ± 13.82 nmol m-2 s-1) than in other seasons (p < 0.05). eFCH4 had a strong temperature dependence. Sediment total organic carbon (STOC) is an important ecosystem level Q10 driver of eFCH4. The meta-analysis also verified that across ecosystems the ecosystem-level Q10 of eFCH4 was significantly positively correlated with STOC and latitude (p < 0.05). This study suggests that eFCH4 will become increasingly crucial in shallow lake ecosystems as climate change and human activities increase. The potential increase in ebullition fluxes in high-latitude lakes is of great importance.

Recent greening may curb urban warming in Latin American cities of better economic conditions.

Rising temperatures have profound impacts on the well-being of urban residents. However, factors explaining the temporal variability of urban thermal environment, or urban warming, remain insufficiently understood, especially in the Global South. Addressing this gap, we studied the relationship between city-level economic conditions and urban warming, and how urban green space mediated this relationship, focusing on 359 major Latin American cities between 2001 and 2022. While effect sizes varied by economic and temperature measures used, we found that better economic conditions were associated with lower baseline greenness in 2011, which contributed to faster warming. There was modest evidence that this faster warming associated with lower baseline greenness and improved economic conditions was partially offset by cooling from recent greening (2001-2022) in cities of better economic conditions. This offset was more evident in arid cities. Together, these findings provide insights into the urban warming mechanism manifested through the effect of economic conditions on urban green space, for Latin American cities and other high-density cities transforming in a similar context.

Can datasets from long-term biomonitoring programs detect climate change effects on stream benthos?

We analyzed datasets from a long-term monitoring program of stream ecosystems in British Columbia, Canada, to determine whether or not it could detect climate change effects. In the Fraser River Basin (monitoring timespan 1994-2019), there was a marked (∼50%) increase in alpha diversity in reference streams, while BC North Coast (2004-2021) streams showed a modest trend of decreasing diversity and Columbia River Basin (2003-2018) and Vancouver Island (2001-2019) streams showed modestly increasing diversity. In all four regions, diversity across all sites in a specific period was primarily a function of sampling effort during this period rather than a temporal trend. Across all the regions, only three of 21 groups of faunally similar sites defined by Reference Condition Approach predictive modeling showed a suggestion of a directional change in community structure over time. Only 1 of 15 reference sites that were repeatedly sampled over several years showed a pattern that may indicate a response to changing climate. Three, not mutually exclusive, reasons why we did not see a clear effect of climate change on BC stream ecosystems were: 1) Little or no effect of climate change relative to other, potentially interacting biotic and abiotic factors, 2) The timespan of monitoring was too short to detect cumulative effects of climate change, and, most importantly, 3) The sampling design and protocol were unable to detect climate change effects. To better detect and characterize the effects of climate change on streams in monitoring programs, we recommend annual re-sampling of a few reference sites and detailed analysis of the natural and human environment of the sites along with better characterization of the benthic community (e.g. with eDNA) at all monitored sites.

Safe Today, Unsafe Tomorrow: Tanzanian Households Experience Variability in Drinking Water Quality.

Measuring Escherichia coli in a single-grab sample of stored drinking water is often used to characterize drinking water quality. However, if water quality exhibits variability temporally, then one-time measurement schemes may be insufficient to adequately characterize the quality of water that people consume. This study uses longitudinal data collected from 193 households in peri-urban Tanzania to assess variability in stored water quality and to characterize uncertainty with different data collection schemes. Households were visited 5 times over the course of a year. At each visit, information was collected on water management practices, and a sample of stored drinking water was collected for E. coli enumeration. Water quality was poor for households, with 80% having highly contaminated (>100 CFU per 100 mL) water during at least one visit. There was substantial variability of water quality for households, with only 3% of households having the same category (low, medium, or high) of water quality for all five visits. These data suggest a single sample would inaccurately characterize a household's drinking water quality over the course of a year and lead to misestimates of population level access to safe drinking water.

Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.

Introduction: The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb. Methods: This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function. Results: The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP. Discussion: The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.

Zooplankton biodiversity and temporal dynamics (2005-2015) in a coastal station in western Portugal (Northeastern Atlantic Ocean).

Long-term monitoring of zooplankton assemblages provides essential knowledge to assess key factors impacting marine ecosystems. Despite the importance of this type of data, monitoring stations worldwide are spatially and temporally limited due to the difficulty of maintaining them. In the northeastern Atlantic area, Cascais-Watch is one monitoring site operating since 2005, despite some constraints throughout the years, and has allowed the collection of important data on the zooplankton communities of the area. The present work summarizes the knowledge collected until 2015 on the biodiversity and dynamics of zooplankton in the site. The results showed a year-round high productivity of the zooplankton abundance, biomass and diversity for the area, with no significant general trends or periodicity, despite the relatively lower winter and higher spring values. The results revealed two main transition periods with marked changes in species composition and dominance of the most abundant taxa. This shift was tentatively attributed to the extended annual dry season verified in Portugal after 2011, the low values of upwelling and precipitation, and the warmer waters. The zooplankton abundance presented an interannual increase for spring periods, and the proportion of Copepoda, the dominant taxa, was lower during summer months, corresponding to increased abundances of Mollusca, Diplostraca (Cladocera) and Cnidaria. In particular, the study shows an increasing abundance of the gelatinous species (particularly Cnidaria) for spring/summer months in recent years, suggesting changes in primary production and prey dynamics. Other relevant tendencies were the higher abundance of meroplankton, such as Bivalvia and fish larvae/eggs, and the decreasing trend in the abundance of the meroplanktonic coastal crustaceans, Decapoda and Cirripedia taxa, highlighting possible changes in the benthic coastal populations in the study region. The present study highlights probable changes and trends in the zooplankton community that should be monitored in the following years.

Determinants of immunosuppressive therapy in renal transplant recipients: an Italian observational study (the CESIT project).

BACKGROUND: Very scanty evidence is available on factors influencing the choice of immunosuppressive drug therapy after kidney transplantation. METHODS: An Italian multiregional real-world study was conducted integrating national transplant information system and claims data. All patients undergoing kidney transplantation for the first time during 2009-2019 (incident patients) were considered. Multilevel logistic models were used to estimate Odds Ratio (OR) and corresponding 95% Confidence intervals. Factors with statistically significance were identified as characteristics associated with treatment regimens: cyclosporin-CsA vs tacrolimus-Tac and, within the latter group, mTOR inhibitors vs mycophenolate-MMF. RESULTS: We identified 3,622 kidney patients undergoing transplantation in 17 hospitals located in 4 Italian regions, 78.3% was treated with TAC-based therapy, of which 78% and 22% in combination with MMF and mTOR, respectively. For both comparison groups, the choice of immunosuppressive regimens was mostly guided by standard hospital practices. Only few recipient and donor characteristics were found associated with specific regimen (donor/receipt age, immunological risk and diabetes). CONCLUSIONS: The choice of post-renal transplant immunosuppressive therapy seems to be mostly driven by standard Centre practices, while only partially based on patient's characteristics and recognized international guidelines.

Response trait diversity and species asynchrony underlie the diversity-stability relationship in Romanian bird communities.

Biodiversity-stability relationships have frequently been studied in ecology, with the recent integration of traits to explain community stability over time. Classical theory underlying the biodiversity-stability relationship posits that different species' responses to the environment should stabilise community-level properties (e.g. biomass or abundance) through compensatory dynamics. However, functional response traits, which aim to predict how species respond to environmental change, are still rarely integrated into studies of ecological stability. Such traits should mechanistically drive community stability, both in terms of community abundance (functional variability) and composition (compositional variability). In turn, whether and how functional or compositional stability scales to affect temporal variation in functional effect traits (a proxy for ecosystem functioning) remains largely unknown, but is key to consistent ecosystem functioning under environmental change. Here, we explore the diversity-stability relationship in bird communities using annual survey data across 98 sites in central Romania, in combination with global trait databases and structural equation models. We show that higher response trait diversity promotes compositional variability directly, and functional variability indirectly via species asynchrony. In turn, functional variability impacts the temporal stability of effect trait diversity. Multiple facets of diversity and community stability differ between natural forests and agricultural or human-dominated survey sites, and the relationship between response diversity and functional variability is mediated by land cover. Further integration of response-and-effect trait frameworks into studies of community stability will enhance understanding of the drivers of biodiversity change, allowing targeted conservation decision-making with a focus on stable ecosystem functioning in the face of global environmental change.

The H9c2(2-1) cell-based sulforhodamine B assay is a non-animal alternative to evaluate municipal wastewater quality over time.

The present study validates the potential of the in vitro H9c2(2-1) cell-based sulforhodamine B (SRB) assay to evaluate the temporal variability of wastewater quality. The impact of effluent disposal on water quality and the efficiency of the wastewater treatment process were also assessed. To correlate standard analytical method results with in vitro results, a total of 16 physicochemical parameters, such as nutrients, pH, chemical oxygen demand, total suspended solids and metals, were determined in both raw and treated wastewater samples. Results revealed that the H9c2(2-1) cell-based SRB assay has an enormous potential to evaluate municipal wastewater quality over time and to discriminate influent and effluent toxic characteristics, as well as for water quality monitoring and surveillance of the efficacy of treatment processes. Finally, the gathered results alerted to the impact of phosphates in a biological system, leading us to recommend the selection of this parameter as a potential environmental health indicator.