Aggregating data is the main line of any discipline dealing with fusion of information from the knowledge-based systems to the decision-making. The purpose of aggregation methods is to convert a list of objects, all belonging to a given set, into a single representative object of the same set usually by an n-ary function, so-called aggregation operator. Since the useful aggregation functions for modeling real-life problems are limit, the basic problem is to construct a proper aggregation operator for each situation. During the last decades, a number of construction methods for aggregation functions have been developed to build new classes based on the well-known operators. This paper reviews some of these construction methods where they are based on transformation, composition and weighted rule.

regulating translational speed and accuracy. Threonylcarbamoyl adenosine (t6A37) and 5-methoxycarbonylmethyl-thiouridine (mcm5s2U34) are critical ASL modifications that have been linked to several human diseases. The model yeast Saccharomyces cerevisiae is viable despite the absence of both modifications, growth is however greatly impaired. The major observed consequence is a subsequent increase in protein aggregates and aberrant morphology. Proteomic analysis of the t6A-deficient strain revealed a global mistranslation leading to protein aggregation without regard to physicochemical properties or t6A-dependent or biased codon usage in parent genes. However, loss of sua5 led to increased expression of soluble proteins for mitochondrial function, protein quality processing/trafficking, oxidative stress response, and energy homeostasis. These results point to a global function for t6A in protein homeostasis very similar to mcm5/s2U modifications.

Triazole derivatives of fluorescein containing N,N-dimethylaminopropyl fragments and their ammonium salts were synthesized with yields of 74 - 85%. The resulting compounds exhibit fluorescent properties in the green region of the visible spectrum. The critical aggregation concentration (CAC) was estimated using a py-rene fluorescent probe corresponding to a range of 0.28 - 1.43 mM, and at concentrations above the CAC the compounds form stable aggregates ranging from 165 to 202 nm. A relative quantum yield of 5-24% has been calculated based on fluorescence and UV spectra. The best value is shown by a derivative containing a tetradecyl substituent. When studying the photocatalytic properties of synthesized compounds through the reaction between N-substituted 1,2,3,4-tetrahydroisoquinoline and malonic ester, the mono-tetradecyl deriva-tive demonstrated the best results. According to gas chromatography-mass spectrometry (GC-MS) data, the conversion of the initial heterocycle reached 95%. Therefore, these resulting compounds have the potential to act as an effective photocatalysts.

Synucleins are a family of small, soluble proteins mainly expressed in neural tissue and in certain tumors. Since their discovery, tens of thousands of scientific reports have been published about this family of proteins as they are associated to severe human diseases. Although the physiological function of these proteins still elusive, their relation with neurodegeneration and cancer was clearly described over the years. In this review, we summarize data connecting synucleins and cancer, going from the structural description of these molecules, to their involvement in tumor-related processes, and discuss the putative use of this proteins as cancer molecular biomarkers.

The increasing micro and nanoplastic pollution is of immense concern. Recent studies show that biofilm substances in contact with nanoplastics play an important role in aggregation and sedimentation of nanoplastics. Consequences of these processes are changes in biofilm formation and stability and changes in transport and fate of pollutants in the environment. Having a deeper understanding of the nanoplastics-biofilm interaction would help to evaluate the risks posed by uncontrolled nanoplastic pollution. These interactions are impacted by environmental changes due to climate change, such as e.g. acidification of surface waters. We apply fluorescence correlation spectroscopy (FCS) to investigate the pH-dependent aggregation tendency of non-functionalized polystyrene (PS) nanoparticles (NPs) due to intermolecular forces with model extracellular biofilm substances. Our biofilm model consists of bovine serum albumin (BSA), which serves as a representative for globular proteins, and the polysaccharide alginate, which is a main component in many biofilms, in solutions containing Na+ with ionic strength being realistic for fresh water conditions. Biomolecule concentrations ranging from 0.5 g/l up to at maximum 21 g/l are considered. We use non-functionalized polystyrene NPs as representative for mostly negatively charged nanoplastics. BSA promotes NP aggregation through adsorption onto the NPs and BSA mediated bridging. In BSA-alginate mixtures the alginate hampers this interaction, most likely due to alginate-BSA complex formation. Thus, the NP are more stable in the BSA-alginate mixtures compared to solutions containing solely BSA. In most BSA-alginate mixtures and alginate alone, other weaker attractive forces, mainly depletion forces, seem to cause NP aggregation. These forces are not electrostatic in nature and thus are less influenced by the pH-value. At high BSA contents the electrostatic BSA-BSA attraction is not sufficiently screened leading to a destabilization of the NP. This study clearly shows that it is crucial to consider correlative effects between multiple biofilm components to better understand the NP aggregation in the presence of complex biofilm substances. Single component biofilm model systems based on comparing the total organic carbon content of the extracellular biofilm substances, as usually considered, would have led to an misjudgment of the stability towards aggregation. Nevertheless, if the protein content is not too high, a simple model that only depends on polysaccharide concentration could be feasible to predict aggregation under environmental relevant conditions.

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws were derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N ̅ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N ̅∝t^(-m). Simultaneously, the scaling behavior of the average cluster size S ̅ is characterized by S ̅∝t^m. Our findings reveals the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induce alterations in the dynamic procession of the system.

A single paragraph Arbuscular mycorrhizal fungi (AMF) establish symbiotic relationships with many crops. These soil microbiotas improve the soil fertility through the soil physical, chemical and biological properties. extending the root absorbing area. In return, the symbiont receives plant carbohydrates for the completion of its life cycle. AMF also helps plants to cope with biotic and abiotic stresses such as extreme temperature, heavy metal, diseases, and pathogens. For soil physical properties, the mechanisms used by AMF are the production of a glycoprotein, glomalin, which creates a high quality of soil macro-aggregations. These macro-aggregations control soil erosion, nutrients and organic matter losses. For soil chemical properties, AMF produce acids and an enzyme called phosphatase. This enzyme hydrolyzes the inorganic phosphorus and the rock phosphate (RP) hence making P available in the soil for plant uptake. AMF also are involved in soil nitrogen, carbon and trace element cycling. Regarding the biological component of the soil, AMF influence the composition, diversity and activity of microbial communities in the hydrosphere. They also work in synergy with others soil microorganisms to improve soil fertility, plant growth and resistance against some diseases. In this review, we present the contribution of AMF on soil fertility and importance in polluted soils.

Amyloid β-peptide (Aβ) oligomerization is believed to contribute to the neuronal dysfunction in Alzheimer disease (AD). Despite decades of research, many details of Aβ oligomerization in neurons still need to be revealed. Förster Resonance Energy Transfer (FRET) is a simple but effective way to study molecular interactions. Here we use a confocal microscope with a sensitive Airyscan detector for FRET detection. By live cell FRET imaging, we detect Aβ42 oligomerization in primary neurons. The neurons were incubated with fluorescently labelled Aβ42 in the cell culture medium for 24 hours. Aβ42 were internalized and oligomerized into the lysosomes/late endosomes in a concentration-dependent manner. Both the cellular uptake and intracellular oligomerization of Aβ42 were significantly higher than for Aβ40. These findings provide a better understanding of Aβ42 oligomerization in neurons.

Multiple sensor nodes known as detection stations make the sensor networks; each node is light and portable. Every sensor node contains power source, microcomputer, transducer and transceiver. Power source provides power to each node. Micro-computer is used for storing and processing the output coming from the sensors. The transducer is used to generate the signals and the transceiver is used to receive and transmit data to the central computer. Eavesdropping gets facilitated with wireless communication, and it has many useful applications in military, homeland, hostile and uncontrolled environments. So it is prone to the high level of security. The process in which information is gathered to form a summarized type for analysis is known as data aggregation, as it is used to reduce the energy consumption in wireless sensor networks. The security issues have become crucial in data aggregation, especially when gets deployed in hostile and remote environment. In wireless sensor networks many secure aggregations have been proposed. It still faces some resource constrained that’s why new techniques are needed. In our survey we will discuss those approaches and their pros and cons.

In human body, carboxylesterases (CEs) play the crucial roles in xenobiotic metabolism and lipid homeostasis. But the abnormal CEs expression is highly associated with some diseases, such as hyperlipidemia, diabetes and liver cancer. Therefore, it is of great significance to develop an efficient tool for the accurate detection of CEs in living organisms. Herein, an innovative near-infrared (NIR) fluorescent probe TTAP−AB has been designed for CEs detection based on aggregation-induced emission (AIE) mechanism. This probe exhibits rapid response (2 min), excellent sensitivity (limit of detection = 8.14×10−6 U/mL), and high selectivity towards CEs. Additionally, owing to the good biocompatibility, probe TTAP−AB enables to monitor the dynamic changes of CEs level under drug-induced modulation in living cells and zebrafish. More importantly, probe TTAP−AB is successfully employed to image the liver tumor and assist tumor resection by the real-time monitoring of CEs, indicating that TTAP−AB is promising to guide liver cancer surgery. Therefore, probe TTAP−AB can not only enrich the strategies for CEs detection in biological systems, but also has great potential for some clinical imaging applications, including medical diagnosis, preclinical research, and imaging-guided surgery.

Glycerol represents a useful functional component of heat-transfer fluids. Glycerol is known to be triboelectrically active. We report how the flow of glycerol, stopped in a cylindrical coiled pipe, influences horseradish peroxidase (HRP) enzyme. Namely, glycerol flow was pumped through the coil and then stopped. After that, we incubated the 0.1 µM solution of HRP near the side of the coil, and then adsorbed HRP onto mica. This operation markedly increased the HRP aggregation on mica in comparison with the control sample. We explain this phenomenon by the influence of triboelectrically induced electromagnetic fields. Our results can be useful in the development of flow-based systems for biosensors and bioreactors.

Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data for studying land cover and land use change (LCLUC) over the past four decades. However, the availability of cloud-, shadow-, and snow-free observations varies spatially and temporally due to climate and satellite data acquisition schemes. Spatio-temporal heterogeneity poses a major issue for some time-series analysis approaches, but can be addressed with pixel-based compositing that generates temporally equidistant cloud-free or near-cloud free synthetic images. Although much consideration is given to methods identifying the ‘best’ pixel value for each composite, determining the aggregation period receives less attention and is often done arbitrary, or based on expert intuition. Here, we evaluated data compositing windows ranging from five days to one year for 1984-2021 Landsat and 2015-2021 Sentinel‑2 time series across Europe. We considered separate and joint use of both data archives and analyzed spatio-temporal availability of composites during each calendar year and pixel-specific growing season. We reported mean annual composites’ availability investigating differences among biogeographical regions, checked feasibility of pan‑European analyses for three LCLUC applications based on annual, monthly and 10-day composites, and analyzed the shortest feasible compositing window ensuring ≥50% temporal data availability and interpolation of the remaining composites for individual years and across a variety of medium- and long‑term time windows. Our results highlighted low data coverage in the 1980s, 1990s, and in 2012, as well as spatial variability in data availability driven by climate and orbit overlaps, which altogether impact spatio-temporal consistency of medium- and long-term time series, limiting feasibility of some LCLUC analyses. We demonstrated that prior to 2011 monthly composites ensured overall 50-62% data coverage for each calendar year, and ~75% afterwards, with further increase to ~82% when Landsat and Sentinel-2 were combined. Temporal consistency of monthly composites was overall low and temporal interpolation augmenting up to 50% missing data each year and across a time window of interest, ensured feasibility of analyses. Applications based on shorter than monthly composites were challenging without joining Landsat and Sentinel‑2 archives after 2015, and beyond the Mediterranean biogeographical region. Using pixel-specific growing season data typically boosted data availability in most geographies and diminished most of the latitudinal differences, but feasibility of complete time series with sub-monthly compositing windows was still restricted to the most recent years, and required data interpolation. Overall, our analyses provided a detailed assessment of Landsat and Sentinel-2 data availability over Europe, and based on selected application examples, highlighted often lacking spatio-temporal consistency of time series with sub-monthly compositing windows and long-time periods, which might hinder feasibility of some LCLUC applications.

Fiber aggregation in nanocomposites has an important effect on the macroscopic electrical performance. To quantitatively evaluate its effect, an index to characterize the degree of aggregation is imperative, and ideally it should have three features simultaneously, i.e., single-parametric, dimensionless and physically meaningful, applicable to different aggregation topologies, and one-to-one corresponding to material electrical properties. To this end, a new aggregation degree is proposed, which is defined as the average increased number of fibers that connect with each one when fibers aggregate from a uniformly distributed state. This index is applicable to different aggregation topologies from lump-like aggregating clusters to network-like aggregating clusters. This index is proven to only depend on the local features of aggregating clusters, and can be concisely expressed by the characteristic parameters of local structure, via geometric probability analysis and numerical validations. Further, a one-to-one linear relation between the aggregation degree and percolation threshold is established by Monte Carlo simulations, which is independent of the distribution law of the fibers. This work provides a guide to the property characterization, performance prediction and material design of nanocomposites, and also gives a physical insight into the understanding of systems with similar non-uniform distributions.

Alzheimer’s disease (AD) is one of the most prevailing neurodegenerative diseases in the world, which is characterized by memory dysfunction and the formation of tau and amyloid β (Aβ) aggregate in multiple brain regions, including the hippocampus and cortex. The formation of senile plaques involving tau hyperphosphorylation, fibrillar Aβ, and neurofibrillary tangles (NFTs) are used as pathological markers of AD, and eventually produces aggregation or misfolded protein. Importantly, it has been found that failure to degrade these aggregate-prone proteins leads to pathological consequences, such as synaptic impairment, cytotoxicity, neuronal atrophy, and memory deficits associated with AD. Recently, increasing evidences have been suggested that autophagy pathway plays a role as a central cellular protection system to prevent the toxicity induced by aggregate or misfolded proteins. Moreover, it has also been related that AD-related protein aggresomes could be selectively degraded by autophagosome and lysosomal fusion through autophagy pathway which is known as aggrephagy. Therefore, the regulation of autophagy might be served as a useful approach to modulate the formation of aggresome associated in AD. This review focuses on the recent improvements in the application of natural compounds and small molecules as a potential therapeutic approach for AD prevention and treatment via aggrephagy.

We report on the effects of the film morphology on the fluorescence spectra for a thin film including a quinoxaline-based co-polymer (TQ1) and a fullerene derivative (PC₇₀BM). The ratio between the polymer and the fullerene derivative, as well as the processing solvent were varied. Beside the main emission peak at 700 nm in the fluorescence spectra of thin films of this phase-separated blend, a broad emission band is observed with a maximum at 520 - 550 nm. The intensity of this emission band decreases with an increasing degree of mixing in the film and becomes most prominent in thicker films, films with high PC₇₀BM content, and films that were spin-coated from solvents with lower PC₇₀BM solubility. We assign this emission band to aggregated PC₇₀BM.

Meta-aminobenzoic acid, an important model system in the study of polymorphism and crystallization of active pharmaceutical ingredients, exist in water in both the nonionic (mABA) and zwitterionic (mABA^±) forms. However, the constituent molecules of the polymorph that crystallizes from aqueous solutions are zwitterionic. This study reports atomistic simulations of the events surrounding the early stage of crystal nucleation of meta-aminobenzoic acid from aqueous solutions. Ab initio molecular dynamics was used to simulate the hydration of mABA± and mABA, and to quantify the interaction of these molecules with the surrounding water molecules. Density functional theory calculations were conducted to determine the low-lying energy conformers of meta-aminobenzoic acid dimers and compute the Gibbs free energies in water of nonionic, (mABA)₂, zwitterionic, (mABA^±)₂ and nonionic-zwitterionic, (mABA)(mABA^±), species. Classical molecular dynamics simulations of mixed mABA–mABA^± aqueous solutions were carried out to examine the aggregation of meta-aminobenzoic acid. According to these simulations the selective crystallization of the polymorph which constituent molecules are zwitterionic is driven by the formation of zwitterionic dimers in solution, which are thermodynamically more stable than (mABA)₂ and (mABA)(mABA^±) pairs. This work represents a paradigm of the role of molecular processes during the early stages of crystal nucleation in affecting polymorph selection during crystallization from solution.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

We have successfully developed curcumin nanosuspension aimed for oral delivery. The main purpose is to improve bioavailability through enhancing its solubility. The nanoparticles were stabilized using various stabilizers including polyvinyl pyrrolidone (PVP), polyvinyl alcohol (PVA), sodium carboxymethylcellulose (Na-CMC), d-alpha tocopheryl polyethylene glycol 1000 succinate (TPGS), and sodium dodecyl sulfate (SDS). The average diameter of particles, microscopic appearance, and sedimentation of each preparation was observed and compared. Each stabilizer demonstrated different degree of inhibition of particle aggregation under electrolyte-containing simulated gastrointestinal (GIT) fluid. Non-ionic stabilizers (PVA, PVP, and TPGS) has shown to preserve the nanosuspension stability against electrolytes. In contrast, strong ionic surfactants such as SDS were found to be very sensitive to electrolytes. The results can provide useful information for the formulators to choose the most suitable stabilizers by considering the nature of stabilizers and physiological characteristics of target site of the drug.

We compare the effect on amyloid fibril formation by two homologous proteins from the family of cystatins, human stefin B (stB) and cystatin C (cysC) in presence of 3 polyphenols: curcumin, resveratrol and quercetin and 2 non-phenolic anti-oxidants: vitamin C (VitC) and N-acetyl cystein (NAC). Some of the experimental data have already been presented, here we compare, further discuss and highlight the results. The amyloid fibril formation was followed by ThT fluorescence and transmission electron microscopy. Inhibitory effects on amyloid fibrillation reaction depended on anti-oxidant class and concentration. The fact that different effect of polyphenols was observed with the two cystatins; Cur acted inhibitory on stB but not on cysC fibril formation, could be explained if the 3 polyphenols would not bind to the same binding site in the fibrils core. Other differences are pointed out and discussed. Synergistic effects of VitC and chosen polyphenols on amyloid fibrilllation of human stB have been explored and are reported here for the first time.

This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. We developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we used a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds’ rules in a swarm of e-puck robots. Our algorithms outperformed this method in terms of cohesion performance and aggregation completion time.

This study employs a comprehensive computational approach to identify and evaluate potential inhibitors of gamma D crystallin (CRYGD), a key protein implicated in cataract formation. A library of compounds, including natural products and FDA-approved drugs, was screened using molecular docking simulations and binding energy calculations. The top-scoring compounds were further assessed for drug-likeness properties and potential toxicity. Molecular dynamics simulations were conducted on the most promising candidates to investigate their interactions with CRYGD at the atomic level. The study identified CNP0185903, CID 3741, and CNP0151361 as the most promising compounds, with CNP0151361 demonstrating the most favorable combination of binding affinity, structural stabilization, and conformational modulation of CRYGD. These findings provide a foundation for the development of novel pharmacological interventions for cataract treatment, potentially offering alternatives or complements to current surgical approaches.

A tetraphenylethylene-derived compound, TPEPhDAT, has been prepared by successive Suzuki-Miyaura coupling and ring-closing reactions. This compound exhibits aggregation-induced emission (AIE) properties in the DMSO/MeOH system, with a fluorescence emission intensity in the aggregated state that is 5-fold higher than that of its counterpart in a dilute solution. Moreover, the diaminotriazine structure of the molecule is a good acceptor of protons, and thus the TPEPhDAT molecule exhibits acid-responsive fluorescence. TPEPhDAT was protonated by trifluoroacetic acid (TFA), leading to a fluorescence quenching, which was reversibly restored by treatment with ammonia (On-Off switch). Time-dependent density functional theory (TDDFT) computational studies have shown that protonation enhances the electron-withdrawing capacity of the triazine nucleus and reduces the bandgap, producing a more intense intramolecular charge transfer (ICT), thus leading to fluorescence quenching. MeOH can easily remove the protonated TPEPhDAT, and this acid-induced discolouration and erasable property can be applied in anti-counterfeiting.

Wireless Sensor Networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, We present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spacial features of each grid with information from its correlated grids, generating spacial predictions within the spacial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and celluar data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.

In the absence of a holistic view of Landscape Sustainability, credible data and consistent information are needed to help decision making and support adaptive management. However, consolidated tools (system of standards and controls) of assessment exist, but show a complexity of the references on one hand, and are mostly qualitative and environmental indicators based on the other hand. This review article deals with the theme of both environmental and landscape indicators in general, the principles and the fundamental definitions. The main models used, the prerequisites and various fields of application are also taken into consideration. Last but not least, various environmental and landscape indicators are presented and compared.

Coffee is rich in caffeine (CF), chlorogenic acid (CGA) and phenolics. Differing types of coffee beverages and brewing procedures may result in differences in total phenolic contents (TPC) and biological activities. Inflammation and increases of platelet activation and aggregation can lead to thrombosis. We focused on determining the chemical composition, antioxidant activity and inhibitory effects on agonist-induced platelet aggregation and cyclooxygenase (COX) of coffee beverages in relation to their preparation method. We prepared instant coffee and brewed coffee beverages using drip, espresso and boiling techniques. Coffee extracts were assayed for their CF and CGA contents using HPLC, TPC using colourimetry, platelet aggregation with an aggregometer and COX activity using ELISA. The findings have shown all coffee extracts, except the decaffeinated types, contained nearly equal amounts of CF, CGA and TPC. Inhibitory effects of coffee extracts on platelet aggregation differed depending on the activation pathways induced by different agonists. All espresso, drip and boiled coffee extracts caused dose dependent inhibition of platelet aggregation induced by ADP, collagen, epinephrine, and arachidonic acid (ARA). The most marked inhibition was seen at low doses of collagen or ARA. Espresso and drip extracts inhibited collagen-induced platelet aggregation more than purified caffeine or CGA. Espresso, boiled and drip coffee extracts were also a more potent inhibitors of COX-1 and COX-2 than purified caffeine or CGA. We conclude that inhibition of platelet aggregation and COX-1 and COX-2 may contribute to anti-platelet and anti-inflammatory effects of espresso and drip coffee extracts.

Since the discovery of the aggregation-induced emission effect in 2001, diaminodicyanoquinone derivatives (DADQs) have presented interesting fluorescence properties, allowing them to be considered fluorescent dyes capable of showing quantum yields above 90%. Besides, the diaminodiacyanoquinone core represents a versatile building block propense either to modification or integration into different systems to obtain and provide them unique photophysical features. Herein, we carried out a theoretical study on the fluorescence properties of three different diaminodicyanoquinodimethane systems. Therefore, time-dependent density functional theory (TD-DFT) was used to obtain the values associated with the dipole moments, oscillator strengths, and the conformational energies between the ground and the first excited states of each molecule. The results suggest that only two of the three studied systems possess significant luminescent properties. In a further stage, the theoretical insights were confirmed by means of experimental measurements, which not only retrieved the luminescence of the DADQs, but also suggest a preliminary and promising antibacterial activity of these systems.

Crystallization behavior of hexadecane (C₁₆H₃₄), octadecane (C₁₈H₃₈), eicosane (C₂₀H₄₂), and docosane (C₂₂H₄₆) dispersions of similar mean droplet diameter (x_50.2 ≈ 15 µm) was investigated in quiescent systems and compared to crystallization under mechanical stress. In quiescent systems, the required supercooling decreased with increasing chain length of the alkanes to initiate crystallization. Crystallization of alkane dispersions under mechanical stress resulted in similar onset crystallization supercooling, as during quiescent crystallization. Increase of mechanical stress did not affect the onset crystallization supercooling within alkane dispersions.

We report magnetic silver nanoshells (M AgNSs) that have both magnetic and SERS properties for SERS-based detection. The M AgNSs are composed of hundreds of Fe3O4 nanoparticles for rapid accumulation and bumpy silver shell for sensitive SERS detection by near-infrared laser excitation. The intensity of the SERS signal from the M AgNSs was strong enough to provide single particle-level detection. We obtained much stronger SERS signal intensity from the aggregated M AgNSs than from the non-aggregated AgNSs. 4-Fluorothiophenol was detected at concentrations as low as 1 nM, which corresponds to 0.16 ppb. The limit of detection for tetramethylthiuram disulfide was 10 μM, which corresponds to 3 ppm. The M AgNSs can be used to detect trace amounts of organic molecules using a portable Raman system.

Platelet P2Y₁₂ is an important ADP receptor that is involved in agonist-induced platelet aggregation and is a valuable target for the development of anti-platelet drugs. Here we characterise the effects of thio-analogues of uridine triphosphate (UTP) on ADP-induced platelet aggregation. Using human platelet-rich plasma we demonstrate that UTP inhibits P2Y₁₂ but not P2Y₁ receptors and antagonises 10 μM ADP-induced platelet aggregation in a concentration-dependent manner with an IC₅₀ value of ~250 μM. An 8-fold higher platelet inhibitory activity was observed with a 2-thio analogue of UTP (2S-UTP), with an IC₅₀ of 30 μM. The 4-thio analogue (4S-UTP) with an IC₅₀ of 7.5 μM was 33-fold more effective. A 3-fold decrease in inhibitory activity, however, was observed by introducing an isobutyl group at the 4S- position. A complete loss of inhibition was observed with thio-modification of the γ phosphate of the sugar moiety, which yields an enzymatically stable analogue. The interaction of UTP analogues with P2Y₁₂ receptors was verified by P2Y₁₂ receptor binding and cAMP assays. These novel data demonstrate for the first time that 2- and 4-thio analogues of UTP are potent P2Y₁₂ receptor antagonists that may be useful for therapeutic intervention.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

Cellulose nanofibrils (CNFs) are materials with high aspect ratio. Typically, TEMPO-oxidised, or cationic CNFs consist of long fibrils (up to 2 μm) with very low thickness (less than 10 nm). Derived from their high aspect ratio, CNFs form strong hydrogels with high elasticity at low concentrations. Thus, CNF suspensions appear as an interesting rheology modifier to be applied in cosmetics, paints, foods, as a mineral suspending agent, among other applications. The high viscosity results from the strong 3D fibril network, which is related to good fibrillation of the material, allowing the nanofibrils to overlap. The overlap concentration (c*) was found to vary from ca. 0.13 to ca. 0.60 wt.% depending on the type and intensity of the pre-treatment ap-plied during the preparation of the CNFs. The results confirm the higher tendency for the fibres treated with CHPTAC (cationic) and TEMPO to form a 3D network, resulting in the lowest c*. For the TEMPO-oxidised CNF suspensions, it was also found that aggregation is improved at acidic pH conditions due to lower charge repulsion among fibrils, leading to an increase in the suspension viscosity, as well as higher apparent yield stresses. TEMPO CNF suspensions with low content of carboxylic groups tend to precipitate at moderate acidic pH values.

Landsat and Sentinel-2 acquisitions are among the most widely used medium-resolution optical data adopted for terrestrial vegetation applications, such as land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. When combined, both data archives provide over 40 years, and counting, of continuous and consistent observations. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of cloud-, snow-, and shade-free observations at the pixel level is lacking and should be explored individually for each study to correctly parametrize subsequent analyses. However, data exploration is time and resource consuming, thus is rarely performed a-priori. Consequently, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present precomputed data on the daily 1982‑2023 availability of usable Landsat and Sentinel-2 acquisitions across the globe, sampled at a pixel-level in a regular 0.18°-point grid. The dataset comprises separate Landsat- and Sentinel‑2‑specific data records, and is accompanied by a pixel-specific growing season information to facilitate data exploration. The dataset was derived based on freely available 1982-2023 Landsat surface reflectance (Collection 2) and Sentinel-2 top-of-the-atmosphere reflectance (pre‑Collection-1 and Collection-1) scenes from 2015 through 2023, following the methodology developed in the recent study on data availability over Europe [1]. Growing season information was derived based on 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6) [1]. As such, the dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much needed a-priori information aiding identification of appropriate methods and challenges for terrestrial vegetation analyses at local to global scale.

Silver powder, as the primary component of solar silver paste, significantly influences various aspects of paste performance, including printing, sintering, and conductivity. Silver powders prepared using the liquid-phase reduction method exhibit different microstructures depending on whether the growth process is dominated by aggregation or crystalline growth. This study explores the impact of different silver powder microstructures on packing density and sintering activity. It is observed that polycrystalline aggregated silver powder possesses higher surface energy, lower packing density, and greater sintering activity. Investigation into the influence of different silver powders on the electrical conductivity, adhesion, and structural density of silver paste reveals that polycrystalline aggregated silver powder is more suitable for lower sintering temperature pastes, whereas crystalline growth-type silver powder is better suited for higher sintering temperature pastes, resulting in a denser sintered silver layer. Finally, an analysis of the impact of various silver powders on the aspect ratio and electrical performance of solar cell silver grid lines yields higher aspect ratios (0.40) and photoelectric conversion efficiency (19.26%).

Training has a significative effect on physiology of blood coagulation both in humans and in horses. Several hemostatic changes have been reported after exercise in the horse but data available are inconclusive. The aim of this study was to investigate the platelet activation and primary platelet-related hemostasis modifications in young untrained Thoroughbred racehorses in the first 4-month timeframe incremental training period in order to improve knowledge on this topic. Twenty-nine clinically healthy, untrained, 2-year-old Thoroughbred racehorses. Never trained Thoroughbreds were followed during their incremental 4-month sprint exercise schedule. Blood collection was performed once a month (T-30, T0, T30, T60, and T90). Platelet aggregation was measured by light transmission aggregometer in response to various agonists: adenosine diphosphate (ADP), collagen and calcium ionophore A23187. Platelet function was evaluated using the Platelet Function Analyzer (PFA-100®) using collagen/ADP and collagen/adrenaline cartridges. Nitrite-nitrate (NOx) plasma concentrations were measured by a colorimetric assay to assess in vivo nitric oxide bioavailability. Platelet activation was investigated also through gene expression analyses (selectin P-SELP, ectonucleotidase CD39-ENTPD1, prostaglandin I2 synthase-PTGIS, endothelial nitric oxide synthase 3-NOS3). Differences among the time points were analyzed and mean±SEM were calculated. Significant modifications were identified compared with T-30 with increase of platelet aggregation (collagen:32.6±4.8 vs 21.6±4.9%; ADP: 35.5±2.0 vs 24.5±3.1%; A23187: 30±4.7 vs 23.8±4%) and a shorter closure time of C-ADP cartridges (75.6±4.4 vs 87.7±3.4 sec) that tended to return to baseline value at T90. NOx concentrations in plasma significantly increased after 30 days of training program compared with baseline. The first long-term training period induce platelet hyperactivity already after 30 days in untrained Thoroughbreds. Regular physical training blunts the negative effects of acute efforts on platelets activation.

Raman scattering has been employed to study in detail the concentration dependence of the vibrational modes for the hexamethylenetetramine (HMTA) aqueous solutions. The formation of protonated and/or aggregated species has been clarified by comparing the experimental with the theoretically predicted vibrational spectra by means of quantum mechanical calculations. The analysis has shown that the vibrational modes of the solutions arise from a contribution of the vibrational modes of the HMTA self-aggregates and hetero-aggregates of HMTA with water molecules that are formed in the low- and intermediate-concentration region, respectively. The protonation of HMTA is ruled out due to the large differences between the experimental and the theoretically calculated spectra of the protonated molecules of HTMA in the fingerprint region. In the low-concentration solutions, the hetero-aggregation reaction of HMTA with water is the dominant mechanism, while at higher concentrations a self-aggregation mechanism occurs. Ultrasonic absorption and velocity measurements were carried out for hexamethylenetetramine aqueous solutions. The acoustic spectra reveal the presence of only one single Debye-type relaxation process that is assigned to the aggregation mechanism of HMTA. The sound absorption data follow two different dependencies on HMTA mole fraction. The crossover 0.018 mole fraction signifies two separate regions with distinct structural characteristics. The relaxation mechanism observed in dilute solutions is attributed to hetero-association of HMTA with water molecules, while at higher concentrations the observed relaxation process is assigned to the self-association reaction of HMTA molecules. This structural transformation is also reflected in several physicochemical properties of the system, including the kinematic viscosity, the mass density, the sound speed, and the adiabatic compressibility of the HMTA aqueous solutions. The combination of vibrational and acoustic spectroscopies with molecular orbital calculations allowed us to disentangle the underlying processes and to elucidate the observed relaxation mechanism in the HMTA aqueous solutions.

Proliferating cell nuclear antigen (PCNA) is the key regulator of human DNA metabolism. One important interaction partner is p15, involved in DNA replication and repair. Targeting the PCNA-p15 interaction is a promising therapeutic strategy against cancer. Here, a Förster resonance energy transfer (FRET)-based assay for the analysis of PCNA-p15 interaction was developed. Next to the application as screening tool for the identification and characterization of PCNA-p15 interaction inhibitors, the assay is also suitable for the investigation of mutation-induced changes in their affinity. This is particularly useful for analysing disease associated PCNA or p15 variants at the molecular level. Recently, the PCNA variant C148S has been associated with Ataxia-telangiectasia-like disorder type 2 (ATLD2). ATLD2 is a neurodegenerative disease based on defects in DNA repair due to an impaired PCNA. Incubation time dependent FRET measurements indicated no effect on PCNAC148S-p15 affinity, but on PCNA stability. The impaired stability and increased aggregation behaviour of PCNAC148S was confirmed by intrinsic tryptophan fluorescence, DSF and AF4 measurements. The analysis of the disease associated PCNA variant demonstrated the versatility of the interaction assay as developed.

Alzheimer’s disease is associated with prion-like aggregation of the amyloid β (Aβ) peptide and the subsequent accumulation of misfolded neurotoxic aggregates in the brain. Therefore, it is critical to clearly identify the factors that trigger the cascade of Aβ misfolding and aggregation. Numerous studies have pointed out the association between microorganisms and their virulence factors and Alzheimer’s disease; however, their exact mechanisms of action remain unclear. Recently, we discovered a new pathogenic role of bacterial extracellular DNA, triggering the formation of misfolded Tau aggregates. In this study, we investigated the possible role of DNA extracted from different bacterial and eukaryotic cells in triggering Aβ aggregation in vitro. Interestingly, we found that the extracellular DNA of some, but not all, bacteria is an effective trigger of Aβ aggregation. Furthermore, the acceleration of Aβ nucleation and elongation can vary based on the concentration of the bacterial DNA and the bacterial strain from which this DNA had originated. Our findings suggest that bacterial extracellular DNA might play a previously overlooked role in the Aβ protein misfolding associated with Alzheimer’s disease pathogenesis. Moreover, it highlights a new mechanism of how distantly localized bacteria can remotely contribute to protein misfolding and diseases associated with this process. These findings might lead to the use of bacterial DNA as a novel therapeutic target for the prevention and treatment of Alzheimer’s disease.

Relapsed leukemia following initial therapeutic response and remission is difficult to treat and causes high patient mortality. Leukemia relapse is due to residual quiescent leukemia cells that escape conventional therapies and later reemerge. Eliminating not only growing but quiescent leukemia cells is critical to effectively treating leukemia and preventing its recurrence. Such therapeutic agents, however, are lacking in the clinic. Here we report that a 4-arylmethyl derivative of the natural anticancer compound curcumin demonstrates a dual effect in eliminating both growing and quiescent leukemia cells. This curcumin derivative, C212, on the one hand, inhibits growing leukemia cells at a higher efficacy than curcumin by inducing apoptosis and cell cycle arrest; it, on the other hand, kills quiescent leukemia cells that are resistant to conventional chemotherapy drugs. Furthermore, C212 drives leukemia cells into and kills them at deep quiescence, avoiding the potential risk associated with awaking therapy-resistant subpopulation of quiescent leukemia cells. Lastly, we show that C212 induces apoptosis and drives cells into deep dormancy at least partially by binding to and inhibiting Hsp90, leading to client protein degradation and protein aggregation. Further elucidating the molecular mechanisms underlying the dual function of C212 in eliminating both growing and quiescent leukemia cells will aid the development of novel therapies against leukemia relapse.

The human androgen receptor (AR) is a ligand inducible transcription factor harboring an amino terminal domain (AR-NTD) hosting the ligand independent activation function. AR-NTD is intrinsically disordered and display aggregation properties conferred by the presence of a poly-glutamine (polyQ) sequence of 22 residues. The length of the polyQ sequence, as well as the presence of adjacent sequence motifs modulate this aggregation property. AR-NTD contains also a conserved sequence motif KELCKAVSVSM that displays an intrinsic property to form amyloid fibrils under mild oxidative conditions of its conserved cysteine residue. As peptide sequences with intrinsic ability to oligomerize are reported to have an impact on the aggregation of polyQ tract, we determined the effect of the KELCKAVSVSM on the polyQ stretch in the context of the AR NTD, using Atomic Force Microscopy (AFM). Here, we present evidence for a crosstalk between the amyloidogenic properties of the KELCKAVSVSM motif and the polyQ stretch at the AR NTD.

Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.

The objective of the present contribution was to design and characterize resveratrol (RSV) and tocopherol (TOC) loaded 11S quinoa seed protein nanocomplexes. Firstly, molecular docking was performed to describe the probable binding sites between protein and ligands. Isothermal titration calorimetry allowed to obtain the thermodynamic parameters that described the molecular interactions between RSV or TOC with the protein. 11S globulin intrinsic fluorescence spectra showed a quenching effect exerted by RSV and TOC, demonstrating protein-bioactive compounds interactions. Stern-Volmer, Scatchard and Förster resonance energy transfer models application confirmed static quenching and allowed to obtain parameters that described the 11S-RSV or 11S-TOC complexation process. Secondly, protein aggregation induced by bioactive compounds interactions was confirmed by dynamic light scattering and atomic force microscopy with diameters <150 nm, detected by both techniques. Finally, it was found that antioxidant capacity of single 11S globulin did not decrease, meanwhile was additive for 11S-RSV. These nanocomplexes could constitute a real platform for the design of nutraceutical products.

Photodynamic therapy (PDT) has garnered significant attention in the fields of cancer treatment and drug-resistant bacteria eradication due to its non-invasive nature and spatiotemporal controllability. Iridium complexes have captivated researchers owing to their tunable structure, exceptional optical properties, and substantial Stokes displacement. However, most of these complexes suffer from aggregation-induced quenching, leading to diminished luminous efficiency. In contrast to conventional photosensitizers, photosensitizers exhibiting aggregation-induced luminescence (AIE) properties retain the ability to generate a large number of reactive oxygen species when aggregated. To overcome these limitations, we designed and synthesized a novel iridium complex named Ir-TPA in this study. It incorporates quinoline triphenylamine cyclomethylated ligands that confer AIE characteristics for Ir-TPA. We systematically investigated the photophysical properties, AIE behavior, spectral features, and reactive oxygen generation capacity of Ir-TPA. The results demonstrate that Ir-TPA exhibits excellent optical properties with pronounced AIE phenomenon and robust capability for producing singlet oxygen species. This work not only introduces a new class of metal iridium complex photosensitizer with AIE attributes but also holds promise for achieving remarkable photodynamic therapeutic effects in future cellular experiments and biological studies.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

The supramolecular aggregation processes occurring on metallic (aluminum and gold) surfaces in aqueous solutions of bovine serum albumin (BSA) during drying were studied using advanced scanning electron microscopy (SEM). The possible mechanism for the formation of amazing intricate fractal structures on metallic surfaces was proposed based on the analysis of SEM images, size distribution diagrams and EDX-scanning elements distribution maps.

As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested based approaches in terms of energy consumption, network control overhead, and packet loss rate.

The complexity of Mixed-Integer Linear Programs (MILPs) increases with the number of nodes in energy system models. An increasing complexity constitutes a high computational load that can limit the scale of the energy system model. Especially in microgrid optimisation problems with multiple buildings and energy systems with a number of rival supply, distribution and storage technologies, methods are sought to reduce this complexity. In this paper, we present a new 2-Level Approach to MILP energy system models that determine the system design through a combination of continuous and discrete decisions. On the first level, data reduction methods are used to determine the discrete design decisions in a simplified solution space. Those decisions are then fixed, and on the second level the full dataset is used to extract the exact scaling of the chosen technologies. The performance of the new 2-Level Approach is evaluated for a case study of an urban energy system with six buildings and an island system based on a high share of renewable energy technologies. The results of the studies show a high accuracy with respect to the total annual costs, chosen system structure, installed capacities and peak load with the 2-Level Approach compared to the results of a single level optimization. The computational load is thereby reduced by more than one order of magnitude, while a significantly higher accuracy is reached in comparison to the common time series aggregation approach.

The applicability of the distance aggregation problem has attracted the interest of many authors. Motivated by this fact, in this paper we face the modular quasi-(pseudo-)metric aggregation problem. We characterize those functions that allow merging a collection of modular quasi-(pseudo-)metrics into a single one. Specifically, a description of such functions in terms of triangle triplets is given and, in addition, the relationship between modular quasi-(pseudo-)metric aggregation functions and modular (pseudo-)metric aggregation functions is discussed. Such characterizations are illustrated with appropriate examples. A few methods to construct modular quasi-(pseudo-)metrics are yielded. Several properties of modular quasi-(pseudo-)metric aggregation functions are explored and used to develop quick tests for discarding candidate functions to aggregate modular quasi-(pseudo-)metrics. Moreover, a characterization of those modular quasi-(pseudo-)metric aggregation functions that preserve modular quasi-(pseudo-)metrics is also provided. Furthermore, the relationship between modular quasi-(pseudo-)metric aggregation functions and quasi-(pseudo-)metric aggregation functions is studied in such a way that significative differences are displayed.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

In our study, we assessed the effects of verbenone, the most widely studied bark beetle aggregation inhibitor, on saproxylic beetles of a Mediterranean pine forest in Tuscany. Verbenone pouches were devised in the laboratory and then applied to Ips sexdentatus pheromone traps so that catches could be compared to those of traps with just the bait. The trial was carried out in spring-summer 2023, collecting insect catches every two weeks. A total of 9440 beetles were collected, belonging to 32 different families and 57 species. About 80% of captures were bark beetles, mainly Orthotomicus erosus. Beetle predators accounted for about 17% of the captures, with a total of 12 species. Some of these predator species had never been studied yet in relation to verbenone effects, like other saproxylic beetles recorded in this study. A significant reduction of captures was recorded for some beetles (e.g. I. sexdentatus and O. erosus), for other species no differences emerged, and in some cases captures increased significantly when verbenone was present in the traps (i.e.. Hylurgus ligniperda, Corticeus pini, Aulonium ruficorne). The diversity of caught saproxylic beetles increased significantly in verbenone traps, highlighting possible implications of the use of verbenone when managing bark beetle outbreaks.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

Amyloids were conventionally referred to as extracellular and intracellular accumulation of Aβ42 peptide which causes the formation of plaques and neurofibrillary tangles inside the brain leading to the pathogenesis in Alzheimer’s disease. Subsequently, amyloid-like deposition was found in the etiology of Prion diseases, Parkinson disease, Type II Diabetes and Cancer which was attributed to the aggregation of Prion protein, α-Synuclein, Islet Amyloid Polypeptide Protein and p53 protein respectively. Hence, traditionally amyloids were considered as aggregates formed by the proteins or peptides exclusively. However, since the last decade it has been discovered that other metabolites like single amino acids, nucleobases, lipids, glucose derivatives etc. have propensity to form amyloid-like toxic assemblies. Several studies suggest direct implications of these metabolite assemblies in the patho-physiology of various Inborn errors of metabolisms like Phenylketonuria, Tyrosinemia, Cystinuria and Gaucher’s disease to name a few. In this review, we present a comprehensive literature overview which suggests amyloid-like structure formation as a common phenomenon for the disease progression and pathogenesis in multiple syndromes. The review on this topic is urgently required to create awareness about the understanding of fundamental molecular mechanism behind the origin of diseases from an amyloid perspective and possibly look for a common therapeutic strategy for the treatment of these maladies by designing generic amyloid inhibitors.

Silicon nanoparticles (SiNPs) are highly promising for biological and biomedical applications, including bioimaging, due to their unique opltical properties (i.e. strong fluorescence and very high photostability). Their low or negligible in vitro toxicity has been reported, but in vivo toxicity and biological effects of SiNPs are still uncertain. Uncoated SiNPs were dispersed in distilled water via sonication, and their rapid aggregation was observed (319.0 ± 2.4 nm particle size). In vivo toxicity was studied using Danio rerio embryos and larvae. Rapid aggregation in their incubation medium was observed; besides that, SiNPs at 25 mg/L or higher concentration induced swim bladder malformation and/or death of the fish. The estimated LC50 value for 7-day larvae was 180 mg/L. This is the first in vivo toxicity study of uncoated and unfunctionalized SiNPs. To achieve better stability in biological media and lower toxicity, SiNPs should be covered with hydrophilic layers, but their absorption by cellular membranes may be weaker in this case.

One of the major problems caused by repeated subcutaneous insulin injections in diabetic patients is insulin amyloidosis. Understanding the molecular mechanism of amyloid fibril formation of insulin and finding effective compounds to its inhibit or eliminate is very important and extensive research has been done on it. In this study, the anti-amyloidogenic and destabilizing effects of the pyrogallol, as a phenolic compound, on human insulin protein were investigated by CR absorbance, ThT and ANS fluorescence, FTIR spectroscopy and atomic force microscopy. According to the obtained results, the formation of amyloid fibrils at pH 2.0 and 50 °C was confirmed by CR, ThT, ANS, FTIR assays. Microscopic images also showed the twisted and long structures of amyloid fibrils. Simultaneous incubation of the protein with pyrogallol at different concentrations reduced the intensities of CR, ThT and ANS in a dose-dependent manner and no trace of fibrillar structures was observed in the microscopic images. FTIR spectroscopy also showed that the position of amide I band in the spectrum of samples containing pyrogallol was shifted. Based on the findings in this study, it can be concluded that pyrogallol can be effective in preventing and suppressing human insulin amyloid fibrils.

In recent years, the photophysical properties of crystalline metal-organic frameworks (MOFs) have become increasingly relevant for their potential application in light-emitting devices, photovoltaics, nonlinear optics and sensing. The availability of high-quality experimental data for such systems makes them ideally suited for a validation of quantum mechanical simulations, aiming at an in-depth atomistic understanding of photophysical phenomena. Here we present a computational DFT study of the absorption and emission characteristics of a Zn-based surface-anchored metal-organic framework (Zn-SURMOF-2) containing anthracenedibenzoic acid (ADB) as linker. Combining band-structure and cluster-based simulations on ADB chromophores in various conformations and aggregation states, we are able to provide a detailed explanation of the experimentally observed photophysical properties of Zn-ADB SURMOF-2: The unexpected (weak) red-shift of the absorption maxima upon incorporating ADB chromophores into SURMOF-2 can be explained by a combination of excitonic coupling effects with conformational changes of the chromophores already in their ground state. As far as the unusually large red-shift of the emission of Zn-ADB SURMOF-2 is concerned, based on our simulations, we attribute it to a modification of the exciton coupling compared to conventional H-aggregates, which results from a relative slip of the centers of neighboring chromophores upon incorporation in Zn-ADB SURMOF-2.

The misfolding and aggregation of proteins is the neuropathological hallmark of numerous diseases including Alzheimer’s disease, Parkinson’s disease, and prion diseases. It is believed that misfolded and abnormal -sheets forms of wild-type proteins are the vectors of these diseases by acting as seeds for the aggregation of endogenous proteins. Cellular prion protein (PrPC) is a glycosyl-phosphatidyl-inositol (GPI) anchored glycoprotein which is able to misfold to a pathogenic isoform PrPSc, the causative agent of prion diseases which present as sporadic, dominantly inherited and transmissible infectious disorders. Increasing evidence highlight the importance of prion-like seeding as a mechanism for pathological spread in Alzheimer’s disease and tauophaty, as well as other neurodegenerative disorders. Here, we report the latest findings on the mechanisms controlling protein folding, focusing on the ER quality control of GPI-anchored proteins and describe the “prion-like” properties of amyloid- and tau assemblies. Furthermore, we highlight the importance of pathogenic assemblies interactions with protein and lipid membrane components and their implications in both prion and Alzheimer’s diseases.

Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spectral bands than a general image, thereby permitting the multi-spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-spectral analysis and explores the middle-level statistics of spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-spectral analysis firstly learns the multi-spectral prototypes (codebook) for representing any pixel-wise spectral data, and then based on the learned codebook, a sparse coded spectral vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded spectral vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is much more accurate than even the deep learning framework for spatial analysis.

Despite the technological advancements of 3-D city models, the 3-D shape abstraction methods for urban vegetation are recent and still limited to support spatial analysis of individual or groups of trees and forest patches. In this paper, a scheme for individual urban tree abstraction, which retains the semantic complexity of the object, is proposed. Our contribution is three-fold. First, an initial tree structure based on a new 3-D aggregation operator is proposed. Second, we developed Gestalt rules for 3-D urban vegetation mapping. Third, current state-of-the-art deep learning is adapted for the abstraction of individual urban trees. Quantitative and qualitative results show the effectiveness of our proposed approach in accurately reducing the spatial density of trees and the degree of fragmentation of the total green area, which can access information about the location, size, and spatial distribution of mapped urban trees.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

Symmetry-breaking is a phenomenon that is observed in various contexts, from the early universe to complex organisms, and is considered a key puzzle in understanding the emergence of life. The presence of enantiomeric amino acids and proteins highlights its critical role. However, the origin of symmetry-breaking has yet to be comprehensively explained, particularly from an energetic standpoint. This article explores a novel approach by considering energy dissipation, specifically lost free energy, as a crucial factor in elucidating symmetry-breaking. Through a thorough thermodynamic analysis applicable to enantiomeric systems at various scales, from elementary particles to aggregated matter like crystals, we present experimental findings that establish a direct correlation between non-equilibrium free energy and energy dissipation during formation processes. Results emphasize the pivotal role of energy dissipation not only as an outcome but as the trigger for symmetry-breaking. This insight suggests that understanding the origins of complex systems, from cells to living beings and the universe itself, requires a lens focused on non-equilibrium processes, ultimately facilitating the formation of non-racemic systems.

Sporadic inclusion body myositis (sIBM) is the most common muscle disease of older people and is clinically characterized by slowly progressive asymmetrical muscle weakness, predominantly affecting quadriceps, deep finger flexors and foot extensors. At present there are no enduring treatments for this relentless disease that eventually leads to severe disability and wheelchair dependency. Although sIBM is considered a rare muscle disorder, its prevalence is certainly higher as the disease is often undiagnosed or misdiagnosed. The histopathogical phenotype of sIBM muscle biopsy includes muscle fiber degeneration and endomysial lymphocytic infiltrates that mainly consist of cytotoxic CD8+ T cells surrounding nonnecrotic muscle fibers expressing MHCI. Muscle fiber degeneration is characterized by vacuolization and accumulation of congophilic misfolded multi-protein aggregates, mainly in their non-vacuolated cytoplasm. Many players have been identified in sIBM pathogenesis including environmental factors, autoimmunity, abnormalities of protein transcription and processing, accumulation of several toxic proteins, impairment of autophagy and ubiquitin proteasome system, oxidative and nitrative stress, endoplasmic reticulum stress, myonuclear degeneration, and mitochondrial dysfunction. Aging has been also proposed as a contributor to the disease. However, the interplay between these processes and the primary event that leads to the coexistence of autoimmune and degenerative changes is still under debate. Here, we outline our current understanding of disease pathogenesis, focusing on degenerative mechanisms, and discuss the possible involvement of aging.

RNA, as an important substance for regulating biological growth and development, has significant implications for visualization research. However, many spontaneously fluorescent substances in plants greatly interfere with the effectiveness of plant bioimaging. Aggregetion- induced emission luminogens (AIEgens), due to their luminescent properties, tunable molecular size, high fluorescence intensity, good photostability, and low cell toxicity, have been widely applied in the animal and medical fields. We have found that AIEgens have great potential as RNA fluorescent probes for efficient imaging in plants. In this review, we first introduce several common RNA labeling forms and point out their pros and cons. We briefly describe the development of AIEgens and the AIE mechanism, and then present various practical applications of AIEgens, including detailed examples of their use as biological markers. To further promote the application of AIE in the field of RNA, we suggest the use of AIEgens to modify target RNA via techniques such as click chemistry or CRISPR/Cas, to achieve RNA visualization in plants. highly possible to modify target RNA with AIEgens in vivo for RNA visualization.

Prior studies have focused on the overall behavior of randomly moving particle swarms. However, the characteristics of the stochastic-constrained particles that form ubiquitously within these swarms remain oblivious. This study demonstrates a generalized diffusion equation for stochastic-constrained particles that considers the velocity and location aggregation effects observed from their parent particle swarm (i.e., a completely random particle swarm). This equation can be approximated as the form of Schr\"odinger equation in the microcosmic case (low relative density) and describe the dynamics of the total mass distribution in the macrocosmic case (high relative density). The predicted density distribution of the particle swarm in the stable aggregation state is consistent with the total mass distribution of massive, relaxed galaxy clusters (at least in the range of r < r_s), preventing cuspy problems in the empirical Navarro-Frenk-White (NFW) profile. This study opens a window to observe the dynamics of stochastic-constrained particles from a third perspective, from which the aggregation effect of particles without gravitation can be saw.

Most neurodegenerative diseases have the characteristics of proteinopathies, i.e. they cause lesions to appear in vulnerable regions of the nervous system, corresponding to protein aggregates that progressively spread through the neuronal network as the symptoms progress. Alzheimer's disease is one of these proteinopathies. It is characterized by two lesions, neurofibrillary tangles (NFTs) and senile plaques, formed essentially of amyloid peptides (Aβ). A combination of factors ranging from genetic mutations to age-related changes in the cellular context converge in this disease to accelerate Aβ deposition. Over the last two decades, numerous studies have attempted to elucidate how structural determinants of its precursor (APP) modify Aβ production, and to understand the processes leading to the formation of different Aβ aggregates; e.g. fibrils and oligomers. The synthesis proposed in this review indicates that the same motifs can control APP function and Aβ production essentially by regulating membrane dimerization, and subsequently Aβ aggregation processes. The distinct properties of these motifs and the cellular context regulate the APP conformation to trigger the transition to the amyloid pathology. This concept can be transposed to the study of other proteinopathies, providing a framework for improving our understanding of these mechanisms that devastate neuronal functions.

Desmin is the major intermediate filament protein of all three muscle cell types and connects different cell organelles and multi-protein complexes like the cardiac desmosomes. Several pathogenic mutations in the DES gene cause different skeletal and cardiac myopathies. However, the significance of the majority of DES missense variants is currently unknown since functional data are lacking. To determine whether desmin missense mutations within the highly conserved 1A coil domain cause a filament assembly defect, we generated a set of variants with unknown significance and analyzed systematically the filament assembly in transfected SW13 and H9c2 cells using confocal microscopy. We found that mutations in the N-terminal part of the 1A coil domain affect the filament assembly leading to the cytoplasmic desmin aggregation. In contrast, mutant desmin in the C-terminal part of the 1A coil domain form filamentous structures comparable to wild-type desmin. Our findings suggest that the N-terminal part of the 1A coil domain is a hot spot for pathogenic desmin mutations, which affect the desmin filament assembly leading in consequence to skeletal and/or cardiac myopathies. This study may have relevance for the genetic counselling of patients carrying variants in the 1A coil domain of the DES gene.

Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetic described by the Smoluchowski theory. Molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, without having to conduct large scale experiments on living organisms.

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, more importantly reduce down the signaling load at the control channels. Thus, in the literature many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets and so on. In this paper, a cyclostationary feature detection based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. In the paper, after the proposed method is formulated, the measurement setup which is developed to collect the data for the validation of the proposed method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of SNR values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. Measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions.

Induction motors are the most numerous and significant dynamic loads in power systems. In transient voltage stability analysis, dynamic equivalent models are commonly used to simplify the representation of a group of induction motors. This paper presents an method for the grouping and aggregation of induction motors at a common bus. Firstly, grouping rules were provided for clustering induction motors into several subgroups based on the mechanical principles of rotor force and motion, and Aggregation Rules were provided for aggregating a motor subgroup into a single-unit model based on the relationship between voltage drop and power transmission in distribution networks. Secondly, guided by the grouping rules, high-speed remaining electromagnetic torque and low-speed remaining electromagnetic torque were defined as new clustering indicators, and an adaptive K-means clustering method using silhouette coefficient verification was provided to obtain the optimal motor subgroups. Thirdly, guided by the Aggregation Rules, a dynamic equivalent method was further provided to obtain the equivalent single-unit model from a motor subgroup. Lastly, a transient voltage stability simulation in a typical distribution network was presented to illustrate that the proposed clustering and equivalent methods are more reasonable, accurate, and effective than traditional methods, as the obtained model has better dynamic characteristics and can more accurately reproduce the process of voltage collapse.

Forests have multifunctional roles. They not only increase forest carbon sinks through afforestation and management, but also indirectly realize multiple benefits of "economy + ecology + society". Therefore, we construct a multiple output-benefit system of forest carbon sinks based on the development of forest carbon sinks in provinces of China. This system is also able to distinguish the importance of forest carbon sinks from other outputs. For the efficiency evaluation of multi-level output systems, this paper constructs a cross-evaluation model based on evidential reasoning method, the original efficiency information by conducting efficiency evaluation before information fusion. Further, the spillover effects and influencing factors of forest carbon sink efficiency are analyzed through a spatial regression model to explore the spatial characteristics of forest carbon sink efficiency in China. The results show that: (1) there is a large gap between the cross-efficiency levels of forest carbon sinks in various regions of China, with significant spatial correlation. For the perspective of direct output benefits, the cross-efficiency of the eastern region is higher, while the cross-efficiency of the northwestern region is lower; for the perspective of indirect output benefits, the cross-efficiency of the southeastern region is higher. (2) China's forest carbon sink has a significant positive spillover effect, and the indirect output cross-efficiency has a significant negative impact on the forest carbon sink in its own region. Further analyzing the impact of indirect output cross-efficiency, it is found that the cross-efficiency of the ecological dimension and the cross-efficiency of the social dimension have a significant negative impact on the forest carbon sink in its own region, and the economic dimension has a significant positive impact on the forest carbon sink in its own region. In conclusion, corresponding policy recommendations are proposed for the development of forest carbon sinks to promote the realization of the dual-carbon goal.

A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to wake effect. The main engine of the algorithm is a gradient-descent method, in which an analogy is established between power deficit and repulsive electrostatic potential energy. To achieve a maximum dispersion in the initial solution, throughout the optimization process, new turbines are included in the centre of the concession area, and quickly expand outward looking for areas with less potential, in turn pushing the previous ones. When the optimization process ends, to avoid local maximums, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a potential map of the entire area is created and a greedy algorithm includes new turbines to complete the layout with the final number of turbines. To speed up the search process and the creation of the potential map, two simplifications have been validated and added: the spatial distribution of velocities has been converted into its equivalent with point symmetry; and, for turbines affected by multiple wakes, the power deficit has been calculated by using a linear aggregation of powers, instead of the usual linear (or quadratic) aggregation of speeds. Owing to this type of aggregation, the process is accelerated in a factor of 90.

Glycerol has found its applications as a heat-transfer fluid in heat exchangers, and as a compo-nent of functional solutions in biosensor analysis. Flowing non-aqueous fluids are known to be able to induce electromagnetic fields due to triboelectric effect. These triboelectrically generated electromagnetic fields can affect biological macromolecules. Horseradish peroxidase (HRP) is widely employed as a convenient model object for studying how external electric, magnetic, and electromagnetic fields affect enzymes. Herein, we have studied whether the flow of glycerol in a ground-shielded cylindrical coil affects the HRP enzyme incubated at a 2 cm distance near the coil’s side. Atomic force microscopy (AFM) has been employed in order to study the effect of glycerol flow on HRP at the nanoscale. Increased aggregation of HRP on mica has been observed after the incubation of the enzyme near the coil. Moreover, the enzymatic activity of HRP has also been affected. The results reported can find their application in biotechnology, food technology and life sciences application, considering the development of triboelectric generators, en-zyme-based biosensors and bioreactors with surface-immobilized enzymes. Our data can also be of interest for scientists studying triboelectric phenomena.

More than fifty million people over the age of fifty have osteoporosis issues due to low bone mass, disease contribution, and not exercising continuously. Certain most important risk factors and causes of osteoporosis contain age, gender, hormones, visits to inexperienced doctors, and certain medical conditions. In this manuscript, we diagnosed certain frank operational laws under the availability of complex intuitionistic fuzzy (CIF) information. Furthermore, using the evaluated operational laws, we pioneered the theory of CIF frank weighted averaging (CIFFWA), CIF frank ordered weighted averaging (CIFFOWA), CIF frank hybrid averaging (CIFFHA), CIF frank weighted geometric (CIFFWG), CIF frank ordered weighted geometric (CIFFOWG), CIF frank hybrid geometric (CIFFHG) operators, and described their beneficial and valuable results with certain useful properties. Moreover, we aim to evaluate the most dangerous type of osteoporosis based on their symptoms, causes, and risk factors using diagnosed approaches. Finally, to enhance the worth of the evaluated operators, we compare the diagnosed result with certain prevailing results and illustrated their geometrical representation with the help of MATLAB.

The Lewy Body Dementia Association (LBDA) held a virtual event, the LBDA Biofluid/Tissue Biomarker Symposium, on January 25, 2021, to present advances in biomarkers for Lewy Body Dementia (LBD), which includes Dementia with Lewy Bodies (DLB) and Parkinson's Disease Dementia (PDD). The meeting featured eight internationally known scientists from Europe and the United States and attracted over 200 scientists and physicians from academic centers, the National Institutes of Health and the pharmaceutical industry. Methods for confirming and quantifying the presence of Lewy body and Alzheimer pathology as well as novel biomarkers were discussed.

Patients with schizophrenia (SCZ) are at high risk of cardiovascular disease (CVD) due to an inherited predisposition, a sedentary life style and the use of antipsychotic medications. Several approaches have been taken to minimize this risk but results continue to be unsatisfactory. A potential alternative is prescribing Selective Serotonin Reuptake Inhibitors (SSRIs). SSRIs decrease platelet aggregation and reduce the risk of coronary heart disease in patients with depression. We therefore aim to investigate whether there is evidence that supports the use of SSRIs to reduce the risk for CVD in SCZ. A systematic review of the literature revealed five published reports relating to the impact of SSRIs on CV risk in SCZ. Three trials assessed the influence on metabolic parameters of fluvoxamine when combined with clozapine. Two of those studies found improvements with fluvoxamine. Of the other two reports, one indicates SSRIs as a group caused minimal but statistically significant increments in total cholesterol, LDL and triglyceride. The second report suggests that when SSRIs are combined with antipsychotics, the metabolic impact depends on the antipsychotic prescribed. While there are promising results, further studies are needed to establish the impact of SSRIs on CV risk in SCZ.

Spinocerebellar ataxia type 3 is a neurodegenerative disease caused by an abnormal expansion of CAG repeats in the disease gene ATXN3, leading to prolonged polyglutamine (polyQ) tracts in the respective protein ataxin-3. The polyQ-expanded protein forms polyQ-containing aggregates in several brain regions which leads to neuronal cell loss. Autophagy is thought to play a significant role in clearing these polyQ-containing protein aggregates. Therefore, the autophagy mechanism was investigated in SCA3 with focus on the autophagic protein p62/sequestosome-1. Human and mouse RNA sequencing data were analyzed to determine RNA expression level of autophagic genes. Additionally, autophagy was studied in the SCA3 304Q knock-in mouse model and through several cell culture experiments using HEK-293T cells transfected with ATXN3 of differ-ent polyQ lengths. For cell culture experiments, autophagy was induced with rapamycin treat-ment, proteasomal degradation was blocked with MG132 and degradation of autophagic pro-teins was investigated using cycloheximide treatment. Here, we report an increased autophagy impairment with SCA3 disease progression seen in HEK-293T cells transfected with ATXN3 plasmid with expanded polyQ tracts, higher aged 304Q-knock-in mice, or human post-mortem brain material. Furthermore, co-localized aggregation of the autophagic protein p62 and the dis-ease protein ATXN3 was observed in the course of SCA3 disease. Therefore, we suggest a signifi-cant role for p62 in aggregate formation seen in the neurodegenerative disease SCA3.

The study of precipitation amount at different time scales constitutes an important issue in climate research and risk assessment. When dealing with daily totals, a frequent but sometimes underestimated problem is at what time the observation day begins. The choice of different starting times may lead to incompatibility between stations and incorrect identification of extreme events. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting time of the observation day is analyzed. The most widely used adjustment methods (1day and uniform shift) and two methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics and percentile distributions. As test series, the precipitation amount collected from 9 a.m. local time (09 LT) on the previous day to 09 LT on the target day (9-9 datasets) of the Padua and nearby stations in the period 1993-2022 have been selected. Results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless the station. But, for the periods in which reanalysis data are not available, “1day” method, which shifts the daily amount back one calendar day, and “unif” method, which distributes uniformly the daily total from a 2-day moving window surrounding the target date, can be considered valid alternatives. On the other side, concerning the precipitation statistics, the reanalysis-based methods are not the best option, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. Nevertheless, the uniform method provides a larger deviation from the original daily series. The use of the series of a station nearby the target one, which is mandatory in case of missing data, gives similar or better results than applying any adjustment method to the 9-9 series. General conclusions can hardly be drawn as they depend on the method and station. For the Padua dataset, the analysis was repeated at monthly and seasonal resolution. In general, the adjustment series show the most relevant changes in the precipitation statistics in summer and less temporal alignment with the original series in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentiles distribution, analyzed for all the methods and stations, indicates that any adjustment method underestimates the percentile values, except ERA5. Only Legnaro, the station most correlated with Padua, gives results like ERA5.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

Oxidative stress-mediated damage is often a downstream result of Parkinson’s disease (PD), which is marked by sharp decline in dopaminergic neurons within the nigrostriatal regions of the brain, accounting for the symptomatic motor deficits in patients. Regulating the level of oxidative stress may present a beneficial approach in preventing PD pathology. Here, we assessed the efficacy of a nicotinamide adenine phosphate (NADPH) oxidase (NOX) inhibitor, an exogenous reactive oxygen species (ROS) regulator synthesized by Aptabio therapeutics with the specificity to NOX-1, 2, and 4. Utilizing N27 rat dopaminergic cells and C57Bl/6 mice, we confirmed that the exposures of alpha-synuclein preformed fibrils (PFF) induced protein aggregation, a hallmark in PD pathology. In vitro assessment of the novel compound revealed an increase in cell viability and decreases in cytotoxicity, ROS, and protein aggregation (Thioflavin-T stain) against PFF exposure at the optimal concentration of 10nM. Concomitantly, the oral treatment alleviated motor deficits in behavioral tests, such as hindlimb clasping, rotarod, pole, nesting, and grooming test, via reducing protein aggregation, based on rescued dopaminergic neuronal loss. The suppression of NOX-1, 2, and 4 within the striatum and ventral midbrain regions including SNc contributed to neuroprotective/recovery effects, making it a potential therapeutic option for PD.

In the present study we tested the effect of small molecular weight redox molecules on collagen-induced platelet aggregation. We used N-acetylcysteine amide (AD4), the amide form of N-acetylcysteine (NAC), a thiol antioxidant with improved lipophilicity and bio-availability compared to NAC, and the thioredoxin mimetics (TXM) peptides, TXM-CB3, TXM-CB13, and TXM-CB30. All compounds significantly inhibited platelet aggregation induced by collagen, with TXM-peptides and AD4 being more effective than NAC. The levels of TxB2 and 12-HETE, the main metabolites derived from the cyclooxygenase and lipoxygenase pathways following platelet activation, were significantly reduced in the presence of AD4, TXM-peptides, or NAC, when tested at the highest concentration (0.6 mM). The effect of AD4, TXM-peptides, and NAC was tested also on the clotting time (CT) of whole blood. TXM-CB3 and TXM-CB30 showed the highest increase of CT. Furthermore, two representative compounds, TXM-CB3, and NAC, showed an increase in the an-ti-oxidant free sulfhydryl groups of plasma detected by Ellman’s method, suggesting a contribution of plasma factors to the antiaggregating effects. Our results suggest that these small molecular weight redox peptides might become useful for the prevention and/or treatment of oxidative stress conditions associated with platelet activation.

Background: Intermittent claudication is a frequent complaint in lower extremity artery disease, but approximately two thirds of patients are asymptomatic, mostly in diabetic population. Non-invasive angiological and microrheological tests among diabetic patients with and without intermittent claudication were studied. Methods: 98 diabetic patients were included and divided into two groups: 20 patients (63.5±8.8 yrs, 55% men, 45% women) had intermittent claudication, 78 patients (65.5±9.3 yrs, 61.5% men, 38.5% women) were asymptomatic. Hand-held Doppler, transcutaneous tissue partial oxygen pressure (tcpO2) measurement, tuning fork test and 6-minute walk test were performed and erythrocyte aggregation was investigated. Results: Ankle/brachial index (p<0.02), and tcpO2 measured during provocational tests (p<0.003), 6-minute walk test (p<0.0001) were significantly deteriorated in the symptomatic group. Higher erythrocyte aggregation index and faster aggregate formation could be observed among claudicant patients (p<0.02). In spite of the statistically better results of the asymptomatic group, 13% of these patients had severe limb ischemia based on the result of tcpO2 measurement. Conclusion: Claudication could be associated with worse hemodynamical and hemorheological status in diabetics; nevertheless, severe ischemia can develop even in asymptomatic subjects which can be revealed by noninvasive vascular tests, which highlights the importance of the early instrumental screening of the lower limbs.

Previous evidence links the formation of extranuclear inclusions of transcription factors, such as ERK, Jun, TDP-43, and REST with oxidative, endoplasmic-reticulum, proteasomal, and osmotic stress. To further characterize its extranuclear location, we performed a high-content screening based on confocal microscopy and automatized image analyses of an epithelial cell culture treated with hydrogen peroxide, thapsigargin, epoxomicin, or sorbitol at different concentrations and times to recreate the stresses mentioned above. We also performed subcellular fractionation of the brain from transgenic mice overexpressing the Q331K mutated TARDBP, and we analyzed REST-regulated mRNAs. The results show that these nuclear proteins exhibit a mitochondrial location, together with significant nuclear/extranuclear ratio changes, in a protein and stress-specific manner. The presence of these proteins in enriched mitochondrial fractions in vivo confirmed the results of image analyses. TDP-43 aggregation was associated with alteration in mRNA levels of REST target genes involved in calcium homeostasis, apoptosis, and metabolism. In conclusion, cell stress increased mitochondrial translocation of nuclear proteins, increasing the chance of proteostasis alterations. Further, TDP-43 aggregation impacts REST target genes, disclosing an exciting interaction between these two transcription factors in neurodegenerative processes.

We report the development of an extrinsic self-healing coating system that shows no fluorescence from the intact coating, yellowish fluorescence in cracked regions, and greenish fluorescence in healed regions, thus allowing the separate monitoring of cracking and healing of coatings. This fluorescence monitoring self-healing system consisted of a top coating, an epoxy matrix resin containing mixed dye-loaded in single microcapsule. The dye-loaded microcapsules consisted of a poly(urea-formaldehyde) shell encapsulating a healing agent containing MAT-PDMS and styrene, a photo-initiator and a mixture of two dyes, one that fluoresces only in the solid state (DCM) and a second that fluoresces dramatically increased in the solid than solution state (4-TPAE). A mixture of the healing agent, photo-initiator and the two dyes was yellow due to fluorescence from DCM. On UV curing of this mixture, however, the color changed from yellow to green and the fluorescence intensity increased due to fluorescence from 4-TPAE in the solid state. When a self-healing coating embedded with microcapsules containing the DCM/4-TPAE dye mixture was scratched, the damaged region exhibited a yellowish color that changed to green after healing. Thus, the self-healing system reported here allows the separate monitoring of cracking and healing based on changes in fluorescence color.

Energy-efficient microprocessors are essential for a wide range of applications. While near-threshold computing is a promising technique to improve energy efficiency, optimal supply demands from logic core and on-chip memory are conflicting. In this paper, we perform static reliability analysis of 6T SRAM and discover the variance among different sizing configuration and asymmetric minimum voltage requirements between read and write operations. We leverage this asymmetric property in near-threshold processors equipped with voltage boosting capability by proposing an opportunistic dual-supply switching scheme with a write aggregation buffer. Our results show that proposed technique improves energy efficiency by more than 21.45% with approximate 10.19% performance speed-up.

Hybrid fiber reinforced composites can be controlled by price, weight and various mechanical properties depending on fiber ratio and lamination method. Despite these excellent hybrid properties, there is a disadvantage that inter-laminar fracture due to external impact, which is the biggest weakness of fiber reinforced composite materials, is weak. The test specimens were prepared by using a vacuum bag method, which is manufactured by using an autoclave device. The pre-preg is manufactured in the form of a B-stage. In the process of fabricating the nanoparticle pre-preg, the homogeneizer using an ultrasonic wave was used to disperse the epoxy subject without the curing agent into nanoparticles. The dispersion of the nanoparticles was dispersed by the weight of the epoxy resin. This is to take into account the cohesion of HNT and to understand the range of cohesion of HNT in a matrix with viscosity and its phenomenon. According to the Comparison of the interlayer interfacial properties and mechanical properties of Aramid / Basalt fiber hybrid composites by HNT addition, the fracture toughness, ILSS and bending strength of specimens with HNT content of more than a certain level were decreased because of the aggregation of HNT.

The core of risk aggregation in the Solvency II Standard Formula is the so-called square root formula. We argue that it should be seen as a means for the aggregation of different risks to an overall risk rather than being associated with variance-covariance based risk analysis. Considering the Solvency II Standard Formula from the viewpoint of linear geometry, we immediately find that it defines a norm and therefore provides a homogeneous and sub-additive tool for risk aggregation. Hence Euler's Principle for the reallocation of risk capital applies and yields explicit formulas for capital allocation in the framework given by the Solvency II Standard Formula. This gives rise to the definition of diversification functions, which we define as monotone, subadditive, and homogeneous functions on a convex cone. Diversification functions constitute a class of models for the study of the aggregation of risk, and diversification. The aggregation of risk measures using a diversification function preserves the respective properties of these risk measures. Examples of diversification functions are given by seminorms, which are monotone on the convex cone of non-negative vectors. Each L^p norm has this property, and any scalar product given by a non-negative positive semidefinite matrix does as well. In particular, the Standard Formula is a diversification function and hence a risk measure that preserves homogeneity, subadditivity, and convexity.

Lithium-metal batteries offer high energy density, but are challenged by dendrite growth, which can lead to short circuits and battery failure. Various models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. In this study, a simple simulation model to predict macroscale dendrite growth on lithium metal electrodes is presented. The model uses a 3D single-particle diffusion-limited aggregation (DLA) algorithm, biased by the electric field, to simulate dendrite growth. The model was validated using 3D MRI of dendrite growth in a lithium symmetric cell. It was used to simulate deposition between consecutive measured dendrite structures. The model also introduces an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. This parameter was calibrated using the overlap and displacement of deposition sites metrics, achieving optimal results at c = 0.7. The model predictions were in good agreement with experimental data confirmed by fractal dimension analysis. This model provides a useful tool for predicting and studying dendrite formation in lithium and other metal batteries.

Neurodegeneration is becoming one of the leading causes of death worldwide as the population expands and grows older. There is a growing desire to understand the mechanisms behind prion proteins as well as the prion-like proteins that make up neurodegenerative diseases (NDs) including Alzheimer’s Disease (AD). Both amyloid β (Aβ) and hyperphosphorylated tau (p-tau) proteins behave in ways similar to that of the infectious form of the prion protein, PrPSc, such as aggregating, seeding, and replicating under not yet fully understood mechanisms, thus the designation of prion-like. This review aims to highlight the shared mechanisms between prion-like proteins and prion proteins in the structural variations associated with aggregation and disease development. These mechanisms are largely focusing on the dysregulation of protein homeostasis, self-replication, and protein aggregation, and how this knowledge could contribute to diagnoses and treatments for the given NDs.

Harbor porpoise are typically seen in small groups of 1-3 individuals, with aggregations of 20+ individuals treated as rare events. Since the 1990s, the harbor porpoise population in the Salish Sea has seen a significant recovery, which has led to an increased number of observed aggregations that exceed the more usual small group sizes. By combining the observational data of United States and Canadian research organizations, community scientists, and whale watch captains or naturalists, we demonstrate that harbor porpoise aggregations appear to be more common than previously known, with 160 aggregations documented in 2022 alone. Behavioral data also indicated that foraging behaviors were common and social behaviors, like mating, were seen more often during these encounters compared to small groups. Other behaviors that are considered to be rare or unknown, were also observed during these encounters, including cooperative foraging and vessel approach. These aggregations are likely important foraging and social gatherings for harbor porpoises. This holistic approach integrating data from two countries and multiple sources provides an ecosystem level assessment that more effectively reflects the habitat use of harbor porpoise in this region that do not recognize the socio-political boundaries imposed upon the natural world.

Given the pathological role of hyperphosphorylated Tau aggregation in Alzheimer’s disease (AD) our laboratory previously developed the novel Tau aggregation inhibitor peptide, RI-AG03. As Tau aggregates accumulate intracellularly, it is essential that Tau-targeting peptides efficiently traverse the cell membrane. In addition, these peptides need to avoid sequestration into cell organelles, particularly those involved in degradation, to ensure interaction with Tau in the cytoplasm. Here, we examine the cellular uptake and intracellular trafficking of RI-AG03 when the peptide is unconjugated or linked to nanoliposome carrier particles. The impact of adding the cell penetrating peptide (CPP) sequences, polyarginine (polyR) and transactivator of transcription (TAT), to RI-AG03 was also characterised. Our data show that the conjugation of CPP-containing RI-AG03 to liposomes dramatically increases cellular liposomes uptake. The majority of this uptake occurs via direct cell penetration. However, we find that macropinocytosis is also an important mechanism for unconjugated and RI-AG03-polyR-conjugated, but not RI-AG03-TAT-conjugated, liposome uptake. In agreement with this macropinocytosis-mediated internalisation, liposome lipids localise to lysosomes and macropinosomes following cellular uptake. However, while unconjugated RI-AG03 peptides also localise to lysosomes, liposome conjugation prevents this localisation, with the peptide detaching from the liposomes at the cell surface and evading organelle entrapment. Overall, this study demonstrates that CPP sequences and nanoliposomes carriers enhance the intracellular delivery of RI-AG03, furthering the possibility of the peptide’s use in targeting Tau pathology in AD.

Alzheimer’s and Parkinson's diseases (AD and PD) are amongst top of the prevalent neurodegenerative disease. One-third of PD patients are diagnosed with dementia, a pre-symptom of AD, but the underlying mechanism is elusive. Amyloid beta (Aβ) and α-synuclein are two of the most investigated proteins, whose pathological aggregation and spreading are crucial to the pathogenesis of AD and PD, respectively. Transcriptomic studies of the mammalian central nervous system shed light on gene expression profiles at molecular levels, regarding the complexity of neuronal morphologies and electrophysiological inputs/outputs. In the last decade, the booming of the single-cell RNA sequencing technique helped to understand gene expression patterns, alternative splicing, novel transcripts, and signal pathways in the nervous system at single-cell levels, providing insight for molecular taxonomy and mechanistic targets of the degenerative nervous system. Here, we re-visited the cell-cell transmission mechanisms of Aβ and α-synuclein in medi-ating disease propagation, and summarized recent single-cell transcriptome sequencing from different perspectives and discussed its understanding of neurodegenerative diseases.

The abnormal assembly of α-synuclein (α-Syn) is an initial step in the formation of Lewy bodies in the brain, which finally causes the neuronal death, being considered as a pathological hallmark in Parkinson’s disease (PD). Certain food bioactives or their metabolites at very low concentrations can trespass the blood brain barrier (BBB) that might, thereafter, act simultaneously. The aim of this work was to evaluate the inhibitory and destabilising capacities on α-Syn kinetics and the neuroprotective effects of three well-known bioactive compounds able to cross the BBB and present in foods; melatonin (MEL), protocatechuic acid (PCA) and hydroxytyrosol (HT), and their combinations. For this purpose, different in vitro techniques (Thioflavin T (ThT), Transmission Electronic Microscopy (TEM), electrophoresis and MTT assay) were used. All tested compounds and their combinations were able to abolish the toxicity induced by α-Syn. In addition, the combination of PCA (100 µM) +HT (100 µM) showed the highest inhibitory effect against α-Syn fibril formation and destabilises α-Syn fibrils (88 and 62%, respectively). This is the first time that MEL, PCA and HT prove a joint effect against α-Syn aggregation and toxicity when they are tested together.

Bioprocesses for the production of renewable energies and materials lack efficient separation processes for the utilized microorganisms such as algae and yeasts. Dissolved air flotation (DAF) and microflotation are promising approaches to overcome this problem. The efficiency of these processes depends on the ability of microorganisms to aggregate with microbubbles in the flotation tank. In this study, different new or adapted aggregation models for microbubbles and microorganisms are compared and investigated for their range of suitability to predict the separation efficiency of microorganisms from fermentation broths. The complexity of the heteroaggregation models range from an algebraic model to a 2D population balance model (PBM) including the formation of clusters containing several bubbles and microorganisms. The effect of bubble and cell size distributions on the flotation efficiency is considered by applying PBMs, as well. To determine the impact of the model assumptions, the modeling approaches are compared and classified for their range of applicability. Evaluating computational fluid dynamics (CFD) of a DAF system shows the heterogeneity of the fluid dynamics in the flotation tank. Since analysis of the streamlines of the tank show negligible backmixing, the proposed aggregation models are coupled to the CFD data by applying a Lagrangian approach.

The Pythagorean fuzzy set (PFS), which is characterized by a membership and a non-membership degree and the square sum of them is less or equal to one, can act as an effective tool to express decision makers’ fuzziness and uncertainty. Considering that the Heronian mean (HM) is a powerful aggregation operator which can take the interrelationship between any two arguments, we study the HM in Pythagorean fuzzy environment and propose new operators for aggregating interval-valued Pythagorean fuzzy information. First, we investigate the HM and geometric HM (GHM) under interval-valued intuitionistic fuzzy environment and develop a series of aggregation operators for interval-valued intuitionistic fuzzy numbers (IVIFNs) including interval-valued intuitionistic fuzzy Heronian mean (IVIFHM), interval-valued intuitionistic fuzzy geometric Heronian mean (IVIFGHM), interval-valued intuitionistic fuzzy weighted Heronian mean (IVIFWHM) and interval-valued intuitionistic fuzzy weighted geometric Heronian mean (IVIFWGHM). Second, some desirable and important properties of these aggregation operators are discussed. Third, based on these aggregation operators, a novel approach to multi-attribute decision making (MADM) is proposed. Finally, to demonstrate the validity of the approach, a numerical example is provided and discussed. Moreover, we discuss several real-world applications of these operators within policy-making contexts.

Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (<1000 s^-1) relies on platelet activation and coagulation. Thrombosis at elevated high shear rates (>10,000 s-1) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modelling informed by biology can provide new pathphysiological mechanisms that are otherwise not accessible experimentally, holding promise for novel first-principle-based therapeutics. In this review, we summarize the key aspects of platelet biorheology and mechanobiology, focusing on the molecular and cellular scale events and how they build up to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight recent advancements in multiscale modeling of platelet biorheology and mechanobiology and how they can lead to better prediction and quantification of thrombus formation, exemplifying the exciting paradigm of digital medicine.

Blood image intensity has been used to detect erythrocyte sedimentation rate (ESR). However, it does not give an information on biophysical properties of blood sample under continuous ESR. In this study, three physical properties, including, τ₀, ESR_τ, and AI_I, are suggested to quantify mechanical variations of blood under continuous ESR. To demonstrate the proposed method, blood sample is loaded into a driving syringe. The blood flow rate is set in a periodic on–off pattern. Blood sample is then supplied into a capillary chip, and microscopic blood images are captured at specific intervals. Blood shear stress is quantified from the interface of blood stream in the coflowing channel. Both τ₀ and ESR_τ are then obtained by analyzing the blood shear stress. Simultaneously, the AI_I is evaluated by analyzing the image intensity of blood flow. According to the experimental results, the τ₀ exhibits consistent trends with respect to hematocrit as well as diluent. The ESR_τ and AI_I showed a reciprocal relationship each other. Three suggested properties represented substantial differences for suspended blood samples (i.e., hardened red blood cells, different concentration of dextran solution and fibrinogen). In conclusion, the present method can detect variations of blood sample under continuous ESR effectively.

Fluorophores with a tetraphenylethylene (TPE) core are attracting increasing interest because of their aggregation-induced emission (AIE) behaviour, which makes them particularly suitable when dispersed in thin-layer films. We propose the synthesis and use of a novel TPE fluorophore (hereafter called TPE-BPAN) decorated with two dimethylamino push groups and a 4-biphenylacetonitrile pull moiety. In tetrahydrofuran solutions, TPE-BPAN showed a broad absorption spectrum, and an intense emission is observed when a non-solvent as water is added in amounts of 70% and above. We selected and synthetized five different polymer matrices for TPE-BPAN with decreasing polarity: a) poly(methylmetacrylate) homopolymer (PMMA); b), c), and d) three copolymers of methyl methacrylate and cyclohexyl methacrylate at different compositions (P(MMA-co-CHMA) 75:25, 50:50, 25:75 mol%); e) poly(cyclohexyl methacrylate) homopolymer (PCHMA). The presence of a less polar comonomer as cyclohexyl methacrylate appeared to enhance the emission behaviour of TPE-BPAN, which displayed the highest quantum yield (QY) in P(MMA-co-CHMA) 75:25 in all the range of concentration studied from 0.4 wt.% to 2.0 wt.%. Further reduction in polarity lowered QY to the values of PMMA films; also, lower adhesion to the glass and a marked fragility of the films were observed. Referring to the P(MMA-co-CHMA) 75:25 films, QY values decreased from over 40% at 0.4 wt.% to about 30% at 2.0 wt.%, which is still helpful for energy harvesting applications such as luminescent solar concentrators (LSC). LSC performances were not significantly affected by the matrix's lower polarity and resulted in around one-third of the state-of-the-art due to the lower QY of TPE-BPAN. The theoretical investigation on density functional theory (DFT) clarified the origin of the observed AIE and the role played by the environment in modulating the photophysical behaviour.

The extensive use of engineered nanoparticles (ENPs) such as zinc oxide nanoparticles (ZnO NPs) in various commercial fields has spurred significant concern about its possible toxicological effects of ZnO NPs on human health and eco-system. Therefore, it is substantial to understand the aggregation phenomena which could attenuate this effect. This data article provides information on aggregation of ZnO NPs under the influence of various parameters of water chemistry. Moreover, Taguchi orthogonal array L27(313) design matrix was used to evaluate the effect of multiple parameters in aqueous solution. The data were obtained from single beam UV-Vis spectrophotometer (Optizen 2120 UV, Mecasys, Korea) using Optizenview 4.2.5 PC interference software, Fourier transform infrared spectrometer (FT/IR-4700, spectroscopy, JASCO Analytical Instruments, Easton, USA) and particle electrophoresis (NanoZS, Zetasizer, Malvern Instruments Ltd, Worcestershire, UK). The dataset draws attention on dominant parameters influencing aggregation of ZnO NPs in water. The analysis of variance (ANOVA) data revealed that electrolyte concentration, type and natural organic matter (NOM) concentration were the most significant parameters. Furthermore, FT-IR data presents a possible mechanism of ZnO NPs stabilization in the presence of different NOM. This data will be helpful for the development of environmental risk assessment strategies and prediction of fate and mobility of other ENPs in the aquatic environment.

We have successfully measured amyloid beta (Aβ) (1-40) protein added in human serum by a NiCr strain gauge cantilever biosensor immobilized with liposomes incorporating cholesterol. Importantly, we investigated the effect of incorporation of cholesterol in the liposome in order to suppress the interaction between the liposome and many different proteins included in human serum. It was revealed that incorporating cholesterol suppresses the interaction between the proteins other than Aβ in human serum and the liposome. Finally, we detected Aβ(1-40) in human serum with typical chronological behaviors due to Aβ aggregation and fibrillization. Furthermore, as a digital low-pass filtering procedure could reduce external noises, the cantilever sensor immobilized with liposome incorporating cholesterol can detect low-concentrated Aβ in human serum.

Background: Cloud-native software systems often have a much more decentralized structure and many independently deployable and (horizontally) scalable components, making it more complicated to create a shared and consolidated picture of the overall decentralized system state. Today, observability is often understood as a triad of collecting and processing metrics, distributed tracing data, and logging. The result is often a complex observability system composed of three stovepipes whose data is difficult to correlate. Objective: This study analyzes whether these three historically emerged observability stovepipes of logs, metrics and distributed traces could be handled more integrated and with a more straightforward instrumentation approach. Method: This study applied an action research methodology used mainly in industry-academia collaboration and common in software engineering. The research design utilized iterative action research cycles, including one long-term use case. Results: This study presents a unified logging library for Python and a unified logging architecture that uses the structured logging approach. The evaluation shows that several thousand events per minute are easily processable. Conclusion: The results indicate that a unification of the current observability triad is possible without the necessity to develop utterly new toolchains.

We are witnessing the severe third outbreak mediated by coronaviruses affecting global public health with unprecedented economic consequences. A better understanding of its phylogenetics, exploration of sequence features and mutational changes could unveil its genealogy to gain insights into the mechanism of transmission and development of possible interventions. Our comparative genomic analyses of >160 isolates of SARS-CoV-2 reveal phylogenetic kinship with other coronaviruses and emergence of evolutionary divergence in clinical isolates. t-SNE-based clustering revealed different clades but no continent specific clusters. Amino acid substitutions at RBD of spike protein provide possible reasons for rapid transmission. Few proteins specific to SARS-CoV-2 were identified which could have implications as therapeutic targets and diagnostic biomarkers. Virtual screening identified repurposed drugs, known nutraceuticals, for specific interventions. These phylogenetic observations reveal the ancestry and computational studies reveal the emergency measures to interject this emerging pathogen that pose threat to whole of mankind.