Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphometric analysis of glenoid cavity in adult human scapulae and its clinical importance

Background: The glenoid cavity of scapula is quite small, which allows space for only limited fixation devices. Appropriate fixation of the glenoid component of shoulder is of key importance in total shoulder arthroplasty to prevent loosening, which is a common indication of revision surgery. Orthopedic surgeons and prosthetic designers must have a thorough understanding of the glenoid’s dimensions and shape, in order to create the finest feasible prosthesis. Aims and objectives: To determine the glenoid cavity index (GCI) and the form of the glenoid cavity, as well as the supero-inferior diameter, shortest antero-posterior diameter, and longest antero-posterior diameter. Materials and methods: The study was conducted on 50 dry unpaired adult human scapulae of North Indian population obtained from GMC, Srinagar. All the important morphometric parameters were studied using a vernier calliper. Result: Supero-inferior diameter, shortest antero-posterior diameter, and longest antero-posterior diameter were found to have respective mean values of 35.06 mm, 15.8074 mm, and 23.3814 mm. Glenoid cavity index had a mean value of 66.68. 42% of the glenoid cavities had an inverted comma shape, 36% had a pear shape, and 22% had an oval shape. Conclusion: The results showed that mean supero-inferior diameter of glenoid is about 35mm while the average prosthesis for shoulder arthroplasty in the market starts from 40mm. This suggests that while developing and fitting glenoid components for total shoulder arthroplasty in the North Indian population, it may be necessary to take into account the smaller dimensions of the glenoid cavities.
BUILT ENVIRONMENT AND SOCIAL FACTORS ASSOCIATED WITH CYCLING BEHAVIOUR IN PUTRAJAYA

Cycling as a mode of transport has been identified as one of the solutions to traffic congestion, high carbon emission, and health issues in both urban and residential areas. Lately, cycling is growing in popularity among residents, and many are involved in cycling activities for various personal reasons: now, bicycle is not only for transport, but it is also used for sports’ training, exercise, and recreation. This cycling trend offers multiple benefits, and to help increase the momentum, some forms of intervention should take place. Two intervention factors that contribute to cycling behaviours are the built environment and social factor. Thus, this study seeks to explore the built environment and social factors that influence cycling behaviour in Putrajaya area. The respondents were selected among cyclists in Putrajaya using snowball and convenience sampling procedures. Through the use of descriptive analysis and after finalising the suitable variables by Factor Analysis, the finding reveals the suitable and reliable factors for future intervention. The implications and recommendations from this research contribute to the existing body of knowledge on cycling behaviour.
Materials in the Na2O–CaO–SiO2–P2O5 System for Medical Applications

Calcium phosphate materials and materials based on silicon dioxide have been actively studied for more than 50 years due to their high biocompatibility and bioactivity. Hydroxyapatite and tricalcium phosphate are the most known among calcium phosphate materials, and Bioglass 45S5 is the most known material in the Na2O–CaO–SiO2–P2O5 system. Each of these materials has its application limits; however, some of them can be eliminated by obtaining composites based on calcium phosphate and bioglass. In this article, we provide an overview of the role of silicon and its compounds, including Bioglass 45S5, consider calcium phosphate materials, talk about the limits of each material, demonstrate the potential of the composites based on them, and show the other ways of obtaining composite ceramics in the Na2O–CaO–SiO2–P2O5 system.
Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

Over half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.
Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration

In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under the framework of the EM algorithm, and the number of components is determined based on the minimum message length criterion (MML). The KL divergence between these two mixture models is utilized as the loss function for stochastic optimization to find the optimal parameters of the transformation model. The self-built point clouds are used to evaluate the performance of the proposed algorithm on rigid registration. Experiments demonstrate that the algorithm dramatically reduces the impact of noise and outliers and effectively extracts the key features of the data-intensive regions.
DIAGNOSIS SITOLOGI PASIEN DENGAN DIAGNOSIS KLINIS TUMOR PARU BERDASARKAN BERBAGAI METODE PENGAMBILAN SAMPEL SITOLOGI DI RSUP SANGLAH TAHUN 2014-2018

Tumor paru adalah tumor yang sangat berbahaya karena berkaitan erat dengan kasus kanker paru. Tumor paru dapat didiagnosis melalui pemeriksaan sitopatologi. Hasil pemeriksaan sitopatologi atau diagnosis sitologi dipengaruhi oleh metode pengambilan sampel yang digunakan. Penelitian ini bertujuan untuk mengetahui diagnosis sitologi pasien dengan diagnosis klinis tumor paru berdasarkan berbagai metode pengambilan sampel sitologi di Instalasi Laboratorium Patologi Anatomi RSUP Sanglah tahun 2014-2018. Penelitian ini menggunakan desain penelitian potong lintang retrospektif dengan mengumpulkan data sekunder berupa registrasi data pasien dengan diagnosis klinis tumor paru di Instalasi Laboratorium Patologi Anatomi RSUP Sanglah pada tahun 2014-2018. Data diagnosis sitologi yang diperoleh dikelompokkan secara kategorikal berdasarkan Papanicolaou Class (Pap Class). Penelitian ini memperoleh 885 sampel yang sesuai dengan kriteria inklusi dan eksklusi. Setelah diolah secara deskriptif, di dapatkan 23 sampel Pap Class I, 481 sampel Pap Class II, 32 sampel Pap Class III, 8 sampel Pap Class IV, 24 sampel Pap Class V dan 317 sampel Pap Class VI. Proporsi diagnosis positif keganasan (Pap Class VI) untuk setiap metode pengambilan sampel adalah sputum (1,3%), efusi pleura (9%), bronkoskopi (17,4%), Fine Needle Aspiration (FNAB) Superficial (79,6%) dan Transthoracal Biopsy (TTB) (58,6%). Melalui penelitian ini diketahui bahwa metode pengambilan sampel FNAB merupakan metode pengambilan sampel dengan hasil diagnosis sitologi positif keganasan (Pap Class VI) terbesar. Selain itu di dapatkan juga data bahwa kelompok diagnosis sitologi terbanyak pada kasus tumor paru adalah kelompok negatif keganasan (Pap Class II). Kata kunci: Tumor paru, metode diagnosis, sampel, sitologi
Improving Application Support in 6G Networks With CAPOM: Confluence-Aided Process Organization Method

Systems requiring terahertz transmission and high sampling capabilities can be supported by sixth-generation (6G) technology with minimal latency and excellent service throughput. Regardless of the distributions of data and services, High-Performance Computing (HPC) enhances speed and provides diversified applications and functionality. The Confluence-Aided Process Organization Method (CAPOM) is suggested in this article to take advantage of process allocations while using an HPC paradigm. The process allocations and completions are scheduled based on prior and current system conditions to minimize waiting time based on the 6G qualities. This implies that state requirements for process allocation, distribution, and completion are carried out with the assistance of federated learning. The initial state allocations are based on the user/application request; in other allocations, the application’s request for completion time and capacity for processing are considered. Offloading and shared processing are, therefore combined to maximize resource deliveries. The federated learning states are checked post-completion times to mitigate the waiting duration of dense service demands. Indicators such as distribution ratios, latency, wait time, and processing rate are considered for the effectiveness of the proofs. The suggested CAPOM achieves an 8.67% higher processing rate, 9.09% reduced latency, 8.76% less wait time, and a 6.73% higher distribution ratio for the various capacities.
Outbreak of severe myocarditis in England: Havoc by a harmless virus

The World Health Organization (WHO) has been alerted to a concerning upsurge of severe myocarditis, an inflammatory heart condition, in neonates within Wales and South West England. The myocarditis cases are being intricately associated with enterovirus infection, belonging to the Picornaviridae family. The concerned pathogen poses a significant global disease burden, with an estimated 10 to 15 million symptomatic cases occurring annually in the United States alone. Neonates are particularly vulnerable with children under the age of one accounting for approximately 40% of enterovirus infections.
Small Molecules Targeting Viral RNA

The majority of antivirals available target viral proteins; however, RNA is emerging as a new and promising antiviral target due to the presence of highly structured RNA in viral genomes fundamental for their replication cycle. Here, we discuss methods for the identification of RNA-targeting compounds, starting from the determination of RNA structures either from purified RNA or in living cells, followed by in silico screening on RNA and phenotypic assays to evaluate viral inhibition. Moreover, we review the small molecules known to target the programmed ribosomal frameshifting element of SARS-CoV-2, the internal ribosomal entry site of different viruses, and RNA elements of HIV.
Selected neonicotinoids and associated risk for aquatic organisms

Neonicotinoids are one of the newest groups of systemic pesticides, effective on a wide range of invertebrate pests. The success of neonicotinoids can be assessed according to the amount used, for example, in the Czech Republic, which now accounts for 1/3 of the insecticide market. The European Union (EU) has a relatively interesting attitude towards neonicotinoids. Three neonicotinoid substances (imidacloprid, clothianidin and thiamethoxam) were severely restricted in 2013. In 2019, imidacloprid and clothianidin were banned, while thiamethoxam and thiacloprid were banned in 2020. In 2022, another substance, sulfoxaflor, was banned. Therefore, only two neonicotinoid substances (acetamiprid and flupyradifurone) are approved for outdoor use in the EU. Neonicotinoids enter aquatic ecosystems in many ways. In European rivers, neonicotinoids usually occur in nanograms per litre. Due to the low toxicity of neonicotinoids to standard test species, they were not expected to significantly impact the aquatic ecosystem until later studies showed that aquatic invertebrates, especially insects, are much more sensitive to neonicotinoids. In addition to the lethal effects, many studies point to sublethal impacts - reduced reproductive capacity, initiation of downstream drift of organisms, reduced ability to eat, or a change in feeding strategies. Neonicotinoids can affect individuals, populations, and entire ecosystems.
Fuzzy adaptive control of a class of nonlinear systems with unmodeled dynamics

This paper investigates the tracking control problem for a class of pure‐feedback systems with unmodeled dynamics. The useful properties of the fuzzy basis functions and membership are explored to be used for stability analysis, and an alternative Lyapunov function depending on both control input and system state is utilized. Then, an adaptive fuzzy controller is designed to ensure that the tracking error is within a small adjustable neighborhood of the origin, where some conventional assumptions imposed on the unmodeled dynamics have been relaxed. Finally, simulation results are given to validate the theoretical results.
Effect of Quadriceps Set and Manual Traction on Pain in Osteoarthritis

Background: Osteoarthritis (OA) is a joint disorder in which progressive loss of articular cartilage occurs and can lead to disability. The most common symptom of osteoarthritis is pain. Usually the pain is exacerbated by use of the joint and relieved by rest. treatment is only aimed at relieving pain, maintaining or increasing mobility, minimizing disability, and improving quality of life. Objective: To determine the effect of quadriceps sets and manual traction on pain in knee osteoarthritis. Methods: This study used a pre-experimental method with a one group pretest and posttest design. Pain measurement was performed using a visual analogue scale (VAS). The data normality analysis test will use Shapiro Wilk while the bivariate analysis uses the paired sample t test, and the multivariate analysis uses a linear regression test. Results: The results obtained from this study, there was a significant effect of manual traction and quadriceps sets on pain p=0.000 (p <0.05) and there was no significant relationship between changes in pain with age (p=0.266) and BMI (p=0.241). Conclusion: There is an effect of giving quadriceps sets and manual traction on the incidence of reducing pain in knee osteoarthritis.
Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma

Objectives In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors. Methods In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM–based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO–selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values. Results Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results. Conclusion In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.
Long-Term Changes of Source Apportioned Particle Number Concentrations in a Metropolitan Area of the Northeastern United States

The northeastern United States has experienced significant emissions reductions in the last two decades leading to a decrease in PM2.5, major gaseous pollutants (SO2, CO, NOx) and ultrafine particles (UFPs) concentrations. Emissions controls were implemented for coal-fired power plants, and new heavy-duty diesel trucks were equipped with particle traps and NOx control systems, and ultralow sulfur content is mandatory for both road and non-road diesel as well as residual oil for space heating. At the same time, economic changes also influenced the trends in air pollutants. Investigating the influence of these changes on ultrafine particle sources is fundamental to determine the success of the mitigation strategies and to plan future actions. Particle size distributions have been measured in Rochester, NY since January 2002. The particle sources were investigated with positive matrix factorization (PMF) of the size distributions (11–470 nm) and measured criteria pollutants during five periods (2002–2003, 2004–2007, 2008–2010, 2011–2013, and 2014–2016) and three seasons (winter, summer, and transition). Monthly, weekly, and hourly source contributions patterns were evaluated.
Neuropsychology of Generalized Anxiety Disorder in Clinical Setting: A Systematic Evaluation

This research paper provides a systematic review of the neuropsychology of generalized anxiety disorder (GAD), examining relevant articles’ methodologies and subject matter and highlighting key findings. It suggests potential cognitive deficits in GAD patients, such as subtle attention, executive function, and working memory deficiencies. It also discusses neural correlates of GAD, particularly the hyperactivity in the amygdala and insula, and the additional impact of comorbidity with other psychiatric disorders. The paper uses the PRISMA methodology and draws data from the PsycINFO, Scopus, PubMed, and Elsevier databases. Although the reviewed research has contributed to understanding GAD’s cognitive and neural mechanisms, further research is required. Additionally, the paper mentions the clinical neuropsychology of GAD, including strategies and treatments, such as cognitive behavioral therapy (CBT), mindfulness, and medication. Lastly, the review identifies the limitations of the existing research and recommends future directions to enhance the understanding of GAD’s underlying cognitive and neural mechanisms. The neural underpinnings of GAD encompass heightened activity within the amygdala and insula, which are brain regions implicated in processing adverse emotional reactions. Co-occurring psychiatric disorders, such as major depressive disorder (MDD), can also impact neuropsychological functioning. Additional investigation is warranted to better understand the intricate interplay between GAD, cognitive performance, and underlying neural processes.
Design, development, and evaluation of spray dried flurbiprofen loaded sustained release polymeric nanoparticles using QBD approach to manage inflammation

Abstract Spray-dried Flurbiprofen (FLB) loaded polymeric nanoformulation using Eudragit L 100 and Ethylcellulose. They were optimized and evaluated. This study determined drug release (%) and encapsulation efficiency (%) by developing a nanoparticulate system using the design of experiment (DoE) approach. FLB is slightly soluble in water; it dissolves slowly and has a low oral bioavailability. FLB-loaded polymeric nanoparticles were produced by solvent evaporation and Spray drying technology. In this research, nanoparticle formulation was prepared by screening and optimization by approaching two different statistical methods (Plackett-Burman and Central composite Designs). The polymeric nanoparticles were evaluated for various characteristics, including drug release, percentage of encapsulation efficiency, X-ray diffraction (X-RD), surface morphology, and Fourier transform infrared (FTIR) spectroscopy. Based on the X-RD analysis, it was found that the drug was successfully incorporated into the polymeric nanoparticles. As a result of nanoparticles containing FLB, % Drug release values were found to be nearly 85-90% increased while % EE was observed in the range of 79-89%. An excellent sustained release, i.e., 14 h, is possible by combining Ethylcellulose (EC) and Eudragit L 100 (ED-100) polymers. The results are beneficial in identifying the ideal formulation parameters for effective encapsulation. Abbreviations: FLB: Flurbiprofen; API: Active pharmaceutical ingredient; DR: Drug release; EE: Encapsulation efficiency; DoE: Design of experiment; X-RD: X-ray diffraction; FTIR: Fourier transform infrared spectroscopy. ; PVA: Polyvinyl alcohol; EC: Ethylcellulose; EUGD: Eudragit L 100; NPs: Nanoparticles; PBD: Placket- Burman Design; CCD: Central Composite Design; CMV: Critical method variables; GIT: Gastrointestinal track; SLN: Solid lipid nanoparticles; NLC: Nanostructured lipid carriers; NSAID: Nonsteroidal anti-inflammatory drug; BCS: Biopharmaceutical classification system; PS: Particle size; PDI: Polydispersity Index; ZP: Zeta potential; FE-SEM: Field emission scanning electron microscopy; 2D: 2 Dimensional; 3D: 3 Dimensional GRAPHICAL ABSTRACT
MODELING AND QUANTITATIVE RETRIEVAL OF FINITE FIELD FOR THE TROPICAL SEA SURFACE WIND SPEED OF THE FY-3B MICROWAVE IMAGER

The purpose of this study is to select a suitable sea wind retrieval method for FY-3B(MWRI). Based on the traditional empirical model of retrieving sea surface wind speed, and in the case of small sample size of FY-3B satellite load regression analysis, this paper analyzes the channel differences between the FY-3B satellite microwave radiation imager(MWRI) and TMI onboard the TRMM. The paper also analyzes the influence of these differences on the channel in terms of receiving temperature, including channel frequency, sensitivity and scaling precision. Then, the limited range of new model coefficient regression analysis is determined(in which the channel range settings include the information and features of channel differences), the regression methods of the finite field are proposed, and the empirical model of wind speed retrieval applicable to MWRI is obtained, which achieves robust results. Compared to the TAO buoy data, the mean deviation of the new model is 0.4 m/s, and the standard deviation is 1.2 m/s. In addition,the schematic diagram of the tropical sea surface wind speed retrieval is provided.