Effect of Crystallization Modes in TIPS-Pentacene/Insulating Polymer Blends on the Gas Sensing Properties of Organic Field-Effect Transistors

Blending organic semiconductors with insulating polymers has been known to be an effective way to overcome the disadvantages of single-component organic semiconductors for high-performance organic field-effect transistors (OFETs). We show that when a solution processable organic semiconductor (6,13-bis(triisopropylsilylethynyl)pentacene, TIPS-pentacene) is blended with an insulating polymer (PS), morphological and structural characteristics of the blend films could be significantly influenced by the processing conditions like the spin coating time. Although vertical phase-separated structures (TIPS-pentacene-top/PS-bottom) were formed on the substrate regardless of the spin coating time, the spin time governed the growth mode of the TIPS-pentacene molecules that phase-separated and crystallized on the insulating polymer. Excess residual solvent in samples spun for a short duration induces a convective flow in the drying droplet, thereby leading to one-dimensional (1D) growth mode of TIPS-pentacene crystals. In contrast, after an appropriate spin-coating time, an optimum amount of the residual solvent in the film led to two-dimensional (2D) growth mode of TIPS-pentacene crystals. The 2D spherulites of TIPS-pentacene are extremely advantageous for improving the field-effect mobility of FETs compared to needle-like 1D structures, because of the high surface coverage of crystals with a unique continuous film structure. In addition, the porous structure observed in the 2D crystalline film allows gas molecules to easily penetrate into the channel region, thereby improving the gas sensing properties

Grain Boundary Induced Bias Instability in Soluble Acene-Based Thin-Film Transistors

Since the grain boundaries (GBs) within the semiconductor layer of organic field-effect transistors (OFETs) have a strong influence on device performance, a substantial number of studies have been devoted to controlling the crystallization characteristics of organic semiconductors. We studied the intrinsic effects of GBs within 5,11-bis(triethylsilylethynyl) anthradithiophene (TES-ADT) thin films on the electrical properties of OFETs. The GB density was easily changed by controlling nulceation event in TES-ADT thin films. When the mixing time was increased, the number of aggregates in as-spun TES-ADT thin films were increased and subsequent exposure of the films to 1,2-dichloroethane vapor led to a significant increase in the number of nuleation sites, thereby increasing the GB density of TES-ADT spherulites. The density of GBs strongly influences the angular spread and crystallographic orientation of TES-ADT spherulites. Accordingly, the FETs with higher GB densities showed much poorer electrical characteristics than devices with lower GB density. Especially, GBs provide charge trapping sites which are responsible for bias-stress driven electrical instability. Dielectric surface treatment with a polystyrene brush layer clarified the GB-induced charge trapping by reducing charge trapping at the semiconductor-dielectric interface. Our study provides an understanding on GB induced bias instability for the development of high performance OFETs

Real-time monitoring of tissue property in a liver phantom using an internal electrode and weighted frequency difference conductivity during microwave ablation

We measured the time difference and weighted frequency difference conductivity images to monitor the changes of temperature and tissue property in a liver phantom due to the microwave ablation. Pixels in regions of interest were compared between conventional boundary surface electrode method and focused configuration with an internal electrode

Continuous non-destructive conductivity monitoring of chondrogenesis using bioimpedance tensor probe

A continuous non-destructive monitoring method is required to apply proper feedback controls during chondrogenesis. We measured the apparent conductivity and the amount of anisotropy on the top and bottom surfaces of samples in the chondrogenesis process to evaluate the ECM structure and composition changes. We compared them with histological trait to analyse the results

Continuous Nondestructive Monitoring Method Using the Reconstructed Three-Dimensional Conductivity Images via GREIT for Tissue Engineering

A continuous Nondestructive monitoring method is required to apply proper feedback controls during tissue regeneration. Conductivity is one of valuable information to assess the physiological function and structural formation of regenerated tissues or cultured cells. However, conductivity imaging methods suffered from inherited ill-posed characteristics in image reconstruction, unknown boundary geometry, uncertainty in electrode position, and systematic artifacts. In order to overcome the limitation of microscopic electrical impedance tomography (micro-EIT), we applied a 3D-specific container with a fixed boundary geometry and electrode configuration to maximize the performance of Graz consensus reconstruction algorithm for EIT (GREIT). The separation of driving and sensing electrodes allows us to simplify the hardware complexity and obtain higher measurement accuracy from a large number of small sensing electrodes. We investigated the applicability of the GREIT to 3D micro-EIT images via numerical simulations and large-scale phantom experiments. We could reconstruct multiple objects regardless of the location. The resolution was 5 mm3 with 30 dB SNR and the position error was less than 2.54 mm. This shows that the new micro-EIT system integrated with GREIT is robust with the intended resolution. With further refinement and scaling down to a microscale container, it may be a continuous nondestructive monitoring tool for tissue engineering applications

Altered resting-state connectivity in subjects at ultra-high risk for psychosis: an fMRI study

<p>Abstract</p> <p>Background</p> <p>Individuals at ultra-high risk (UHR) for psychosis have self-disturbances and deficits in social cognition and functioning. Midline default network areas, including the medial prefrontal cortex and posterior cingulate cortex, are implicated in self-referential and social cognitive tasks. Thus, the neural substrates within the default mode network (DMN) have the potential to mediate self-referential and social cognitive information processing in UHR subjects.</p> <p>Methods</p> <p>This study utilized functional magnetic resonance imaging (fMRI) to investigate resting-state DMN and task-related network (TRN) functional connectivity in 19 UHR subjects and 20 matched healthy controls. The bilateral posterior cingulate cortex was selected as a seed region, and the intrinsic organization for all subjects was reconstructed on the basis of fMRI time series correlation.</p> <p>Results</p> <p>Default mode areas included the posterior/anterior cingulate cortices, the medial prefrontal cortex, the lateral parietal cortex, and the inferior temporal region. Task-related network areas included the dorsolateral prefrontal cortex, supplementary motor area, the inferior parietal lobule, and middle temporal cortex. Compared to healthy controls, UHR subjects exhibit hyperconnectivity within the default network regions and reduced anti-correlations (or negative correlations nearer to zero) between the posterior cingulate cortex and task-related areas.</p> <p>Conclusions</p> <p>These findings suggest that abnormal resting-state network activity may be related with the clinical features of UHR subjects. Neurodevelopmental and anatomical alterations of cortical midline structure might underlie altered intrinsic networks in UHR subjects.</p

Cystatin C, a novel indicator of renal function, reflects severity of cerebral microbleeds

Background: Chronic renal insufficiency, diagnosed using creatinine based estimated glomerular filtration rate (GFR) or microalbumiuria, has been associated with the presence of cerebral microbleeds (CMBs). Cystatin C has been shown to be a more sensitive renal indicator than conventional renal markers. Under the assumption that similar pathologic mechanisms of the small vessel exist in the brain and kidney, we hypothesized that the levels of cystatin C may delineate the relationship between CMBs and renal insufficiency by detecting subclinical kidney dysfunction, which may be underestimated by other indicators, and thus reflect the severity of CMBs more accurately. Methods: Data was prospectively collected for 683 patients with ischemic stroke. The severity of CMBs was categorized by the number of lesions. Patients were divided into quartiles of cystatin C, estimated GFR and microalbumin/creatinine ratios. Ordinal logistic regression analysis was used to examine the association of each renal indicator with CMBs. Results: In models including both quartiles of cystatin C and estimated GFR, only cystatin C quartiles were significant (the highest vs. the lowest, adjusted OR, 1.88; 95% CI 1.05-3.38; p = 0.03) in contrast to estimated GFR (the highest vs. the lowest, adjusted OR, 1.28; 95% CI 0.38-4.36; p = 0.70). A model including both quartiles of cystatin C and microalbumin/creatinine ratio also showed that only cystatin C quartiles was associated with CMBs (the highest vs. the lowest, adjusted OR, 2.06; 95% CI 1.07-3.94; p = 0.03). These associations were also observed in the logistic models using log transformed-cystatin C, albumin/creatinine ratio and estimated GFR as continuous variables. Cystatin C was a significant indicator of deep or infratenorial CMBs, but not strictly lobar CMBs. In addition, cystatin C showed the greatest significance in c-statistics for the presence of CMBs (AUC = 0.73 ± 0.03; 95% CI 0.66-0.76; p = 0.02). Conclusion: Cystatin C may be the most sensitive indicator of CMB severity among the renal disease markers.Peer Reviewe

Reduced cortical folding of the anterior cingulate cortex in obsessive-compulsive disorder

Background: Anterior cingulate cortex (ACC) abnormalities have been implicated consistently in the pathophysiology of obsessive-compulsive disorder (OCD), yet it remains unclear whether these abnormalities originated during early neurodevelopment. In this study, we examined the ACC sulcal/gyral patterns to investigate whether neurodevelopmental anomalies of the ACC were present in patients with OCD. We hypothesized that patients with OCD would show reduced cortical folding of the ACC compared with controls. Methods: We used magnetic resonance imaging (MRI) of 169 healthy volunteers and 110 patients with OCD to examine the paracingulate sulcus and cingulate sulcus. We assessed cortical folding patterns according to established classification criteria and constructed 3 categories of paracingulate sulcus morphology according to its presence and anteroposterior extent: "prominent," "present" and "absent." We classified the cingulate sulcus as "interrupted" or "continuous" according to the interruptions in its course. In addition, we evaluated ACC sulcal asymmetry based on interhemispheric comparisons of paracingulate sulcus morphology. Results: Analyses revealed that patients with OCD were significantly less likely than controls to show a well-developed left paracingulate sulcus: 50.0% of patients and 65.1% of controls showed a "prominent" or "present" paracingulate sulcus in the left hemisphere. However, there were no differences in regard to cingulate sulcus continuity, and patients also showed the same leftward ACC sulcal asymmetry as controls. Limitations: Our study was limited by the fact that we obtained the MRI scans from 2 different scanners, and we did not calculate cerebral fissurization as our study was restricted to 1 specific brain region. Moreover, patients and controls differed significantly in terms of sex ratio and IQ, although we controlled these variables as covariates. Conclusion: Our findings imply a subtle deviation in the early neurodevelopment of the ACC in patients with OCD, but the extent to which these anomalies contributed to the pathogenesis of OCD remains unclear. 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Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles

Background and purposeMultiple attempts at intracranial hemorrhage (ICH) detection using deep-learning techniques have been plagued by clinical failures. We aimed to compare the performance of a deep-learning algorithm for ICH detection trained on strongly and weakly annotated datasets, and to assess whether a weighted ensemble model that integrates separate models trained using datasets with different ICH improves performance.MethodsWe used brain CT scans from the Radiological Society of North America (27,861 CT scans, 3,528 ICHs) and AI-Hub (53,045 CT scans, 7,013 ICHs) for training. DenseNet121, InceptionResNetV2, MobileNetV2, and VGG19 were trained on strongly and weakly annotated datasets and compared using independent external test datasets. We then developed a weighted ensemble model combining separate models trained on all ICH, subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and small-lesion ICH cases. The final weighted ensemble model was compared to four well-known deep-learning models. After external testing, six neurologists reviewed 91 ICH cases difficult for AI and humans.ResultsInceptionResNetV2, MobileNetV2, and VGG19 models outperformed when trained on strongly annotated datasets. A weighted ensemble model combining models trained on SDH, SAH, and small-lesion ICH had a higher AUC, compared with a model trained on all ICH cases only. This model outperformed four deep-learning models (AUC [95% C.I.]: Ensemble model, 0.953[0.938–0.965]; InceptionResNetV2, 0.852[0.828–0.873]; DenseNet121, 0.875[0.852–0.895]; VGG19, 0.796[0.770–0.821]; MobileNetV2, 0.650[0.620–0.680]; p &lt; 0.0001). In addition, the case review showed that a better understanding and management of difficult cases may facilitate clinical use of ICH detection algorithms.ConclusionWe propose a weighted ensemble model for ICH detection, trained on large-scale, strongly annotated CT scans, as no model can capture all aspects of complex tasks