Broad Range Research Articles

Structural, mechanical and in vitro studies on pulsed laser deposition of hydroxyapatite on additive manufactured polyamide substrate

Additive manufacturing (AM) is an emerging field that merges engineering and life sciences to produce components that can effectively act as a replacement in the human body. This AM encompasses biofabrication using cells, biological or biomaterials as building blocks to fabricate biological and bio-application oriented substance, device and therapeutic products through a broad range of engineering and biological processes. Furthermore, bioactive coating on BAM surface facilitates biological fixation between the prosthesis and the hard tissue which increases the long term stability and integrity of the implant. In this paper, hydroxyapatite (HA) powder was coated over AM polyamide substrate using pulsed laser deposition. Coating morphology was characterised using scanning electron microscope (SEM) analysis and observed that the coating was dominated by the presence of particle droplet with different sizes. Compounds like tricalcium phosphate and a few amorphous calcium phosphates were found along with HA which was confirmed by X-ray diffraction (XRD) analysis. Fourier transform infrared spectroscopy (FTIR) techniques shows the presence of phosphate and carbonate groups in the HA structure. Nano-indentation and pull-out test reveals that the layer was strong enough and withstands higher load before it peels off. In vitro analysis was evaluated with human osteosarcoma MG-63 cells with respect to the cell viability and results shows that the good viability was observed on coated surface due to combinational effect of Ca2+ and PO4 3− ions. The multitude of characterisation conducted on the coating has established that coating polyamide with HA results in a positive combination for an implant.

Role of Bioactive Compounds in the Regulation of Hemostasis: Lessons from Nature

Abstract: The maintenance of hemostasis is a crucial component of the healing process for wounds. Hemostasis may be disrupted when there is a genetic abnormality in the process of platelet production or blood coagulation. The use of herbal remedies derived from plants native to India that exhibit a broad range of physicochemical qualities is becoming more common. This study uses an electronic database to investigate the hemostatic action of commonly accessible medicinal herbs, as well as the bioactive components and potential mechanisms behind this activity. It is possible that the transformation of traditional medicine into pharmaceuticals might be facilitated by taking into account the therapeutic characteristics of spices, garden plants, and wild plants. This review discusses a variety of topics related to plants, including their bioactivities, clinical uses, Indian traditional medicinal approaches, and modern developments. It includes several herbs and spices that have therapeutic and hemostatic properties. Extensive study is required prior to the use of herbal treatments within the context of the present healthcare system. This study deals with making a strategy to discover new medical plant-based therapeutic compounds and keeping track of the ones you already have.

Synthesis and in-silico Studies of 4-phenyl thiazol-2-amine Derivatives as Putative Anti-breast Cancer Agents.

Breast cancer (BC) is the second-leading cause of cancer-related fatalities in women after lung cancer worldwide. The development of BC is significantly influenced by estrogen receptors (ERs). The problem with current cancer treatments is selectivity, target specificity, cytotoxicity, and developing resistance. Thiazole scaffolds are gaining popularity in drug discovery due to their broad range of biological activity. It has the extraordinary capacity to control a variety of cellular pathways, and its potential for selective anticancer activity can be explored. Synthesis and in-silico</i> studies of 4-Phenyl thiazol-2-amine derivatives as anti-breast cancer agents and molecular docking was used to assess the compounds' capacity to bind ER-α protein target. In this study, 4-Phenylthiazol-2-amine derivatives (3a-j) have been synthesized, and using Schrodinger software, molecular docking and ADME studies of the compounds were conducted. Most of the synthesized compounds have shown dock scores ranging from -6.658 to - 8.911 kcal/mol, which is better than the standard drug tamoxifen (-6.821 kcal/mol). According to molecular docking, all compounds fit in the protein's active site and have the same hydrophobic pocket as the standard drug tamoxifen. Further, all of the compounds' ADME properties are below acceptable limits. Compound 3e showed the best docking score of -8.911. All compounds' ADME properties are within acceptable limits, and their p/o coefficients fall within a range, suggesting they will all have sufficient absorption at the site of action. These compounds can be evaluated invitro</i> and in-vivo</i> in the future.

Tiny Object Tracking: A Large-Scale Dataset and a Baseline.

Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in many vision tasks, such as object detection and segmentation. To promote the research and development of tiny object tracking, we create a large-scale video dataset, which contains 434 sequences with a total of more than 217K frames. Each frame is carefully annotated with a high-quality bounding box. In data creation, we take 12 challenge attributes into account to cover a broad range of viewpoints and scene complexities, and annotate these attributes for facilitating the attribute-based performance analysis. To provide a strong baseline in tiny object tracking, we propose a novel multilevel knowledge distillation network (MKDNet), which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination, and localization abilities in tracking tiny objects. Extensive experiments are performed on the proposed dataset, and the results prove the superiority and effectiveness of MKDNet compared with state-of-the-art methods. The dataset, the algorithm code, and the evaluation code are available at https://github.com/mmic-lcl/Datasets-and-benchmark-code.

Synthesis of Cs3Cu2I5 Nanocrystals in a Continuous Flow System.

Achieving the goal of generating all of the world's energy via renewable sources and significantly reducing the energy usage will require the development of novel, abundant, nontoxic energy conversion materials. Here, a cost-efficient and scalable continuous flow synthesis of Cs3Cu2I5 nanocrystals is developed as a basis for the rapid advancement of novel nanomaterials. Ideal precursor solutions are obtained through a novel batch synthesis, whose product served as a benchmark for the subsequent flow synthesis. Realizing this setup enabled a reproducible fabrication of Cs3Cu2I5 nanocrystals. The effect of volumetric flow rate and temperature on the final product's morphology and optical properties are determined, obtaining 21% quantum yield with the optimal configuration. Consequently, the size and morphology of the nanocrystals can be tuned with far more precision and in a much broader range than previously achievable. The flow setup is readily applicable to other relevant nanomaterials. It should enable a rapid determination of a material's potential and subsequently optimize its desired properties for renewable energy generation or efficient optoelectronics.

Numerical investigation of the heat transfer and flow pattern on tandem triangle-grooved cylinders

Modifying surface characteristics and geometric structure to regulate heat transfer and flow fields is a classical issue. The study of wake dynamics and heat transfer efficiency in tandem grooved cylinders hold fundamental importance. This research proposes a configuration of tandem cylinders with triangle-grooved surfaces and numerical simulates the vorticity, fluid forces, Strouhal numbers, and convective heat transfer in tandem grooved cylinders. The parameters investigated include orientation angles θ = 0°, 30°, 45°, 60°, 90° and Reynolds numbers Re = 75, 100, 125, 150, 175, 200. This study focuses on how the triangle-grooved angle (θ) and Re affect wake dynamics and thermal performance. Numerical results are validated against available data in the literature for tandem smooth and tandem grooved cylinders. The results indicate that, compared to tandem smooth cylinders, the positive vorticity intensity of tandem grooved cylinders is 35.7 % higher, while the negative vorticity intensity is 23.5 % lower. Further observations show that at θ = 45°, the pressure variation on the upstream cylinder is most significant, approximately twice that at θ = 0° and θ = 90°. Notably, the variation values of the downstream cylinder (β = 0°) at θ = 0° and θ = 45° are triple and double that of the upstream cylinder in TF mode, respectively. Additionally, at θ = 0°, the root mean square lift coefficient of the downstream cylinder reaches the maximum value (Re = 75–100), approximately 1.34. When Re = 125, CL(rms),1 decreases by 15.7 % to 1.13. Particularly, when Re = 100–150, the time-averaged drag coefficient of the downstream cylinder shows a peak value of approximately 1.02. Interestingly, at Re = 200, a tadpole-shaped thermal distribution forms in the wake region of the upstream cylinder. Furthermore, the time-averaged Nusselt number ratio (θ = 0°) exhibits a trend opposite to other cases over a broader range (125 ≤ Re ≤ 200) and peaks at Re = 200, approximately 1.24.

Geochemical characteristics of peridotite xenoliths from the Vitim volcanic field: Insight to late Cenozoic mantle upwelling in SE Siberia

The Vitim volcanic field, comprising Cenozoic basaltic lava flows associated with the Baikal Rift volcanism, contains abundant mantle xenoliths that offer insights into the subcontinental lithospheric mantle (SCLM) beneath the region. This study investigates the mineralogical and geochemical compositions of these mantle xenoliths, entrained in Miocene and Pleistocene basalts. The xenoliths, predominantly garnet-, spinel-, and garnet-spinel-bearing lherzolites, exhibit high modal content of clinopyroxene and low fosterite content of olivine, suggesting a relatively fertile SCLM. Distinct variations in CaO and Al2O3 observed in spongy rims and cores of clinopyroxenes likely result from decompression-induced partial melting. Trace element patterns of clinopyroxene reveal three types: light rare-earth elements (LREE) depleted, LREE enriched, and transitional LREE types. Whole-rock trace element compositions of the four Vitim lherzolites showing flat to LREE and large ion lithophile elements enrichments are entirely consistent with those of their clinopyroxenes. The LREE depleted type indicates that these lherzolites were residual mantle after melt extraction, whereas the LREE enriched type suggests that they were metasomatized after residual mantle formed by partial melting. The transitional LREE type showing in-between features among the above two end-members could represent that those lherzolite underwent less or incomplete metasomatism, thus clinopyroxene cores still retain primary depleted LREE type feature of residual mantle. Most Vitim lherzolites were affected by cryptic metasomatism with less stealth metasomatism, whereas only those with secondary amphibole and apatite could be influenced by modal metasomatism. The lithospheric mantle beath the Vitim volcanic field representing by these lherzolites was metasomatized predominantly by hydrous fluids with minor silicate melts. Using the clinopyroxene melting model, it was found that most Vitim lherzolites have experienced <10% partial melting. Geothermal gradients estimated from mineral geothermobarometry indicate that lherzolites in the Pleistocene basalts equilibrated at shallower depths with higher temperatures compared to those in the Miocene basalts with deeper depths and lower temperatures. SrNd isotopic ratios, combined with previous results (Ionov et al., 2005), demonstrate that the Miocene lherzolites have a broader range from depleted to enriched components, whereas lherzolites in the Pleistocene basalts show lesser enrichment. It is proposed that the Pleistocene basalts captured shallower SCLM lherzolites experienced less degrees of melt metasomatism than those deeper SCLM lherzolites hosted by Miocene basalts. Considering the deeper SCLM is more vulnerable to metasomatism by ascending melts, such temporal and geochemical variation further emphasizes progressive asthenosphere upwelling beneath the Vitim region from the Miocene to Pleistocene time.

Spatial distribution of physicochemical parameters and drinking and irrigation water quality indices in the Jhelum River, Pakistan.

Sustainable management of river systems is a serious concern, requiring vigilant monitoring of water contamination levels that could potentially threaten the ecological community. This study focused on the evaluation of water quality in the Jhelum River (JR), Azad Jammu and Kashmir, and northern Punjab, Pakistan. To achieve this, 60 water samples were collected from various points within the JR Basin (JRB) and subjected to a comprehensive analysis of their physicochemical parameters. The study findings indicated that the concentrations of physicochemical parameters in the JRB water remained within safety thresholds for both drinking and irrigation water, as established by the World Health Organization and Pakistan Environmental Protection Agency. These physicochemical parameters refer to various chemical and physical characteristics of the water that can have implications for both human health (drinking water) and agricultural practices (irrigation water). The spatial variations throughout the river course distinguished between the upstream, midstream, and downstream sections. Specifically, the downstream section exhibited significantly higher values for physicochemical parameters and a broader range, highlighting a substantial decline in its quality. Significant disparities in mean values and ranges were evident, particularly in the case of nitrates and total dissolved solids, when the downstream section was compared with its upstream and midstream counterparts. These variations indicated a deteriorating downstream water quality profile, which is likely attributable to a combination of geological and anthropogenic influences. Despite the observed deterioration in the downstream water quality, this study underscores that the JRB within the upper Indus Basin remains safe and suitable for domestic and agricultural purposes. The JRB was evaluated for various irrigation water quality indices. The principal component analysis conducted in this study revealed distinct covariance patterns among water quality variables, with the first five components explaining approximately 79% of the total variance. Recommending the continued utilization of the JRB for irrigation, we advocate for the preservation and enhancement of water quality in the downstream regions.

Interaction-and-Response Network for Distantly Supervised Relation Extraction.

Distantly supervised relation extraction (DSRE) aims to identify semantic relations from massive plain texts. A broad range of the prior research has leveraged a series of selective attention mechanisms over sentences in a bag to extract relation features without considering dependencies among the relation features. As a result, potential discriminative information existed in the dependencies is ignored, causing a decline in the performance of extracting entity relations. In this article, we focus on going beyond the selective attention mechanisms and propose a new framework termed interaction-and-response network (IR-Net) that adaptively recalibrates the features of sentence, bag, and group levels by explicitly modeling interdependencies among the features on each level. The IR-Net consists of a series of interactive and responsive modules throughout feature hierarchy, seeking to strengthen its power of learning salient discriminative features for distinguishing entity relations. We conduct extensive experiments on three benchmark DSRE datasets, including NYT-10, NYT-16, and Wiki-20m. The experimental results demonstrate that the IR-Net brings obvious improvements in performance when comparing ten state-of-the-art DSRE methods for entity relation extraction.

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium.

The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have leveraged a microfluidics-based Plate-Seq technology to survey drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Built on this dataset, we implemented an efficient computational framework to define a tissue-specific protein activity landscape of these drugs and reported almost 50 million differential protein activities derived from drug perturbations vs. vehicle controls. These analyses revealed thousands of highly reproducible and novel, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide drug functional network, characterization of MoA-related drug clusters and off-target effects, dramatical expansion of druggable human proteome, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins, most never reported before. The drug perturbation profile resource described here represents the first, large-scale, whole-genome-wide, RNA-Seq based dataset assembled to date, with the proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, facilitates mechanistic exploration of drug functions, supports systematic and quantitative approaches to precision oncology, and serves as a rich data foundation for drug discovery.

Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework

Crash scenario-based testing is crucial for assessing autonomous driving safety. However, existing studies on scenario generation tend to prioritize concrete scenarios for direct testing, neglecting the construction of fundamentally functional scenarios with a broader range. Police-reported historical crash data is a valuable supplement, yet detecting all potential crash scenarios is laborious. In order to address this issue, this study proposes an adaptive search sampling framework based on deep generative model and surrogate model (SM) to extract master scenario samples from police-reported historical crash data. The framework starts with selecting representative samples from the full crash dataset as initial master scenario samples using various sampling techniques. Evaluation indexes are then constructed, and derived scenario samples are synthesized using the deep generative model. To enhance efficiency, an SM is established to replace the generative model’s training and data generation process. Based on the SM, an adaptive search sampling method is developed, which iteratively adjusts the sampling strategy using the Similarity Score to achieve comprehensive sampling. Experimental results demonstrate the notable advantage of the adaptive search sampling method over other sampling methods. Furthermore, statistical analysis and visualization assessments confirm the effectiveness and accuracy of the proposed method.

High incidence of dural tears with 3-column osteotomies: a systematic reviewof adult spinal deformity surgery literature for the past decade.

Dural tear (DT) is a well-known complication of spinal surgery. We aimed to systematically review the literature from the past decade and determine the incidence and risk factors for DT in the adult spinal deformity (ASD) population to improve both the surgical strategy and counseling of patients undergoing ASD correction. A systematic review from 2013 to 2023 utilizing PRISMAguidelines was performed. The MEDLINE database was used to collect primary English language articles. The inclusion criterion for patients was degenerative ASD. Pediatric studies, animal studies, review articles, case reports, studies investigating minimally invasive surgery (MIS), studies lacking data on DT incidence, and articles pertaining to infectious, metastatic or neoplastic, traumatic, or posttraumatic etiologies of ASD were excluded. Our results demonstrate that the incidence of DT in ASD surgery ranges from 2.0% to 35.7%, which is a much broader range than the reported incidence for non deformity surgery. Moreover, the average rate of DT during ASD surgery stratified by surgical technique was greater for osteotomy overall (19.5% +/- 7.9%), especially for 3-column osteotomy (3CO), and lower for interbody fusion (14.3% +/- 9.9%). Risk factors for DT in the ASD surgery cohort included older age, revision surgery, chronic severe compression, higher-grade osteotomy, complexity of surgery, rheumatoid arthritis (RA), and higher Anesthesiology Society of America (ASA) grade. To our knowledge, this is the first systematic review discussing the incidence of and risk factors for DT in the ASD population. We found that the risk factors for DT in ASD patients were older age, revision surgery, chronic severe compression, a greater degree of osteotomy, complexity of surgery, RA, and a higher ASA grade. These findings will help guide spine surgeons in patient counseling as well as surgical planning.

Monolithic integrated optoelectronic chip for vector force detection

Sensors with a small footprint and real-time detection capabilities are crucial in robotic surgery and smart wearable equipment. Reducing device footprint while maintaining its high performance is a major challenge and a significant limitation to their development. Here, we proposed a monolithic integrated micro-scale sensor, which can be used for vector force detection. This sensor combines an optical source, four photodetectors, and a hemispherical silicone elastomer component on the same sapphire-based AlGaInP wafer. The chip-scale optical coupling is achieved by employing the laser lift-off techniques and the flip-chip bonding to a processed sapphire substrate. This hemispherical structure device can detect normal and shear forces as low as 1 mN within a measurement range of 0–220 mN for normal force and 0–15 mN for shear force. After packaging, the sensor is capable of detecting forces over a broader range, with measurement capabilities extending up to 10 N for normal forces and 0.2 N for shear forces. It has an accuracy of detecting a minimum normal force of 25 mN and a minimum shear force of 20 mN. Furthermore, this sensor has been validated to have a compact footprint of approximately 1.5 mm2, while maintaining high real-time response. We also demonstrate its promising potential by combining this sensor with fine surface texture perception in the fields of compact medical robot interaction and wearable devices.

A novel algorithmic approach for automatic virtual articulation to avoid dynamic interferences in dental restoration designs.

There is an ongoing debate about the benefits of the facebow and individual articulator settings in prosthodontics when compared with simpler methods. The present study aims to determine whether the implementation of novel algorithmic articulator concepts may be an alternative to avoid dynamic interference contacts during the design process of fixed posterior restorations, and to what extent the occlusal morphology of the restoration is affected. From a chairside CAD database, a total of 50 clinical patient cases documenting 61 planned fixed restorations in the posterior tooth region were selected. A common CAD software program was used for the automated knowledge-based design process. When designing the restorations, functional concepts were applied, including the pure static occlusion, the average articulation, three different articulator settings as a control, and a combination of a broad range of articulation parameters (the full range dynamic articulation). The resulting dynamic contact points were compared both visually and metrically with a monitoring software program. There is a highly significant difference in avoiding dynamic interference contacts when applying the full range dynamic articulation compared with the pure static occlusion (P < 0.001) and the average articulation (P < 0.001). Furthermore, the superimposition revealed that the surface of the restorations showed nearly no visual morphologic changes after virtually grinding-in the interpenetrating contact points. The full range dynamic articulation can be used for the design of small fixed posterior restorations to avoid most dynamic interference contacts without the need for determining individual parameters for each patient.

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements.

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

Structure, Function, and Physicochemical Properties of Pore-forming Antimicrobial Peptides.

Antimicrobial peptides (AMPs), a class of antimicrobial agents, possess considerable potential to treat various microbial ailments. The broad range of activity and rare complete bacterial resistance to AMPs make them ideal candidates for commercial development. These peptides with widely varying compositions and sources share recurrent structural and functional features in mechanisms of action. Studying the mechanisms of AMP activity against bacteria may lead to the development of new antimicrobial agents that are more potent. Generally, AMPs are effective against bacteria by forming pores or disrupting membrane barriers. The important structural aspects of cytoplasmic membranes of pathogens and host cells will also be outlined to understand the selective antimicrobial actions. The antimicrobial activities of AMPs are related to multiple physicochemical properties, such as length, sequence, helicity, charge, hydrophobicity, amphipathicity, polar angle, and also self-association. These parameters are interrelated and need to be considered in combination. So, gathering the most relevant available information will help to design and choose the most effective AMPs.

An Exceptional Valorization of CuO Nanoparticles in Ionic Liquids as an Efficient Medium for the Electrophilic Substitution of Indole Towards the Formation of Bis(indolyl)methanes

Aim: Ionic liquids are promising green solvents with simple but unique structure-related physical properties such as negligible vapour pressure, exceptional thermal conductivity, remarkable thermal stability and their suitability and inertness towards a broad range of catalytic applications. CuO NPs have been addressed as a cost-effective and a reagent of a choice that necessitates only mild reaction conditions to offer a high yield of the desired products with exceptional selectivity in a short duration of time. Therefore, in the present work, attempts have been made to explore the catalytic potentials of CuO NPs in an ionic liquid medium to synthesize biologically important bis(indolyl)methanes. Background: Catalytic explorations of metal oxide nanoparticles in ionic liquids offers a cooperative effect that has a significant impact on the kinetics as well as on the outcome of the reaction. Therefore, such catalytic systems in the present times have seized the scientific community's interest from the perspectives of sustainable development in synthetic organic chemistry. The combination of metal oxide nanoparticles with highly tunable ionic liquids is not only used to synthesize simple organic molecules but also explored in the synthesis of complex organic molecules of high commercial and biological relevance. Objectives: The current work offers a rapid and robust protocol for synthesizing bis(indolyl)methanes via electrophilic substitution reaction between indole and various aldehydes in the presence of a CuO nanoparticles-ionic liquid system. The discussion focuses on the high tolerance of different functionalities by the catalytic system leading to the synthesis of bis(indolyl)methanes. Methods: CuO NPs have been synthesized via the co-precipitation method using ionic liquid. The applicability of metal oxide nanoparticles-IL matrix was further investigated in synthesizing bis(indolyl)methanes. Results: The FT-IR absorption below 600 cm-1 and the XRD pattern showing all the peaks in the diffraction diagram revealed the formation of CuO NPs. FESEM images show the flake-shaped morphology of CuO NPs and are found to be separated from the agglomerated clusters Conclusion: Ionic liquid-CuO NPs matrix reveals good to exceptional catalytic properties, and their advancements as a catalytic system at room temperature open new avenues for synthetic organic chemists.

Egg-free mayonnaise-type emulsions stabilized with proteins derived from the larvae of Tenebrio molitor

In view of demanding alternative protein sources to replace the conventional ones, egg-yolk free mayonnaise-type emulsions were made from protein preparations extracted from Tenebrio molitor larvae by applying alkaline extraction (ASP), alkaline extraction followed by isoelectric precipitation (AIP) and salt-assisted treatment followed by dialysis (SSP). Their droplet size, microstructure and rheological properties were compared with egg-yolk mayonnaise (CEY). All emulsions exhibited similar droplet size distribution, with the CEY-mayonnaise having a broader range. SSP showed the smallest mean droplet size acting as a more effective emulsifier. Steady-state flow properties and dynamic oscillatory experiments within the linear viscoelastic region of mayonnaise-type emulsions were performed. The higher value of G′ compared to the value of G″ indicated the elastic character of all samples. Both moduli were no-highly frequency dependent, which was in line with their solid-like nature. The thixotropic behaviour of the emulsions was evaluated by three-interval thixotropy tests revealing higher recovery rate in the case of CEY and ASP. Temperature sweeps were also performed and revealed that AIP and SSP showed less temperature dependence, indicating their lower sensitivity to temperature variations. Overall, the results of this study suggest that the protein preparations extracted from Tenebrio molitor larvae have the potential for producing egg yolk-free mayonnaise-type emulsions.

Testing the human superorganism approach tomorality.

Current theories in moral psychology do not agree about the kinds and range of offenses that people should moralize. In this study, a new approach to defining the moral domain, Human Superorganism Theory (HSoT), is presented and tested. HSoT proposes that the primary function of moral action is the suppression of cheaters in the unusually large societies recently established by our species (i.e., human 'superorganisms'). It suggests that a broad range of moral concerns exist beyond traditional notions of harm and fairness, including actions that inhibit functions such as group-level social control, physical and social structuring, reproduction, communication, signaling and memory. Roughly 80,000 respondents completed a web-based experiment hosted by the British Broadcasting Corporation, which elicited a suite of responses to characteristics of a set of 33 short scenarios representing the areas identified by the HSoT perspective. Results indicate that all 13 superorganism functions are moralized, while violations of scenarios falling outside this area (social customs and individual decisions) are not. Several hypotheses derived specifically from HSoT were also supported. Given this evidence, we believe this new approach to defining a broader moral domain has implications for fields ranging from psychology to legal theory.

Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals

Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms. To further alleviate the challenge of limited sample size, we introduced a data combination strategy that improved prediction accuracy and mitigated overfitting by combining different datasets. The Light Gradient Boosting Machine (LightGBM) with MF and Random Forest (RF) with MD models based on the unified dataset were finally selected as the optimal models. The SHapley Additive exPlanations revealed insights: the MF-LightGBM model successfully captured the influence of electron-withdrawing/donating groups, while autocorrelation, walk count and information content descriptors in the MD-RF model were identified as key features. Furthermore, the important contribution of pH was emphasized. The results of the applicability domain analysis further supported that the developed model can make reliable predictions for query compounds across a broader range. Finally, a practical web application for logk calculations was built.