Lehmannhaslund9490

Then, the aligned fully-sampled reference image joins the multi-modal reconstruction of the under-sampled target image. Also, considering the contrast difference between the target and reference images, we have designed a cross-modality-synthesis-based registration loss in combination with the reconstruction loss, to jointly train the spatial alignment network and the reconstruction network. The experiments on both clinical MRI and multi-coil k -space raw data demonstrate the superiority and robustness of the multi-modal MRI reconstruction empowered with our spatial alignment network. Our code is publicly available at https//github.com/woxuankai/SpatialAlignmentNetwork.3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori. However, previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry, which is attributed to insufficient ground-truth 3D shapes, unreliable training strategies and limited representation power of 3DMM. To alleviate this issue, this paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person. Specifically, given a 2D image as the input, we virtually render the image in several calibrated views to normalize pose variations while preserving the original image geometry. A many-to-one hourglass network serves as the encode-decoder to fuse multiview features and generate vertex displacements as the fine-grained geometry. Besides, the neural network is trained by directly optimizing the visual effect, where two 3D shapes are compared by measuring the similarity between the multiview images rendered from the shapes. Finally, we propose to generate the ground-truth 3D shapes by registering RGB-D images followed by pose and shape augmentation, providing sufficient data for network training. Experiments on several challenging protocols demonstrate the superior reconstruction accuracy of our proposal on the face shape.Transformer with self-attention has led to the revolutionizing of NLP field, and recently inspires the emergence of Transformer-style architecture design with competitive results in numerous CV tasks. 3',3'-cGAMP price Nevertheless, most of existing designs directly employ self-attention over a 2D feature map to obtain the attention matrix based on pairs of isolated queries and keys, but leave the rich contexts among neighbor keys under-exploited. Here we design a novel Transformer-style module, i.e., Contextual Transformer (CoT) block, for visual recognition. It fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation. Technically, CoT block first contextually encodes input keys via 3×3 convolution, leading to a static contextual representation. We further concatenate the encoded keys with input queries to learn the dynamic multi-head attention matrix through two consecutive 1×1 convolutions. The learnt attention matrix is multiplied by values to achieve the dynamic contextual representation. The fusion of static and dynamic contextual representations are finally taken as outputs. Our CoT block can readily replace each 3×3 convolution in ResNet architectures, yielding a Transformer-style backbone named as Contextual Transformer Networks (CoTNet). Through extensive experiments over a wide range of applications, we validate the superiority of CoTNet as a stronger backbone.We had released MoNuSAC2020 as one of the largest publicly available, manually annotated, curated, multi-class, and multi-instance medical image segmentation datasets. Based on this dataset, we had organized a challenge at the International Symposium on Biomedical Imaging (ISBI) 2020. Along with the challenge participants, we had published an article summarizing the results and findings of the challenge (Verma et al., 2021). Foucart et al. (2022) in their "Analysis of the MoNuSAC 2020 challenge evaluation and results metric implementation errors" have pointed ways in which the computation of the segmentation performance metric for the challenge can be corrected or improved. After a careful examination of their analysis, we have found a small bug in our code and an erroneous column-header swap in one of our result tables. Here, we present our response to their analysis, and issue an errata. After fixing the bug the challenge rankings remain largely unaffected. On the other hand, two of Foucart et al.'s other suggestions are good for future consideration, but it is not clear that those should be immediately implemented. We thank Foucart et al. for their detailed analysis to help us fix the two errors.For over 40 years, the Bicoid-hunchback (Bcd-hb) system in the fruit fly embryo has been used as a model to study how positional information in morphogen concentration gradients is robustly translated into step-like responses. A body of quantitative comparisons between theory and experiment have since questioned the initial paradigm that the sharp hb transcription pattern emerges solely from diffusive biochemical interactions between the Bicoid transcription factor and the gene promoter region. Several alternative mechanisms have been proposed, such as additional sources of positional information, positive feedback from Hb proteins or out-of-equilibrium transcription activation. By using the MS2-MCP RNA-tagging system and analysing in real time, the transcription dynamics of synthetic reporters for Bicoid and/or its two partners Zelda and Hunchback, we show that all the early hb expression pattern features and temporal dynamics are compatible with an equilibrium model with a short decay length Bicoid activity gradient as a sole source of positional information. Meanwhile, Bicoid's partners speed-up the process by different means Zelda lowers the Bicoid concentration threshold required for transcriptional activation while Hunchback reduces burstiness and increases the polymerase firing rate.The resistance of Staphylococcus aureus (S. aureus) to various antibiotics has increased dramatically due to the misuse of antibiotics, and thus the development of new anti-infective drugs with new targets is urgently needed to combat resistance. Caseinolytic peptidase P is a case in hydrolase that regulates the virulence level of S. aureus. Here, we found that nepetin, a small-molecule compound from traditional Chinese herbal flavonoids, effectively inhibits ClpP activity. Nepetin suppressed the virulence of S. aureus and effectively combated the lethal pneumonia caused by MRSA. The results of cellular thermal shift assay showed that nepetin could bind to ClpP and reduce the thermal stability of ClpP, and the KD value of 602 nM between them was determined using localized surface plasmon resonance. The binding mode of nepetin and ClpP was further investigated by molecular docking, and it was found that Ser-22 and Gln-47 of ClpP residues were found to be involved in the binding of nepetin to ClpP. In conclusion, we determined that nepetin is a ClpP inhibitor and an effective lead compound for the development of a virulence factor-based treatment for MRSA infection.This paper reports the design and qualification of the first purpose-built, bench-scale reactor system to model the municipal waste-to-energy combustion of fluorinated polymers. Using the principle of similarity, the gas-phase combustion zone of a typical municipal waste-to-energy plant has been scaled down to the bench with a focus on chemical similarity. Chemical similarity is achieved in large part through the use of methanol as a surrogate for municipal solid waste (MSW). Review of prior research shows that methanol is one of the major volatile products expected during MSW thermal conversion in the fuel bed of waste-to-energy plants. Like full-scale waste-energy plants, the design of the bench-scale model includes a flame zone and a post-flame zone. Maintaining steady methanol vapor flow premixed with air to the model reactor system ensures stable combustion resulting in bench-scale CO emission levels comparable to those of full-scale waste-to-energy plants. Since investigation of fluorinated polymer combas-phase municipal waste combustion behavior upstream of air pollution control and generating representative exhaust gas samples for off-line trace-level analysis of PFOA.

Motorcycles are a common mode of transport, especially in low-middle-income countries like Pakistan. The pattern and severity of injuries in motorcycle trauma depends on the mechanism of accident, which may be classified as collision accidents (CAs) or loss-of-control accidents (LOCAs). In this study, we aimed to investigate patterns of trauma due to motorcycle CAs and LOCAs, with a focus on injuries, management, complications, and outcomes.

A retrospective cohort study was conducted at the Aga Khan University Hospital (AKUH), Pakistan (a level 1 trauma facility), enrolling all patients presenting with motorcycle trauma between January 2018 and March 2019.

The most common sites of major injury were the lower limb (40.9%), head and neck (38.1%), and upper limb (27.5%). A significantly higher percentage of CA victims had head and neck injuries (43.4% vs. 30.5%), abdominal injuries (5.5% vs. 1.1%), pelvic fracture (5.9% vs. 0%), and polytrauma (22.8% vs. 11.1%). Compared to LOCA victims, CA victims had a sugh both motorcycle CAs and LOCAs stress trauma systems in developing countries, the dynamics of CAs mean that they result in worse injuries and outcomes. Specific measures to reduce CAs and LOCAs are urgently indicated in developing countries to reduce the burden of morbidity and mortality of motorcycle accidents.

Sickle cell disease (SCD) is a rare genetic disease with limited therapeutic options. Gene-based therapies are being investigated in clinical trials to evaluate their curative potential. The expected life-long benefits of one-time administration of genetically corrected stem cells present uncharted challenges in estimating value of these treatments. Our objective is to conduct a landscape analysis of clinical trials and prompt a discussion estimating the value of gene therapy as a therapeutic option for SCD.

We searched

to identify and characterize clinical trials in gene therapies for SCD. We report available results and discuss current concerns and elements of value necessary to consider as these products come to market.

Gene therapies could represent a major advance in SCD treatment. Although clinical trials are ongoing, reports of serious adverse events have led to pause of these trials, emphasizing the need to prove long-term tolerability. Measured using the methods of health economic evaluation, we anticipate high up-front costs may be offset by potential life-long benefits of these treatments. During development and after treatment approval, attention should be focused on ensuring adequate availability and equitable access to emerging therapies in underserved areas and low-middle-income countries (LMIC).

Gene therapies could represent a major advance in SCD treatment. Although clinical trials are ongoing, reports of serious adverse events have led to pause of these trials, emphasizing the need to prove long-term tolerability. Measured using the methods of health economic evaluation, we anticipate high up-front costs may be offset by potential life-long benefits of these treatments. During development and after treatment approval, attention should be focused on ensuring adequate availability and equitable access to emerging therapies in underserved areas and low-middle-income countries (LMIC).