Potential Applicability Research Articles

Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo.

With the specter of accelerating climate change, securing access to potable water has become a critical global challenge. Atmospheric water harvesting (AWH) through metal-organic frameworks (MOFs) emerges as one of the promising solutions. The standard numerical methods applied for rapid and efficient screening for optimal sorbents face significant limitations in the case of water adsorption (slow convergence and inability to overcome high energy barriers). To address these challenges, we employed grand canonical transition matrix Monte Carlo (GC-TMMC) methodology and proposed an efficient interpolation scheme that significantly reduces the number of required simulations while maintaining accuracy of the results. Through the example of water adsorption in three MOFs: MOF-303, MOF-LA2-1, and NU-1000, we show that the extrapolation of the free energy landscape allows for prediction of the adsorption properties over a continuous range of pressure and temperature. This innovative and versatile method provides rich thermodynamic information, enabling rapid, large-scale computational screening of sorbents for adsorption, applicable for a variety of sorbents and gases. As the presented methodology holds strong applicative potential, we provide alongside this paper a modified version of the RASPA2 code with a ghost swap move implementation and a Python library designed to minimize the user's input for analyzing data derived from the TMMC simulations.

Deep semi-supervised regression via pseudo-label filtering and calibration

Semi-supervised learning (SSL) is a widely used model training paradigm that effectively utilizes a limited set of labeled data and a substantially larger pool of unlabeled data. Historically, the focus of SSL research has predominantly been on classification tasks, employing methods such as consistency regularization and pseudo-labeling. However, the direct application of these methods to regression tasks presents significant challenges, primarily due to the complexities associated with evaluating the reliability of pseudo-labels in a regression context. This paper introduces SimRegMatch, a novel semi-supervised regression (SSR) framework devised to overcome this specific challenge, by combining an uncertainty-based filtering mechanism with a similarity-based pseudo-label calibration approach. The former component is tasked with discerning which unlabeled examples possess pseudo-labels of sufficient reliability, achieved through the estimation of uncertainty levels. The latter component then refines these pseudo-labels by propagating information from labeled to unlabeled examples, thereby enhancing the overall quality of the pseudo-labels. The efficacy of SimRegMatch was rigorously tested through experiments conducted on the publicly available AgeDB dataset, which is centered around age prediction, as well as on a practical regression problem focused on the detection of interior noise levels in automobiles using accelerometer data. When benchmarked against current state-of-the-art methods in semi-supervised regression, SimRegMatch exhibited notable improvements in regression performance. Additionally, a series of ablation studies were carried out to dissect and understand the specific elements of the framework that were instrumental in achieving these performance enhancements. SimRegMatch addresses a pivotal issue in semi-supervised regression - the assessment of regression pseudo-label reliability - and substantially elevates model performance. By combining the strengths of uncertainty estimation and pseudo-label calibration, SimRegMatch emerges as a robust and versatile framework with significant potential for broad applicability in various SSR scenarios. A PyTorch implementation is publicly available at https://github.com/YongwonJo/SimRegMatch.

Can I see myself there? How Black potential applicants use diversity cues to learn about graduate program climate.

In academia, showcasing diversity, equity, and inclusion (DEI) values has become increasingly prominent in efforts to recruit students and faculty with marginalized identities, yet little work has examined the empirical effects that such DEI practices and identity safety cues have on the perceptions of these institutions. In the present study, we examine the contextual factors that shape how Black science, technology, engineering, and mathematics (STEM) students learn and draw inferences about diversity and cultural climate in the graduate programs to which they consider applying. A sample of 217 Black participants with background in a STEM discipline viewed a mock academic department website which presented varied combinations of expressed diversity cues (diversity vs. neutral mission statement) and/or evidence-based diversity cues (racially diverse vs. all-White faculty). Participants reported perceptions of the department's DEI culture, their own perceived fit within the program, and belief of future personal success within that institution. Results indicated a significant main effect of evidence-based cue, in the form of faculty diversity, on all examined outcomes, with Black participants more positively assessing a program exhibiting this cue. An expressed cue, in the form of diversity statement, did not have significant effects. These results indicate that in higher education, as in other settings, evidence-based cues may be more effective means to cue identity safety. This study provides a foundation for future research to help guide efforts of academic programs seeking to create a welcoming and supportive climate for all potential applicants. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design and assembly of ZIF@PPy-derived hierarchical structure CeCo-NxCC electrocatalyst towards oxygen reduction reaction

The research of high-performance catalysts for ORR is always a great challenge in the field of zinc-air batteries (ZABs) applications. Herein, a near-regular dodecahedral ZIF-67-derived nitrogen-doped carbon material (CeCo-NxCC-700) with abundant hollow nanospheres on the surface is successfully constructed by introducing Ce ions to replace part of the Co ions and polymerizing pyrrole on the surface of the polyhedrons. PPy-coating is beneficial to improving the electrochemical stability and increasing the number of Co-Nx active sites, especially the pyrrole N and pyridine N sites. Ce-doping adjusts the charge density around the active sites. Meanwhile, the pyrolysis at 700 °C during the preparation process does not cause the structural collapse of CeCo-NxCC. Based on their decent ORR/OER performance, ZABs have been further assembled with CeCo-NxCC-700, which exhibit a high specific capacity of 815.4 mAh·g (Zn)−1 and cycling stability of more than 80 h, indicating a great application potentiality in ZABs manufacturing.

Ethylene control in fruit quality assurance: A material science perspective

AbstractThe waste of resources associated with fruit decay is rapidly spreading globally, threatening the interests of relevant practitioners and the health of consumer groups, and demanding precise solutions. Controlling fruit ripening through ethylene regulation is one of the most important strategies for providing high‐quality fruits. However, current materials for ethylene regulation still have difficulty realizing their application potential due to high manufacturing costs and performance deficiencies. In this review, the ethylene‐controlled release materials for ripening based on molecular encapsulation and the ethylene scavengers for preservation based on mechanisms such as oxidation, photodegradation, and adsorption are presented. We discuss and analyze a wide range of materials in terms of mechanism, performance, potential of applicability, and sustainability. The ethylene release behavior of encapsulating materials depends on the form in which the ethylene binds to the material as well as on environmental factors (humidity and temperature). For ethylene scavengers, there are a variety of scavenging mechanisms, but they generally require porous materials as adsorption carriers. We highlight the great opportunity of designing soft crystalline porous materials as efficient ethylene adsorbent due to their unique structural properties. We present this review, including a summary of practical characteristics and deficiencies of various materials, to establish a systematic understanding of fruit quality assurance materials applied to ethylene regulation, anticipating a promising prospect for these new materials.

Ultra-highly efficient adsorbent for CO2 capture from air by directional deprotonation regulation of MOFs-based amine grafting

In recent years, the solid amine of Metal-Organic Frameworks (MOFs) has shown promise as a direct air capture (DAC) adsorbent. However, the efficiency of amine groups under ultra-low CO2 concentrations requires improvement. In this study, we propose a novel method to regulate proton migration through solvent effects during amine grafting, to enhance the synthesis of MOFs as solid amine adsorbents and improve amine group utilization. This approach yielded outstanding results with a MIL-100(Cr)-based DAC adsorbent, demonstrating a CO2 adsorption capacity of 2.15 mmol/g and an ultra-high amine group utilization of 89.6 % at ambient conditions. By directional deprotonation regulation, the CO2 adsorption performance of the UiO-66(Zr)-based adsorbent significantly increased from 0.06 mmol/g of using H2O as solvent to 1.71 mmol/g of using NMP, with a 27.5-fold increase. Specifically, organic polar aprotic solvents enhanced the binding energy and reduced the energy barrier for deprotonation during the grafting process, thereby improving CO2 adsorption performance and amine group utilization, which shed a new light on the development of MOFs based CO2 adsorbent. Furthermore, due to the coordination of secondary amine groups with metal sites leads to the decrease of hydrophilic functional groups in the CO2 adsorption products, the hydrophobicity of products was reduced significantly resulting in an increase in the ΔS of the hydrated ion pairs after the adsorption, which reduced the heat of CO2 adsorption about 15 % than that reported in the literature. Based on changes in humidity conditions, the regeneration energy consumption of the MF-Cr-AEEA is less than 2.5 GJ/t CO2 under a relatively lower humidity, demonstrating outstanding industrial applications potential of the adsorbent.

Carothers and Flory-Stockmayer Theory as a new perspective on currently ineffective poly(glycerol sebacate) synthesis and crosslinking

Since 2002, intensive research on glycerol polyester has been ongoing. Poly(glycerol sebacate) (PGS) is most often studied because of its high applicable potential in many medical uses. This polyester is commonly synthesised from glycerine and sebacic acid in a molar ratio G/SA = 1:1. Nowadays, synthesis and thermal crosslinking processes are very time-consuming. At least 24 h for each, sometimes even a few days. There is a lack of works comparing these processes with polymerisation theory, such as Carothers Equations or Flory-Stockmayer Theory. In this work, experiments were performed at characteristic points of molar ratio determined from mathematical modelling. Currently published syntheses and crosslinking processes are characterised by long times, probably due to the use of the wrong molar ratio of monomers. The most effective glycerine to sebacic acid ratio is 2:3, according to both theories. This parameter should be considered for further synthesis optimisation and scaling up. This is also important for industrial implementation due to economic and ecological aspects. Moreover, the study of the G/SA ratio outside the range of 0.5–1.0 has no theoretical and experimental justification.

Exploring the Ecological Implications, Gastronomic Applications, and Nutritional and Therapeutic Potential of Juglans regia L. (Green Walnut): A Comprehensive Review.

The green walnut, which is frequently overlooked in favor of its more mature sibling, is becoming a topic of great significance because of its unique ecological role, culinary flexibility, and therapeutic richness. The investigation of the bioactive substances found in green walnuts and their possible effects on human health has therapeutic potential. Juglans regia L. is an important ecological component that affects soil health, biodiversity, and the overall ecological dynamic in habitats. Comprehending and recording these consequences are essential for environmental management and sustainable land-use strategies. Regarding cuisine, while black walnuts are frequently the main attraction, green walnuts have distinct tastes and textures that are used in a variety of dishes. Culinary innovation and the preservation of cultural food heritage depend on the understanding and exploration of these gastronomic characteristics. Omega-3 fatty acids, antioxidants, vitamins, and minerals are abundant in green walnuts, which have a comprehensive nutritional profile. Walnuts possess a wide range of pharmacological properties, including antioxidant, antibacterial, antiviral, anticancer, anti-inflammatory, and cognitive-function-enhancing properties. Consuming green walnuts as part of one's diet helps with antioxidant defense, cardiovascular health, and general well-being. Juglans regia L., with its distinctive flavor and texture combination, is not only a delicious food but also supports sustainable nutrition practices. This review explores the nutritional and pharmacological properties of green walnuts, which can be further used for studies in various food and pharmaceutical applications.

Graduate program collaborations to identify and cultivate graduate enrollment best practices

AbstractDetermining best practices for graduate enrollment requires continuous dedication by graduate enrollment personnel to identify innovative and relevant recruitment strategies to attract potential graduate applicants. The dynamic landscape of higher education creates further complexity as these best practices must also align with institutional expectations and the expectations of those overseeing graduate academic programs. To establish a robust enrollment strategy, it is crucial for graduate program officers and graduate enrollment personnel to establish a collaborative relationship that supports timely and ongoing dialogues to attract and retain quality admissions candidates. This manuscript explores how graduate enrollment personnel at a mid‐size regional institution collaborate with graduate program officers to achieve the enrollment goals of the institution and to support continued program growth.Practical Takeaways Establishing collaborative relationships between graduate program officers and graduate enrollment personnel are essential to support program growth and the continuous evaluation of admissions criteria to promote holistic admissions. Recognizing the diverse expectations of applicants and establishing a graduate enrollment framework that allows for individual program needs creates a supportive environment that encourages program growth. The elimination of extraneous admissions requirements can support program growth without negatively impacting the quality of applicants. Supporting graduate program officers throughout the graduate enrollment process is imperative to foster positive experiences for applicants throughout and following the admissions cycle.

Prophylactic efficacy of an intranasal spray with 2 synergetic antibodies neutralizing Omicron.

BACKGROUNDAs Omicron is prompted to replicate in the upper airway, neutralizing antibodies (NAbs) delivered through inhalation might inhibit early-stage infection in the respiratory tract. Thus, elucidating the prophylactic efficacy of NAbs via nasal spray addresses an important clinical need.METHODSThe applicable potential of a nasal spray cocktail containing 2 NAbs was characterized by testing its neutralizing potency, synergetic neutralizing mechanism, emergency protective and therapeutic efficacy in a hamster model, and pharmacokinetics/pharmacodynamic (PK/PD) in human nasal cavity.RESULTSThe 2 NAbs displayed broad neutralizing efficacy against Omicron, and they could structurally compensate each other in blocking the Spike-ACE2 interaction. When administrated through the intranasal mucosal route, this cocktail demonstrated profound efficacy in the emergency prevention in hamsters challenged with authentic Omicron BA.1. The investigator-initiated trial in healthy volunteers confirmed the safety and the PK/PD of the NAb cocktail delivered via nasal spray. Nasal samples from the participants receiving 4 administrations over a course of 16 hours demonstrated potent neutralization against Omicron BA.5 in an ex vivo pseudovirus neutralization assay.CONCLUSIONThese results demonstrate that the NAb cocktail nasal spray provides a good basis for clinical prophylactic efficacy against Omicron infections.TRIAL REGISTRATIONwww.chictr.org.cn, ChiCTR2200066525.FUNDINGThe National Science and Technology Major Project (2017ZX10202203), the National Key Research and Development Program of China (2018YFA0507100), Guangzhou National Laboratory (SRPG22-015), Lingang Laboratory (LG202101-01-07), Science and Technology Commission of Shanghai Municipality (YDZX20213100001556), and the Emergency Project from the Science & Technology Commission of Chongqing (cstc2021jscx-fyzxX0001).

First principles study on the structural, electronic, mechanical and optical properties of Pb-free double perovskites M2FeMnO6 (M = Rb, Cs)

The researchers are looking for efficient smart materials to cope with the energy crisis. In this regard, double perovskites are extensively studied for various technological advantages. In the present work, we investigated the structural, electronic, optical, and elastic properties of double perovskites M2FeMnO6 (M = Rb, Cs) using the WIEN2k package within density functional theory (DFT). The structural properties reveal that both compounds are stable and can be fabricated experimentally. The electronic study indicates that both compounds possess a metallic nature in spin up configuration while in the spin down configuration these materials are insulators overall they possess half metallic nature. The elastic parameters indicates that these compounds are mechanical stable. Furthermore, the ductile and anisotropic nature is conformed from the computed elastic constant obtained through IR-Elast package. Optical parameters such as dielectric constants, optical absorption, optical conductivity, and optical reflectivity are studied in the range of 0 to 40 eV energy range. From the study of these compounds, we recognize that these materials can be potential applicants for spintronic and optoelectronic devices.

Guizhi Fuling Wan ameliorates concanavalin A-induced autoimmune hepatitis in mice

BackgroundAutoimmune hepatitis (AIH) is an immune-mediated hepatic disease associated with intense complications. AIH is more common in females and needs effective drugs to treat. Guizhi Fuling Wan (GZFLW) is a traditional Chinese herbal formula used to treat various gynecologic diseases. In this study, we aim to extend the new use of GZFLW for AIH. MethodsThe tandem MS-based analysis was used to identify secondary metabolites in GZFLW. Therapeutic effects of GZFLW were tested in a concanavalin A (Con A)-induced AIH model in mice. Ethnopharmacological mechanisms underlying the antiapoptotic, antioxidant, and immunomodulatory protective effects were determined. ResultsOral administration of GZFLW attenuates AIH in a Con A-induced hepatotoxic model in vivo. The tandem MS-based analysis identified 15 secondary metabolites in GZFLW. The Con A-induced AIH syndromes, including hepatic apoptosis, inflammation, reactive oxygen species accumulation, function failure, and mortality, were significantly alleviated by GZFLW in mice. Mechanistically, GZFLW restrained the caspase-dependent apoptosis, restored the antioxidant system, and decreased pro-inflammatory cytokine production in the livers of Con A-treated mice. Besides, GZFLW repressed the Con A-induced hepatic infiltration of inflammatory cells, splenic T cell activation, and splenomegaly in mice. ConclusionsOur findings demonstrate the applicable potential of GZFLW in treating AIH. It prompts further investigation of GZFLW as a treatment option for AIH and possibly other hepatic diseases.

Synthesis, Structure, Properties, and Applications of Fluorinated Polyurethane.

Fluorinated polyurethane (FPU) is a new kind of polyurethane (PU) material with great applicational potential, which is attributed to its high bond energy C-F bonds. Its unique low surface energy, excellent thermal stability, and chemical stability have attracted considerable research attention. FPU with targeted performance can be precisely synthesized through designing fluorochemicals as hard segments, soft segments, or additives and changes to the production process to satisfy the needs of coatings, clothing textiles, and the aerospace and biomedical industries for materials that are hydrophobic and that are resistant to weathering, heat, and flames and that have good biocompatibility. Here, the synthesis, structure, properties, and applications of FPU are comprehensively reviewed. The aims of this research are to shed light on the design scheme, synthesis method, structure, and properties of FPU synthesized from different kinds of fluorochemicals and their applications in different fields and the prospects for the future development of FPU.

Playful personnel selection: The use of traditional versus game‐related personnel selection methods and their perception from the recruiters’ and applicants’ perspectives

AbstractGame‐related assessments (GRAs) are increasingly gaining interest in practice and research. However, little is known about how recruiters view these methods. This study examined recruiters’ (N = 158) reasons for using GRAs, the influence of their characteristics on their use of these methods, and recruiters’ perceptions of GRAs compared to those of potential applicants (N = 144). Results indicated that perceived ease of use, perceived usefulness, innovativeness in IT, and video game pursuit partly explained recruiters’ intentions to use GRAs, which then predicted actual usage. The expected positive impact on the organizational image and applicant reactions were among the most important reasons for using GRAs. Recruiters and potential applicants perceived GRAs as more enjoyable and modern than traditional assessment methods.

Real-time Attentive Dilated U-Net for Extremely Dark Image Enhancement

Images taken under low-light conditions suffer from poor visibility, color distortion and graininess, all of which degrade the image quality and hamper the performance of downstream vision tasks, such as object detection and instance segmentation in the field of autonomous driving, making low-light enhancement an indispensable basic component of high-level visual tasks. Low-light enhancement aims to mitigate these issues, and has garnered extensive attention and research over several decades. The primary challenge in low-light image enhancement arises from the low signal-to-noise ratio (SNR) caused by insufficient lighting. This challenge becomes even more pronounced in near-zero lux conditions, where noise overwhelms the available image information. Both traditional image signal processing (ISP) pipeline and conventional low-light image enhancement methods struggle in such scenarios. Recently, deep neural networks have been used to address this challenge. These networks take unmodified RAW images as input and produce the enhanced sRGB images, forming a deep learning-based ISP pipeline. However, most of these networks are computationally expensive and thus far from practical use. In this paper, we propose a lightweight model called attentive dilated U-Net (ADU-Net) to tackle this issue. Our model incorporates several innovative designs, including an asymmetric U-shape architecture, dilated residual modules (DRMs) for feature extraction, and attentive fusion modules (AFMs) for feature fusion. The DRMs provide strong representative capability while the AFMs effectively leverage low-level texture information and high-level semantic information within the network. Both modules employ a lightweight design but offer significant performance gains. Extensive experiments demonstrate our method is highly-effective, achieving an excellent balance between image quality and computational complexity, i . e ., taking less than 4ms for a high-definition 4K image on a single GTX 1080Ti GPU and yet maintaining competitive visual quality. Furthermore, our method exhibits pleasing scalability and generalizability, highlighting its potential for widespread applicability.

SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model

Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges, for better generating high-quality and precisely controllable spherical panoramic images. For the spherical distortion characteristic, we embed the semantics of the distorted object with text encoding, then explicitly construct the relationship with text-object correspondence to better use the pre-trained knowledge of the planar images. Meanwhile, we employ a deformable technique to mitigate the semantic deviation in latent space caused by spherical distortion. For the spherical geometry characteristic, in virtue of spherical rotation invariance, we improve the data diversity and optimization objectives in the training process, enabling the model to better learn the spherical geometry characteristic. Furthermore, we enhance the denoising process of the diffusion model, enabling it to effectively use the learned geometric characteristic to ensure the boundary continuity of the generated images. With these specific techniques, experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average.

Interactions of monolayer molybdenum disulfide sheets with metalloid antimony in aquatic environment: Adsorption, transformation, and joint toxicity

The tremendous application potentiality of transitional metal dichalcogenides (TMDs), such as molybdenum disulfide (MoS2) nanosheets, will unavoidably lead to increasing release into the environment, which could influence the fate and toxicity of co-existed contaminants. The present study discovered that 59.8 % of trivalent antimony [Sb(III)] was transformed by MoS2 to pentavalent Sb [Sb(V)] in aqueous solutions under light illumination, which was due to hole oxidation on the nanosheet surfaces. A synergistic toxicity between MoS2 and Sb(III, V) to algae (Chlorella vulgaris) was observed, as demonstrated by the lower median-effect concentrations of MoS2 + Sb(III)/Sb(V) (13.1 and 20.9 mg/L, respectively) than Sb(III)/Sb(V) (38.8 and 92.5 mg/L, respectively) alone. Particularly, MoS2 at noncytotoxic doses notably increased the bioaccumulation of Sb(III, V) in algae, causing aggravated oxidative damage, photosynthetic inhibition, and structural alterations. Metabolomics indicated that oxidative stress and membrane permeabilization were primarily associated with down-regulated amino acids involved in glutathione biosynthesis and unsaturated fatty acids. MoS2 co-exposure remarkably decreased the levels of thiol antidotes (glutathione and phytochelatins) and aggravated the inhibition on energy metabolism and ATP synthesis, compromising the Sb(III, V) detoxification and efflux. Additionally, extracellular P was captured by the nanosheets, also contributing to the uptake of Sb(V). Our findings emphasized the nonignorability of TMDs even at environmental levels in affecting the ecological hazard of metalloids, providing insight into comprehensive safety assessment of TMDs.

Enhanced co-design and evaluation of a collective robotic construction system for the assembly of large-scale in-plane timber structures

Collective robotic construction (CRC) is an emerging approach to construction automation based on the collaboration among teams of small mobile robots. This paper enhances an existing modular CRC system, showcasing its capability to assemble full-scale in-plane timber structures. Utilizing strategies of co-design, the robotic actuators were updated to accommodate material tolerance in the passive building material, timber struts, which they use for locomotion and assemble into structures. A custom effector was also developed to establish architecturally relevant, structural connections between struts. The enhancements address two major challenges in CRC system development: material tolerance and connections. To showcase these research findings and compare with other construction automation systems, a reinforced slab structure was assembled. With a building envelop of 0.485 m3, this is one of the largest structures assembled by a CRC system. As such, the work highlights the potential applicability of CRC in real-world architectural projects.

Theory-Guided Feature Selection in Cybercrime Data Science

Cybercrime data science is being significantly hampered by the presence of 'noisy' features within vast and complex datasets. We draw from the theoretical insights of the behavioural sciences to propose a feature selection model to enrich and improve the value and interpretability of cybercrime intelligence datasets. We piloted our theory-guided feature selection approach on a subset of intelligence datafeeds provided by a global fraud and cybercrime tracking firm. The results of the proposed social influence feature selection model show significant improvement in the interpretability of the machine learning-based exploratory analysis and advanced visualization techniques in an experimental setting. The feature selection model yielded rich insights about cybercriminal psychological tactics from social engineering scam data and has potential applicability in the areas of cyberthreat response and cybersecurity awareness training. Our study shows the value of an interdisciplinary theory-guided approach to cybercrime data analytics that integrates scientific knowledge from the behavioural sciences and data science expertise. Our paper concludes by suggesting avenues for future research on theory-guided feature selection seeking to incorporate behavioural science knowledge in cybercrime data science. We intend to refine, automate, evaluate, and scale our model in future research to assess its effectiveness in producing insights about cybercriminal activities and informing decision-making in a naturalistic and real-time setting. In future research efforts, we aim to automate the encoding of features and apply a wider range of machine learning tools and evaluation metrics to extract more meaningful insights into cybercriminal psychological tactics. We also intend to refine our model on larger datasets to enhance its efficiency and responsiveness to real-time cybercrime data.  We call on data scientists and cybercrime domain experts to work together to apply theory-guided feature selection to improve processes of knowledge discovery that enhance our cybersecurity capabilities.

Increased reactive carboxyl and free alfa-amino groups from fish type I collagen peptides by Alcalase® hydrolysis exhibit higher antibacterial and antioxidant activities

Abstract This study aimed to generate low molecular weight peptides (LMWP) from fish collagen type I hydrolysed by increasing activity of Alcalase® from 0.0 to 12.0 (AU-A per 100 g) at 55 °C and 7.4 of pH for 3 h 40 min. The results showed that all enzyme activity levels caused 34–55 % reductions in protein recovery, 1.0–3.0 folds’ increase in free α-amino groups and 1.7–3.2 folds’ increase in carboxyl groups. Degree of hydrolysis ranged from 20 to 30 % with increasing enzyme activity. The number average molecular weight significantly reduced from 3200 g/mol in 0.0 AU-A per 100 g enzyme activity to 1151, 1398, 1175, 1040 and 1246 g/mol in 2.4, 4.8, 7.2, 9.6 and 12.0 AU-A per 100 g enzyme activities, respectively. Depending upon enzyme activity level, the produced LMWP with reactive carboxyl and amino end-groups exhibited 2.5- to 4.0-fold increases in antioxidant capacity and 1.0–3.5 log cfu/ml inhibition of four pathogen bacteria. Highest inhibition of 2.5 log cfu/ml in Escherichia coli was obtained from 2.4 AU-A per 100 g enzyme activity and 3.5 log cfu/ml in Listeria monocytogenes from 9.6 and 12.0 AU-A per 100 g enzyme activity levels. Infrared spectroscopy clearly identified reactive end-groups and showed remarkably differences in molar absorptivity of various molecular regions between non-enzyme and enzyme treated collagen type I molecule. A 9.6 and 12.0 AU-A per 100 g enzyme activity levels were found optimally effective to generate LMWP. In conclusion, LMWP exhibited high antioxidant and antibacterial activity due to increased functional reactive end-groups, and these bio-active peptides may have greater potentialities in various food and pharmaceutical applications.