Minimal Information Loss Attention U-Net for abdominal CT of kidney cancers segmentation

Recent work has shown that U-net is a straight-forward and successful architecture, it quickly evolved to a commonly used benchmark in medical image segmentation, Which nnU-Net had better performance We improved the nnUNet model by incorporating a new image pyramid to preserve contextual features and attention gate. In order to let different kinds of class details more easily accessible at different scales, we injected the encoder layers with an input image pyramid before each of the max-pooling layers. We proposed a new image pyramid mechanism with dilated convolution that counters the loss of information caused by max-pooling, re-introducing the original image at multiple points within the network. We evaluated this model in the 2019 Kidney Tumor Segmentation Challenge. and got the dice coefficient 0.958 of kidney and 0.847 of tumors

Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis

ObjectiveEpilepsy is considered as a neural network disorder. Seizure activity in epilepsy may disturb brain networks and damage brain functions. We propose using resting-state functional magnetic resonance imaging (rs-fMRI) data to characterize connectivity patterns in drug-resistant epilepsy.MethodsThis study enrolled 47 participants, including 28 with drug-resistant epilepsy and 19 healthy controls. Functional and effective connectivity was employed to assess drug-resistant epilepsy patients within resting state networks. The resting state functional connectivity (FC) analysis was performed to assess connectivity between each patient and healthy controls within the default mode network (DMN) and the dorsal attention network (DAN). In addition, dynamic causal modeling was used to compute effective connectivity (EC). Finally, a statistical analysis was performed to evaluate our findings.ResultsThe FC analysis revealed significant connectivity changes in patients giving 64.3% (18/28) and 78.6% (22/28) for DMN and DAN, respectively. Statistical analysis of FC was significant between the medial prefrontal cortex, posterior cingulate cortex, and bilateral inferior parietal cortex for DMN. For DAN, it was significant between the left and the right intraparietal sulcus and the frontal eye field. For the DMN, the patient group showed significant EC connectivity in the right inferior parietal cortex and the medial prefrontal cortex for the DMN. There was also bilateral connectivity between the medial prefrontal cortex and the posterior cingulate cortex, as well as between the left and right inferior parietal cortex. For DAN, patients showed significant connectivity in the right frontal eye field and the right intraparietal sulcus. Bilateral connectivity was also found between the left frontal eye field and the left intraparietal sulcus, as well as between the right frontal eye field and the right intraparietal sulcus. The statistical analysis of the EC revealed a significant result in the medial prefrontal cortex and the right intraparietal cortex for the DMN. The DAN was found significant in the left frontal eye field, as well as the left and right intraparietal sulcus.ConclusionOur results provide preliminary evidence to support that the combination of functional and effective connectivity analysis of rs-fMRI can aid in diagnosing epilepsy in the DMN and DAN networks

Multiple Frequency Bands Analysis of Large Scale Intrinsic Brain Networks and Its Application in Schizotypal Personality Disorder

The human brain is a complex system composed by several large scale intrinsic networks with distinct functions. The low frequency oscillation (LFO) signal of blood oxygen level dependent (BOLD), measured through resting-state fMRI, reflects the spontaneous neural activity of these networks. We propose to characterize these networks by applying the multiple frequency bands analysis (MFBA) to the LFO time courses (TCs) resulted from the group independent component analysis (ICA). Specifically, seven networks, including the default model network (DMN), dorsal attention network (DAN), control executive network (CEN), salience network, sensorimotor network, visual network and limbic network, are identified. After the power spectral density (PSD) analysis, the amplitude of low frequency fluctuation (ALFF) and the fractional amplitude of low frequency fluctuation (fALFF) is determined in three bands: <0.1 Hz; slow-5; and slow-4. Moreover, the MFBA method is applied to reveal the frequency-dependent alternations of fALFF for seven networks in schizotypal personality disorder (SPD). It is found that seven networks can be divided into three categories: the advanced cognitive networks, primary sensorimotor networks and limbic networks, and their fALFF successively decreases in both slow-4 and slow-5 bands. Comparing to normal control group, the fALFF of DMN, DAN and CEN in SPD tends to be higher in slow-5 band, but lower in slow-4. Higher fALFF of sensorimotor and visual networks in slow-5, higher fALFF of limbic network in both bands have been observed for SPD group. The results of ALFF are consistent with those of fALFF. The proposed MFBA method may help distinguish networks or oscillators in the human brain, reveal subtle alternations of networks through locating their dominant frequency band, and present potential to interpret the neuropathology disruptions

SPD-rsfMRI-NEU-ZS.zip

We have MRI images of 18 Schizotypal Personality Disorder (SPD) and 18 healthy controls. T1 weighted MRI and DWI images are provided for each projects

Effects of fermentation by-products and inhibitors on pervaporative recovery of biofuels from fermentation broths with novel silane modified silicalite-1/PDMS/PAN thin film composite membrane

The influence of different bioethanol fermentation broths components (fermentation by-products, and the common inhibitory compounds present in lignocellulosic hydrolysates) on the pervaporation performance of the vinyltriethoxysilane (VTES) modified silicalite-1/PDMS/PAN thin-film composite membrane was detailedly investigated in this work. The results showed that succinic acid and glycerol are impermeable components, whereas formic acid, acetic acid, and furfural can permeate through the membrane. Succinic acid have no obvious influence on the membrane performance. Meanwhile, the composite membrane can effectively remove formic acid, acetic acid, and furfural present in lignocellulosic hydrolysates. The maximum furfural/water selectivity of 95 and furfural flux of 130 g/m(2) h were obtained with adding 15 g/L furfural into 2 wt.% ethanol binary solution at the feed temperature of 35 degrees C. This research reveals a novel detoxification method of lignocellulosic hydrolysates and a potential approach for furfural production as well. (C) 2015 Elsevier B.V. All rights reserved