bluuebunny/crossref_metadata_2025 huggingface dataset:
Autism Spectrum Disorder Therapy: Analysis of Artificial Intelligence integrated Robotic Approach
Authors: Ananth Rao Ananya , Qien Yeau Tan Shaun , Raghavi R , Srivastava Archit , Renumadhavi C H
Published: January 2022

Autism Spectrum Disorder is a developmental disorder that may manifest in a myriad of ways such as difficulties in social interaction and a tendency to engage in repetitive patterns of behaviour. Over the years, several kinds of treatment protocols have been proposed and implemented. One such area that is attracting the attention of researchers in the field is a robot-based approach in the treatment of children diagnosed with the disorder. Here we propose a viable method via the integration of apex technological methods like Artificial Intelligence, Machine Learning and Medical Robotics, coupling it with problem specific algorithms in OpenCV along with principles of Applied Behavioural Analysis to help possibly alleviate a key symptom displayed by children in terms of level of social interaction - that of eye-contact. This would be achieved via an AI-integrated Robotic Framework. The project also considers the possibility of inclusion of the growing research field of Quantum Computing to realize the process and investigates its viability as a potential source of innovation in the future.
Motorsport Data Acquisition System and Live Telemetry using FPGA based CAN controller
Authors: Banerjee A , Jindal A V , Shankar A , Sachdeva V , Kanthi M
Published: January 2022

The paper describes the design and working of a motorsport data acquisition, logging, live telemetry, and display system developed using the Controller Area Network (CAN) communication protocol as the backbone of the arrangement. The main controller of the CAN system is the myRIO which was programmed using LabVIEW. A Formula One car hosts over a hundred sensors during each of its races. The data acquisition/logging system, although does not directly affect the car’s performance, is indispensable when it comes to the testing and design phase of the car. Designers can validate their assumptions and calculations, real-time data during testing can be a safety indicator and it provides insight to the driver about the performance of the vehicle. The FPGA-based controller for CAN is designed for data acquisition and live telemetry system with the interest of the formula car team in mind. The design choices were made to improve and deliver a more effective system than the pre-existing ones. All choices of controllers, sensors, formatting were custom made for the requirements of the team. All programmable devices were coded individually to suit the system and the graphical user interface was designed internally. Data acquired by the proposed system helps in making sure that the car achieves the goals that were envisioned when it was designed.
Network intrusion detection: a comparative study of four classifiers using the NSL-KDD and KDD’99 datasets
Authors: Devarakonda Ananya , Sharma Nilesh , Saha Prita , Ramya S
Published: January 2022

As most of the population acquires access to the internet, protecting online identity from threats of confidentiality, integrity, and accessibility becomes an increasingly important problem to tackle. By definition, a network intrusion detection system (IDS) helps pinpoint and identify anomalous network traffic to bring forward and classify suspicious activity. It is a fundamental part of network security and provides the first line of defense against a potential attack by alerting an administrator or appropriate personnel of possible malicious network activity. Several academic publications propose various artificial intelligence (AI) methods for an accurate network intrusion detection system (IDS). This paper outlines and compares four AI methods to train two benchmark datasets- the KDD’99 and the NSL-KDD. Apart from model selection, data preprocessing plays a vital role in contributing to accurate solutions, and thus, we propose a simple yet effective data preprocessing method. We also evaluate and compare the accuracy and performance of four popular models- decision tree (DT), multi-layer perceptron (MLP), random forest (RF), and a stacked autoencoder (SAE) model. Of the four methods, the random forest classifier showed the most consistent and accurate results.
Smart Farming enabled by IoT and Spectral Imaging
Authors: Mohanty Pratik , Valagadri Vivek , Ramya S
Published: January 2022

Smart Farming System is an emerging concept which utilizes sensors in the field enabled through IoT to get live data from the farm. This paper aims at developing such a Smart Farming system using the highly advanced technology of Texas instruments microcontrollers, MSP430 and TIVA C Series TM4C1294. Along with IoT the system uses Multispectral Imaging in conjunction with Wireless Soil Embedded Sensor Networks. The goal of the system is to provide reliable live data which is obtained from the multiple sensor nodes placed throughout the farm, that use the sink nodes to transfer the data to the cloud. The farmer can access this data using the Blynk Mobile app and can thus take further calculated actions towards maintaining the farm and further monitor the soil/crop health to increase the ultimate yield from his farm.
Formal Verification of CHAP PPP authentication Protocol for Smart City/Safe City Applications.
Authors: Pradeep R , Sunitha N R
Published: January 2022

A smart city is a technologically advanced metropolitan region with several connected devices that collects data using various electronic technologies, voice activation methods, and sensors. The information obtained from the data is utilised to efficiently manage assets, resources, and services; in turn, the data is used to enhance operations throughout the city. Achieving security for smart cities is one of the major challenges as the number of connected devices increases the vulnerability also increases. The security of a smart city system depends on the reliability of the security protocols used by the security systems. To design and develop a highly secure system for a smart city the security protocols used must be highly reliable. To prove the reliability of a security protocol the validation technique is not desirable because of its several drawbacks, these drawbacks can be overcome using the formal verification technique which provides the mathematical proof for its correctness. In this work, The Challenge-Handshake Authentication Protocol Point-to-Point (CHAP PPP) which is more commonly used in PPP authentication of smart cities is formally verified using the well-known verification technique known as the model checking technique. The Scyther model checker is the tool used to build the abstract security protocol model.
An approach framework of transfer learning, adversarial training and hierarchical multi-task learning - a case study of disinformation detection with offensive text
Authors: Chandra Shravan , Das Bhaskarjyoti
Published: January 2022

With society going online and disinformation getting accepted as a phenomena that we have to live with, there is a growing need to automatically detect offensive text on modern social media platforms. But the lack of enough balanced labeled data, constantly evolving socio-linguistic patterns and ever-changing definition of offensive text make it a challenging task. This is a common pattern witnessed in all disinformation detection tasks such as detection of propaganda, rumour, fake news, hate etc. The work described in this paper improves upon the existing body of techniques by bringing in an approach framework that can surpass the existing benchmarks. Firstly, it addresses the imbalanced and insufficient nature of available labeled dataset. Secondly, learning using relates tasks through multi-task learning has been proved to be an effective approach in this domain but it has the unrealistic requirement of labeled data for all related tasks. The framework presented here suitably uses transfer learning in lieu of multi-task learning to address this issue. Thirdly, it builds a model explicitly addressing the hierarchical nature in the taxonomy of disinformation being detected as that delivers a stronger error feedback to the learning tasks. Finally, the model is made more robust by adversarial training. The work presented in this paper uses offensive text detection as a case study and shows convincing results for the chosen approach. The framework adopted can be easily replicated in other similar learning tasks facing a similar set of challenges.
Identification of Diabetic Retinopathy (DR) using Image Processing
Authors: Vikramathithan A C , Pooja P , Bhaskar M S , Navya S , Rakshith K B
Published: January 2022

Diabetes appears in two varieties: Type-1 and Type-2. The former is chronic and can last for years together, whereas the latter can be cured if identified and treated at a premature stage. The symptoms of diabetes affecting the eyes appear very subtle and hence, identifying irregularities in retinal images is a demanding process for medical practitioners. Thus, there was a need to find a method to detect these abnormalities by observing the retinal images non-invasively. After going through research projects and recent developments in identifying DR, we found various techniques/strategies employed, their advantages and drawbacks followed by the objective of overall findings, and the importance of a good DR detection system. Our proposed method calls to attention the importance of early screening, using geometrical relations, multiple thresholding methods and usage of convolutional neural networks as means of overcoming the factors that stand as obstacles in timely detection.
Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models
Authors: Ashwini B P , Sumathi R , Sudhira H S
Published: January 2022

Congested roads are a global problem, and increased usage of private vehicles is one of the main reasons for congestion. Public transit modes of travel are a sustainable and eco-friendly alternative for private vehicle usage, but attracting commuters towards public transit mode is a mammoth task. Commuters expect the public transit service to be reliable, and to provide a reliable service it is necessary to fine-tune the transit operations and provide well-timed necessary information to commuters. In this context, the public transit travel time is predicted in Tumakuru, a tier-2 city of Karnataka, India. As this is one of the initial studies in the city, the performance comparison of eight Machines Learning models including four linear namely, Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator Regression, and Support Vector Regression; and four non-linear models namely, k-Nearest Neighbors, Regression Trees, Random Forest Regression, and Gradient Boosting Regression Trees is conducted to identify a suitable model for travel time predictions. The data logs of one month (November 2020) of the Tumakuru city service, provided by Tumakuru Smart City Limited are used for the study. The time-of-the-day (trip start time), day-of-the-week, and direction of travel are used for the prediction. Travel time for both upstream and downstream are predicted, and the results are evaluated based on the performance metrics. The results suggest that the performance of non-linear models is superior to linear models for predicting travel times, and Random Forest Regression was found to be a better model as compared to other models.
A Comparative Analysis of Machine Learning Algorithms in Design Process of Adaptive Traffic Signal Control System
Authors: Savithramma R M , Sumathi R , Sudhira H S
Published: January 2022

In recent decades machine learning technology has proved its efficiency in most sectors by making human life easier. With this popularity and efficiency, it is applied to design traffic signal control systems to mitigate traffic congestion and distribute waiting delays. Hence, many researchers around the world are working to address this issue. As a part of the solution, this article presents a comparative analysis of various machine learning models to come up with a suitable model for an isolated intersection. In this context, eight machine learning models including Linear Regression, Ridge, Lasso, Support Vector Regression, k-Nearest Neighbour, Decision Tree, Random Forest, and Gradient Boosting Regression Tree are selected. Shivakumara Swamiji Circle (SSC), one of the intersections in Tumakuru, Karnataka, India is selected as a case study area. Essential data is collected from SSC through videography. The selected models are developed to predict green time based on traffic classification and volume in Passenger Car Units (PCU) for each phase on the PyCharm platform. The models are evaluated based on various performance metrics. Results revealed that all the selected models predict green splits with 91% accuracy using traffic classification as input, whereas, models showed 85% accuracy with PCU as input. And also, Gradient Boosting Regression Tree is the best suitable model for the selected intersection, whereas, Decision Tree is not referred model for this application.
Airlight (A UAVs based light guidance and search light system)
Authors: Paleja Aman , Kumar Shreyash , Choraria Amit , Hariharan Arjun , Tendolkar Atharv , Pai M M Manohara
Published: January 2022

Airlight, an UAV (Unmanned Aerial Vehicle) based real time focus and searching light system is a proposed solution which ensures guiding people safely on their path during a dark environment is one less thing to worry about. It is designed to identify a person through algorithms of object detection and also through usage of IoT and sensorics to enable power efficient use of spotlight and hence autonomously guiding people on their path during night-time. Airlight proves to be useful in various sectors such as helping farmers to look after their fields during night-time, security purposes, guiding construction workers while lifting heavy objects, military applications etc.
Aerodock (a smart, autonomous charging and docking station for unmanned aerial vehicles)
Authors: Mukerjee Laaboni , Yadav Mukul , Choraria Amit , Tendolkar Atharv , Hariharan Arjun , Manohara Pai M M
Published: January 2022

The COVID-19 pandemic has laid bare the need for contactless operations. While unmanned aerial vehicles (UAVs) are being developed to aid humans in countless domains, the need for effective battery management and performance optimization remains a huge task. The proposed solution, the “AeroDock”, aims to tackle these challenges by using wireless power transfer (WPT) technology coupled with smart monitoring of the drone’s health. The performance and hardware checks are assessed at the user end via cloud computing and IoT technology. This system is contact-less, safe, reliable and its usage is not affected by external factors. Thus, the AeroDock is a smart docking station for UAVs which eliminates the need for human intervention in effective charging and maintenance.
Smart Aero-Spider: An adaptive and selective smart switching aerator system
Authors: Yadav Shubham , Bharadwaj Yashas , Hariharan Arjun , Choraria Amit , Tendolkar Atharv , Manohara Pai M M
Published: January 2022

The recovery from the COVID-19 pandemic hit shows the emergence of increase in quality of life across various parts of the world. With this lifestyle change, people are looking towards high quality food. Fish being a major source of protein, the industry producing fish from aquaculture is booming. The proposed smart aerator system provides an integrated array of underwater systems for selective aeration of the water body. The smart system ensures targeted aeration to guarantee optimal levels of dissolved oxygen at all times. This is beneficial for perfect survival, growth, and reproduction of fishes. The strategically placed spider aerators are turned on when readings from the dissolved oxygen level at the location is below the optimum range of values. The air blower system consists of an intelligent switching system to activate the right aerator based on the requirement. The sensor data is relayed to the cloud with a wireless communication module. This data can be used for useful insights and all-round monitoring of the water body. The respective aerators have IR sensor to detect movement alongside on-board LEDs to indicate functioning status. Overall, this ensures maximum accelerated growth of healthy fishes. Thus, the solution aims at efficiently boosting the ability of the aquaculture industry to meet the ever-growing demand of consumers.
Aqua-Vision (An underwater panoramic camera with computer vision and analytics)
Authors: Sudhakar Siddharth , Yadav Shubham , Dhelia Manav , Taysheti Pranav , Hariharan Arjun , Choraria Amit , Tendolkar Atharv , Manohara Pai M M
Published: January 2022

In the present social media-bound lifestyle, capturing memories and keeping them accessible is gaining a significant demand globally. For this purpose, a robust, portable camera system for recreational or commercial purposes can be of substantial advantage to society. Aqua-Vision intends to bring an affordable underwater camera system with various innovative features to the hands of consumers. The smart module consists of a waterproof gimbal camera that can be used underwater, providing a hassle-free and reliable user experience and offers rotary motion along two axes. The camera features various general modes like panorama, burst shot, and smart modes using inbuilt computer vision algorithms. The gimbal camera setup can be controlled and switched remotely between all possible modes using a mobile application. All the above features will allow the user to capture photos/videos in any possible setup and use the camera module for various applications. The advent of such innovative, convenient, and robust modules will help cater to the market demands effectively.
Comparative Analysis on Various Types of Energy Storage Devices for Wind Power Generation
Authors: Prasad Ganthia Bibhu , Suriyakrishnaan K. , Prakash N. , Harinarayanan J. , Thangaraj M. , Mishra Sthitprajna
Published: January 2022

Specifically for wind and photovoltaic, energy Storage is well regarded as an important tool for renewable energy. Distributed generation could also give benefits, but the position and use of wind energy are almost reciprocal to the PV system. So the needs of energy storage devices are coming into account for enhancing the power generations. This chapter gives brief idea about the conventional and flow based battery system for energy storage in power system. Here various conventional battery system compared with flow battery system for maintaining the power stabilities and power quality. The objective for this study is to find the better energy storage device which can regulate both stability and efficiency of the renewable energy system. Basically wind energy battery storage systems are depicted here with their construction, operation and usability. This paper can be effective for the researchers to study and to implement the better energy storage device in the wind or solar system to regulate the power quality. A brief comparison was highlighted in the discussion section for better analysis.
Region Detection and Segmentation of Brain Hemorrhage using Algorithmic Approach of Image Processing
Authors: Pai Krithika M
Published: January 2022

Brain is one of the most important part of the body. Brain Hemorrhage is a severe head injury that deteriorates the performance and function of an individual. Brain Hemorrhage can be detected through CT (Computer Tomography) scan of the brain. CT scan uses narrow X-ray beam which rotates around the part of the body and provides a set of images from different angles and the computer creates a cross-sectional view. It is challenging to detect and segment the region of the brain having Hemorrhage. Hence an automated system would be handy at those times. In the proposed work an attempt has been made to segment and identify the hemorrhaged region of the brain in the CT scan slices of the image. Brain hemorrhage segmentation helps to identify the region of brain hemorrhage which in turn helps to treat the patients at an early stage. The region of brain hemorrhage is appropriately identified from the proposed algorithm.
A web-based application for face detection in real-time images and videos
Authors: Arora Mehul , Naithani Sarthak , Shaju Areeckal Anu
Published: January 2022

Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.
Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification
Authors: Hemavati  , Susheela Devi V , Aparna R
Published: January 2022

Nowadays, multi-label classification can be considered as one of the important challenges for classification problem. In this case instances are assigned more than one class label. Ensemble learning is a process of supervised learning where several classifiers are trained to get a better solution for a given problem. Feature reduction can be used to improve the classification accuracy by considering the class label information with principal Component Analysis (PCA). In this paper, stacked ensemble learning method with augmented class information PCA (CA PCA) is proposed for classification of multi-label data (SEMML). In the initial step, the dimensionality reduction step is applied, then the number of classifiers have to be chosen to apply on the original training dataset, then the stacking method is applied to it. By observing the results of experiments conducted are showing our proposed method is working better as compared to the existing methods.
A review on friction stir processing over other surface modification processing techniques of magnesium alloys
Authors: N M Siddesh Kumar , S Chethan , Nikhil Talluri , Dhruthi
Published: January 2022

An enormous amount of research has been conducted on aluminium alloys in friction stir processing (FSP), despite magnesium alloys reporting severe weight reduction when compared to aluminium alloys; a very slight amount of research has testified for FSP of magnesium alloys. Magnesium is highly reactive and susceptible to corrosion in the presence of an aggressive environment. This highly corrosive nature of magnesium limits its applications. Surface properties like crystal structure, composition, and microstructure influence the corrosion and wear properties of the material. Coating techniques and alloying techniques like laser surface modifications are performed to passivate the magnesium surface from corrosion. Coating techniques, however, have been found to be insufficient in corrosion protection due to coating defects like pores, cracks, etc, adhesion problems due to poor surface preparation of the substrate, and impurities present in the coating which provide microgalvanic cells for corrosion. The current study gives a detailed overview of different types of surface modification methods, such as physical vapour deposition, chemical vapour deposition, chemical conversion coating, and ion implantation coating techniques, and also focuses on a few alloying or surface processing methods, such as laser surface modification – namely laser surface melting, laser surface cladding, laser shot peening, laser surface alloying and FSP. FSP is a novel surface modification method derived from friction stir welding, which modifies the microstructure and composition of surface layer without changing the bulk properties to enhance corrosion resistance. FSP enhances and homogenizes the microstructure but also eliminates the breakup of the brittle-network phases and cast microstructure imperfections. Indeed, FSP can produce particle and fibre-reinforced magnesium-based surface composites. FSP empowers the manufacturing of magnesium by adding additives. The different methods of coating and surface modification are compared with FSP.
Racial Inequities in Self-Rated Health Across Brazilian Cities: Does Residential Segregation Play a Role?
Authors: Guimarães Joanna M N , Yamada Goro , Barber Sharrelle , Caiaffa Waleska Teixeira , Friche Amélia Augusta de Lima , Menezes Mariana Carvalho de , Santos Gervasio , Santos Isabel , Cardoso Leticia de Oliveira , Diez Roux Ana V
Published: January 2022

Racial health inequities may be partially explained by area-level factors such as residential segregation. In this cross-sectional study, using a large, multiracial, representative sample of Brazilian adults (n = 37,009 individuals in the 27 state capitals; National Health Survey (Pesquisa Nacional de Saúde), 2013), we investigated 1) whether individual-level self-rated health (SRH) (fair or poor vs. good or better) varies by race (self-declared White, Brown, or Black) and 2) whether city-level economic or racial residential segregation (using dissimilarity index values in tertiles: low, medium, and high) interacts with race, increasing racial inequities in SRH. Prevalence of fair or poor SRH was 31.5% (Black, Brown, and White people: 36.4%, 34.0%, and 27.3%, respectively). Marginal standardization based on multilevel logistic regression models, adjusted for age, gender, and education, showed that Black and Brown people had, respectively, 20% and 10% higher prevalence of fair or poor SRH than did White people. Furthermore, residential segregation interacted with race such that the more segregated a city, the greater the racial gap among Black, Brown, and White people in fair or poor SRH for both income and race segregation. Policies to reduce racial inequities may need to address residential segregation and its consequences for health.
LLPSDB v2.0: an updated database of proteins undergoing liquid–liquid phase separation in vitro
Authors: Wang Xi , Zhou Xiang , Yan Qinglin , Liao Shaofeng , Tang Wenqin , Xu Peiyu , Gao Yangzhenyu , Li Qian , Dou Zhihui , Yang Weishan , Huang Beifang , Li Jinhong , Zhang Zhuqing
Published: January 2022

Emerging evidences have suggested that liquid–liquid phase separation (LLPS) of proteins plays a vital role both in a wide range of biological processes and in related diseases. Whether a protein undergoes phase separation not only is determined by the chemical and physical properties of biomolecule themselves, but also is regulated by environmental conditions such as temperature, ionic strength, pH, as well as volume excluded by other macromolecules. A web accessible database LLPSDB was developed recently by our group, in which all the proteins involved in LLPS in vitro as well as corresponding experimental conditions were curated comprehensively from published literatures. With the rapid increase of investigations in biomolecular LLPS and growing popularity of LLPSDB, we updated the database, and developed a new version LLPSDB v2.0. In comparison of the previously released version, more than double contents of data are curated, and a new class ‘Ambiguous system’ is added. In addition, the web interface is improved, such as that users can search the database by selecting option ‘phase separation status’ alone or combined with other options. We anticipate that this updated database will serve as a more comprehensive and helpful resource for users.

Availability and implementation

LLPSDB v2.0 is freely available at: http://bio-comp.org.cn/llpsdbv2.

Supplementary information

Supplementary data are available at Bioinformatics online.
Turning the Party State Inside Out: CCP Intra-party Regulations as a Theoretical Challenge
Authors: Seppänen Samuli
Published: January 2022

Scholarship on the Chinese party state commonly treats the Chinese Communist Party (CCP) as a black box: the Party acts in the world, using the law as its tool to achieve its political goals. The study of intra-party regulations turns this anthropomorphic image inside out, requiring scholars to regard the very notion of the Party and rule-based governance as ideologically and theoretically contentious matters. This article maps out—and criticizes—prominent approaches to the study of intra-party regulations and rule-based governance within the CCP. It is certainly possible to explain CCP intra-party regulations through external social theoretical perspectives, such as Ernst Fraenkel’s dual state model. However, such perspectives do not necessarily help one understand the CCP leaders’ and ideologues’ idiosyncratic approach to rule-based governance. Fraenkel’s dual state model, in particular, fails to fully describe the role of rule-based governance in the CCP’s governance project.
Cardiovascular disease and ANCA-associated vasculitis: are we missing a beat?
Authors: Floyd Lauren , Morris Adam D , Woywodt Alexander , Dhaygude Ajay
Published: January 2022

The association between cardiovascular (CV) disease and anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is well documented. The recent work by Massicotte-Azarniouch et al. confirms the risk and adds to the existing evidence by describing the highest risk in the first 3 months after diagnosis. In this review, we aim to put their findings into perspective and formulate implications for the care of AAV patients. We discuss mechanisms for increased CV disease in AAV, including the impact of traditional risk factors and disease-related risks such as renal impairment and anti-myeloperoxidase (MPO) ANCA serotype. We also provide a brief primer on the impact of inflammatory-driven endothelial dysfunction and platelet activation on accelerated atherosclerosis in AAV patients. These features alongside the impact of disease activity and systemic inflammation provide potential explanations to why the incidence of CV events is highest in the first 3 months from diagnosis. We suggest future avenues of research, provide some suggestions to address and treat CV risk based on current evidence, and highlight the importance of addressing this topic early on. Addressing modifiable risk factors, dialogue with patients, patient information and a structured approach overall will be key to improve CV outcomes in AAV.
Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations
Authors: Du Manuel , Bernstein Richard , Hoppe Andreas , Bienefeld Kaspar
Published: January 2022

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models that attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlled on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This study elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.
Driving Performance in Older Adults: Current Measures, Findings, and Implications for Roadway Safety
Authors: Toups Robert , Chirles Theresa J , Ehsani Johnathon P , Michael Jeffrey P , Bernstein John P K , Calamia Matthew , Parsons Thomas D , Carr David B , Keller Jeffrey N
Published: January 2022

Background and Objectives

Over 10,000 people a day turn 65 in the United States. For many older adults, driving represents an essential component of independence and is one of the most important factors in overall mobility. Recent survey studies in older adults suggest that up to 60% of older adult drivers with mild cognitive impairment, and up to 30% with dementia, continue to drive. The purpose of this review is to provide a comprehensive and detailed resource on the topics of cognition and driving for clinicians, researchers, and policymakers working on efforts related to older adult drivers.

Research Design and Methods

Publications on PubMed and Medline and discussions with experts working in geriatrics, technology, driving policy, psychology, and diverse aspects of driving performance were utilized to inform the current review.

Results

Research indicates that there is a complex and inverse correlation between multiple cognitive measures, driving performance, and risky driving behaviors. The fragmented nature of available peer-reviewed literature, and a reliance on correlative data, do not currently allow for the identification of the temporal and reciprocal nature of the interplay between cognition and driving endpoints.

Discussion and Implications

There are currently no widely accepted definitions, conceptual models, or uniform set of analyses for conducting geriatric research that is focused on driving. Establishing conventions for conducting research that harmonizes the fields of geriatrics, cognition, and driving research is critical for the development of the evidence base that will inform clinical practice and road safety policy.
Why Men Fare Worse: A Mixed-Methods Study Examining Gender Differences in Nursing Home Resident Quality of Life
Authors: Davila Heather , Ng Weiwen , Akosionu Odichinma , Thao Mai See , Skarphol Tricia , Virnig Beth A , Thorpe Roland J , Shippee Tetyana P
Published: January 2022

Background and Objectives

Despite research documenting gender differences in numerous outcomes in later life, we know little about gender differences in quality of life (QoL) for nursing home (NH) residents. This study examines the relationship between gender and residents’ QoL, including possible reasons for differences observed.

Research Design and Methods

We used a mixed-methods design including surveys with a random sample of Minnesota NH residents using a multidimensional measure of QoL (n = 9,852), resident clinical data, facility-level characteristics (n = 364), interviews with residents (n = 64), and participant observations. We used linear mixed models and thematic analysis of resident interviews and observations to examine possible gender-related differences in residents’ QoL.

Results

After controlling for individual and facility characteristics, men reported lower overall QoL than women, including significantly lower ratings in several QoL domains. In interviews, men noted being less satisfied with activities than women, having fewer friends, and being less able to rely on family for support. Some women described the NH as a place of respite, but men more often described being dissatisfied with life in the NH and undesirable for long-term living. Our observations were consistent with interview findings but provide additional nuances, such as that some men organized their own social groups.

Discussion and Implications

Our findings suggest that men and women experience QoL differently in NHs, with men reporting lower QoL in several domains. Tailoring more activities for men and finding ways to strengthen relationships for men in NHs could help reduce the gender differences in QoL we observed.
Pilot study of peer modeling with psychological inoculation to promote coronavirus vaccination
Authors: Ramirez A G , Despres C , Chalela P , Weis J , Sukumaran P , Munoz E , McAlister A L
Published: January 2022

Promoting coronavirus vaccination is deterred by misinformation, ranging from elaborate conspiracy theories about sinister purposes to exaggeration of side effects, largely promulgated by social media. In this pilot study, we tested the effects of different messages on actions leading to vaccination. Two theory-based advertisements were produced for Facebook, which provided video testimonials from peer role models recommending vaccination and its benefits while providing psychological inoculation through the models’ acknowledging misinformation, rejecting it and receiving the vaccine. These ads were paid to appear on Facebook users’ feeds in rural counties in South Texas, along with a generic vaccine promotion ad from the CDC without peer models or psychological inoculation. Ad viewers could click a link to ‘find a vaccine near you’; these responses served as the outcome variable for assessing experimental effects. Ads featuring peer modeling with psychological inoculation yielded a significantly higher rate of positive responses than CDC ads (30.5 versus14.9/1000 people reached in English and 49.7 versus 31.5/1000 in Spanish; P < 0.001 for both English and Spanish rate comparisons). This provides useful pilot data supporting the hypothesis that theory-based communication, i.e. peer modeling with psychological inoculation, may be more effective than more traditional forms of advertising for promoting coronavirus vaccination.
XBP1 variant 1 promotes mitosis of cancer cells involving upregulation of the polyglutamylase TTLL6
Authors: Zhong Yongwang , Yan Wenjing , Ruan Jingjing , Fang Mike , Yu Changjun , Du Shaojun , Rai Ganesha , Tao Dingyin , Henderson Mark J , Fang Shengyun
Published: January 2022

XBP1 variant 1 (Xv1) is the most abundant XBP1 variant and is highly enriched across cancer types but nearly none in normal tissues. Its expression is associated with poor patients’ survival and is specifically required for survival of malignant cells, but the underlying mechanism is not known. Here we report that Xv1 upregulates the polyglutamylase tubulin tyrosine ligase-like 6 (TTLL6) and promotes mitosis of cancer cells. Like the canonical XBP1, Xv1 mRNA undergoes unconventional splicing by IRE1α under endoplasmic reticulum stress, but it is also constitutively spliced by IRE1β. The spliced Xv1 mRNA encodes the active form of Xv1 protein (Xv1s). RNA sequencing in HeLa cells revealed that Xv1s overexpression regulates expression of genes that are not involved in the canonical unfolded protein response, including TTLL6 as a highly upregulated gene. Gel shift assay and chromatin immunoprecipitation revealed that Xv1s bind to the TTLL6 promoter region. Knockdown of TTLL6 caused death of cancer cells but not benign and normal cells, similar to the effects of knocking down Xv1. Moreover, overexpression of TTLL6 partially rescued BT474 cells from apoptosis induced by either TTLL6 or Xv1 knockdown, supporting TTLL6 as an essential downstream effector of Xv1 in regulating cancer cell survival. TTLL6 is localized in the mitotic spindle of cancer cells. Xv1 or TTLL6 knockdown resulted in decreased spindle polyglutamylation and interpolar spindle, as well as congression failure, mitotic arrest and cell death. These findings suggest that Xv1 is essential for cancer cell mitosis, which is mediated, at least in part, by increasing TTLL6 expression.
The variant rs77559646 associated with aggressive prostate cancer disrupts ANO7 mRNA splicing and protein expression
Authors: Wahlström Gudrun , Heron Samuel , Knuuttila Matias , Kaikkonen Elina , Tulonen Nea , Metsälä Olli , Löf Christoffer , Ettala Otto , Boström Peter J , Taimen Pekka , Poutanen Matti , Schleutker Johanna
Published: January 2022

Prostate cancer is among the most common cancers in men, with a large fraction of the individual risk attributable to heritable factors. A majority of the diagnosed cases does not lead to a lethal disease, and hence biological markers that can distinguish between indolent and fatal forms of the disease are of great importance for guiding treatment decisions. Although over 300 genetic variants are known to be associated with prostate cancer risk, few have been associated with the risk of an aggressive disease. One such variant is rs77559646 located in ANO7. This variant has a dual function. It constitutes a missense mutation in the short isoform of ANO7 and a splice region mutation in full-length ANO7. In this study, we have analyzed the impact of the variant allele of rs77559646 on ANO7 mRNA splicing using a minigene splicing assay and by performing splicing analysis with the tools IRFinder (intron retention finder), rMATS (replicate multivariate analysis of transcript splicing) and LeafCutter on RNA sequencing data from prostate tissue of six rs77559646 variant allele carriers and 43 non-carriers. The results revealed a severe disruption of ANO7 mRNA splicing in rs77559646 variant allele carriers. Immunohistochemical analysis of prostate samples from patients homozygous for the rs77559646 variant allele demonstrated a loss of apically localized ANO7 protein. Our study is the first to provide a mechanistic explanation for the impact of a prostate cancer risk SNP on ANO7 protein production. Furthermore, the rs77559646 variant is the first known germline loss-of-function mutation described for ANO7. We suggest that loss of ANO7 contributes to prostate cancer progression.
Effect of a vegetation fire event ban on hospital visits for respiratory diseases in Upper Northern Thailand
Authors: Uttajug Athicha , Ueda Kayo , Seposo Xerxes Tesoro , Honda Akiko , Takano Hirohisa
Published: January 2022

Background

Upper Northern Thailand (UNT) has been episodically affected by air pollution from vegetation burning, which causes adverse respiratory health effects. However, no study has evaluated the effect of regulatory actions to prohibit vegetation burning on respiratory morbidity. We examined the effect of a burning ban enforced in May 2016 on hospital visits for respiratory diseases in UNT.

Methods

This study used data from eight provinces in UNT. Analyses were conducted for January to April of 2014–2016 (before ban enforcement) and January to April of 2017–2018 (after ban enforcement). Particulate matter of 10 microns in diameter or smaller (PM10) concentrations, numbers of satellite fire hotspots and age-standardized rates of hospital visits for respiratory diseases before and after ban enforcement were compared. The effect of the ban on hospital visits for respiratory diseases was evaluated using an interrupted time-series analysis controlled for season-specific temporal trends, day of week, public holiday, temperature, relative humidity, number of hospitals and offset population, with gastrointestinal diseases as a negative control. A meta-analysis was performed to pool province-specific effect estimates.

Results

The daily average PM10 concentration and the number of fire hotspots decreased after ban enforcement in all provinces in UNT, with percent changes ranging from 5.3 to 34.3% and 14.3 to 81.5%, respectively. The adjusted pooled effect estimates of hospital visits for respiratory diseases decreased by 9.1% (95% CI: 5.1, 12.9), whereas a null association was observed for gastrointestinal diseases.

Conclusion

The burning ban had a positive impact on both air pollution levels and rates of hospital visits for respiratory diseases in UNT.
Dual 5-HT3 and 5-HT6 Receptor Antagonist FPPQ Normalizes Phencyclidine-Induced Disruption of Brain Oscillatory Activity in Rats
Authors: Castañé Anna , Cano Montserrat , Ruiz-Avila Luis , Miquel-Rio Lluís , Celada Pau , Artigas Francesc , Riga Maurizio S
Published: January 2022

Schizophrenia is a severe mental disorder featuring psychotic, depressive, and cognitive alterations. Current antipsychotic drugs preferentially target dopamine D2-R and/or serotonergic 5-HT2A/1A-R. They partly alleviate psychotic symptoms but fail to treat negative symptoms and cognitive deficits. Here we report on the putative antipsychotic activity of (1-[(3-fluorophenyl)sulfonyl]-4-(piperazin-1-yl)-1H-pyrrolo[3,2-c]quinoline dihydrochloride) (FPPQ), a dual serotonin 5-HT3-R/5-HT6-R antagonist endowed with pro-cognitive properties. FPPQ fully reversed phencyclidine-induced decrease of low-frequency oscillations in the medial prefrontal cortex of anaesthetized rats, a fingerprint of antipsychotic activity. This effect was mimicked by the combined administration of the 5-HT3-R and 5-HT6-R antagonists ondansetron and SB-399 885, respectively, but not by either drug alone. In freely moving rats, FPPQ countered phencyclidine-induced hyperlocomotion and augmentation of gamma and high-frequency oscillations in medial prefrontal cortex, dorsal hippocampus, and nucleus accumbens. Overall, this supports that simultaneous blockade of 5-HT3R and 5-HT6-R-like that induced by FPPQ-can be a new target in antipsychotic drug development.
Uhlenbeck Compactification as a Bridgeland Moduli Space
Authors: Tajakka Tuomas
Published: January 2022

Let $(X,H)$ be a smooth, projective, polarized surface over ${\mathbb {C}}$, and let $v \in K_{\textrm {num}}(X)$ be a class of positive rank. We prove that for certain Bridgeland stability conditions $\sigma = ({\mathcal {A}}, Z)$ “on the vertical wall” for $v$, the good moduli space $M^\sigma (v)$ parameterizing S-equivalence classes of $\sigma $-semistable objects of class $v$ in ${\mathcal {A}}$ is projective. Moreover, we construct a bijective morphism $M^{\textrm {Uhl}}(v) \to M^\sigma (v)$ from the Uhlenbeck compactification of $\mu $-stable vector bundles.