Characterization of Escherichia coli pathogenicity and drug resistance in yolk peritonitis.

Yolk Peritonitis can lead to a rapid decline in egg production, which seriously affects the health of laying hens and the profitability of chicken farms. Escherichia coli (E. coli) is the most common cause of yolk peritonitis in laying hens. In this study, bacterial samples were collected from the ovaries and fallopian tubes of laying hens with suspected yolk peritonitis from a laying farm in Jiangsu Province, and their pathogenicity and drug resistance were investigated. Initially, morphological and biochemical detection methods were employed to isolate and identify the pathogenic bacteria. The results showed that a total of 16 strains of E. coli were isolated from laying hens with yolk peritonitis. Subsequently, the drug resistance and pathogenicity of a randomly selected E. coli strain were analyzed and predicted by genome sequencing technology, and the drug resistance of E. coli was verified by drug sensitivity test and PCR. Finally, the virulence was verified by infection experiment in mice. The study revealed that the egg-yolk peritonitis in laying hens was caused by E. coli infection, and the genome sequencing analysis revealed that the bacteria had multidrug resistance and high virulence. The drug susceptibility testing indicates that E. coli exhibited resistance to aminoglycosides, ß-lactam, macrolides, fluoroquinolones, and sulfonamides. In this study, resistance genes including KdpE, aadA5, APH(3 ")-ID, APH(6)-ID, and TEM-1 were identified, and their expression levels varied across different stages of bacterial growth. The results of virulence analysis indicated a mortality rate of 50% in mice infected with E. coli at a concentration of 2.985 × 107 CFU/mL. E. coli infection resulted in damage to various tissues and organs in mice, with the intestinal tissue structure being the most severely affected. This study provides a reference for the study of drug resistance mechanisms in E. coli and provides valuable insights into the selection of drugs for the treatment of vitelline peritonitis.

A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm.

PIN diodes, due to their simple structure and variable resistance characteristics under high-frequency high-power excitation, are often used in radar front-end as limiters to filter high power microwaves (HPM) to prevent its power from entering the internal circuit and causing damage. This paper carries out theoretical derivation and research on the HPM effects of PIN diodes, and then uses an optimized neural network algorithm to replace traditional physical modeling to calculate and predict two types of HPM limiting indicators of PIN diode limiters. We proposes a neural network model for each of the following two prediction scenarios: in the scenario of time-junction temperature curves under different HPM irradiation, the weighted mean squared error (MSE) between the predicted values from the test dataset and the simulated values is below 0.004. While in predicting PIN limiter's power limitation threshold, insertion loss, and maximum isolation under different HPM irradiation, the MSE of the test set prediction values and simulation values are all less than 0.03. The method proposed in this research, which applies an optimized neural network algorithm to replace traditional physical modeling algorithms for studying the high-power microwave effects of PIN diode limiters, significantly improves the computational and simulation speed, reduces the calculation cost, and provides a new method for studying the high-power microwave effects of PIN diode limiters.

A novel angiotensin I-converting enzyme inhibitory peptide from walnut (Juglans sigillata) protein hydrolysates and its evaluation in Ang II-induced HUVECs and hypertensive rats.

This study aims to seek angiotensin-I-converting enzyme inhibitory (ACEi) peptides from walnut using different enzymatic hydrolysis, and further to validate the potent ACEi peptides identified and screened via peptidomics and in silico analysis against hypertension in spontaneously hypertensive rats (SHRs). Results showed that walnut protein hydrolysate (WPH) prepared by combination of alcalase and simulated gastrointestinal digestion exhibited high ACEi activity. WPH was separated via Sephadex-G25, and four peptides were identified, screened and verified based on their PeptideRanker score, structural characteristic and ACE inhibition. Interestingly, FDWLR showed the highest ACEi activity with IC50 value of 8.02 µg/mL, which might be related to its close affinity with ACE observed in molecular docking. Subsequently, high absorption and non-toxicity of FDWLR was predicted via in silico absorption, distribution, metabolism, excretion and toxicity. Furthermore, FDWLR exhibited positively vasoregulation in Ang II-induced human umbilical vein endothelial cells, and great blood pressure lowering effect in SHRs.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis.

AIM: Computer-aided drug design (CADD) is a drug design technique for computing ligand‒receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics. METHODS: A systematic review of studies published between 2000 and July 20, 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analysed using software such as Excel, VOSviewer, RStudio, and CiteSpace. RESULTS: A total of 2,031 publications were included. These publications primarily originated from 99 countries or regions, led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications. CONCLUSIONS: Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modelling, pharmacophore modelling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics direction for future development. Furthermore, this paper will be helpful for better understanding the frontiers and hotspots of CADD.

Prediction of electrical properties of GAAFET based on integrated learning model.

As device feature sizes continue to decrease and fin field effect transistors reach their physical limits, gate all around field effect transistors (GAAFETs) have emerged with larger gate control areas and stackable characteristics for better suppression of second-order effects such as short-channel effects due to their gate encircling characteristics. Traditional methods for studying the electrical characteristics of devices are mostly based on the technology computer-aided design. Still, it is not conducive to developing new devices due to its time-consuming and inefficient drawbacks. Deep learning (DL) and machine learning (ML) have been well-used in recent years in many fields. In this paper, we propose an integrated learning model that integrates the advantages of DL and ML to solve many problems in traditional methods. This integrated learning model predicts the direct current characteristics, capacitance characteristics, and electrical parameters of GAAFET better than those predicted by DL or ML methods alone, with a linear regression factor (R2) greater than 0.99 and very small root mean square error. The proposed integrated learning model achieves fast and accurate prediction of GAAFET electrical characteristics, which provides a new idea for device and circuit simulation and characteristics prediction in microelectronics.

A False Trigger-Strengthened and Area-Saving Power-Rail Clamp Circuit with High ESD Performance.

A power clamp circuit, which has good immunity to false trigger under fast power-on conditions with a 20 ns rising edge, is proposed in this paper. The proposed circuit has a separate detection component and an on-time control component which enable it to distinguish between electrostatic discharge (ESD) events and fast power-on events. As opposed to other on-time control techniques, instead of large resistors or capacitors, which can cause a large occupation of the layout area, we use a capacitive voltage-biased p-channel MOSFET in the on-time control part of the proposed circuit. The capacitive voltage-biased p-channel MOSFET is in the saturation region after the ESD event is detected, which can serve as a large equivalent resistance (~106 Ω) in the structure. The proposed power clamp circuit offers several advantages compared to the traditional circuit, such as having at least 70% area savings in the trigger circuit area (30% area savings in the whole circuit area), supporting a power supply ramp time as fast as 20 ns, dissipating the ESD energy more cleanly with little residual charge, and recovering faster from false triggers. The rail clamp circuit also offers robust performance in an industry-standard PVT (process, voltage, and temperature) space and has been verified by the simulation results. Showing good performance of human body model (HBM) endurance and high immunity to false trigger, the proposed power clamp circuit has great potential for application in ESD protection.

Osteoporosis treatment using stem cell-derived exosomes: a systematic review and meta-analysis of preclinical studies.

BACKGROUND: The increasing incidence of osteoporosis in recent years has aroused widespread public concern; however, existing effective treatments are limited. Therefore, new osteoporosis treatment methods, including stem cell transplantation and exosome therapy, have been proposed and are gaining momentum. Exosomes are considered to have greater potential for clinical application owing to their immunocompatibility. This study summarises the latest evidence demonstrating the efficacy of exosomes in improving bone loss in the treatment of osteoporosis. MAIN TEXT: This systematic review and meta-analyses searched PubMed, Embase, and Cochrane Library databases from inception to 26 March 2022 for osteoporosis treatment studies using stem cell-derived exosomes. Six endpoints were selected to determine efficacy: bone mineral density, trabecular bone volume/tissue volume fraction, trabecular number, trabecular separation, trabecular thickness, and cortical thickness. The search generated 366 citations. Eventually, 11 articles that included 15 controlled preclinical trials and 242 experimental animals (rats and mice) were included in the meta-analysis. CONCLUSION: The results were relatively robust and reliable despite some publication biases, suggesting that exosome treatment increased bone mass, improved bone microarchitecture, and enhanced bone strength compared with placebo treatments. Moreover, stem cell-derived exosomes may favour anabolism over catabolism, shifting the dynamic balance towards bone regeneration.

Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT.

Objective: To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR). Methods: This study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images via the proposed CLG model. We used the Sct images as the fixed images instead of the CBCT images to achieve the multi-modality image registration accurately. The deformation vector field is applied to propagate the target contour from the pCT to CBCT to realize the automatic target segmentation on CBCT images. We calculate the Dice similarity coefficient (DSC), 95 % Hausdorff distance (HD95), and average surface distance (ASD) between the prediction and reference segmentation to evaluate the proposed method. Results: The DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region. Conclusion: The CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning.

Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 102 and 1.38 × 103 times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

Trends in incidence and survival in patients with gastrointestinal neuroendocrine tumors: A SEER database analysis, 1977-2016.

Objectives: We aimed to determine trends in incidence and survival in patients with gastrointestinal neuroendocrine tumors (GI-NETs) from 1977 to 2016, and then analyze the potential risk factors including sex, age, race, grade, Socioeconomic status (SES), site, and stage. Methods: Data were obtained from Surveillance, Epidemiology, and End Results Program (SEER) database. Kaplan-Meier survival analysis, relative survival rates (RSRs), and Cox proportional risk regression model were used to evaluate the relationship between these factors and prognosis. Results: Compared with other sites, the small intestine and rectum have the highest incidence, and the appendix and rectum had the highest survival rate. The incidence was higher in males than in females, and the survival rate in males was close to females. Blacks had a higher incidence rate than whites, but similar survival rates. Incidence and survival rates were lower for G3&4 than for G1 and G2. Age, stage, and grade are risk factors. Conclusions: This study described changes in the incidence and survival rates of GI-NETs from 1977 to 2016 and performed risk factor analyses related to GI-NETs.

Visual analysis of research hotspots and trends of external therapies in traditional Chinese medicine for insomnia / 数字中医药（英文）

@#【Objective】 To explore the current status and development trend of research on external therapies in traditional Chinese medicine (TCM) for insomnia over the past 10 years through bibliometrics and visual analysis, to provide references for further research on the topic. 【Methods】 Literature relating to TCM external therapies for insomnia from January 1, 2012 to December 31, 2021 was retrieved from Chinese databases, including China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and from the Web of Science Core Collection (WOSCC) for English articles. CiteSpace, VOSviewer, Scimago Graphica, and NoteExpress software were used to analyze publication volumes of the papers and how they were distributed in different journals, as well as to visualize the data of the countries, authors, institutions, and keywords. 【Results】 A total of 6 085 papers were obtained, of which 5 592 were from the Chinese databases and 493 were from the English database, with their publication volumes growing steadily year on year. Approximately 45 countries and regions were found to have published research on the topic. In terms of Chinese publications, the author with the most papers published was CHEN Yunfei from Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine. The closest collaboration was between LIU Chengyong from the Affiliated Hospital of Nanjing University of Chinese Medicine and YUE Zenghui from Hunan University of Chinese Medicine. In terms of English publications, the author with the most papers published was MAO Junj from Sloan-Kettering Cancer Research Center, USA, and LAO Lixing from the University of Hong Kong was his closest partner in collaboration. Heilongjiang University of Chinese Medicine was the institution with the most Chinese publications, and Shanghai University of Traditional Chinese Medicine was the one with the most English papers published. Studies on the topic were published in 386 Chinese journals and 205 English journals, respectively. Nowadays, the clinical application of TCM external treatments for insomnia, the selection of meridians and acupoints, therapies for insomnia and its related diseases are research hotspots. The combined use of different TCM external therapies is a trend in the treatment of insomnia and its concomitant diseases, especially in the fields of oncology, nursing, and psychiatric disorders. The exploration of mechanisms of TCM external therapies for insomnia is also a key direction for future research. In clinical practice, the commonly used external therapies for insomnia include acupuncture, ear-acupressure with beans, acupoint application, etc. The commonly selected acupoints are auricular points, Sishencong (EX-HN1), Shenmen (HT7), etc. The frequently studied meridians are Ren, Du, Qiao, etc. The insomnia concomitant diseases are depression, stroke, anxiety, etc. 【Conclusion】 A wealth of research results have been accumulated in the treatment of insomnia by TCM external therapies, but authoritative research results are not so many. Therefore, institutions in different countries should strengthen communications and cooperation, and researchers should be encouraged to make innovations and breakthroughs on the basis of inherited TCM external therapies, so as to produce more valuable research results and improve TCM external therapies for providing better treatments for patients with sleep disorders.

A Novel DTSCR Structure with High Holding Voltage and Enhanced Current Discharge Capability for 28 nm CMOS Technology ESD Protection.

To cope with the much narrower ESD design window in 28 nm CMOS technology, a novel diode-triggered silicon-controlled rectifier with an extra discharge path (EDP-DTSCR) for ESD protection is proposed in this paper. Compared with the traditional DTSCR, the proposed DTSCR has an enhanced current discharge capability that is achieved by creating a slave SCR path in parallel with the master SCR path. Moreover, the improved triggering and holding characteristic can be obtained by the proposed EDP-DTSCR. By sharing the anode emitter junction, a slave SCR path is constructed that is symmetrical to the position of the master SCR path to add an additional ESD discharge path to the EDP-DTSCR. In this way, the current discharge capability of the entire device is obviously improved. The TCAD simulation result shows that the proposed device has a remarkably lower on-resistance compared with the traditional DTSCR and the DTSCR with p-type guard ring (PGR-DTSCR). In addition, it is structurally optimized to further increase the holding voltage and reduce the trigger voltage to improve the anti-latching capability of the device, which is more conducive to the ESD protection window application of 28 nm CMOS technology.

MR-based synthetic CT image for intensity-modulated proton treatment planning of nasopharyngeal carcinoma patients.

PURPOSE: To develop an advanced deep convolutional neural network (DCNN) architecture to generate synthetic CT (SCT) images from MR images for intensity-modulated proton therapy (IMPT) treatment planning of nasopharyngeal cancer (NPC) patients. METHODS: T1-weighted MR images and paired CT (PCT) images were obtained from 206 NPC patients. For each patient, deformable image registration was performed between MR and PCT images to create an MR-CT image pair. Thirty pairs were randomly chosen as the independent test set and the remaining 176 pairs (14 for validation and 162 for training) were used to build two conditional generative adversarial networks (GANs): 1) GAN3D: using a 3D U-net enhanced with residual connections and attentional mechanism as the generator and 2) GAN2D: using a 2D U-net as the generator. For each test patient, SCT images were generated using the generators with the MR images as input and were compared with respect to the corresponding PCT image. A clinical IMPT plan was created and optimized on the PCT image. The dose was recalculated on the SCT images and compared with the one calculated on the PCT image. RESULTS: The mean absolute errors (MAEs) between the PCT and SCT, within the body, were (64.89 ± 5.31) HU and (64.31 ± 4.61) HU for the GAN2D and GAN3D. Within the high-density bone (HU > 600), the GAN3D achieved a smaller MAE compared with the GAN2D (p < 0.001). Within the body, the absolute point dose deviation was reduced from (0.58 ± 1.61) Gy for the GAN2D to (0.47 ± 0.94) Gy for the GAN3D. The (3 mm/3%) gamma passing rates were above 97.32% for all SCT images. CONCLUSIONS: The SCT images generated using GANs achieved clinical acceptable dosimetric accuracy for IMPT of NPC patients. Using advanced DCNN architecture design, such as residual connections and attention mechanism, SCT image quality was further improved and resulted in a small dosimetric improvement.

A Novel L-Gate InGaAs/GaAsSb TFET with Improved Performance and Suppressed Ambipolar Effect.

A heterojunction tunneling field effect transistor with an L-shaped gate (HJ-LTFET), which is very applicable to operate at low voltage, is proposed and studied by TCAD tools in this paper. InGaAs/GaAsSb heterojunction is applied in HJ-LTFET to enhance the ON-state current (ION). Owing to the quasi-broken gap energy band alignment of InGaAs/GaAsSb heterojunction, height and thickness of tunneling barrier are greatly reduced. However, the OFF-state leakage current (IOFF) also increases significantly due to the reduced barrier height and thickness and results in an obvious source-to-drain tunneling (SDT). In order to solve this problem, an HfO2 barrier layer is inserted between source and drain. Result shows that the insertion layer can greatly suppress the horizontal tunneling leakage appears at the source and drain interface. Other optimization studies such as work function modulation, doping concentration optimization, scaling capability, and analog/RF performance analysis are carried out, too. Finally, the HJ-LTFET with a large ION of 213 µA/µm, a steep average SS of 8.9 mV/dec, and a suppressed IOFF of 10-12 µA/µm can be obtained. Not only that, but the fT and GBP reached the maximum values of 68.3 GHz and 7.3 GHz under the condition of Vd = 0.5 V, respectively.

Single event effects prediction of MOSFET device using deep learning.

Single event effect (SEE) is an important problem in the reliability research of integrated circuits. The study of SEE of traditional MOSFET devices is mainly based on simulation software, which is characterized by slow simulation speed, large computation and time-consuming. In this paper, a SEE research method based on deep learning is proposed. The method relies on 28 nm MOSFET. The complete drain transient current pulse, transient current peak value and total collected charge can be obtained in a short time by inputting relevant parameters that affect the SEE. The accuracy of the network for predicting transient current peak and total collected charge is 96.95% and 97.53% respectively, and the mean goodness of fit of the network for predicting the drain transient current pulse curve is 0.985. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.89 × 103and 1.50 × 103times respectively. This method has good prediction effect and provides a new possibility for the study of SEE.

Dynamic Characteristics and Predictive Capability of Tumor Voxel Dose-Response Assessed Using 18F-FDG PET/CT Imaging Feedback.

Purpose: Tumor voxel dose-response matrix (DRM) can be quantified using feedback from serial FDG-PET/CT imaging acquired during radiotherapy. This study investigated the dynamic characteristics and the predictive capability of DRM. Methods: FDG-PET/CT images were acquired before and weekly during standard chemoradiotherapy with the treatment dose 2 Gy × 35 from 31 head and neck cancer patients. For each patient, deformable image registration was performed between the pretreatment/baseline PET/CT image and each weekly PET/CT image. Tumor voxel DRM was derived using linear regression on the logarithm of the weekly standard uptake value (SUV) ratios for each tumor voxel, such as SUV measured at a dose level normalized to the baseline SUV0. The dynamic characteristics were evaluated by comparing the DRMi estimated using a single feedback image acquired at the ith treatment week (i = 1, 2, 3, or 4) to the DRM estimated using the last feedback image for each patient. The predictive capability of the DRM estimated using 1 or 2 feedback images was evaluated using the receiver operating characteristic test with respect to the treatment outcome of tumor local-regional control or failure. Results: The mean ± SD of tumor voxel SUV measured at the pretreatment and the 1st, 2nd, 3rd, 4th, and last treatment weeks was 6.76 ± 3.69, 5.72 ± 3.43, 3.85 ± 2.22, 3.27 ± 2.25, 2.5 ± 1.79, and 2.23 ± 1.27, respectively. The deviations between the DRMi estimated using the single feedback image obtained at the ith week and the last feedback image were 0.86 ± 4.87, -0.06 ± 0.3, -0.09 ± 0.17, and -0.09 ± 0.12 for DRM1, DRM2, DRM3, and DRM4, respectively. The predictive capability of DRM3 and DRM4 was significant (p < 0.001). The area under the curve (AUC) was increased with the increase in treatment dose level. The DRMs constructed using the single feedback image achieved an AUC of 0.86~1. The AUC was slightly improved to 0.94~1 for the DRMs estimated using 2 feedback images. Conclusion: Tumor voxel metabolic activity measured using FDG-PET/CT fluctuated noticeably during the first 2 treatment weeks and obtained a stabilized reduction rate thereafter. Tumor voxel DRM constructed using a single FDG-PET/CT feedback image after the 2nd treatment week (>20 Gy) has a good predictive capability. The predictive capability improved continuously using a later feedback image and marginally improved when two feedback images were applied.

Evaluation of DIR schemes on tumor/organ with progressive shrinkage: Accuracy of tumor/organ internal tissue tracking during the radiation treatment.

PURPOSE: Accuracy of intratumoral treatment dose accumulation and response assessment highly depends on the accuracy of a DIR method. However, achievable accuracy of the existing DIR methods for tumor/organ with large and progressive shrinkage during the radiotherapy course have not been explored. This study aimed to use a bio-tissue phantom to quantify the achievable accuracy of different DIR schemes. MATERIALS /METHODS: A fresh porcine liver was used for phantom material. Sixty gold markers were implanted on the surface and inside of the liver. To simulate the progressive radiation-induced tumor/organ shrinkage, the phantom was heated using a microwave oven incrementally from 30 s to 200 s in 8 phases. For each phase, the phantom was scanned by CT. Two extra image sets were generated from the original images: 1) the image set with overriding the high-density gold markers (feature image); 2) the image set with overriding the entire phantom to the mean soft tissue intensity (featureless image). Ten DIR schemes were evaluated to mimic clinical treatment situations of tumor/critical organ with respect to their surface and internal condition of image features, availability of intermediate feedback images and DIR methods. The internal marker's positions were utilized to evaluate DIR accuracy quantified by target registration error (TRE). RESULTS: Volume reduction was about 20 % ∼ 40 % of the initial volume after 90 s âˆ¼ 200 s of the heating. Without image features on the surface and inside of the phantom, the hybrid-DIR (image-based DIR followed by biomechanical model-based refinement) with the surface constraint achieved the registration TRE from 2.6 ± 1.2 mm to 5.3 ± 2.6 mm proportional to the %volume shrinkage. Meanwhile, the hybrid-DIR with the surface-marker constraint achieved the TRE from 2.4 ± 1.2 mm to 2.6 ± 1.0 mm. If both the surface and internal image features would be viable on the feedback images, the achievable accuracy could be minimal with the TRE from 1.6 ± 0.9 mm to 1.9 ± 1.2 mm. CONCLUSIONS: Standard DIR methods cannot guarantee intratumoral tissue registration accuracy for tumor/organ with large progressive shrinkage. Achievable accuracy with using the hybrid DIR method is highly dependent on the surface registration accuracy. If the surface registration mean TRE can be controlled within 2 mm, the mean TRE of internal tissue can be controlled within 3 mm.

Prediction of electrical properties of FDSOI devices based on deep learning.

Fully depleted Silicon on insulator technology (FDSOI) is proposed to solve the various non-ideal effects when the process size of integrated circuits is reduced to 45 nm. The research of traditional FDSOI devices is mostly based on simulation software, which requires a lot of calculation and takes a long time. In this paper, a deep learning (DL) based electrical characteristic prediction method for FDSOI devices is proposed. DL algorithm is used to train the simulation data and establish the relationship between the physical parameters and electrical characteristics of the device. The network structure used in the experiment has high prediction accuracy. The mean square error of electrical parameters and transfer characteristic curve is only 4.34 × 10-4and 2.44 × 10-3respectively. This method can quickly and accurately predict the electrical characteristics of FDSOI devices without microelectronic expertise. In addition, this method can be extended to study the effects of various physical variables on device performance, which provides a new research method for the field of microelectronics.

Developing an accurate model of spot-scanning treatment delivery time and sequence for a compact superconducting synchrocyclotron proton therapy system.

BACKGROUND: A new compact superconducting synchrocyclotron single-room proton solution delivers pulsed proton beams to each spot through several irradiation bursts calculated by an iterative layer delivery algorithm. Such a mechanism results in a new beam parameter, burst switching time (BST) in the total beam delivery time (BDT) which has never been studied before. In this study, we propose an experimental approach to build an accurate BDT and sequence prediction model for this new proton solution. METHODS: Test fields and clinical treatment plans were used to investigate each beam delivery parameter that impacted BDT. The machine delivery log files were retrospectively analyzed to quantitatively model energy layer switching time (ELST), spot switching time (SSWT), spot spill time (SSPT), and BST. A total of 102 clinical IMPT treatment fields' log files were processed to validate the accuracy of the BDT prediction model in comparison with the result from the current commercial system. Interplay effect is also investigated as a clinical application by comparing this new delivery system model with a conventional cyclotron accelerator model. RESULTS: The study finds that BST depends on the amount of data to be transmitted between two sequential radiation bursts, including a machine irradiation log file of the previous burst and a command file to instruct the proton system to deliver the next burst. The 102 clinical treatment fields showed that the accuracy of each component of the BDT matches well between machine log files and BDT prediction model. More specifically, the difference of ELST, SSWT, SSPT, and BST were (- 3.1 ± 5.7)%, (5.9 ± 3.9)%, (2.6 ± 8.7)%, and (- 2.3 ± 5.3)%, respectively. The average total BDT was about (2.1 ± 3.0)% difference compared to the treatment log files, which was significantly improved from the current commercial proton system prediction (58 ± 15)%. Compared to the conventional cyclotron system, the burst technique from synchrocyclotron effectively reduced the interplay effect in mobile tumor treatment. CONCLUSION: An accurate BDT and sequence prediction model was established for this new clinical compact superconducting synchrocyclotron single-room proton solution. Its application could help users of similar facilities better assess the interplay effect and estimate daily patient treatment throughput.