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Abstract Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

IntroductionRecent work on bats flying over long distances has revealed that single hippocampal cells have multiple place fields of different sizes. At the network level, a multi-scale, multi-field place cell code outperforms classical single-scale, single-field place codes, yet the performance boundaries of such a code remain an open question. In particular, it is unknown how general multi-field codes compare to a highly regular grid code, in which cells form distinct modules with different scales.MethodsIn this work, we address the coding properties of theoretical spatial coding models with rigorous analyses of comprehensive simulations. Starting from a multi-scale, multi-field network, we performed evolutionary optimization. The resulting multi-field networks sometimes retained the multi-scale property at the single-cell level but most often converged to a single scale, with all place fields in a given cell having the same size. We compared the results against a single-scale single-field code and a one-dimensional grid code, focusing on two main characteristics: the performance of the code itself and the dynamics of the network generating it.ResultsOur simulation experiments revealed that, under normal conditions, a regular grid code outperforms all other codes with respect to decoding accuracy, achieving a given precision with fewer neurons and fields. In contrast, multi-field codes are more robust against noise and lesions, such as random drop-out of neurons, given that the significantly higher number of fields provides redundancy. Contrary to our expectations, the network dynamics of all models, from the original multi-scale models before optimization to the multi-field models that resulted from optimization, did not maintain activity bumps at their original locations when a position-specific external input was removed.DiscussionOptimized multi-field codes appear to strike a compromise between a place code and a grid code that reflects a trade-off between accurate positional encoding and robustness. Surprisingly, the recurrent neural network models we implemented and optimized for either multi- or single-scale, multi-field codes did not intrinsically produce a persistent “memory” of attractor states. These models, therefore, were not continuous attractor networks.

Abstract Attribute inference—the process of analyzing publicly available data in order to uncover hidden information—has become a major threat to privacy, given the recent technological leap in machine learning. One way to tackle this threat is to strategically modify one’s publicly available data in order to keep one’s private information hidden from attribute inference. We evaluate people’s ability to perform this task, and compare it against algorithms designed for this purpose. We focus on three attributes: the gender of the author of a piece of text, the country in which a set of photos was taken, and the link missing from a social network. For each of these attributes, we find that people’s effectiveness is inferior to that of AI, especially when it comes to hiding the attribute in question. Moreover, when people are asked to modify the publicly available information in order to hide these attributes, they are less likely to make high-impact modifications compared to AI. This suggests that people are unable to recognize the aspects of the data that are critical to an inference algorithm. Taken together, our findings highlight the limitations of relying on human intuition to protect privacy in the age of AI, and emphasize the need for algorithmic support to protect private information from attribute inference.

Microplastic pollution is a pervasive issue, with remarkably high concentrations observed even in the most remote locations such as Arctic sea ice and snow. The reason for such large microplastic abundances in sea ice is still speculative and applies mainly to saline or freshwater conditions. In this study, we investigated seasonal ice core samples collected in March 2021 from the northern Baltic Sea (Gulf of Bothnia) for their microplastic distributions. The Baltic Sea is characterized by low salinity and can be ice-covered for up to six months annually. Microplastics were analyzed in the melted ice samples using an adsorption technique and Raman microscopy to identify their abundances, colors, shapes, and sizes to calculate their masses. Due to the strong dynamic of the ice layer and the repeated melting and freezing processes during the ice formation, no discernible trends in microplastic abundances, masses, or polymer types were observed throughout the ice core length. The average microplastic abundance (±SD) in the Baltic Sea ice was determined to be 22.3 ± 8.6 N L−1, with 64.9% of the particles exhibiting a particulate shape and 35.1% having a fibrous shape. The most prevalent polymer type was polyethylene terephthalate (PET), accounting for 44.4% of all polymers. This is likely due to the high proportion of PET fibers (93.8%). The majority of particle-shaped microplastics were identified as polyethylene (PE; 37.2%), followed by PET (17.2%), polyvinyl chloride (PVC; 15.9%), and polypropylene (PP; 15.9%). No correlations were found between microplastic concentrations and proximity to land, cities, industries, or rivers, except for PP mass concentrations and particle sizes, which correlated with distances to industries in Luleå, Sweden.

Abstract Inflammation modifies the incidence and progression of Parkinson’s disease (PD). By using 30 inflammatory markers in CSF in 498 people with PD and 67 people with dementia with Lewy bodies (DLB) we show that: (1) levels of ICAM-1, Interleukin-8, MCP-1, MIP-1 beta, SCF and VEGF were associated with clinical scores and neurodegenerative CSF biomarkers (Aβ1-42, t-Tau, p181-Tau, NFL and α-synuclein). (2) PD patients with GBA mutations show similar levels of inflammatory markers compared to PD patients without GBA mutations, even when stratified by mutation severity. (3) PD patients who longitudinally developed cognitive impairment during the study had higher levels of TNF-alpha at baseline compared to patients without the development of cognitive impairment. (4) Higher levels of VEGF and MIP-1 beta were associated with a longer duration until the development of cognitive impairment. We conclude that the majority of inflammatory markers is limited in robustly predicting longitudinal trajectories of developing cognitive impairment.

Endocrine disrupting compounds and in particular estrogenic substances have the ability to interact with the hormone system of organisms. Among them are not only synthetic but also natural substances that potentially stress the aquatic ecosystem. High human population densities such as around the Pearl River Estuary (PRE) are suspected of exerting significant anthropogenic pressure onto coastal areas. In this study, natural and synthetic estrogens as well as estrogen-like substances derived from plants and fungi were investigated in the PRE and at the adjacent northern shelf of the South China Sea. Maximum concentration of 3.6 ng L−1 for estrone (E1), 0.7 ng L−1 for 17α-ethinylestradiol (EE2), 12.9 ng L−1 for genistein (GEN), 11.9 ng L−1 for daidzein (DAI) and 1.9 ng L−1 for zearalenone (ZEN) were observed. While E1 and EE2 were detected in fresh and saltwater samples, GEN, DAI and ZEN were observed only at freshwater sampling sites. During the investigations, the analysis of 17β-estradiol (E2) and EE2 indicated a strong matrix dependence. Additionally, an estrogen screen observation showed estrogenic activity in form of estradiol equivalent quotients up to 0.18 ng L−1.

Abstract As the COVID-19 pandemic has demonstrated, identifying the origin of a pandemic remains a challenging task. The search for patient zero may benefit from the widely-used and well-established toolkit of contact tracing methods, although this possibility has not been explored to date. We fill this gap by investigating the prospect of performing the source detection task as part of the contact tracing process, i.e., the possibility of tuning the parameters of the process in order to pinpoint the origin of the infection. To this end, we perform simulations on temporal networks using a recent diffusion model that recreates the dynamics of the COVID-19 pandemic. We find that increasing the budget for contact tracing beyond a certain threshold can significantly improve the identification of infected individuals but has diminishing returns in terms of source detection. Moreover, disease variants of higher infectivity make it easier to find the source but harder to identify infected individuals. Finally, we unravel a seemingly-intrinsic trade-off between the use of contact tracing to either identify infected nodes or detect the source of infection. This trade-off suggests that focusing on the identification of patient zero may come at the expense of identifying infected individuals.

Abstract Nations worldwide are mobilizing to harness the power of Artificial Intelligence (AI) given its massive potential to shape global competitiveness over the coming decades. Using a dataset of 2.2 million AI papers, we study inter-city citations, collaborations, and talent migrations to uncover dependencies between Eastern and Western cities worldwide. Beijing emerges as a clear outlier, as it has been the most impactful city since 2007, the most productive since 2002, and the one housing the largest number of AI scientists since 1995. Our analysis also reveals that Western cities cite each other far more frequently than expected by chance, East–East collaborations are far more common than East–West or West–West collaborations, and migration of AI scientists mostly takes place from one Eastern city to another. We then propose a measure that quantifies each city’s role in bridging East and West. Beijing’s role surpasses that of all other cities combined, making it the central gateway through which knowledge and talent flow from one side to the other. We also track the center of mass of AI research by weighing each city’s geographic location by its impact, productivity, and AI workforce. The center of mass has moved thousands of kilometers eastward over the past three decades, with Beijing’s pull increasing each year. These findings highlight the eastward shift in the tides of global AI research, and the growing role of the Chinese capital as a hub connecting researchers across the globe.

W szeroko rozumianym obszarze „pracy z ludźmi” superwizja jest jedną z podstawowych form uczenia się i rozwoju zawodowego. W jej ramach można również umieścić badania naukowe – przede wszystkim te prowadzone w terenie i oparte na interakcjach z badanymi. Kwestia krytycznej refleksji nad podejmowanymi przez badaczki i badaczy działaniami, nad ich postawami oraz relacjami z osobami badanymi, nad dylematami etycznymi, a także nad rozwojem umiejętności postępowania z (często dotkniętym cierpieniem) Innym jest szczególnie znacząca w socjologii, naukowej refleksji nad pracą socjalną, antropologii czy pedagogice, czyli dziedzinach bazujących w istotnej części na badaniach jakościowych. Celem artykułu jest przedstawienie podstawowych zagadnień związanych z pojawiającymi się ostatnio postulatami wprowadzenia superwizji w obszar społecznych badań jakościowych. Przedstawiono w nim krótko uwagi na temat superwizji w kontekście uwarunkowań pracy naukowej we współczesnej akademii, a następnie rozważono problem podatności badaczek i badaczy na zranienie i wskazano możliwe funkcje przeznaczonej dla nich superwizji. W ostatniej części tekstu odniesiono się do kwestii terminologicznych dotyczących pojęciowego zakreślenia granic superwizji dla badaczy, oddzielenia jej od superwizji klinicznej czy też promotorstwa i opieki naukowej, także określanych jako superwizja, ukazując skrótowo, jak superwizja badawcza opisywana jest w ostatnich latach w literaturze obcojęzycznej.

Abstract Lewy-body pathology with aggregation of abnormal conformations of the protein alpha-synuclein (α-Syn) represent the histopathological hallmarks of Parkinson’s disease (PD). Genetic prototypes such as PD due to mutations in the alpha-synuclein gene (SNCA) offer the opportunity to evaluate α-Syn-related profiles in patient-derived biomaterial. We identified a family with a SNCA triplication and assessed the index patient for CSF α-Syn seeding capacity and levels of total α-Syn along with other neurodegenerative CSF markers (Aβ1-42, total-Tau, phospho-Tau, NFL). As no published CSF data in patients with SNCA triplication are available, we descriptively compared his CSF profiles to those of sporadic PD patients and PD patients with GBA mutations as these are also specifically associated with prominent α-Syn pathology. Additionally, skin biopsies with staining for phospho-α-Syn were done. To assess cerebral glucose metabolism and brain atrophy combined positron emission tomography and magnetic resonance imaging ([18F]FDG-PET/MRI) was performed. Age at onset was 24 years and motor impairment was accompanied by prominent non-motor symptoms with early development of dementia, depression, REM sleep behavior disorder, hyposmia, and dysautonomia. Correspondingly, PET-MRI showed hypometabolism and atrophy in frontal, temporoparietal and occipital regions. CSF levels of total α-Syn were threefold higher and RT-QuIC showed remarkable α-Syn seeding activity in all kinetic categories in the SNCATriplication patient compared to patients with GBA mutations. Our results are consistent with findings that not only mutant forms but also overexpression of the wild-type α-Syn protein lead to PD and PD dementia and show a striking CSF α-Syn seeding profile, thus substantiating the role of RT-QuIC as a specific in vivo biomarker of α-Syn brain pathology.

Sinking particles were studied by analyzing samples collected in a sediment trap at 180 m depth in the Gotland Basin, eastern Baltic Sea between 1999 and 2020. The aim of this study was to determine the temporal variability of the particle flux and its components and how their changes are linked to phytoplankton blooms. The variables studied included total particle flux, particulate organic carbon and nitrogen, biogenic silica, C:N ratio and the isotopic composition of organic carbon and nitrogen. The total particle flux and its components reached maximum values in 2003, 2012 and 2015. Long-term means over the 22-year period of the total particle flux and its components particulate organic carbon and nitrogen, biogenic silica were estimated at around 152, 22, 3 and 8 mg m−2 d−1, respectively. The C:N ratio and the isotopic composition of organic carbon and nitrogen showed high variability around their long-term means of 9, -25‰ and 4‰, respectively. The annual variability of the components of the flux particulate organic carbon (3–65 mg m−2 d−1), particulate organic nitrogen (0.4–9 mg m−2 d−1) and biogenic silica (1–24 mg m−2 d−1) exhibited the same general pattern as the total particle flux (11–450 mg m−2 d−1) over the study period. On the seasonal scale, sinking material in summer contributed roughly one-third (31%) to the total particle flux, followed by winter (27%), spring (24%) and autumn (19%). The highest particle flux occurred mostly in April, July and November, during and after the appearance of phytoplankton blooms in the Gotland Basin. The phytoplankton community changed from silicon-rich species to nitrogen-fixing cyanobacteria, indicating a shift in nitrogen sources from nitrate-based to N2-based over the year. The spring bloom, dominated by diatoms, was characterized by a lighter carbon and heavier nitrogen isotopic composition, while the summer bloom, mainly of diazotrophic cyanobacteria, was characterized in contrary by heavier carbon and lighter nitrogen isotopes. Although no trend was found in the data, the variability observed in the sinking material was related to the changes over time in the phytoplankton community in the Gotland Basin. The findings of this study provide new and valuable information for our understanding of the temporal variability of sinking material linked to the development of phytoplankton blooms and nutrient sources in the Gotland Basin, and underscore the importance of continued monitoring to understand the potential impacts of environmental changes on this fragile ecosystem.

Recent studies have hypothesized that the reinforcing effects of boehmite nanoparticles (BNPs) in polymer nanocomposites (PNCs) are partly related to the particles themselves and partly to the water released from the BNP during curing. In this work, PNCs made from dried BNP (dBNP) with concentrations up to 15 wt% are investigated to differentiate particle and water related effects. The observed trend of the storage modulus in dynamic mechanical thermal analysis measurements was found to be independent of the drying procedure. Stiffness maps from intermodulation atomic force microscopy showed that dBNP leads to a stiffening of the interphase surrounding the particles compared with the unaffected epoxy matrix, while a softer interphase was reported for PNCs with as received BNP. A slight decrease in the glass transition temperature was observed by broadband dielectric spectroscopy related to a lowered crosslink density due to the particles. A significantly higher decrease was reported for PNCs with BNP, attributed to water influencing the curing process. In conclusion, the stiffening of PNC with BNP is related to the particles themselves, while the release of water causes the formation of a soft interphase in the vicinity of the particles and a significant decrease in crosslink density. [ABSTRACT FROM AUTHOR]