Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation and physicochemical characterization of whitlockite/PVA/Gelatin composite for bone tissue regeneration

This work used a straightforward solvent casting approach to synthesize bone whitlockite (WH) based PVA/Gelatin composites. WH nanoparticles (NPs) were synthesized using the wet precipitation method, followed by their addition into the PVA/Gelatin matrix at concentrations from 1% to 10%. The physicochemical characterization of the prepared PVA/Gelatin/WH composite was carried out using ATR-FTIR, Optical profilometry, a Goniometer, a Universal tensile testing machine (UTM), and scanning electron microscopy (SEM) techniques. The ATR-FTIR analysis confirmed the formation of noncovalent interactions between polymeric chains and WH NPs and the incorporation of WH NPs into the polymer cavities. SEM analysis demonstrated increased surface roughness with the addition of WH NPs, supporting the results obtained through optical profilometry analysis. The mechanical properties of the prepared composite showed an increase in the tensile strength with the addition of WH filler up to 7% loading. The prepared composite has demonstrated an excellent swelling ability and surface wettability. The reported results demonstrate the exceptional potential of the prepared composite for bone tissue regeneration.
Long Non-Coding RNAs: New Insights in Neurodegenerative Diseases

Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS), are gradually becoming a burden to society. The adverse effects and mortality/morbidity rates associated with these NDDs are a cause of many healthcare concerns. The pathologic alterations of NDDs are related to mitochondrial dysfunction, oxidative stress, and inflammation, which further stimulate the progression of NDDs. Recently, long non-coding RNAs (lncRNAs) have attracted ample attention as critical mediators in the pathology of NDDs. However, there is a significant gap in understanding the biological function, molecular mechanisms, and potential importance of lncRNAs in NDDs. This review documents the current research on lncRNAs and their implications in NDDs. We further summarize the potential implication of lncRNAs to serve as novel therapeutic targets and biomarkers for patients with NDDs.
A case study of monthly values probability using Global Historical Climatology Network data.

The paper presents a study of a monthly temperatures time series, using data available from the Global Historical Climatology Network for Sibiu. Only by analysing the time series variability it is possible to correctly identify the range of “normal” climate conditions and the fluctuations or eventually climate changes that could affect forest dynamics. A simple empirical method, the quantile plot, was used for estimating the probabilities associated with different temperature monthly values, which are very useful and possible to be easily estimated using this procedure by researchers and managers from forestry, agriculture, land planning etc.
Novel anti-neuroinflammatory pyranone-carbamate derivatives as selective butyrylcholinesterase inhibitors for treating Alzheimer’s disease

Abstract Butyrylcholinesterase (BuChE) and neuroinflammation have recently emerged as promising therapeutic directions for Alzheimer’s disease (AD). Herein, we synthesised 19 novel pyranone-carbamate derivatives and evaluated their activities against cholinesterases and neuroinflammation. The optimal compound 7p exhibited balanced BuChE inhibitory activity (eqBuChE IC50 = 4.68 nM; huBuChE IC50 = 9.12 nM) and anti-neuroinflammatory activity (NO inhibition = 28.82% at 10 μM, comparable to hydrocortisone). Enzyme kinetic and docking studies confirmed compound 7p was a mix-type BuChE inhibitor. Additionally, compound 7p displayed favourable drug-likeness properties in silico prediction, and exhibited high BBB permeability in the PAMPA-BBB assay. Compound 7p had good safety in vivo as verified by an acute toxicity assay (LD50 > 1000 mg/kg). Most importantly, compound 7p effectively mitigated cognitive and memory impairments in the scopolamine-induced mouse model, showing comparable effects to Rivastigmine. Therefore, we envisioned that compound 7p could serve as a promising lead compound for treating AD.
Metamerism Appreciation of Jadeite‐Jade Green under the Standard Light Sources D65, A and CWF

We tested 288 pieces of the polished cabochon jadeite‐jade with high quality green and smooth surfaces using the colorimeter Color i5 based on the CIE 1976 L*a*b* uniform color space. The color changes after alternating three CIE standard light sources D65, A and CWF were compared, and the degree of metamerism was evaluated. The results show that the CIE standard light sources D65, A and CWF increase the chroma of green and turn bluish green into vivid green, which are similar or to nearly colorless and transparent glass‐like samples with fine textures. When the three light sources were individually alternated, the lightness of the jadeite‐jades showed little change, but the tone changed significantly. Metamerism occurred only in 3 pairs among the 16 samples that were selected by cluster analysis for quality, and the metamerism colors were highly consistent of L*D65∊(43.47, 46.75), C*D65∊(65.20, 68.80) and ho*D65∊(138.10, 140.23). We can conclude that the jadeite‐jade green gradually turns to blue when the color temperature of the light source increases. But when considering both the light sources and the samples, D65 is more suitable to be the light source of jadeite‐jade green when compared with the light sources CWF and A. Compared with the CIE standard green and high chroma green, high‐quality jadeite‐jade green has a slight yellow tone, low lightness and high chroma. The special metamerism index decreased to 1 when the light source alternated among D65, A and CWF. Therefore, the color of jadeite‐jade green might be slightly influenced by alternating of the light sources.
Knowledge Reasoning Based on Knowledge Graph

Nowadays, with the rapid development of Internet technology and application mode, the scale of Internet data is growing geometrically, and a large number of valuable knowledge are also in it. How to organize and express these knowledge and carry out in-depth calculation and analysis have aroused widespread concern. Grow up under the environment. Knowledge reasoning based on knowledge graph is one of the hotspots in knowledge graph research. It plays an important role in vertical search, intelligent answering and other applications. Knowledge reasoning oriented to knowledge graph aims at reasoning out new knowledge or identifying wrong knowledge based on existing knowledge. Unlike traditional knowledge reasoning, the knowledge reasoning method facing knowledge graph is also more diverse because of the concise, flexible and flexible knowledge representation in knowledge graph. This article will be based on the basic knowledge reasoning. The concept of knowledge based knowledge reasoning is introduced in recent years. In particular, knowledge reasoning can be divided into single step and multistep reasoning, because the methods are not the same. Each class can be divided into rule based reasoning, distributed representation, reasoning based on the divine network, and mixed reasoning.
Influence of atmospheric rivers on vegetation productivity and fire patterns in the southwestern U.S.

In the southwestern U.S., the meteorological phenomenon known as atmospheric rivers (ARs) has gained increasing attention due to its strong connections to floods, snowpacks, and water supplies in the West Coast states. Relatively less is known about the ecological implications of ARs, particularly in the interior Southwest, where AR storms are less common. To address this gap, we compared a chronology of AR landfalls on the west coast between 1989 and 2011 and between 25°N and 42.5°N to annual metrics of the normalized difference vegetation index (NDVI; an indicator of vegetation productivity) and daily resolution precipitation data to assess influences of AR‐fed winter precipitation on vegetation productivity across the southwestern U.S. We mapped correlations between winter AR precipitation during landfalling ARs and (1) annual maximum NDVI and (2) area burned by large wildfires summarized by ecoregion during the same year as the landfalls and during the following year. Interannual variations of AR precipitation strongly influenced both NDVI and area burned by wildfire in some dryland ecoregions. The influence of ARs on dryland vegetation varied significantly depending on the latitude of landfall, with those ARs making landfall below 35°N latitude more strongly influencing these systems, and with effects observed as far as 1300 km from the landfall location. As climatologists' understanding of the synoptic patterns associated with the occurrence of ARs continues to evolve, an increased understanding of how AR landfalls, in aggregate, influence vegetation productivity and associated wildfire activity in dryland ecosystems may provide opportunities to better predict ecological responses to climate and climate change.
THERMAL INFRARED ANALYSIS OF VOLCANIC PROCESSES

Due to the dangerous and remote nature of many volcanoes, field-based data collection of active processes and precursory activity is problematic. Spaceborne remote sensing instruments enable these data to be recorded, monitored, and studied. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one such sensor that currently collects data in the visible near infrared (VNIR) and thermal infrared (TIR) wavelength regions and has an archive with the highest spatial resolution TIR data (90 m) currently available to the scientific community. ASTER is capable of recording precursory volcanic activity that is unidentifiable with other sensors. By combining ASTER data with those gathered from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), temporal resolution is improved and processes such as the cooling rate of pyroclastic flows and subtle precursory activity are able to be quantified. Rigorous modeling of these datasets further allows results such as estimation of pyroclastic flow volume, the specific eruption mechanisms and the onset of a future eruption. The work outlined in this dissertation demonstrates how data collected from the ASTER sensor greatly improves current monitoring capabilities. New methods for processing these high spatial resolution data allow scientists to understand and better evaluate the risks associated with specific volcanoes and their common eruption styles.
Heavy Metal Contamination of Groundwater – Due to Industrial Solid Waste Disposal

Heavy metal contamination has been recognized as a major environmental concern in India over the last few decades. The three percent of global fresh water is large enough to meet the requirements of man for million of years etc., Due to lack of efficient solid waste management system and improper dumping of ISW as open landfills, the ground water and surface water in the Kakinada city is found to be contaminated in industrial area. The leaches of the open dump yards directly contaminate the groundwater and surface water resources leading unsuitability of water for drinking at many places. The ground Water samples are collected in Monsoon, Post Monsoon, summer and Winter Seasons at different industrial areas of Kakinada. These samples were tested for water analysis to identify the heavy metals. After testing of analysis Copper (Cu), Zinc (Zn), Iron (Fe) and Manganese (Mn) were crossed Exceeding limits in the ground water due to contamination of industrial solid waste disposal.
The Deep Earth

Seismic anisotropy has been found in many regions of the Earth’s interior. Its presence in the Earth’s crust has been known since the 19th century, and is due in part to the alignment of anisotropic crystals in rocks, and in part to patterns in the distribution of fractures and pores. In the upper mantle, seismic ani- sotropy was discovered 50 years ago, and can be attributed for the most part, to the alignment of intrinsically anisotropic olivine crystals during large scale deformation associated with convection. There is some indication for anisotropy in the transition zone, particularly in the vicinity of subducted slabs. Here we focus on the deep Earth – the lower mantle and core, where anisotropy is not yet mapped in detail, nor is there consensus on its origin. Most of the lower mantle appears largely isotropic, except in the last 200–300 km, in the D 00 region, where evidence for seismic anisotropy has been accumulating since the late 1980s, mostly from shear wave splitting measurements. Recently, a picture has been emerging, where strong anisotropy is associated with high shear velocities at the edges of the large low shear velocity provinces (LLSVPs) in the central Paciﬁc and under Africa. These observations are consistent with being due to the presence of highly anisotropic MgSiO 3 post-perovskite crystals, aligned dur- ing the deformation of slabs impinging on the core-mantle boundary, and upwelling ﬂow within the LLSVPs. We also discuss mineral physics aspects such as ultrahigh pressure deformation experiments, ﬁrst principles calculations to obtain information about elastic properties, and derivation of dislocation activity based on bonding characteristics. Polycrystal plasticity simulations can predict anisotropy but models
The use of artificial intelligence to assess diabetic eye disease among the Greenlandic population

ABSTRACT Background: Retina fundus images conducted in Greenland are telemedically assessed for diabetic retinopathy by ophthalmological nurses in Denmark. Applying an AI grading solution, in a Greenlandic setting, could potentially improve the efficiency and cost-effectiveness of DR screening. Method: We developed an AI model using retina fundus photos, performed on persons registered with diabetes in Greenland and Denmark, using Optos® ultra wide-field scanning laser ophthalmoscope, graded according to ICDR. Using the ResNet50 network we compared the model’s ability to distinguish between different images of ICDR severity levels in a confusion matrix. Results: Comparing images with ICDR level 0 to images of ICDR level 4 resulted in an accuracy of 0.9655, AUC of 0.9905, sensitivity and specificity of 96.6%. Comparing ICDR levels 0,1,2 with ICDR levels 3,4, we achieved a performance with an accuracy of 0.8077, an AUC of 0.8728, a sensitivity of 84.6% and a specificity of 78.8%. For the other comparisons, we achieved a modest performance. Conclusion: We developed an AI model using Greenlandic data, to automatically detect DR on Optos retina fundus images. The sensitivity and specificity were too low for our model to be applied directly in a clinical setting, thus optimising the model should be prioritised.
Determining design criteria for indoor positioning system projects in hospitals: A design science approach

Objectives Indoor navigation systems (indoor positioning systems) can improve orientation for patients in hospitals and help employees to track assets. Many hospitals would like to implement indoor positioning systems but do not know how. To support them in doing this, and to gain knowledge about the requirements for indoor positioning system implementation, our research identifies the design criteria relevant to indoor positioning system implementation projects. Methods A design science research process is built to design and evaluate an artifact. For this, five indoor positioning system developers and five hospital IT management representatives from various hospitals and companies in Germany are interviewed. Further, controlled experiments are conducted in Germany, using an ultrasound-based indoor positioning system. Results We determined and tested indoor positioning system functions, evaluated indoor positioning system performance criteria, and identified the operating conditions in hospitals. Our results show that indoor positioning system functions should provide a benefit to a hospital's daily operations, that some performance criteria are more important than others, and that operating conditions are important, e.g., radiation. Conclusion As a theoretical contribution, we show how design science research can be applied to the context of indoor positioning systems in hospitals. In addition, we make a practical contribution in that our propositions can be used for future indoor positioning system developments.
Drought propagation through the hydrological cycle

Time series of simulated recharge, groundwater heads and streamflow were obtained for temperate humid and semiarid climate regions and for quickly and slowly-responding catchments. These were analysed to investigate the propagation of drought through the subsurface, i.e. the change in characteristics (onset, duration and severity) from a meteorological drought to a hydrological drought. In two selected contrasting climate regions (Spain and The Netherlands) the propagation of recharge droughts generally leads to smaller hydrological droughts, except for severe recharge droughts, which might generate very severe hydrological droughts. In quickly-responding catchments more minor droughts will occur, whereas the probability of a very severe drought is higher in slowly-responding catchments. It is anticipated that quantitative knowledge on drought propagation will advance if recharge droughts derived from real instead of synthetic recharge can be better defined, i.e. by use of recharge anomalies.
SEA SURFACE TEMPERATURE DAN CHLOROPHYLL-A CONCENTRATION VARIABILITIES OF JAKARTA BAY AND ITS SURROUNDING WATERS

Variability of sea surface temperature (SST) and the concentration of chlorophyll-a (Chla) in Jakarta Bay and its surrounding waters were analyzed based on the time series data of monthly mean SST and Chla estimated by NOAA AVHRR, SeaWiFS, and MODIS satellites for 17 years (1997 to 2014). Generally, the monthly mean SST values fluctuated seasonally with the semi-annual pattern with the maximum value occurred in the second transitional season (Oct-Nov) and relatively high in the first transitional season (Mar). In contrast, the minimum of monthly mean SST values was found during the east season (Jul) and relatively low during the west season (Des-Jan). SST variabilities were generally associated with the sun position, sea water circulation, and rainfall. Based on MODIS data, the minimum-maximum of monthly mean SST ranged of 28.39°C-32.58°C. Seasonal fluctuations with the semi-annual pattern also occurred in the monthly mean Chla values with the maximum value occurred during the west season (Des-Jan) and relatively high values occurred during the east season (Jun). In contrast, the minimum of the monthly mean Chla was found during the second transition season (Sep-Oct) and relatively low during the first transitional season (March-Apr). The minimum-maximum of monthly mean Chla ranged of 0.09 mg/m 3 -2.50 mg/m 3 . Chla fluctuations were generally associated with the variabilities of rainfall, river water discharge, and sea water circulation of the Java Sea. Anomalous climatic factors such as El Nino Southern Oscillation (ENSO) and La Nina produced a significant impact on the SST and Chla interannual variability. Generally, the ENSO phenomenon lo-wers the value SST and Chla, while La Nina events tended to increase Chla values. Keywords: sea surface temperature, chlorophyll-a, fluctuation, NOAA AVHRR, SeaWiFS, MODIS
Relationships Between Speech, Spatial and Qualities of Hearing Short Form SSQ12 Item Scores and their Use in Guiding Rehabilitation for Cochlear Implant Recipients

Cochlear implantation successfully improves hearing in most adult recipients. However, in rare cases, post-implant rehabilitation is required to maximize benefit. The primary aim of this investigation was to test if self-reports by cochlear implant users indicate the need for post-implant rehabilitation. Listening performance was assessed with the Speech, Spatial and Qualities short-form SSQ12, which was self-administered via a web-based survey. Subjects included over 2000 adult bilateral or unilateral cochlear implant users with at least one year of experience. A novel application of regression tree analysis identified core SSQ12 items that serve as first steps in establishing a plan for further rehabilitation: items 1, 8, and 11 dealing with single-talker situations, loudness perception, and clarity, respectively. Further regression and classification tree analyses revealed that SSQ12 item scores were weakly related to age, degree of tinnitus, and use of bilateral versus unilateral implants. Conversely, SSQ12 scores were strongly associated with self-rated satisfaction and confidence in using their cochlear implant. The SSQ12 total scores did not vary significantly over 1–9 or more years’ experience. These findings suggest that the SSQ12 may be a useful tool to guide rehabilitation at any time after cochlear implantation. Identification of poor performance may have implications for timely management to improve the outcomes, through various techniques such as device fitting adjustments, counseling, active sound exposure, and training spatial hearing.
Understanding determinants related to farmers’ protective measures towards pesticide exposure: A systematic review

Objective Pesticide poisoning is the main cause of adverse effects and mortality worldwide. Protective measures can reduce the intensity of the effects of pesticides on the health of farmers. Numerous cross-sectional studies have been conducted on the determinants of performing protective measures to reduce exposure to pesticides, but there is no systematic study that comprehensively examines the impact of these factors. Therefore, the aim of this study is to identify existing studies on the determinants of effective protective measures to reduce exposure to pesticides among farmers. Methods In this systematic review, studies were obtained from PubMed, Web of Science and Scopus databases using a search strategy that covered articles from the first years of database design to April 20, 2023. The inclusion and exclusion criteria were based on the PICOs criteria. The study included cross-sectional studies that measured the implementation of protective measures using objective or valid subjective tools. The data were extracted and analyzed based on several criteria and ecological levels. The Ecological Model of Health Behavior was used to classify the determinants that affect the performance of protective behaviors. The National Heart, Lung and Blood Institute (NHLBI) has developed a quality assessment tool for studies. Results A total of 39 studies were ultimately selected for inclusion in this analysis. Many of these studies were conducted in developing countries. The most important factors that have an impact on protective measures include a variety of socio-demographic characteristics (age, gender, level of education, income, farming experience, experience of using pesticides), individual level (knowledge, attitude, risk perception, intention), interpersonal level (subjective norms), organizational level (education), and public policy level (government attention, health costs, governmental extension services). The quality of most studies was fair. Conclusions Research indicates that several factors influence the use of personal protective equipment and safe behaviors when handling pesticides. These include farmers’ education level, knowledge, and attitudes towards safety measures. Environmental factors such as access to information, extension services, training programs, and media coverage can also help minimize exposure to pesticides.