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المدارس العالمية : ربط الفصول المدرسية بالطلاب في جميع أنحاء العالم
يقدم المؤلف في الكتاب لمعلمي القرن الحادي والعشرين خارطة طريق أساسية ترشدهم إلى كيفية تضمين التعليم العالمي في الفصل المدرسي، ولذلك يتضمن الكتاب أفكارا ومشروعات وخططا للدروس ومصادر واختبارات للتقييم، وهو ما يجعله مرجعا قيما للغاية. ويقدم المؤلف مرة أخرى وصفا للمعلمين الذين بدأوا في قيادة مسيرة إشراك طلابهم في عملية التفكير النقدي والعمل التعاوني والتواصل والكثير من الأنشطة الأخرى، فهذا الكتاب مفيد ومهم للجميع بدءا من مدرس الفصل إلى مدير المدرسة.
BOOK
Stytra: An open-source, integrated system for stimulation, tracking and closed-loop behavioral experiments
We present Stytra, a flexible, open-source software package, written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments. It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations. Stytra logs all recorded quantities, metadata, and code version in standardized formats to allow full provenance tracking, from data acquisition through analysis to publication. The package is modular and expandable for different experimental protocols and setups. Current releases can be found at https://github.com/portugueslab/stytra. We also provide complete documentation with examples for extending the package to new stimuli and hardware, as well as a schema and parts list for behavioral setups. We showcase Stytra by reproducing previously published behavioral protocols in both head-restrained and freely-swimming larvae. We also demonstrate the use of the software in the context of a calcium imaging experiment, where it interfaces with other acquisition devices. Our aims are to enable more laboratories to easily implement behavioral experiments, as well as to provide a platform for sharing stimulus protocols that permits easy reproduction of experiments and straightforward validation. Finally, we demonstrate how Stytra can serve as a platform to design behavioral experiments involving tracking or visual stimulation with other animals and provide an example integration with the DeepLabCut neural network-based tracking method.
Journal Article
Ubiquitination in the regulation of inflammatory cell death and cancer
The ubiquitin system is complex, multifaceted, and is crucial for the modulation of a vast number of cellular processes. Ubiquitination is tightly regulated at different levels by a range of enzymes including E1s, E2s, and E3s, and an array of DUBs. The UPS directs protein degradation through the proteasome, and regulates a wide array of cellular processes including transcription and epigenetic factors as well as key oncoproteins. Ubiquitination is key to the dynamic regulation of programmed cell death. Notably, the TNF signaling pathway is controlled by competing ubiquitin conjugation and deubiquitination, which governs both proteasomal degradation and signaling complex formation. In the inflammatory response, ubiquitination is capable of both activating and dampening inflammasome activation through the control of either protein stability, complex formation, or, in some cases, directly affecting receptor activity. In this review, we discuss the enzymes and targets in the ubiquitin system that regulate fundamental cellular processes regulating cell death, and inflammation, as well as disease consequences resulting from their dysregulation. Finally, we highlight several pre-clinical and clinical compounds that regulate ubiquitin system enzymes, with the aim of restoring homeostasis and ameliorating diseases.
Journal Article
Population-based guiding for evolutionary neural architecture search
Neural Architecture Search (NAS)—combined with biology-inspired evolutionary methods—can help discover suitable architectures tailored to a given objective. A guided evolutionary approach can enhance efficiency, aiming to accelerate the discovery of top-performing architectures within a given search space. We propose a novel algorithmic framework that implements selection, crossover, and mutation operations to generate new candidate architectures during an evolutionary Neural Architecture Search: A greedy selection operator, relying solely on model accuracy data, promotes exploitation. Incorporating architecture embeddings to further refine the mutation process enhances exploration. We introduce a guided mutation approach to steer the search toward unexplored regions of the current population. The proposed strategy, PBG (Population-Based Guiding), synergizes both explorative and exploitative methods. It substantially outperforms baseline methods such as regularized evolution by being up to three times faster on NAS-Bench-101. This combined approach not only leverages the strengths of both explorative guided mutation and exploitative greedy selection strategies, but also provides a robust and efficient framework reaching competitive performance for evolutionary Neural Architecture Search across benchmarks.
Journal Article
Predicting semantic segmentation quality in laryngeal endoscopy images
Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to assess laryngeal physiology. However, due to the diversity of patients presenting, it is necessary to judge the segmentation quality. In this study, we present a fully automatic system to evaluate the segmentation performance in laryngeal endoscopy images. We showcase on glottal area segmentation that the predicted segmentation quality represented by the intersection over union metric is on par with human raters. Using a traffic light system, we are able to identify problematic segmentation frames to allow human-in-the-loop improvements, important for the clinical adaptation of automatic analysis procedures.
Journal Article
DeepD3, an open framework for automated quantification of dendritic spines
Dendritic spines are the seat of most excitatory synapses in the brain, and a cellular structure considered central to learning, memory, and activity-dependent plasticity. The quantification of dendritic spines from light microscopy data is usually performed by humans in a painstaking and error-prone process. We found that human-to-human variability is substantial (inter-rater reliability 82.2±6.4%), raising concerns about the reproducibility of experiments and the validity of using human-annotated ‘ground truth’ as an evaluation method for computational approaches of spine identification. To address this, we present DeepD3, an open deep learning-based framework to robustly quantify dendritic spines in microscopy data in a fully automated fashion. DeepD3’s neural networks have been trained on data from different sources and experimental conditions, annotated and segmented by multiple experts and they offer precise quantification of dendrites and dendritic spines. Importantly, these networks were validated in a number of datasets on varying acquisition modalities, species, anatomical locations and fluorescent indicators. The entire DeepD3 open framework, including the fully segmented training data, a benchmark that multiple experts have annotated, and the DeepD3 model zoo is fully available, addressing the lack of openly available datasets of dendritic spines while offering a ready-to-use, flexible, transparent, and reproducible spine quantification method.
Journal Article
Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care
Deep Learning has a large impact on medical image analysis and lately has been adopted for clinical use at the point of care. However, there is only a small number of reports of long-term studies that show the performance of deep neural networks (DNNs) in such an environment. In this study, we measured the long-term performance of a clinically optimized DNN for laryngeal glottis segmentation. We have collected the video footage for two years from an AI-powered laryngeal high-speed videoendoscopy imaging system and found that the footage image quality is stable across time. Next, we determined the DNN segmentation performance on lossy and lossless compressed data revealing that only 9% of recordings contain segmentation artifacts. We found that lossy and lossless compression is on par for glottis segmentation, however, lossless compression provides significantly superior image quality. Lastly, we employed continual learning strategies to continuously incorporate new data into the DNN to remove the aforementioned segmentation artifacts. With modest manual intervention, we were able to largely alleviate these segmentation artifacts by up to 81%. We believe that our suggested deep learning-enhanced laryngeal imaging platform consistently provides clinically sound results, and together with our proposed continual learning scheme will have a long-lasting impact on the future of laryngeal imaging.
Journal Article
A cerebellar internal model calibrates a feedback controller involved in sensorimotor control
Animals must adapt their behavior to survive in a changing environment. Behavioral adaptations can be evoked by two mechanisms: feedback control and internal-model-based control. Feedback controllers can maintain the sensory state of the animal at a desired level under different environmental conditions. In contrast, internal models learn the relationship between the motor output and its sensory consequences and can be used to recalibrate behaviors. Here, we present multiple unpredictable perturbations in visual feedback to larval zebrafish performing the optomotor response and show that they react to these perturbations through a feedback control mechanism. In contrast, if a perturbation is long-lasting, fish adapt their behavior by updating a cerebellum-dependent internal model. We use modelling and functional imaging to show that the neuronal requirements for these mechanisms are met in the larval zebrafish brain. Our results illustrate the role of the cerebellum in encoding internal models and how these can calibrate neuronal circuits involved in reactive behaviors depending on the interactions between animal and environment.
Animals can adjust their behavior in response to changes in the environment when these changes can be predicted. Here the authors show the role of the cerebellum in zebrafish that change their swimming as they adjust to long-lasting changes in visual feedback
Journal Article
Impaired RIPK1 ubiquitination sensitizes mice to TNF toxicity and inflammatory cell death
Receptor-interacting protein 1 (RIP1; RIPK1) is a key regulator of multiple signaling pathways that mediate inflammatory responses and cell death. TNF-TNFR1 triggered signaling complex formation, subsequent NF-κB and MAPK activation and induction of cell death involve RIPK1 ubiquitination at several lysine residues including Lys376 and Lys115. Here we show that mutating the ubiquitination site K376 of RIPK1 (K376R) in mice activates cell death resulting in embryonic lethality. In contrast to
Ripk1
K376R/K376R
mice,
Ripk1
K115R/K115R
mice reached adulthood and showed slightly higher responsiveness to TNF-induced death. Cell death observed in
Ripk1
K376R/K376R
embryos relied on RIPK1 kinase activity as administration of RIPK1 inhibitor GNE684 to pregnant heterozygous mice effectively blocked cell death and prolonged survival. Embryonic lethality of
Ripk1
K376R/K376R
mice was prevented by the loss of TNFR1, or by simultaneous deletion of caspase-8 and RIPK3. Interestingly, elimination of the wild-type allele from adult
Ripk1
K376R/cko
mice was tolerated. However, adult
Ripk1
K376R/cko
mice were exquisitely sensitive to TNF-induced hypothermia and associated lethality. Absence of the K376 ubiquitination site diminished K11-linked, K63-linked, and linear ubiquitination of RIPK1, and promoted the assembly of death-inducing cellular complexes, suggesting that multiple ubiquitin linkages contribute to the stability of the RIPK1 signaling complex that stimulates NF-κB and MAPK activation. In contrast, mutating K115 did not affect RIPK1 ubiquitination or TNF stimulated NF-κB and MAPK signaling. Overall, our data indicate that selective impairment of RIPK1 ubiquitination can lower the threshold for RIPK1 activation by TNF resulting in cell death and embryonic lethality.
Journal Article