Max Planck Institute of Molecular Cell Biology and Genetics

In this paper, a novel C2NOT optical gate by using a hexagonal two-dimensional photonic crystal lattice has been introduced and analyzed. The presented structure consists of five waveguides with three inputs and three outputs. The input and output are connected by six ring resonators. By creating defects in the structure and removing the rod, ring resonators and waveguides have been created. The design was analyzed using both the finite difference time domain (FDTD) and the plane wave expansion (PWE) method. The PWE method was employed to determine the photonic band gap of the structure, while the FDTD method was used to analyze the behavior of electromagnetic fields within the photonic crystal lattice. The main advantage of this design is the high contrast ratio (contrast ratio of 13.1 dB in switching mode) and low footprint. Also, other advantages include the use of silicon with a refractive index of 3.46 in the background air, as well as increasing the maximum output power in the case of equal to one and reducing the minimum output power in the case of equal to zero presentation and design in the form of a hexagonal lattice, the use of linear materials and the use of linear defects, low delay and reduction of footprint compared to previous designs and the ability to be used in integrated circuits.

This paper contributes significantly to time series analysis by discussing the empirical properties of white noise and their implications for model selection. This paper illustrates the ways in which the standard assumptions about white noise typically fail in practice, with a special emphasis on striking differences in sample ACF and PACF. Such findings prove particularly important when assessing model adequacy and discerning between residuals of different models, especially ARMA processes. This study addresses issues involving testing procedures, for instance, the Ljung–Box test, to select the correct time series model determined in the review. With the improvement in understanding the features of white noise, this work enhances the accuracy of modeling diagnostics toward real forecasting practice, which gives it applied value in time series analysis and signal processing.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition that can affect professional scepticism, a critical aspect of effective auditing. This study examines the impact of ADHD, assessed using the Integrated Visual and Auditory‐2 Continuous Performance Test, on the professional scepticism of external auditors. Results indicate a negative association between ADHD and professional scepticism, suggesting that higher levels of ADHD reduce sceptical tendencies. However, the study finds that job satisfaction significantly mitigates the adverse effects of ADHD on professional scepticism. Specifically, findings from the Johnson‐Neyman analysis reveal that high job satisfaction mitigates the adverse impact of ADHD on scepticism. This study highlights the need for audit firms to consider ADHD‐related challenges. It emphasises the importance of creating a supportive work environment to sustain professional scepticism, which is vital for enhancing audit quality and effectively managing cognitive diversity in the workforce.

Introduction. Diffuse B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin’s lymphoma. Currently, the standard method for evaluating patients at the initial stages of cancer diagnosis in Mashhad oncology centers involves computed tomography scans (CT scans), histopathological evaluation of tissue, bone marrow sampling, and cytogenetic studies, all of which are time-consuming and costly. It is worth mentioning that at present, the most recommended approach for determining lymphoma staging is the FDG-PET/CT scan, which combines labeled glucose with CT scan and offers a more accurate alternative. The objective of this study is to explore the potential of FDG-PET/CT scan as a tool for detecting high-grade lymphoma.Methods. In this study, patients with different types of DLBCL who underwent FDG-PET Scan for staging at Razavi Hospital, Mashhad, Iran between 2017 and 2021 were examined. The necessary clinical and paraclinical information, including the stage of the disease, the involved site at the time of diagnosis, the result of immunohistochemical examination, and the response to treatment were collected. FDG-PET Scan information including the extent of involvement and metabolic activity of the tumor before the start of treatment, pathological characteristics of the tumor, clinical behavior, and response to treatment in the form of response rate (RR), disease-free survival (DFS) and overall survival (OS) of the patients. Was also investigated. Aggressive histology in the present study was classified based on morphological characteristics and immunohistochemical staining, prognostic indicators, clinical behavior and response to treatment. Data were analyzed using SPSS software at a significance level of p<0.05.Results. Comparing the two groups of patients with high grade histology (n=12) and NOS (n=14), the results showed that SUV max values in patients with aggressive lymphoma were 27.5 ± 15.6 (median 25.6) and in patients with NOS lymphoma was 15.4 ± 9.8 (median 14.4) (p=0.01). The overall survival of patients in the aggressive group was 10 months and in the non-aggressive group was 24 months (p=0.002). Also, the cut — off -point of 21.1 for SUV max has a sensitivity of 66 % and a specificity of 72 % in differentiating aggressive from non-aggressive types.Conclusion. The results revealed that FDG PET CT Scan can provide valuable insights into differentiating lymphomas with a more aggressive type from their usual types, as those with heightened metabolic activity (SUVmax) are often indicative of aggressive behaviors.

Background/Objectives: The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. Traditionally, research in this domain has focused primarily on binary classifications of disorders. This study aims to classify five distinct states, including one control group and four categories of psychological disorders. Methods: Our investigation will utilize algorithms based on Granger causality and local graph structures to improve classification accuracy. Feature extraction from connectivity matrices was performed using local structure graphs. The extracted features were subsequently classified employing K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes classifiers. Results: The KNN classifier demonstrated the highest accuracy in the gamma band for the depression category, achieving an accuracy of 89.36%, a sensitivity of 89.57%, an F1 score of 94.30%, and a precision of 99.90%. Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band. Conclusions: The proposed methodology provides a novel approach for analyzing EEG signals and holds potential applications in the classification of psychological disorders.

The rapid advancement of computer technologies, along with the significant role of emotions in daily life, has driven interest in intelligent emotion recognition systems. Electroencephalography (EEG) serves as a prominent objective tool in affective computing. However, effectively integrating multichannel EEG spatial and temporal information remains a critical challenge. This study introduces a novel emotion recognition model grounded in cognitive and biological principles, emphasizing the importance of spatiotemporal dynamics in emotional processing. In this research, brain frequency bands were extracted through wavelet analysis, and the signals within predefined time windows were quantified. These features were then concatenated across distinct brain channels to create a comprehensive matrix representing spatiotemporal brain information. The matrix was characterized using both the summation of matrix cells and the highest singular value to optimize computational costs during classification. The resulting attributes were input into a classification module for emotion detection. Experimental results on the Database for Emotion Analysis using Physiological Signals (DEAP) achieved a maximum accuracy of 89.55%. This work introduces a novel approach to analyzing and classifying EEG signals elicited by various emotional stimuli, demonstrating that the proposed model is competitive with the state-of-the-art classification schemes, thereby paving the way for future development of a robust spatiotemporal-based EEG emotion recognition system.

Remote state preparation (RSP) is a method to transfer a known state from a sender to a receiver some distance away. Based on its importance, many kinds of RSP have been proposed in the literature. In this paper, the controlled cyclic RSP protocol is extended to an arbitrary $$n$$ number of parties. To accomplish this goal, two protocols are proposed and compared. The first one is based on a $$2n+1$$ entangled state as a channel, and the other is with $$2n$$ EPR states. Then, the proposed protocols are analyzed from the controller power’s point of view, and the improved versions are presented. Finally, the protocols have been proposed to send the states with an arbitrary number of qubits. Furthermore, the performance of the protocol is analyzed in the noisy environments.

Gliomas account for 75 % of primary malignant CNS tumors. High-grade glioma (CNS WHO grades 3 and 4) have an unfavorable treatment response and poor outcome. CXCR4 is a G protein-coupled receptor that plays an important part in the signaling pathway between cancer cells and tumor microenvironment. CXCR4 overexpression has been shown in a variety of cancers. In this study, we evaluate the potential value of [

This study aimed to investigate the PSMA-avid distribution of disease in newly diagnosed prostate cancer (PC) and the correlation between [68Ga]Ga-PSMA-11 PET-derived parameters with serum PSA levels, biopsy Gleason Score (GS), and the presence of metastasis. Additionally, we explored whether machine learning-based analysis of PET-derived parameters predicts PSA value and biopsy GS. We retrospectively evaluated 256 newly diagnosed PC patients who had undergone [68Ga]Ga-PSMA-11 PET/CT for staging after biopsy. Several primary tumors and whole-body SUV and volumetric parameters were extracted from PET images. The relationship between PSA value, GS, and metastatic tendency with PET-derived parameters was evaluated. Several classifiers were trained with PET-derived parameters to predict GS > 7 and PSA > 20. Of the 256 evaluated patients, only seven cases (2.7%) showed a negative scan. Out of 249 positive cases, 137 (55%) exhibited only localized disease, while 112 (45%) showed signs of metastasis. There was a significant correlation between GS and PSA value with all PET-derived parameters related to the primary tumor (P < 0.05). In patients with metastatic scans, PET-derived parameters in the primary tumor were significantly higher compared to patients with only local disease (P < 0.05). Based on ROC curve analysis with AUC, the optimal PSA cut-off for a metastatic scan was 16.79 ng/ml. Furthermore, the optimal cut-off values for SUVmean, SUVmax, PSMA-TV, and TL-PSMA in the primary tumor for a metastatic c scan were 4.4, 12.99, 18.91, and 98.69, respectively. TL-PSMA demonstrated the highest AUC to predict GS ≤ 7 vs. >7 with an optimal cut-off of 75.37 cm3 and a sensitivity of 86% and specificity of 65%. Likewise, in the metastatic scans, wbTL-PSMA exhibited the highest AUC to predict GS ≤ 7 vs. >7 with an optimal cut-off of 106.60 cm3 and a sensitivity of 92% and specificity of 59%. TL-PSMA showed the highest AUC to predict PSA ≤ 20 vs. PSA > 20 with an optimal cut-off of 70.31 cm3 and a sensitivity of 81% and specificity of 66%. Additionally, in the metastatic scans, wbPSMA-TV demonstrated the highest AUC to predict PSA ≤ 20 vs. PSA > 20 with an optimal cut-off of 59.46 cm3 and a sensitivity of 76% and specificity of 63%. Among evaluated classifiers, linear support vector classifier (SVC), calibrated classifier CV and logistic regression demonstrated the highest accuracy for categorization of GS ≤ 7 and GS > 7. Furthermore, calibrated classifier CV, nearest centroid, and logistic regression showed the optimal accuracy in predicting PSA ≤ 20 and PSA > 20. In conclusion, [68Ga]PSMA PET/CT is a valuable tool for evaluating primary PC, detecting lymph node spread and bone metastases. There is a correlation between GS and PSA value with PET-derived parameters, which can predict GS and metastatic potential. Lastly, utilizing machine learning to analyze PET-derived parameters can aid in predicting PSA value and GS in primary PC. These findings indicate a possible connection between the distribution and amount of PSMA expression detected on [68Ga]Ga-PSMA PET scans with both biopsy GS and PSA level.

AbstractEconomic sanctions have increasingly become a pivotal tool in modern foreign policy, especially in the context of U.S. international strategy. Despite their growing significance, there is a notable lack of empirical research on how these sanctions affect corporate behavior. This study aims to explore the impact of U.S. economic sanctions on innovation and fraudulent activities within Iranian firms subjected to these measures. Utilizing a difference‐in‐differences approach, the findings reveal a weak and negative relationship between sanctions and innovation, while no significant link is found between sanctions and fraudulent activities. Notably, the study uncovers a substantial divergence in companies' responses to sanctions based on their innovation background. Companies with a strong innovation background experience a significant increase in innovation efforts and a corresponding decline in fraudulent behavior upon facing sanctions. In contrast, companies lacking an established innovation background exhibit a reduction in innovation and, in turn, an escalation in fraudulent practices in response to sanctions. These findings remain robust across various sensitivity analyses. This research contributes to the expanding body of empirical literature on sanction outcomes by highlighting the profound impact of innovation background on the responsible and ethical decisions made by sanctioned companies.

To predict Gleason Score (GS) using radiomic features from 68Ga-PSMA-PET/CT images in primary prostate cancer. 138 patients undergoing 68Ga-PSMA-PET/CT imaging were categorized based on GS, with GS above 4 + 3 as malignant and under 3 + 4 as benign tumors. radiomic features were extracted from tumors’ volume of interest in both PET and CT images, using Feature Elimination with cross-validation. Fusion features were generated by combining features at the feature level; average of features (PET/CTAveFea) or concatenated features (PET/CTConFea). The performance of various models was compared using area under the curve, sensitivity and specificity. Wilcoxon test and F1-score test were used to find the best model. Predictive models were developed for CT-only, PET-only, and PET/CT feature-level fusion models. Random Forest achieved the highest accuracy on CT with 0.74 ± 0.01 AUCMean, 0.75 ± 0.07 sensitivity, and 0.62 ± 0.08 specificity. Logistic regression (LR) exhibited the best predictive performance on PET images with 0.74 ± 0.05 AUCMean, 0.7 ± 0.13 sensitivity, and 0.78 ± 0.14 specificity. The best predictive PET/CTAveFea was achieved by LR, resulting in 0.72 ± 0.07 AUCMean, 0.74 ± 0.12 sensitivity, and 0.63 ± 0.02 specificity. In the case of PET/CTConFea, LR showed the best predictive performance with 0.78 ± 0.08 AUCMean, 0.81 ± 0.09 sensitivity, and 0.66 ± 0.15 specificity. The results demonstrated that radiomic models derived from 68Ga-PSMA-PET/CT images could differentiate between benign and malignant tumors based on GS.

Background The ankle-knee-hip joint systems have structures that can produce mechanical work through elastic, viscoelastic mechanisms or muscle activity. This study aimed to compute sit-to-stand (STS) joint works in lower limbs between blind and sighted children to find the relationship between visual memory and STS joint work variables. Methods This study included fifteen female children with congenitally blind (CB) and 30 healthy girls without visual impairments. The children with no visual impairments were randomly divided into two condition groups with 15 each, the eyes open (EO) and the eyes closed (EC). Inverse dynamics calculated joint works by integrating multiple the moment and angular velocity (F1) and force and velocity (F2). They were normalized to body mass and body height. Results Generally, the sensitivity of F1 (on both sides in the sagittal and frontal planes) was more than F2 (on the non-dominant side in the mediolateral and vertical axes). In the ML axis, the EC group had insufficient maximal non-dominant hip work relative to the EO group (p = 0.002). In addition, the CB group suffered from low hip efficiency (p = 0.003) and high knee (p < 0.001) mechanical work. Conclusions Numerous differences between CB and EC groups (on knee and hip works) showed that the time of visual input deprivation could change the type of human body's strategies to reach the consolidation process and keep adequate balance during STS. Therefore, rehabilitation programs should be aimed at addressing the impairments in the management of restricted visual input during STS performance.

Schizophrenia (SZ) is an enduring, intricate, and debilitating neuropsychological illness. Traditional approaches for diagnosing SZ face several challenges, including the arduous and time-consuming nature of the examination process. Recently, the development of a pipeline for automatic, objective detection of SZ using electroencephalography (EEG) has become of great interest. Scholars have utilized all EEG channels or conventional channel selection techniques where an optimal brain channel is selected for the entire recording period. However, brain neurons continuously receive information from the surroundings or internals. As a result, different brain areas are activated over time. This study presents a novel approach to updating optimal selected/fused brain channels that float over time. Four strategies are proposed for dynamically selecting or integrating brain channels. After reducing the number of EEG electrodes, either by channel selection or fusion, EEG dynamicity is characterized by some indices of Poincaré plot asymmetry. The feature vector is then utilized in the principal component analysis, and the resulting outcome is fed into various machine learning algorithms to complete the SZ detection scheme. Our results show that floating brain channel selection or fusion could result in 100% classification accuracy. The highest classification performances are achieved by utilizing dynamic channel fusion. The process entails the calculation of the summation of EEGs in individual hemispheres, which is subsequently followed by the computation of the absolute difference between the obtained signals. The accuracy of our proposed system is superior or comparable to state-of-the-art EEG SZ detection tools.