Wide Variety Research Articles

Guiding Principles for the Design of a Chemical Vapor Deposition Process for Highly Crystalline Transition Metal Dichalcogenides

2D transition metal dichalcogenides (TMDs) for advanced logic transistor technologies are deposited by various modifications of the chemical vapor deposition (CVD) method using a wide variety of precursors. Being a major electrical performance limiter, the TMD crystal grain size strongly differs between the various CVD precursor chemistries from nano‐ to millimeter‐sized crystals. However, it remains unclear how the CVD precursor chemistry affects the nucleation density and resulting TMD crystal grain size. This work postulates guiding principles to design a CVD process for highly crystalline TMD deposition using a quantitative analytical model benchmarked against the literature. The TMD nucleation density reduces favorably under low supersaturation conditions where the metal precursor sorption on the starting surface is reversible and the corresponding metal precursor desorption rate exceeds the overall deposition rate. Such reversible precursor adsorption guarantees efficient long‐range gas‐phase lateral diffusion of precursor species in addition to short‐range surface diffusion, which vitally increases crystal grain size. The proposed model explains the large spread in experimentally observed TMD nucleation densities and crystal grain sizes for state‐of‐the‐art CVD chemistries. Ultimately, it empowers the reader to interpret and modulate precursor adsorption and diffusion reactions through designing CVD precursor chemistries.

PIEZO ion channels: force sensors of the interoceptive nervous system.

Many organs are designed to move: the heart pumps each second, the gastrointestinal tract squeezes and churns to digest food, and we contract and relax skeletal muscles to move our bodies. Sensory neurons of the peripheral nervous system detect signals from bodily tissues, including the forces generated by these movements, to control physiology. The processing of these internal signals is called interoception, but this is a broad term that includes a wide variety of both chemical and mechanical sensory processes. Mechanical senses are understudied, but rapid progress has been made in the last decade, thanks in part to the discovery of the mechanosensory PIEZO ion channels (Coste etal., 2010). The role of these mechanosensors within the interoceptive nervous system is the focus of this review. In defining the transduction molecules that govern mechanical interoception, we will have a better grasp of how these signals drive physiology.

MONITORING OF GLACIERS ON HORSESHOE ISLAND, ANTARCTICA BASED ON A DEEP LEARNING APPROACH FROM HIGH-RESOLUTION ORTHOPHOTOS (TAE-6 & TAE-7)

Abstract. Global climate change is a phenomenon that seriously affects the balance of a wide variety of ecosystems and is the intense focus of climate scientists and environmental researchers. In this context, periodic monitoring of glacier areas in terms of a better understanding of atmosphere-ocean interactions; thus, predicting the effects of climate change and planning against future threats by evaluating environmental impacts is an important research area. Especially the polar regions, where the melting of glaciers and the rise of sea levels are visibly observed, are important for climate scientists in providing crucial observations to understand and predict global climate change. In this study, within the scope of the international bilateral cooperation project carried out in cooperation with Istanbul Technical University (ITU) and the Bulgarian Academy of Sciences (BAS) (Project No: 121N033), the spatial changes in snow/glacier areas obtained from UAV Photogrammetry products generated during the 6th and 7th Antarctic National Science Expeditions. Snow/glacier areas were segmented with the K-Net deep learning approach which has been previously tested for accuracy and provides glacier mapping with accuracy metrics over 99%, on the high spatial resolution orthophotos produced during the two periods. The snow/glacier areas difference between the two periods were calculated and compared and water bodies which are critical areas, were specifically examined. The result of this comparison shows that the glacier area decreased by approximately 11% in just 1 year. However, to better understand these changes in snow/glacier areas, the region needs to be observed closely for longer time periods. It is thought that future studies will contribute to efforts to manage global environmental impacts and cope with climate change by focusing on monitoring and better understanding changes in these critical regions.

A General Model of Impact Sensitivity for Nitrogen-Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study.

In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R2≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R2≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.

GNAS AS2 methylation status enables mechanism-based categorization of pseudohypoparathyroidism type 1B.

Pseudohypoparathyroidism type 1B (PHP1B) results from aberrant genomic imprinting at the GNAS gene. Defining the underlying genetic cause in new patients is challenging because various genetic alterations (e.g., deletions, insertions) within the GNAS genomic region, including the neighboring STX16 gene, can cause PHP1B, and the genotype-epigenotype correlation has not been clearly established. Here, by analyzing patients with PHP1B with a wide variety of genotypes and epigenotypes, we identified a GNAS differentially methylated region (DMR) of distinct diagnostic value. This region, GNAS AS2, was hypomethylated in patients with genetic alterations located centromeric but not telomeric of this DMR. The AS2 methylation status was captured by a single probe of the methylation-sensitive multiplex ligation-dependent probe amplification (MS-MLPA) assay utilized to diagnose PHP1B. In human embryonic stem cells, where NESP55 transcription regulates GNAS methylation status on the maternal allele, AS2 methylation depended on 2 imprinting control regions (STX16-ICR and NESP-ICR) essential for NESP55 transcription. These results suggest that the AS2 methylation status in patients with PHP1B reflects the position at which the genetic alteration affects NESP55 transcription during an early embryonic period. Therefore, AS2 methylation levels can enable mechanistic PHP1B categorization based on genotype-epigenotype correlation and, thus, help identify the underlying molecular defect in patients.

An entropic theory of homogeneous ice nucleation in non-ionic aqueous solutions.

The nucleation of ice from aqueous solutions is a process essential to myriad environmental and industrial processes, but the physical factors affecting the capacity of different solutes to depress the homogeneous nucleation temperature of ice are yet poorly understood. In this work, we demonstrate that for many binary aqueous solutions of non-ionic solutes, this depression is dominated by the entropy of the liquid phase. Employing the classic Turnbull interpretation of the interfacial free energy γ∼TSliquid-Ssolid and estimating solution entropies with a Flory-style modification of the ideal entropy of mixing that accounts for solute size effects, we demonstrate that mixing entropy alone predicts experimental homogeneous nucleation temperatures across a wide variety of non-ionic solutions. We anticipate that this physical insight will not only enhance a fundamental understanding of homogeneous nucleation processes across fields but also open new avenues to the rational design of aqueous solutions for desired nucleation behaviors.

Biodegradable Honeycomb-Mimic Scaffolds Consisting of Nanofibrous Walls.

The scaffold is a porous three-dimensional (3D) material that supports cell growth and tissue regeneration. Such 3D structures should be generated with simple techniques and nontoxic ingredients to mimic bio-environment and facilitate tissue regeneration. In this work, simple but powerful techniques are demonstrated for the fabrication of lamellar and honeycomb-mimic scaffolds with poly(L-lactic acid). The honeycomb-mimic scaffolds with tunable pore size ranging from 70 to 160µm are fabricated by crystal needle-guided thermally induced phase separation in a directional freezing apparatus. The compressive modulus of the honeycomb-mimic scaffold is ≈4 times higher than that of scaffold with randomly oriented pore structure. The fabricated honeycomb-mimic scaffold exhibits a hierarchical structure from nanofibers to micro-/macro-tubular structures. Pre-osteoblast MC3T3-E1 cells cultured on the honeycomb-mimic nanofibrous scaffolds exhibit an enhanced osteoblastic phenotype, with elevated expression levels of osteogenic marker genes, than those on either porous lamellar scaffolds or porous scaffolds with randomly oriented pores. The advanced techniques for the fabrication of the honeycomb-mimic structure may potentially be used for a wide variety of advanced functional materials.

Effect of egg production dynamics on the functional response of two parasitoids.

Functional response describes the number of hosts attacked by a parasitoid in relation to host densities and plays an important role by connecting behavioral-level processes with community-level processes. Most functional response studies were carried out using simple experimental designs where the insects were confined to a plain and small arena with different host densities during a fixed period of time. With these designs, other factors that might affect the functional response of parasitoids were not analyzed, such as fecundity, age, and experience. We proposed a series of latent-variables Markovian models that comprised an integrated approach of functional response and egg production models to estimate the realized lifetime reproductive success of parasitoids. As a case study, we used the parasitoids Anagyrus cachamai and A. lapachosus (Hymenoptera: Encyrtidae), two candidate agents for neoclassical biocontrol of the Puerto Rican cactus pest mealybug, Hypogeococcus sp. (Hemiptera: Pseudococcidae). The tested species were assessed according to their physiology and prior experience. We estimated the number of mature eggs after emergence, egg production on the first day, egg production rate, the proportion of eggs resorbed, egg resorption threshold, and egg storage capacity. Anagyrus cachamai and A. lapachosus both presented a type III functional response. However, the two parasitoids behaved differently; for A. cachamai, the number of parasitized hosts decreased with female age and depended on the number of mature eggs that were available for oviposition, whereas A. lapachosus host parasitism increased with female age and was modulated by its daily egg load and previous experience. The methodology presented may have large applicability in pest control, invasive species management, and conservation biology, as it has the potential to increase our understanding of the reproductive biology of a wide variety of species, ultimately leading to improved management strategies.

Bidirectional graphics-based digital twin framework for quantifying seismic damage of structures using deep learning networks

Tremendous effort has been devoted toward developing automated post-earthquake inspection techniques, including automated image collection and damage identification. However, few studies have attempted to establish the complex relationship between visible damage and structural conditions. Moreover, the lack of training data further hinders the potential use of deep learning algorithms. This paper proposes a framework, termed Bidirectional Graphics-based Digital Twin (Bi-GBDT), that allows for assessment of the structural condition based on post-earthquake photographic surveys. The procedure contains two parts: (i) A GBDT of a target structure, containing a computer graphics model and a finite element (FE) model, is developed (ii) Synthetic data subsequently is generated from the GBDT to train neural networks to predict the damage measures and structural conditions. To demonstrate the proposed approach, a seismically-designed reinforced concrete shear wall is considered. First, the GBDT of the shear wall is constructed and calibrated so that the associated damage patterns simulated by the corresponding FE model match well with the experimental results. Next, synthetic images of the damage patterns are created from the validated GBDT, along with the corresponding structural damage measures, and used to train Residual Neural Network and Conditional Generative Adversarial Networks to determine the maximum drift, the stress/strain fields and the structural condition. The neural networks trained on synthetic data are shown to perform well for the experimental data, confirming the proposed approach. Subsequently, the neural networks are tested on the synthetic data for a wide variety of loading conditions to demonstrate the robustness of the approach. In addition, the realistic images rendered from the GBDT are utilized as input to predict the structural condition, showcasing the comprehensive Bi-GBDT framework. These results demonstrate the efficacy of the proposed approach to generate accurate digital twins and point toward its future application for development of automated post-earthquake assessment strategies.

Situation Awareness Oriented Design: Review and Future Directions

Situation awareness (SA) is critical for decision making and performance in a wide variety of fields. It is quite challenging to achieve however, due to information overload, poorly integrated technologies, system complexity, workload, and automation. SA-oriented design (SAOD) was developed as a systematic methodology for addressing these challenges. The goal of the current paper is to review the contribution of SAOD and its usefulness for system design. SAOD is summarized and compared to other design approaches. An example of SAOD applied to command and control is provided. Results of SAOD applications are reviewed and directions for further development of SAOD are presented.

Opportunities and Perspectives for Three Dimensional Culture of Mesenchymal Stem Cell-Derived Exosomes.

Exosomes are nanosized extracellular vesicles (EVs) that participate in intercellular communication through surface proteins and the delivery of internal cargo. The exosomes have gained attention for their potential as disease biomarkers and therapeutic agents. The therapeutic ability of exosomes has been verified by copious previous studies. Effective methods for extensive clinical applications are being researched for exosome-based regenerative therapies, including the application of 3D cultures to enhance exosome production and secretion, which can resolve limited exosome secretion from the parent cells. Cell culture has emerged as a crucial approach for biomedical research because of its many benefits. Both well-established continuous cell lines and primary cell cultures continue to be invaluable for basic research and clinical application. Previous studies have shown that three-dimensional cultured exosomes (3D-Exo) improve therapeutic properties and yields compared with traditional culture systems. Since the majority of studies have focused on exosomes derived from mesenchymal stem cells (MSC-Exo), this review will also concentrate on MSC-Exo. In this review, we will summarize the advantages of 3D-Exo and introduce the 3D culture system and methods of exosome isolation, providing scientific strategies for the diagnosis, treatment, and prognosis of a wide variety of diseases.

SPIRIT: Style-guided Patch Interaction for Fashion Image Retrieval with Text Feedback

Fashion image retrieval with text feedback aims to find the target image according to the reference image and the modification from the user. This is a challenging task, as it requires not only the synergistic understanding of both visual and textual modalities but also the ability to model a wide variety of styles that fashion images contain. Hence, the crucial aspect of addressing this problem lies in exploiting the abundant semantic information inherent in fashion images and correlating it with the textual description of style. Recognizing that style is generally situated at the local level, we explicitly define style as the commonalities and differences between local areas of fashion images. Building upon this, we propose a Style-guided Patch InteRaction approach for fashion Image retrieval with Text feedback (SPIRIT), which focuses on the decisive influence of local details of fashion images on their style. Three corresponding networks are designed pertinently. The Patch-level Style Commonality network is introduced to fully leverage the semantic information among patches and compute their average as the style commonality. Subsequently, the Patch-level Style Difference network employs a graph reasoning network to model the patch-level difference and filter out insignificant patches. By considering the above two networks, mutual information about style is obtained from the interaction between patches. Finally, the Visual Textual Fusion network is utilized to integrate visual features with rich semantic information and textual features. Experimental results on four benchmark datasets demonstrate that our proposed SPIRIT achieves state-of-the-art performance. Source code is available at https://github.com/PKU-ICST-MIPL/SPIRIT_TOMM2024 .

Incorporating Diversity, Equity, and Inclusion Content Into Bioengineering Curricula: A Program-Level Approach.

Diversity, equity, and inclusion (DEI) are interconnected with bioengineering, yet have historically been absent from accreditation standards and curricula. Toward educating DEI-competent bioengineers and meeting evolving accreditation requirements, we took a program-level approach to incorporate, catalog, and assess DEI content through the bioengineering undergraduate program. To support instructors in adding DEI content and inclusive pedagogy, our team developed a DEI planning worksheet and surveyed instructors pre- and post-course. Over the academic year, 74% of instructors provided a pre-term and/or post-term response. Of responding instructors, 91% described at least one DEI curricular content improvement, and 88% incorporated at least one new inclusive pedagogical approach. Based on the curricular adjustments reported by instructors, we grouped the bioengineering-related DEI content into five DEI competency categories: bioethics, inclusive design, inclusive scholarship, inclusive professionalism, and systemic inequality. To assess the DEI content incorporation, we employed direct assessment via course assignments, end-of-module student surveys, end-of-term course evaluations, and an end-of-year program review. When asked how much their experience in the program helped them develop specific DEI competencies, students reported a relatively high average of 3.79 (scale of 1 = "not at all" to 5 = "very much"). Additionally, based on student performance in course assignments and other student feedback, we found that instructors were able to effectively incorporate DEI content into a wide variety of courses. We offer this framework and lessons learned to be adopted by programs similarly motivated to train DEI-competent engineering professionals and provide an equitable, inclusive engineering education for all students.

Streptolysin S is required for Streptococcus pyogenes nasopharyngeal and skin infection in HLA-transgenic mice.

Streptococcus pyogenes is a human-specific pathogen that commonly colonizes the upper respiratory tract and skin, causing a wide variety of diseases ranging from pharyngitis to necrotizing fasciitis and toxic shock syndrome. S. pyogenes has a repertoire of secreted virulence factors that promote infection and evasion of the host immune system including the cytolysins streptolysin O (SLO) and streptolysin S (SLS). S. pyogenes does not naturally infect the upper respiratory tract of mice although mice transgenic for MHC class II human leukocyte antigens (HLA) become highly susceptible. Here we used HLA-transgenic mice to assess the role of both SLO and SLS during both nasopharyngeal and skin infection. Using S. pyogenes MGAS8232 as a model strain, we found that an SLS-deficient strain exhibited a 100-fold reduction in bacterial recovery from the nasopharynx and a 10-fold reduction in bacterial burden in the skin, whereas an SLO-deficient strain did not exhibit any infection defects in these models. Furthermore, depletion of neutrophils significantly restored the bacterial burden of the SLS-deficient bacteria in skin, but not in the nasopharynx. In mice nasally infected with the wildtype S. pyogenes, there was a marked change in localization of the tight junction protein ZO-1 at the site of infection, demonstrating damage to the nasal epithelia that was absent in mice infected with the SLS-deficient strain. Overall, we conclude that SLS is required for the establishment of nasopharyngeal infection and skin infection in HLA-transgenic mice by S. pyogenes MGAS8232 and provide evidence that SLS contributes to nasopharyngeal infection through the localized destruction of nasal epithelia.

Pt(II)/Pd(II)-Based Metallosupramolecular Architectures as Light Harvesting Systems and their Applications.

The development of artificial light-harvesting systems mimicking the natural photosynthesis method is an ever-growing field of research. Numerous systems such as polymers, metal complexes, POFs, COFs, supramolecular frameworks etc. have been fabricated to accomplish more efficient energy transfer and storage. Among them, the supramolecular coordination complexes (SCCs) formed by non-covalent metal-ligand interaction, have shown the capacity to not only undergo single and multistep energy migration but also to utilize the harvested energy for a wide variety of applications such as photocatalysis, tunable emissive systems, encrypted anti-counterfeiting materials, white light emitters etc. This review sheds light on the light-harvesting behavior of both the 2D metallacycles and 3D metallacages where design ingenuity has been executed to afford energy harvesting by both donor ligands as well as metal acceptors.

Pt(II)/Pd(II)‐Based Metallosupramolecular Architectures as Light Harvesting Systems and their Applications

AbstractThe development of artificial light‐harvesting systems mimicking the natural photosynthesis method is an ever‐growing field of research. Numerous systems such as polymers, metal complexes, POFs, COFs, supramolecular frameworks etc. have been fabricated to accomplish more efficient energy transfer and storage. Among them, the supramolecular coordination complexes (SCCs) formed by non‐covalent metal‐ligand interaction, have shown the capacity to not only undergo single and multistep energy migration but also to utilize the harvested energy for a wide variety of applications such as photocatalysis, tunable emissive systems, encrypted anti‐counterfeiting materials, white light emitters etc. This review sheds light on the light‐harvesting behavior of both the 2D metallacycles and 3D metallacages where design ingenuity has been executed to afford energy harvesting by both donor ligands as well as metal acceptors.

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives

Recommender systems research and practice are fast-developing topics with growing adoption in a wide variety of information access scenarios. In this article, we present an overview of research specifically focused on the evaluation of recommender systems. We perform a systematic literature review, in which we analyze 57 papers spanning six years (2017–2022). Focusing on the processes surrounding evaluation, we dial in on the methods applied, the datasets utilized, and the metrics used. Our study shows that the predominant experiment type in research on the evaluation of recommender systems is offline experimentation and that online evaluations are primarily used in combination with other experimentation methods, e.g., an offline experiment. Furthermore, we find that only a few datasets (MovieLens, Amazon review dataset) are widely used, while many datasets are used in only a few papers each. We observe a similar scenario when analyzing the employed performance metrics—a few metrics are widely used (precision, normalized Discounted Cumulative Gain, and Recall), while many others are used in only a few papers. Overall, our review indicates that beyond-accuracy qualities are rarely assessed. Our analysis shows that the research community working on evaluation has focused on the development of evaluation in a rather narrow scope, with the majority of experiments focusing on a few metrics, datasets, and methods.

Identification of critical industry skills in an industrial hygiene graduate training program.

Graduates of industrial hygiene training (IH) programs must be able to meet continuously evolving health and safety needs in a wide variety of occupational settings. Therefore, academic IH graduate programs must regularly evaluate their curricula and solicit input from industry professionals to make curricular changes that will better prepare their students for professional roles in industry. The purpose of this study was to identify the training gaps that existed between industry needs and the current curriculum for a United States-accredited IH graduate training program. The research team facilitated two group interviews with the IH program advisory board, collected alumni survey data, and performed a qualitative analysis to identify skills gaps/needs for the IH Program graduates. The research team identified 3 themes from participant interviews and alumni surveys (technical, applied, and essential skills) and selected several skills within each theme that interview participants thought were necessary proficiencies for junior IH professionals. The skills identified in the qualitative interview and survey data can be incorporated into the curriculum to improve the training of IH graduate students. Additionally, by using qualitative analysis, the researchers uncovered essential skills previously unidentified in IH needs assessments, providing valuable information for all IH graduate programs.

Adopting a reductionist approach to advance acoustic deterrents in fish conservation

IntroductionDeterrents that use acoustics to guide fish away from dangerous areas (e.g., water intakes) depend on the elicitation of avoidance in the target species. Background noise is often neglected when testing acoustic deterrents, but it is important to account for its effects as freshwater environments present a wide variety of ambient soundscapes.MethodsUsing the widely studied goldfish (Carassius auratus) as a suitable experimental model, this study adopted a reductionist approach to investigate the relationship between the startle response to a pure tone signal and background noise. Under laboratory conditions, the startle responses of individual goldfish exposed to 120 ms tones at 250 Hz and four Sound Pressure Levels (SPLs: 115, 125, 135, 145 dB re 1 μPa) were quantified in the presence (treatment) and absence (ambient - control) of band-limited random noise (105 dB re 1 μPa).Results and discussionWhen observing the dose-response relationship, the proportion of fish that startled to the signal increased with SPL in both the treatment and control, although there was no difference between them, suggesting that the signal-to-noise ratio was not influential under the conditions tested. However, further analysis using Signal Detection Theory indicated that the was higher in the noisy treatment than the control when responding to both false alarms (startle during a pre-signal period) and hits (startle to an external stimulus during the pre-signal period). Furthermore, fish were better able to discriminate (d?) external stimuli over time (during the pre-signal period) in the treatment than control. There is a need to consider the role of background noise when designing acoustic fish deterrents that depend on the exhibition of avoidance behaviors.