Common Search Research Articles

Large language models’ performances regarding common patient questions about osteoarthritis: A comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Perplexity

BackgroundLarge Language Models (LLMs) have gained much attention and, in part, have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses. Osteoarthritis (OA) is a common topic in skeletal muscle disorders, and patients often seek information about it online. Our study evaluated the ability of 3 LLMs (ChatGPT-3.5, ChatGPT-4.0, and Perplexity) to accurately answer common OA-related queries. MethodsWe defined 6 themes (pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis) based on a generalization of 25 frequently asked questions about OA. Three consultant-level orthopedic specialists independently rated the LLMs' replies on a 4-point accuracy scale. The final ratings for each response were determined using a majority consensus approach. Responses classified as “satisfactory” were evaluated for comprehensiveness on a 5-point scale. ResultsChatGPT-4.0 demonstrated superior accuracy, with 64% of responses rated as “excellent”, compared to 40% for ChatGPT-3.5 and 28% for Perplexity (Pearson's chi-squared test with Fisher's exact test, all p < 0.001). All 3 LLM-chatbots had high mean comprehensiveness ratings (Perplexity = 3.88; ChatGPT-4.0 = 4.56; ChatGPT-3.5 = 3.96, out of a maximum score of 5). The LLM-chatbots performed reliably across domains, except for “treatment and prevention” However, ChatGPT-4.0 still outperformed ChatGPT-3.5 and Perplexity, garnering 53.8% “excellent” ratings (Pearson's chi-squared test with Fisher's exact test, all p < 0.001). ConclusionOur findings underscore the potential of LLMs, specifically ChatGPT-4.0 and Perplexity, to deliver accurate and thorough responses to OA-related queries. Targeted correction of specific misconceptions to improve the accuracy of LLMs remains crucial.

Automated fault diagnosis of rotating machinery using sub domain greedy Network Architecture search

Network Architecture Search (NAS) automates hyperparameter adjustments in deep learning models, offering a potent solution for building intelligent fault diagnosis models. Despite its potential, NAS faces challenges in this application. The primary issues include the excessive time required when searching across the full data domain and the inefficacy of the common random search strategy in yielding high-performance sub networks. This study addresses these challenges through several key initiatives: It defines a specific library of hyperparameters tailored for intelligent fault diagnosis tasks; constructs sub data domain model clusters to reduce the computational costs associated with NAS; and introduces a hyperparameter search strategy leveraging a greedy algorithm, which enhances the search efficiency for optimal diagnostic sub network and minimizes its prediction errors. When applied to datasets involving gear and bearing faults, the proposed method demonstrates a reduction in both the time cost of searches and the prediction errors of sub networks, compared to traditional Network Architecture Search. Moreover, it outperforms manually tuned deep learning models by simultaneously optimizing multiple sets of hyperparameter and automatically generating higher accuracy diagnostic sub networks.

Patient education resources for oral mucositis: a google search and ChatGPT analysis.

Oral mucositis affects 90% of patients receiving chemotherapy or radiation for head and neck malignancies. Many patients use the internet to learn about their condition and treatments; however, the quality of online resources is not guaranteed. Our objective was to determine the most common Google searches related to "oral mucositis" and assess the quality and readability of available resources compared to ChatGPT-generated responses. Data related to Google searches for "oral mucositis" were analyzed. People Also Ask (PAA) questions (generated by Google) related to searches for "oral mucositis" were documented. Google resources were rated on quality, understandability, ease of reading, and reading grade level using the Journal of the American Medical Association benchmark criteria, Patient Education Materials Assessment Tool, Flesch Reading Ease Score, and Flesh-Kincaid Grade Level, respectively. ChatGPT-generated responses to the most popular PAA questions were rated using identical metrics. Google search popularity for "oral mucositis" has significantly increased since 2004. 78% of the Google resources answered the associated PAA question, and 6% met the criteria for universal readability. 100% of the ChatGPT-generated responses answered the prompt, and 20% met the criteria for universal readability when asked to write for the appropriate audience. Most resources provided by Google do not meet the criteria for universal readability. When prompted specifically, ChatGPT-generated responses were consistently more readable than Google resources. After verification of accuracy by healthcare professionals, ChatGPT could be a reasonable alternative to generate universally readable patient education resources.

Yoga as a Potential Tool for Filling Implementation Gap in Promoting Better Mental Health in Geriatric Age Group at Global Level: A Scoping Review

This scoping review aims to review the effectiveness of yoga as a complementary intervention for improving mental health outcomes in the geriatric population globally. Yoga is practiced by millions worldwide. Since practitioners of yoga claim to have calmer minds and better psychological and physical health, this topic needs to be reviewed at length. There is a considerable treatment gap worldwide in treating mental disorders, especially in old age, and there is a shortage of low-cost, feasible, and acceptable preventive or treatment options available to tackle mental ailments for the senior population at large. Yoga can potentially act as a low-cost intervention for promoting better mental health. The article reviews yoga as an implementation strategy for meeting this global challenge. Information related to “Yoga and Mental Health” and “Yoga and old age” was searched in two databases, i.e., PubMed and Google Scholar, using a standard search strategy and search strings. Only the articles published in the English language were selected. A standard data extraction form was used to extract and tabulate the data. A comprehensive search strategy (annexed) yielded only two papers as the work on implementation research on mental health is dismal. Both were excluded as the papers did not belong to implementation research. However, for the sake of narrative review, a common search strategy in PubMed included key terms such as “yoga and aging and mental health” and yielded 54 results out of which 7 were included for this scoping review. Comprehensive advanced search from Google Scholar revealed 4850 articles. Screening found 1 implementation research abstract only and two clinical trials. None was included as they did not belong to implementation research. Considering the potential that yoga has as a preventive and therapeutic option, scientific work on implementation research involving mental health and yoga is required. Considering the bludgeoning amount of work done on the subject, there is a possibility of including the same in mainstream medicine as a low-cost preventive or therapeutic option to address the implementation gap in mental health, especially for the senior age group.

Both the transcriptional activator, Bcd, and repressor, Cic, form small mobile oligomeric clusters

Transcription factors play an essential role in pattern formation during early embryo development, generating a strikingly fast and precise transcriptional response that results in sharp gene expression boundaries. To characterize the steps leading up to transcription, we performed a side-by-side comparison of the nuclear dynamics of two morphogens, a transcriptional activator, Bicoid (Bcd), and a transcriptional repressor, Capicua (Cic), both involved in body patterning along the anterior-posterior axis of the early Drosophila embryo. We used a combination of fluorescence recovery after photobleaching, fluorescence correlation spectroscopy, and single-particle tracking to access a wide range of dynamical timescales. Despite their opposite effects on gene transcription, we find that Bcd and Cic have very similar nuclear dynamics, characterized by the coexistence of a freely diffusing monomer population with a number of oligomeric clusters, which range from low stoichiometry and high mobility clusters to larger, DNA-bound hubs. Our observations are consistent with the inclusion of both Bcd and Cic into transcriptional hubs or condensates, while putting constraints on the mechanism by which these form. These results fit in with the recent proposal that many transcription factors might share a common search strategy for target gene regulatory regions that makes use of their large unstructured regions, and may eventually help explain how the transcriptional response they elicit can be at the same time so fast and so precise.

Quality of Information About Kidney Stones from Artificial Intelligence Chatbots.

Introduction: Kidney stones are common and morbid conditions in the general population with a rising incidence globally. Previous studies show substantial limitations of online sources of information regarding prevention and treatment. The objective of this study was to examine the quality of information on kidney stones from artificial intelligence (AI) chatbots. Methods: The most common online searches about kidney stones from Google Trends and headers from the National Institute of Diabetes and Digestive and Kidney Diseases website were used as inputs to four AI chatbots (ChatGPT version 3.5, Perplexity, Chat Sonic, and Bing AI). Validated instruments were used to assess the quality (DISCERN instrument from 1 low to 5 high), understandability, and actionability (PEMAT, from 0% to 100%) of the chatbot outputs. In addition, we examined the reading level of the information and whether there was misinformation compared with guidelines (5 point Likert scale). Results: AI chatbots generally provided high-quality consumer health information (median DISCERN 4 out of 5) and did not include misinformation (median 1 out of 5). The median understandability was moderate (median 69.6%), and actionability was moderate to poor (median 40%). Responses were presented at an advanced reading level (11th grade; median Flesch-Kincaid score 11.3). Conclusions: AI chatbots provide generally accurate information on kidney stones and lack misinformation; however, it is not easily actionable and is presented above the recommended reading level for consumer health information.

Quantifying Threat or Challenge Response of Undergraduate Paramedicine Students During High-Stress Clinical Scenarios

Paramedic work can be stressful. Encountering clinically unwell patients, long shift hours and dealing with the unknown expose paramedics to mental, physical and emotional stress. In the learning environment, these types of stresses are difficult for educators to replicate. Traditionally, students have been tested under pressure in scenario-based situations as a means of stress inoculation. However, the literature is unclear as to whether this enhances or hinders learning. A recent scoping review identified an acceptable level of stress during simulation can be beneficial, although a level of a balance is required. Too much stress can hinder learning and lead to underperformance. Ideally, high-acuity patient scenarios should be designed to invoke a challenging state of appraisal in the student to support both their learning and knowledge retention. To obtain an understanding of how students appraise these types of scenarios, quantitative physiological and psychometric data needs to be obtained and analyzed. However, across the health care education literature, inconsistent methodologies and a variety of physiological and cognitive measures make it challenging to draw firm conclusions. This narrative review searched three prominent databases using common search terms to produce a subset of high-quality publications that we thought were most pertinent and insightful. Our paper establishes recommendations for appropriate physiological assessment and interpterion of challenge appraisal in students undertaking high-stress, low-frequency clinical scenarios.

Reaction rebalancing: a novel approach to curating reaction databases

PurposeReaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need.MethodsThe SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities.ResultsThe rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively.ConclusionThe SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning.Scientific ContributionSynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.

Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides.

What Questions Do Patients Ask About Biologic Therapies for Chronic Sinusitis With Nasal Polyps?

Objective: The objective of this study is to explore the questions commonly asked online about biologic therapies for use in treatment of chronic sinusitis with nasal polyps (CRSwNP) and the quality of the available content. Methods: Most common search terms were identified via Google Trends. People Also Ask (PAA) questions were identified and extracted with their associated website using an online data-scraping program [Search Engine Optimization (SEO) Minion, Keywords Everywhere]. Sources were evaluated using Flesch Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE, higher number = better) score for readability; Journal of American Medical Association (JAMA) Benchmark criteria for quality assessment (0-4, 4 = all criteria met). Results: A total of 143 unique PAA questions and unique websites were identified. Questions were organized into 3 categories: questions about biologic therapies overall (38.46%), about CRSwNP (37.76%), and about treatment options for CRSwNP (23.78%). Websites answering PAA questions were from commercial (60.14%), medical practice (13.99%), academic (13.29%), and government (12.59%) sources. FKGL scores found the average reading level to be at approximately a 12th grade level (SD = 3.297) alongside a low reading ease FRE score of 37.6 (SD = 16.77). Mean JAMA criteria scores were 0.9895 (SD = 0.848), indicating largely low-quality materials. Conclusion: Biologic therapies are a novel treatment option for CRSwNP, and participants are seeking more information about these treatments and disease state. Online resources regarding biologics should be presented at a lower reading level. Sources with evidence-based information are needed. Physicians should be aware of these limitations in online material and counsel accordingly by curating and directing patients to good sources.Level of Evidence: Step 4.

Exploring the Role of Video Playback Visual Cues in Object Retrieval Tasks.

Searching for objects is a common task in daily life and work. For augmented reality (AR) devices without spatial perception systems, the image of the object's last appearance serves as a common search assistance. Compared to using only images as visual cues, videos capturing the process of object placement can provide procedural guidance, potentially enhancing users' search efficiency. However, complete video playback capturing the entire object placement process as visual cues can be excessively lengthy, requiring users to invest significant viewing time. To explore whether segmented or accelerated video playback can still assist users in object retrieval tasks effectively, we conducted a user study. The results indicated that when video playback is covering the first appearance of the object's destination to the object's final appearance (referred to as the destination appearance, DA) and playing at normal speed, search time and cognitive load were significantly reduced. Subsequently, we designed a second user study to evaluate the performance of video playback compared to image cues in object retrieval tasks. The results showed that combining the DA playback starting point with images of the object's last appearance further reduced search time and cognitive load.

High mirror symmetry in mouse exploratory behavior.

The physicality of the world in which the animal acts-its anatomical structure, physiology, perception, emotional states, and cognitive capabilities-determines the boundaries of the behavioral space within which the animal can operate. Behavior, therefore, can be considered as the subspace that remains after secluding all actions that are not available to the animal due to constraints. The very signature of being a certain creature is reflected in these limitations that shape its behavior. A major goal of ethology is to expose those constraints that carve the intricate structure of animal behavior and reveal both uniqueness and commonalities between animals within and across taxa. Exploratory behavior in an empty arena seems to be stochastic; nevertheless, it does not mean that the moving animal is a random walker. In this study, we present how, by adding constraints to the animal's locomotion, one can gradually retain the 'mousiness' that characterizes the behaving mouse. We then introduce a novel phenomenon of high mirror symmetry along the locomotion of mice, which highlights another constraint that further compresses the complex nature of exploratory behavior in these animals. We link these findings to a known neural mechanism that could explain this phenomenon. Finally, we suggest our novel finding and derived methods to be used in the search for commonalities in the motion trajectories of various organisms across taxa.

Searching for the fine particulate matter (PM 2.5) pollutant emission source using a drone

In this research, we propose a method with three search algorithms, namely Greedy, Dynamic, and Hybrid, to exploit the ability of a drone to efficiently and accurately locate air pollution (i.e., particulate matter) emission sources. Currently, most of the existing stationed environment sensor systems can provide continuous monitoring information of air quality such as PM 2.5 (Particulate Metter 2.5) or CO2 (carbon dioxide) but are unable to pinpoint the location of the emission source. The proposed method utilizes the PM2.5 concentration information provided by the existing sensing system to initialize the search plan by limiting a search area. Based on the initial sensing information, a drone with an onboard air quality sensor will adjust its searching direction and distance to an intermediate point. After an intermediate location point is reached, the drone will pause and sense the PM2.5 concentration at the current location. Next, the drone continues to adjust the search path with its searching direction and distance based on the sensed PM 2.5 concentration level until the emission source is located. Utilizing the information provided by both an existing sensing system and an onboard sensor, the drone is able to make correct decisions when searching for pollution sources. In the experiments, three proposed algorithms and two common search methods are compared under different settings. Experiment results showed our proposed method is able to achieve a location estimation error below 2 m within 20 min.

Emotional and relational problems of adolescents with and without a migrant background in Europe: a systematic review.

Mental health of migrant adolescents is a topic that has been widely studied in the past decades. Emotional and behavioural problems are amongst the most explored areas; however, little attention has been paid to the relational sphere, which represents another key aspect of mental health and is paramount during adolescence. This systematic review analysed the available evidence on emotional and relational problems comparing adolescents with and without a migrant background in Europe between 2010 and 2021. The search was conducted in four databases using a common search strategy composed of terms addressing adolescence, migrant population, and emotional and relational problems. Three rounds of screening produced 36 eligible studies. Factors affecting both types of problems were identified and categorised using thematic synthesis, dividing them into factors affecting both types of problems jointly or separately and analysing them according to three systemic levels affecting the adolescents' lives (intrapersonal, interpersonal and external). Critical analysis of the results pointed to mixed findings, with a mild tendency in migrant-background adolescents to portray more emotional problems than their native peers and a stronger tendency for relational problems in the same direction. Several limitations were identified and, along with the conclusions, point to suggestions for future research focusing on studying relational problems as a key component of mental health and its link to emotional problems. Further suggestions entail designing studies that target adolescents with different migrant-background profiles and cultural origins to establish differences between them and identify additional factors affecting emotional and relational problems during the pre-migration and transit phases of the migratory journey to help prevent the onset of these problems.

Patient Questions Surrounding Posterior Nasal Nerve Ablation for Chronic Rhinitis

AbstractObjectiveIn‐office ablation of the posterior nasal nerve (PNN) has emerged as an effective treatment option for chronic rhinitis patients. This study explored questions patients commonly search online regarding this therapy and the quality of content available.Study DesignA retrospective analysis of online search criteria and sources was performed with subsequent analysis of results.SettingSearch and data acquisition was in September of 2023.MethodsMost common search terms related to cryotherapy and radiofrequency neurolysis of the PNN were identified with associated People Also Ask (PAA) questions. Questions were categorized and organized into subtopics and sources evaluated using readability and quality metrics.ResultsA total of 255 unique PAA questions and 175 unique websites were identified. The most common subtopics were related to facts about chronic rhinitis (26.7%) and rhinitis treatment options (25.1%). Nearly a quarter (24.3%) of websites were from commercial sources. Quality metrics indicate difficult‐to‐read and low‐quality materials.ConclusionExisting online resources need improvement to provide patients material that is easier to read. Physicians counseling patients should be aware of these areas for adequate shared decision making.

IJCM_371A: Patterns of patient self-identification of ailments: A cross-sectional study from tertiary care centers in Mangalore

Background: Self-diagnosis is the process of determining a disease or disorder without seeking medical care. Advances in technology have made it easier for patients to get medical information online, fostering self-diagnosis and doctor cross-examination. The aim of this study is to estimate the proportion of study respondents who use the internet and other sources for self-diagnosis and explore their attitudes concerning self-diagnosis. Methodology: This cross- sectional study was conducted among 186 participants from hospitals affiliated to Kasturba Medical college, Mangalore (Govt Wenlock hospital and KMC Hospital, Attavar). The data was collected using a semi-structured, internally validated questionnaire- consisting of 3 sections: 1) General information of study participants 2A) Questions on self- diagnosis through different sources 2B) On their attitudes concerning self-diagnosis. Data was analyzed using SPSS version 24. Results were expressed in descriptive statistics. Results: This study on self-diagnosis was conducted among OPD attendees and found a mean age of 31 years, with 66.0 % males and 34.0 % females. Majority (98.4%) had media access, in which 67.7% online media for self-diagnosis. Specific diseases like cancer and infectious disease were common search topics (76.3%). One third of the total participants reported they have done online search for ailments related to their relatives or friends. Around 61.3% considered their sources reliable, while 50.5% intended to continue self-diagnosing. Notably, 61.3% made correct self- diagnoses which were confirmed later after the physical consultation with their treating physician, particularly among participants under 25 with higher education. Conclusion: Due to the development of artificial intelligence and internet access, even non-medical people are now searching the media for medical issues. Although there is a growing dependence on internet resources, most individuals still say they trust in future self-diagnosis but still prefer visiting a doctor rather than self-medicating. To educate the public about the risks associated with self-diagnosis and the purchase of over-the-counter medications, community-based behavioral change communication should be employed.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

SPIMulator: A Spintronic Processing-In-Memory Simulator for Racetracks

In-memory processing is becoming a popular method to alleviate the memory bottleneck of the von Neumann computing model. With the goal of improving both latency and energy cost associated with such in-memory processing, emerging non-volatile memory technologies, such as Spintronic magnetic memory, are of particular interest as they can provide a near-SRAM read/write performance and eliminate nearly all static energy without experiencing any endurance limitations. Spintronic Racetrack Memory (RM) further addresses density concerns of spin-transfer torque memory (STT-MRAM). Moreover, it has recently been demonstrated that portions of RM nanowires can function as a polymorphic gate, which can be leveraged to implement multi-operand bulk bitwise operations. With more complex control, they can also be leveraged to build arithmetic integer and floating point processing in memory (PIM) primitives. This paper proposes SPIMulator, a Spintronic PIM sim ulator that can simulate the storage and PIM architecture of executing PIM commands in Racetrack memory. SPIMulator functionally models the polymorphic gate properties recently proposed for Racetrack memory, which allows transverse access that determines the number of ‘1’s in a segment of each Racetrack nanowire. From this simulation, SPIMulator can report real-time performance statistics such as cycle count and energy. Thus, SPIMulator simulates the multi-operand bit-wise logic operations recently proposed and can be easily extended to implement new PIM operations as they are developed. Due to the functional nature of SPIMulator, it can serve as a programming environment that allows development of PIM-based codes for verification of new acceleration algorithms. We demonstrate the value of SPIMulator through the modeling and estimations of performance and energy consumption of a variety of example applications, including the Advanced Encryption Standard (AES) for encryption primarily based on logical and look-up operations; multiplication of matrices, a frequent requirement in scientific, signal processing, and machine learning algorithms; and bitmap indices a common search table employed for database lookups.

Public interest and seasonal peaks for wisdom teeth related web inquiries - A google trends analysis.

With the help of the online search query tool, Google Trends, the public interest in the primary search term "wisdom teeth" for the timeframe between January 1st, 2004 and September 31st, 2021 was analyzed. To do so, a country-specific search was conducted in English-speaking countries (the USA, the UK, Canada, and Australia) in the northern and southern hemispheres. The extracted time series was examined for reliability, and a Cosinor analysis evaluated the statistical significance of seasonal interest peaks. The reliability of averaged time series data on the search term "wisdom teeth" was excellent in all examined countries. In all countries analyzed, "wisdom teeth removal" was one of the most common related search terms. Significant interest peaks for wisdom teeth-related search terms were found in Canada and the USA during summer (p < .001). In Canada and the USA, significant seasonal patterns with the highest interest during the summer months, could be displayed. This phenomenon could be caused by increased wisdom teeth-related complaints induced by seasonal climate changes.

Descending control and regulation of spontaneous flight turns in Drosophila

The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of locomotion, selecting for a common search motif in which straight movements through resource-poor regions alternate with zig-zag exploration in resource-rich domains. For example, during local search, flying flies spontaneously execute rapid flight turns, called body saccades, but suppress these maneuvers during long-distance dispersal or when surging upstream toward an attractive odor. Here, we describe the key cellular components of a neural network in flies that generate spontaneous turns as well as a specialized pair of neurons that inhibits the network and suppresses turning. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional units-one for right turns and one for left-with each unit consisting of an excitatory (DNae014) and an inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly, we identified a pair of large, distinct interneurons (VES041) that form inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As predicted by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it promotes straight flight to regulate the transition between local search and long-distance dispersal. These results thus identify the key elements of a network that may play a crucial role in foraging ecology.