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The electrospinning technique has been considered an attractive route for processing conjugated polymers in a significant quantity for large-scale applications. The processing-structure-property relationship of the electrospinning process for conjugated polymers is not well understood. Here, we report the electrospinning of poly(3-hexylthiophene) (P3HT) for three different molecular weights of P3HT: 31, 58, and 83 kDa. Chloroform was used as a solvent, and a high molecular weight poly(ethylene oxide) (PEO) was utilized to facilitate the processing of P3HT. The electrospinning was performed on the freshly prepared and 24 h aged spinning solutions. The aging of the spinning solution led to the self-assembly of P3HT chains, particularly with dominant H-aggregation for 83 kDa P3HT. The structure development and properties of the fibers were investigated, including the single-fiber electrical conductivity measured using a custom-built setup. The electrical conductivity has been found to be increasing with increasing molecular weight, and as high as a five-fold enhancement in single fiber electrical conductivity was obtained compared to the fiber from the freshly-prepared solution. Despite a 25% PEO concentration in the fibers, the maximum electrical conductivity of a single fiber was found to be ≈2.7 X 10^(-5) S/cm, similar to the pristine P3HT thin films. Our study provides an additional understanding of P3HT structure development in electrospun fibers as a function of polymer molecular weight and processing steps and relates that to fiber properties.

The development of open-shell organic molecules that magnetically order at room temperature,which can be practically applied, remains a grand challenge in chemistry, physics, and materials science. Despite the exploration of vast chemical space, design paradigms for organic paramagnetic centers generally result in unpaired electron spins that are unstable or isotropic. Here, a high-spin conjugated polymer is demonstrated, which is composed of alternating cyclopentadithiophene and benzo[1,2-c;4,5-c']bis[1,2,5]thiadiazole heterocycles, in which macromolecular structure and topology coalesce to promote the spin center generation and intermolecular exchange coupling. Electron paramagnetic resonance (EPR) spectroscopy is consistent with spatially localized spins, while magnetic susceptibility measurements show clear anisotropic spin ordering and exchange interactions that persist at room temperature. The application of long-range π-correlations for spin center generation promotes remarkable stability. This work offers a fundamentally new approach to the implementation of this long-sought-after physical phenomenon within organic materials and the integration of manifold properties within emerging technologies.

Polymer microspheres have been widely investigated because of their applications in areas such as drug delivery, latex diagnostics, and affinity bioseparators. The effect of annealing on polymer microspheres, however, has been rarely studied. In this work, we demonstrate the morphology transformation of polystyrene (PS) microspheres annealed thermally on poly(methyl methacrylate) (PMMA) films. During the annealing process, the PS microspheres gradually sink into the PMMA films and transform into PS hemispheres, driven by the reduction of the surface and interfacial energies. The effect of the film thicknesses on the morphology transformation is also studied. In addition, porous PMMA films or PS hemispheres can be obtained by removing the PS or the PMMA domains of the polymer composites using cyclohexane or acetic acid, respectively.

Although various strategies have been developed to prepare anisotropic polymeric particles, it remains challenging to fabricate monolayers of anisotropic polymeric particles, which can extend the applications of anisotropic particles. Here, we develop a novel and facile approach to fabricate monolayers of anisotropic polymeric particles. Monolayers of polystyrene (PS) microspheres with a mean diameter of 10 μm are deposited on glass substrates coated with poly(methyl methacrylate) films, followed by sequential selective solvent on-film annealing processes. Monolayers of anisotropic polymeric particles, such as the snowman-like PS particles, are successfully fabricated. Such unique structures possess the long-range ordering of monolayers (the structure factor) and the anisotropic geometry of individual particles (the form factor). The nanomechanical properties of the PS particles are also characterized using atomic force microscopy force volume measurements, showing a decrease in the Young's moduli of the PS particles owing to the looser packing of the polymer chains. This work provides the most facile and versatile strategy by far to fabricate monolayers of ordered anisotropic polymeric particles, which are inaccessible by other traditional means.

Mutli-camera vehicle tracking and re-identification (re-ID) have gradually gained attention due to their applications in the intelligent transportation system. However, these problems are fundamentally challenging. Specifically, for vehicle tracking, we observe that the results generated from single camera tracking algorithm usually recognize tracklets with same identity as different vehicles when the tracklets are occluded. Hence, we propose a Tracklet Re-connection technique to refine tracking results with pre-defined zone areas and GPS information. The proposed method can efficiently filter invalid tracklet pairs and reconnect the split tracklets into complete ones, which is important for the afterwards multi-target multi-camera tracking. As for re-ID, we also find that when a large-scale auxiliary dataset is used to assist the learning of main dataset for better model capability and generalization, there is a performance drop caused by data imbalance when the full auxiliary dataset is applied. To tackle this problem, we introduce Balanced Cross-Domain Learning to avoid the overemphasis on larger auxiliary dataset by a newly introduced training data sampler and loss function. The extensive experiments validate the empirical effectiveness of our proposed components.

Recently, anodic aluminum oxide (AAO) membranes with ordered and monodispersed nanopores have attracted significant attention because the pore sizes and lengths of the AAO membranes can be systematically controlled by changing the anodization parameters. Asymmetries of geometry, wettability, charge, and polarity in nanoporous AAO membranes are now highly desired for applications such as drug delivery, membrane filtration, and catalysis. Although the asymmetries in AAO membranes have been widely studied, AAO membranes whose nanopores simultaneously possess more than two asymmetric properties still need to be developed. In this work, we present a simple method to fabricate asymmetric AAO membranes by combining the post-treatments with the three-step anodization method. The asymmetries of the AAO membranes simultaneously possessing larger nanopores with higher hydrophobicity and smaller nanopores with lower hydrophobicity are characterized by scanning electron microscopy, water contact angle measurements, an...

Convolutional neural networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many filters to extract the knowledge behind it. However, while the depth of convolutional layers gets deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, inspired by rate-distortion optimization in image and video coding, we propose a computation-performance optimization (CPO) method to remove the redundant convolution filters in a CNN with performance constraints. To prove the effectiveness of the proposed method, CPO is applied to the networks for image super-resolution and image classification. Under almost the same PSNR drop and accuracy drop for performance evaluation in these two tasks, we can achieve the best parameter and computation reduction when compared with previous works.

The Plateau–Rayleigh instability is a phenomenon driven by the reduction of the surface energies, in which a liquid cylinder transforms to a series of droplets with distinct spacing. This instability phenomenon is also applied broadly for the transformation of cylindrical materials to spherical particles in homogeneous environments. The demonstration of the instability in heterogeneous environments, however, has been less studied. Here, we study the Plateau–Rayleigh instability of electrospun polystyrene (PS)/poly(methyl methacrylate) (PMMA) core–shell fibers annealed on n-octadecyltrichlorosilane (ODTS)-modified glass substrates with toluene vapors. After the annealing procedures, the PS/PMMA core–shell fibers transform to anisotropic sunny-side-up egg-shaped polymer structures, driven by the reduction of the total surface and interfacial energies. Quantitative analyses are performed on the sizes of the structures. Fluorescent fibers are also prepared by electrospinning and annealed on the ODTS-modified ...

In this work, we study the structure transformations of cylinder-forming polystyrene-block-polydimethylsiloxane (PS31k-b-PDMS14.5k) confined in cylindrical nanopores. PS-b-PDMS nanotubes, nanospheres, and curved nanodiscs are ingeniously prepared by a facile template wetting strategy using anodic aluminum oxide (AAO) templates. Quantitative analyses of the structure transformations from nanospheres to curved nanodiscs are also conducted, showing that the lengths of the curved nanodiscs can be controlled by adjusting the annealing temperature and time. Furthermore, the PDMS domains of the nanostructures can be selectively etched using HF solutions, generating porous PS nanostructures. This work not only offers versatile routes to prepare block copolymer nanostructures with controlled shapes but also provides a deeper understanding of the structure transformation of block copolymers in confined geometries.

Interactions between charged amino acids significantly influence the structure and function of proteins. The encoded charged amino acids Asp, Glu, Arg, and Lys have different number of hydrophobic methylenes linking the backbone to the charged functionality. It remains to be fully understood how does this difference in the number of methylenes affect protein structure stability. Protein secondary structures are the fundamental three-dimensional building blocks of protein structures. β-Sheet structures are particularly interesting, because these structures have been associated with a number of protein misfolding diseases. Herein, we report the effect of charged amino acid side chain length at two β-strand positions individually on the stability of a β-hairpin. The charged amino acids include side chains with a carboxylate, an ammonium, or a guanidinium group. The experimental peptides, fully folded reference peptides, and fully unfolded reference peptides were synthesized by solid phase peptide synthesis and analyzed by 2D NMR methods including TOCSY, DQF-COSY, and ROESY. Sequence specific assignments were performed for all peptides. The chemical shift data were used to derive the fraction folded population and the folding free energy for the experimental peptides. Results showed that the fraction folded population increased with increasing charged amino acid side chain length. These results should be useful for developing functional peptides that adopt the β-conformation.

Person re-identification (re-ID) has received great success with the supervised learning methods. However, the task of unsupervised cross-domain re-ID is still challenging. In this paper, we propose a Hard Samples Rectification (HSR) learning scheme which resolves the weakness of original clustering-based methods being vulnerable to the hard positive and negative samples in the target unlabelled dataset. Our HSR contains two parts, an inter-camera mining method that helps recognize a person under different views (hard positive) and a part-based homogeneity technique that makes the model discriminate different persons but with similar appearance (hard negative). By rectifying those two hard cases, the re-ID model can learn effectively and achieve promising results on two large-scale benchmarks., Comment: This paper was accepted by IEEE International Conference on Image Processing (ICIP) 2021

In the interactive segmentation, users initially click on the target object to segment the main body and then provide corrections on mislabeled regions to iteratively refine the segmentation masks. Most existing methods transform these user-provided clicks into interaction maps and concatenate them with image as the input tensor. Typically, the interaction maps are determined by measuring the distance of each pixel to the clicked points, ignoring the relation between clicks and mislabeled regions. We propose a Dynamic Click Transform Network~(DCT-Net), consisting of Spatial-DCT and Feature-DCT, to better represent user interactions. Spatial-DCT transforms each user-provided click with individual diffusion distance according to the target scale, and Feature-DCT normalizes the extracted feature map to a specific distribution predicted from the clicked points. We demonstrate the effectiveness of our proposed method and achieve favorable performance compared to the state-of-the-art on three standard benchmark datasets., Comment: This paper was accepted by IEEE International Conference on Image Processing (ICIP) 2021

Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. However, there exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. To address this issue, we present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets. Furthermore, we introduce an improved model called Coarse-to-Fine Axial-Attention Network (CF-AAN). Based on the typical Non-local Network, we replace the non-local module with three 1-D position-sensitive axial attentions, in addition to our proposed coarse-to-fine structure. With the developed CF-AAN, compared to the original non-local operation, we can not only significantly reduce the computation cost but also obtain the state-of-the-art performance (91.3% in rank-1 and 86.5% in mAP) on the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for data alignment, to our surprise, several baseline models can achieve better or comparable results with the current state-of-the-arts. Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS. We hope that our work can help the community for the further development of invariant representation without the hassle of the spatial and temporal alignment and dataset noise. The code, corrected labels, evaluation protocol, and the aligned data will be available at https://github.com/jackie840129/CF-AAN., Comment: This paper was accepted by CVPR 2021 Biometrics Workshop

Current state-of-the-art deep learning based face recognition (FR) models require a large number of face identities for central training. However, due to the growing privacy awareness, it is prohibited to access the face images on user devices to continually improve face recognition models. Federated Learning (FL) is a technique to address the privacy issue, which can collaboratively optimize the model without sharing the data between clients. In this work, we propose a FL based framework called FedFR to improve the generic face representation in a privacy-aware manner. Besides, the framework jointly optimizes personalized models for the corresponding clients via the proposed Decoupled Feature Customization module. The client-specific personalized model can serve the need of optimized face recognition experience for registered identities at the local device. To the best of our knowledge, we are the first to explore the personalized face recognition in FL setup. The proposed framework is validated to be superior to previous approaches on several generic and personalized face recognition benchmarks with diverse FL scenarios. The source codes and our proposed personalized FR benchmark under FL setup are available at https://github.com/jackie840129/FedFR., Comment: This paper was accepted by AAAI 2022 Conference on Artificial Intelligence and selected as an oral paper

Person re-identification (Re-ID) aims to match images of the same person across distinct camera views. In this paper, we propose the Space-Time Guided Association Learning (STGAL) for unsupervised Re-ID without ground truth identity nor image correspondence observed during training. By exploiting the spatial-temporal information presented in pedestrian data, our STGAL is able to identify positive and negative image pairs for learning Re-ID feature representations. Experiments on a variety of datasets confirm the effectiveness of our approach, which achieves promising performance when comparing to the state-of-the-art methods.

This study aimed to investigate the diagnostic performance of semi-quantitative parameters of thallium-201 myocardial perfusion imaging (MPI) for coronary artery disease (CAD). From January to December 2017, patients were enrolled who had undergone Tl-201 MPI and received cardiac catheterization for coronary artery disease within three months of MPI. Receiver operating characteristics (ROC) analysis was used to determine the optimal cutoff values of semi-quantitative parameters. A comparison of the sensitivity and specificity of these parameters based on different subgroupings was further performed. A total of 130 patients were enrolled for further analysis. Among the collected parameters, the stress total perfusion deficit (sTPD) had the highest value of the area under curve (0.813) under the optimal cutoff value of 3.5%, with a sensitivity and specificity of 73.5% and 74.5%, respectively (p = 0.0000), for the diagnosis of CAD. With further subgrouping analysis based on history of diabetes or dyslipidemia, the sensitivity and specificity showed similar results. Based on the currently collected data and image acquisition conditions, the sTPD parameter has a clinical role for the diagnosis of CAD with a cutoff value of 3.5%.

Filter pruning is an efficient way to structurally remove the redundant parameters in convolutional neural network, where at the same time reduces the computation, memory storage and transfer cost. Recent state-of-the-art methods globally estimate the importance of each filter based on its impact to the loss and iteratively remove those with smaller values until the pruned network meets some resource constraints, such as the commonly used number (or ratio) of filter left. However, when there is a more practical constraint like the total number of FLOPs, they ignore its relation to the estimation of filter importance. We propose a novel method called Constraint-Aware Importance Estimation (CAIE) that integrates information of the impact on the given resource into the original importance estimation only based on loss when pruning each filter. Moreover, our CAIE can be generalized to the pruning problem under multiple resource constraints simultaneously. Extensive experiments show that under the same multiple resource constraints, the model pruned with our CAIE method can not only accurately meet the constraints but also achieve the optimal performance results when comparing to existing state-of-the-art methods.

Vehicle re-identification (re-ID) matches images of the same vehicle across different cameras. It is fundamentally challenging because the dramatically different appearance caused by different viewpoints would make the framework fail to match two vehicles of the same identity. Most existing works solved the problem by extracting viewpoint-aware feature via spatial attention mechanism, which, yet, usually suffers from noisy generated attention map or otherwise requires expensive keypoint labels to improve the quality. In this work, we propose Viewpoint-aware Channel-wise Attention Mechanism (VCAM) by observing the attention mechanism from a different aspect. Our VCAM enables the feature learning framework channel-wisely reweighing the importance of each feature maps according to the "viewpoint" of input vehicle. Extensive experiments validate the effectiveness of the proposed method and show that we perform favorably against state-of-the-arts methods on the public VeRi-776 dataset and obtain promising results on the 2020 AI City Challenge. We also conduct other experiments to demonstrate the interpretability of how our VCAM practically assists the learning framework., CVPR Workshop 2020

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention mechanisms to help vehicle re-ID, they often require expensive keypoint labels or suffer from noisy attention mask if not trained with expensive labels. In this work, we propose a dedicated Semantics-guided Part Attention Network (SPAN) to robustly predict part attention masks for different views of vehicles given only image-level semantic labels during training. With the help of part attention masks, we can extract discriminative features in each part separately. Then we introduce Co-occurrence Part-attentive Distance Metric (CPDM) which places greater emphasis on co-occurrence vehicle parts when evaluating the feature distance of two images. Extensive experiments validate the effectiveness of the proposed method and show that our framework outperforms the state-of-the-art approaches.

Hierarchical structures are commonly observed in nature and possess unique properties. The fabrication of hierarchical structures with well-controlled sizes in different length scales, however, is still a great challenge. To further understand the morphologies and properties of the hierarchical structures, here we present a novel strategy to prepare hierarchical polymer structures by combining the modified breath figure method and the template method. Poly(methyl methacrylate) (PMMA) honeycomb films with regular micropores are first prepared using the modified breath figure method by dipping PMMA films into mixtures of chloroform and methanol. The polymer chains on the honeycomb films are then annealed and wetted into the nanopores of anodic aluminum oxide templates via capillary forces, resulting in the formation of hierarchical polymer structures. The morphologies of the polymer structures, which can be controlled by the molecular weights of the polymers and the concentrations of the polymer solutions, are characterized by scanning electron microscopy. The surface wettabilities of the polymer structures are also examined by water contact angle measurements, and the hierarchical structures are observed to be more hydrophobic than the flat films and honeycomb films. This work not only provides a feasible approach to fabricate hierarchical polymer structures with controlled sizes but also gives a better understanding of the relationship between surface morphologies and properties.

Poly(3-hexylthiophene) (P3HT) films have been usually prepared by spin-coating for the applications of electronic devices such as organic photovoltaic devices (OPV) and organic field-effect transistors (OFETs). The wetting and dewetting behaviors of the swollen P3HT films during the spin-coating processes, however, are still poorly understood. In this work, we investigate the dewetting behaviors of P3HT thin films and the formation of ring structures during the spin-coating process by controlling the spin rates and the solution temperatures. Quantitative studies of the dewetting phenomena are conducted by measuring the sizes of the ring structures of the dewetting patterns. It is observed that the sizes of the ring structures are larger at lower spin rates because of the longer dewetting times allowed during the spin-coating processes. More importantly, the dewetting behaviors of the P3HT films are discovered to be affected by the formation of the P3HT nanowhiskers (nanowires). This work offers a deeper u...

Ordered arrays of polymer nanostructures have been widely investigated because of their promising applications such as solar-cell devices, sensors, and supercapacitors. It remains a great challenge, however, to manipulate the shapes of individual nanostructures in arrays for tailoring specific properties. In this study, an effective strategy to prepare anisotropic polymer nanopillar arrays via photo-fluidization is presented. Azobenzene-containing polymers (azopolymers) are first infiltrated into the nanopores of ordered anodic aluminum oxide (AAO) templates. After the removal of the AAO templates using weak bases, azopolymer nanopillar arrays can be prepared. Upon exposure of linearly polarized lights, azobenzene groups in the azopolymers undergo trans-cis-trans photoisomerization, causing mass migration and elongation of the nanopillar along with the polarization directions. As a result, anisotropic nanopillar arrays can be fabricated, of which the deformation degrees are controlled by the illumination times. Furthermore, patterned nanopillar arrays can also be constructed with designed photomasks. This work presents a practical and versatile strategy to fabricate arrays of anisotropic nanostructures for future technical applications.

With excellent mechanical and physical properties, polyimides (PIs) possess diverse applications in different fields. PI-based materials can also be converted to carbon materials by high temperature carbonization processes. The fabrication and properties of PI-based nanomaterials and corresponding carbon nanomaterials, however, have been less investigated, especially the packing behaviors of the polymer chains. In this work, we study the fabrication and characterization of PI nanostructures using the solution wetting (SW) method and the solvent-annealing-induced nanowetting in templates (SAINT) method. Poly(amic acid) (PAA) tubes are first prepared using anodic aluminum oxide (AAO) membranes. After thermal imidization and carbonization processes, PI and carbon tubes can be obtained, respectively. Bamboo-like structures are also observed in the tubes, which can be attributed to the Marangoni effect induced by the gradient of the surface tensions during the solvent evaporation process. Moreover, the laser Raman micro-spectroscopy results show that the packing behaviors of the polymer chains in the nanostructures prepared by the SAINT method are better than those prepared by the SW method, indicating that the solvent annealing process can improve the chain packing while assisting the infiltration of polymer chains in the nanopores.

Vehicle re-identification (Re-ID) is fundamentally challenging due to the difficulties in data labeling, visual domain mismatch between datasets and diverse appearance of the same vehicle. We propose the adaptive feature learning technique based on the space-time prior to address these issues. The idea is demonstrated effectively in both the human Re-ID and the vehicle Re-ID tasks. We train a vehicle feature extractor in a multi-task learning manner on three existing vehicle datasets and fine-tune the feature extractor with the adaptive feature learning technique on the target domain. We then develop a vehicle Re-ID system based on the learned vehicle feature extractor. Finally, our meticulous system design leads to the second place in the 2018 NVIDIA AI City Challenge Track 3.

We study the dewetting behaviors of poly(methyl methacrylate) (PMMA) thin films coated in the cylindrical nanopores of anodic aluminum oxide (AAO) templates by thermal annealing. Self-assembled monolayers (SAMs) of n-octadecyltrichlorosilane (ODTS) are introduced to modify the pore surfaces of the AAO templates to induce the dewetting process. By using scanning electron microscopy (SEM), the dewetting-induced morphology transformation from the PMMA thin films to PMMA nanoparticles with asymmetric shapes can be observed. The sizes of the PMMA nanoparticles can be controlled by the original PMMA solution concentrations. The dewetting phenomena on the modified nanopores are explained by taking into account the excess intermolecular interaction free energy (ΔG). This work opens a new possibility for creating polymer nanoparticles with asymmetric shapes in confined geometries.

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop., This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS)

Technetium-99m-labeled red blood cells subcutaneous radionuclide venography can apply to evaluate deep venous flow of lower extremities. We introduce additional abdominal/pelvic hybrid SPECT/CT (single-photon emission computed tomography with computed tomography) to scrutinize the proximal deep venous flow defects in the iliac vein. SPECT/CT imaging is collected when disturbed or asymmetrical iliac venous flow is noted on the planar imaging of Tc-99m-labeled red blood cells subcutaneous radionuclide venography. The region of SPECT/CT imaging includes abdominal aorta, inferior vena cava and bilateral iliac vessels. We present three cases with suspicion of deep vein thrombosis in the iliac vein. Tc-99m-labeled red blood cells subcutaneous radionuclide venography with SPECT/CT revealed not only provides us the venous flow but also other information including abnormal vessel structure, space-occupying lesion and abnormality in other organs. In conclusion, if disturbed or asymmetrical iliac venous flow and abnormal collateral venous drainage in the abdomen are noted on the Tc-99mlabeled red blood cells subcutaneous radionuclide venography, SPECT/CT imaging may arrange for further information and differential diagnosis of lower extremities swelling; not only in detecting deep vein thrombosis.

The fabrication of electrospun polymer fibers is demonstrated with anisotropic cross-sections by applying a simple pressing method. Electrospun polystyrene or poly(methyl methacrylate) fibers are pressed by flat or patterned substrates while the samples are annealed at elevated temperatures. The shapes and morphologies of the pressed polymer fibers are controlled by the experimental conditions such as the pressing force, the pressing temperature, the pressing time, and the surface pattern of the substrate. At the same pressing force, the shape changes of the polymer fibers can be controlled by the pressing time. For shorter pressing times, the deformation process is dominated by the effect of pressing and fibers with barrel-shaped cross-sections can be generated. For longer pressing times, the effect of wetting becomes more important and fibers with dumbbell-shaped cross-sections can be obtained. Hierarchical polymer fibers with nanorods are fabricated by pressing the fibers with porous anodic aluminum oxide templates.

We present a case with swelling of the lower extremities, shortness of breath, and hyponatremia. (99m)Tc-labeled red blood cells subcutaneous radionuclide venography was performed to rule out deep vein thrombosis. Disturbed iliac venous flow and a photopenic lesion were noted in the lower abdomen on the planar imaging. SPECT/CT imaging revealed a hypodense lesion in the lower abdomen. Distended urinary bladder and retention were impressed. After approximately 4000 mL of urine was drained, symptoms were dramatically relieved, and improvement of hyponatremia was also observed.