Cluster Headache: What's New?

BACKGROUND: Cluster headache is a highly disabling primary headache disorder which is widely described as the most painful condition a human can experience. AIM: To provide an overview of the clinical characteristics, epidemiology, risk factors, differential diagnosis, pathophysiology and treatment options of cluster headache, with a focus on recent developments in the field. METHODS: Structured review of the literature on cluster headache. RESULTS: Cluster headache affects approximately one in 1000 of the population. It is characterised by attacks of severe unilateral head pain associated with ipsilateral cranial autonomic symptoms, and the tendency for attacks to occur with circadian and circannual periodicity. The pathophysiology of cluster headache and other primary headache disorders has recently become better understood and is thought to involve the hypothalamus and trigeminovascular system. There is good quality evidence for acute treatment of attacks with parenteral triptans and high flow oxygen; preventive treatment with verapamil; and transitional treatment with oral corticosteroids or greater occipital nerve injection. New pharmacological and neuromodulation therapies have recently been developed. CONCLUSION: Cluster headache causes distinctive symptoms, which once they are recognised can usually be managed with a variety of established treatments. Recent pathophysiological understanding has led to the development of newer pharmacological and neuromodulation therapies, which may soon become established in clinical practice

Titanium Dioxide Modifications for Energy Conversion: Learnings from Dye-Sensitized Solar Cells

During the last two and half decade modifying anatase TiO2 has appreciably enhanced our understanding and application of this semiconducting, non-toxic material. In the domain of DSCs, the main focus has been to achieve band adjustment to facilitate electron injection from anchored dyes, and high electronic mobility for photo-generated electron collection. In retrospection, there is a dire need to assimilate and summarize the findings of these studies to further catalyze the research, better understanding and comparison of the structure–property relationships in modifying TiO2 efficiently for crucial photocatalytic, electrochemical and nanostructured applications. This chapter aims at categorizing the typical approaches used to modify TiO2 in the domain of DSCs such as through TiO2 paste additives, TiO2 doping, metal oxides inclusion, dye solution co-adsorbing additives, post staining surface treatment additives and electrolyte additives. A summary of the consequences of these modifications on electron injection, charge extraction, electronic mobility, conduction band shift and surface states has been presented. This chapter is expected to hugely benefit the researchers employing TiO2 in energy, catalysis and battery applications

Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflict

The ongoing Russo-Ukrainian conflict has been a subject of intense media coverage worldwide. Understanding the global narrative surrounding this topic is crucial for researchers that aim to gain insights into its multifaceted dimensions. In this paper, we present a novel dataset that focuses on this topic by collecting and processing tweets posted by news or media companies on social media across the globe. We collected tweets from February 2022 to May 2023 to acquire approximately 1.5 million tweets in 60 different languages. Each tweet in the dataset is accompanied by processed tags, allowing for the identification of entities, stances, concepts, and sentiments expressed. The availability of the dataset serves as a valuable resource for researchers aiming to investigate the global narrative surrounding the ongoing conflict from various aspects such as who are the prominent entities involved, what stances are taken, where do these stances originate, and how are the different concepts related to the event portrayed.Comment: Dataset can be found at https://zenodo.org/record/804345

Analysis of temperature variations in fixed-bed columns using non-isothermal and non-equilibrium transport model

A non-isothermal and non-equilibrium two-component lumped kinetic model of fixed-bed column liquid chromatography is formulated with the linearized isotherm and solved analytically to study the influence of temperature variations on the process. The model equations constitute a system of convection-diffusion PDE for mass and energy balances in the bulk phase coupled with differential equations for mass and energy balances in the stationary phase. The analytical solutions are derived for Dirichlet boundary conditions by implementing the Laplace transformation, Tschirnhaus-Vieta approach, the linear decomposition technique and an elementary solution technique of ODE. An efficient and accurate numerical Laplace inversion technique is applied to bring back the solution in the actual time domain. In order to validate the derived analytical solutions for concentration and temperature fronts, the high resolution upwind finite volume scheme is applied to approximate the model equations numerically. Various case studies are carried out assuming realistic model parameters. The results obtained will be beneficial for interpreting mass and energy profiles in non-equilibrium and non-isothermal liquid chromatographic columns and provide deeper insight into the sensitivity of the separation process without performing costly and time-consuming laboratory experiments

Check square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features

In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first prob-lem, claim check-worthiness prediction, we explore the fusion of syntac-tic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similar-ity, and perform KD-search to retrieve verified claims with respect to a query tweet

TIB's visual analytics group at MediaEval '20: Detecting fake news on corona virus and 5G conspiracy

Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifi-cally, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The Fak-eNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection

TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy

Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.Comment: MediaEval 2020 Fake News Tas

Characterization of sperm heparin binding proteins (HBPs) using polyclonal antibodies raised against seminal plasma HBPs: Application in buffalo bull fertility

26-33This study aimed to evaluate rabbit polyclonal antibodies raised against purified seminal plasma sperm membrance extracts (SP) heparin binding protein (HBP) for identifying HBPs in buffalo bull spermatozoa by western blotting. Anti-SP-HBP recognized 11 polypeptides in SDS-sperm membrance extracts (SME) of 31 tested bulls. Thirty one bulls were divided into G-1 (>40%) and G-II (≤40%) based on acrosome reaction. Immunoblotting revealed that HBPs of 24, 30, 38 and 43 kDa were present in 3%, 7.02%, 1.16% and 4.83% more bulls of G-I, whereas, 20 and 46 kDa HBPs were present in 13.2 and 9.65% more bulls of G-II. Immunoblotting of anti-HBP with sperm extracts of 10 bulls (22-31) indicated that 31 kDa positive bulls had 10.9% higher conception rate than 31 kDa negative bulls. Although 24 kDa HBP was detected in 10 bulls, but its expression was very weak in bull number 22, 23 and 26, which had 10.7% lower conception rate than the bulls with strong expression of 24 kDa HBP. In the present study, 17/20 kDa positive bulls exhibited 4.46% and 8.67% low conception rate than 17/20 kDa negative bulls. Mass spectrometry analysis revealed matching of 24, 31, 33 and 38 kDa proteins with MHC class 1 antigen, tRNA methyl transferase 11 homolog partial, parvalbumin alpha-like and cilia- and flagella-associated protein 99. This study suggests that buffalo bull fertility can be predicted from sperm HBP