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In this paper, we present a framework of modeling memristor noise for circuit simulators using physics-informed neural networks (PINNs). The variability of the memristor that is directly related to the neuromorphic system can be handled with this approach. The memristor noise model is transformed into a Fokker–Planck equation (FPE) from a probabilistic perspective. The translated equations are physically interpreted through the PINN. The weights and biases extracted from the PINN are implemented in Verilog-A through simple operations. The characteristics of the stochastic system under the noise are obtained by integrating the probability density function. This approach allows for the unification of different memristor models and the analysis of the effects of noise.

Introduction: TMEM16 family proteins are involved in a variety of functions, including ion transport, phospholipid scrambling, and the regulation of membrane proteins. Among them, TMEM16F has dual functions as a phospholipid scramblase and a nonselective ion channel. TMEM16F is widely expressed and functions in platelet activation during blood clotting, bone formation, and T cell activation. Despite the functional importance of TMEM16F, the modulators of TMEM16F function have not been sufficiently studied.Method: In this study, we generated TMEM16F-specific affibodies by performing phage display with brain-specific TMEM16F (hTMEM16F) variant 1 purified from GnTi− cells expressing this variant in the presence of digitonin as a detergent. Purified human TMEM16F protein, which was proficient in transporting phospholipids in a Ca2+-dependent manner in proteoliposomes, was coated onto plates and then the phage library was added to fish out TMEM16F-binding affibodies. For the validation of interaction between affibodies and TMEM16F proteins, ELISA, bio-layer interferometry, and size exclusion chromatography were conducted.Results and Discussion: As a result, the full sequences of 38 candidates were acquired from 98 binding candidates. Then, we selected 10 candidates and purified seven of them from E. coli expressing these candidates. Using various assays, we confirmed that two affibodies bound to human TMEM16F with high affinity. These affibodies can be useful for therapeutical and diagnostic applications of TMEM16F-related cancer and neurodegenerative diseases. Future studies will be required to investigate the effects of these affibodies on TMEM16F function.

Abstract In age-related neurodegenerative diseases, pathology often develops slowly across the lifespan. As one example, in diseases such as Alzheimer’s, vascular decline is believed to onset decades ahead of symptomology. However, challenges inherent in current microscopic methods make longitudinal tracking of such vascular decline difficult. Here, we describe a suite of methods for measuring brain vascular dynamics and anatomy in mice for over seven months in the same field of view. This approach is enabled by advances in optical coherence tomography (OCT) and image processing algorithms including deep learning. These integrated methods enabled us to simultaneously monitor distinct vascular properties spanning morphology, topology, and function of the microvasculature across all scales: large pial vessels, penetrating cortical vessels, and capillaries. We have demonstrated this technical capability in wild-type and 3xTg male mice. The capability will allow comprehensive and longitudinal study of a broad range of progressive vascular diseases, and normal aging, in key model systems.

Memristor devices have diverse physical models depending on their structure. In addition, the physical properties of memristors are described using complex differential equations. Therefore, it is necessary to integrate the various models of memristor into an unified physics-based model. In this paper, we propose a physics-informed neural network (PINN)-based compact memristor model. PINNs can solve complex differential equations intuitively and with ease. This methodology is used to conduct memristor physical analysis. The weight and bias extracted from the PINN are implemented in a Verilog-A circuit simulator to predict memristor device characteristics. The accuracy of the proposed model is verified using two memristor devices. The results show that PINNs can be used to extensively integrate memristor device models.

Purpose Wordvice AI Proofreader is a recently developed web-based artificial intelligence-driven text processor that provides real-time automated proofreading and editing of user-input text. It aims to compare its accuracy and effectiveness to expert proofreading by human editors and two other popular proofreading applications—automated writing analysis tools of Google Docs, and Microsoft Word. Because this tool was primarily designed for use by academic authors to proofread their manuscript drafts, the comparison of this tool’s efficacy to other tools was intended to establish the usefulness of this particular field for these authors. Methods We performed a comparative analysis of proofreading completed by the Wordvice AI Proofreader, by experienced human academic editors, and by two other popular proofreading applications. The number of errors accurately reported and the overall usefulness of the vocabulary suggestions was measured using a General Language Evaluation Understanding metric and open dataset comparisons. Results In the majority of texts analyzed, the Wordvice AI Proofreader achieved performance levels at or near that of the human editors, identifying similar errors and offering comparable suggestions in the majority of sample passages. The Wordvice AI Proofreader also had higher performance and greater consistency than that of the other two proofreading applications evaluated. Conclusion We found that the overall functionality of the Wordvice artificial intelligence proofreading tool is comparable to that of a human proofreader and equal or superior to that of two other programs with built-in automated writing evaluation proofreaders used by tens of millions of users: Google Docs and Microsoft Word.

Photovoltaic systems are emerging as an important device to address the environmental pollution generated from conventional energy production. The objectives of this study are to accurately predict the power of photovoltaic systems under partial shading conditions and to model high-efficiency photovoltaic systems. First, the power loss under partial shading conditions was addressed using a bypass diode. In previous studies, for the power prediction, one or two parameters were trained through artificial neural networks. In this study, we employ five main parameters to improve the accuracy: the photo-current (Iph), diode saturation current (I0 ), diode idealization factor (n), series resistance (Rs), and shunt resistance (Rsh). Compared to the results of previous studies, the proposed model yielded consistent results. As a result, more accurate power predictions are possible with variations in temperature and irradiation.

Introduction: To enhance photothermal treatment (PTT) efficiency, a delivery method that uses cell vector for nanoparticles (NPs) delivery has drawn attention and studied widely in recent years. Objectives: In this study, we demonstrated the feasibility of M1 activated macrophage as a live vector for delivering NPs and investigated the effect of NPs loaded M1 stimulated by Lipopolysaccharide on PTT efficiency in vivo. Methods: M1 was used as a live vector for delivering NPs and further to investigate the effect of NPs loaded M1 on PTT efficiency. Non-activated macrophage (MФ) was stimulated by lipopolysaccharide (LPS) into M1 and assessed for tumor cell phagocytic capacity towards NPs Results: We found M1 exhibited a 20-fold higher uptake capacity of NPs per cell volume and 2.9-fold more active infiltration into the tumor site, compared with non-activated macrophage MФ. We injected M1 cells peritumorally and observed that these cells penetrated into the tumor mass within 12 h. Then, we conducted PTT using irradiation of a near-infrared laser for 1 min at 1 W/cm2. As a result, we confirmed that using M1 as an active live vector led to a more rapid reduction in tumor size within 1 day indicating that the efficacy of PTT with NPs-loaded M1 is higher than that with NPs-loaded MФ. Conclusion: Our study demonstrated the potential role of M1 as a live vector for enhancing the feasibility of PTT in cancer treatment.

Heart regeneration after myocardial infarction (MI) using human stem cell-derived cardiomyocytes (CMs) is rapidly accelerating with large animal and human clinical trials. However, vascularization methods to support the engraftment, survival, and development of implanted CMs in the ischemic environment of the infarcted heart remain a key and timely challenge. To this end, we developed a dual remuscularization-revascularization therapy that is evaluated in a rat model of ischemia-reperfusion MI. This study details the differentiation of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) for engineering cardiac tissue containing patterned engineered vessels 400 μm in diameter. Vascularized engineered human myocardial tissues (vEHMs) are cultured in static conditions or perfused in vitro prior to implantation and evaluated after two weeks. Immunohistochemical staining indicates improved engraftment of hiPSC-CMs in in vitro-perfused vEHMs with greater expression of SMA+ vessels and evidence of inosculation. Three-dimensional vascular reconstructions reveal less tortuous and larger intra-implant vessels, as well as an improved branching hierarchy in in vitro-perfused vEHMs relative to non-perfused controls. Exploratory RNA sequencing of explanted vEHMs supports the hypothesis that co-revascularization impacts hiPSC-CM development in vivo. Our approach provides a strong foundation to enhance vEHM integration, develop hierarchical vascular perfusion, and maximize hiPSC-CM engraftment for future regenerative therapy.

We present a deep learning and simulation-based method to measure cortical capillary red blood cell (RBC) flux using Optical Coherence Tomography (OCT). This method is more accurate than the traditional peak-counting method and avoids any user parametrization, such as a threshold choice. We used data that was simultaneously acquired using OCT and two-photon microscopy to uncover the distribution of parameters governing the height, width, and inter-peak time of peaks in OCT intensity associated with the passage of RBCs. This allowed us to simulate thousands of time-series examples for different flux values and signal-to-noise ratios, which we then used to train a 1D convolutional neural network (CNN). The trained CNN enabled robust measurement of RBC flux across the entire network of hundreds of capillaries.

Abstract Recently, viral infectious diseases, including COVID‐19 and Influenza, are the subjects of major concerns worldwide. One strategy for addressing these concerns focuses on nasal vaccines, which have great potential for achieving successful immunization via safe, easy, and affordable approaches. However, conventional nasal vaccines have major limitations resulting from fast removal when pass through nasal mucosa and mucociliary clearance hindering their effectiveness. Herein a nanoparticulate vaccine (NanoVac) exhibiting photochemical immunomodulation and constituting a new self‐assembled immunization system of a photoactivatable polymeric adjuvant with influenza virus hemagglutinin for efficient nasal delivery and antigen‐specific immunity against pathogenic influenza viruses is described. NanoVac increases the residence period of antigens and further enhances by spatiotemporal photochemical modulation in the nasal cavity. As a consequence, photochemical immunomodulation of NanoVacs successfully induces humoral and cellular immune responses followed by stimulation of mature dendritic cells, plasma cells, memory B cells, and CD4+ and CD8+ T cells, resulting in secretion of antigen‐specific immunoglobulins, cytokines, and CD8+ T cells. Notably, challenge with influenza virus after nasal immunization with NanoVacs demonstrates robust prevention of viral infection. Thus, this newly designed vaccine system can serve as a promising strategy for developing vaccines that are active against current hazardous pathogen outbreaks and pandemics.

This study proposes a method to acquire an accurate 3D point cloud in radioactive and underwater environments using industrial 3D scanners. Applications of robotic systems at nuclear facility dismantling require 3D imaging equipment for localization of target structures in radioactive and underwater environments. The use of industrial 3D scanners may be a better option than developing prototypes for researchers with basic knowledge. However, such industrial 3D scanners are designed to operate in normal environments and cannot be used in radioactive and underwater environments. Modifications to environmental obstacles also suffer from hidden technical details of industrial 3D scanners. This study shows how 3D imaging equipment based on the industrial 3D scanner satisfies the requirements of the remote dismantling system, using a robotic system despite insufficient environmental resistance and hidden technical details of industrial 3D scanners. A housing unit is designed for waterproofing and radiation protection using windows, mirrors and shielding. Shielding protects the industrial 3D scanner from radiation damage. Mirrors reflect the light required for 3D scanning because shielding blocks the light. Windows in the waterproof housing also transmit the light required for 3D scanning with the industrial 3D scanner. The basic shielding thickness calculation method through the experimental method is described, including the analysis of the experimental results. The method for refraction correction through refraction modeling, measurement experiments and parameter studies are described. The developed 3D imaging equipment successfully satisfies the requirements of the remote dismantling system: waterproof, radiation resistance of 1 kGy and positional accuracy within 1 mm. The proposed method is expected to provide researchers with an easy approach to 3D scanning in radioactive and underwater environments.

A new machine learning model is presented to predict the dynamic behavior of threshold voltage shifts induced by bias temperature instability (BTI) in CMOS devices. The model is constructed by combining physical theories with machine learning such as an artificial neural network and a Gaussian mixture model (GMM). To enlarge the capture–emission energy (CEE) window and to perform independent estimations of two distinct components of CEE distribution, the GMM with soft clustering is utilized, enabling full lifetime modeling of BTI. By training the CEE map with the consideration of the occupancy probability of traps and then executing the integration along the CEE, the threshold voltage shifts are obtained. This approach forms data-driven modeling that naturally encodes underlying physical theories as prior information. The resulting model exhibits a good performance for predicting the dynamic characteristics of BTI under various stress-recovery conditions.

“Prick of conscience” is a phrase to express feelings of guilt in both English and Korean. Particularly in South Korea, guilt is metaphorically associated with a sense of touch by pricking. Koreans commonly express feelings of guilt by using the metaphor, “It pricks my conscience.” Across three studies, we examined whether prick of conscience (i.e., feelings of guilt) is grounded in bodily experiences of physical prick (e.g., a needle prick), using a sample of Koreans. Participants who recalled past unethical acts were less likely to choose a needle prick rather than medication as a treatment for indigestion, whereas those who recalled ethical acts presented no significant difference in their willingness to receive either treatment (Study 1). Participants who decided to lie sensed the finger prick deeper and felt more pain as compared to those in the truth group or the control group (Study 2). Lastly, participants who had the finger prick rendered harsher moral judgments than participants in the control condition (Study 3). In line with an embodied cognition framework, these findings suggest that prick of conscience is not just a linguistic metaphor but can be embodied as physical sensations in forms of pricking.

[This corrects the article DOI: 10.1371/journal.pone.0222692.].

Optical coherence tomography angiography (OCTA) has emerged as an advanced in vivo imaging modality, which is widely used for the clinic ophthalmology and neuroscience research in the rodent brain cortex among others. Based on the high numerical aperture (NA) probing lens and the motion-corrected algorithms, a high-resolution imaging technique called OCT micro-angiography is applied to resolve the small blood capillary vessels ranging from 5μm to 10μm in diameter. As OCT-based techniques are recently evolving further from the structural imaging of capillaries toward spatio-temporal dynamic imaging of blood flow in capillaries, here we present a review on the latest techniques for the dynamic flow imaging. Studies on capillary blood flow using these techniques will help us better understand the roles of capillary blood flow for normal functioning of the brain as well as how it malfunctions in diseases.

A single unified analytical model is presented to predict the shot noise for both the source-to-drain (SD) and the gate tunneling current in sub-10 nm MOSFETs with ultrathin oxide. Based on the Landauer formula, the model is constructed from the sequential tunneling flows associated with number fluctuations. This approach provides the analytical formulation of the shot noise as a function of the applied voltages. The model performs well in predicting the Fano factor for shot noise in the SD and gate tunneling currents.

Herein, we describe the anesthetic management during the first combined heart-liver transplant (CHLT) performed in Korea. Though CHLT is a rare procedure, accumulating evidence suggests that it is a feasible option for patients with coexisting heart and liver failure. A 45-year-old female patient presented with severe cardiac dysfunction requiring extracorporeal membrane oxygenation (ECMO) support and secondary congestive hepatopathy. The patient underwent consecutive heart and liver transplantation using extracorporeal circulatory devices—heart transplant with cardiopulmonary bypass, and liver transplant with peripheral ECMO. In this case report, we focus on the specific anesthetic considerations for CHLT pertaining to the challenges associated with dual pathophysiology.

[This corrects the article DOI: 10.1371/journal.pone.0220810.].

Freely crawling cells are often viewed as randomly moving Brownian particles but they generally exhibit some directional persistence. This property is often related to their zigzag motile behaviors that can be described as a noisy but temporally structured sequence of "runs" and "turns." However, its underlying biophysical mechanism is largely unexplored. Here, we carefully investigate the collective actin wave dynamics associated with the zigzag-crawling movements of microglia (as primary brain immune cells) employing a number of different quantitative imaging modalities including synthetic aperture microscopy and optical diffraction tomography, as well as conventional fluorescence imaging and scanning electron microscopy. Interestingly, we find that microglia exhibit two distinct types of actin waves working at two quite different time scales and locations, and they seem to serve different purposes. One type of actin waves is fast "peripheral ruffles" arising spontaneously with an oscillating period of about 6 seconds at some portion of the leading edge of crawling microglia, where the vigorously biased peripheral ruffles seem to set the direction of a new turn (in 2-D free space). When the cell turning events are inhibited with a physical confinement (in 1-D track), the peripheral ruffles still exist at the leading edge with no bias but showing phase coherence in the cell crawling direction. The other type is "dorsal actin waves" which also exhibits an oscillatory behavior but with a much longer period of around 2 minutes compared to the fast "peripheral ruffles". Dorsal actin waves (whether the cell turning events are inhibited or not) initiate in the lamellipodium just behind the leading edge, travelling down toward the core region of the cell and disappear. Such dorsal wave propagations seem to be correlated with migration of the cell. Thus, we may view the dorsal actin waves are connected with the "run" stage of cell body, whereas the fast ruffles at the leading front are involved in the "turn" stage.

A new approach for modeling low frequency noise is presented to enable the predictions of noise behavior from negative bias temperature instability (NBTI). The noise model is based on a capture-emission energy (CEE) map describing the probability density function of widely distributed defect capture-emission activation energies. To enlarge the capture-emission energy window and to perform the accurate estimation of the recoverable component of CEE, the Gaussian mixture model (GMM) is applied to the CEE map. This approach provides an efficient identification of noise sources and an in-depth noise analysis under both stationary and cyclo-stationary conditions.

Fluorescence monitoring of endogenous microRNA (miRNA or miR) activity related to neuronal development using nano-sized materials provides crucial information on miRNA expression patterns in a noninvasive manner. In this study, we report a new method to monitor intracellular miRNA124a using quantum dot-based molecular beacon (R9-QD-miR124a beacon). The R9-QD-miR124a beacon was constructed using QDs and two probes, miR124a-targeting oligomer and arginine rich cell-penetrating peptide (R9 peptide). The miR124a-targeting oligomer contains a miR124a binging sequence and a black hole quencher 1 (BHQ1). In the absence of target miR124a, the R9-QD-miR124a beacon forms a partial duplex beacon and remained in quenched state because the BHQ1 quenches the fluorescence signal of the R9-QD-miR124a beacon. The binding of miR124a to the miR124a binding sequence of the miR124a-targeting oligomer triggered the separation of the BHQ1 quencher and subsequent signal-on of a red fluorescence signal. Moreover, enhanced cellular uptake was achieved by conjugation with the R9 peptide, which resulted in increased fluorescent signal of the R9-QD-miR124a beacons in P19 cells during neurogenesis due to the endogenous expression of miR124a.

Studying gene expression profile in a single cancer cell is important because multiple genes are associated with cancer development. Quantum dots (QDs) have been utilized as biological probes for imaging and detection. QDs display specific optical and electrical properties that depend on their size that can be applied for imaging and sensing applications. In this study, simultaneous imaging of the cancer biomarkers, tenascin-C and nucleolin, was performed using two types of aptamer-conjugated QDs. The simultaneous imaging of these two different cancer markers in three cancer cell lines was reliable and cell line-specific. Current requirements for cancer imaging technologies include the need for simple preparation methods and the ability to detect multiple cancer biomarkers and evaluate their intracellular localizations. The method employed in this study is a feasible solution to these requirements.

Evaluation of the function of microRNAs (miRNAs or miRs) through miRNA expression profiles during neuronal differentiation plays a critical role not only in identifying unique miRNAs relevant to cellular development but also in understanding regulatory functions of the cell‐specific miRNAs in living organisms. Here, we examined the microarray‐based miRNA expression profiles of G2 cells (recently developed human neural stem cells) and monitored the expression pattern of known neuron‐specific miR‐9 and miR‐124a during neuronal differentiation of G2 cellsin vitro andin vivo. Of 500 miRNAs analyzed by microarray of G2 cells, the expression of 90 miRNAs was significantly increased during doxycycline‐dependent neuronal differentiation of G2 cells and about 60 miRNAs showed a gradual enhancement of gene expression as neuronal differentiation progressed. Real‐time PCR showed that expression of endogenous mature miR‐9 was continuously and gradually increased in a pattern dependent on the period of neuronal differentiation of G2 cells while the increased expression of neuron‐specific mature miR‐124a was barely observed during neurogenesis. Our recently developed miRNA reporter imaging vectors (CMV/Gluc/3×PT_miR‐9 and CMV/Gluc/3×PT_miR‐124a) containing Gaussia luciferase, CMV promoter and three copies of complementary nucleotides of each corresponding miRNA showed that luciferase activity from CMV/Gluc/3×PT_miR‐9 was gradually decreased bothin vitro andin vivo in G2 cells induced to differentiate into neurons. However,in vitro andin vivo bioluminescence signals for CMV/Gluc/3×PT_miR‐124a were not significantly different between undifferentiated and differentiated G2 cells. Our results demonstrate that biogenesis of neuron‐specific miR‐124a is not necessary for doxycycline‐dependent neurogenesis of G2 cells.