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Rapid and accurate detection of plant pathogens in the field is crucial to prevent the proliferation of infected crops. Polymerase chain reaction (PCR) process is the most reliable and accepted method for plant pathogen diagnosis, however current conventional PCR machines are not portable and require additional post-processing steps to detect the amplified DNA (amplicon) of pathogens. Real-time PCR can directly quantify the amplicon during the DNA amplification without the need for post processing, thus more suitable for field operations, however still takes time and require large instruments that are costly and not portable. Microchip PCR systems have emerged in the past decade to miniaturize conventional PCR systems and to reduce operation time and cost. Real-time microchip PCR systems have also emerged, but unfortunately all reported portable real-time microchip PCR systems require various auxiliary instruments. Here we present a stand-alone real-time microchip PCR system composed of a PCR reaction chamber microchip with integrated thin-film heater, a compact fluorescence detector to detect amplified DNA, a microcontroller to control the entire thermocycling operation with data acquisition capability, and a battery. The entire system is 25 × 16 × 8 cm(3) in size and 843 g in weight. The disposable microchip requires only 8-µl sample volume and a single PCR run consumes 110 mAh of power. A DNA extraction protocol, notably without the use of liquid nitrogen, chemicals, and other large lab equipment, was developed for field operations. The developed real-time microchip PCR system and the DNA extraction protocol were used to successfully detect six different fungal and bacterial plant pathogens with 100% success rate to a detection limit of 5 ng/8 µl sample.

A thin FePt film was deposited onto a CrV seed layer at 400°C and showed a high coercivity (~3,400 Oe) and high magnetization (900–1,000 emu/cm 3 ) characteristic of L 1 0 phase. However, the magnetic properties of patterned media fabricated from the film stack were degraded due to the Ar-ion bombardment. We employed a deposition-last process, in which FePt film deposited at room temperature underwent lift-off and post-annealing processes, to avoid the exposure of FePt to Ar plasma. A patterned medium with 100-nm nano-columns showed an out-of-plane coercivity fivefold larger than its in-plane counterpart and a remanent magnetization comparable to saturation magnetization in the out-of-plane direction, indicating a high perpendicular anisotropy. These results demonstrate the high perpendicular anisotropy in FePt patterned media using a Cr-based compound seed layer for the first time and suggest that ultra-high-density magnetic recording media can be achieved using this optimized top-down approach.

Co 40 Fe 17 Si 32 B 11 thin film (150 nm) was deposited by using an RF magnetron co-sputtering system on a Si (100) substrate. A self-designed substrate holder, including various hard magnets arranged around the substrates, was utilized so that various external magnetic fields could be applied to the Si substrates during the film deposition. By the effect of this deposition field, the applied field during the sample deposition, the squareness of the CoFeSiB thin film was significantly enhanced. From the angular analysis of the magnetic hysteresis curves from easy (parallel to external magnetic field) to hard (perpendicular to external magnetic field) direction of magnetization, it can be deduced that Co and Fe atoms were magnetically arranged to the easy magnetization axis by an external magnetic field which was induced during deposition. Also, the compositional cluster size of the CoFeSiB thin film was remarkably reduced by Zr addition, causing a decrease in the value of coercivity. These results suggest that apparent magnetic anisotropy was achieved, and the soft magnetic property of the CoFeSiB thin film was remarkably enhanced by the combination of the deposition field and Zr addition.

[...]institutions should revise existing international student recruitment and admissions standards. [...]the federal government must revisit visa policies and find areas of improvement for a better pre/post-student experience. The hardship and anxiety of being an international student begins months before stepping foot in the U.S. The obstacles of obtaining a student visa could be viewed as a deterrent, especially in light of other countries with straightforward processes.Through COVID-19, we learned that changes to systems are possible. [...]the federal government should look into providing more legitimate pathways for graduated students to gain employment in the U.S. and ultimately attain permanent residency.

Non-Protective Personal Equipment (PPE) detection is crucial on construction sites. Although deep learning models are adept at identifying such information from on-site cameras, their success relies on large, diverse, and high-quality datasets. Image augmentation offers an alternative for artificially broadening dataset diversity. However, its impact on non-PPE detection in construction environments has not been adequately examined. This study introduces a methodology applying eight distinct augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, translation, and a combined strategy incorporating all methods. Model performance was assessed by comparing accuracy across different classes and architectures, both with and without augmentation. While most of these augmentations improved accuracy, their effectiveness was found to be task-dependent. Moreover, the most beneficial augmentation varied by non-PPE class and architecture, suggesting that augmentation strategies should be tailored to the unique features of each class and model. Although the primary focus here is on non-PPE, the evaluated techniques could also extend to related tasks on construction sites, such as detecting heavy equipment or identifying hazardous worker behavior.

The naturalistic viewing of a video clip enables participants to obtain more information from the clip compared to conventional viewing of a static image. Because changing the field-of-view (FoV) allows new visual information to be obtained, we were motivated to investigate whether naturalistic viewing with varying FoV based on active eye movement can enhance the viewing experience of natural stimuli, such as those found in a video clip with a 360° FoV in an MRI scanner. To this end, we developed a novel naturalistic viewing paradigm based on real-time eye-gaze tracking while participants were watching a 360° panoramic video during fMRI acquisition. The gaze position of the participants was recorded using an eye-tracking computer and then transmitted to a stimulus presentation computer via a TCP/IP connection. The identified gaze position was then used to alter the participants' FoV of the video clip in real-time, so the participants could change their FoV to fully explore the 360° video clip (referred to in this paper as active viewing). The gaze position of one participant while watching a video was used to change the FoV of the same video clip for a paired participant (referred to as yoked or passive viewing). Four 360° panoramic videos were used as stimuli, divided into categories based on the brightness level (i.e., bright vs. dark) and location (i.e., nature vs. city). Each of the subjects participated in the active viewing of one of the two nature videos and one of the two city videos and then engaged in the passive viewing of the other video in each category, followed by conventional viewing with a fixed FoV (referred to as fixed viewing) after each of the active or passive viewings. Forty-eight healthy volunteers participated in the study, and data from 42 of these participants were used in the analysis. Representational similarity analysis (RSA) was conducted in a multiple regression framework using representational dissimilarity matrix (RDM) codes to accommodate all of the information regarding neuronal activations from fMRI analysis and the participants' subjective ratings of their viewing experience with the four video clips and with the two contrasting viewing conditions (i.e., “active–fixed” and “passive–fixed”). It was found that the participants' naturalistic viewing experience of the video clips was substantially more immersive with active viewing than with passive and fixed viewing. The RSA using the RDM codes revealed the brain regions associated with the viewing experience, including eye movement and spatial navigation in the superior frontal area (of Brodmann's area 6) and the inferior/superior parietal areas, respectively. Brain regions potentially associated with cognitive and affective processing during the viewing of the video, such as the default-mode networks and insular/Rolandic operculum areas, were also identified. To the best of our knowledge, this is the first study that has used the participants' eye movements to interactively change their FoV for 360° panoramic video clips in real-time. Our method of utilizing the MRI environment can be further extended to other environments such as electroencephalography and behavioral research. It would also be feasible to apply our method to virtual reality and/or augmented reality systems to maximize user experience based on their eye movement. •A novel naturalistic viewing based on 360° panoramic video and eye-gaze tracking was developed.•A representational dissimilarity matrix (RDM) was used to encode all of the experimental conditions.•Two-step multiple regression using the RDM codes was applied to address multicollinearity.•Our proposed method substantially enhanced the naturalistic viewing experience.•Our proposed method can also be employed in virtual reality and/or augmented reality.

Background/Objectives: Frank’s sign, a diagonal earlobe crease, may reflect systemic microvascular dysfunction. We investigated whether Frank’s sign serves as a clinical marker of white matter hyperintensity (WMH) burden in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), a monogenic model of pure cerebral small vessel disease. Methods: We analyzed 81 genetically confirmed CADASIL patients (61.8 ± 12.6 years, 40.7% female) and 54 age/sex-matched controls (70.3 ± 6.6 years, 48.1% female). Frank’s sign was detected using deep learning from brain MRI-reconstructed 3D facial surfaces. WMH volumes were automatically quantified and adjusted for confounders using Random Forest regression residuals. We compared Frank’s sign prevalence between groups, assessed within-CADASIL associations, and evaluated dose–response relationships across WMH tertiles. Results: Frank’s sign prevalence was significantly higher in CADASIL versus controls (66.7% vs. 42.6%, p = 0.020), with strengthened association after multivariate adjustment (OR = 4.214, 95% CI: 1.128–15.733, p = 0.032). Within CADASIL, Frank’s sign-positive patients showed 72% greater WMH burden (51.5 ± 27.1 vs. 30.0 ± 26.1 mL, p < 0.001) and lower Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total scores (57.7 ± 19.6 vs. 71.2 ± 22.8, p = 0.006), but similar lacunes, microbleeds, and hippocampal volumes. A robust dose–response relationship emerged across WMH tertiles, with Frank’s sign prevalence increasing from 37.0% (lowest) to 74.1% (highest tertile; adjusted OR = 3.571, 95% CI: 1.134–11.253, p = 0.030). Conclusions: Frank’s sign represents an accessible biomarker of WMH burden in CADASIL, demonstrating disease-specificity and dose–response characteristics independent of vascular risk factors. The automated MRI-based detection method of Frank’s sign enables retrospective analysis of existing neuroimaging databases, transforming a bedside observation into a quantifiable neuroimaging biomarker for genetic small vessel disease stratification.

Frank’s sign (FS) is a diagnostic marker associated with aging and various health conditions. Despite its clinical significance, there lacks a standardized method for its identification. This study aimed to develop a deep learning model for automated FS detection in 3D facial images derived from MRI scans. Four deep learning architectures were evaluated for FS segmentation on a dataset of 400 brain MRI scans. The optimal model was subsequently validated on two external datasets, comprising 300 brain MRI scans each with varying FS presence. Dice similarity coefficient (DSC) and receiver operating characteristic (ROC) analysis were employed to assess model performance. The U-net architecture demonstrated superior performance in terms of accuracy and efficiency. On the validation datasets, the model achieved a DSC of 0.734, an intra-class correlation coefficient of 0.865, and an area under the ROC curve greater than 0.9 for FS detection. Additionally, the model identified optimal voxel thresholds for accurate FS classification, resulting in high sensitivity, specificity, and accuracy metrics. This study successfully developed a deep learning model for automated FS segmentation in MRI scans. This tool has the potential to enhance FS identification in clinical practice and contribute to further research on FS and its associated health implications.

Obesity is a major risk factor for developing nonalcoholic fatty liver disease (NAFLD). NAFLD is the most common form of chronic liver disease and is closely associated with insulin resistance, ultimately leading to cirrhosis and hepatocellular carcinoma. However, knowledge of the intracellular regulators of obesity-linked fatty liver disease remains incomplete. Here we showed that hepatic Rho-kinase 1 (ROCK1) drives obesity-induced steatosis in mice through stimulation of de novo lipogenesis. Mice lacking ROCK1 in the liver were resistant to diet-induced obesity owing to increased energy expenditure and thermogenic gene expression. Constitutive expression of hepatic ROCK1 was sufficient to promote adiposity, insulin resistance, and hepatic lipid accumulation in mice fed a high-fat diet. Correspondingly, liver-specific ROCK1 deletion prevented the development of severe hepatic steatosis and reduced hyperglycemia in obese diabetic (ob/ob) mice. Of pathophysiological significance, hepatic ROCK1 was markedly upregulated in humans with fatty liver disease and correlated with risk factors clustering around NAFLD and insulin resistance. Mechanistically, we found that hepatic ROCK1 suppresses AMPK activity and a ROCK1/AMPK pathway is necessary to mediate cannabinoid-induced lipogenesis in the liver. Furthermore, treatment with metformin, the most widely used antidiabetes drug, reduced hepatic lipid accumulation by inactivating ROCK1, resulting in activation of AMPK downstream signaling. Taken together, our findings establish a ROCK1/AMPK signaling axis that regulates de novo lipogenesis, providing a unique target for treating obesity-related metabolic disorders such as NAFLD.

The triple networks, namely the default-mode network (DMN), the central executive network (CEN), and the salience network (SN), play crucial roles in disorders of the brain, as well as in basic neuroscientific processes such as mindfulness. However, currently, there is no consensus on the underlying functional features of the triple networks associated with mindfulness. In this study, we tested the hypothesis that (a) the partial regression coefficient (i.e., slope): from the SN to the DMN, mediated by the CEN, would be one of the potential mindfulness features in the real-time functional magnetic resonance imaging (rtfMRI) neurofeedback (NF) setting, and (b) this slope level may be enhanced by rtfMRI-NF training. Sixty healthy mindfulness-naïve males participated in an MRI session consisting of two non-rtfMRI-runs, followed by two rtfMRI-NF runs and one transfer run. Once the regions-of-interest of each of the triple networks were defined using the non-rtfMRI-runs, the slope level was calculated by mediation analysis and used as neurofeedback information, in the form of a thermometer bar, to assist with participant mindfulness during the rtfMRI-NF runs. The participants were asked to increase the level of the thermometer bar while deploying a mindfulness strategy, which consisted of focusing attention on the physical sensations of breathing. rtfMRI-NF training was conducted as part of a randomized controlled trial design, in which participants were randomly assigned to either an experimental group or a control group. The participants in the experimental group received contingent neurofeedback information, which was obtained from their own brain signals, whereas the participants in the control group received non-contingent neurofeedback information that originated from matched participants in the experimental group. Our results indicated that the slope level from the SN to the DMN, mediated by the CEN, was associated with mindfulness score (rtfMRI-NF runs: r = 0.53, p = 0.007; p-value was corrected from 10,000 random permutations) and with task-performance feedback score (rtfMRI-NF run: r = 0.61, p = 0.001) in the experimental group only. In addition, during the rtfMRI-NF runs the level of the partial regression coefficient feature was substantially increased in the experimental group compared to the control group (p < 0.05 from the paired t-test; the p-value was corrected from 10,000 random permutations). To the best of our knowledge, this is the first study to demonstrate a partial regression coefficient feature of mindfulness in the rtfMRI-NF setting obtained by triple network mediation analysis, as well as the possibility of enhancement of the partial regression coefficient feature by rtfMRI-NF training. •Mediation analysis using triple networks was employed to estimate functional feature of mindfulness (MF).•Real-time fMRI (rtfMRI) neurofeedback (NF) training based on this functional feature of MF was presented.•The partial regression coefficient from the SN to the DMN, mediated by the CEN appeared to be a potential feature of MF.•The validity of this functional feature of MF was evaluated by comparing alternative functional connectivity levels in the triple networks.•The possibility of enhancement of this functional feature of MF was demonstrated via rtfMRI-NFbased training.