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One of the IpSAP homologs from other ixodid ticks showed similar effects on Lyme spirochete transmission. Together, our findings suggest that LTβR signaling plays an important role in blocking the transmission and pathogenesis of tick-borne Lyme disease spirochetes, and that IpSAP and its homologs are promising candidates for broad-spectrum vaccine development.The increase of CO2 emissions due to human activity is one of the preeminent reasons for the present climate crisis. In addition, considering the increasing demand for renewable resources, the upcycling of CO2 as a feedstock gains an extensive importance to establish CO2-neutral or CO2-negative industrial processes independent of agricultural resources. Here we assess whether synthetic autotrophic Komagataella phaffii (Pichia pastoris) can be used as a platform for value-added chemicals using CO2 as a feedstock by integrating the heterologous genes for lactic and itaconic acid synthesis. SB 204990 inhibitor 13C labeling experiments proved that the resulting strains are able to produce organic acids via the assimilation of CO2 as a sole carbon source. Further engineering attempts to prevent the lactic acid consumption increased the titers to 600 mg L-1, while balancing the expression of key genes and modifying screening conditions led to 2 g L-1 itaconic acid. Bioreactor cultivations suggest that a fine-tuning on CO2 uptake and oxygen demand of the cells is essential to reach a higher productivity. We believe that through further metabolic and process engineering, the resulting engineered strain can become a promising host for the production of value-added bulk chemicals by microbial assimilation of CO2, to support sustainability of industrial bioprocesses.
To validate the "Patient Evaluation of Emotional Care During Hospitalization" (PEECH) questionnaire, which assesses hospitalised patients' emotional experiences, in patients admitted to the intensive care unit (ICU).

Prospective study. The PEECH consists of three sections and four sub-scales "level of security", "level of knowing", "level of personal value", and "level of connection". The questionnaire was completed by 253 hospitalised patients. Expert judgement was used to analyse the content validity and factor analysis was performed to confirm construct validity. Cronbach's alpha was used to measure the internal consistency of the four sub-scales.

In the confirmatory factor analysis of the four sub-scales, the weights of all questions were found to be significant (>1). The internal consistency of the PEECH questionnaire was 0.86 (Cronbach's alpha) and the homogeneity index was high (>0.50).

The PEECH questionnaire is a valid and reliable tool to evaluate the perception of emotional care in ICU patients. The information gathered can help provide more comprehensive care for patients in the ICU and in other hospitalised patients.
The PEECH questionnaire is a valid and reliable tool to evaluate the perception of emotional care in ICU patients. The information gathered can help provide more comprehensive care for patients in the ICU and in other hospitalised patients.A graphene disk metasurface-inspired refractive index sensor (RIS) with a subwavelength structure is numerically investigated to enhance the functionality of flexible metasurface in the biosensor sector. The main aim behind the sensor development is to detect amino acids with high sensitivity. The results in form of transmittance and the electric field intensity are carried out to verify the sensor's performance. The optimal design of the proposed sensor is also obtained by varying several structural parameters such as glass-based substrate thickness, the inner radius of the graphene disk metasurface, and the angle of incidence. The proposed sensor is also wide-angle insensitive for the angle of incidence ranging from 0° to 60°. Furthermore, the sensor's attributes are analyzed based on numerous parameters with an achieved maximum sensitivity of 333.33 GHz/RIU, Figure of Merit (FOM) of 3.11 RIU-1, and Q-factor of 7.3 are achieved. As a result, these insights offered an enhanced direction for designing metasurface biosensors with a high Q-factor and FOM with high sensitivity for the detection of amino acids.Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at sub-wavelength resolution. However, challenges remain in ultrafast ULM implementation where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL) based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data (L frames) are still required to accumulate enough localized microbubble events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this paper, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to reconstruct a super-resolution image directly from a temporal mean low-resolution image generated by averaging l-frame raw US images with l being significantly smaller than L. To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM-GAN allows ~40-fold reduction in data acquisition time and ~61-fold reduction in computational time compared with the conventional Gaussian fitting method, without compromising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM-GAN offers an implementation of ULM with ultrafast data acquisition time (~0.33 s) and ultrafast data processing speed (~0.60 s) that makes it promising to observe rapid biological activities in vivo.We have previously shown that healthy subjects can transfer coordination skills to the unpracticed hand by performing a unimanual task with the other hand and visualizing a bimanual action using a game-like interactive system. However, whether this system could be used to transfer coordination skills to the paretic hand after stroke and its underlying neural mechanism remain unknown. Here, using a game-like interactive system for visualization during physical practice in an immersive virtual reality environment, we examined coordination skill improvement in the unpracticed/paretic hand after training in 10 healthy subjects and 13 chronic and sub-acute stroke patients. The bimanual movement task was defined as simultaneously drawing non-symmetric three-sided squares (e.g., U and C), while the training strategy was performing a unimanual task with the right/nonparetic hand and visualizing a bimanual action. We found large decreases in the intra-hand temporal and spatial measures for movement in the unpracticed/paretic hand after training. Furthermore, a substantial reduction in the inter-hand temporal and spatial interference was observed after training. Additionally, we examined the related cortical network evolution using EEG in both the healthy subjects and stroke patients. Our studies show that the cortical network became more efficient after training in the healthy subjects and stroke patients. These results demonstrate that our proposed method could contribute to the transference of coordination skill to the paretic/unpracticed hand by promoting the efficiency of cortical networks.Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical settings, where it is imperative that erroneous predictions are caught and corrected. We propose Quantile Binning, a data-driven method to categorize predictions by uncertainty with estimated error bounds. Our framework can be applied to any continuous uncertainty measure, allowing straightforward identification of the best subset of predictions with accompanying estimated error bounds. We facilitate easy comparison between uncertainty measures by constructing two evaluation metrics derived from Quantile Binning. We compare and contrast three epistemic uncertainty measures (two baselines, and a proposed method combining aspects of the two), derived from two heatmap-based landmark localization model paradigms (U-Net and patch-based). We show results across three datasets, including a publicly available Cephalometric dataset. We illustrate how filtering out gross mispredictions caught in our Quantile Bins significantly improves the proportion of predictions under an acceptable error threshold. Finally, we demonstrate that Quantile Binning remains effective on landmarks with high aleatoric uncertainty caused by inherent landmark ambiguity, and offer recommendations on which uncertainty measure to use and how to use it. The code and data are available at https//github.com/schobs/qbin.Optical Coherence Tomography Angiography (OCTA), a functional extension of OCT, has the potential to replace most invasive fluorescein angiography (FA) exams in ophthalmology. So far, OCTA's field of view is however still lacking behind fluorescence fundus photography techniques. This is problematic, because many retinal diseases manifest at an early stage by changes of the peripheral retinal capillary network. It is therefore desirable to expand OCTA's field of view to match that of ultra-widefield fundus cameras. We present a custom developed clinical high-speed swept-source OCT (SS-OCT) system operating at an acquisition rate 8-16 times faster than today's state-of-the-art commercially available OCTA devices. Its speed allows us to capture ultra-wide fields of view of up to 90 degrees with an unprecedented sampling density and hence extraordinary resolution by merging two single shot scans with 60 degrees in diameter. To further enhance the visual appearance of the angiograms, we developed for the first time a three-dimensional deep learning based algorithm for denoising volumetric OCTA data sets. We showcase its imaging performance and clinical usability by presenting images of patients suffering from diabetic retinopathy.Identifying squamous cell carcinoma and adenocarcinoma subtypes of metastatic cervical lymphadenopathy (CLA) is critical for localizing the primary lesion and initiating timely therapy. B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), ultrasound elastography (UE) and dynamic contrast-enhanced ultrasound provide effective tools for identification but synthesis of modality information is a challenge for clinicians. Therefore, based on deep learning, rationally fusing these modalities with clinical information to personalize the classification of metastatic CLA requires new explorations. In this paper, we propose Multi-step Modality Fusion Network (MSMFN) for multi-modal ultrasound fusion to identify histological subtypes of metastatic CLA. MSMFN can mine the unique features of each modality and fuse them in a hierarchical three-step process. Specifically, first, under the guidance of high-level BUS semantic feature maps, information in CDFI and UE is extracted by modality interaction, and the static imaging feature vector is obtained.
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