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Objective : The objective was to describe the clinical presentations, radiologic features, and outcomes of patients with autoimmune encephalitis associated with myelin oligodendrocyte glycoprotein antibody (MOG). Background: During the past decade, the spectrum of the myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD) has expanded. Recently, patients with MOG antibody encephalitis (MOG-E) who do not fulfill the criteria for ADEM have been reported. In this study, we aimed to describe the spectrum of MOG-E. Methods: Sixty-four patients with MOGAD were screened for encephalitis-like presentation. We collected the clinical, radiological, laboratory, and outcome data of the patients who presented with encephalitis and compared it with the non-encephalitis group. Results: We identified sixteen patients (nine males and seven females) with MOG-E. The median age of the encephalitis population was significantly lower than the non-encephalitis group (14.5 years (11.75–18) vs. 28 years (19.75–42), p = 0.0004). Twelve out of sixteen patients (75%) had fever at the time of encephalitis. Headache and seizure were present in 9/16 (56.2%) and 7/16 (43.75%) patients, respectively. FLAIR cortical hyperintensity was present in 10/16 (62.5%) patients. Supratentorial deep gray nuclei were involved in 10/16 (62.5%) patients. Three patients had tumefactive demyelination, and one patient had a leukodystrophy-like lesion. Twelve of 16 (75%) patients had a good clinical outcome. Patient with leukodystrophy pattern and other with generalized CNS atrophy showed a chronic progressive course. Conclusion: MOG-E can have heterogeneous radiological presentations. FLAIR cortical hyperintensity, tumefactive demyelination, and leukodystrophy-like presentations are novel radiological presentations associated with MOGAD. Though majority of MOG-E have a good clinical outcome, few patients can have chronic progressive disease even on immunosuppressive therapy. [ABSTRACT FROM AUTHOR]

Purpose of Review: Recognition of node of Ranvier as the site of injury in inflammatory neuropathies contributed to discovery of antibodies against the nodal/paranodal structures. These antibodies mediate a unique type of inflammatory neuropathies that are different from typical chronic inflammatory demyelinating polyneuropathy. This review discusses the advancements made in the field of autoimmune neuropathies secondary to antibodies to nodal and paranodal proteins. Recent Findings: Neuropathies caused by antibodies to nodal-paranodal antigens including neurofascin 186, neurofascin 155, contactin1, and contactin-associated protein1 were termed as autoimmune nodopathies (AN) in 2021. Since the initial description almost a decade ago, newer cohorts have expanded the clinical spectrum of AN. In addition to IgG4, other subclasses of IgG such as IgG1/IgG3 have been identified, particularly in relation to acute presentations and anti-pan neurofascin antibody disease. In vitro and in vivo studies have also supported antibody-mediated pathogenicity of many of these biomarkers. Summary: Antibodies to nodal-paranodal antigens have emerged as a biomarker for a novel type of immune-mediated neuropathies. These antibodies have distinct pathogenic mechanisms and produce a unique set of clinicopathologic features. Their clinical profile and treatment may also vary depending on the antibody isotype. B cell depleting therapies are effective in managing some of these patients. [ABSTRACT FROM AUTHOR]

Microwave-metal discharge-based drilling of metallic materials involves use of thermal energy from the plasma column for material removal. However, poor control over plasma generated in atmospheric air causes wide heat-affected zone (HAZ) and poor surface integrity around the drilled hole. The present work is an attempt to minimize drilling-related defects by incorporating circulating liquid dielectric as the drilling media. Performance of microwave drilling was assessed after machining in liquid dielectric and atmospheric air media. Stainless steel (AISI 304) and pure tungsten were chosen as the work and tool materials, respectively. Process performance parameters, such as diametrical overcut, circularity, HAZ and material removal rate (MRR), were evaluated for the drilling intervals of 40, 50 and 60 s. It was observed that the dissipation of microwave power within the liquid dielectric medium reduces the intensity of the plasma column. In addition, pressure applied by the liquid dielectric medium over the plasma column aids in its confinement. As a result, a relatively less intense and more uniform plasma column is generated in the liquid dielectric medium, which reduces erosive action on the work material. This controlled removal of work material results in a significant reduction (up to 90%) in HAZ. The uniform nature of plasma column yielded better hole circularity in liquid dielectric medium. [ABSTRACT FROM AUTHOR]

Purpose of Review: To critically appraise the literature on the application, methods, and advances in emergency electroencephalography (EEG). Recent Findings: The development of rapid EEG (rEEG) technologies and other reduced montage approaches, along with advances in machine learning over the past decade, has increased the rate and access to EEG acquisition. These achievements have made EEG in the emergency setting a practical diagnostic technique for detecting seizures, suspected nonconvulsive status epilepticus (NCSE), altered mental status, stroke, and in the setting of sedation. Summary: Growing evidence supports using EEG to expedite medical decision-making in the setting of suspected acute neurological injury. This review covers approaches to acquiring EEG in the emergency setting in the adult and pediatric populations. We also cover the clinical impact of this data, the time associated with emergency EEG, and the costs of acquiring EEG in these settings. Finally, we discuss the advances in artificial intelligence for rapid electrophysiological interpretation. [ABSTRACT FROM AUTHOR]

Aspect category detection is an essential task in aspect-based sentiment analysis. Most previous works use labeled data and apply a supervised learning approach to detect the aspect category. However, to avoid the dependency on labeled data, some researchers have also applied unsupervised learning approaches, wherein variants of topic models and neural network-based models have been built for this task. These unsupervised methods focus on co-occurrences of words and ignore the contextual meaning of the words in the given sentence. Thus, such models perform reasonably well in detecting the explicitly expressed aspect category but often fail in identifying the implicitly expressed aspect category in the sentence. This paper focuses on the contextual meaning of the word. It adopts a document clustering approach requiring minimal user guidance, i.e., only a small set of seed words for each aspect category to detect efficiently implicit and explicit aspect categories. A novel BERT-based Attentive Representation Learning with Adversarial Training (BARLAT) model is presented in this paper, which utilizes domain-based contextual word embedding (BERT) for generating the sentence representation and uses these representations for clustering the sentences through attentive representation learning. Further, the model parameters are generalized better by performing adversarial training, which adds perturbations to the cluster representations. BARLAT is the first nearly unsupervised method that uses the contextual meaning of the words for learning the aspect categories through an adversarial attentive learning approach. The performance of BARLAT is compared with various state-of-the-art models using F1-score on Laptop and Restaurant datasets. The experimental results show that BARLAT outperforms the best existing model by a margin of 1.1% and 2.3% on Restaurant and Laptop datasets, respectively. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) includes two subtasks, namely, aspect term extraction and aspect-level sentiment classification. Most existing works address these subtasks independently. Recently, many researchers have attempted to solve both the subtasks of ABSA with a unified framework. However, previous works have not focused on the generalization and robustness of such unified frameworks. This paper proposes a novel BERT-Based Interactive Learning with Ensemble Adversarial Training (BILEAT) to solve complete ABSA by using a unified tagging scheme. We build white-box adversarially post-trained domain knowledge BERT (WBDK-BERT) using a domain-specific dataset. During post-training, we regularize the training objective by adding perturbations in the embedding space to maximize the adversarial loss, enhancing the generalization and robustness of WBDK-BERT. BILEAT uses WBDK-BERT to generate contextualized embeddings and produce collaborative signals through interactive learning. Further, to build a highly reliable model, we generate adversarial examples using a black-box technique. These adversarial examples are grammatically fluent, semantically coherent with original input, and can mislead the neural network. Our proposed model is trained using original inputs and such adversarial examples in a combined way. Experimental results demonstrate that WBDK-BERT and black-box adversarial examples complement each other, and combining these two helps BILEAT become highly generalized and robust compared to existing methods. To the best of our knowledge, this is the first study that generates quality adversarial examples and evaluates the robustness of models for unified ABSA1. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) includes two sub-tasks namely, aspect extraction and aspect-level sentiment classification. Most existing works address these sub-tasks independently by applying a supervised learning approach using labeled data. However, obtaining such labeled sentences is difficult and extremely expensive. Hence, it is important to solve ABSA without taking a dependency on labeled sentences. In this work, we propose a three-step semi-supervised hybrid approach that jointly detects an aspect and its associated sentiment in a given review sentence. The first step of our approach takes a small set of seed words for each aspect and sentiment class to construct respective semantically coherent class vocabularies. The second step makes use of these constructed vocabularies along with POS tags to label a subset of sentences from the training corpus. As we adopt a semi-automated approach to label the data, this process may induce noise in the labels during the annotation. In the last step, we use such labeled sentences to build a noise-robust deep neural network for aspect and sentiment classification. We conduct experiments on two real data sets to verify the effectiveness of our model (https://github.com/Raghu150999/UnsupervisedABSA). [ABSTRACT FROM AUTHOR]

A novel CMOS transistor implementation of three stage amplifier is presented in this work. The floating gate metal oxide semiconductor (FGMOS) transistor current mirror is exploited here to minimize supply requirements and to overcome the bandwidth reduction due to FGMOS transistor, frequency compensation techniques have been applied in subsequent stages. This amplifier is useful for application of low-voltage low-power VLSI as it requires a dual supply of only ± 0.5 V. The power consumption is 0.157 mW and slew rate is 6.5 V/μs. The gain bandwidth product of the circuit is calculated as 59.1 MHz and dc gain is 111.5 dB. All simulations are carried out in 180 nm technology with spice tools. [ABSTRACT FROM AUTHOR]