bluuebunny/crossref_metadata_2025_split huggingface dataset:
How do additions of submerged macrophytes, large-bodied cladocerans and nutrients impact tropical plankton communities? A mesocosm experiment
Authors: Amorim Cihelio A. , Jeppesen Erik , Moura Ariadne N. 
Published: July 2024

AbstractWe investigated the individual and combined impacts of manipulation of submerged macrophytes, large-bodied cladocerans, and nutrients on plankton communities in a tropical hypereutrophic shallow reservoir. We tested how the addition of the macrophyte Ceratophyllum demersum, the cladoceran Sarsilatona serricauda, and nutrients affected phytoplankton and zooplankton diversity, composition, and structure using mesocosms and a factorial design (3 × 3) with eight treatments. During the experiment, the reservoir experienced an intense bloom of algae (207 mg l−1 of biomass), mainly composed of cyanobacteria (> 98%). The submerged macrophytes were found to significantly reduce the biomass of cyanobacteria (by 85%), diatoms (80%), and green algae (78%), while the addition of zooplankton and nutrients led to a 96% reduction for diatoms. While both submerged macrophytes and the added cladocerans impacted the native zooplankton community, the macrophytes exerted stronger effects on phytoplankton and zooplankton diversity, composition, and structure. Intriguingly, nutrient addition did not alter the main effects of macrophytes and large cladocerans. Our findings reveal the positive potential of introducing submerged macrophytes in tropical shallow lakes, even at a low to moderate percentage of the volume inhabited, to control toxic cyanobacterial blooms. Under our experimental conditions, the method was effective even without extra zooplankton grazing and at increased nutrient input.
In Some Ways: Syādvāda as the Synthesis of Anekāntavāda and Nayavāda in Akalaṅka’s Philosophical Treatises
Authors: Nahata Shree 
Published: July 2024

AbstractThis paper investigates the relationship between the theory of many-sidedness (anekāntavāda), the theory of viewpoints (nayavāda), and the theory of sevenfold conditional predication (syādvāda) in the philosophical monographs of the Jaina philosopher Akalaṅka (c. 720-780 CE). Despite being celebrated as novel Jaina contributions to Indian philosophy, the mutual relationship between these three theories is poorly understood. Do these three theories represent three distinct frameworks with distinct purposes? Or do these three theories ultimately synthesise to form a single unified framework? Through a careful study of the Laghīyastraya and the Nyāyaviniścaya, this paper shows how Akalaṅka understands syādvāda as the central organising framework for integrating the theory of many-sidedness and the theory of viewpoints. On the one hand, syādvāda is analysed as the synthesis of multiple viewpoints because each of the seven conditional predications, when taken individually, represents a genuine viewpoint. On the other hand, the seven conditional predications of syādvāda, when taken collectively and iterated with respect to different characteristics, represent the linguistic formalisation of many-sided (anekānta) Jaina ontology. This paper shows how syādvāda as the grand synthesis of the theory of many-sidedness and the theory of viewpoints creates new possibilities as well as new problems within Akalaṅka’s philosophical system.
Properties, characterization and biomedical applications of polyvinylidene fluoride (PVDF): a review
Authors: Concha Viktor O. C. , Timóteo Laura , Duarte Leila A. N. , Bahú Juliana O. , Munoz Federico L. , Silva Aline P. , Lodi Leandro , Severino Patrícia , León-Pulido Jeffrey , Souto Eliana B. 
Published: July 2024

AbstractPolyvinylidene fluoride (PVDF) boosted its technological applications because of its piezoelectric and pyroelectric properties, together with mechanical, chemical, and thermal resistance. In this review, we address the PVDF chemical structure and its synthesis procedure, also describing the occurrence of polymorphism (alpha (α), beta (β), gamma (γ), and delta (δ) of this semicrystalline polymer, and the processes involved in phase transformation that govern the adaptability of the PVDF crystalline phases and properties for several industrial uses. It can be used as a component of implants and sutures, as engineering plastic to play a structural role in aggressive environments, acting in the field of process control as a component of transducers and sensors, as well as in the synthesis of separation membranes, fuel cells, and a long list of new applications that include an important role in robotics. The main physicochemical techniques used for PVDF characterization, i.e., differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy, that highlight its unique properties, are also discussed, showing the versatility of PVDF for a range of biomedical and pharmaceutical applications.
Transverse relaxation optimized spectroscopy of NH2 groups in glutamine and asparagine side chains of proteins
Authors: Tugarinov Vitali , Torricella Francesco , Ying Jinfa , Clore G. Marius 
Published: July 2024

AbstractA transverse relaxation optimized spectroscopy (TROSY) approach is described for the optimal detection of NH2 groups in asparagine and glutamine side chains of proteins. Specifically, we have developed NMR experiments for isolating the slow-relaxing 15N and 1H components of NH2 multiplets. Although even modest sensitivity gains in 2D NH2-TROSY correlation maps compared to their decoupled NH2–HSQC counterparts can be achieved only occasionally, substantial improvements in resolution of the NMR spectra are demonstrated for asparagine and glutamine NH2 sites of a buried cavity mutant, L99A, of T4 lysozyme at 5 ºC. The NH2-TROSY approach is applied to CPMG relaxation dispersion measurements at the side chain NH2 positions of the L99A T4 lysozyme mutant — a model system for studies of the role of protein dynamics in ligand binding.
Stress, hypothalamic-pituitary-adrenal axis, hypothalamic-pituitary-gonadal axis, and aggression
Authors: Mbiydzenyuy Ngala Elvis , Qulu Lihle-Appiah 
Published: July 2024

AbstractThis comprehensive review explores the intricate relationship between the hypothalamic-pituitary-adrenal (HPA) axis, the hypothalamic-pituitary-gonadal (HPG) axis, and aggression. It provides a detailed overview of the physiology and functioning of these axes, as well as the implications for aggressive behavior. The HPA axis, responsible for the stress response, is activated in response to various stressors and can influence aggressive behavior. Glucocorticoids, such as cortisol, play a crucial role in stress-induced activation of the HPA axis and have been implicated in aggressive tendencies. Chronic stress can dysregulate the HPA axis, leading to alterations in cortisol levels and potentially contributing to aggressive behavior. The HPG axis, particularly the androgen hormone testosterone, is also closely linked to aggression. Animal and human studies have consistently shown a positive association between testosterone levels and aggression. The androgen receptors in the brain’s neural circuitry play a critical role in modulating aggressive behavior. Interactions between the HPA and HPG axes further contribute to the regulation of aggression. Feedback mechanisms and crosstalk between these axes provide a complex system for the modulation of both stress and reproductive functions, which can impact aggressive behavior. Additionally,the influence of stress on reproductive functions, particularly the role of androgens in stress-induced aggression, adds further complexity to this relationship. The review also discusses the future directions and implications for clinical interventions. Understanding the neurobiological mechanisms underlying aggression requires integrating molecular, cellular, and circuit-level approaches. Translational perspectives, including animal models and human studies, can bridge the gap between basic research and clinical applications. Finally, therapeutic strategies for aggression-related disorders are explored, highlighting the importance of targeted interventions based on a comprehensive understanding of the interactions between the HPA and HPG axes. In conclusion, this review provides a comprehensive overview of the physiological and neurobiological mechanisms underlying aggression, with a specific focus on the interplay between the HPA and HPG axes. By elucidating the complex interactions between stress, hormones, and aggressive behavior, this research paves the way for future investigations and potential therapeutic interventions for aggression-related disorders.
Role of environmental awareness & self-identification expressiveness in electric-vehicle adoption
Authors: Mustafa Sohaib , Shi Yangyan , Adan Dur e , Luo Wen , Al Humdan Eias 
Published: July 2024

AbstractGlobal warming is a serious threat to humanity, and greenhouse gases are behind it. CO2 is one of the main greenhouse gasses that cause global warming. The second most important reason behind CO2 emission is the transport sector. Electric-powered Vehicles introduced by the automobile sector are regarded as the main alternative to cope with this issue. Despite their advantages, EV adoption rates vary by country, and the expected reductions in energy shortages and pollution are not fully achieved. We took the opportunity to assess the consumer EV adoption intention by using the Value-based technology adoption model. We integrated Environmental awareness (EA) and Self-identification expressiveness (SIE) as new factors in this model to assess the role of EA and SIE in EV adoption intentions. We have collected the response of 704 EV users from China and applied the SEM-ANN dual-stage hybrid model to test proposed hypotheses and rank the variables according to their importance. Study findings revealed that Environmental awareness is a significant predictor of EV adoption but is not the most important factor. Study results also revealed that the perceived benefits of EV adoption and perceived sacrifices for its adoption have a significant impact on the perceived value of EV, and this value leads to the adoption intentions. PV is revealed as a significant mediator in our proposed model. We conclude that people need to educate about environmental issues and the benefits of using EVs to achieve the CO2 reduction and EV sale target. The study has several theoretical and practical implications for the government and the electric vehicle manufacturing industry.
Heat Kernel Asymptotics for Scaling Limits of Isoradial Graphs
Authors: Schwarz Simon , Sturm Anja , Wardetzky Max 
Published: July 2024

AbstractWe consider the asymptotics of the discrete heat kernel on isoradial graphs for the case where the time and the edge lengths tend to zero simultaneously. Depending on the asymptotic ratio between time and edge lengths, we show that two different regimes arise: (i) a Gaussian regime and (ii) a Poissonian regime, which resemble the short-time asymptotics of the heat kernel on (i) Euclidean spaces and (ii) graphs, respectively.
Assessment of Different Methods for Estimation of Missing Rainfall Data
Authors: Hırca Tuğçe , Eryılmaz Türkkan Gökçen 
Published: July 2024

AbstractMissing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.
A real-time and energy-efficient SRAM with mixed-signal in-memory computing near CMOS sensors
Authors: Diaz-Madrid Jose-Angel , Domenech-Asensi Gines , Ruiz-Merino Ramon , Zapata-Perez Juan-Francisco 
Published: July 2024

AbstractIn-memory computing (IMC) represents a promising approach to reducing latency and enhancing the energy efficiency of operations required for calculating convolution products of images. This study proposes a fully differential current-mode architecture for computing image convolutions across all four quadrants, intended for deep learning applications within CMOS imagers utilizing IMC near the CMOS sensor. This architecture processes analog signals provided by a CMOS sensor without the need for analog-to-digital conversion. Furthermore, it eliminates the necessity for data transfer between memory and analog operators as convolutions are computed within modified SRAM memory. The paper suggests modifying the structure of a CMOS SRAM cell by incorporating transistors capable of performing multiplications between binary (−1 or +1) weights and analog signals. Modified SRAM cells can be interconnected to sum the multiplication results obtained from individual cells. This approach facilitates connecting current inputs to different SRAM cells, offering highly scalable and parallelized calculations. For this study, a configurable module comprising nine modified SRAM cells with peripheral circuitry has been designed to calculate the convolution product on each pixel of an image using a $$3 \times 3$$

3
×
3

mask with binary values (−1 or 1). Subsequently, an IMC module has been designed to perform 16 convolution operations in parallel, with input currents shared among the 16 modules. This configuration enables the computation of 16 convolutions simultaneously, processing a column per cycle. A digital control circuit manages both the readout or memorization of digital weights, as well as the multiply and add operations in real-time. The architecture underwent testing by performing convolutions between binary masks of 3 × 3 values and images of 32 × 32 pixels to assess accuracy and scalability when two IMC modules are vertically integrated. Convolution weights are stored locally as 1-bit digital values. The circuit was synthesized in 180 nm CMOS technology, and simulation results indicate its capability to perform a complete convolution in 3.2 ms, achieving an efficiency of 11,522 1-b TOPS/W (1-b tera-operations per second per watt) with a similarity to ideal processing of 96%.
Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning
Authors: Rahmani Maryam , Dierker Donna , Yaeger Lauren , Saykin Andrew , Luckett Patrick H. , Vlassenko Andrei G. , Owens Christopher , Jafri Hussain , Womack Kyle , Fripp Jurgen , Xia Ying , Tosun Duygu , Benzinger Tammie L. S. , Masters Colin L. , Lee Jin-Moo , Morris John C. , Goyal Manu S. , Strain Jeremy F. ,   for the ADOPIC, ADNI Investigators, Kukull Walter , Weiner Michael ,   Biostats, Database and Bioinformatics, Burnham Samantha , CoxDoecke Tim James , Fedyashov Victor , Fripp Jurgen , Shishegar Rosita , Xiong Chengjie , Marcus Daniel , Raniga Parnesh , Li Shenpeng ,   Cognition, Aschenbrenner Andrew , Hassenstab Jason , Lim Yen Ying , Maruff Paul , Sohrabi Hamid , Robertson Jo , Markovic Shaun ,   Imaging, Bourgeat Pierrick , Doré Vincent , Mayo Clifford Jack , Mussoumzadeh Parinaz , Rowe Chris , Villemagne Victor ,   CSF and Blood, Bateman Randy , Fowler Chris , Li Qiao-Xin , Martins Ralph , Schindler Suzanne , Shaw Les ,   Genetics, Cruchaga Carlos , Harari Oscar , Laws Simon , Porter Tenielle , O’Brien Eleanor ,   Neuropathology, Perrin Richard ,   NACC, Kukull Walter ,   DIAN, Bateman Randy , McDade Eric ,   Cerebrovascular Disease (CVD) Risk, Jack Clifford , Morris John , Yassi Nawaf ,   Hippocampal Sclerosis (HS-TDP43) Risk, Bourgeat Pierrick , Perrin Richard , Roberts Blaine , Villemagne Victor ,   Artificial Intelligence and Machine Learning, Fedyashov Victor , Goudey Benjamin 
Published: July 2024

AbstractThis systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.
Preliminary Observations of the Loke Microdialysis in an Experimental Pig Model: Are We Ready for Continuous Monitoring of Brain Energy Metabolism?
Authors: Svedung Wettervik Teodor , Hånell Anders , Ahlgren Kerstin M. , Hillered Lars , Lewén Anders 
Published: July 2024

Abstract

Background
Brain energy metabolism is often disturbed after acute brain injuries. Current neuromonitoring methods with cerebral microdialysis (CMD) are based on intermittent measurements (1–4 times/h), but such a low frequency could miss transient but important events. The solution may be the recently developed Loke microdialysis (MD), which provides high-frequency data of glucose and lactate. Before clinical implementation, the reliability and stability of Loke remain to be determined in vivo. The purpose of this study was to validate Loke MD in relation to the standard intermittent CMD method.

Methods
Four pigs aged 2–3 months were included. They received two adjacent CMD catheters, one for standard intermittent assessments and one for continuous (Loke MD) assessments of glucose and lactate. The standard CMD was measured every 15 min. Continuous Loke MD was sampled every 2–3 s and was averaged over corresponding 15-min intervals for the statistical comparisons with standard CMD. Intravenous glucose injections and intracranial hypertension by inflation of an intracranial epidural balloon were performed to induce variations in intracranial pressure, cerebral perfusion pressure, and systemic and cerebral glucose and lactate levels.

Results
In a linear mixed-effect model of standard CMD glucose (mM), there was a fixed effect value (± standard error [SE]) at 0.94 ± 0.07 (p < 0.001) for Loke MD glucose (mM), with an intercept at − 0.19 ± 0.15 (p = 0.20). The model showed a conditional R
2 at 0.81 and a marginal R
2 at 0.72. In a linear mixed-effect model of standard CMD lactate (mM), there was a fixed effect value (± SE) at 0.41 ± 0.16 (p = 0.01) for Loke MD lactate (mM), with an intercept at 0.33 ± 0.21 (p = 0.25). The model showed a conditional R
2 at 0.47 and marginal R
2 at 0.17.

Conclusions
The established standard CMD glucose thresholds may be used as for Loke MD with some caution, but this should be avoided for lactate.
Complex network analysis of climate and landscape satellite data to explore spatio-temporal patterns in urban environment: the case of Athens
Authors: Charakopoulos Avraam , Karakasidis Theodoros , Ziliaskopoulos Konstantinos , Laspidou Chrysi 
Published: July 2024

AbstractMotivated by the significance and complexity of exploring spatiotemporal patterns - regions within an urban environment, particularly in the context of extreme heat events- this research analyzes meteorological time series through complex network analysis. The data collected for the examination area is focused on Athens, Greece, and covers sections of the city’s urban landscape. The data was obtained from the Copernicus observation component of the European Union. Initially, the time series are transformed into networks using correlation network methodology, followed by examination of the discriminative capability of the topological measures of networks degree and modularity as community - region detection methods. Of particular interest is that our findings suggest that the proposed complex network analysis can lead to the extraction of spatial urban regions closely linked to land use and building heights in corresponding areas. These results may help investigate the spatial variability of heat in the urban environment and inform urban planning and management strategies in policy decision-making regarding the intensity of urban heat throughout the city and the planning of climate change adaptation strategies.
Unintended sprouts as additional resource for pathogen-free seed potato (Solanum tuberosum) propagation
Authors: de Souza-Dias José Alberto Caram , Feldmann Falko 
Published: July 2024

AbstractUnintended sprouting of potato tubers (Solanum tuberosum) occurs within the professional production chain during transportation or storage due to the break of dormancy by changing environmental conditions. The sprouting of high-quality seed potatoes, for example, results in huge amounts of sprouts which complicate the planting process and are, therefore, manually removed and discarded. However, such sprouts can be very useful and sustainable when used as propagation material, helping to avoid losses of seed potatoes and increasing productivity per individual seed potato tuber.
Existential risk and equal political liberty
Authors: Porter J. Joseph , Gibbons Adam F. 
Published: July 2024

Abstract
Rawls famously argues that the parties in the original position would agree upon the two principles of justice. Among other things, these principles guarantee equal political liberty—that is, democracy—as a requirement of justice. We argue on the contrary that the parties have reason to reject this requirement. As we show, by Rawls’ own lights, the parties would be greatly concerned to mitigate existential risk. But it is doubtful whether democracy always minimizes such risk. Indeed, no one currently knows which political systems would. Consequently, the parties—and we ourselves—have reason to reject democracy as a requirement of justice in favor of political experimentalism, a general approach to political justice which rules in at least some non-democratic political systems which might minimize existential risk.