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Cross-scale phenological overseeing within do ecosystems: a content-analysis-based evaluation.
Aphelids (Aphelida) are intracellular parasitoids of algae and represent one of the early diverging or sister lineages of the kingdom Fungi. Although Aphelida is a small group, molecular phylogenetic analyses revealed that many environmental sequences belong to Aphelida, suggesting that aphelids are distributed worldwide; however, the extent of their diversity is unclear. Here, we investigated a novel aphelid culture APH2 that parasitizes the green alga Coccomyxa sp. APH2 produced posteriorly uniflagellate zoospores, a defining character of the genus Aphelidium. The residual body of APH2 was spherical in the mature plasmodium, but became amorphous during zoospore formation and collapsed after zoospore discharge, which has not been described for other Aphelidium species. Zoospores of APH2 possessed a striated rhizoplast that extended anteriorly from the kinetosome to the posterior end of the nucleus, and a microtubular root arising from the side of the kinetosome and lying almost parallel to the rhizoplast, both of which are unique among aphelid taxa. A molecular phylogenetic analysis based on the 18S rDNA sequences placed APH2 as sister lineage to all other known aphelid sequences. Based on these results, we describe this aphelid as a new species, Aphelidium collabens.Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency, adoption of MRF into the clinics is hindered by its dictionary matching-based reconstruction, which is computationally demanding and lacks scalability. Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction. We evaluated the method using MRF T1-FF, an MRF sequence for T1 relaxation time of water (T1H2O) and fat fraction (FF) mapping. We demonstrate the method's performance on a highly heterogeneous dataset consisting of 164 patients with various neuromuscular diseases imaged at thighs and legs. We empirically show the benefit of incorporating spatial regularization during the reconstruction and demonstrate that the method learns meaningful features from MR physics perspective. Further, we investigate the ability of the method to handle highly heterogeneous morphometric variations and its generalization to anatomical regions unseen during training. The obtained results outperform the state-of-the-art in deep learning-based MRF reconstruction. The method achieved normalized root mean squared errors of 0.048 ± 0.011 for T1H2O maps and 0.027 ± 0.004 for FF maps when compared to the dictionary matching in a test set of 50 patients. Coupled with fast MRF sequences, the proposed method has the potential of enabling multiparametric MR imaging in clinically feasible time.To fully define the target objects of interest in clinical diagnosis, many deep convolution neural networks (CNNs) use multimodal paired registered images as inputs for segmentation tasks. However, these paired images are difficult to obtain in some cases. Furthermore, the CNNs trained on one specific modality may fail on others for images acquired with different imaging protocols and scanners. Therefore, developing a unified model that can segment the target objects from unpaired multiple modalities is significant for many clinical applications. In this work, we propose a 3D unified generative adversarial network, which unifies the any-to-any modality translation and multimodal segmentation in a single network. Since the anatomical structure is preserved during modality translation, the auxiliary translation task is used to extract the modality-invariant features and generate the additional training data implicitly. To fully utilize the segmentation-related features, we add a cross-task skip connection with feature recalibration from the translation decoder to the segmentation decoder. Experiments on abdominal organ segmentation and brain tumor segmentation indicate that our method outperforms the existing unified methods.Due to the development of deep learning, an increasing number of research works have been proposed to establish automated analysis systems for 3D volumetric medical data to improve the quality of patient care. However, it is challenging to obtain a large number of annotated 3D medical data needed to train a neural network well, as such manual annotation by physicians is time consuming and laborious. Self-supervised learning is one of the potential solutions to mitigate the strong requirement of data annotation by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical data. Specifically, we propose a pretext task, i.e., Rubik's cube+, to pre-train 3D neural networks. The pretext task involves three operations, namely cube ordering, cube rotating and cube masking, forcing networks to learn translation and rotation invariant features from the original 3D medical data, and tolerate the noise of the data at the same time. Compared to the strategy of training from scratch, fine-tuning from the Rubik's cube+ pre-trained weights can remarkablely boost the accuracy of 3D neural networks on various tasks, such as cerebral hemorrhage classification and brain tumor segmentation, without the use of extra data.The sidestreams produced during fish processing end in a separation tank where the resulting fractions follow biogas production or wastewater treatment. These streams can alternatively be used for production of protein-rich fungal biomass for e.g. fish feed applications, a product in increasing demand. These streams and upper streams originated during fish processing were used in this study for production of biomass using the edible filamentous fungus Rhizopus oryzae. The COD of the streams varied between 11 and 54 kg/m3 and, after fungal conversion of organic matter into protein-rich biomass and separation, a reduction of 34-69% was achieved. The stream origin had an effect on the final production and composition of the fungal biomass 480 kg of biomass containing 33% protein per ton of COD were produced after cultivation in the separation tank streams, while 220 kg of biomass containing 62% protein per ton of COD were produced in upper sidestreams with lower amounts of suspended solids. Changing the initial pH (6.1-6.5) to 5.0 had a negative influence on the amount of biomass produced while medium supplementation had no influence. Thus, fish processing sidestreams can be diverted from biogas production and wastewater treatment to the production of protein-rich biomass for feed applications.This article looks at the effect paternal death can have on non-cognitive outcomes at age 15 and 22 depending on whether a child lost the father in middle childhood or adolescence. The article uses the potential outcome framework to estimate results using five rounds of longitudinal survey data for Ethiopia collected between 2002 and 2016. It finds that the loss of the father in middle childhood reduces an orphan's self-esteem significantly by 0.15 standard deviations and subjective wellbeing by 16 per cent. buy Amprenavir These effects are not persistent. link2 Instead, the loss of the father between ages 12-22, encompassing early, middle and late adolescence have significant positive effects on agency, self-efficacy, self-esteem and peer relationships as a young adult aged 22, improving them by 0.31, 0.28, 0.31 and 0.26 standard deviations respectively. This suggests that a father's death during a child's adolescent years may be associated with positive adaptive behavior.Objective We evaluated four imaging techniques, i.e. Electroencephalography (EEG)-functional Magnetic Resonance Imaging (MRI) (EEG-fMRI), High-resolution EEG (HR-EEG), Magnetoencephalography (MEG) and 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (PET), for the identification of the epileptogenic zone (EZ) in 41 patients with negative MRI, candidate to neurosurgery. Methods For each technique, results were compared to the Stereo-EEG. Diagnostic measures were calculated with respect to the post-surgical outcome, either for all the patients (39/41, two patients excluded) and for the subgroup of patients with the EZ involving more than one lobe (20/41). Results When considered individually, each functional technique showed accuracy values ranging 54,6%-63,2%, having PET, MEG and HR-EEG higher sensitivity, and EEG-fMRI higher specificity. In patients with multilobar epileptogenic zone, functional techniques achieved the best accuracies (up to 80%) when three techniques, including EEG-fMRI, were considered together. Conclusions The study highlights the accuracy of a combination of functional imaging techniques in the identification of EZ in MRI negative focal epilepsy. The best diagnostic yield was obtained if the combination of PET, MEG (or HR-EEG as alternative), EEG-fMRI were considered together. Significance The functional imaging techniques may improve the presurgical workup of MRI negative focal epilepsy, if epileptogenic zone involves more than one lobe.Introduction Epidermoid and dermoid cysts in the floor of the mouth are uncommon. A large cyst can lead to functional morbidities including airway, swallowing and speech problems. The treatment of choice is surgical excision. To obtain a good result, the decision for optimal surgical technique is valuable. Case presentation A young female patient presented with a slowly growing mass at the submandibular area and swelling in the floor of mouth. The MRI showed large sublingual cyst extended posteriorly to the parapharyngeal space. She underwent surgical excision of the cyst size 6.5 × 3.2 × 2.5 cm via an intraoral approach. The pathological examination revealed a benign stratified squamous epithelium-lined cyst with no evidence of adnexal structure consistent with an epidermoid cyst. The patient remained disease-free after two years of follow-up. Discussion Generally, large sublingual cysts over 6 cm located or transgress below the mylohyoid muscle are removed by an extraoral approach. Our team reported the first case of a large lateral sublingual epidermoid cyst with parapharyngeal extension removed by an intraoral approach. Preoperative imaging is important for surgical approach selection. Conclusion Successful management of sublingual epidermoid cyst extended to the parapharynx is feasible by an intraoral approach with excellent functional and cosmetic outcomes.Introduction Minimally invasive techniques such as stereotactic aspiration of spontaneous intracerebral hematoma (SICH) evacuation can minimize brain tissue damage due to surgery. We share our experience of adopting stereotactic aspiration of SICH in our center without compromising safety. Presentation of cases Three stereotactic aspiration procedures for SICH immediately after 24 h of onset are reported. All cases showed good results. link3 Level of consciousness of all the patient returned to normal. Two patients could carry out routine activities independently. One patient has left hemiparesis. Discussion Stereotactic aspiration of SICH has been newly implemented at our center with acceptable results achieved. Patient selection has an important role in determining the choice of technique. The procedures were done without anticoagulant agent. Stereotactic aspiration of SICH is associated with limited brain tissue damage, shorter duration of surgery, reduced length of stay, faster postoperative healing, and better functional improvement.
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