Porter's diamond model was used because it is an index in the field of competitiveness and increasing the index of competition in the international arena. In order to measure actions in the field of commerce and marketing trends and its compliance with Porter's diamond model, an organization named Isfahan Chamber of Commerce, Industries, Mines and Agriculture was selected from among the existing non-governmental organizations in Iran. This non-governmental organization, having different departments has members and subgroups related and influential in the field of commerce and marketing. In order to answer the question raised in this research, that is, to determine the position of the Isfahan Chamber of Commerce, Industries, Mines and Agriculture in Porter's diamond model, by referring to this organization in person, the available documents, information and data available in a descriptive-survey manner. It was reviewed until October 2022. All key activities and works performed in this organization in the period from March 2019 to March 2022 were identified and separated according to the internal factors of Porter's Diamond model. Finally, the credibility and effectiveness of this non-governmental organization as a link between the private sector and the government was measured in the Porter's diamond model.

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 accompanying mechanical behavior during the flow of ore-rock dispersion is an important factor leading to the instability and failure of the orepass in the ore storage section. It is of great significance to accurately understand the instability characteristics of the well wall in the ore section, during the ore drawing process by understanding the flow characteristics and internal mechanical transfer mechanism of ore-rock dispersion. Using the discrete element method to analyze the flow characteristics, contact compactness, stress distribution characteristics and contact force probability distribution of the ore-rock dispersion, it realizes the quantitative characterization of the damage degree of ore-rock flow, and reveals the damage mechanism of mine storage wall. The results show that: (1) The friction between ore-rock particles, between ore-rock and well wall is an important reason for the change of the macroscopic flow form of ore-rock from “one” to “V” type. (2) The compactness and stress concentration of ores are increasing with the increase of storage depth, which is shown that with the increase of storage depth, the greater the probability of well wall instability. (3) The probability distribution of contact force strength between particles decreases exponentially in the whole process of ore drawing, in which the strong chain plays a major role in the stability of the dispersion system. (4) The number of overpressure and the overpressure coefficient can be used to quantitatively characterize the wall damage degree under the action of ore-rock flow. The dynamic side pressure decreases periodically in the form of exponential fluctuations, and the dynamic load formed by the ore-rock flow mainly played a role in the lower area of the ore storage section.

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.

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.

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.

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.

Asphaltene is a component of crude oil that, if mishandled, may create substantial transportation and industrial concerns. It is a solid by nature, with a diverse set of molecular compositions. Furthermore, the oil industry has identified emulsion generation and clogging precipitate as key concerns. The emulsified water droplets in the process are just what give the raw oil its increased viscosity; hence, power is squandered to transport the oil around. The information has been separated into sections based on simulation, solubilization, agglomeration, precipitation, and evidence of model chemicals in the literature. We have included research that investigates the bulk rheology and interfacial rheology of asphaltene to describe its interfacial activity more clearly.

Modeling the flow of polymer solutions requires knowledge at various length and time scales. The macroscopic behavior is described by the overall velocity, pressure and stress. The polymeric contribution to the stress requires knowledge of the evolution of polymer chains. In this work, we use a microscopic model, the finitely extensible nonlinear elastic (FENE) model, to capture the polymer behavior. The benefit of microscopic models is they remain faithful to the polymer dynamics without information loss via averaging. The downside is the computational cost needed to solve the thousands to millions of differential equations describing the microstructure. We thus describe a multiscale flow solver that utilizes GPUs for massively parallel, efficient simulations. We compare and contrast the microscopic model with its macroscopic counterpart under various flow conditions. In particular, significant differences are observed under nonlinear flow conditions, where the polymers become highly stretched and oriented.

This work presents the results regarding the synthesis of tungsten nanoparticles (W NPs) by magnetron sputtering combined with gas aggregation (MSGA) when H2 mixed with Ar is used as working gas, the amount of H2 being increased up to 82%. The results show that up to 40% H2 in discharge the synthesis rate increases over 60 times, rapidly decreasing for over 50% H2 in discharge. The W dust is still produced in H2 dominated discharge (82%), the deposition rate being small but not negligible (20 mg/hour). The obtained WNPs are isolated, with the diameter decreasing from 50 nm to 15 nm when the amount of H2 in discharge is smaller than 41%. Over this value, the particles tend to agglomerate, forming structures similar to film-like deposits. Also, the diameter of the deposited spots on substrates is dependent on the H2 content in discharge. This allows the efficient coating of substrates with dimensions up to 26 mm by translating them in front of the MSGA cluster source exit aperture. Supplementary, for 41% H2 in discharge, the influence of synthetic air leaks (0-5%) in discharge was investigated. The deposition rate decreases rapidly (ceasing for around 4% air in discharge) and the obtained nanoparticles tend to agglomerate (at 2% air content, the dust deposit has the aspect of a near continuous film). Chemical composition investigations show a pronounced tendency of oxidation, nitridation and oxynitride formation in the presence of air leaks.

This review article is devoted to the colloidal properties of fullerene solutions. According to generally accepted understanding, all solvents in relations to fullerenes are divided into “good” (or “strong” or “high-solubility” ones), “poor” (usually polar) and “reactive”. Water occupies a special place, close to “poor” solvents. We have consistently considered and discussed the state of fullerenes in these systems. In "good", predominantly non-polar aromatic solvents and CS2, nonequilibrium dissolution methods lead to the formation of colloidal aggregates, whereas utilization of equilibrium methods results in the formation of molecular solutions. The latter, however, have some unusual properties; new results confirm previously expressed ideas about colloidal properties of these solutions. Both experimental and theoretical methods devoted to such systems are considered. In “poor” (polar) solvents, typical lyophobic colloidal systems appear. Both “bottom-up” and “top-down” methods of preparation are now well documented in the literature. However, some of these polar solvents dissolve fullerenes rather easily and with less energy consumption. In addition to N-methylpyrrolidin-2-one, which has been well studied for fullerenes, DMSO and DMF have now been described. These solvents can be considered a subset of poor solvents that have some features of being “reactive” at the expense of basic (“cationophilic”) properties. Room temperature ionic liquids are also used to dissolve fullerenes, but there is little experimental research. Hydrosols and aqueous suspensions of fullerenes are typical hydrophobic colloids that obey the Schulze–Hardy rule and other regularities in the presence of electrolytes. New experimental data confirm the previously obtained results. Organosols in acetonitrile and methanol are much less stable with respect to electrolytes; the difference in critical coagulation concentrations of electrolytes reaches two to three orders of magnitude. This allows us to conclude that there is an additional, so-called non-DLVO, stabilizing factor in the hydrosols. Accordingly, the value of the Hamaker constant of fullerene-fullerene interaction may be higher than that derived from coagulation data in water. Contrary to the mentioned “poor” polar solvents, the stability of fullerene sols in DMSO is even higher than in water. These results, as well as data obtained with DMF demonstrate strong solvation of fullerenes with basic solvents.

Globally, inequalities in educational provision are prevalent between genders, various geographical regions, and among different socio-economic backgrounds. Consequently, this study set-out to assess the level of disparity among the Federal Regional States of Ethiopia using Gini-coefficient and Lorenz curve from the statistical data of MoE. Moreover, data were collected from 656 respondents found in the sample regions. The result of the Gini-coefficient indicated that disparity in educational provision has been reduced over the past couple of decades both at primary (0.145 to 0.032) and secondary levels (0.277 to 0.126). Emerging regions are by far lagging behind the central and established regions. The sources of variation were mainly the demand-side variables, especially the economic and contextual related issues like drought and conflicts. Therefore, educational policies designed at the central level are advised to consider the strategies to bridge the existing inequalities through equitable provision of the education system to its citizen.

New experimental and clinical findings question the historic view of hydrocephalus and its 100-year-old classification. In particular, real-time MRI evaluation of CSF flow and detailed insights into brain water regulation on the molecular scale indicate the existence of at least three main mechanisms that determine the dynamics of neurofluids. (i) Inspiration is a major driving force (ii) Adequate filling of brain ventricles by balanced cerebrospinal fluid upsurge is sensed by cilia (iii) The perivascular glial network connects the ependymal surface to the pericapillary Virchow-Robin spaces. Hitherto, these aspects have not been considered a common physiologic framework improving knowledge and therapy for severe disorders of normal-pressure and post-haemorrhagic hydrocephalus, spontaneous intracranial hypotension and spaceflight disease.

Background: The antisense oligonucleotide against APOC3 mRNA volanesorsen was recently introduced to treat Familial Chylomicronemia Syndrome (FCS). Cases of decreased platelet count are reported among patients treated with volanesorsen. The aim of the study is to evaluate platelet function and thrombin generation (TG) assessment in FCS patients receiving volanesorsen. We performed a cross-sectional study on FCS patients treated with volanesorsen. Methods: changes in PLC were assessed from baseline to Tw12 and Tw36. To assess TG, samples were processed by CAT (with PPP-reagent LOW). The results were expressed by the trombogram graphic (thrombin variation over time); lagTime; Endogenous Thormbin Potential(ETP); peak; time to reach peak(ttpeak), startTail e velocity Index. Platelet aggregation was assessed by testing different agonists using turbidimetry method. Results: Four FCS patients and 4 matched healthy controls were included in the present study. Changes in PLC at Tw12 was 30% and 34% at T36w. Thrombin generation results show values in normal range (for patients and controls respectively lagTime:10,42±4,40 and 9,25±0,99; ttPeak:14,33± 4,01 and 13,10±0,67; startTail:32,13±3,54 and 29,46±1,69; velocityIndex:20,21±3,63 and 33,05±13,21; ETP 599,80±73,47 and 900,2±210,99; peak value 76,84±1,07 and 123,30±39,45) and no significant difference between cases and controls. Platelet aggregation test showed values in range with no significant difference with healthy controls Conclusions: Our study showed for the first time that no significant changes in the general haemostasis assessed by TG and in platelet function have been observed in FCS patients receiving volanesorsen.

Advanced glycation end-products (AGEs) form through non-enzymatic glycation of various proteins. Optic nerve degeneration is a frequent complication of diabetes, and retinal AGEs accumulation is strongly linked to diabetic retinopathy development. Type 2 diabetes mellitus (T2DM) is a major risk factor for Alzheimer's disease (AD), with patients often exhibiting optic axon degeneration in the nerve fiber layer. Notably, a gap exists in our understanding of how AGEs contribute to neuronal degeneration in the optic nerve within the context of both diabetes and AD. Our previous work demonstrated that glyceraldehyde (GA)-derived toxic advanced glycation end-products (TAGE) disrupt neurite outgrowth through TAGE-β-tubulin aggregation and tau phosphorylation in neural cultures. In this study, we further illustrated GA-induced suppression of optic nerve axonal elongation via abnormal β-tubulin aggregation in mouse retinas. Elucidating this optic nerve degeneration mechanism holds promise for bridging the knowledge gap regarding vision loss associated with DM and AD.

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.

Amyloids result from the aggregation of several unrelated proteins, due to either specific mutations or promoting intra- or extra-cellular conditions. Structurally, they are rich in intermolecular β-sheets and are the causative agents of several diseases, both neurodegenerative and systemic. It is believed that the most toxic species are small aggregates, referred to as oligomers, rather than the final fibrillar assemblies. Their mechanisms of toxicity are mostly mediated by aberrant interactions with the cell membranes, with resulting derangement of membrane-related functions. Much effort is being put in the search for natural antiamyloid agents, and/or in the development of synthetic molecules. Actually, it is well documented that the prevention of amyloid aggregation results in several cytoprotective effects. Here, we portray the state of the art in the field. Several natural compounds are effective antiamyloid agents, notably tetracyclines and polyphenols. They are generally non-specific, as documented by their partially overlapping mechanisms, and the capability to interfere with the aggregation of several unrelated proteins. Among rationally designed molecules, we mention the prominent examples of β-breakers peptides, whole antibodies and fragments thereof, and the special case of drugs contrasting transthyretin aggregation. In this framework, we stress the pivotal role of the computational approaches. When combined with biophysical methods, in several cases they have helped clarify in detail the protein/drug modes of interaction, which make it plausible that more effective drugs will be developed in the future.

According to Quantum Mechanics a narrow wave-package of the center of mass of macroscopic objects, due to their heavy mass, remains stable over astronomical times. However Quantum Mechanics allows and energetically even prefers largely smeared out c.m. states that never been seen. Why is this the real state of the world we know? Does it suggest a micro-macro threshold with different theoretical descriptions?

Stratocumulus (Sc) is the most common cloud type in China. Sc clouds may or may not be accompanied by various types of precipitation that are representative of different macro- and microphysical characteristics. The finely resolved CloudSat data products are used in this study to quantitatively investigate the macro- and microphysical characteristics of precipitating and non-precipitating Sc (PS and NPS, respectively) clouds over Eastern China (EC). Based on statistical information extracted from the CloudSat data, Sc clouds are highly likely to occur alone, in association with liquid precipitation, or in association with drizzle over 25.65% of EC. The cloud bases of NPS clouds are higher than those of PS clouds, although the latter display higher cloud top heights and thicker cloud thicknesses. The spatial distributions of microphysical characteristics differ between PS and NPS clouds. The magnitudes of microphysical characteristics in NPS clouds are relatively small and decrease with height, whereas the magnitudes of microphysical characteristics in PS clouds are relatively large and peak in response to certain circulation patterns and over certain terrain. The variations in microphysical characteristics in Sc clouds with height and contoured frequency by altitude diagrams (CFADs) of radar reflectivity may indicate that different microphysical processes operate in PS and NPS clouds. In NPS clouds, hydrometeor particles accumulate by coalescence as they rise; once the particles are too large to be supported by updrafts, the cloud droplets form raindrops. In PS clouds, raindrops increase continuously in size via collision-coalescence processes as they fall. The levels between 2.5 and 3.0 km represent the space where particles grow most rapidly. Particles are affected by updrafts and accumulate at levels between 2.5 and 1.0 km as height decreases.

The long-term use of fertilizers and pesticides in conventional cultivation has resulted in a decrease in soil productivity and vegetable yields in greenhouses. However, only a limited number of studies have explored the impacts of organic cultivation on greenhouse soil. Specifically, there is a scarcity of research examining the alterations in soil aggregate mediated organic carbon and microbial communities, which are known to enhance soil productivity. This study investigated the properties of soil aggregates, including the levels of organic carbon fractions, microbial community, and enzyme activity with the three aggregate classes microaggregates (<0.25 mm), small macroaggregates (2-0.25 mm) and large macroaggregates (>2 mm). These investigations were conducted under three different cultivation methods in a greenhouse: conventional cultivation (CC), integrated cultivation (IC), and organic cultivation (OC) in greenhouse. The results showed that (1) OC and IC promoted the formation of small macro-aggregates and enhanced aggregate stability compared to CC, (2) SOC in the three size fractions of OC increased by 98.99%-92.06% compared to CC; EOC increased by 98.47%-117.59%; POC increased by 138.59%-208.70%; MBC increased by 104.71%-230.61%; DOC increased by 21.93%-40.90%, respectively; (3) Organic cultivation significantly increased enzyme activity in all three particle size aggregates and increased the relative abundance of bacteria in micro-aggregates as well as the relative abundance of fungi in small macro-aggregates. Structural equation model (SEM) analysis revealed that organic farming practices fostered the development of smaller macro-aggregates, elevated microbial and enzyme activities within soil aggregates, and facilitated the conversion of soil nutrients and carbon sequestration. consequently, this led to an increase in soil organic carbon content and improved vegetable yield in greenhouse environments. In conclusion, the soil carbon content and vegetable yield are enhanced through long-term organic cultivation in greenhouses, primarily by increasing the proportion of small macro-aggregates.

The macro-litter (plastic) sea bottom pollution of 14 city harbors and marinas in North Africa and in the western Baltic Sea was investigated using a new simple mobile underwater camera system. The study was complemented by a harbor manager survey and 3D-hydrodynamic transport simulations. The average pollution in German marinas was 0.1 particles/m² sea floor (0.04–1.75). The pollution in North African marinas on average was 7 times higher (0.7 particles/m²) and exceeded 3 particles/m² in city center harbors. The resulting >100,000 litter particles per harbor indicate the existence of a problem. With 73%-74%, plastic particles are dominating. Existing legal and management frameworks explain the lack of plastic bottles and bags on sea floors in Germany and are one reason for the lower pollution levels. Items that indicate the role of untreated sewage water were not found. Harbor festivals seem not to be quantitatively relevant for open sea bottom pollution. Our method tends to underestimate the pollution level. Model simulations indicate that storms can cause litter reallocations and sediment cleanings. However, marina sea floor monitoring is recommendable, because it addresses pollution hot-spots, is cost-effective and takes place close to emission sources. Further, the effectiveness of land-based pollution reduction measures can easily be assessed.

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.

The soil organic matter (SOM) content and stability in natural gypsum soils and soils from mining/industrial areas were compared, in terms of their physical, chemical and micromorphological properties, to determine whether the old brownfield soils were acting as carbon (C) sinks, counteracting increasing carbon dioxide concentrations in the atmosphere. The mining/industrial soils were 50–400 years old, so had been left sufficiently long for full self-restoration. Despite having very different origins, both natural and brownfield soils stored similar amounts of SOM, and had similar pH values, calcium carbonate contents and textures. They were also fully covered with similar grass plant communities. The mining/industrial soils exhibited similar microstructures to the gypsum soils and were often quite porous. The most striking difference between them and the natural gypsum soils was that the mining/industrial soils were strongly polluted with zinc, lead and cadmium. The 50-year-old brownfield soils differed from the others in terms of having a lower state of SOM transformation, with a much higher C/N ratio, lower aggregate water resistance index, lower dehydrogenase activity and greater areas of undecomposed or slightly decomposed plant residues. These parameters in the older (150–400 years) brownfield soils were similar to those of the natural soils.

Candida albicans is an opportunistic fungus that becomes pathogenic and problematic under certain biological conditions. C.albicans may cause painful and uncomfortable symptoms, as well as deaths in immunocompromised patients. Therefore, early detection of c.albicans is essential. However, conventional detection methods are costly, slow and inaccessible to women in remote or developing areas. To address these concerns, we have developed a wearable and discrete naked-eye detectable, colorimetric platform for c.albicans detection. With some modification, this platform is designed to be directly adhered to existing feminine hygiene pads. Our platform is rapid, inexpensive, user-friendly, disposable and only requires three steps: i) addition of vaginal fluid onto sample pads; ii) addition of gold nanoparticle gel and running buffer, and iii) naked eye detection. Our platform is underpinned by selective thiolated aptamer-based recognition of 1,3-β-D glucan molecules- a hallmark of c.albicans cell walls. In the absence of c.albicans, wearable sample pads turn bright pink. In the presence of c. albicans, the wearable pads turn dark blue due to significant nanoparticle target-induced aggregation. We demonstrate naked eye colorimetric detection of 4.4 ×106 c.albicans cells per ml. We believe that this proof-of-concept platform has the potential to have a significant impact on women’s health, globally.

The paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons. The probability distribution is derived both by tracing out the environment from a bosonic eigenstate of the union of environment and system of interest (the empirical approach) and by tracing out the environment from the mixed state of the union of environment and system of interest (the Bayesian approach). In the thermodynamic limit, the two coincide and are equal to the multinomial distribution. Furthermore, the paper proposes to identify the physical entropy of the bosonic system with the Shannon entropy of the occupancy numbers, fixing certain contradictions that arise in the classical analysis of thermodynamic entropy. Finally, by leveraging an information-theoretic inequality between the entropy of the multinomial distribution and the entropy of the multivariate hypergeometric distribution, Bayesianism of information theory and empiricism of statistical mechanics are integrated into a common ''infomechanical'' framework.

Information regarding the cost of a construction project is available to the investor and project participants in order to determine the subsequent success of a project, given that the information they collect has an impact on the decisions they make. Cost calculations, especially in the initial phase of a project, often generate large errors. This paper presents the new approach based on a combination of the Case Based Reasoning method (CBR) with the originally selected criteria for the description of a construction project (as a result of Pearson correlation coefficient and Spearman's rank correlation coefficient) and Building Information Modeling (BIM) technology. The CBR method fulfils expectations for a simple and fast system supporting the cost estimation process. It does not require any specialist knowledge, so it will be comprehensible to cost estimation practitioners. The BIM-based model gives the opportunity for the calculation of quantity take-offs and enables the use of the information contained in the BIM model in the cost estimation process. In order to prepare the model an appropriate relational database had to be developed. With extensive research, a database of 173 construction projects, including the construction of a sports field, was obtained. There were 14 variables defined originally by authors; however, only 10 (as a result of the correlation analysis) were used for the calculation. Data related to the project were collected in the BIM model. Results estimating the project’s unit price, using the CBR method, were presented and discussed. The Mean Absolute Estimate Error was used to evaluate the model.

Plant biomass could become a new world-class "currency" because almost everything can be made of it: fuels, foods, feeds, constructing materials, furniture, etc. Currently, growing biomass for energy needs is particularly relevant in so complicated socioeconomical situation. There are important its productivity and the lowest possible ecological footprint. Can increasing crop biodiversity address these challenges? A field experiment was started at the Vytautas Magnus University Agricultural Academy in 2020-2022. Maize, hemp and faba bean were grown as single, binary and ternary crops to determine its effect on soil properties. Studies have shown that most intercrops failed to stabilize soil aggregate composition, NPK levels and pH, and enzyme urease activity. Binary maize-hemp and the ternary crops reduced the amount of the main macroelements in the soil the most because of the highest yields, which were removed from the fields. Regardless of the ecological services provided by faba beans, biomass removal leads to degradation of most soil properties and making the soil vulnerable to erosion. In the future, the cultivation of such multicrops should be investigated under conditions of more abundant fertilization or the effect of green manure should be used by increasing the number of fast- and short-growing interior plant species in the cultivation.

This paper is referred to a top frontline research in life science, showing that in spite of the large variation of the physiological architecture and behavioral forms of the living systems, the fundamental structure of their informational system is basically the same, allowing the body growth/development by micro to macro-structuration processes, maintaining the same informational configuration and functions like the basic unit of life – the eukaryotic cell. Starting and based on the determination of the special/specific info-operability in living structures and the role of information in structuration and their functionality, there are discussed the informational functions of the eukaryotic/prokaryotic cells, the simplest units of life and is elaborated and presented the Informational Model of the Living Structures, showing that these structures operate on the basis of two main informational systems: one of them defined as the Programmed Informational System, composed by three components, assuring respectively the info-generation, maintenance and the proliferation/reproduction, and another one defined as the Operative Informational System, necessary for the continuous communication with the environment for adaptation, consisting in four main components referred to memory, decision, sentience and info-connection/info-selection. The revealed properties are compared with the Informational System of the Human Body, as a prototype of a living system on the top of the evolutionary scale, showing/demonstrating the compatibility of structuration and functions on the entire scale, from prokaryotic/eukaryotic unit cells to multicellular organisms – plants, animals and human, by means of a continuous adaptation process, which always follows the same line of micro to macro-structuration process based on the same Info-organization and functionality. These models are new and revolutionary in the information and life sciences

The best business relationships are built as relationships between people and organizations that work together to achieve common goals. In the current digital era, B2B collaboration relationships are essential for business success. The response time to customer requests can be a key factor in maintaining these relationships and obtaining a competitive advantage. The use of VBA automation for repetitive tasks can help speed up this process, but it is important that computer operators have medium knowledge about using VBA macros. This work deals with issues related to improving response times to requests received by email, using a macro program for repetitive tasks. The empirical study was conducted on an average of 50 daily emails for order confirmation, with an average writing time of 68 s per email, resulting in a potential reduction in working time for the operator of approximately 1 h/day, only for order confirmation. The results show a significant improvement in working time for computer operators, using VBA buttons for repetitive tasks.

Utilizing lignocellulosic biosourced platforms, we synthesized novel cyanostilbene (CS) deriva-tives featuring the 3,4-dimethoxyphenyl moiety. These derivatives were investigated for their emission properties in both solution and solid states. The two simple CS derivatives exhibit very weak luminescence in solution but significant luminescence in the solid state, indicating distinct Aggregation-Induced Emission (AIE) characteristic. Furthermore, combining these two CS units, without conjugation and with quasi perpendicular orientation, results in Dual-State Emission (DSE) fluorophore showing luminescence both in solution and solid states. X-ray crys-tallography studies on the solid-state compounds reveal a structure-emission relationship, demonstrating that the colour emission correlates with the conformations adopted by the mol-ecules in the solid state, which influence the type of stacking.

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.

Centralized/Cloud Radio Access Networks (C-RAN) are deployed in converged fixed-mobile networks to exploit the flexibility coming from joint application of Network Function Virtualization (NFV) and Software Defined Networking (SDN). In this context, optical links connecting C-RAN nodes, possibly based on the Ethernet standards, may carry traffic with different requirements in terms of latency and throughput. This paper considers the problem of traffic aggregation on C-RAN optical Ethernet links with latency control for fronthaul traffic and throughput capability for backhaul traffic. Integrated hybrid nework technique is applied to show how time transparency can be enforced for Ethernet encapsulated Common Public Radio Interface (CPRI) traffic while allowing statistical multiplexing of backhaul traffic. Simulation results show the effectiveness of segmentation of backhaul traffic to allow exploitation of the available bandwidth even with high capacity CPRI options.

Some evidence supports the influence of the implant macro-design and primary stability. Additionally, tactile perception can be used to assess implant stability when placing the implant. This research aimed to quantify the primary and secondary stability of three implant systems with two different macro geometries (cylindrical and conical) determined through the insertion torque and the implant stability quotient (ISQ) at the moment of implant placement as a function of implant-related factors (length, width, dental arch, and implant location in the arch), intraoperative factors (bone density determined subjectively by the clinician's tactile perception), and patient-related factors (age, gender and bone density determined objectively in the cone beam computed tomography (CBCT). Methods: 102 implants of three implant systems with two different macro geometries (conical and cylindrical) were placed in 53 patients. The insertion torque, the ISQ at the implant placement (ISQ0), and bone quality according to the clinician's tactile sensation were recorded on the day of the surgery. After a three-month healing period, the ISQ was re-evaluated (ISQ3). Results: Cylindrical implants exhibited significantly higher insertion torque and ISQ at the moment of the surgery and after three months than conical implants. Cylindrical implants also showed significantly lower indices of tactile evaluation of bone quality during the implant placement surgery. However, no differences were demonstrated in the bone density measured objectively in the CBCT. (4) Conclusions: The cylindrical implants achieved the highest values for primary stability (Ncm and ISQ) and secondary stability (ISQ after three months). Insertion torque was the variable that most influenced ISQ on the day of the surgery. Implant location (incisors-canines, bicuspids-molars) and the implant macro geometry were the variables that most influenced the secondary stability (ISQ at three months).

Tree peony flowers are traditional ornamental and medicinal materials in China. In this study, 23 tree peony flowers at a broad color spectrum were analyzed. Gas chromatography-mass spectrometer (GC-MS) revealed that tree peony flowers are rich in sugars and organic acids. Up to 18 amino acids were identified by liquid chromatography-mass spectrometer (LC-MS), including all essential amino acids, except for methionine. The majority of amino acids were significant positively correlated with each other and were significant negatively correlated with glucose, fructose and galactose. A total of 11 polyphenols were identified in these tree fresh peony flowers by LC-MS. There was a high consistency in grouping peony flowers by using sugars and organic acids and amino acids, which differed from that based on color components and polyphenols. Tree peony flowers are also rich in K, Ca, Mg and Fe. In together, peony flowers can be a good resource of health-promoting compounds.

Neurodegenerative diseases, including Alzheimer's, Huntington's, Parkinson’s, and Amyotrophic Lateral Sclerosis, involve neuron degeneration linked to the misfolding of amyloidogenic proteins, resulting in β-sheet-enriched aggregates. This phenomenon disrupts the normal folding of proteins critical for cellular function. Genetic mutations, proteolytic cleavage, oxidative stress, and aging contribute to misfolding. Parkinson's disease (PD) is closely tied to alpha-synuclein (αS) misfolding, with A53T mutation and Lewy bodies (LB) in PD brains pivotal to understanding the disease mechanism. Other synuclein homologues (βS, γS) further contribute to synucleinopathies. Mutants (A30P, A53T, E46K, H50Q, and G51D) underscore αS's role in PD development. This concise review focuses on molecular and pathological aspects of neurodegenerative diseases, emphasizing αS in PD. The integration of genetic, structural, and mechanistic insights highlights the disorders. complexity, paving the way for future therapeutic strategies targeting protein misfolding and aggregation.

Three-dimensional (3D) printing has allowed for production of geometrically complex 3D objects with extreme flexibility, which is currently undergoing rapid expansions in terms of materials, functionalities, as well as areas of application. When attempting to print 3D microstructures in glass, femtosecond laser induced chemical etching (FLICE) – which is a subtractive 3D printing technique – has proved itself a powerful approach. Here, we demonstrate fabrication of macro-scale 3D glass objects of large heights up to ~3.8 cm with an identical lateral and longitudinal spatial resolution of ~20 μm. The remarkable accomplishment is achieved by revealing an unexplored regime in the interaction of ultrafast laser pulses with fused silica which results in aberration-free focusing of the laser pulses deeply inside fused silica.

Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, which have been widely recognized. However, such models (i) ignore the fact that information quality is uneven in propagation, which makes semantic representations unreliable. (ii) Most models do not fully leverage spatial and temporal structure in combination. (iii) Finally, internal decision-making processes and results are non-transparent and unexplained. In this study, we develop a trust-aware evidence reasoning and spatiotemporal feature aggregation model for more interpretable and accurate fake news detection. Specifically, we first design a trust-aware evidence reasoning module to calculate the credibility of posts based on a random walk model to discover high-quality evidence. Next, from the perspective of spatiotemporal structure, we design an evidence-representation module to capture the semantic interactions granularly and enhance the reliable representation of evidence. Finally, a two-layer capsule network is designed to aggregate the implicit bias in evidence while capturing the false portions of source information in a transparent and interpretable manner. Extensive experiments on two benchmark datasets indicate that the proposed model can provide explanations for fake news detection results, as well as can achieve better performance, boosting 3.5% in F1-score on average.

In response to the growing demand for high-strength and high-toughness materials in industries like aerospace and automobiles, there is a need for metal matrix composites (MMCs) that can simultaneously enhance strength and toughness. This paper focuses on the design configurations of MMCs, which include both the configurations resulting from reinforcements and the inherent heterogeneity of the matrix itself. The mechanical properties of MMCs are influenced by factors such as reinforcement content, shape, size, and spatial distribution within the composite architecture. Among them, Aluminum matrix composites (AMCs) are particularly significant in aerospace, electronics, and electric vehicles due to their potential for weight reduction and enhanced performance. However, the challenge lies in the inverse relationship between strength and toughness, hindering the widespread utilization and large-scale development of MMCs. The design configuration of composites plays a critical role in achieving concurrent improvements in strength and toughness. This review explores the advantages of toughness, toughening mechanisms, reinforcement distribution characteristics, and structural parameters in the design of composite architectures. Drawing inspiration from biological composites like bone, the development of synthetic composites with homogeneous structural designs provides insights into attaining exceptional strength and toughness in lightweight engineering structures. Additionally, understanding fracture behavior and toughening mechanisms in heterogeneous nanostructures is vital for advancing the field of metal matrix composites. Summarily, the design of composite architectures holds tremendous potential for tailoring AMCs with outstanding strength and toughness, addressing the requirements of lightweight engineering structures in various industries.

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.

In cross-country skiing the time difference between a race winner and the person coming second is typically very small. Since much of the energy is spent on overcoming resistive forces, a relatively small reduction of these can have a significant impact on the results. The resistive forces come partly from the friction in the tribological interface, between the ski and the snow, and as with many tribological applications the characterisation of its origin, plays an important role in determining the frictional properties. Also in cross-country ski friction, there are several scales impacting the frictional performance, with the major contributors being the ski-camber profile and ski-base structure. Macro-scale measurements of the ski's camber profile under load, are often used to determine how adequate the ski is for a specific condition. The characteristic properties usually obtained are, the force required to collapse the ski to a certain camber height, the topography of the kick-wax zone, and by simple means a determined lengths of the frictional interface, i.e., the apparent contact length. To this date, there are some mathematical models, but there is no robust way of determining the macro-scale contact properties between a cross-country ski and a counter surface using simulations. In the present paper an Artificial neural networks (ANN) is trained to predict the ski-camber profile for various loads placed at different positions, and a well established deterministic approach is used to simulate the contact between the ANN-predicted ski-camber profile and a linearly elastic body with a flat surface, representing the snow. The results suggest that this method is feasible for the determination of the apparent contact characteristics of different skis. Moreover, we show that the apparent contact area does not linearly depend on the load, and that the elastic properties of the counter surface also has a large impact on the apparent contact area and the average apparent contact pressure.

This paper presents a numerical two-scale framework for the simulation of fiber reinforced concrete under impact loading. The numerical homogenization framework considers the full balance of linear momentum at the microscale. This allows for the study of microscopic inertia effects affecting the macroscale. After describing the ideas of the dynamic framework and the material models applied at the microscale, the experimental behavior of the fiber and the fiber-matrix bond under varying loading rates are discussed. To capture the most important features, a simplified matrix cracking and a strain rate sensitive fiber pullout model are utilized at the microscale. A split Hopkinson bar tension test is used as an example to present the capabilities of the framework to analyze different sources of dynamic behavior measured at the macroscale. The induced loading wave is studied and the influence of structural inertia on the measured signals within the simulation are verified. Further parameter studies allow the analysis of the macroscopic response resulting from the rate dependent fiber pullout as well as the direct study of the microscale inertia. Even though the material models and the microscale discretization used within this study are still simplified, the value of the numerical two-scale framework to study material behavior under impact loading is shown.

The multidrug-resistant fungal pathogens belonging to the Candida haemulonii complex and the phylogenetically related species Candida auris are well-known for causing infections that are difficult to treat due to their multidrug-resistance profile. C. auris is even more worrisome due to its ability to cause outbreaks in healthcare settings. These emerging yeasts produce a wide range of virulence factors that facilitate the development of the infectious process. In recent years, the aggregative phenotype has been receiving attention, as it is mainly associated with defects in cellular division and its possible involvement in helping the fungus to escape from host immune responses. In this study, we initially investigated the aggregation ability of 18 clinical isolates belonging to the C. haemulonii species complex and C. auris. Subsequently, we evaluated the effects of physicochemical factors on the fungal aggregation competence. The results demonstrated that cell aggregation was a typically time-dependent event, in which almost all studied fungal isolates exhibited high aggregation after 2 h of incubation at 37°C. The aggregation was not impacted by pH, temperature, -mercaptoethanol (a protein-denaturing agent) and EDTA (a chelator agent). Conversely, proteinase K, trypsin and sodium dodecyl sulfate significantly diminished the aggregation. Collectively, our results demonstrated that the aggregation ability in these opportunistic yeast pathogens is time-dependent, and surface proteins and hydrophobic interactions seem to mediate cell aggregation, since the presence of proteases and anionic detergent affected the aggregation ability. However, further studies are necessary to better elucidate this phenomenon.

Information Technology (IT) has become an essential part of our lives and due to the emergence of Internet-of-Things (IoT), technology has encompassed a majority of things that humans rely on in their daily lives. Further, as IT becomes more relevant in daily lives, the need for IT to serve public emergency services has become more important. However, due to the infancy status of IoT, there is a need for a data consortium that would prove to be best used in servicing policing in a technological driven society. This paper will discuss the plausibility of creating a universal format for use in carrying out public services, such as emergency response by the police and regular law maintenance. In this research we will discuss what the police requires in their line-of-duty and how smart devices can be used to satisfy those needs. A data formatting framework is developed and demonstrated, with the goal of showing what can be done to unifying data from smart city sensors.

Epidermal growth factor (EGF) repeats are present in various proteins and form well-defined structures with three disulfide bonds. One representative protein is the Notch receptor. Each EGF repeat contains unique atypical O-linked glycans such as O-linked N-acetylglucosamine (O-GlcNAc). To generate a monoclonal antibody against the O-GlcNAc moiety in mouse Notch1, we expressed the recombinant C-terminal His6-tagged Notch1 EGF14-15 protein in HEK293T cells to prepare the immunogen. Most of the proteins were not secreted and showed higher molecular weight ladders in the cell lysate, suggesting protein aggregation. To overcome this issue, we fused Sparcl1 as an extracellular escorting tag to the N-terminus of Notch1 EGF14-15-His6. The fusion protein was efficiently secreted extracellularly without protein aggregates in the lysates. Following PreScission protease treatment, EGF14-15-His6 was efficiently released from the escorting tag. EGF14-15-His6 prepared using this method was indeed O-GlcNAcylated. The optimal length of the escorting tag was determined by generating deletion mutants to improve the extracellular secretion of EGF14-15-His6. Hence, a large amount of EGF14-15-His6 was successfully prepared from the culture supernatant of HEK293T cells, which were otherwise prone to aggregation.

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 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.

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.

In recent years, the analysis of macro- and micro-expression has drawn the attention of researchers since they provide visual cues of an individual's emotions for a broad range of potential applications such as lie detection and criminal detection. In this paper, we address the challenge of spotting facial macro- and micro-expression from videos and present compelling results by using a deep learning approach to analyze the optical flow features. Different from other deep learning approaches that are mainly based on Convolutional Neural Networks (CNNs), we propose a Transformer-based deep learning approach that predicts a score indicating the probability of a frame being within an expression interval. In contrast to other Transformer-based models that achieve high performance by being pre-trained on large datasets, our deep learning model, called SL-Swin, which incorporates Shifted Patch Tokenization and Locality Self-Attention into the backbone network Swin Transformer, effectively spots macro- and micro-expressions by being trained from scratch on small-size expression datasets. Our evaluation outcomes surpass the MEGC 2022 spotting baseline result, obtaining an overall F1-score of 0.1366. Additionally, our approach also performs well in the MEGC 2021 spotting task with an overall F1-score of 0.1824 and 0.1357 on CAS(ME)^2 and SAMM Long Videos, respectively. The code is publicly available on GitHub (https://github.com/eddiehe99/pytorch-expression-spotting).

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.

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.

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.

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.

The energy loss of photons due to the cosmological red shift is interpreted, here, as a periodical process of transferring electromagnetic field energy into the space. The transferred energy portions are independent on photon energy if this transfer occurs with their frequency. The amounts of periodically released energy are so small that the related ultra-long wave length photons have to be understood as perfectly delocalized. Thus, the described point of view bridges the quantum micro cosmos with the macro cosmos. It is proposed to regard this energy exchange as a typical property of universal time arrow and to interpret the “large numbers”, in particular the dimensionless reciprocal of product of Hubbles constant and Planck time as the fundamental parameter describing cosmic evolution.

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.

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

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.

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.

The concept of neutrosophic number is a significant mathematical tool to deal with real scientific problems because it can tackle indeterminate and incomplete information which exists generally in real problems. In this article, we use neutrosophic numbers (a + bI), where a and bI denote determinate component and indeterminate component respectively. We explore the situations in which the input information is needed to express in terms of neutrosophic numbers. We define score functions and accuracy functions for ranking neutrosophic numbers. We then define a cosine function to determine unknown criteria weights. We define neutrosophic number harmonic mean operators and proved their basic properties. Then, we develop two novel MCGDM strategies using the proposed aggregation operators. We solve a numerical example to demonstrate the feasibility and effectiveness of the proposed two strategies. Sensitivity analysis with variation of “I” on neutrosophic numbers is performed to demonstrate how the preference ranking order of alternatives is sensitive to the change of “I”. The efficiency of the developed strategies is ascertained by comparing the obtained results from the proposed strategies with the existing strategies in the literature.

Biochar is a carbon-rich material that recently received attention due to its increasing agronomical potential. The agricultural utilization of biochar relates to its potential to act in the soil as a soil conditioner, nevertheless, the complex information on the direct dependence of biochar physical properties (texture, particle size) and corresponding leaching and availability of organic molecules (e.g. the polycyclic and heterocyclic organic compounds) and inorganic mineral salts (based on micro– and macroelements) are still not consistent. These aspects were addressed in our study. A multi-elemental analysis (ICP-OES) was used to access the information on the aqueous-extractable and total contents of inorganic macro- and macroelements in used commercial biochar samples (purchased from Sonnenerde GmbH, Novoterrra Sonnenerde GmbH, Biouhel.cz, s.r.o.). Potassium (K) was identified as the major macroelement, responsible for alkaline pH and the conductivity of biochar extract. Our results indicate an indirect relation between the size fraction of biochar and the content of extractable K, sodium (Na), and calcium (Ca) and a direct relation with the contents of phosphorus (P) and magnesium (Mg). As the total determined amounts of inorganic elements were significantly higher compared to aqueous-extractable amounts, we believe it could indicate the potential for repeated release to the surrounding soil. These findings were also confirmed by the analysis of biochar samples obtained after the cultivation (duration of 2 years) in selected representative soil samples from the Czech Republic (Cambisol, Fluvisol, Regosol, Chernozem). Moreover, gas chromatography (GC) coupled with mass spectroscopy (MS) was further used to reveal the concentrations of available polycyclic aromatic and heterocyclic compounds in biochar samples. The detected concentration of these types of organic compounds were far below the certified limits. We believe our in-depth analysis could provide the necessary insight into the description of biochar mineral content, its connection to biochar texture, corresponding physicochemical properties and potential availability to leach nutrients to the soil. These findings could help in further assessment of the potential applications of biochar in modern agriculture as a soil conditioner.

Three neutral Pt(II) complexes with diphenylamino-modified 2-phenylpyridine derivatives as cy- clometalating ligands and acetylacetone as the ancillary ligand exhibit aggregation-induced phosphorescent emission (AIPE) properties in THF/H2O. The crystal structures of the complexes highlight the contributions of non-covalent Pt···Pt interactions and hydrogen bonds to the AIPE properties. These AIPE-active Pt(II) complexes 1-3 have been successfully applied to detect picric acid (PA) in aqueous media, affording the lowest limit of detection at 70 nM. Furthermore, three Pt(II) complexes are able to detect PA in common water samples. The quenching of luminescence in the detection can be attributed to photo-induced electron transfer.

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.

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.

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.

Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations, as well as research. In order to make use of pathogen genomics data, it must be interpreted using contextual data (metadata). Contextual data includes sample metadata, laboratory methods, patient demographics, clinical outcomes, and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration, and its use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating, and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool’s web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission.In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.

We present first-principles DFT+U investigation of the bulk properties including structural, energetic and mechanical properties for uranium-americium mixed oxides (U, Am)O₂. The various Am aggregation contents have been investigated to better understand the impact of Am in the nuclear fuel UO₂. The supercell defect models at different scales were used to describe the solid solution (U, Am)O₂. The obtained results show that different contents of Am has a significant impact on the volume and energy of the mixed oxides systems. The results on the electronic structure calculations exhibit a narrow band gap owing to the mixing of UO₂ and AmO₂. The mixing enthalpy of the Am aggregation systems are used to describe the phase stability for the solid solution. In particular, mixing enthalpy of the (U, Am)O₂ significantly reduced as the Am content increases. The elastic properties of the (U, Am)O₂ mixed oxides have also been compared as a function of Am content. Moreover, the impacts of the whole Pu aggregation content range on the bulk properties for the (U, Pu)O₂ mixed oxides is also discussed.

Detecting rotated objects in remote sensing images poses a substantial challenge. These images usually cover a wide field of view, containing diverse and complex backgrounds with variously sized ground objects densely distributed. Therefore, identifying objects of interest in remote-sensing images is challenging. While integrating Convolutional Neural Networks (CNNs) and Transformer networks has exhibited progress in detecting rotated objects, there is still scope for enhancing feature extraction and fusion. To address this issue, we propose a feature extraction module, a feature context aggregation module, and a multi-scale feature fusion module. Initially, we substitute the Spatial Pyramid Pooling Bottleneck (SPPFBottleneck) with a new module aimed at extracting multi-scale features, thereby enhancing the detection of small objects in complex backgrounds. Next, we develop a novel module for the multi-scale fusion of contextual information within feature maps, extracting valuable information. Finally, we combine the original features with the fused ones to prevent the loss of specific features in the fusion process. We refer to our newly proposed model as the "Multi-scale Feature Context Aggregation Network" (MFCANet). We assess our approach on three challenging remote sensing datasets: MAR20, SRSDD, and HRSC. Comprehensive experimental results show that our method surpasses baseline models by 2.13%, 10.28%, and 1.46% mAP on the MAR20, SRSDD, and HRSC datasets, respectively.

Detecting rotational objects in remote sensing imagery is a significant challenge. These images typically encompass a broad field of view, featuring diverse and intricate backgrounds, with ground objects of various sizes densely scattered. As a result, identifying objects of interest within these images is a daunting task. While the integration of Convolutional Neural Networks (CNN) and Transformer networks leads to some advancements in rotational object detection, there is still room for improvement, particularly in enhancing the extraction and utilization of information related to smaller objects. To address this, our paper presents a multi-scale feature fusion module and a global feature context aggregation module. Initially, we fuse original, shallow, and deep features to reduce the loss of shallow feature information, thereby improving the detection performance of small objects in complex backgrounds. Subsequently, we compute the correlation of contextual information within feature maps to extract valuable insights. We name the newly proposed model the "Multiscale Feature Context Aggregation Module" (MFCA). We evaluate our proposed methodology on three challenging remote sensing datasets: DIOR-R, HRSC, and MAR20. Comprehensive experimental results show that our approach surpasses baseline models by 2.07\% mAP, 1.02\% mAP, and 1.98\% mAP on the DIOR-R, HRSC2016, and MAR20 datasets, respectively.

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.

Global models have been developed to tackle the challenge of forecasting sets of series that are related or share similarities, but not for heterogeneous datasets. Various methods of partitioning by relatedness have been introduced to enhance the similarities of the set, resulting in improved forecasting accuracy but often at the cost of a reduced sample size, which could be harmful. To shed light on how the relatedness between series impacts the effectiveness of global models in real-world demand forecasting problems we perform an extensive empirical study using the M5 competition dataset. We examined cross-learning scenarios driven by the product hierarchy commonly employed in retail planning, which allow global models to capture interdependencies across products and regions more effectively. Our findings show that global models outperform state-of-the-art local benchmarks by a considerable margin, indicating that they are not inherently more limited than local models and can handle unrelated time series data effectively. The accuracy of data partitioning approaches increases, as the size of the data pools and the models' complexity decrease. However, there is a trade-off between data availability and data relatedness. Smaller data pools lead to increased similarity among time series, making it easier to capture cross-product and cross-region dependencies, but this comes at the cost of a reduced sample, which may not be beneficial. Finally, it's worth noting that the successful implementation of global models for heterogeneous datasets can significantly impact forecasting practice.

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.

The study of interactions between polyelectrolytes (PE) and surfactants is of great interest for both fundamental and applied research. These mixtures can represent, for example, models of self-assembly and molecular organization in biological systems, but they are also relevant in industrial applications. Amphiphilic block polyelectrolytes represent an interesting class of PE, but their interactions with surfactants have not been extensively explored so far, most studies being restricted to non-associating PE. In this work, interactions between an anionic amphiphilic triblock polyelectrolyte and different types of surfactants bearing respectively negative, positive and no charge, are investigated via surface tension and solution rheology measurements for the first time. It is evidenced that the surfactants have different effects on viscosity and surface tension, depending on their charge type. Micellization of the surfactant is affected by the presence of the polymer in all cases; shear viscosity of polymer solutions decreases in presence of same charge or nonionic surfactants, while the opposite charge surfactant cause precipitation. This study highlights the importance of the charge type, and the role of the associating hydrophobic block in the PE structure, on the solution behavior of the mixtures. Moreover, a possible interaction model is proposed, based on the obtained data.

Optical forces are used to aggregate plasmonic nanoparticles and create SERS-active hot spots in liquid. When biomolecules are added to the nanoparticles, high sensitivity SERS detection is accomplished. Here we tailor this methodology to detect catalase and hemoglobin, two Raman resonant biomolecules, at concentrations down to 10 nM and 1 pM. Subsequently, we study the SERS signal in Bovine Serum Albumin as a function of the concentration, finding a monotonic dependence that suggests the possibility of quantitative detection. Finally, by exploiting nanoparticles functionalized with specific aptamers, we obtain first results on the SERS detection of Ochratoxin A, a fungal toxin found in food commodities and wine. This represents a first step towards the addition of molecular specificity to this novel biosensor strategy.

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.

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.

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。

This paper uses preliminary studies of archaeological sites (burial mounds and settlements) from the Iron Age through Medieval periods (ca. 800 BCE to 1200 CE) in the Juuku Valley on the South side of Lake Issyk-Kul to model land-use across vertical mountain zones. To do so, we have established a radiometric chronology, conducted test excavations of an Iron Age settlement and a Turkic period burial, undertaken preliminary archaeobotanical research, and pedestrian surveys. Seed remains of wheat (Triticum aestivum), barley (Hordeum vulgare), broomcorn millet (Panicum milaceum) and foxtail millet (Setaria italica) and legumes were recovered in very small quantities from an Iron Age upland settlement at 2100 m asl and a Turkic period burial mound at 1934 m asl. These preliminary archaeobotanical results are compared to the Talgar Iron Age settlements on the north side of the Tian Shan Mountain range in Kazakhstan studied earlier by Robert Spengler. Small samples of faunal remains found at the Turkic period kurgan and from a profile at the upland Iron Age settlement indicate livestock herding of sheep/goats, cattle, and horses in Juuku Valley. The goal of this paper is to test the hypothesis that pastoral transhumance and agropastoralism were economic strategies used by ancient societies during the Iron Age through Medieval periods in mountain-river valley areas between 750 m to 2500 m asl. These economic strategies that combined the pasturing of sheep, goats, cattle and horses with the cultivation of cereals were adapted to the use of different vegetational zones along a vertical gradient. This paper based on preliminary research of upland sites of the Juuku Valley in Kyrgyzstan, initiates a long-term research study of four millennia of settlements that ranged from pastoral transhumance, pastoral nomadism, and combined mountain agriculture using survey data and test excavations.

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.

An ambitious per capita GDP target has been envisioned by the Chinese government since 2020 to project its sustainable economic growth rate by 2035. Can China fully achieve its goal? This is a question worth investigating. By inserting relevant TABLO modules of the final goods trade, the intermediate goods trade, and factor-strengthening technology spillovers, along with technology absorption thresholds effects of the global value chain, this study builds a global dynamic recursive computational general equilibrium (CGE ) model on basis of GTAP-RD. This approach allows considering the 'dual circulation' system strongly advocated and pointed out as the sole way to realize China’s technological (total factor productivity (TFP)) progress by the Chinese government, which integrates both domestic internal and international external circulations (trade and foreign direct investment (FDI)). We simulate China's technological progress under eight scenarios, and use the latest GTAP Version 11 production and trade data (released in April 2023) for 141 countries and regions and results are aggregated into 10 countries groups with 9 activity sectors and 10 commodities. Main conclusions are as follows: (1) If China maintains its trade opening policy, the 2035 vision goal can be achieved, with external circulation being more important than internal circulation for technological spillovers. (2) The economic growth impacts of external and internal circulation operate relatively independently. However, FDI provides a relatively larger synergistic effect for all forms of external trade circulation than to the internal one. Regarding factor-strengthening effects, spillovers are more favorable to 'capital' than to labor factors. (3) We find that the Regional Comprehensive Economic Partnership Agreement is the most important strategic partner for China. (4) It appears that FDI is not an effective way to lift the productive services sector's total factor productivity (TFP), and more realistic for China is to open up the productive services market more widely, as its efficiency is a pivotal reflection of China's independent innovation capability. (5) China–US decoupling has an enormous global impact. In all the Scenarios we consider, the United States is always the country that loses the most, and Europe would be the group of countries that benefits when there is a large increase in TFP in the US.

Background: this interventional pilot study aimed to evaluate the short-term (3 years) efficacy of focal laser ablation (FLA) in treating the index lesion of low-intermediate risk prostate cancer, along with assessing the safety of the procedure (ClinicalTrials.gov ID NCT04045756). Methods: forty patients aged between 46 and 86 with histologically proven organ-confined prostate cancer and low to intermediate progression risk were included. FLA was performed under percutaneous fusion magnetic resonance/ultrasound guidance in a Day Hospital setting under local anesthesia. Patients underwent regular clinical assessments, PSA measurements, post-procedure MRI scans, and biopsies at 36 months or if positive findings were detected earlier. Statistical analyses were conducted to assess trends in PSA levels and cavity dimensions over time. Results: forty patients were initially included, with 15 lost to follow-up. At 36 months, a mean PSA reduction of 60% was observed, and 80% of MRI scans showed no signs of in-field clinically significant residual/recurrent cancer. Biopsies at 36 months revealed no malignant findings in 20 patients. No deterioration in sexual function or urinary symptoms was recorded. Conclusions: FLA appears to be safe, feasible and effective in the index lesion treatment of low-intermediate risk prostate cancer, with a high rate of tumor eradication and preservation of quality of life.

Both natural and anthropogenic factors can cause long-term and irreversible damage to the environment, in particular to the soil. To solve several environmental problems, it is important to study the seasonal changes in the concentration of macro- and trace elements in soil samples and to identify the factors that regulate the concentration of chemical elements in soil and their toxicity This study employs an innovative integrated methodology to detect alterations in the concentrations of certain chemical elements in soil samples from various regions of Armenia throughout the current agricultural seasons. Elemental analysis of soil samples is carried out using a portable XRF analyzer with direct X-ray exposure. The clastogenicity of the soil-plant system was evaluated using Tradescantia (clone 02). A pollution index based on the established Clark concentration for chemical elements was calculated. Trace elements exhibited a higher pollution index than those in other groups. The genotoxic analysis of soils from the study sites revealed a significant increase in soil clastogenicity, beginning in autumn and peaking in the spring season. During the subsequent summer sampling season, all genotoxicity evaluation criteria for the materials under study notably decreased. The results analysis revealed positive and negative correlations among all chemical elements in the soil samples from all sites during the experimental cycle, with varying degrees of significance.

Magnetic nanoparticles (MNPs) can be functionalized with antibodies to give them an affinity for a biomarker of interest. Functionalized MNPs (fMNPs) cluster in the presence of a multivalent target, causing a change in their magnetization. Target concentration can be proportional to the third harmonic phase of the fMNP magnetization signal. fMNP clustering can also be induced with salt. Generally, salt can alter the stability of charge stabilized fMNPs causing a change in magnetization that is not proportional to the target concentration. We have developed a model system consisting of biotinylated MNPs (biotin-MNPs) that target streptavidin to study the effects of salt concentration on fMNP-based biosensing in simulated in vivo conditions. We have found that biotin-MNP streptavidin targeting was independent of salt concentration for 0.005x to 1.00x phosphate buffered saline (PBS) solutions. Additionally, we show that our biosensor’s measurable concentration range (dynamic range) can be tuned with biotin density. Our results can be leveraged to design an in vivo nanoparticle (NP)-based biosensor with enhanced efficacy in the event of varying salt concentrations.

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.

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.

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.

This is the second of a two-paper series describing a multi-scale modeling approach developed to simulate crack sensing in polymer fibrous composites by exploiting interruption of electrically conductive carbon nanotube (CNT) networks. The approach is based on the finite element (FE) method. FE models at three different scales, namely the micro-scale, the meso-scale and the macro-scale, have been developed using the ANSYS PDL environment. In the present paper, the meso- and macro-scale analyses are described. In the meso-scale, a two-dimensional model of the CNT/polymer matrix reinforced by carbon fibers is used to develop a crack sensing methodology from a parametric study which relates the crack position and length with the reduction of current flow. In the meso-model, the effective electrical conductivity of the CNT/polymer computed from the micro-scale is used as input. In the macro-scale, the final implementation of the crack sensing methodology is performed on a CNT/polymer/carbon fiber composite volume using as input the electrical response of the cracked CNT/polymer derived at the micro-scale and the crack sensing methodology. Analyses have been performed for cracks of two different lengths. In both cases, the numerical model predicts with good accuracy both the length and position of the crack. These results highlight the prospect of conductive CNT networks to be used as a localized structural health monitoring technique.

Many neurodegenerative diseases are directly related to the formation of toxic protein aggregates, such as Parkinson’s and Alzheimer’s diseases, which are associated with the aggregation of alpha-synuclein (α-syn) and beta-amyloid (Aβ ), respectively. In this context, protein fibrils are the hallmark of these neurodegenerative diseases. In this sense, planning or designing compounds that are capable of preventing or reducing the formation of protein aggregation in the brain can be of fundamental importance for the curative treatment of these diseases. This work presents an in silico and experimental study to investigate the inhibitory potential of the ligands Octovespin (bioinspired by the wasp venom Polybia Occidentalis) and Fraternine-10 (bioinspired by the wasp venom Parachartergus fraternus wasp) against the aggregation of fibrils of the peptides Aβ(17-42) and α-syn, respectively. Using molecular docking and molecular dynamics simulations with 100 ns, the disaggregation capacity of these ligands was studied. The results obtained indicate that Octovespin and Fraternine-10 interact with the Aβ and α-syn protein fibrils throughout the simulation time. The RMSD values and the interactions with amino acids responsible for fibril aggregation demonstrate that both Octovespin and Fraternine-10 have a significant disaggregation potential, which corroborates the observations in vitro and in vivo. Furthermore, experimental results of Fraternine-10 demonstrate an anti-aggregation effect, indicating that it can promote the disaggregation of fibrils and prevent them from aggregating again to form oligomers. These discoveries qualify these molecules as potential candidates to become medicines against Parkinson’s and Alzheimer’s diseases.

Cytochrome P450 oxidoreductase (POR) is an essential redox partner for steroid and drug-metabolizing cytochromes P450 located in the endoplasmic reticulum. Mutations in POR lead to metabolic disorders, including congenital adrenal hyperplasia, and affect the metabolism of steroids, drugs, and xenobiotics. In this study, we examined approximately 450 missense variants of the POR gene listed in the Genome Aggregation Database (gnomAD) using eleven different in silico prediction tools. We found that 64 novel variants were consistently predicted to be disease-causing by most tools. To validate our findings, we conducted population analysis and selected two variations in POR for further investigation. The human POR wild type, along with the R268W and L577P variants, were expressed in bacteria and subjected to enzyme kinetic assays using model substrate. We also examined the activities of several cytochrome P450 proteins in the presence of POR (WT or variants) by combining P450 and reductase proteins in liposomes. We observed a decrease in enzymatic activities (ranging from 35% to 85%) of key drug metabolizing enzymes, supported by POR variants R288W and L577P compared to WT-POR. These results validate our approach of curating a vast amount of data from genome projects and provide an updated and reliable reference for diagnosing POR deficiency.

Soil aggregates, as the basic component of soil, make great contributions to the stability of soil structure and soil carbon (C) sequestration. Recently, grassland is experiencing continuous grazing, which greatly affects soil aggregation and soil C storage. However, how soil aggregates and soil C in different grassland respond to grazing remain unclear. In this study, three national fenced grassland monitoring field stations which represented mountain meadow (MM), temperate steppe (TS), temperate steppe desert (TSD) were selected, soil samples at 0–10 cm depth of inside and outside the fence were collected to explore the effect of grazing and grassland type on composition, stability and nutrients of soil aggregates. The results showed that the bulk soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN) and available phosphorus (AP) varied greatly among the three grassland types, with the highest values in MM. Soil aggregates composition showed significantly response to both grassland type and grazing, especially the proportion of soil aggregates >2 mm, which significantly decreased 51.68% on average in grazing plots compared with fenced plots, whereas sharply decreased in MM and TSD. A significant decrease (on average 25.08%) of the mean weight diameter (MWD) of soil aggregates under grazing was detected across all grassland types. The effect of grazing on nutrients in macroaggregates (>0.25 mm) was greater than that in microaggregates (<0.25 mm). Aggregate-associated SOC concentration decreased under grazing in MM and TSD, however, SOC density showed different response to grazing across all grassland types, with minor effect on MM, decreasing SOC density in TS and increasing on TSD, respectively. The magnitude of grazing effect size on aggregate-associated SOC varied in different soil particle size, with greater response in aggregates > 2 mm and the biggest value in TDS. In addition, correlation analysis showed that MWD of soil aggregates had significant negative correlation with soil bulk density, but significant positive correlation with SOC, TN and TP concentration. Results of RDA showed that BD, soil nutrients were the main influence factors of the composition and stability of aggregates. Overall, grazing had significant influence on soil aggregation, stability and SOC, played a crucial role in grassland soil stability and the accumulation of SOC.

Numerous structures at the nano- and microscale manifest distinctive properties with far-reaching implications across diverse fields, including electronics, electricity, medicine, and surface engi-neering. Established methods such as nanoimprint lithography, photolithography, and self-assembly play crucial roles in the fabrication of nano- and microstructures; however, they exhibit limitations in generating high-aspect-ratio structures when utilizing high-viscosity pho-tocurable resins. In response to this inherent challenge, we propose a highly cost-effective ap-proach facilitating the direct replication of high-aspect-ratio structures, specifically nanowires, through the utilization of anodized aluminum substrates. This study elucidates the streamlined fabrication process for multiscale porous surfaces achieved through the evaporation-induced in-tegration of solid nanowires printed with high-viscosity photocurable resin.

In spite of its incidence on pistachio trees, the chemical ecology of Labidostomis lusitanica (Germar) (Coleoptera: Chrysomelidae) has been neglected so far. In this work we provide the first evidence of a biologically active male-specific compound that may be promoting field aggregation. Headspace collections by solid-phase microextraction from feral males and females reported the presence of 2-isobutyl-3-methoxypyrazine exclusively on males. Electroantennographic recordings revealed that males and females responded in a dose-dependent manner to increasing stimuli of 2-isobutyl-3-methoxypyrazine, with females overall displaying a higher response than males. In dual-choice tests, both males and females showed a significant preference for the compound in comparison to a pure air stimulus. In the light of these results, the possible role of 2-isobutyl-3-methoxypyrazine as an aggregation cue in L. lusitanica is discussed.

The amyloid fibril formation by $\alpha$-synuclein is a hallmark of various neurodegenerative disorders, most notably Parkinson's disease. Epigallocatechin gallate (EGCG) has been reported to be an efficient aggregation inhibitor of numerous proteins, among them $\alpha$-synuclein. Here we show that this applies only to a small region of relevant parameter space and that under some conditions, EGCG can even accelerate α-synuclein amyloid fibril formation through facilitating its heterogeneous primary nucleation. Furthermore, we show through quantitative seeding experiments that contrary to previous reports, EGCG is not able to re-model α-synuclein amyloid fibrils into seeding-incompetent structures. Taken together, our results paint a complex picture of EGCG as a compound that can under some conditions inhibit the amyloid fibril formation of α-synuclein, but the inhibitory action is not robust against various relevant changes in experimental conditions. Our results are important for the development of strategies to identify and characterise promising amyloid inhibitors.

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.

This feature study attempts on setting a granules involving mesoscopic matter-aggregation context as a problem of passing locally by the matter-aggregating system from classical stochastic (mesoscopic limit) to a quantum description (nanoscale quantum-size effect limit). A d-dimensional entropy-production aggregation of the granules-involving matter is considered in terms of a (sub)diffusive realization. It turns out that when taking a full d-dimensional pathway of the aggregation toward the nanoscale, one is capable of disclosing a Heisenberg type (diffusional) relation, setting up an upper uncertainty bound for the (sub)diffusive very slow granules-including environment that matches the quantum limit of h/2pm (m– average mass of a granule; h – the Planck’s constant) for the diffusion coefficient of the aggregation, likely first proposed by Fürth in 1933, and qualitatively foreseen by Schrödinger some years before, both in the context of a diffusing particle cluster. The classical-quantum passage uncovered here, also termed insightfully the quantum-size effect (as borrowed from the quantum dots’ parlance), works properly for the three-dimensional (d=3) case, making use of a substantial physical fact that the (nano)granules interact readily via their surfaces with the also granular surroundings in which they are immersed. This natural observation is embodied in the basic averaging construction of the diffusion coefficient of the aggregation of interest. Certain, possibly biomedical, applications of the model can also be viewed by prospective inspection of tiny inhomogeneities in nanofibrillar and/or lamellar matter for which certain numerical realizations are plausible to prepare based on thorough nanoscale experiments.

High fat-diet (HFD) feeding is known to induce metabolic dysregulation, however less is known on its impact in promoting the hypercoagulable state. The current study aimed to evaluate platelet-monocyte aggregate (PMA) formation following short-term HFD feeding. This is particularly important for understanding the link between inflammation and the hypercoagulable state during the early onset of metabolic dysregulation. To explore such a hypothesis, mice were fed a HFD for 8 weeks, with body weights as well as insulin and blood glucose levels monitored on weekly basis during this period. Basal hematological measurements were determined and the levels of spontaneous peripheral blood PMAs were assessed using whole blood flow cytometry. The results showed that although there were no significant differences in body weights, mice on HFD displayed impaired glucose tolerance and markedly raised insulin levels. These metabolic abnormalities were accompanied by elevated baseline PMA levels as an indication of hypercoagulation. Importantly, it was evident that baseline levels of monocytes, measured using the CD14 monocyte marker were significantly decreased in HFD-fed mice when compared to controls. In summary, the current evidence shows that in addition to causing glucose intolerance, such as that identified in a prediabetic state, HFD-feeding can promote undesirable hypercoagulation, the major consequence implicated in the development of cardiovascular complications.

The determination of the parameters that characterise the air-void system in hardened concrete elements becomes crucial for structures under freezing and thawing cycles. The ASTM C457 standard describes some procedures to accomplish this task, but they are not easy to apply, require specialised equipment such as a stereoscopic microscope and result in highly tedious tasks to be performed. This paper describes an alternative procedure to the modified point-count method described in the Standard that makes use of macro photography. This alternative procedure is successfully applied to a large set of samples and presents some advantages over the traditional method, since the required equipment is less expensive and provides a more comfortable and less tedious procedure for the operator.

Toxic advanced glycation end-products (TAGE), formed by glyceraldehyde (GA) as an intermediate in the non-enzymatic reaction with intracellular proteins, are highly cytotoxic and have been implicated in the pathogenesis of various diseases. However, the mechanisms underlying the degradation and removal of TAGE remain largely unknown. In the present study, we identified the checkpoint kinase-1 (CHK1) mutant, d270KD, which was rapidly degraded intracellularly by GA, and showed that its degradation was mainly mediated by the ubiquitin-proteasome pathway. The high-molecular-weight complexes formed by the GA stimulation of d270KD were abundant in the RIPA-insoluble fraction, which also contained high levels of TAGE. The knockdown of p62/SQSTM1 reduced the amount of high-molecular-weight complexes in the RIPA-insoluble fraction, indicating its involvement in the formation of TAGE aggregates. The present results suggest that the ubiquitin-proteasome pathway and p62 play a role in the degradation and aggregation of intracellular TAGE formed by GA. This study provides new insights into the mechanisms underlying TAGE metabolism and may lead to the development of novel therapeutic strategies for diseases associated with TAGE accumulation.

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.

In the existing group decision-making fuzzy analytic hierarchy process (FAHP) methods, the consensus among experts has rarely been fully reached. To fill this gap, in this study, a pre-aggregation fuzzy collaborative intelligence (FCI)-based FAHP approach is proposed. In the proposed pre-aggregation FCI-based FAHP approach, fuzzy intersection is applied to aggregate experts’ pairwise comparison results if these pairwise comparison results overlap. The aggregation result is a matrix of polygonal fuzzy numbers. Subsequently, alpha-cut operations are applied to derive the fuzzy priorities of criteria from the aggregation result. The pre-aggregation FCI-based FAHP approach has been applied to select suitable alternative suppliers for a wafer foundry in Taiwan amid the COVID-19 pandemic. The experimental results revealed that the pre-aggregation FCI-based FAHP approach significantly reduced the uncertainty inherent in the decision-making process by deriving fuzzy priorities with very narrow ranges.

Patients with neurodegenerative diseases (NDDs) are rapidly increasing all over the world. Alzheimer’s disease (AD), Parkinson’s disease, Huntington’s disease (HD), frontotemporal lobar degeneration (FTLD) and many other diseases are classified as NDDs, however, no therapeutic drugs have been developed against NDDs yet. As common phenomenon, protein oligomers and aggregates (inclusion bodies) are formed in neurons, and they trigger neuronal cell death and/or axonal degeneration. We have investigated the mechanisms of NDDs, and discovered several chemicals and peptides have potential to cure NDDs. Here, we show phorbol 12-myristate 13-acetate (PMA) significantly inhibits aggregate formation and promotes neurite regeneration in human neuronal cell culture model of NDDs, FTLD and AD. We expressed R406W mutant tau protein found in FTLD patients in human neuroblastoma SH-SY5Y cells as FTLD cell culture model. These FTLD neuronal cells showed intracellular aggregate formation and impaired neurite growth. Addition of PMA to culture medium 60% suppressed aggregate formation. PMA also enhanced neurite growth to the normal level at nanomolar concentration. We cultured SH-SY5Y cells in the medium containing -amyloid (1-42) as AD cell culture model. In these cells, neurite growth was limited to less than 50% of controls by the treatment of -amyloid (1-42). Surprisingly, addition of PMA increased the number of cells with >10-fold neurite extension to seven times even in -amyloid (1-42) contained medium. Also, PMA increased the expression of several genes and proteins important for cell survival in both cell culture models. These results suggest PMA has unexpected potential for the drug development against NDDs.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p < 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.