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Abstract Immune checkpoint blockade (ICB) has become a standard anti-cancer treatment, offering durable clinical benefits. However, the limited response rate of ICB necessitates biomarkers to predict and modulate the efficacy of the therapy. The gut microbiome’s influence on ICB efficacy is of particular interest due to its modifiability through various interventions. However, gut microbiome biomarkers for ICB response have been inconsistent across different studies. Here, we identify TANB77, an uncultured and distinct bacterial clade, as the most consistent responder-enriched taxon through meta-analysis of ten independent ICB recipient cohorts. Traditional taxonomy fails to distinguish TANB77 from unrelated taxa, leading to its oversight. Mice with higher gut TANB77 abundance, either naturally or through transplantation, show improved response to anti-PD-1 therapy. Additionally, mice injected with TANB77-derived pilin-like protein exhibit improved anti-PD-1 therapy response, providing in vivo evidence for the beneficial role of the pilin-like protein. These findings suggest that pilins from the TANB77 order may enhance responses to ICB therapy across diverse cohorts of cancer patients.

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

Background/Aims The major histocompatibility class II (MHC II) transactivator, known as CIITA, is induced by Interferon gamma (IFN-γ) and plays a well-established role in regulating the expression of class II MHC molecules in antigen-presenting cells. Methods Primary human hepatocytes (PHH) were isolated via therapeutic hepatectomy from two donors. The hepatocellular carcinoma (HCC) cell lines HepG2 and Huh7 were used for the mechanistic study, and HBV infection was performed in HepG2-NTCP cells. HBV DNA replication intermediates and secreted antigen levels were measured using Southern blotting and ELISA, respectively. Results We identified a non-canonical function of CIITA in the inhibition of hepatitis B virus (HBV) replication in both HCC cells and patient-derived PHH. Notably, in vivo experiments demonstrated that HBV DNA and secreted antigen levels were significantly decreased in mice injected with the CIITA construct. Mechanistically, CIITA inhibited HBV transcription and replication by suppressing the activity of HBV-specific enhancers/promoters. Indeed, CIITA exerts antiviral activity in hepatocytes through ERK1/2-mediated down-regulation of the expression of hepatocyte nuclear factor 1α (HNF1α) and HNF4α, which are essential factors for virus replication. In addition, silencing of CIITA significantly abolished the IFN-γ-mediated anti-HBV activity, suggesting that CIITA mediates the anti-HBV activity of IFN-γ to some extent. HBV X protein (HBx) counteracts the antiviral activity of CIITA via direct binding and impairing its function. Conclusions Our findings reveal a novel antiviral mechanism of CIITA that involves the modulation of the ERK pathway to restrict HBV transcription. Additionally, our results suggest the possibility of a new immune avoidance mechanism involving HBx.

Abstract The Ferromagnetic Resonance (FMR) phenomenon, marked by the selective absorption of microwave radiation by magnetic materials in the presence of a magnetic field, plays a pivotal role in the development of radar absorbing materials, high speed magnetic storage, and magnetic sensors. This process is integral for technologies requiring precise control over microwave absorption frequencies. We explored how variations in resonance fields can be effectively modulated by adjusting both the shape and stress anisotropies of magnetic materials on a flexible substrate. Utilizing polyethylene-naphthalate (PEN) as the substrate and Permalloy (Ni79Fe21, noted for its positive magnetostriction coefficient) as the magnetic component, we demonstrated that modifications in the aspect ratio and bending repetitions can significantly alter the resonance field. The results, consistent with Kittel’s equation and the predictions of a uniaxial magnetic anisotropy model, underscore the potential for flexible substrates in enhancing the sensitivity and versatility of RF-based magnetic devices.

Abstract Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

The integration of virtual reality (VR) and multisensory experiences offers a novel approach to enhancing workspace design, particularly for home office environments. This preliminary study examines the effects of visual and olfactory stimuli in a VR-simulated smart home environment on presence, task performance, heart rate, and mood. Sensory conditions, including visual, olfactory, and combined stimuli, were controlled in an experiment with 60 participants to assess both physiological and psychological responses. Task performance was measured through cognitive tests, while heart rate and mood states were monitored using a heart rate sensor and the Positive and Negative Affect Schedule (PANAS), respectively. Statistical results showed that the sense of presence provided by VR and specific conditions—such as visual stimuli (e.g., intelligent lighting systems)—effectively enhanced work efficiency, with task performance under visual stimuli significantly outperforming that under olfactory stimuli (e.g., peppermint scent). However, these sensory stimuli did not significantly affect heart rate or emotions. Our findings underscore the potential of sensory design to improve task performance and highlight the importance of the sensory environment in designing home offices, suggesting that sensory stimuli can enhance work efficiency in remote or virtual work settings.

This study was performed to evaluate the effects of rye silage treated with sodium formate (Na-Fa) and lactic acid bacteria (LAB) inoculants on the ruminal fermentation characteristics, methane yield and energy balance in Hanwoo steers. Forage rye was harvested in May 2019 and ensiled without additives (control) or with either a LAB inoculant or Na-Fa. The LAB (Lactobacillus plantarum) were inoculated at 1.5 × 1010 CFU/g fresh matter, and the inoculant was sprayed onto the forage rye during wrapping at a rate of 4 L/ton of fresh rye forage. Sixteen percent of the Na-Fa solution was sprayed at a rate of approximately 6.6 L/ton. Hanwoo steers (body weight 275 ± 8.4 kg (n = 3, group 1); average body weight 360 ± 32.1 kg (n = 3, group 2)) were allocated into two pens equipped with individual feeding gates and used in duplicated 3 × 3 Latin square design. The experimental diet was fed twice daily (09:00 and 18:00) during the experimental period. Each period comprised 10 days for adaptation to the pen and 9 days for measurements in a direct respiratory chamber. The body weights of the steers were measured at the beginning and at the end of the experiment. Feces and urine were collected for 5 days after 1 day of adaptation to the chamber, methane production was measured for 2 days, and ruminal fluid was collected on the final day. In the LAB group, the ratio of acetic acid in the rumen fluid was significantly lower (p = 0.044) and the ratio of propionic acid in the rumen fluid was significantly higher (p = 0.017). Methane production per DDMI of the Na-FA treatment group was lower than that of the other groups (p = 0.052), and methane production per DNDFI of the LAB treatment group was higher than that of the other groups (p = 0.056). The use of an acid-based additive in silage production has a positive effect on net energy and has the potential to reduce enteric methane emissions in ruminants.

Abstract Synaptic transistors (STs) with a gate/electrolyte/channel stack, where mobile ions are electrically driven across the solid electrolyte, have been considered as analog weight elements for neuromorphic computing. The current (ID) between the source and drain in the ST is analogously updated by gate voltage (VG) pulses, enabling high pattern recognition accuracy in neuromorphic systems; however, the governing physical mechanisms of the ST are not fully understood yet. Our previous physics-based simulation study showed that ion movement in the electrolyte, rather than the electrochemical reactions that occur in the channel, plays an important role in switching. In this study, we experimentally explore the properties of the HfOx electrolyte and show that by tuning the density of oxygen vacancies, it can assume the dual role of electrolyte and channel. We demonstrate analog synaptic behavior using a novel ST with a two-layer stack of CuOx/HfOx, where the CuOx is the gate and Cu ion reservoir, and the HfOx is the electrolyte and channel. To improve state retention and linearity, we introduce a Cu ion transport barrier in the form of a dense and stoichiometric Al2O3 layer. The CuOx/Al2O3/HfOx exhibits excellent state retention and improved potentiation and depression response. Energy dispersive spectroscopy mapping following potentiation confirms the role of the Al2O3 layer in confining the Cu ions in the HfOx layer. We also show that a two-step programming scheme can further enhance synaptic response and demonstrate high recognition accuracy on the Fashion-MNIST dataset in simulation.

Abstract Although robot-based automation in chemistry laboratories can accelerate the material development process, surveillance-free environments may lead to dangerous accidents primarily due to machine control errors. Object detection techniques can play vital roles in addressing these safety issues; however, existing detection models still suffer from insufficient accuracy in environments involving complex and noisy scenes. With the aim of improving safety in a surveillance-free laboratory, we report a deep learning (DL)-based object detector, namely, DenseSSD. For the foremost and frequent problem of detecting positions of transparent chemical vessels, DenseSSD achieved a mean average precision (mAP) over 95% based on a complex dataset involving both empty and solution-filled vials, greatly exceeding those of conventional detectors; such high precision is critical to minimizing failure-induced accidents. Additionally, DenseSSD was observed to be generalizable to other laboratory environments, maintaining its high precisions under the variations of solution colors, camera view angles, background scenes, experiment hardware and type of chemical vessels. Such robustness of DenseSSD supports that it can universally be implemented in diverse laboratory settings. This study conclusively demonstrates the significant utility of DenseSSD in enhancing safety within automated material synthesis environments. Furthermore, the exceptional detection accuracy of DenseSSD opens up possibilities for its application in various other fields and scenarios where precise object detection is paramount.

Abstract Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

(1) Background: As digital technology continues to reshape visual landscapes, understanding how design elements influence customer experience has become essential. Eye-tracking technology offers a powerful, quantitative approach to assessing visibility, aesthetics, and design components, providing unique insights into visual engagement. (2) Methods: This paper presents a systematic review of eye-tracking methodologies applied in design research. Thirty studies were selected for analysis from recognized academic databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Employing the Population, Intervention, Comparison, and Outcomes (PICO) framework, this review focused on experimental studies in visual communication and new media design that utilized visual symbols for communication and leveraged new media technologies. (3) Results: The findings corroborated that eye-tracking technology offers in-depth insights into gaze patterns, visual perception, and attention, which can inform design strategies. This review shows that assessing visual designs based on eye-tracking data can enhance consumer-centered interfaces, better align with user preferences, and foster more engaged behaviors in both digital and physical environments. (4) Conclusions: This review deepens our understanding of the cognitive and emotional processes underlying visual engagement. It also suggests new avenues for integrating diverse eye-tracking metrics into design evaluation, offering practical applications for improving design strategies and advancing the field of design research.

Objective: To develop a non-invasive cognitive impairment detection system using speech data analysis, addressing the growing global dementia crisis and enabling accessible early screening through daily health monitoring. Methods: Speech data from 223 Korean patients were collected across eight tasks. Patients were classified based on Korean Mini-Mental State Examination scores. Four machine learning models were tested for three binary classification tasks. Voice acoustic features were extracted and analyzed. Results: The Deep Neural Network model performed best in two classification tasks, with Precision-Recall Area Under the Curve scores of 0.737 for severe vs. no impairment and 0.726 for mild vs. no impairment, while Random Forest achieved 0.715 for severe + mild vs. no impairment. Several acoustic features emerged as potentially important indicators, with DDA shimmer from the /i/ task and stdevF0 from the /puh-tuh-kuh/ task showing consistent patterns across classification tasks. Conclusions: This preliminary study suggests that certain acoustic features may be associated with cognitive status, though demographic factors significantly influence these relationships. Further research with demographically matched populations is needed to validate these findings.

Abstract BackgroundIn South Korea, the cancer incidence rate has increased by 56.5% from 2001 to 2021. Nevertheless, the 5-year cancer survival rate from 2017 to 2021 increased by 17.9% compared with that from 2001 to 2005. Cancer survival rates tend to decline with lower socioeconomic status, and variations exist in the survival rates among different cancer types. Analyzing socioeconomic patterns in the survival of patients with cancer can help identify high-risk groups and ensure that they benefit from interventions. ObjectiveThe aim of this study was to analyze differences in survival rates among patients diagnosed with six types of cancer—stomach, colorectal, liver, breast, cervical, and lung cancers—based on socioeconomic status using Korean nationwide data. MethodsThis study used the Korea Central Cancer Registry database linked to the National Health Information Database to follow up with patients diagnosed with cancer between 2014 and 2018 until December 31, 2021. Kaplan-Meier curves stratified by income status were generated, and log-rank tests were conducted for each cancer type to assess statistical significance. Hazard ratios with 95% CIs for any cause of overall survival were calculated using Cox proportional hazards regression models with the time since diagnosis. ResultsThe survival rates for the six different types of cancer were as follows: stomach cancer, 69.6% (96,404/138,462); colorectal cancer, 66.6% (83,406/125,156); liver cancer, 33.7% (23,860/70,712); lung cancer, 30.4% (33,203/109,116); breast cancer, 91.5% (90,730/99,159); and cervical cancer, 78% (12,930/16,580). When comparing the medical aid group to the highest income group, the hazard ratios were 1.72 (95% CI 1.66‐1.79) for stomach cancer, 1.60 (95% CI 1.54‐1.56) for colorectal cancer, 1.51 (95% CI 1.45‐1.56) for liver cancer, 1.56 (95% CI 1.51‐1.59) for lung cancer, 2.19 (95% CI 2.01‐2.38) for breast cancer, and 1.65 (95% CI 1.46‐1.87) for cervical cancer. A higher deprivation index and advanced diagnostic stage were associated with an increased risk of mortality. ConclusionsSocioeconomic status significantly mediates disparities in cancer survival in several cancer types. This effect is particularly pronounced in less fatal cancers such as breast cancer. Therefore, considering the type of cancer and socioeconomic factors, social and medical interventions such as early cancer detection and appropriate treatment are necessary for vulnerable populations.

Abstract As competitiveness to obtain a residency position in the field of Otolaryngology–Head and Neck Surgery (Oto‐HNS) has continued to rise, applicants have endeavored to set themselves apart. One increasingly popular strategy is maximizing research output. Over the past 6 years, applicant metrics such as board scores and volunteer and work experiences have risen incrementally, while research production has more than doubled, from 8.4 mean number of abstracts, presentations, and publications in 2016 to 17.2 in 2022. This coincides with the exponential surge of new research fellowships among Oto‐HNS departments over a similar period, which is now up to at least 68 advertised positions. With a significant difference between the research production of matched and unmatched applicants, programs may be signaling a positive bias towards research‐heavy applicants. Whether this is intended and/or preferable should be examined more closely.

Abstract Tumor necrosis factor superfamily (TNFSF) resistance contributes to the development and progression of tumors and resistance to various cancer therapies. Tumor-intrinsic alterations involved in the adaptation to the TNFSF response remain largely unknown. Here, we demonstrate that protein kinase C substrate 80K-H (PRKCSH) abundance in lung cancers boosts oncogenic IGF1R activation, leading to TNFSF resistance. PRKCSH abundance is correlated with IGF1R upregulation in lung cancer tissues. Specifically, PRKCSH interacts with IGF1R and extends its half-life. The PRKCSH-IGF1R axis in tumor cells impairs caspase-8 activation, increases Mcl-1 expression, and inhibits caspase-9, leading to an imbalance between cell death and survival. PRKCSH deficiency augmented the antitumor effects of natural killer (NK) cells, representative TNFSF effector cells, in a tumor xenograft IL-2Rg-deficient NOD/SCID (NIG) mouse model. Our data suggest that PRKCSH plays a critical role in TNFSF resistance and may be a potential target to improve the efficacy of NK cell-based cancer therapy.

Abstract Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

Abstract The multilevel current states of synaptic devices in artificial neural networks enable next-generation computing to perform cognitive functions in an energy-efficient manner. Moreover, considering large-scale synaptic arrays, multiple states programmed in a low-current regime may be required to achieve low energy consumption, as demonstrated by simple numerical calculations. Thus, we propose a three-terminal Cu-ion-actuated CuOx/HfOx/WO3 synaptic transistor array that exhibits analogously modulated channel current states in the range of tens of nanoamperes, enabled by WO3 channel engineering. The introduction of an amorphous stoichiometric WO3 channel formed by reactive sputtering with O gas significantly lowered the channel current but left it almost unchanged with respect to consecutive gate voltage pulses. An additional annealing process at 450 °C crystallized the WO3, allowing analog switching in the range of tens of nanoamperes. The incorporation of N gas during annealing induced a highly conductive channel, making the channel current modulation negligible as a function of the gate pulse. Using this optimized gate stack, Poole–Frenkel conduction was identified as a major transport characteristic in a temperature-dependent study. In addition, we found that the channel current modulation is a function of the gate current response, which is related to the degree of progressive movement of the Cu ions. Finally, the synaptic characteristics were updated using fully parallel programming and demonstrated in a 7 × 7 array. Using the CuOx/HfOx/WO3 synaptic transistors as weight elements in multilayer neural networks, we achieved a 90% recognition accuracy on the Fashion-MNIST dataset.

Abstract Socioeconomic barriers to cancer screening exist at a regional level. The deprivation index is used to estimate socioeconomic gradients and health disparities across different geographical regions. We aimed to examine the impact of deprivation on breast cancer screening participation rates among South Korean women. Municipal breast cancer screening participation rates in women were extracted from the National Cancer Screening Information System and linked to the Korean version of the deprivation index constructed by the Korea Institute for Health and Social Affairs. A generalised linear mixed model was employed to investigate the association between the deprivation index and age-standardised breast cancer screening participation rates in 2005, 2012, and 2018. Participation rates increased gradually across all age groups from 2005 to 2018. Participants in their 60 s consistently had one of the highest participation rates (2005: 30.37%, 2012: 61.57%, 2018: 65.88%). In 2005, the most deprived quintile had a higher estimate of breast cancer screening participation than the least deprived quintile (2nd quintile; estimate: 1.044, p = 0.242, 3rd quintile; estimate: 1.153, p = 0.192, 4th quintile; estimate: 3.517, p = 0.001, 5th quintile; estimate: 6.913, p =

This study shows how the threshold switching (TS) characteristics of a NbOx layer with noninert W electrodes can be improved by introducing an oxide barrier. The ∼10-nm-thick NbOx layer exhibits TS, which is known to originate from NbO2, after electroforming. However, an unwanted formation of a Nb2O5 layer at the top electrode (TE) interface has been observed, which deteriorates the TS, given the switching capability of Nb2O5. Therefore, a thin oxide layer was introduced to overcome the issue and enhance the TS behavior. Furthermore, the role of the oxide barrier is investigated systematically considering its location and unique properties. This study reveals that depositing a barrier at the bottom electrode interface makes the formation of NbO2 difficult, resulting in poor TS behavior, while placing the barrier at the TE interface could mitigate the Nb2O5 creation, promoting TS. Finally, the effect of incorporating various oxide types has been investigated, showing different switching behavior. Through examining the temperature dependence of the TS obtained from the W/NbOx/W and W/ZrO2/NbOx/W stacks, it was found that the suppression of Nb2O5 at the top interface is indirectly shown as a strengthened Schottky barrier from an electrical measurement perspective. As a result, excellent cell-to-cell uniformity of the TS is achieved in the ZrO2/NbOx stack, which not only prevents the formation of Nb2O5 but also stabilizes NbO2 in the NbOx layer.

This study investigated the lay perception of inequalities in society, healthcare utilization, and health in Korea. We also examined the association between socioeconomic status (SES) and perception of inequalities. Data from an online survey on the perception of health and healthcare inequalities related to cancer conducted between October 19, 2021, and November 12, 2021, were used. Data of 3,769 participants aged 20 to 74 years from 17 provinces were analyzed. We examined the perceived level of inequalities according to SES or residential area using frequency analysis, a t-test or analysis of variance (ANOVA), multiple linear regression analysis, and multiple logistic regression analysis. The mean score for perception of social inequality was 5.99 [Standard Deviation (SD) = 1.95] and that for perception of healthcare utilization inequality was 4.75 (SD = 1.96). The perception rate of health inequality according to SES and residential area was approximately 59 % and 61 %, respectively. Higher the income level, lower the level of perception of social inequalities β = -0.22, p

Abstract Non-alcoholic fatty liver disease (NAFLD) is an increasingly prevalent immuno-metabolic disease that can progress to hepatic cirrhosis and cancer. NAFLD pathogenesis is extremely complex and is characterized by oxidative stress, impaired mitochondrial function and lipid metabolism, and cellular inflammation. Thus, in-depth research on its underlying mechanisms and subsequent investigation into a potential drug target that has overarching effects on these features will help in the discovery of effective treatments for NAFLD. Our study examines the role of endogenous paraoxonase-2 (PON2), a membrane protein with reported antioxidant activity, in an in vitro cell model of NAFLD. We found that the hepatic loss of PON2 activity aggravated steatosis and oxidative stress under lipotoxic conditions, and our transcriptome analysis revealed that the loss of PON2 disrupts the activation of numerous functional pathways closely related to NAFLD pathogenesis, including mitochondrial respiratory capacity, lipid metabolism, and hepatic fibrosis and inflammation. We found that PON2 promoted the activation of the autophagy pathway, specifically the mitophagy cargo sequestration, which could potentially aid PON2 in alleviating oxidative stress, mitochondrial dysfunction, lipid accumulation, and inflammation. These results provide a mechanistic foundation for the prospect of PON2 as a drug target, leading to the development of novel therapeutics for NAFLD.

Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites.

Abstract Background While many studies investigated changes in working status in cancer survivors, most studies have been performed in survivors of breast cancer and few studies evaluated factors associated with changes in the working status of cancer survivors comprehensively. We aimed to evaluate the changes in the working status of cancer survivors after diagnosis and socio-demographic, clinical, work-related and psychological factors associated with it. Methods We conducted a cross-sectional survey of adult patients with cancer who were working at the time of diagnosis. A trained interviewer inquired about participants’ current working status, including leave of absence, discontinuing, continuing, and changing work. Sociodemographic, clinical, work-related and psychological factors were measured. Multinomial logistic regression was used to identify factors associated with changes in the working status. Results Among the 730 patients, 29%, 18% and 6% were currently on a discontinued working, leave of absence and had changed jobs, respectively. Patients who discontinued working after cancer diagnosis were more likely to be female, have ≥ $3,000 of monthly family income, not be the principal wage earners for their families and be blue-collar workers. In clinical characteristics, advanced-stage cancer and experienced cancer recurrence was associated with leave of absence and discontinued working. In work-related and psychological factors, stress due to insufficient job control (relative risk ratio [RRR] = 2.26), interpersonal conflict (RRR = 1.86), job insecurity (RRR = 2.63), organizational system (RRR = 3.49), and lack of reward (RRR = 11.76), and less meaning to work were more likely to discontinue working after a cancer diagnosis. Conclusion Occupational health care professionals and other stakeholders need to openly communicate with patients with cancer about potential barriers during the return-to-work trajectory.

With the development of high-throughput technologies for producing genomic data, more advanced statistical methods such as regularization and machine learning techniques have been adapted to survival analysis. However, the clinical information such as age, gender and medical history plays a critical role in constructing a survival prediction model. Machine learning technique such as the support vector machine (SVM) can improve the predictability of the survival model by using the available clinical information. When implementing SVM for a predictive survival model, the clinical kernel was proposed by Daemen et al. by equalizing the influence of clinical variables and taking account of the range of these variables. However, this clinical kernel uses the same weight for all clinical variables without considering the different effect of those variables on the survival time. In this study, we proposed a simple kernel, called ensemble kernel, by combining a clinical kernel with model fitting. Since the proposed ensemble kernel is based on model fitting, two different kernels are considered by using either Cox model or accelerated failure time (AFT) model. We compare the performance of these two ensemble kernels with that of the linear kernel and the clinical kernel by the concordance index (C-index) using the four real data sets. While both linear and clinical kernels use all clinical variables in defining global kernels, the proposed two ensemble kernels can use only significant variables from either a Cox model or an AFT model. The comparative result shows that the proposed two ensemble kernels perform similarly as the existing clinical kernel does and the performance of four kernels vary according to data sets. [ABSTRACT FROM AUTHOR]

BackgroundThe COVID-19 pandemic increased the importance of technology for all Americans, including older adults. Although a few studies have indicated that older adults might have increased their technology use during the COVID-19 pandemic, further research is needed to confirm these findings, especially among different populations, and using validated surveys. In particular, research on changes in technology use among previously hospitalized community-dwelling older adults, especially those with physical disability, is needed because older adults with multimorbidity and hospital associated deconditioning were a population greatly impacted by COVID-19 and related distancing measures. Obtaining knowledge regarding previously hospitalized older adults’ technology use, before and during the pandemic, could inform the appropriateness of technology-based interventions for vulnerable older adults. ObjectiveIn this paper, we 1) described changes in older adult technology-based communication, technology-based phone use, and technology-based gaming during the COVID-19 pandemic, compared to before the COVID-19 pandemic and 2) tested whether technology use moderated the association between changes in in-person visits and well-being, controlling for covariates. MethodsBetween December 2020 and January 2021 we conducted a telephone-based objective survey with 60 previously hospitalized older New Yorkers with physical disability. We measured technology-based communication through three questions pulled from the National Health and Aging Trends Study COVID-19 Questionnaire. We measured technology-based smart phone use and technology-based video gaming through the Media Technology Usage and Attitudes Scale. We used paired t tests and interaction models to analyze survey data. ResultsThis sample of previously hospitalized older adults with physical disability consisted of 60 participants, 63.3% of whom identified as female, 50.0% of whom identified as White, and 63.8% of whom reported an annual income of $25,000 or less. This sample had not had physical contact (such as friendly hug or kiss) for a median of 60 days and had not left their home for a median of 2 days. The majority of older adults from this study reported using the internet, owning smart phones, and nearly half learned a new technology during the pandemic. During the pandemic, this sample of older adults significantly increased their technology-based communication (mean difference=.74, P=.003), smart phone use (mean difference=2.9, P=.016), and technology-based gaming (mean difference=.52, P=.030). However, this technology use during the pandemic did not moderate the association between changes in in-person visits and well-being, controlling for covariates. ConclusionsThese study findings suggest that previously hospitalized older adults with physical disability are open to using or learning technology, but that technology use might not be able to replace in-person social interactions. Future research might explore the specific components of in-person visits that are missing in virtual interactions, and if they could be replicated in the virtual environment, or through other means.

Abstract Background Psychiatric disorders increase risk of neuropsychiatric disease and poor outcomes, yet little is known about the neuropsychiatric manifestations of COVID-19 in the psychiatric population. The primary objective is to synthesize neuropsychiatric outcomes of COVID-19 in people with preexisting psychiatric disorders. Methods Data were collected during an ongoing review of the impact of pandemics on people with existing psychiatric disorders. All study designs and gray literature were included. Medline, PsychInfo, CINAHL, EMBASE, and MedRx were searched from inception to September 1 2020. Risk of bias was assessed using a published tool that can accommodate all study types. Two independent authors screened the studies and extracted data. Data were narratively synthesized, as there were insufficient data to meta-analyze. Evidence was appraised according to GRADE. Results Four case reports were included, comprising 13 participants from three countries. Many large-sample, relevant papers were omitted for not reporting psychiatric history, despite reporting other comorbidities. Included participants (n = 13) were hospitalized with COVID-19 and appeared to meet criteria for delirium. Myoclonus, rigidity, and alogia were also reported. The most commonly reported preexisting psychiatric diagnoses were mood disorders, schizophrenia, and alcohol use disorder. Conclusions People with preexisting psychiatric disorders may experience delirium, rigidity, myoclonus, and alogia during COVID-19 infection; although higher quality and longitudinal data are needed to better understand these phenomena. Relevant COVID-19 literature does not always report psychiatric history, despite heightened neuropsychiatric vulnerability within this population. Trial Registration: PROSPERO (CRD42020179611).

This paper investigated the conductance-state stability of TiN/PrCaMnOx (PCMO)-based resistive random-access memory (RRAM), which serves as a kernel weight element in convolutional neural networks (CNNs), to realize accurate feature extraction from images. On application of the initial forming process that actively drives more oxygen ions to form an interfacial layer between TiN and PCMO to RRAM devices with a high voltage of ±4 V, resistive switching behavior with a noticeable memory window was observed. However, the achieved conductance states continued to decrease during repeated cycling. The oxidation at the interface tended to occur thermodynamically, implying an increase in interfacial layer thickness. Considering the hardware implementation of the kernel weight matrix, with specifically assigned conductance values of the RRAM, state instability in the RRAM renders image edge detection difficult, eventually degrading the overall recognition accuracy of the CNN. Thus, we introduced an asymmetric programming voltage method, wherein a higher set voltage of −3 V than a reset voltage of +2.5 V can shift more oxygen ions back into PCMO. Consequently, when the RRAM devices programmed to different states were maintained without degradation in the 1 K cross-point array, eight clearly distinct weighted sum currents were demonstrated in the 3 × 1 subarray. Based on the measurement results, we performed feature extraction in CNN algorithms through MATLAB simulation, demonstrating input image edge detection with a high accuracy of 92%.

Neuroinflammation activated by microglia affects inflammatory pain development. This study aimed to explore the anti-inflammatory properties and mechanisms of 1,6,7-trihydroxy-2-(1,1-dimethyl-2-propenyl)-3-methoxyxanthone (THMX) from Cudrania tricuspidata in microglia activation-mediated inflammatory pain. In RAW 264.7 and BV2 cells, THMX has been shown to reduce lipopolysaccharide (LPS)-induced inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and pro-inflammatory mediators and cytokines, including nitric oxide (NO), prostaglandin (PG) E2, interleukin (IL)-6, and tumor necrosis factor alpha (TNF-α). THMX also decreased LPS-induced phosphorylation of mitogen-activated protein kinase (MAPK) and the activation of p65 nuclear factor kappa B (NF-κB). Interestingly, THMX also activated heme oxygenase (HO)-1 expression. These findings suggest that THMX is a promising biologically active compound against inflammation through preventing MAPKs and NF-ĸB and activating HO-1 signaling pathways.

While electro-chemical RAM (ECRAM)-based cross-point synaptic arrays are considered to be promising candidates for energy-efficient neural network computational hardware, array-level analyses to achieve energy-efficient update operations have not yet been performed. In this work, we fabricated CuOx/HfOx/WOx ECRAM arrays and demonstrated linear and symmetrical weight update capabilities in both fully parallel and sequential update operations. Based on the experimental measurements, we showed that the source-drain leakage current (ISD) through the unselected ECRAM cells and resultant energy consumption—which had been neglected thus far—contributed a large portion to the total update energy. We showed that both device engineering to reduce ISD and the selection of an update scheme—for example, column-by-column—that avoided ISD intervention via unselected cells were key to enable energy-efficient neuromorphic computing.

This study aims at investigating how consumers experience the retail environment visually, thus establishing a foundation for deeper insights into visual merchandising strategies. Specifically, we experimentally recorded and analyzed the visual attention and emotional arousal of the consumers in a test setting and examined the influence of various elements as well as gender differences in the recorded consumer responses. We conducted an experiment utilizing eye-tracking and virtual reality to analyze visual attention and emotional arousal in response to spatial and design elements in an immersive retail environment. We examined real-time measures of consumer interest and emotional responses during the retail experience. Valid gaze data from 24 male and 22 female participants were used for the analysis of total dwell time (TDT), total fixation count (TFC), and average pupil diameter (APD). The visual attention and emotional arousal of consumers showed different responses to specific areas of interest according to different spatial arrangements in the sales and service areas. This study statistically analyzed gender differences in consumer responses and performed a correlation analysis between visual attention and emotional arousal. Our findings provide insight into improving the design of retail environments for target consumers and contribute to building visual merchandising strategies.

This paper reviews studies that have quantified humans’ emotions in architectural environments using biometrics. We analyzed the design variables that induced positive emotions and compared the study conditions for each result, including the biometric tools utilized. The objective was to analyze the design characteristics of the architectural environment, biometric tools, and study conditions that measure emotional responses in design research, their limitations, and future uses of biometric devices. Twenty papers were selected using the preferred reporting items for systematic reviews and meta-analyses. The population, intervention, control, outcome, and setting study design were used as an analysis framework. The reviewed papers used biometric tools such as electroencephalography, functional near-infrared spectroscopy, functional magnetic resonance imaging, galvanic skin response, electromyography, electrocardiography, and eye-tracking to analyze emotions. Environmental factors that produce positive emotional responses in the architectural spaces included form, layout, material, color, furniture, and lighting. The findings from methodology using biometric tools have the potential to develop an evidence-based design to produce positive emotions in architectural environments that improve our mental health and well-being.

Despite advanced machine learning methods, the implementation of emotion recognition systems based on real-world video content remains challenging. Videos may contain data such as images, audio, and text. However, the application of multimodal models using two or more types of data to real-world video media (CCTV, illegally filmed content, etc.) lacking sound or subtitles is difficult. Although facial expressions in image sequences can be utilized in emotion recognition, the diverse identities of individuals in real-world content limits computational models of relationships between facial expressions. This study proposed a transformation model which employed a video vision transformer to focus on facial expression sequences in videos. It effectively understood and extracted facial expression information from the identities of individuals, instead of fusing multimodal models. The design entailed capture of higher-quality facial expression information through mixed-token embedding facial expression sequences augmented via various methods into a single data representation, and comprised two modules: spatial and temporal encoders. Further, temporal position embedding, focusing on relationships between video frames, was proposed and subsequently applied to the temporal encoder module. The performance of the proposed algorithm was compared with that of conventional methods on two emotion recognition datasets of video content, with results demonstrating its superiority.