bluuebunny/crossref_metadata_2025 huggingface dataset:
Caracterização socioambiental da bacia hidrográfica do rio Itajaí-Mirim - SC
Authors: Roedel Tamily , Walloth Victoria , Cionek Vivian , Branco Joaquim
Published: May 2024

As bacias hidrográficas são unidades de planejamento territorial e concomitantemente refletem os problemas do meio urbano. A bacia hidrográfica do rio Itajaí-Mirim, localizada no Estado de Santa Catarina, drena áreas historicamente ocupadas por agricultura, mineração e urbanização, refletindo negativamente sobre a qualidade de suas águas. Esta pesquisa tem como objetivo fazer a caracterização socioambiental da qualidade e usos da água na bacia do Itajaí-Mirim. Para isso, foram utilizadas base de dados públicos, empresas de saneamento básico dos municípios e/ou através de solicitações via Lei de Acesso à Informação. Para analisar a qualidade da água foram utilizados os indicadores cor, turbidez, pH e bacteriológico. Os dados foram interpretados descritivamente e processados no software R. A pesquisa apontou que na bacia hidrográfica do rio Itajaí-Mirim são encontradas 23 Unidades de Conservação e o rio Itajaí-Mirim e seus afluentes são utilizados para a coleta de água do abastecimento público para atender às atividades comerciais, industriais, residenciais e públicas em seu entorno. Também, o volume de água captado varia entre os pontos de captação e ao longo dos meses, sendo que todos os pontos de captação apresentaram valores de cor, turbidez e pH de acordo com os padrões da legislação ambiental para água tratada. Por fim, sugere-se a utilização de diferentes parâmetros para avaliar a qualidade da água, bem como oportunizar e ampliar discussões sobre a gestão dos recursos hídricos na região, visto que o aumento da demanda populacional coloca em risco a manutenção e distribuição deste recurso no território.
Variant pathogenicity prediction based on the ESGMM algorithm
Authors: Liu Jing , Zhang Chen , Liu Xiyi , Zhang Yu , Liu Jin , Guan Xiao
Published: May 2024

Modeling the functional impact of sequence variation is a critical issue for both understanding and developing proteins. An Evolutionary Sequence and Gaussian Mixture Model (ESGMM) for predicting variant pathogenicity is presented in this paper. The model is trained on 2715 clinical proteins and their homologous sequences, using a Transformer-based protein language model to discover evolutionary patterns of amino acids from multiple sequence alignment (MSA). To fully mine deep information of MSA two-dimensional data, an axial attention mechanism is introduced during training. The model estimates the probability of all variants compared to the wild type and calculates variant scores. To categorize variations as pathogenic or benign, a global-local Gaussian mixture model is then constructed for each variant, and ESGMM scores are produced for each variant employing a combination of global and local information. Particle swarm optimization (PSO) is introduced to optimize the local Gaussian mixture model and further quantify the uncertainty of the classification, which enhances the model prediction precision. Experimental results demonstrate the superiority of the optimized ESGMM algorithm in predicting the pathogenicity of variants.
Menopausal symptoms and utilization of menopausal hormone therapy among women aged 40-60 years in Addis Ababa, Ethiopia: A cross-sectional study
Authors: Asfaw Tewodros Getahun , Gebreyohannes Rahel Demissew , Tesfaye Milcah Temesgen
Published: May 2024

Background: The onset of menopause leads to diminished estrogen exposure, resulting in a high morbidity burden related to menopausal symptoms. Menopausal hormonal therapy is an effective therapy that offers more advantages than disadvantages for women aged less than 60 years or who have had menopause for less than 10 years.
OBJECTIVE: This study aimed to assess the prevalence of menopausal symptoms, identify factors associated with menopausal symptoms, and assess the use of menopausal hormone therapy among women aged 40-60 years who visited the gynecological clinics of three hospitals in Addis Ababa, Ethiopia.
Methods: A facility-based cross-sectional study was conducted from January 2022 to June 2022 at Gandhi Memorial Hospital, Tikur Anbessa Hospital and Zewditu Memorial Hospital on atotal of 296 middle-aged women. Data were collected using an interviewer-administered structured questionnaire and analyzed for sociodemographic factors, utilization of menopausal hormone therapy, and prevalence of menopausal symptoms using the menopause rating scale. Data were analyzed using SPSS version 25. Bivariate and multivariate logistic regression analyses were performed to identify independent predictors of each subscale of menopausal symptoms. The strength of the association was measured using odds ratios with 95% confidence intervals, and statistical significance was set at value P<0.05.
Result: The prevalence of menopausal symptoms was 89.9%. According to menopausal rating scale, the frequency of reported symptoms was hot flushes (54.7%), muscle and joint pain (32.1%) on somatic subscale; physical and mental exhaustion (55.1%), irritability (48.6%) on psychological subscale; and sexual problems (41.3%), bladder problems (39.2%) on urogenital subscale. This study also showed that the age of women and monthly family income were significantly associated with all three subscales of the menopausal rating scale. There was no utilization of menopausal hormonal therapy to treat menopausal symptoms and to prevent complications.
Conclusion: The prevalence of menopausal symptoms is high; however, the utilization of individualized administration of menopausal hormone therapy according to symptoms is negligible. It appears essential for these institutions to work on service availability and delivery of menopausal hormone therapy for those in need of wider benefits of their clients.
Autonomous password generation and setting system with Cosmic Coding and Transfer (COSMOCAT) and Cosmic Time Calibrator (CTC)
Authors: Tanaka Hiroyuki , Cantoni Elena , Sellone Marco , Cerretto Giancarlo , Duffy Alan , Krishnan Shanti , Holma Marko , Oláh László , Steigerwald Sara , Varga Dezső
Published: May 2024

As wireless sensor networks (WSNs) with Internet of Things (IoT) devices become increasingly widespread and more complex, the threat of cyber-attacks is also increasing. One of the most common ways WSNs can be hijacked is when passwords/IDs are leaked. If the passwords do not frequently change, it is easier for the system to be compromised. However, many organizations and individuals retain old passwords to avoid the hassle and challenge of continually remembering and managing new passwords. COSMO-PASS is a new technique that combines COSMOCAT and CTC to enable hardware-level protection of the WSN nodes. It removes the inconvenience of having its users create, remember, and change multiple passwords. Based on the test experiments and simulations with a 102-cm2-sized (a smartphone-sized) detector, 6-7-digit passwords are automatically generated and transferred to the sensor node within the time range from 1 second to 1 minute, depending on the nodal distance. Consequently, it is confirmed that automatically generated and frequent password updates are possible with COSMO-PASS, which will effectively protect the data and network. It is anticipated that the enhanced security level capabilities of COSMO-PASS can easily be applied to the next generation of secured wireless sensor networks to achieve the realization of safer and smarter communities.
Bazı Kinoa Çeşitlerinin Verim ve Bazı Verim Öğelerine Farklı Sıra Aralığının Etkileri
Authors: Keskin Bilal , Duman Abdurrahman
Published: May 2024

Ekstrem iklim ve toprak şartlarına dayanıklı olan kinoa (Chenepodium quinoa Willd.) alternatif yem kaynağı olarak avantajlara sahiptir. Bu çalışmada sulu koşullarda 4 farklı sıra aralığı mesafede (17.5, 35.0, 52.5, 70.0 cm) yetiştirilen 2 kinoa çeşidinin (Cherry Vanilla ve Read Head) bitki boyu, ana sap kalınlığı, yaprak oranı, sap oranı, salkım oranı ve yaş ot verimleri belirlenmiştir. Araştırma sonucunda farklı sıra aralıklarının ana sap kalınlığı, yaprak oranı ve yaş ot verimi üzerine etkisi önemli bulunmuştur. Sıra aralığı arttıkça yaprak oranı ve yaş ot verimi düşerken, ana sap kalınlığının arttığı belirlenmiştir. Kullanılan çeşitlerin ise bitki boyu, yaprak oranı, sap oranı ve yaş ot verimi üzerine önemli etkisi olmuştur. Red Head çeşidinin bitki boyu, sap oranı ve yaş ot verimi yüksek olurken, Cherry Vanilla çeşidinin ise yaprak oranı daha yüksek bulunmuştur. Araştırma sonuçlarına göre, yüksek yaş ot veriminin alınması için Red head çeşidinin 17.5 cm sıra aralığında ekilmesi gerektiği belirlenmiştir.
A Study of Some Morphological Problems of Chadian Learners of English
Authors: Tompte Ndoubangar , Safotso Gilbert Tagne
Published: May 2024

The study of new Englishes has been the focus of many researchers who evaluate the spoken and written quality of non-native English in different parts of the globe. This study investigates morphological errors made by Chadian learners of English in their written and oral production. 250 essays by Première and Terminale learners of Lycée Pascal Yoadimnadji and 300 ones by students of the University of Doba were scrutinised. This was completed with oral presentations and free conversations on topics of general interest by 30 secondary school learners and 50 university students. The paper particularly looked at their production of plural forms, third person singular markers, omission/addition, and the production of the -ed morpheme of the simple past tense and past participle. The results show that irrespective of their levels of education, Chadian learners of English generally omit or mispronounce the inflection morphemes in their various forms. The findings also reveal that some of the errors found are general features of non-native English, while others are specific to Chadian setting where English varieties from Cameroon (CamFE), Nigeria (NigE & Pidgin English), and from Sudan lead to a real mixture in classrooms.
Adware Detection using LSTM and Comparison with other ML Models
Authors: Khare Mr. Himanshu Kishor
Published: May 2024

Abstract: In recent years, the internet has become the predominant communication medium, paralleled by a surge in cyber threats where attackers actively exploit vulnerabilities to obtain sensitive information. Among the various malware tactics employed, Adware poses a significant challenge. Addressing this concern, cyber security specialists leverage Machine Learning (ML) techniques, with this paper proposing a novel Long Short-Term Memory (LSTM) algorithm for adware detection. The research employs a holistic methodology involving diverse data pre-processing techniques, feature selection, and ML algorithms to effectively identify adware samples within the dataset. A comparative analysis of ML classifiers, including Random Forest (RF), k - Nearest Neighbors (k-NN), Decision Tree (DT), and Logistic Regression (LR), reveals optimal detection accuracies of 98.66%, 98.10%, and 98.05% for DT and k-NN, respectively. These findings underscore the efficacy of the proposed approach in fortifying cyber security against adware threats.
Assessment of Traffic at Selected Intersection of Porbandar City
Authors: Lakhana Niralee
Published: May 2024

Abstract: Transportation is the most important aspect for the development of urban areas and also country’s economy relies on it. In this project, traffic at intersections of Porbandar city is studied. Owing to tremendous growth in population, vehicle growth and concentrated development resulted into traffic congestion which calls for the demand of changing the geometry especially at the intersections which are not controlled appropriately and it has also decreased the facilities of pedestrian. Due to lack of enough land, adding new lanes or constructing a grade separator to increase capacity or improving the traffic flow is not an easy task. However, efficient usage of the existing infrastructure which doesn’t cost much could be a proper solution to overcome the congestion issue.
Multi-Level Encryption System using AES and RSA Algorithms
Authors: Phatangare Sheetal
Published: May 2024

Abstract: As the volume of sensitive data entrusted to the cloud explodes, from healthcare records to financial transactions, the need for robust security solutions intensifies. Traditional encryption techniques, while effective, remain vulnerable to key compromise, leaving data exposed to malicious factors. This study proposes a multi-level encryption strategy that leverages the power of AES for efficient data encryption, RSA for secure key protection with public-key cryptography, and role-based encryption for granular access control. This layered approach significantly increases the computational complexity for unauthorized access, making brute-force or dictionary attacks exponentially more difficult. Our research demonstrates successful integration of these algorithms, achieving a 50% reduction in key compromise risk and enhanced data integrity through MAC verification. Moving forward, we envision seamless integration with multiple cloud platforms and exploring advanced access control mechanisms based on user context and behavior. This multi-level encryption strategy holds immense potential to reshape the landscape of cloud data security, fostering a more secure and trustworthy digital future.
Fully Automatic Solar Grass cutter
Authors: Joshi Mohit
Published: May 2024

Abstract: "The term 'Embedded' signifies the integration of multiple independent devices into a single chip, commonly referred to as a System on Chip (SoC). The advent of VLSI technology has enabled the creation of high-speed, cost-effective, low-power, and co impact integrated circuits. The microcontroller employed for these applications is often referred to as an IBP (Itty Bitty Processor) or an embedded processor. "Multi-task oriented systems may introduce time delays, which can be inefficient in certain applications. In such scenarios, microcontrollers play a crucial role in embedded system design. In this context, we have developed a versatile embedded system application titled 'PC-controlled pick and place Robot.' This project serves as a demonstration of the effective utilization of automation and is structured around the integration of a microcontroller and a personal computer (PC). "The objective of our project is to manage the robot using commands transmitted via Zigbee protocol communication from a personal computer. These commands include F (forward), B (backward), L (left), R (right)
Photovoltaic Powered Dual Thermoelectric Air Conditioning System
Authors: Pal Ritu
Published: May 2024

Abstract: Nowadays energy efficient and environment friendly systems are among the emerging technologies, due to growing concerns about climate change and sustainability. According to the present scenario, the renewable energy resources are becoming the source of energy due to the decrease in non-renewable energy resources (Fossil fuels). So we presented a “Photovoltaic-Powered Dual Thermoelectric Air Conditioning System” that incorporates Peltier modules(thermoelectric material) which works on peltier effect known for their eco-friendly characteristics. Harnessing solar energy through a highefficiency PV array, this system aims to provide efficient and sustainable air conditioning while minimizing the reliance on traditional power sources. The additional benefit of this system is that it provides year round comfort i.e. cooling and heating facility in one system.
Energy Harvesting System
Authors: Rajas Neha
Published: May 2024

Abstract: This paper presents a novel energy harvesting system designed to harness ambient mechanical energy using piezoelectric sensors, with the Arduino Uno microcontroller serving as the central processing unit. The system's primary objective is to convert mechanical vibrations into electrical energy, storing it for potential use in powering low-energy electronic devices. The proposed setup incorporates piezoelectric materials strategically positioned to capture mechanical vibrations from various sources. The core of the system is an Arduino Uno microcontroller, programmed to efficiently manage the energy harvesting process. The harvested energy is stored in a rechargeable battery, ensuring a consistent power supply for applications with intermittent energy demands. To validate the effectiveness of the system, a light bulb is employed as a demonstrative load, allowing for real-time observation of the energy harvesting capabilities. The paper details the experimental setup, including the placement and configuration of piezoelectric sensors, the Arduino Uno programming logic, and the integration of a light bulb for visual confirmation of the generated current. Performance metrics such as voltage output, current production, and power generation efficiency are thoroughly analyzed under varying environmental conditions and mechanical vibrations. Results demonstrate the feasibility of the proposed energy harvesting system, showcasing its potential as a sustainable power source for low-power electronic devices. The combination of piezoelectric materials, Arduino Uno control, and a practical load application illustrates a promising approach to energy harvesting, paving the way for advancements in self-sufficient and ecofriendly power solutions
Prediction and Analysis of Liver Patient Data
Authors: Singh Priyanka
Published: May 2024

Abstract: Prediction and Analysis of Liver Patient Data is a project that aims to gain insights into liver diseases and develop a system for predicting the likelihood of liver-related health conditions by using data analysis techniques and predictive modeling. This project examines data from liver patients, focusing on the relationships between a key list of liver enzymes, proteins, age, and gender in order to predict the likelihood of liver disease. In this case, we are constructing a model by employing various machine learning algorithms to find the most accurate model. And connect to a flask-based web application. The user can predict the disease by entering parameters into the web application. The project's goal is to uncover significant indicators and build a predictive model to aid in diagnosing liver diseases and identifying potential risks by exploring patterns and relationships within the data.
Use of Biopolymers as Soil Stabilizers: A Review
Authors: Gupta Pragya
Published: May 2024

Abstract: Soil enhancement methods offer engineers solutions for addressing stability issues encountered during project execution. Rather than employing traditional soil treatment techniques, soil stabilization focuses on enhancing the inherent characteristics of the soil. Currently, polysaccharide biopolymers like xanthan gum, guar gum, welan gum, beta glucan, and chitosan are utilized in soil behavior enhancement to mitigate environmental concerns. Biopolymers show promise in improving soil shear strength and reducing subsidence potential at varying water contents. The effectiveness of treatment relies on factors such as soil composition and the specific biopolymer used.
Virtual Fitness Trainer Using AI: A Research Paper
Authors: Chaudhari Mr. H. P.
Published: May 2024

Abstract: Our research delves into the creation of a Virtual Fitness Trainer using Artificial Intelligence (AI) to offer personalized workout guidance and real-time feedback. This project harnesses advanced computer vision, machine learning, and natural language processing techniques to develop an intelligent system capable of accurately detecting human poses, tracking exercise repetitions, and providing corrective feedback. Utilizing Python's OpenCV library to capture live webcam feeds, processed by MediaPipe's BlazePose tool for precise pose estimation, our application employs a novel topology with 33 keypoints, enhancing the accuracy of body movement analysis. The frontend interface, designed with Flask, HTML, CSS, and Bootstrap, ensures an intuitive user experience, allowing selection from various exercises such as squats, curls, jumping jacks, push-ups, lateral raises, and pull-ups. Each exercise page offers detailed instructions and demonstration videos for correct technique execution. The system processes live video streams frame-by-frame, converting them into formats suitable for pose analysis and accuracy assessment. BlazePose tracks user movements in real-time, displaying a 33-keypoint exoskeleton via OpenCV, aiding in understanding body movements and tracking progress through repetition counts. The system also provides real-time feedback on exercise form, suggesting corrective actions to optimize the workout and minimize injury risks. Our Virtual Fitness Trainer addresses the limitations of existing fitness apps by enabling accurate, independent home workouts, bridging the gap left by the absence of gyms and personal trainers. It highlights AI's potential in fitness, with future advancements expected to enhance gesture recognition, adaptive workout plans, and integration with biometric data from wearables. As AI algorithms evolve, the Virtual Fitness Trainer aims to become indispensable for achieving health and wellness goals, offering a convenient, engaging, and supportive platform.
Machine Learning Approach to Detect Depression through Social Media Posts
Authors: Verma Aloukik
Published: May 2024

Abstract: Depression is one of the biggest issues which is increasing rapidly day by day. It has always been a topic for various researchers to discover new methods and techniques to overcome it. It is basically a kind of disorder in which people do not feel happy and joy. Since with the increase of the social networking sites people are expressing their feelings on the social media platforms which are helping the various researchers to analyze these posts and find an effective solution. Now a days there are various studies that aim to exploit machine learning techniques for detecting a probable depressed Twitter (Now X) user tweets. Textual data can be collected from the comments on the post, exchanging information etc. Using Facebook, Twitter, and Instagram. There is a huge amount of data which is present on these platforms which can be used for research. The aim of this study is that whether machine learning can be able to effectively detect the various signs of depression by analyzing the user post. This paper is basically on the sentimental analysis using NLP that identifies that the post is positive, negative or neutral.
Lung Cancer Detection
Authors: Rane Milind
Published: May 2024

Abstract: This paper presents a comprehensive approach to lung cancer detection utilizing state-of-the-art machine learning techniques, specifically Convolutional Neural Networks (CNNs). Using CNN[1] the model is trained and it can detect whether the given lung cancer cell image contains cancerous cells or not. The first component of the proposed approach involves high-resolution medical imaging, such as computed tomography (CT) scans, to capture detailed anatomical information about the lungs. Image processing algorithms are applied to enhance the quality of the images and extract relevant features. Additionally, innovative three-dimensional reconstruction techniques are employed to visualize the lung tissue at a microscopic level, facilitating the identification of subtle abnormalities.
Mobile Tracking System Using SMTP Protocol
Authors: Kale Prof. Madhuri
Published: May 2024

Abstract: The objective of this project is to enhance mobile security through a robust Android application capable of tracking, locking, and recovering lost or stolen mobile devices. Utilizing the capabilities of Android devices, the system offers real-time tracking, remote locking, device wiping, and geo fencing features. It supports Android OS and allows users to control their devices remotely via SMS commands or through a web-based dashboard using their account credentials. Users can track the location of their device, remotely lock it, and even manipulate the camera to capture images of the possessor. The rising incidence of mobile device theft, which often results in unauthorized access and personal data breaches, underscores the importance of this sophisticated Mobile Tracking System (MTS). This system addresses these security concerns by providing comprehensive functionalities that enhance user safety and device recovery.
Predictive Modeling for Lung Cancer Detection: Unveiling Insights through Machine Learning Techniques
Authors: Zargar Nida Aijaz
Published: May 2024

Abstract: Lung cancer stands as a formidable global health challenge, characterized by its widespread prevalence and high mortality rates, placing significant burdens on both public health systems and clinical practitioners. Despite notable advancements in diagnostic and therapeutic modalities, early detection remains paramount in improving patient outcomes and enhancing survival rates. In response to this imperative, the integration of predictive modelling techniques within healthcare has garnered substantial attention in recent years, offering promising avenues for bolstering early detection strategies and optimizing patient care pathways.
Skin Disease Detection Model
Authors: Soundharya Ms. R
Published: May 2024

Abstract: The human skin is a remarkable organ susceptible to a myriad of know and unknown diseases. Many of these ailment are widespread, with some ranking among common worldwide. The complexity of diagnosing these diseases is compounded by challenges such as variations in skin texture, the presence of hair, and diverse skin colors. In some areas have limited access to medical facilities, individuals often neglect early symptoms, leading to exacerbated conditions over time. Furthermore, traditional diagnostic methods for skin diseases are time consuming. To address these challenges, there is a critical need to develop advanced diagnostic methods utilizing machine learning techniques to enhance accuracy cross various skin diseases. Machine learning algorithms have proven valuable in medical applications, leveraging image feature values to facilitate decision making. The diagnostic process involves three key stages: feature extraction, training, and testing. By employing machine learning technology, these algorithms learn from a diverse set of skin images o enhance their diagnostic capabilities. The primary goal is to significantly improved the accuracy of skin disease detection. This study focuses on utilizing color and texture features for the classification of skin diseases. The distinctive color of healthy skin differs from that affected by disease, while texture features effectively discern smoothness, coarseness, and regularity in images. Key features such as texture, color, and shape phyla pivotal role in image classification. The incorporation of convolution neural networks (CNN) further augments the capabilities of image classification in the realm of skin disease diagnosis
Impact of regulatory, normative and cognitive institutional pressures on rural agribusiness entrepreneurial opportunities and performance: empirical evidence from Ghana
Authors: Osei Charles Dwumfour , Zhuang Jincai , Adu Daniel
Published: May 2024

Entrepreneurs in the agribusiness sector in Ghana encounter numerous challenges due to the weak institutional framework in the country. This often leads to business failure and poor performance of agribusiness ventures. In this paper, we aim to highlight the significance of institutional support for agribusiness entrepreneurs and its crucial role in creating more opportunities for farm entrepreneurs and ensuring the success of farm ventures. The study analyzes empirical data collected from 246 maize farm entrepreneurs in Ghana using the Structural Equation Modelling technique (PLS-SEM). The findings show that regulatory and cognitive institutions have a more positive significant influence on the opportunity recognition and exploitation strategies of maize farm entrepreneurs. Both opportunity recognition and exploitation partially mediate the effects of regulatory, normative and cognitive institutional pressures and farm business performance. The study suggests that maize farm entrepreneurs appear to benefit from regulatory and cognitive institutions. The findings, therefore, have strong policy relevance. The results of this study contribute significantly to food policy aimed at boosting maize supply chain business and food security.
Causal associations between COVID-19 and diseases of seven organs: A proteome-wide Mendelian randomization study
Authors: Shen Yunhan , Zhang Yi , Xu Ye-yang , Li Xinyi , Wu Jiachen , Pei Hao , Wang Linyan , Zhu Tiansheng
Published: May 2024

The coronavirus disease 2019 (COVID-19) pandemic poses an enormous
threat to public health worldwide. Many retrospective studies and case
reports to date have shown associations between severe COVID-19 and
multi-organs. However, the research on the causal mechanisms behind this
phenomenon is neither extensive nor comprehensive. We conducted a
proteome-wide Mendelian randomization (MR) study using summary
statistics from a genome-wide association study (GWAS) of severe
COVID-19 and diseases related to seven organs: lung, spleen, liver,
heart, kidney, testis, and thyroid, based on the European ancestry. We
filtered the data using multi-organ proteomic data from autopsies of
COVID-19 cases from previous studies. The primary analytical method used
is the radial inverse variance-weighted (radial IVW) method,
supplemented with the inverse variance-weighted (IVW), weighted-median
(WM), MR-Egger methods. Our findings have confirmed the association
between severe COVID-19 and multiple organ-related diseases, such as
Hypothyroidism, strict autoimmune (HTCBSA), Thyroid disorders (TD), and
Graves’ disease (GD). However, we did not find correlations between
severe COVID-19 and certain organ-related diseases that have been
clinically established. And we’ve also identified some proteins that are
associated with organ-related disease.
NSQIP Outcomes of Superficial Parotidectomy: The Case for Extracapsular Dissection
Authors: McWilliams Michael P. , Reiter Evan R.
Published: May 2024

Objectives: To compare outcomes of patients with benign
neoplasms undergoing superficial parotidectomy, with or without facial
nerve dissection, the latter a surrogate for extracapsular dissection
technique. Methods: Retrospective review of the American
College of Surgeons National Surgical Quality Improvement Project
(NSQIP) database providing 30-day postoperative outcome data from 676
sites nationwide. Inclusion criteria were primary diagnosis of benign
salivary neoplasm, age 18-89 years, primary procedure superficial
parotidectomy either with (CPT 42415 (SPwFND)) or without (CPT 42410
(SPwoFND)) facial nerve dissection between 2012-2019. We analyzed
demographics, operative time, length of stay, perioperative
complications, readmission, and reoperation rates between the two
procedure groups. NSQIP contained no information on postoperative facial
paralysis or estimated blood loss. Results: In total, 2651
patients underwent SPwFND while 764 underwent SPwoFND. Operative time
was shorter (111.59 +/- 63.417) for SPwoFND than for SPwFND (135.05 +/-
64.313, p<;0.0001). Fewer patients undergoing SPwoFND required
overnight stay than those undergoing SPwFND (41.5% vs 59.4%,
p<0.001). There were no significant differences between rates
of reoperation, unplanned readmission, or perioperative complications
between the groups. Conclusion: SPwoFND nerve dissection had a
shorter operative time and led to fewer postoperative overnight stays
than SPwFND, with no significant differences in rates of reoperation,
unplanned readmission, or perioperative complications. This suggests
that for appropriately selected patients, SPwoFND may be a comparable
treatment option with the benefit of lower resource utilization than
SPwFND.
Shared proteomic patterns among high BMI and comorbidities may indicate potential biomarkers in a Brazillian population
Authors: Silva Carlos Vinicius da , Silva Carlos José Ferreira Da , Scapin Sandra Mara Naressi , Sade Youssef Bacila , Thompson Cristiane Carneiro , THOMPSON FABIANO , Silva-Boghossian Carina Maciel da , Santos Eidy de Oliveira
Published: May 2024

This study sought to analyze the impact of Metabolic Syndrome (MeS) and
Type 2 Diabetes (T2DM) on metabolism and their relationship of Body Mass
Index (BMI) and to identify potential predictive protein biomarkers for
MeS and T2DM. The proteomes of saliva and blood, clinical parameters
were analyzed in 103 adult individuals from the State of Rio de Janeiro,
Brazil, a mixed-race population. Healthy individuals were sorted by
their Body Mass Index (BMI) in normal (n=29), overweight (n=25) and
obese (n=15) and were compared with individuals with MeS (n=23) and T2DM
(n=11). Random forest predictive model revealed that 3 clinical
variables, BMI, HOMA-IR, and fasting blood glucose, are most important
for predicting MeS and T2DM. A total of 6 plasmatic proteins (ABCD4,
LDB1, PDZ, Podoplanin, Lipirin-alpha-3 and WRS) and 6 salivary proteins
(Hemoglobin subunit beta, POTE ankyrin domain family member E, T cell
receptor alpha variable 9-2, Lactotransferrin, Cystatin-S, Carbonic
anhydrase 6), are enhanced in T2DM and in MeS. Salivary and plasmatic
proteomes, in a population of brazil, demonstrates that the
physiopathological conditions associated with abnormal weight gain,
T2DM, and MeS share similar modifications in protein composition,
offering potential predictive biomarkers, potentially mitigating the
adverse health consequences of these metabolic disorders.
Respiratory Sinus Arrhythmia -- Common and distinct mechanisms of emotional adjustment in the mood and anxiety disorders spectrum?
Authors: Adolph Dirk , Zhang Xiao Chi , Teismann Tobias , Margraf Jürgen
Published: May 2024

Here, we assessed resting respiratory sinus arrythmia (rRSA) and RSA
reactivity (ΔRSA) as common and distinct emotion-adjustment mechanisms
for affective and anxiety disorders and their treatments. We recruited
samples of healthy controls and patients with anxiety and affective
disorders, assessed rRSA during baseline and ΔRSA as RSA-change from
baseline to viewing emotional films. Patients then underwent
disorder-specific Cognitive Behavior Therapy. Although both patient
groups exhibited lower rRSA than controls, depression-, but not
anxiety-symptomatology was transdiagnostically associated with less rRSA
and ΔRSA. Complementing these depression-specific results, better ΔRSA
predicted better treatment-outcome in depression, but not anxiety. Our
data confirm RSA as a transdiagnostic marker for mood and anxiety,
support recent attempts towards transdiagnostic, dimensional
classification systems (HiToP, RDoC) and provide evidence for a more
robust association of RSA with depression-symptomatology and -treatment.
They thus suggest rRSA and ΔRSA as potential markers to assess common
and distinct mechanisms associated with depression and anxiety.
Impact of sensitisation programme on awareness among school teachers engaged in National Deworming Day regarding Adverse Drug Reaction reporting
Authors: Sheoran Babita , Deb Tirthankar , Tuteja Mayur , Sharma Nitika , Saranjhna Ashish , Kaur Manmeet
Published: May 2024

Background: The study examines critical role of school teachers
in executing National Deworming Day (NDD) and their awareness of adverse
drug reaction (ADR) reporting. The study aims to evaluate teachers’
knowledge and attitudes on ADR reporting to improve pharmacovigilance
during NDD, ensuring children’s safety during preventative chemotherapy
with Albendazole. Method: After prior approval from
Institutional Ethics Committee, a total of 164 participants were
selected from 20 cluster heads overseeing 94 schools. The study utilized
a validated questionnaire, scored on a binary system (0-10). Employing a
prospective quasi-experimental one group pre-test and post- test
research design, the primary aim was to evaluate the impact of the
awareness program, with secondary objectives focused on demographic
correlations with program effectiveness. Results: The study
shows a significant improvement in participants’ scores from the
pre-test (mean score of 5.60) to the post-test (mean score of 8.28),
resulting in a mean deviation of -2.681 (t = -19.7, p < .001),
suggesting that the intervention significantly enhanced outcomes.
Additionally, the results indicate that the mean post-test score was
slightly higher among urban participants compared to rural participants,
with mean scores of 8.45 and 8.10, respectively. Conclusion:
The sensitisation programme significantly improved awareness among
school teachers engaged in NDD regarding ADR reporting, as evidenced by
the substantial increase in post-test scores compared to pre-test
scores. However, further studies with larger sample size are needed to
corroborate these findings.
Parameningeal rhabdomyosarcoma- clinical profile, outcomes and prognostic factors in children treated at a single center over a decade
Authors: PARAMBIL BADIRA CHERIYALINKAL , Gollamudi Venkata , Prasad Maya , Patel Vasundhara , Laskar Siddhartha , Khanna Nehal , Manjali Jifmi , Qureshi Sajid , Ramadwar Mukta , Panjwani Poonam , Shah Sneha , Baheti Akshay , Pawar Akash , Adhav Komal , Chinnaswamy Girish
Published: May 2024

1 Background Parameningeal Rhabdomyosarcomas (PM-RMS) in
children are challenging to treat. While ten-year Event Free Survival
(EFS) of 62% have been reported from High-Middle Income Countries
(HMICs) for localized disease, data is limited from Low-Middle Income
Countries (LMICs). We studied the clinical profile, outcomes, and
prognostic factors in PM-RMS. 2 Materials and Methods
Children≤15 years with PM-RMS treated on a uniform chemotherapy protocol
from January 2013-December 2021 were retrospectively analysed. Local
therapy at 10-12weeks of induction was radiotherapy (RT)+/-surgery where
possible with early RT for intracranial extension (ICE). 3
Results Seventy-six patients with a median age of 6.7years
(range,3.2-15years), male to female ratio of 1.8:1 formed the study
cohort. Eleven patients (14.5%) had metastasis (lungs-8, bone-2, bone
marrow-1) and ICE seen in 46.1%(n=35). Twenty-five patients (49.0%)
had alveolar histology with PAX3/7 positive in 17/59 (28.8%). Median
tumor size(t ) at baseline was
5.2cm(range,1.2-12.8cm). Seventy-one patients received RT, 5 also
underwent surgery. At a median follow-up of 65months (range,53-76months)
4year EFS, OS of the whole cohort were 47.3%(95%CI:34.8%-58.8%),
51.7%(95%CI:38.0%-64.0%) respectively. Four-year EFS, OS of
localized and metastatic cohort were 54.7%(95%CI:41.3%-68.1%),
56.0%(95%CI:42.0%-70.0%) and 9.1%(95%CI:0%-26.5%),
18.2%(95%CI:0%-47.8%) respectively. Metastases
(HR-3.38,95%CI:1.57-7.26,p=0.002), t
(HR-1.17,95%CI:1.02-1.34,p=0.026) were prognostic for survival on
multivariate analysis. 4 Conclusions Survival of children with
localized PM-RMS in our study is relatively fair compared to the
reported literature probably due to application of RT in all despite
higher proportion of larger tumors, unfavorable sites of primary and
intracranial extension. Identification of high-risk subsets and
optimizing current treatment strategies, both systemic and local therapy
may partly improve outcomes.
Challenges in accuracy in molecular genetic diagnosis of childhood AML: case series
Authors: Mota Fernanda , Toledo Silvia , Tesser-Gamba Francine , Pires Michele Gaboardi de Carvalho , Gouveia Juliana Thomazini , Oliveira Indhira , Santos Nancy da Silva , Delbuono Elizabete , Rhein Bruno Nicolaz , Guimarães Renata Fittipaldi da Costa , VG Zecchin , Lee Maria Lucia , Sousa Ana de
Published: May 2024

Comprehending the genetic background of pediatric Acute Myeloid Leukemia
(AML) is the key to risk stratification. Next Generation Sequencing
(NGS) technology uses target panels that may detect additional genetic
subsets. The study describes the experience of using NGS at an
institution. Patients who showed poor outcome aberration were referred
to hematopoietic stem cell transplant (HSCT). 11 patients were tested.
Aberrations were found in all subjects, indicating referral to HSCT in
first remission in 2 cases and helping to outline the genetic features
in all cases. NGS is a valuable tool in the precision medicine era and
should be widely accessible.
Foliar anatomical studies of five Kalanchoe species in Nigeria
Authors: OLUTAYO TOLULOPE MARY , Arogundade Oluwabunmi , Akinloye Akinwumi , Ogunlowo Ifeoluwa
Published: May 2024

Abstract: A Foliar anatomical study of five Kalanchoe species in Nigeria
was carried out for taxonomic purposes. The study tends to provide
anatomical information on Nigerian Kalanchoe species, for the purpose of
documentation and delimiting the taxa studied. Matured fresh samples of
the leaves of Kalanchoe crenata (Andr.) Haw, Kalanchoe gastonis-boonieri
Raym.-Hamet & H.Perrier , Bryophyllum pinnatum (Lam.) Oken, Bryophyllum
delagoense (Eckl. and Zeyh.) Schinz, Bryophyllum houghtonii (D.B Ward)
P.I.Forst were used. Epidermal peels and transverse sections were
carried out according to standard procedures. Unifying and diagnostic
characters important for the taxonomy of the selected five species of
the genus Kalanchoe were identified. Unifying characters include
amphistomatic leaves, a non-pitted anticlinal wall, mesophyll not
differentiated into palisades and a spongy layer. Diagnostic characters
such as uniseriate to multiseriate epidermis in Bryophyllum houghtonii.
The study provides concise information on the anatomical attributes of
the epidermis and transverse section of the leaf of the Kalanchoe
species studied
Unlocking Rivers Hidden Diversity and Ecological Status using DNA Metabarcoding in Northwest Spain.
Authors: Rodríguez Álvaro Fueyo , Fernández Omar Sánchez , Carleos Carlos , Marina Amando Escudero , Cordón Javier , Castro Javier Granero , Borrell Yaisel
Published: May 2024

1. Rivers are crucial ecosystems supporting biodiversity and human
well-being, yet they face increasing degradation globally. Traditional
river biomonitoring methods based on morphological identification of
macroinvertebrates present challenges in terms of taxonomic resolution
and scalability. 2. This study explores the application of DNA
metabarcoding analysis in both bulk and environmental DNA (eDNA) samples
for comprehensive assessment of macrozoobenthic biodiversity, detection
of invasive and endangered species, and evaluation of river ecological
status in northwestern Spain. 3. DNA metabarcoding of homogenised bulk
samples and water eDNA revealed a mean of 100 and 87 macrozoobenthos
species per sample respectively. However, it was a significantly
different specific composition with only 27.3% of the total species
shared. It was not possible to identify all the OTUs to species level,
only 17.43% and 49.4% of the OTUs generated could be identified to
species level In the Bulk and eDNA samples respectively. However, a
total of 11 exotic species (2 first records for the Iberian Peninsula
and another 3 first records for Asturias region) and 1 endangered
species were detected by molecular tools. Molecular methods showed
significant correlations with morphological identification for EQR
values (Ecological quality ratio) of IBMWP index, yet differences in
inferred river ecological status were noted, with bulk samples tending
to indicate higher status. 4. Overall, DNA metabarcoding offers a
promising approach for river biomonitoring, providing insights into
biodiversity, invasive species, and ecological status within a single
analysis. Further optimization and intercalibration are needed for its
implementation in routine biomonitoring programs, but its scalability
and multi-tasking capabilities position it as a valuable tool for
integrated monitoring of river ecosystems.
Real hypersurfaces in S6(1) equipped with structure Jacobi operator satisfying Lxl = ∇xl
Authors: Kocic Djordje
Published: May 2024

The study of hypersurfaces of almost Hermitian manifolds by means of their
Jacobi operators has been highly active in recent years. Specially, many
recent results answer the question of the existence of hypersurfaces with a
structure Jacobi operator that satisfies conditions related to their
parallelism. We investigate real hypersurfaces in nearly K?hler sphere S6(1)
whose Lie derivative of structure Jacobi operator coincides with the
covariant derivative of it and show that such submanifolds do not exist.
Riemannian and sub-Riemannian structures on a cotangent bundle of Heisenberg group
Authors: Sukilovic Tijana , Vukmirovic Srdjan
Published: May 2024

In this paper we give a classification of left invariant sub-Riemannian
structures on cotangent bundle of 2n+1 dimensional Heisenberg group T*H2n+1.
We show that the sub-Riemannian metric is tamed by the corresponding
Riemannian metric on T*H2n+1. We also describe Riemannian and sub-Riemannian
geodesics on T*H2n+1.
Classification of second orders symmetric tensors on manifolds through an associated fourth order tensor
Authors: Hall Graham
Published: May 2024

For a manifold M admitting a metric 1 and given a second order symmetric
tensor T on M one can construct from 1 and (the trace-free part of) T a
fourth order tensor E on M which is related in a one-to-one way with T and
from which T may be readily obtained algebraically. In the case when dimM =
4 this leads to an interesting relationship between the Jordan-Segre
algebraic classification of T, viewed as a linear map on the tangent space
to M with respect to 1, and the Jordan-Segre classification of E, viewed as
a linear map on the 6?dimensional vector space of 2?forms to itself (with
respect to the usual metric on 2?forms). This paper explores this
relationship for each of the three possible signatures for 1.
On general solutions of equidistant vector fields on two-dimensional (pseudo-)Riemannian spaces
Authors: Peska Patrik , Mikes Josef , Rýparová Lenka , Chepurna Olena
Published: May 2024

The paper is devoted to studying equidistant two-dimensional (pseudo-)
Riemannian spaces. Embeddings of these spaces in three-dimensional Euclidean
and Minkowski spaces as revolution or helical surfaces are given. The
general solution of equidistant equations is found beyond these spaces under
minimal requirements for the differentiability of the studied objects. These
vector fields are associated with Killing vector fields on those spaces.
Surfaces defined by bending of knots
Authors: Rancic Svetozar , Najdanovic Marija , Velimirovic Ljubica
Published: May 2024

We consider the definition of the infinitesimal bending of a curve as a
vector parametric equation of a surface defined by two free variables: one
of them is free variable u which define curve and another is bending
parameter ?. In this way, while being bent curve is deformed and moved
through the space forming a surface. If infinitesimal bending field is of
constant intensity, deformed curves form a ruled surface that represents a
ribbon. In particular, we consider surfaces obtain by bending of knots both
analytically and graphically. We pay attention to the torus knot and
possibility of its infinitesimal bending so that the surface determined by
bending is a part of the initial torus.
Modulus of continuity of normal derivative of a harmonic functions at a boundary point
Authors: Arsenovic Milos , Mateljevic Miodrag
Published: May 2024

We give sufficient conditions which ensure that harmonic extension u = P[f]
to the upper half space {(x, y) | x ? Rn, y > 0} of a function f ? Lp(Rn)
satisfies estimate ?u/?y (x, y) ? C?(y)/y for every x in E ? Rn, where ? is
a majorant. The conditions are expressed in terms of behaviour of the Riesz
transforms Rjf of f near points in E. We briefly investigate related
questions for the cases of harmonic and hyperbolic harmonic functions in the
unit ball.