Brain tumor segmentation using U-Net in conjunction with EfficientNet.

According to the Ten Leading Causes of Death Statistics Report by the Ministry of Health and Welfare in 2021, cancer ranks as the leading cause of mortality. Among them, pleomorphic glioblastoma is a common type of brain cancer. Brain cancer often occurs in the brain with unclear boundaries from normal brain tissue, necessitating assistance from experienced doctors to distinguish brain tumors before surgical resection to avoid damaging critical neural structures. In recent years, with the advancement of deep learning (DL) technology, artificial intelligence (AI) plays a vital role in disease diagnosis, especially in the field of image segmentation. This technology can aid doctors in locating and measuring brain tumors, while significantly reducing manpower and time costs. Currently, U-Net is one of the primary image segmentation techniques. It utilizes skip connections to combine high-level and low-level feature information, leading to significant improvements in segmentation accuracy. To further enhance the model's performance, this study explores the feasibility of using EfficientNetV2 as an encoder in combination with U-net. Experimental results indicate that employing EfficientNetV2 as an encoder together with U-net can improve the segmentation model's Dice score (loss = 0.0866, accuracy = 0.9977, and Dice similarity coefficient (DSC) = 0.9133).

Effect of age on postural performance and control strategies during changes in visual input and dual-tasking stances.

Background: With age, people begin to experience deterioration in standing balance, especially when sensory input is suddenly removed or added. Here, we sought to explore the effects of age on postural performance and postural control strategies. Methods: The convenience sample consisted of 15 young, 10 middle-aged, and 14 elderly healthy adults. They were instructed to stand with their feet together in four randomly administered conditions involving visual input removal/addition and single-/dual-tasking. Dual-tasking involved continuous subtraction by 3s. Results: Postural sway displacement in the two older groups seemed larger than that in the younger group; however, neither the main effect of group (F2, 36 = 1.152, p = .327) nor the group × time interaction effect (F4, 27 = 0.229, p = .922) was significant. Greater stiffness of the lower leg muscles was observed in the vision-addition condition than in the vision-removal condition in only the elderly group (t13 = -2.755, p = .016). The dual-tasking condition resulted in smaller sway displacement (F1, 36 = 7.690, p = .009) and greater muscle stiffness (F1, 36 = 5.495, p = .025). In the vision-removal condition, the increase in muscle stiffness due to dual-tasking was significantly larger in the middle-aged (t9 = -3.736, p = .005) and elderly groups (t13 = -2.512, p = .026). Conclusions: In healthy older individuals, age-related changes were observed in control strategies used to maintain standing balance upon changes in visual input. The dual-task paradigm induced the use of an ankle-stiffening strategy in middle-aged and elderly adults.

Development of revised ResNet-50 for diabetic retinopathy detection.

BACKGROUND: Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness. If DR is detected early, ophthalmologists can use lasers to create tiny burns around the retinal tears to inhibit bleeding and prevent the formation of new blood vessels, in order to prevent deterioration of the disease. The rapid improvement of deep learning has made image recognition an effective technology; it can avoid misjudgments caused by different doctors' evaluations and help doctors to predict the condition quickly. The aim of this paper is to adopt visualization and preprocessing in the ResNet-50 model to improve module calibration, to enable the model to predict DR accurately. RESULTS: This study compared the performance of the proposed method with other common CNNs models (Xception, AlexNet, VggNet-s, VggNet-16 and ResNet-50). In examining said models, the results alluded to an over-fitting phenomenon, and the outcome of the work demonstrates that the performance of the revised ResNet-50 (Train accuracy: 0.8395 and Test accuracy: 0.7432) is better than other common CNNs (that is, the revised structure of ResNet-50 could avoid the overfitting problem, decease the loss value, and reduce the fluctuation problem). CONCLUSIONS: This study proposed two approaches to designing the DR grading system: a standard operation procedure (SOP) for preprocessing the fundus image, and a revised structure of ResNet-50, including an adaptive learning rating to adjust the weight of layers, regularization and change the structure of ResNet-50, which was selected for its suitable features. It is worth noting that the purpose of this study was not to design the most accurate DR screening network, but to demonstrate the effect of the SOP of DR and the visualization of the revised ResNet-50 model. The results provided an insight to revise the structure of CNNs using the visualization tool.

Improvement of brain-computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP.

Motor imagery (MI) can produce a specific brain pattern when the subject imagines performing a particular action without any actual body movements. According to related previous research, the improvement of the training of MI brainwaves can be adopted by feedback methods in which the analysis of brainwave characteristics is very important. The aim of this study was to improve the subject's MI and the accuracy of classification. In order to ameliorate the accuracy of the MI of the left and right hand, the present study designed static and dynamic visual stimuli in experiments so as to evaluate which one can improve subjects' imagination training. Additionally, the filter bank common spatial pattern (FBCSP) method was used to divide the frequency band range of the brainwaves into multiple segments, following which linear discriminant analysis (LDA) was adopted for classification. The results revealed that the averaged false positive rate (FPR) under FBCSP-LDA in the dynamic MI experiment was the lowest FPR (23.76%). As such, this study suggested that a combination of the dynamic MI experiment and the FBCSP-LDA method improved the overall prediction error rate and ameliorated the performance of the MI brain-computer interface.

The multi-targeted tyrosine kinase inhibitor SKLB610 resensitizes ABCG2-overexpressing multidrug-resistant cancer cells to chemotherapeutic drugs.

The overexpression of ATP-binding cassette (ABC) transporter ABCB1 (P-glycoprotein) or ABCG2 (BCRP/MXR/ABCP) in cancer cells is frequently associated with the development of multidrug resistance (MDR) in cancer patients, which remains a major obstacle to effective cancer treatment. By utilizing energy derived from ATP hydrolysis, both transporters have been shown to reduce the chemosensitivity of cancer cells by actively effluxing cytotoxic anticancer drugs out of cancer cells. Knowing that there are presently no approved drugs or other therapeutics for the treatment of multidrug-resistant cancers, in recent years, studies have investigated the repurposing of tyrosine kinase inhibitors (TKIs) to act as agents against MDR mediated by ABCB1 and/or ABCG2. SKLB610 is a multi-targeted TKI with potent activity against vascular endothelial growth factor receptor 2 (VEGFR2), platelet-derived growth factor receptor (PDGFR), and fibroblast growth factor receptor 2 (FGFR2). In this study, we investigate the interaction of SKLB610 with ABCB1 and ABCG2. We discovered that neither ABCB1 nor ABCG2 confers resistance to SKLB610, but SKLB610 selectively sensitizes ABCG2-overexpressing multidrug-resistant cancer cells to cytotoxic anticancer agents in a concentration-dependent manner. Our data indicate that SKLB610 reverses ABCG2-mediated MDR by attenuating the drug-efflux function of ABCG2 without affecting its total cell expression. These findings are further supported by results of SKLB610-stimulated ABCG2 ATPase activity and in silico docking of SKLB610 in the drug-binding pocket of ABCG2. In summary, we reveal the potential of SKLB610 to overcome resistance to cytotoxic anticancer drugs, which offers an additional treatment option for patients with multidrug-resistant cancers and warrants further investigation.

P-glycoprotein Mediates Resistance to the Anaplastic Lymphoma Kinase Inhiitor Ensartinib in Cancer Cells.

Ensartinib (X-396) is a promising second-generation small-molecule inhibitor of anaplastic lymphoma kinase (ALK) that was developed for the treatment of ALK-positive non-small-cell lung cancer. Preclinical and clinical trial results for ensartinib showed superior efficacy and a favorable safety profile compared to the first-generation ALK inhibitors that have been approved by the U.S. Food and Drug Administration. Although the potential mechanisms of acquired resistance to ensartinib have not been reported, the inevitable emergence of resistance to ensartinib may limit its therapeutic application in cancer. In this work, we investigated the interaction of ensartinib with P-glycoprotein (P-gp) and ABCG2, two ATP-binding cassette (ABC) multidrug efflux transporters that are commonly associated with the development of multidrug resistance in cancer cells. Our results revealed that P-gp overexpression, but not expression of ABCG2, was associated with reduced cancer cell susceptibility to ensartinib. P-gp directly decreased the intracellular accumulation of ensartinib, and consequently reduced apoptosis and cytotoxicity induced by this drug. The cytotoxicity of ensartinib could be significantly reversed by treatment with the P-gp inhibitor tariquidar. In conclusion, we report that ensartinib is a substrate of P-gp, and provide evidence that this transporter plays a role in the development of ensartinib resistance. Further investigation is needed.

Age-related differences in alpha and beta band activity in sensory association brain areas during challenging sensory tasks.

Sensory challenges to postural balance are daily threats for elderly individuals. This study examined electroencephalography (EEG) in alpha and beta bands in sensory association areas during the Sensory Organization Test, involving withdrawal of visual or presenting misleading somatosensory inputs, in twelve young and twelve elderly participants. The results showed stepwise deterioration in behavioral performance in four conditions, with group effects that were amplified with combined sensory challenges. With eye closure, alpha and beta activities increased in all sensory association areas. Fast beta activity increased in the bilateral parietal-temporal-occipital areas. Misleading somatosensory information effects on EEG activity were of smaller amplitude than eye closure effects and in a different direction. Decreased alpha activity in left parietal-temporal-occipital areas and decreased beta and fast beta activities in bilateral parietal-temporal-occipital areas were significant. Elderly participants had increased fast beta activity in the left temporal-occipital and bilateral occipital areas, indicative of sustained efforts that they made in all sensory conditions. Similar to the young participants, elderly participants with eyes closed showed increased alpha activity, although to a smaller degree, in bilateral temporal-occipital and left occipital areas. This might indicate a lack of efficacy in redistributing relative sensory weights when elderly participants dealt with eye closure. In summary, EEG power changes did not match the stepwise deterioration in behavioral data, but reflected different sensory strategies adopted by young and elderly participants to cope with eye closure or misleading somatosensory information based on the efficacy of these different strategies.

Neural correlates of mathematical problem solving.

This study explores electroencephalography (EEG) brain dynamics associated with mathematical problem solving. EEG and solution latencies (SLs) were recorded as 11 neurologically healthy volunteers worked on intellectually challenging math puzzles that involved combining four single-digit numbers through basic arithmetic operators (addition, subtraction, division, multiplication) to create an arithmetic expression equaling 24. Estimates of EEG spectral power were computed in three frequency bands - θ (4-7 Hz), α (8-13 Hz) and ß (14-30 Hz) - over a widely distributed montage of scalp electrode sites. The magnitude of power estimates was found to change in a linear fashion with SLs - that is, relative to a base of power spectrum, theta power increased with longer SLs, while alpha and beta power tended to decrease. Further, the topographic distribution of spectral fluctuations was characterized by more pronounced asymmetries along the left-right and anterior-posterior axes for solutions that involved a longer search phase. These findings reveal for the first time the topography and dynamics of EEG spectral activities important for sustained solution search during arithmetical problem solving.

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels.

Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare.

Self-adjustments may account for the contradictory correlations between HRV and motion-sickness severity.

This study investigates the relationship between heart rate variability (HRV) and the level of motion sickness (MS) induced by simulated tunnel driving. The HRV indices, normalized low frequency (NLF, 0.04-0.15 Hz), normalized high frequency (NHF, 0.15-0.4 Hz), and LF/HF ratio were correlated with the subjectively and continuously rated MS levels of 20 participants. The experimental results showed that for 13 of the subjects, the MS levels positively correlated with the NLF and the LF/HF ratio and negatively correlated with the NHF. The remaining seven subjects had negative correlations between the MS levels and the NLF and the LF/HF ratio and a positive correlation between the MS levels and the NHF. To clarify this contradiction, this study also inspected the effects of subjects' self-adjustments on the correlations between the MS levels and the HRV indices and showed that the variations in the relationship might be attributed to the subjects' self-adjustments, which they used to relieve the discomfort of MS.

EEG correlates of haptic feedback in a visuomotor tracking task.

This study investigates the temporal brain dynamics associated with haptic feedback in a visuomotor tracking task. Haptic feedback with deviation-related forces was used throughout tracking experiments in which subjects' behavioral responses and electroencephalogram (EEG) data were simultaneously measured. Independent component analysis was employed to decompose the acquired EEG signals into temporally independent time courses arising from distinct brain sources. Clustering analysis was used to extract independent components that were comparable across participants. The resultant independent brain processes were further analyzed via time-frequency analysis (event-related spectral perturbation) and event-related coherence (ERCOH) to contrast brain activity during tracking experiments with or without haptic feedback. Across subjects, in epochs with haptic feedback, components with equivalent dipoles in or near the right motor region exhibited greater alpha band power suppression. Components with equivalent dipoles in or near the left frontal, central, left motor, right motor, and parietal regions exhibited greater beta-band power suppression, while components with equivalent dipoles in or near the left frontal, left motor, and right motor regions showed greater gamma-band power suppression relative to non-haptic conditions. In contrast, the right occipital component cluster exhibited less beta-band power suppression in epochs with haptic feedback compared to non-haptic conditions. The results of ERCOH analysis of the six component clusters showed that there were significant increases in coherence between different brain networks in response to haptic feedback relative to the coherence observed when haptic feedback was not present. The results of this study provide novel insight into the effects of haptic feedback on the brain and may aid the development of new tools to facilitate the learning of motor skills.

Brain dynamics of mathematical problem solving.

The purpose of this study is to examine brain activities of participants solving mental math problems. The research investigated how problem difficulty affected the subjects' responses and electroencephalogram (EEG) in different brain regions. In general, it was found that solution latencies (SL) to the math problems increased with difficulty. The EEG results showed that across subjects, the right-central beta, left-parietal theta, left-occipital theta and alpha, right-parietal alpha and beta, medial-frontal beta and medial central theta power decreased as task difficulty increased. This study further explored the effects of problem-solving performance on the EEG. Slow solvers exhibited greater frontal theta activities in the right hemisphere, whereas an inverse pattern of hemispheric asymmetry was found in fast solvers. Furthermore, analyses of spatio-temporal brain dynamics during problem solving show progressively stronger alpha- and beta-power suppression and theta-power augmentation as subjects were reaching a solution. These findings provide a better understanding of cortical activities mediating math-based problem solving and knowledge acquisition that can ultimately benefit math learning and education.

A study of Fitts' law on goal-directed aiming task with moving targets.

Most research based on Fitts' law define a log-linear relationship between temporal and spatial accuracy in goal-directed aiming tasks using stationary targets. Whether this relationship holds or not when the targets have varying velocities, and how the behavioral strategies and physical activities may change accordingly are of interest. The aim of this study was to investigate the relationship between temporal and spatial accuracy in goal-directed aiming tasks with moving targets. Participants were asked to aim at two target widths using a joystick. Results demonstrated that in a goal-directed aiming task there was a negative effect on performance when target velocity was increased or target width was decreased. Participants moved faster and then made more systematic errors in a high-velocity target condition. Results may be applicable to the complex perceptual-motor behavior of people who perform tasks using computers.

Synthesis of 1-substituted carbazolyl-1,2,3,4-tetrahydro- and carbazolyl-3,4-dihydro-ß-carboline analogs as potential antitumor agents.

A series of 1-substituted carbazolyl-1,2,3,4-tetrahydro- and carbazolyl-3,4-dihydro-ß-carboline analogs have been synthesized and evaluated for antitumor activity against human tumor cells including KB, DLD, NCI-H661, Hepa, and HepG2/A2 cell lines. Among these, compounds 2, 6, 7, and 9 exhibited the most potent and selective activity against the tested tumor cells. As for inhibition of topoisomerase II, compounds 1-14 and 18 showed better activity than etoposide. Among them, compounds 3, 4, 7, 9, and 10 exhibited potent activity. The structure and activity relationship (SAR) study revealed correlation between carbon numbers of the side chain and biological activities. The molecular complex with DNA for compound 2 was proposed.

Effect of respiratory modulation on relationship between heart rate variability and motion sickness.

This study investigates the interplay among heart rate variability (HRV), respiration, and the severity of motion sickness (MS) in a realistic passive driving task. Although HRV is a commonly used metrically in physiological research or even believed to be a direct measure of sympathovagal activities, the results of MS-effected HRV remain mixed across studies. The goal of this study is to find the source of these contradicting results of HRV associated with MS. Experimental results of this study showed that the group trend of the low-frequency (LF) component and the LF/HF ratio increased and high-frequency (HF) component decreased significantly as self-reported MS level increased (p<0.001), consistent with a perception-driven autonomic response of the cardiovascular system. However, in one of the subjects, the relationship was reversed when individuals intentionally adjust themselves (deep breathing) to relieve the discomfort of MS during the experiments. It appears that the correlations between HRV and MS level were higher when individuals made fewer adjustments (the number of deep breathing) during the passive driving experiments.

An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation.

This study presents a novel wireless, ambulatory, real-time, and autoalarm intelligent telecardiology system to improve healthcare for cardiovascular disease, which is one of the most prevalent and costly health problems in the world. This system consists of a lightweight and power-saving wireless ECG device equipped with a built-in automatic warning expert system. This device is connected to a mobile and ubiquitous real-time display platform. The acquired ECG signals are instantaneously transmitted to mobile devices, such as netbooks or mobile phones through Bluetooth, and then, processed by the expert system. An alert signal is sent to the remote database server, which can be accessed by an Internet browser, once an abnormal ECG is detected. The current version of the expert system can identify five types of abnormal cardiac rhythms in real-time, including sinus tachycardia, sinus bradycardia, wide QRS complex, atrial fibrillation (AF), and cardiac asystole, which is very important for both the subjects who are being monitored and the healthcare personnel tracking cardiac-rhythm disorders. The proposed system also activates an emergency medical alarm system when problems occur. Clinical testing reveals that the proposed system is approximately 94% accurate, with high sensitivity, specificity, and positive prediction rates for ten normal subjects and 20 AF patients. We believe that in the future a business-card-like ECG device, accompanied with a mobile phone, can make universal cardiac protection service possible.

Spatial and temporal EEG dynamics of motion sickness.

This study investigates motion-sickness-related brain responses using a VR-based driving simulator on a motion platform with six degrees of freedom, which provides both visual and vestibular stimulations to induce motion sickness in a manner that is close to that in daily life. Subjects' brain dynamics associated with motion sickness were measured using a 32-channel EEG system. Their degree of motion sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop behavioral references to the recorded EEG changes. The acquired EEG signals were parsed by independent component analysis (ICA) into maximally independent processes. The decomposition enables the brain dynamics that are induced by the motion of the platform and motion sickness to be disassociated. Five MS-related brain processes with equivalent dipoles located in the left motor, the parietal, the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. The parietal and motor components exhibited significant alpha power suppression in response to vestibular stimuli, while the occipital components exhibited MS-related power augmentation in mainly theta and delta bands; the occipital midline components exhibited a broadband power increase. Further, time series cross-correlation analysis was employed to evaluate relationships between the spectral changes associated with different brain processes and the degree of motion sickness. According to our results, it is suggested both visual and vestibular stimulations should be used to induce motion sickness in brain dynamic studies.

Preparation, electrochemical and spectral properties of N-methylated pyridylethynyl porphyrins.

A new series of electron-deficient porphyrins were prepared by attaching one or two N-methylated 2-, 3- or 4-pyridylethynyl groups to the 10,20-meso positions of (5,15-biphenylporphinato)zinc(II). Electrochemical studies showed significant changes in the reduction potentials of these porphyrins, and N-methyl-2-pyridylethyne is the strongest electron-withdrawing substituent in the series. UV-visible spectra demonstrated largely red-shifted absorptions, and N-methyl-4-pyridylethyne has the greatest impact to the porphyrin absorptions. Electrochemical, UV-visible and EPR results concluded that porphyrins Zn2 and Zn6 reversibly undergo two one-electron porphyrin-ring reductions to their anion radicals then dianions. The first reductions of porphyrins Zn1, Zn3, Zn4 and Zn5 were irreversible one-electron transfer processes. The instability of these reduction products was suggested to result from the eletrophilic attacks at the substituents.

Microbial transformation of baccatin VI and 1beta-hydroxy baccatin I by Aspergillus niger.

The biotransformation of baccatin VI (1) and 1beta-hydroxybaccatin I (2) with the filamentous fungus Aspergillus niger produced four new taxane diterpenoids taxumairol S(1) (3), taxumairol T(1) (4) and taxumairol S (5), taxumairol T (6), respectively. 1beta-Dehydroxybaccatin VI (7) remained unreacted under the same condition.