Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety of Bronchoalveolar Lavage in Hematological Patients with Thrombocytopenia. A Retrospective Cohort Study

Background Hospitalized hematological patients often require bronchoalveolar lavage (BAL). Scarce evidence exists regarding the potential risks in patients with very severe thrombocytopenia (VST). Methods This retrospective-cohort study included adult hematological in-patients with VST, defined as platelets<20x103/μL, undergoing BAL during 2012–2021. Mechanically ventilated patients or those with known active bleeding were excluded. Primary outcomes included major bleeding halting the BAL or deemed significant by the treating physician, need for any respiratory support other than low flow O2, or death within 24 hours. Any other bleedings were recorded as secondary outcomes. Results Of the 507 patients included in the final analysis, the 281 patients with VST had lower hemoglobin (Md=0.3, p=0.003), longer prothrombin-time (Md=0.7s, p=0.025), higher chances of preprocedural platelet transfusion (RR 3.68, 95%CI [2.86,4.73]), and only one primary-outcome event (death of septic shock 21h postprocedurally) - compared with 3 (1.3%) events (two bleedings halting procedure and one need for non-invasive-ventilation) in patients with platelets ≥20x103/μL (p=0.219). The risk of minor spontaneously resolved bleeding was higher (RR=3.217, 95% CI [0.919,11.262]) in patients with VST (4.3% vs 1.3%, p=0.051). No association was found between the complications recorded and preprocedural platelets, age, aPTT, P.T. , hematological status, or platelet transfusion. Conclusions This data suggests BAL to be safe even when platelet counts are <20x103/μL.
Machine Learning Model to Detect Parkinson's Disease using MRI Data

The major goal is to create opportunities for deep learning algorithms to be used to understand better and diagnose Parkinson's disease. The loss of dopamine-producing neurons causes Parkinson's disease, a degenerative disorder. When the condition first manifests, patients have tremors, bradykinesia, poor posture, and balance among other mobility impairments that progressively get worse over time. Additionally, as the global ageing population grows exponentially, more people are developing Parkinson's disease, which places a significant financial strain on governments. Structural modifications in the brain of individuals suffering from Parkinson's disease, resulting from a deficiency in dopamine, can be visualized through the application of Magnetic Resonance Imaging (MRI). In this particular investigation, an attempt was made to classify MR images of both Parkinson's disease patients and unaffected individuals by utilizing a sophisticated deep learning neural network. The utilization of a convolutional neural network proves to be instrumental in augmenting the diagnostic capabilities concerning Parkinson's disease. The MR images are meticulously scrutinized and subjected to rigorous evaluation in order to derive accurate metrics pertaining to their classification. The primary objective of this investigation aims to establish an approach for accurately discerning and classifying ailments through the utilization of Magnetic Resonance Imaging (MRI). Deep learning exhibits remarkable proficiency in disease diagnosis and image interpretation. A burgeoning body of scholarly inquiry suggests that the identification and prognostication of Parkinson's disease may necessitate the application of deep learning methodologies. In terms of disease prediction, deep learning models wield immense potency. The best early detection accuracy deep learning algorithms are hence the subject of increased research. In this experiment, the illness stage will be determined by brain imaging. Also, to the study suggests an effective deep learning approach for early disease prediction utilizing MRI data. The data set came from Kaggle. In this article a deep learning method for diagnosing Parkinson's diseases is proposed that makes use of the CNN algorithm.
The Effects of Light Spectrum and Intensity, Seeding Density, and Fertilization on Biomass, Morphology, and Resource Use Efficiency in Three Species of Brassicaceae Microgreens

Light is a critical component of indoor plant cultivation, as different wavelengths can influence both the physiology and morphology of plants. Furthermore, fertilization and seeding density can also potentially interact with the light recipe to affect production outcomes. However, maximizing production is an ongoing research topic, and it is often divested from resource use efficiencies. In this study, three species of microgreens—kohlrabi; mustard; and radish—were grown under five light recipes; with and without fertilizer; and at two seeding densities. We found that the different light recipes had significant effects on biomass accumulation. More specifically, we found that Far-Red light was significantly positively associated with biomass accumulation, as well as improvements in height, leaf area, and leaf weight. We also found a less strong but positive correlation with increasing amounts of Green light and biomass. Red light was negatively associated with biomass accumulation, and Blue light showed a concave downward response. We found that fertilizer improved biomass by a factor of 1.60 across species and that using a high seeding density was 37% more spatially productive. Overall, we found that it was primarily the main effects that explained microgreen production variation, and there were very few instances of significant interactions between light recipe, fertilization, and seeding density. To contextualize the cost of producing these microgreens, we also measured resource use efficiencies and found that the cheaper 24-volt LEDs at a high seeding density with fertilizer were the most efficient production environment for biomass. Therefore, this study has shown that, even with a short growing period of only four days, there was a significant influence of light recipe, fertilization, and seeding density that can change morphology, biomass accumulation, and resource input costs.
Binding Folate Receptor Alpha Autoantibody Is a Biomarker for Leucovorin Treatment Response in Autism Spectrum Disorder

Autism spectrum disorder (ASD) affects up to 1 in 36 children in the United States. It is a heterogeneous neurodevelopmental disorder with life-long consequences. Patients with ASD and folate pathway abnormalities have demonstrated improved symptoms after treatment with leucovorin (folinic acid), a reduced form of folate. However, biomarkers for treatment response have not been well investigated and clinical trials are lacking. In this retrospective analysis, a cohort of prospectively collected data from 110 consecutive ASD clinic patients [mean (SD) age: 10.5 (6.2) years; 74% male] was examined. These patients all underwent testing for folate receptor alpha autoantibodies (FRAAs) and soluble folate binding proteins (sFBPs) biomarkers and were treated with leucovorin, if appropriate. Analyses examined whether these biomarkers could predict response to leucovorin treatment as well as the severity of ASD characteristics at baseline. The social responsiveness scale (SRS), a measure of core ASD symptoms, and the aberrant behavior checklist (ABC), a measure of disruptive behavior, were collected at each clinic visit. Those positive for sFBPs had more severe ASD symptoms, and higher binding FRAA titers were associated with greater ABC irritability. Treatment with leucovorin improved most SRS subscales with higher binding FRAA titers associated with greater response. Leucovorin treatment also improved ABC irritability. These results confirm and expand on previous studies, underscore the need for biomarkers to guide treatment of folate pathways in ASD, and suggest that leucovorin may be effective for children with ASD.
Classification of Lying Postures

The aim of this paper is to classify participants’ lying postures using publicly available data obtained from matrix pressure sensors. Two machine learning algorithms, Convolutional Neural Network (CNN) and Random Forest (RF), are employed for subjects’ postures classification. The evaluation revealed that CNN applied on pressure topographic maps (accuracy of 90%) outperformed the RF algorithm that utilized extracted features (accuracy of 64%). Moreover, additional transformations of topographic maps improved CNN accuracy up to 96%, indicating that processing may play a significant role in the application of deep learning algorithm for lying posture classification.
Fungal Ecology and Ecosystem-based Management of Special Forest Products

Fungi shape the dynamics of natural ecosystems as pathogens, nutritional mutualists, and decomposers. They are also important as Special Forest Products with cultural and economic significance. I took an interdisciplinary approach to understand how fungal reproduction varies in response to abiotic (weather) and biotic (vegetation) factors, and how forest managers in the Pacific Northwest manage collaborative activities associated with Special Forest Products. I used three complementary approaches to examine temporal and spatial variation in reproduction in fungal communities. First, I measured the phenology of airborne fungal spores in coast redwood forests, mixed-evergreen forests, and maritime chaparral in coastal California, USA. Temporal patterns in meteorological factors (relative humidity, temperature) were more important in determining airborne fungal spore abundance than was vegetation type. This suggests that overall patterns of fungal reproductive dynamics may be predictable across heterogeneous landscapes based on local weather. Second, I used novel metagenomic techniques to identify unculturable and culturable airborne fungi across a vegetation mosaic. I found that the assemblages of fungal species vary strongly over time, with little spatial structure associated with habitat types. Third, for airborne spores collected from different vegetation types, I measured physical traits that may be important for survival, dispersal, or response to environmental change. I found larger and elongated spores in dry and structurally open vegetation (chaparral) compared to smaller and rounder spores in wet and structurally closed vegetation (forests). These results suggest that fungi in chaparral possess spore traits that allow them to persist and disperse in harsh, dry, open conditions. Finally, I examined the position of Special Forest Products like mushrooms and berries, in large-scale, collaborative planning for ecosystem-based management and the role of stakeholder participation to improve natural resource management. Working in the Pacific Northwest, I conducted a regional survey and interviews with national forest managers and found that collaborative activities occur at small scales (< 20 acres) and are driven by bi-lateral stakeholder interactions and legal obligations to stakeholders such as federally recognized Native American Tribes. These findings modify existing hypotheses on collaboration of natural resources and can aid national forests in future collaborative resource management.
Effect of cloud microphysics on Indian summer monsoon precipitating clouds: A coupled climate modeling study

The quest for one of the most dominant processes controlling the large‐scale circulations in the tropics is unraveled. The impact of cloud microphysical processes is known to have effects on rainfall and local atmospheric thermodynamics; however, its effect on the prevailing mean circulations is not yet studied. Two sets of coupled global climate model experiments (ICE and NO ICE microphysics) reveal that ice microphysics improves the strength of the Hadley circulation with respect to observation. Results pinpoint that ICE simulation enhances high cloud fraction (global tropics: ~59%, India: ~51%) and stratiform rain (global tropics: ~5%, India: ~15%) contribution. ICE and NO ICE cloud microphysics impacts differently on the outgoing longwave radiation (OLR), tropospheric temperature, and surface shortwave and longwave radiation. The effect of ice microphysics reduces OLR, which signifies deeper convection in the ICE run. The global annual average of the net radiation flux (shortwave and longwave) at the surface in ICE run (108.1 W/m2) is close to the observation (106 W/m2), which is overestimated in NO ICE run (112 W/m2). The result of apparent heat source term over the land and ocean surface eventually modifies regional Hadley circulation. Thus, the effect of ice microphysics in the global coupled model is important not only because of microphysics but also due to the radiation feedbacks. Therefore, better ice‐phase microphysics is required in the new generation of climate forecast model, which may lead to improvements in the simulation of monsoon.
UNUSUAL GROWTH OF POLLEN TUBES IN THE OVARY OF PLUM GENOTYPES DEVELOPED AT FRUIT RESEARCH INSTITUTE CACAK (SERBIA)

Unusual growth of pollen tubes in different pollination variants (self-, crossand open pollination) was observed in the ovary of six promising plum (Prunus domestica L.) genotypes developed at Fruit Research Institute (FRI), Čačak (hybrids 38/62/70, IV/63/81, 32/21/87, 34/41/87 and 22/17/87 and cultivar ‘Nada’) in Serbia. The cross-pollination was performed using the pollen of plum cultivar ‘Čačanska Lepotica’. The occurrence of unusual pollen tubes growth in the ovary was studied over the three-year period (2009‒2011) using fluorescent microscopy method. The appearance of unusual growth of pollen tubes in the ovary of all studied genotypes during the whole examination period was recorded in the region of the obturator and the micropyle, with or without further growth of the pollen tubes to the nucellus. Several different types of unusual growth of pollen tubes were observed, i.e. the growth of pollen tubes in opposite direction in relation to the nucellus, the curling up of pollen tubes, the branching of pollen tubes, as well as combinations of some of the above mentioned types of specific pollen tubes growth. Average values of all analyzed genotypes showed that the highest frequency of the ovaries with unusual growth of pollen tubes was determined in the open pollination variant (7.89%), followed by the cross-pollination variant (6.27%), while the smallest frequency was found in the self-pollination variant (3.12%).
Complicated Appendicitis-Shifting towards Early Appendectomy-A Comprehensive Review

The management of complicated appendicitis has undergone a transformative shift in recent years, moving away from the traditional conservative approach followed by interval appendectomy towards early appendectomy. This review article explores the current landscape of complicated appendicitis management, investigating the roles of conservative treatment, interval appendectomy, and early appendectomy. With no definitive consensus in place, the choice of treatment largely depends on the individual surgeon. The emergence of laparoscopic appendectomy has played a pivotal role in this paradigm shift, further highlighting the need to reevaluate and understand the various management options available. A total of 43 articles, 24 from Asia, 12 from Europe, 5 from the USA and 1 from Africa were retrieved from PubMed, Google scholar and the Cochrane database of clinical reviews. This review synthesizes existing literature, shedding light on the advantages and considerations associated with each approach.
Validation of the Recipe for Broadband Ground‐Motion Simulations of Japanese Crustal Earthquakes

The robustness of broadband ground‐motion simulation can promote seismic‐hazard assessment. A broadband ground‐motion simulation technique called “the recipe” is used in the scenario earthquake shaking maps of the National Seismic Hazard Maps for Japan. The recipe represents a fault rupture based on a multiple asperity model referred to as the characterized source model. Broadband ground‐motion time histories on the engineering bedrock are computed by a hybrid approach of the 3D finite‐difference method and the stochastic Green’s function method for the long‐ (>1 s) and short‐period ( 70 km); such conditions are outside of the target range of the recipe. Simulations using a 1D velocity structure model were also examined. The simulation results for the 1D and 3D velocity structure models indicated that the 3D velocity structure models are important in reproducing PGV and the later phases with long duration, especially on deep sediment sites.
Testing the Representativeness of a Multimode Survey in South Korea: Results from KAMOS

The Korean Academic Multimode Open Survey (KAMOS) is a national survey first conducted in 2016. Stratified cluster random sampling was used in an initial face-to-face survey during which panel members were recruited. The second survey allowed invited panel members to answer online or by phone. KAMOS includes both longitudinal items and omnibus items, i.e., researchers can propose questions to include on KAMOS. This paper seeks to establish that KAMOS is representative of the South Korean adult population. The demographic variables from the first survey were comparable to demographic variables from two well-respected surveys in South Korea: the KOSTAT Social Survey and the Gallup Korea Omnibus Survey. To ensure that there was no substantial difference between those who answered the first survey and those who answered the second survey, we compared the results of 22 items from the first survey. The 2,000 panel members who were invited to participate in the second survey had similar responses to the 1,008 of those who responded to the second survey. Based on our findings, KAMOS can be considered a representative sample.
Airborne microorganism disinfection by photocatalytic HEPA filter.

This study determined the efficacy of photocatalytic HEPA filters on microorganism disinfection in a closed-loop chamber and later applied it in an air purifier and tested its efficacy in an 8-m 3 chamber and in a hospital. The photocatalytic filters were made by dip-coating a HEPA filter in a TiO2 slurry. In order to disinfect the microorganisms retained on the filter, UV-A light was irradiated onto the filter to create strong oxidative radicals which can destroy microorganisms. The findings showed that disinfection efficiency of the photocatalytic filters with high TiO2 loading was insignificantly higher than with lower loading. S. epidermidis was completely eliminated within 2 hours, while 86.8% of B. subtilis, 77.1% of A. niger, and 82.7% of P. citrinum were destroyed within 10 hours. When applying the photocatalytic filters into an air purifier in a 8-m 3 chamber, it was found that as soon as the air purifier was turned on, 83.4% of S. epidermidis, 81.4% of B. subtilis, 88.5% of A. niger, and 75.8% of P. citrinum were removed from the air. In a hospital environment, the PCO air purifier efficacy was lower than that in the chamber. Besides, relative humidity, distances from the air purifier and room size were suspected to affect the efficacy of the photocatalytic filters.
Nonsteroidal anti-in ﬂ ammatory drugs and implications for the cyclooxygenase pathway in embryonic development

Nonsteroidal anti-in ﬂ ammatory drugs (NSAIDs) are a class of analgesics that inhibit the activity of cyclooxygenase isoenzymes, which drive tissue in ﬂ ammation pathways. Caution should be exercised when taking these drugs during pregnancy as they increase the risk of developmental defects. Due to the high rates of NSAID use by individuals, possibilities for in utero exposure to NSAIDs are high, and it is vital that we de ﬁ ne the potential risks these drugs pose during embryonic development. In this review, we characterize the identi ﬁ ed roles of the cyclooxygenase signaling pathway components throughout pregnancy and discuss the effects of cyclooxygenase pathway perturbation on developmental outcomes.
Diagnosis Support Model for Autism Spectrum Disorder Using Neuroimaging Data and Xception

Autism Spectrum Disorder is a neurodevelopmental disease-causing significant problem in social and behavioral interactions. Diagnosis of Autism is still challenging for the living community because there is no specific blood test or medical test for its detection. Medical personnel has to diagnose it by checking the individual's past social and physical behavior. In this current scenario, using advanced technological methods like deep learning for the detection of autism will help in solving the problem. It will save a lot of individuals in the early stage. Moreover, brain imaging data for this classification will also help acquire more detection features. This study aims to predict autism using brain scans using a transfer learning algorithm that involves a deep Conv net named Xception. The ABIDE I dataset extracts resting-state functional MRI scans for this study. The dataset consists of brain scans for individuals whose ages lie in the range of 7–64 years, which makes the median age of individuals 14.6 years. The transfer learning approach is used for this classification problem, which uses a deep convolution neural network. The xception algorithm is built on the given dataset for the prediction of autism. This study yielded an accuracy of 97.66% on model training whereas achieved an accuracy of 99.39% on the validation of the model. The Xception transfer learning model gives an accuracy score of 67.35% on the testing of the respective model. All results were obtained from neuroimaging data from the resting state. This paper shows that it is feasible to forecast autism at an early stage using advanced computer algorithms. With the help of the study results, we can conclude that the Xception transfer learning model performed better for the diagnosis of autism.
COMMUNICATION IN MEDICAL

Teamwork based on proper communication, social skills of interdisciplinary team members and proper division of roles has a direct impact on the quality of patient care. Incorrect interpretation of messages or delayed reaction to the received message cause significant problems in a teamwork. Nurses and doctors are two of the most important healthcare providers. They perform separate but complementary tasks in health care. An electronic literature search was conducted in the PubMed and ScienceDirect databases with the aim of analysing research on cooperation in healthcare teams, with a particular focus on the aspect of mutual communication between doctors and nurses. Empirical evidence may indicate that formal practices that strengthen communication and relationships among providers through participation in joint training have the potential to increase physicians’ awareness of teamwork and thereby support effective team behaviours. Of the studies reviewed, some were conducted using unvalidat-ed survey instruments. Some examined the opinions of only one professional group, without comparing them with the opinions of other groups involved in the collaborative process. Consequently, the present findings are inadequate as a reliable foundation for scientific conclusions.
The Timing of Filler-Gap Dependency Formation in Second Language Comprehension

: This study investigated at what point in a sentence native Japanese speakers begin to build long-distance dependencies while reading English relative clauses (RCs) by conducting two self-paced reading experiments. Experiment 1 revealed that Japanese learners of English construct long-distance dependency immediately after reading a verb by demonstrating “plausibility mismatch effects” at the verb site. On the contrary, Experiment 2 did not find evidence that they form a long-distance dependency before a verb, that is, no “transitivity mismatch effect. ” On the basis of these results, this study proposes that Japanese learners of English initiate long-distance dependency formation immediately after encountering a verb, but it does not precede the appearance of a verb. This extends previous findings that it is difficult to generate predictions in the processing of non-native languages, to a structural processing level. *