Multi-cropping is becoming an increasingly popular technique in agriculture to tackle major and complex agroecosystem problems such as biodiversity and soil fertility loss, erosion and degradation, increased greenhouse gas emissions, etc. Comprehensively assessing the impact of multi-cropping intensity on agroecosystems is a new and still under-researched approach that can provide a better understanding of the impact of individual indicators on the overall functioning of biodiverse agroecosystems. Data from a stationary field experiment using multi-cropping at the Vytautas Magnus University Experimental Station between 2020 and 2022 were used to carry out this study. The study included maize, hemp, and faba bean as single, binary, and ternary crops. A complex assessment approach (CEI value) was used to determine the impact of these crops on the agroecosystem, the interrelationships between the main indicators, and the strength of their effects. It was found that the ternary maize-hemp-faba bean crop had the most positive effect on the agroecosystem. The effectiveness of other crops was 2 to 35% less. The lowest value was calculated for the maize-faba bean crop.

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It is a wellknown belief that weather can influence human health, including pain sensation. However, the current data are controversial, which might be due to the wide range of interindividual differences. The present study aimed to characterize the individual pain–weather associations during chronic pain by utilizing several data analytical methods.

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The study included 3-3 patients with (P1, P3, and P4) or without (P2, P5, P6) diabetes mellitus and signs of trigeminal neuralgia or low back pain. Subjective pain scores (0–10) and 12 weather parameters (terrestrial, geomagnetic, and solar) were recorded for one month repeated three times daily. Nonparametric Spearman’s correlation (Sp), multiple regression (Mx), and principal component (PCA) analyses were performed to evaluate associations between pain and meteorological factors obtained at the day of recorded pain value, 2 days before and 2 days after the recorded pain, and the changes in these parameters (5 × 12 parameters). Complex scores were calculated based on the results of these analyses.

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While the temperature had the highest effects on the pain levels in most of the participants, huge interindividual dif­ferences in the degree and the direction of the associations between pain and weather parameters could be obtained. The analytic methods also revealed subjectspecific results, and the synthesis of different statistical methods as total scores provided a personalized map for each patient, which showed disparate patterns across the study participants. Thus, Participants 2 and 5 had higher scores for Mx compared to Sp; furthermore, certain factors showed opposite direction in their associations with the pain level depending on the type of analysis (Sp vs Mx). In contrast, P3 had a lower score for Mx compared to Sp, which might suggest a low level of weather sensitivity on the association between the different weather parameters in this subject. Furthermore, participants P4 and P6 had a very high level of weather sensitivity, while P1 had an opposite pattern. Regarding the time point-related effects on the pain level, most patients were sensitive to parameters obtained at the same day or two days before, except the P1 subject, who had the highest sensitivity to weather parameters detected two days after.

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The present study highlights the importance of integrating different data analysis approaches to elucidate the individual connections between pain and most of the weather parameters. In conclusion, complex personalized profiling should be considered for the characterization of pain–weather associations by applying different data analytical approaches, which may provide feedback to physicians and patients. 

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A betegek beszámolója alapján jól ismert a fájdalom és az időjárás lehetséges összefüggése. Az eredmények azonban ellentmondóak, ami feltehetően az egyének közötti nagy érzékenységbeli különbségeknek köszönhető. Jelen tanulmány célja, hogy egyénre szabott módon jellemezze a fájdalom és az időjárás lehetséges kapcsolatait többféle statisztikai elemzés alkalmazásának kombinálásával.

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A tanulmányban hat krónikus fájdalom szindrómás beteg vett részt (trigeminus neuralgia, illetve derékfájdalom tüneteivel). Három páciensben a tünetek diabeteses neuropathiához kapcsolódtak (P1, P3, P4), míg három betegben ugyanezek a szindrómák voltak megfigyelhetők cukorbetegség nélkül (P2, P5, P6). A szubjektív fájdalomskálát (0–10) és 12 időjárás-paramétert regisztráltunk egy hónapon keresztül napi három alkalommal. Spearman korrelációs (Sp), többszörös regressziós (Mx) és fő komponens (PCA) elemzéseket végeztünk összehasonlítva a fájdalom- és időjárás-paramétereket az adott napon, 2 nappal a mérés előtt és után, illetve a változások mértékét (5 × 12 paraméter). Az analízisekkel kapott adatokból egy komplex értéket számoltunk, és analizáltuk.

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Bár a hőmérséklet fejtette ki a legnagyobb hatást a fájdalomérzetre, az egyéni különbség igen nagy volt. A fájdalom-időjárás összefüggés nemcsak az időjárás-paraméterektől, hanem az elemzés fajtájától is függött, így egyénenként különböző mintázatot adtak mind az egyes analízisek, mind a komplex értékek. A P2 és P5 beteg nagyobb pontszámot ért el az Mx-, mint az Sp-elem­zéssel, sőt az összefüggés iránya is sok eset­ben ellentétes volt a két elemzésben. A P3 egyénben az Sp-elemzéssel kaptunk magasabb értéket, ami felveti, hogy az egyes időjárás-paraméterek egymásra kevésbé gyakoroltak hatást. A P4 és P6 beteg nagyon érzékeny volt az időjárás-paraméterekre, míg a P1-es egyén ellenkező mintázatot mutatott. A vizsgált időpontok tekintetében azt tapasztaltuk, hogy a legtöbb beteg az aznapi és az azt két nappal megelőző időjárás-paraméterekre volt a legérzékenyebb, kivéve a P1-es egyént, akinek a fájdalomszintje a két nappal későbbi időjárás-paraméterekkel mutatott legnagyobb összefüggést. 

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A jelen tanulmány eredményei felhívják a figyelmet arra, hogy számos környezeti paraméter adatainak integrált elemzése megvilágíthatja a fájdalom és az időjárás összefüggését egyéni szinten. Ezen adatok összetett, egyénre szabott elemzése segíthet a hatékonyabb fájdalomterápia megtervezésében.

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Aging is one of the major etiological factors driving intervertebral disc (IVD) degeneration, the main cause of low back pain. The nucleus pulposus (NP) includes a heterogeneous cell population, which is still poorly characterized. Here, we aimed to uncover main alterations in NP cells with aging. For that, bovine coccygeal discs from young (12 months) and old (10-16 years old) animals were dissected and primary NP cells were isolated. Gene expression and proteomics of fresh NP cells were performed. NP cells were labelled with propidium iodide and analysed by flow cytometry for the expression of CD29, CD44, CD45, CD146, GD2, Tie2, CD34 and Stro-1. Morphological cell features were also dissected by imaging flow cytometry. Elder NP cells (up-regulated bIL-6 and bMMP1 gene expression) presented lower percentages of CD29+, CD44+, CD45+ and Tie2+ cells compared with young NP cells (upregulated bIL-8, bCOL2A1 and bACAN gene expression), while GD2, CD146, Stro-1 and CD34 expression were maintained with age. NP cellulome showed an upregulation of proteins related to endoplasmic reticulum (ER) and melanosome independently of age, whereas proteins upregulated in elder NP cells were also associated with glycosylation and disulfide bonds. Flow cytometry analysis of NP cells disclosed the existence of 4 subpopulations with distinct auto-fluorescence and size with different dynamics along aging. Regarding cell morphology, aging increases NP cell area, diameter and vesicles. These results contribute to a better understanding of NP cells aging and highlighting potential anti-aging targets that can help to mitigate age-related disc disease.

Industrial wastewater, domestic wastewater, and stormwater are the three entry points for microplastics (MP) in wastewater treatment plants. Extreme weather conditions, such as rising temperatures and heavy rainfall caused by climate change, can alter the rate at which MP enters wastewater treatment plants. In this study, wastewater and sludge samples from different treatment stages were collected during a 12-month sampling campaign (seasonal) to determine the efficiency of a municipal wastewater treatment plant in removing microplastic particles. MP ranging from 20 to 1000 µm were detected and classified by shape, color, size, and chemical composition. All samples contained MP particles, with concentration ranging from 1964 ± 50-2982 ± 54 MP/L in influent to 744 ± 13-1244 ± 21 MP/L in effluent and 91.1 ± 8-61.9 ± 5 MP/g in sludge; 71.6-90.1% identified particles were fragment-type with black, white, and transparent colors. Most of the microplastic particles were removed in the activated sludge tank, while the average removal rate in the wastewater treatment plant was 57%. The total concentration of MP was 27% higher in spring than in other seasons. The most common microplastic particles were polyethylene terephthalate (PET), polystyrene (PS), and polypropylene (PP). These results demonstrate the value of long-term monitoring and MP quantification, which would provide a more accurate estimate of MP pollution from wastewater treatment plants.

This paper proposes a new methodology called TOpic modeling Based Index Assessment through Sentiment (TOBIAS). This method aims at modeling the effects of the topics, moods, and sentiments of the comments describing a phenomenon upon its overall rating. TOBIAS is built combining different techniques and methodologies. Firstly, Sentiment Analysis identifies sentiments, emotions, and moods, and Topic Modeling finds the main relevant topics inside comments. Then, Partial Least Square Path Modeling estimates how they affect an overall rating that summarizes the performance of the analyzed phenomenon. We carried out TOBIAS on a real case study on the university courses\' quality evaluated by the University of Cagliari (Italy) students. We found TOBIAS able to provide interpretable results on the impact of discussed topics by students with their expressed sentiments, emotions, and moods and with the overall rating.

The alignment of spindle-shaped cells in two-dimensional geometries induces singular points called topological defects, at which the alignment angle of the cell cannot be defined. To control defects related to biological roles such as cell apoptosis, calculation methods for predicting the defect positions are required. This study proposes an explicit calculation method for predicting cell alignment and defect positions in non-circular geometries. First, a complex potential is introduced to describe the alignment angles of cells, which is used to derive an explicit formula for cell alignment in a unit disc. Then, the derived formula for the unit disc is extended to the case for non-circular geometries using a numerical conformal mapping. Finally, the complex potential allows a calculation of the Frank elastic energy, which can be minimized with respect to the defect positions to predict their equilibrium state in the geometry. The proposed calculation method is used to demonstrate a numerical prediction of multiple defects in circular and non-circular geometries, which are consistent with previous experimental results.

Consider a linear elliptic PDE defined over a stochastic stochastic geometry a function of N random variables. In many application, quantify the uncertainty propagated to a Quantity of Interest (QoI) is an important problem. The random domain is split into large and small variations contributions. The large variations are approximated by applying a sparse grid stochastic collocation method. The small variations are approximated with a stochastic collocation-perturbation method and added as a correction term to the large variation sparse grid component. Convergence rates for the variance of the QoI are derived and compared to those obtained in numerical experiments. Our approach significantly reduces the dimensionality of the stochastic problem making it suitable for large dimensional problems. The computational cost of the correction term increases at most quadratically with respect to the number of dimensions of the small variations. Moreover, for the case that the small and large variations are independent the cost increases linearly.

In this article we analyze the linear parabolic partial differential equation with a stochastic domain deformation. In particular, we concentrate on the problem of numerically approximating the statistical moments of a given Quantity of Interest (QoI). The geometry is assumed to be random. The parabolic problem is remapped to a fixed deterministic domain with random coefficients and shown to admit an extension on a well defined region embedded in the complex hyperplane. The stochastic moments of the QoI are computed by employing a collocation method in conjunction with an isotropic Smolyak sparse grid. Theoretical sub-exponential convergence rates as a function to the number of collocation interpolation knots are derived. Numerical experiments are performed and they confirm the theoretical error estimates.

Peer review is the way in which we, as scientists, criticise, check, and confirm the findings of our colleagues. The process of peer review relies on individuals in all fields applying their particular expertise and determining if they agree with the findings submitted for publication. In recent years, there has been a significant rise in the number of manuscripts submitted for publication that draw from a range of disparate and complementary fields. This has created the curious situation where an expert may be requested to review a manuscript that is only partially within their immediate field of expertise. The issue that arises is that, without full knowledge of the data, techniques, methodologies, and principles that are presented, it is difficult for reviewers to make properly informed decisions, especially when it can take an entire career to reach that specific level of expertise in a single field. From this perspective, we explore these issues and also provide a commentary on how peer review could evolve in the context of a changing cross-disciplinarily-focused scientific landscape.

In this paper we introduce concepts from uncertainty quantification (UQ) and numerical analysis for the efficient evaluation of stochastic high dimensional Newton iterates. In particular, we develop complex analytic regularity theory of the solution with respect to the random variables. This justifies the application of sparse grids for the computation of statistical measures. Convergence rates are derived and are shown to be subexponential or algebraic with respect to the number of realizations of random perturbations. Due the accuracy of the method, sparse grids are well suited for computing low probability events with high confidence. We apply our method to the power flow problem. Numerical experiments on the non-trivial 39 bus New England power system model with large stochastic loads are consistent with the theoretical convergence rates. Moreover, compared to the Monte Carlo method our approach is at least 1011 times faster for the same accuracy.