Browse Articles

Student perceptions of COVID-19 challenges affecting student motivation, well-being, and success in undergraduate education

Our objectives in this study were to understand the impact of COVID-19 disruptions on the academic and personal experiences of undergraduate students at a state land-grant institution in the Western United States, and to use those insights to identify actionable ways to improve student success. We used a mixed method survey to assess strategies used by undergraduates to adapt to COVID-19 disruptions. Results revealed that despite challenges, the majority of students continued toward their academic goals. Face-to-face classes yielded the greatest student satisfaction, and students reported great dissatisfaction with separation from peers and instructors. These insights will be especially helpful to educators and administrators in responding to future challenges and planning future approaches. This overview of students’ attitudes associated with moving from in-person to online coursework may also be useful for advising students considering which of these instructional paradigms to pursue.

First-principles computational analysis of the electronic and charge transport anisotropy of NbO<i>X</i>2 (<i>X</i> <b>=</b> Cl, Br, I) nanoribbons and nanosheets

The use of NbOX2 oxyhalide (X = Cl, Br, I) nanoribbons and nanosheets in next-generation nanoelectronic devices remains unfulfilled because the impact of the fundamental electronic properties of these materials on their practical device applications remains poorly understood. The present work applies first-principles density functional theory calculations to investigate the anisotropic electronic properties and quantum confinement effects of NbOX2 nanoribbons and nanosheets and their performance in field effect transistors. Our results reveal direction-dependent electron transport behaviors, with the most efficient transport occurring along the Nb-X axis. Quantum confinement in nanoribbons leads to bandgap widening, with Nb–O-oriented nanoribbons exhibiting more stable electronic properties. In addition, charge delocalization is confirmed along the Nb-X axis, and it strengthens with increasing halogen constituent mass.

Atlas of proteomic signatures of brain structure and its links to brain disorders

Abstract Individual variation in brain structure influences deterioration due to disease and comprehensive profiling of the associated proteomic signature advances mechanistic understanding. Here, using data from 4997 UK Biobank participants, we analyzed the associations between 2920 plasma proteins and 272 neuroimaging-derived brain structure measures. We identified 5358 associations between 1143 proteins and 256 brain structure measures, with NCAN and LEP proteins showing the most associations. Functional enrichment implicated these proteins in neurogenesis, immune/apoptotic processes and neurons. Furthermore, bidirectional Mendelian randomization revealed 33 associations between 32 proteins and 23 brain structure measures, and 21 associations between nine brain structure associated proteins and ten brain disorders. Moreover, the significant associations between the identified proteins and mental health were mediated by brain volume and surface area. In summary, this study generates a comprehensive atlas mapping the patterns of association between proteome and brain structure, highlighting their potential value for studying brain disorders.

Correlation analysis of mitochondrial DNA maintenance-related genes with HCC prognosis, tumor mutation burden and tumor microenvironment features

Background Mitochondrial DNA (mtDNA) is an important genetic material in eukaryotic cells. Mitochondrial DNA maintenance-related gene (mtDNA MRG) variants contribute to mitochondrial dysfunction in cancer progression and are associated with cancer prognosis. However, the mechanism of mtDNA MRGs in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) remains unclear. Methods Data for a total of 487 HCC samples were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The mitochondrial regulatory pathway gene set was downloaded, and 22 mtDNA MRGs were identified by screening. Based on these 22 genes, the HCC samples were grouped by unsupervised clustering based on a machine learning model. Principal component analysis (PCA) was used to construct the mtDNA score model, and the relationships between the mtDNA score and clinicopathological features, tumor mutation burden (TMB), TME cell infiltration and biological processes were analyzed. Results The expression of 22 mtDNA MRGs significantly different in HCC samples vs. normal controls. In this study, HCC samples were divided into three molecular subtypes based on the expression of mtDNA MRGs. The three subtypes exhibit different clinical characteristics and immune infiltration profiles, and the cell infiltration profiles corresponded to the immune rejection, immune inflammation, and immune-desert phenotypes, respectively. A total of 740 core genes were obtained from different molecular subtypes, and these genes were divided into three gene subtypes. The mtDNA score model, which can be used to assess tumor immune cell invasion, clinicopathological features, genetic variation, and prognosis, was subsequently constructed. A high mtDNA score was associated with a high mutation burden, high clinical stage and poor prognosis. Conclusions mtDNA MRGs play important roles in HCC TMB, prognosis, clinicopathological features and the immune microenvironment. The mtDNA score can be used to evaluate HCC prognosis, TMB and the immune microenvironment, thereby providing guidance for treatment decision making and prognosis prediction in HCC patients.

Low-power optoelectronic synaptic devices with ZnMgO in deep-ultraviolet encryption computation

The realization of vision-based neuromorphic computing relies significantly on advancements in optoelectronic synaptic chip technology. Currently, the development of optoelectronic devices is mainly limited by high power consumption due to high bias voltage and low recognition rates caused by the background noise. A low-power deep-ultraviolet optoelectronic synapse device is developed by doping Mg into zinc oxide to modulate oxygen-vacancy defects. Specifically, the synaptic behavior still has excellent persistent photoconductivity response at 12 mV bias, and the low single synaptic energy consumption is 2.34 pJ. Meanwhile, the artificial neural network of the device is constructed according to the excitation and inhibition characteristics, and the recognition rate of handwritten digits is as high as 95.41%. In addition, on the basis of demonstrating ultraviolet image visual learning and memory, the device provides an encryption algorithm verification array integrating sensing and storage with energy consumption as low as 20 nJ. This low-power and strong anti-interference deep-ultraviolet optoelectronic synapse device supports the development of high-performance visual neural-state computing.

An unbiased tissue transcriptome analysis identifies potential markers for skin phenotypes and therapeutic responses in atopic dermatitis

Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting

LSTM (Long Short-Term Memory Network) is currently extensively utilized for forecasting financial time series, primarily due to its distinct advantages in separating the long-term from the short-term memory information within a sequence. However, the experimental results presented in this paper indicate that LSTM may struggle to clearly differentiate between these two types of information. To overcome this limitation, we propose the ARMA-RNN-LSTM Hybrid Model, aimed at enhancing the separation between the long-term and short-term memory information on top of LSTM framework. The experiment in this paper is inspired by an observation: when LSTMs and RNNs are respectively used to forecast the same time series that contains only short-term memory information, LSTMs exhibit significantly lower forecasting accuracy than RNNs, and we attributed this to LSTMs potentially misclassifying some short-term memory information as long-term during forecasting process. Further, we speculate that this confusion might also arise when LSTMs are used to forecast the time series containing both the long-term and short-term memory information. To verify the aforementioned hypothesis and improve the forecasting accuracy for financial time series, this paper combines RNNs with LSTMs, proposing a method of ARMA-RNN-LSTM Hybrid Modelling, and conducts an experiment with stock index prices. Eventually, the experiment results show that the ARMA-RNN-LSTM Hybrid Model outperforms standalone RNNs and LSTMs in forecasting stock index series containing both long-term and short-term memory information, confirming that the ARMA-RNN-LSTM Hybrid Model has effectively enhanced the separation between the long-term and short-term memory information within sequence. This hybrid modelling approach has innovatively addressed the issue of the confusion between the long-term and the short-term memory information in a sequence during LSTM’s forecasting process, improving the accuracy of forecasting financial time series, and demonstrates that neural network’s forecasting errors is a area worth to explore in the future.

Computationally designing a dual-aspheric compound lens toward multi-depth endomicroscopy imaging

This study presents a computational approach for designing a compound lens system by pairing two infinity-corrected aspheric lenses with matching ray-mapping conditions to enable three-dimensional imaging over an extended volume with minimal aberrations. The surface profiles of each aspherical lens are derived by solving explicit differential equations, reducing dependence on prior optical design expertise. We explore different combinations of the aspheric lenses to create compound lenses that satisfy the Abbe sine or Herschel's condition, validating their tolerance to lateral and axial object point shifts. This computational design approach offers a low-barrier, cost-effective solution for rapidly fabricating miniature objectives and prototyping unconventional optical sectioning endomicroscope designs.

Selective targeting of genome amplifications and repeat elements by CRISPR-Cas9 nickases to promote cancer cell death

Abstract Focal gene amplification serves as an oncogenic driver during tumorigenesis and is a hallmark of many forms of cancer. Oncogene amplifications promote genomic instability, which is integral to cancer cell survival and evolution. However, focal gene amplification potentially affords an opportunity for therapeutic exploitation. As a proof-of-concept, we leverage CRISPR-Cas9 nickase to selectively promote cancer cell death in MYCN-amplified neuroblastoma in a gene amplification-dependent manner. Our analysis demonstrates that CRISPR-Cas9 nickase can generate a lethal number of highly toxic, replication-dependent double-strand breaks in cells harboring amplified loci. Furthermore, we demonstrate that Cas9 nickase—mediated toxicity can be modulated in combination with small molecule inhibitors targeting key regulators of the DNA-damage response or cell death pathways. Importantly, our findings in MYCN-amplified neuroblastoma translate to other cancer types with distinct oncogene amplifications.

Initial development of the Stress Monitoring and Response Tool (SMART): A holistic measure of stress following trauma

In the immediate aftermath of trauma exposure, individuals may experience an acute stress reaction (ASR). ASRs may be transient but for individuals operating in high-stakes occupations, these reactions can potentially endanger themselves and those around them. Thus, a better understanding of ASRs could facilitate development of early interventions that help prevent longer-term sequelae. Although existing measures (e.g., PCL-5, CAPS-5) target symptoms that occur in the weeks following trauma, they do not encompass the range of ASR symptoms identified in emerging research. Using data from a large-scale study conducted across emergency departments in the United States, we employed confirmatory factor analysis to identify survey items that sensitively assess ASR symptoms during the peri- and post-trauma phases. These analyses identified 23 core items that are appropriate for administration both immediately following trauma exposure and at later timepoints, as well as 11 supplementary items that can be added to the core items for assessment at later timepoints. Collectively, these items constitute the Stress Monitoring and Response Tool (SMART). Both the SMART Core Scale and the combined SMART Core Scale with Supplemental Items demonstrate good convergent and concurrent validity with several other measures of mental health, physical health, somatic symptoms, pain, and functioning. In addition, the SMART scale remains moderately-to-strongly correlated with multiple measures of symptoms and functional impairment at three months post-trauma. Consequently, the SMART can be used to assess individuals in clinical settings, predict trajectories of recovery, and inform tailoring of interventions across time. Future studies should be conducted to assess the potential utility of the SMART as a decision aid in high-intensity occupational contexts.

Surface Fermi level tuning in thin epitaxial Bi<i>y</i>Sb2−<i>y</i>Te3−<i>x</i>Se<i>x</i>/Si(111) films

The effects of chemical treatment and aging of BiySb2−yTe3−xSex 3D topological insulator (TI) thin films on the surface electronic structure, morphology, and transport properties have been studied. The surface conditions significantly affect the separation between the Dirac point (DP) and the Fermi level, which is evident in both photoemission and magnetotransport measurements and is explained by the band bending. Depending on the Fermi level's position relative to the DP, the contribution of topological surface states to the nonlinear Hall effect can be solely controlled by the antilocalization effect or enhanced by the effect of gap opening. The wide tuning of the surface Fermi level by the surface/interface modification could be utilized in metal–insulator–semiconductor structures based on a TI thin films.

Microbial potential to mitigate neurotoxic methylmercury accumulation in farmlands and rice

Electrolyte Chemistry Development for Sodium‐Based Batteries: A Blueprint from Lithium or a Step Toward Originality?

AbstractCurrently, electrolyte design for sodium‐based batteries is largely inherited from their lithium‐based counterparts, which often present critical challenges that hinder forging new perspectives and thus further improvements. This work delves into the key properties of representative sodium‐ and lithium‐based electrolytes, encompassing prevailing salt anions. It aims to evaluate the impact of cation chemistry, including their nature and the degree of interactions with counter anions, thereby bridging the gap in effectively transferring the know‐how accumulated in lithium batteries to sodium‐based batteries. The results demonstrate that the unique impact of salt anions on the properties of metal‐ion conducting electrolytes is tightly correlated with the nature of metal cations. By synchronizing the anionic structures with the critical features of sodium cations, the solvating dynamics and transport properties, chemical stability, aluminum corrosion behavior, and other key properties of the electrolytes could be finely tuned to fit the specific requirements of advanced sodium‐based batteries. This work gives an in‐depth insight into the chemical and physical features of sodium‐based electrolytes, with a potential avenue to accelerate the deployment of high‐performance sodium batteries and simultaneously inspire and guide the design of other electrolytes for emerging mono‐ and multivalent cation‐based rechargeable batteries.

An enhanced adaptive dynamic metaheuristic optimization algorithm for rainfall prediction depends on long short-term memory

Sorting and analyzing different types of rainfall according to their intensity, duration, distribution, and associated meteorological circumstances is the process of rainfall prediction. Understanding rainfall patterns and predictions is crucial for various applications, such as climate studies, weather forecasting, agriculture, and water resource management. Making educated decisions about things like agricultural planning, effective use of water resources, precise weather forecasting, and a greater comprehension of climate-related phenomena is made more accessible when many components of rainfall are analyzed. The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. Next, the desired features are trained on five different models: Decision Trees (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and K-Nearest Neighbor (KNN). Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. With coefficient of determination (R2) of 0.9636, the results demonstrate the superior efficacy and performance of the suggested methodology (AD-PSO-Guided WOA-LSTM) compared to other alternative optimization techniques.

Structural, electronic, and superconducting properties of MBE-grown tantalum nitride films on c-plane sapphire

Two single crystal phases of tantalum nitride were stabilized on c-plane sapphire using molecular beam epitaxy. The phases were identified to be δ-TaN with a rock salt cubic structure and γ-Ta2N with a hexagonal structure. Atomic force microscopy scans revealed smooth surfaces for both the films with root mean square roughnesses less than 0.3 nm. Phase purity of these films was determined by x-ray diffraction. The Raman spectrum of the phase-pure δ-TaN and γ-Ta2N obtained will serve as a future reference to determine phase purity of tantalum nitride films. Furthermore, the room temperature and low-temperature electronic transport measurements indicated that both of these phases are metallic at room temperature with resistivities of 586.2 μΩ · cm for the 30 nm δ-TaN film and 75.5 μΩ · cm for the 38 nm γ-Ta2N film and become superconducting below 3.6 and 0.48 K, respectively. The superconducting transition temperature reduces with applied magnetic field as expected. Ginzburg–Landau fitting revealed a 0 K critical magnetic field and coherence length of 18 T and 4.2 nm for the 30 nm δ-TaN film and 96 mT and 59 nm for the 38 nm γ-Ta2N film. These tantalum nitride films are of high interest for superconducting resonators and qubits.

Your time is valuable. Don’t give it away just for ‘exposure’

ELMO2 is an essential regulator of carotid artery development

Abstract Engulfment and cell motility 2 (ELMO2) is a cytoskeletal adaptor protein necessary for cell migration and apoptotic cell removal. Loss-of-function mutations in ELMO2 cause intraosseous vascular malformation (VMOS), a human disease involving progressive expansion of craniofacial bones in combination with anomalies in blood vessels that emerge from the external carotid artery, as well as aneurysms in the internal carotid artery. Here we show that global inactivation of Elmo2 in mice leads to midgestation embryonic lethality due to dilation of the 3 rd pharyngeal arch arteries and aneurysm formation in the common carotids. These vascular malformations are associated to defects in vascular smooth muscle cell organization and are phenocopied upon neural crest-specific deletion. In vitro experiments further confirm that ELMO2 regulates vascular smooth muscle cell adhesion, spreading and contractility through Rac1 activation and modulation of actin dynamics. Our findings provide new insights into the biological function of ELMO2 with relevant implications for understanding VMOS pathogenesis and raise the possibility of vessel-targeted diagnostic and treatment strategies.

Frontispiece: Non‐Carbonized Pd Single‐Atom Catalyst Supported on Lignin‐Functionalized Phenolic Resin for Potent Catalytic Transfer Hydrogenation of Lignin‐Derived Aldehydes

Decline of salt marsh-nesting birds within the lower Chesapeake Bay (1992–2021)

Bird species that depend on tidal marshes throughout the world are threatened by ongoing sea-level rise. How species that differ in their level of marsh-dependency may respond to change over time remains unclear. I surveyed a network of patches (N = 186) within tidal salt marshes located in the lower Chesapeake Bay (1992, 2021) for 12 species of breeding birds to evaluate changes in abundance. Marsh-nesting bird abundance declined by 65.7% during the course of the survey interval. Significant declines in abundance were discovered for eight of ten species evaluated with declines in abundance ranging from 34 to 100%. Four species were extirpated or nearly extirpated within focal marshes during the study period. The magnitude of decline was highest for facultative nesting species (84.2%) followed by marsh obligates (81.6%) and salt marsh obligates (44.2%) respectively. Salt marsh obligates have become an increasingly dominant portion of the species assemblage over time reaching 83% of all detections by 2021. This pattern supports the prediction that specialists may persist longer than generalists as habitats are subjected to change. Despite their relative stability, salt marsh obligates are of high conservation concern over the longer term due to their specialization on a habitat that is currently experiencing rapid disruption. Even though this study did not evaluate the causes of population decline, results are aligned with other recent work within other regions that have implicated ongoing sea-level rise and nest predation.

Integrated fiber supercapacitor coils enabling wireless charging and passive voltage monitoring

Fiber supercapacitors (FSCs) have attracted extensive research interest in the field of flexible electronics and wearable devices. This paper presents integrated FSC coils capable of inducing charging current between two electrodes in an alternating magnetic field, where FSC serves both as energy storage part and as an inductive coil, and rectifier diodes regulate charging current direction and maintain direct current bias of FSC. Using FSC as inductive coils leads to a concurrent increase in both energy storage capacity and energy harvesting ability as the device scale expands. This enables the aggregation of the total charging power when multiple coils are used for charging simultaneously. The charging principles reveal that the charging current is only induced and circulated inside the coil, ensuring each individual coil maintains its independence during multi-module charging and preventing the risk caused by the accumulation of large currents. The charging power of the device can be quantitively reflected by the pulse height (ΔUpulse) between two electrodes and voltage drop (Udrop) in one electrode, and analyzing these parameters enables rapid assessment of device performance. The integrated FSC coils also show compatibility with commercial wireless charging pads, expanding potential applications. In addition, the devices naturally function as LC oscillators, with resonant frequency tunable through the coil structure design. Replacing diodes with varactor diodes allows direct conversion of charging voltage into resonance frequency, permitting wireless readout. Such capability facilitates rapid identification and passive voltage monitoring across individual components during multi-module charging, presenting a viable approach for maintaining and managing large-scale, multi-coil systems.