Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of the COVID-19 Pandemic on Mortality Following Hip and Knee Joint Arthroplasty Surgeries: A Systematic Review and Meta-Analysis

We performed a meta-analysis comparing the mortality rates after hip and knee joint arthroplasty between the coronavirus 2019 (COVID-19) pandemic and pre-pandemic periods. The purpose of present study was to investigate the impact of the COVID-19 pandemic on mortality rates after hip and knee joint arthroplasty. We systematically searched the MEDLINE, Embase, and Cochrane Library databases for studies published up to 28 March 2022. We included studies which directly compared mortality rates after hip and knee joint arthroplasty between the COVID-19 pandemic and pre-pandemic periods. The methodological quality of the included studies was assessed using the Methodological Index for Nonrandomized Studies (MINORS). We compared the overall mortality rate as the primary outcome. For the subgroup analysis, the mortality rates included were: within 30 days and unrelated to COVID-19; we excluded studies with only elective arthroplasties. Readmission rates following arthroplasties were also compared. We included five studies with 3044 patients, of which 838 patients underwent surgeries during the pandemic period and 2206 patients underwent surgeries in the pre-pandemic period. The mean MINORS score was 15.4/24 (range: 15–16). The overall mortality rate showed no significant differences between the pandemic and pre-pandemic periods (OR, 2.71; 95% CI, 0.78–9.35; p = 0.12; I2 = 19%). No differences were observed in mortality following arthroplasties within 30 days and unrelated to COVID-19 nor in the readmission rates. Mortality, after excluding studies with only elective arthroplasty, presented significant differences between the COVID-19 pandemic and pre-pandemic periods (OR, 3.80; 95% CI, 1.18–12.28; p = 0.03, I2 = 0%). The limitation of the present study was that elective arthroplasty and urgent arthroplasty were not completely differentiated. The overall mortality rate in the COVID-19 pandemic period following hip and knee arthroplasty did not show a significant increase. This finding could help to maintain the practice of elective arthroplasty during a pandemic situation in the future (PROSPERO–CRD42022335471).
Novel Targets for a Combination of Mechanical Unloading with Pharmacotherapy in Advanced Heart Failure

LVAD therapy is an effective rescue in acute and especially chronic cardiac failure. In several scenarios, it provides a platform for regeneration and sustained myocardial recovery. While unloading seems to be a key element, pharmacotherapy may provide powerful tools to enhance effective cardiac regeneration. The synergy between LVAD support and medical agents may ensure satisfying outcomes on cardiomyocyte recovery followed by improved quality and quantity of patient life. This review summarizes the previous and contemporary strategies for combining LVAD with pharmacotherapy and proposes new therapeutic targets. Regulation of metabolic pathways, enhancing mitochondrial biogenesis and function, immunomodulating treatment, and stem-cell therapies represent therapeutic areas that require further experimental and clinical studies on their effectiveness in combination with mechanical unloading.
The combination of vermicompost and arbuscular mycorrhizal fungi improves the physiological properties and chemical composition of Opuntia ficus-indica under semi-arid conditions in the field

Abstract Drought is one of the major threats to food security in arid and semi-arid regions. Therefore, significant changes in agricultural production systems will be required to meet the challenges of food security while addressing water scarcity. Prickly pear is a drought tolerant plant, as well as a source of sustainable food and feed. Nonetheless, in dry conditions, its growth and performance are restricted. The aim of this study was to evaluate the effect of arbuscular mycorrhizal fungi (AMF), vermicompost (Vc) or the combination of AMF + Vc on yield, growth and physio-chemical parameters of cactus grown under field drought conditions. Two irrigation regimes were applied, non-irrigated (grown under rainfed conditions) and irrigated plants (8 mm of water per week). After eight months of cultivation, the finding demonstrated that drought reduced growth, physiological and biochemical parameters of cactus. However, the application of biostimulants, especially Vc and AMF + Vc, can mitigate the negative effect of drought by increasing cladode dry weight (93% and 109%), total chlorophyll (78% and 75%), total phenolic content (83% and 81%). Those treatments are also efficient in enhancing activities of enzymatic and non enzymatic antioxidants in cactus cladodes and fruits. These results suggest that the use of these biostimulants can mitigate the challenges associated with drought, by improving the physiological and biochemical responses of cactus.
A Phenomenology Study on Misappropriation of Assets and Corruption in Malaysian Local Authorities: A Reality Check

Year on year, the Auditor General’s Report had reported on the inefficiencies and weaknesses in the government administrative system. Accordingly, this research aims to identify why misappropriation of assets and corruption are still occurring despite the elaborated preventive measures in place. Due the sensitivity of the area, a phenomenological study is the most appropriate method to approach the objective. This research used the evidence collected from both the semi-structured interviews and open-ended survey from local authorities in Malaysia. Information and data from face-to-face interviews served as the primary source of information while responses received by postages served as a secondary support to the primary research instrument. Based on the responses, inefficient leader becomes the centrepiece of the other ten challenges, namely, attitude, weak enforcement, lack of internal strengths, ineffective training, lack of authority, opportunity due to lack of control, burden of proof due to bureaucracy, favouritism/ excessive discretions, organisational culture, and public perception. This research contributes to the literature by investigating a commonly proposed subject but underexplored methodology by incorporating phenomenology and open-ended survey simultaneously in data collection to understand a holistic view of the discussed issues. While detailed research of whistle-blowing programs is outside the scope of this research, future research should acknowledge the importance of whistle-blowing programs in the Malaysian public sector which move beyond the compliance of established policies and procedures.
role of allograft bone gel in osseointegration of immediate loading implant in esthetic zone

Introduction: Immediate implants loading permit the placement of a temporary restoration immediately after the insertion of the fixture that is certainly an aesthetic and comfort for the patient. Immediate nonfunctional loading of single-tooth implants in the upper anterior after augmentation with allograft bone gel can result in successful implant integration and stable peri-implant conditions. Aim: The aim of the study was to assess clinically and radiographically osseointegration of immediate loading implant with allograft bone gel. Patient and methods: involved fourteen immediate loading dental implants in 10 male patients. The age of the patients range from 22- 48 years old with average 35 years old. Fourteen Immediate implants were divided into two main groups: Group 1 (control group), A traumatic extraction of single rooted tooth or remaining root and immediate placement of dental implant into fresh extracted socket. Group 2 (test group), A traumatic extraction of single rooted tooth or remaining root and immediate insertion of the implant in extracted socket and sealed with allograft bone gel (c-blast putty). Results: test group showed significant higher bone density than control group (Independent samples t-test, P<.001) after two weeks ,three months and nine months. Conclusion: Allograft bone gel(C blast putty gel) shows effective role in implant osseointegration. Immediate non-functional loading implant design does not disturb or interfere with bone formation around implant in control or test group.
Outlier-Concerned Data Completion Exploiting Intra- and Inter-Data Correlations in Sparse CrowdSensing

Mobile CrowdSensing (MCS) is a popular data collection paradigm which usually faces the problem of sparse sensed data because of the limited sensing cost. In order to address the situation of sparse data, sparse MCS recruits users to sense important areas and infers completed data by data completion, which is crucial in sparse MCS for urban sensing applications (e.g. enhancing data expression, improving urban analysis, guiding city planning, etc.) To achieve accurate completion results, previous methods usually utilize the universal similarity and conventional tendency while incorporating only a single dataset to infer the full map. However, in real-world scenarios, there may exist many kinds of data (inter-data), that could help to complement each other. Moreover, for each kind of data (intra-data), there usually exist a few but important outliers caused by the special events (e.g., parking peak, traffic congestion, or festival parade), which may behave in a different way as the statistical patterns. These outliers cannot be ignored, while it is difficult to detect and recover them in data completion because of the following challenges: 1) the infrequency and unpredictability of outliers’ occurrence, 2) the large deviations against the means compared to normal values, and 3) the complex spatiotemporal relations among inter-data. To this end, focusing on spatiotemporal data with both intra- and inter-data correlations, we propose a matrix completion method that takes outliers’ effects into consideration and exploits both intra- and inter-data correlations for enhancing performance. Specifically, we first conduct the Deep Matrix Factorization (DMF) with multiple auxiliary Neural Networks, which named Stacked Deep Matrix Factorization (SDMF). Note that the loss function of SDMF is no longer the previous MSE loss function, but replaced with an Outlier Value Loss (OVL) function to effectively detect and recover the outliers. Moreover, a spatiotemporal outlier value memory network is added for further enhancing the outlier inference. Finally, we take extensive qualitative and quantitative experiments on two popular datasets each with two types of sensing data, and the experimental results indicate the advantages of our method that outperforms the state-of-the-art methods.
Defining the baseline of pulmonary microbiota in healthy populations and influencing factors

Lung microbiota and lung diseases have already received increasing attention. However, the lung microbiota lacks a unified healthy baseline. In this review, we collect the healthy pulmonary microbial composition based on the data of existing relevant studies. Subsequently, we discuss and analyze the three aspects of bacterial, fungus and viral at the phylum and genus levels, as well as influence factors like sample type, geography, age, time, hypervariable regions and sequencing method to set up a unified pulmonary baseline. We conclude that Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria and Fusobacteria are the predominant phyla in healthy people. At the genus level, the most common bacterial genera are Veillonella, Streptococcus, Prevotella, Neisseria and Fusobacterium. A significant difference exists at the bacterial genus level between the lung of healthy subjects and the normal tissues of patients, and geography impacts on the healthy baseline significantly. In addition, age, time, hypervariable regions and sequencing method all affect the baseline to various degrees. In healthy people, Ascomycota and Basidiomycota dominate the pulmonary fungal phyla, while bacteriophages are the predominated order in virome. Our investigation provides a healthy lung baseline for the study of lung microbiota, which is conducive to better finding lung disease-related pathogens.
Effects of Ultraviolet Radiation on Sediment Burial Parameters and Photo-Oxidative Response of the Intertidal Anemone Anthopleura hermaphroditica

Anthopleura hermaphroditica is an intertidal anemone that lives semi-buried in soft sediments of estuaries and releases its brooded embryos directly to the benthos, being exposed to potentially detrimental ultraviolet radiation (UVR) levels. In this study, we investigated how experimental radiation (PAR: photosynthetically active radiation; UVA: ultraviolet A radiation; and UVB: ultraviolet B radiation) influences burrowing (time, depth and speed) in adults and juveniles when they were exposed to PAR (P, 400–700 nm), PAR + UVA (PA, 315–700 nm) and PAR + UVA + UVB (PAB, 280–700 nm) experimental treatments. The role of sediment as a physical shield was also assessed by exposing anemones to these radiation treatments with and without sediment, after which lipid peroxidation, protein carbonyls and total antioxidant capacity were quantified. Our results indicate that PAB can induce a faster burial response compared to those anemones exposed only to P. PAB increased oxidative damage, especially in juveniles where oxidative damage levels were several times higher than in adults. Sediment offers protection to adults against P, PA and PAB, as significant differences in their total antioxidant capacity were observed compared to those anemones without sediment. Conversely, the presence or absence of sediment did not influence total antioxidant capacity in juveniles, which may reflect that those anemones have sufficient antioxidant defenses to minimize photooxidative damage due to their reduced tolerance to experimental radiation. Burrowing behavior is a key survival skill for juveniles after they have been released after brooding.
Assistive Technology Education: Experiences of Preservice Special Education Teachers Within an Instructional Material Development Project

The purpose of this study was to explore preservice special education teachers’ experiences within an instructional material development project for students with disabilities during a semester-long technology integration course. Data were collected from 61 preservice special education teachers through an open-ended questionnaire, instructional design reports, and instructional materials. The findings indicated that working with students with disabilities in a real-life environment helped the preservice teachers realize the importance of assistive technology in education to support these students’ needs. This experience also helped the teachers to ascertain what barriers may exist to assistive technology use in education, such as insufficient technology knowledge and skills, and inadequate infrastructure of classrooms, etc. The preservice teachers emphasized that following an instructional design process facilitated the development of effective instructional materials. They also stressed that the instructional material development project contributed to their professional development in different ways, such as practical skills through real-life experiences and technology-related skills.
A Privacy Preservation Masking Method to Support Business Collaboration

This paper introduces a privacy preservation masking method to support business collaboration, called Dimensionality Reduction-Based Transformation (DRBT). This method relies on the intuition behind random projection to mask the underlying attribute values subject to cluster analysis. Using DRBT, data owners are able to find a solution that meets privacy requirements and guarantees valid clustering results. DRBT was validated taking into account five real datasets. The major features of this method are: a) it is independent of distance-based clustering algorithms; b) it has a sound mathematical foundation; and c) it does not require CPU-intensive operations.
Determinants of agricultural commercialization in smallholder farmers in Zimbabwe: The case of Zhombe North Rural District

Agricultural commercialization raises farm incomes and improves living standards in African farming populations. Despite its importance, it remains low in Africa. Limited studies in agricultural commercialization have explored different tools for analysis given the agricultural commercialization indicators with zeros and fractions. This paper addresses methodological issue and adds literature on factors affecting agricultural commercialization. Agricultural commercialization is explored in Zhombe North Rural District using 2017/2018 cross-sectional data collected from 165 households. The zero-inflated beta regression model was used to find the drivers of agricultural commercialization. Crop output market participation share served as a proxy for agricultural commercialization. Land size, fertilizer use, use of credit, and certified seed proportion had significant positive effects on agricultural commercialization. In contrast, household head age, food cropping land size, and off-farm employment significantly influenced agricultural commercialization. The results for the model are similar to the results in previous studies using the Tobit model. The study recommends policies that promote the availability and usage of inputs to improve agricultural commercialization in the study area.
Perturbation Theory in Mathematical Programming and Its Applications

PART I: FOUNDATIONS OF THEORY AND METHODS OF FINITE-DIMENSIONAL OPTIMIZATION: Basic Concepts, Problems and Fields of Applications of Perturbation Theory Perturbation Theory of Smooth Mathematical Programming Problems (Main Results) Examples of Studies in Parametric Optimization Problems, Applications of Perturbation Theory PART II: BASIC PERTURBATION THEORY IN FINITE-DIMENSIONAL OPTIMIZATION: Elements of Convex and Nonconvex Analysis Optimality Criteria in Generating Problems PART III: PERTURBATION THEORY IN MATHEMATICAL PROGRAMMING: General Perturbation Theory Perturbation Theory for Smooth Mathematical Programming Problems Perturbation Theory for Convex Programming Problems Theory of Minimax Perturbations under Bound Constraints.
Impact of Clinical Pharmacist Intervention on Clinical Outcomes in the Critical Care Unit, Taif City, Saudi Arabia: A Retrospective Study

(1) Objectives: Clinical pharmacists are now playing a significant role in hospitals aiming to reduce medication errors, adverse drug reactions, and healthcare costs. Therefore, the main objective of this study was to assess the interventions provided by a clinical pharmacist in the intensive care unit at the King Faisal Hospital in Taif city. (2) Methods: For this single-center retrospective study, patients were included from December 2021 to May 2022. In the present study, all the interventions of clinical pharmacists made over six months were included. The Intensive care unit (ICU) ward was covered by three clinical pharmacists, and the interventions made were categorized into four groups: (1) interventions related to indications; (2) interventions regarding safety; (3) interventions regarding dosing, and (4) miscellaneous. Descriptive statistics was applied to evaluate the results in the form of frequencies and percentages. Analysis was performed using the statistical package SPSS 20.0. (3) Results: Overall, a total of 404 interventions were recommended for 165 patients during the six- month period of study. Among them, 370 interventions (91.5%) were accepted by physicians. Among all the interventions, the majority were suggested regarding ‘indication’ (45.7%), including the addition of drugs, drugs with no indications, and duplication. The acceptance rate of clinical pharmacist intervention was 98.5%. (4) Conclusions: This retrospective study shows that clinical pharmacists played a critical role in optimizing drug therapy which could subsequently help to prevent drug-related issues and lower drug costs. More research is needed to do a thorough cost-benefit analysis.
An Improved Deep Learning Model for High-Impact Weather Nowcasting

Accurate nowcasting (short-term prediction, 0–6 h) of high-impact weather, such as landfalling hurricanes and extreme convective precipitation, plays a critical role in natural disaster monitoring and mitigation. A number of nowcasting approaches have been developed in the past few decades, such as optical flow and the tracking radar echoes by correlation system. Most of these mainstream operational techniques are based on radar echo map extrapolation, which determines the velocity and direction of precipitation systems using historical and current radar observations. However, the skill of the traditional extrapolation method decreases rapidly within the first hour. In order to improve nowcasting skill, recent studies have proposed using deep learning methods, such as convolutional recurrent neural network and trajectory gate recurrent unit. But none of these methods focuses on high-impact weather events, and the deep learning models trained based on general precipitation events cannot meet the demand of accurate warnings and decision-making at the scales required for high-impact weather events, such as hurricanes. Using multiradar observations, this article introduces the idea of self-attention and develops a self-attention-based gate recurrent unit (SaGRU) to enhance its generalization capability and scalability in predicting high-impact weather events. In particular, two types of high-impact weather systems, namely, landfalling hurricanes and extreme convective precipitation events, are investigated. Three models are trained based on hurricane events, heavy rainfall (i.e., nonhurricane) events, and all events combined in the southeast United States during 2015 and 2020. The impacts of different data sources on the nowcasting performance are quantified. The evaluation results of nowcasting products show that our SaGRU performs very well in predicting hurricane-induced rainfall. In the new methodology, the data from nonhurricane events are shown to provide useful information in enhancing the nowcasting performance during hurricane events as the model trained by combining all the hurricane and nonhurricane events has the best performance. In addition, this article quantifies the impact of the sequence length of input radar observations on the nowcasting performance, which shows that five consecutive observations are sufficient to obtain a stable model, and even two consecutive observations can produce reasonable results.
Biochemical Components, Immune Characteristics and Antimicrobial Activity of Epidermal Mucus of the Three Indian Major Carp Species

Fish live in an environment that is full of phylogenetically diverse collections of bacterial pathogens. Fish epidermal mucus is regarded as a barrier between fish and infectious microorganisms in their surroundings protecting it against pathogens. The current study aimed to evaluate the biochemical components of skin mucus, mucosal immunity, and the bactericidal effect of epidermal mucus in three carp species, including Catla catla , Labeo rohita, and Cirrhinus mrigala . Biochemical composition showed the presence of protein, carbohydrate and lipid in the mucus of all three carp species, and concentration varied with respect to species. Protein and lipid
Simple output feedback adaptive control based on passification principle

This paper deals with the output stabilization of time‐delay systems with sector‐bounded nonlinearity and disturbance. In this paper, we will consider the problem of absolute stability for a time‐delay systems class, which can be represented as a feedback connection of a linear dynamical system with unknown parameters and an uncertain nonlinearity satisfying a sector constraint. Control algorithm providing exponential stability of equilibrium position without disturbance and L ∞ ‐stability for disturbed systems is proposed for a class of output. Obtained results are validated with the aid of computer simulation of the two‐stage chemical reactor control. Copyright © 2013 John Wiley & Sons, Ltd.