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A 66-year-old male patient had a history of hepatitis B, was a hepatitis C carrier, and had hypertension. He was referred to our family medicine international clinic by the Canadian Physician's Referral Service. He had had progressive general weakness over 2 years, and the symptom had recently exacerbated. He also had slurred speech, difficulty in swallowing with occasional choking, constipation, and urinary incontinence. He was diagnosed as having Parkinson disease, but his symptoms worsened despite treatment with levodopa 250mg four times daily for 2 years. He had sought medical help in many clinics in Taiwan and Canada and was referred to our outpatient clinic by his Canadian family physician. The neurologist suggested that the diagnosis was multiple system atrophy (MSA) after history taking and neurologic examination. Rehabilitation programs, including physical therapy, occupational therapy and speech therapy, were arranged for him. Many Taiwanese immigrants prefer to come back to Taiwan for medical treatment. If we can integrate medical resources not only between different hospitals but also between different countries, many unnecessary medical expenses could be avoided. In this case, we summarized the case history and provided him with a CD of the images. This will be helpful in further care by a Canadian physician.

Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditional models are trained with knowledge of the imaging operator, they can show poor generalization across variable operators. Unconditional models instead learn generative image priors decoupled from the operator to improve reliability against domain shifts related to the imaging operator. Recent diffusion models are particularly promising given their high sample fidelity. Nevertheless, inference with a static image prior can perform suboptimally. Here we propose the first adaptive diffusion prior for MRI reconstruction, AdaDiff, to improve performance and reliability against domain shifts. AdaDiff leverages an efficient diffusion prior trained via adversarial mapping over large reverse diffusion steps. A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss. Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff outperforms competing conditional and unconditional methods under domain shifts, and achieves superior or on par within-domain performance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Plasticulture, the practice of using plastic materials in agricultural applications, consumes about 6.7 million tons of plastics every year, which is about 2% of the overall global annual plastics production. For different reasons, plastic material used for agriculture is difficult to recycle. Therefore, most of it is either buried in fertile soils, thereby significantly causing deterioration of their properties, or, at best case, end in landfills where its half-life is measured in decades and even centuries. Hence, developing biodegradable plastic materials that are suitable for agricultural applications is a vital and inevitable need for the global human society. In our labs, two types of potentially biodegradable plastic polymer films were prepared and characterized imidazolium in terms of their bio-degradability. In the first approach, polymers made of ionic liquid monomers were prepared using photo radical induced polymerization. The second approach relies on formation of polyethylene-like n-alkane disulfide polymers from 1,ω-di-thiols through thermally activated air oxidation. These two families of materials were tested for their biodegradability in soils by using a simulation system that combines a controlled environment chamber equipped with a respirometer and a proton-transfer-reaction time of flight mass spectrometer (PTR-TOF-MS) system. This system provides a time-dependent and comprehensive fingerprint of volatiles emitted in the degradation process. The results obtained thus far indicate that whereas the ionic-liquid based polymer does not show significant bio-degradability under the test conditions, the building block monomer, 1,10-n-decane dithiol, as well as its disulfide-based polymer, are bio-degradable. The latter reaching, under basic soil conditions and in room temperature, ∼20% degradation within three months. These results suggest that by introduction of disulfide groups into the polyethylene backbone one may be able to render it biodegradable, thus considerably shortening its half-life in soils. Principal component analysis, PCA, of the data about the total volatiles produced during the degradation in soil indicates a distinctive volatile "fingerprint" of the disulfide-based bio-degradable products which comes from the volatile organic compounds portfolio as recorded by the PTR-TOF-MS. The biodegradation volatile fingerprint of this kind of film was different from the "fingerprint" of the soil background which served as a control. These results can help us to better understand and design biodegradable films for agricultural mulching practices., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dar, Wu, Zhang, Yu, Qin, Shen, Zou, Poh, Eichen and Achmon.)

Multi-contrast MRI protocols increase the level of morphological information available for diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors including scan time and patient motion. Synthesis of missing or corrupted contrasts from other high-quality ones can alleviate this limitation. When a single target contrast is of interest, common approaches for multi-contrast MRI involve either one-to-one or many-to-one synthesis methods depending on their input. One-to-one methods take as input a single source contrast, and they learn a latent representation sensitive to unique features of the source. Meanwhile, many-to-one methods receive multiple distinct sources, and they learn a shared latent representation more sensitive to common features across sources. For enhanced image synthesis, we propose a multi-stream approach that aggregates information across multiple source images via a mixture of multiple one-to-one streams and a joint many-to-one stream. The complementary feature maps generated in the one-to-one streams and the shared feature maps generated in the many-to-one stream are combined with a fusion block. The location of the fusion block is adaptively modified to maximize task-specific performance. Quantitative and radiological assessments on T 1 ,- T 2 -, PD-weighted, and FLAIR images clearly demonstrate the superior performance of the proposed method compared to previous state-of-the-art one-to-one and many-to-one methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The design of a three-dimensional structure for an Ir-based catalyst offers a great opportunity to improve the electrocatalytic performance and maximize the use of the precious metal. Herein, a novel wet chemical strategy is reported for the synthesis of an IrRuMn catalyst with a sphere structure and porous features. In the synthetic process, the combined use of citric acid and formamide is requisite for the formation of the sphere structure. This method leads to a favorable 3D IrRuMn sphere structure with many fully exposed active sites. Furthermore, an alloying noble metal, such as Ir or Ru, with the transition metal leads to enhanced oxygen evolution reaction (OER) activity. The doping of a transition metal, such as Mn, is an interesting example, because it exhibits stability and activity in both acidic and alkaline media. For the OER, the IrRuMn sphere catalyst exhibits an overpotential of 260 mV at a current density of 10 mA cm -2 in strongly acidic 0.1 m HClO 4 , which is superior to that of a commercial IrO 2 /C catalyst. This approach provides a novel way to synthesize an Ir-based multimetallic spherical electrocatalyst, which exhibits exceptional efficiency for the acidic OER. It will pave the way for new approaches to the practical utilization of PEM electrolyzers., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A single step fast track method is proposed to determine melamine based on strong hydrogen bonding between exocyclic amine group in melamine moiety and hydroxyl group of tannic acid (TA); acting as reducing and functionalizing agent for synthesis of gold nanoparticles (Au NPs). The proposed strategy is facile and highly time saving; as the detection of melamine with labelfree gold nanoparticles was accomplished within five minutes. The colorimetric probe exhibits high detection limit of 8 nM for melamine determination in raw milk, which is very low as compared to other capping ligands employed for Au NPs such as borohydride, citrate and 3,5-dihydroxybenzoic acid. The results show a highly sensitive linear range (0.03-2.5 μ M) between absorption ratio and concentration of melamine with a correlation coefficient of 0.991. Proposed methodology is proven effective for on-site screening without using costly instrumentation.

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, the current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can, in turn, suffer from the loss of structural details in synthesized images. Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss, and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T 1 - and T 2 - weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to the previous state-of-the-art methods. Our synthesis approach can help improve the quality and versatility of the multi-contrast MRI exams without the need for prolonged or repeated examinations.

A hierarchical excavated iron-nitrogen codoped mesoporous carbon (Ex-FeN-MC) electrocatalyst has been successfully synthesized by pyrolysis of the unique ureidopyrimidinone containing organic precursor MAMIS by using silica as the mesoporous template and iron nitrate as the source of iron. The selected organic precursor has multifunctionalities with diverse distribution of nitrogen and oxygen species which constitute interesting features to prepare the Ex-FeN-MC electrocatalyst. The as-prepared Ex-FeN-MC material shows unexpectedly higher catalytic activities towards ORR/OER performances with an overpotential of 250 mV for OER in alkaline solution at a current density of 10 mA cm -2 , while the ORR activity is almost similar to that of commercial Pt/C. It is believed that the excellent electrochemical performance results from the synergic contribution of the hierarchical uniform mesoporous structure, evenly distributed iron nanoparticles and high density of Ex-FeN-MC catalytically active sites. The presence of the hierarchical uniform mesoporous structure considerably accelerates the mass transfer and efficiently promotes the full utilization of active sites. Furthermore, it also possesses amorphous carbon in coexistence with lattice graphitic carbon which also enhanced the ORR/OER performances. Our research findings can surely provide a new insight to design highly active ORR/OER electrocatalysts by utilizing more novel organic precursors to provide interesting characteristics to electrocatalysts, as well as open up a route for constructing unique multifunctional materials with mesoporous architectures for electrocatalysis and supercapacitor applications.