Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density–phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4−/CD8− double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.
Empirical comparison of deep learning models for fNIRS pain decoding

Introduction Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain. Methods In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features. Results The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models. Discussion Overall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios.
Vegetation Responses to Climate Change by Using the Satellite-Derived Normalized Difference Vegetation Index: A Review

Climate is one of the most important environmental factors affecting spatial and temporal vegetation dynamics. The different responses of vegetation to global warming tend to change over different time and space scales. The rapid development of remote sensing has made it possible to examine vegetation changes at the regional and global scales. Two basic approaches to characterizing vegetation responses to changing climates are to use statistics and modeling. New statistical methods and improved vegetation models have recently been successfully applied to assessing the effects of climate change on vegetation dynamics. The data and analytical methods used and the discoveries made in previous studies of vegetation responses to climate are summarized in this paper, with a focus on vegetation responses to different climate parameters, including precipitation, temperature, and radiation. It is generally accepted that precipitation significantly affects the normalized difference vegetation index (NDVI) in arid and semiarid regions and in regions with distinct dry and wet seasons. The time lag in the response of vegetation to precipitation, and the way the time lag depends on local conditions. Temperature is the dominant climate parameter that affects the NDVI in temperate and boreal regions. The time lag in the vegetation response to temperature is somewhat less pronounced than the time lag in response to precipitation. Solar radiation is the predominant factor limiting vegetation growth in some low latitude areas and areas that have lots of cloud cover, and at certain times in high latitude regions. Future research should focus on 1) quantifying the human role in the response of vegetation to climate change, 2) remeasuring the vegetation responses to different climate parameter changes associated with global warming, and 3) considering the multi-scale assessment of climate change impacts on vegetation.
Accuracy of prognostic serological biomarkers in predicting liver fibrosis severity in people with metabolic dysfunction-associated steatotic liver disease: a meta-analysis of over 40,000 participants

Introduction A prognostic model to predict liver severity in people with metabolic dysfunction-associated steatotic liver disease (MASLD) is very important, but the accuracy of the most commonly used tools is not yet well established. Objective The meta-analysis aimed to assess the accuracy of different prognostic serological biomarkers in predicting liver fibrosis severity in people with MASLD. Methods Adults ≥18 years of age with MASLD were included, with the following: liver biopsy and aspartate aminotransferase-to-platelet ratio (APRI), fibrosis index-4 (FIB-4), non-alcoholic fatty liver disease fibrosis score (NFS), body mass index, aspartate aminotransferase/alanine aminotransferase ratio, diabetes score (BARD score), FibroMeter, FibroTest, enhanced liver fibrosis (ELF), Forns score, and Hepascore. Meta-analyses were performed using a random effects model based on the DerSimonian and Laird methods. The study’s risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Results In total, 138 articles were included, of which 86 studies with 46,514 participants met the criteria for the meta-analysis. The results for the summary area under the receiver operating characteristic (sAUROC) curve, according to the prognostic models, were as follows: APRI: advanced fibrosis (AF): 0.78, any fibrosis (AnF): 0.76, significant fibrosis (SF): 0.76, cirrhosis: 0.72; FIB-4: cirrhosis: 0.83, AF: 0.81, AnF: 0.77, SF: 0.75; NFS: SF: 0.81, AF: 0.81, AnF: 0.71, cirrhosis: 0.69; BARD score: SF: 0.77, AF: 0.73; FibroMeter: SF: 0.88, AF: 0.84; FibroTest: SF: 0.86, AF: 0.78; and ELF: AF: 0.87. Conclusion The results of this meta-analysis suggest that, when comparing the scores of serological biomarkers with liver biopsies, the following models showed better diagnostic accuracy in predicting liver fibrosis severity in people with MASLD: FIB-4 for any fibrosis, FibroMeter for significant fibrosis, ELF for advanced fibrosis, and FIB-4 for cirrhosis. Clinical trial registration: [https://clinicaltrials.gov/], identifier [CRD 42020180525].
Efficient and Selective Removal of Heavy Metals and Dyes from Aqueous Solutions Using Guipi Residue-Based Hydrogel

The presence of organic dyes and heavy metal ions in water sources poses a significant threat to human health and the ecosystem. In this study, hydrogel adsorbents for water pollution remediation were synthesized using Guipi residue (GP), a cellulose material from Chinese herbal medicine, and chitosan (CTS) through radical polymerization with acrylamide (AM) and acrylic acid (AA). The characteristics of the hydrogels were analyzed from a physicochemical perspective, and their ability to adsorb was tested using model pollutants such as Pb2+, Cd2+, Rhodamine B (RhB), and methyl orange (MO). The outcomes revealed that GP/CTS/AA-co-AM, which has improved mechanical attributes, effectively eliminated these pollutants. At a pH of 4.0, a contact duration of 120 min, and an initial concentration of 600 mg/L for Pb2+ and 500 mg/L for Cd2+, the highest adsorption capabilities were 314.6 mg/g for Pb2+ and 289.1 mg/g for Cd2+. Regarding the dyes, the GP/CTS/AA-co-AM hydrogel displayed adsorption capacities of 106.4 mg/g for RhB and 94.8 mg/g for MO, maintaining a stable adsorption capacity at different pHs. Compared with other competitive pollutants, GP/CTS/AA-co-AM demonstrated a higher absorption capability, mainly targeted toward Pb2+. The adsorption processes for the pollutants conformed to pseudo-second-order kinetics models and adhered to the Langmuir models. Even after undergoing five consecutive adsorption and desorption cycles, the adsorption capacities for heavy metals and dyes remained above 70% and 80%. In summary, this study effectively suggested the potential of the innovative GP/CTS/AA-co-AM hydrogel as a practical and feasible approach for eliminating heavy metals and dyes from water solutions.
Do Postural and Walking Stabilities Change over a Decade by Aging? A Longitudinal Study

Background: Previous studies have demonstrated that the center of gravity (COG) is more unstable in the elderly than in young people. However, it is unclear whether aging itself destabilizes the COG. This study aimed to investigate changes in COG sway and gait kinematics over time by a longitudinal study of middle-aged and elderly adults. Methods: This study included 198 healthy middle-aged and elderly people who underwent stabilometry at ten-year intervals. The participants’ mean age at baseline was 62.9 ± 6.5 years, and 77 (39%) of them were male. The results of stabilometry (mean velocity, sway area, postural sway center in the medial–lateral direction [X center], and postural sway center in the anterior–posterior direction [Y center]), and results of exercise tests (the height-adjusted maximum stride length [HMSL] and the 10 m walk test [10MWT]) were analyzed. The destabilized group with 11 participants, whose mean velocity exceeded 3 cm/s after 10 years, was compared with the stable group with 187 participants, whose mean velocity did not exceed 3 cm/s. Results: Mean velocity increased significantly over ten years (open-eye, from 1.53 ± 0.42 cm to 1.86 ± 0.67 cm, p < 0.001); however, the sway area did not change significantly. X center showed no significant change, whereas Y center showed a significant negative shift (open-eye, from −1.03 ± 1.28 cm to −1.60 ± 1.56 cm, p < 0.001). Although the results of 10MWT and initial HMSL did not differ significantly, the HMSL in the destabilized group at ten years was 0.64, which was significantly smaller than the 0.72 of others (p = 0.019). Conclusions: The ten-year changes in COG sway in middle-aged and elderly adults were characterized by a significant increase in mean velocity but no significant difference in sway area. Because the destabilized group had significantly smaller HMSL at ten years, instability at the onset of movement is likely to be affected by COG instability.
Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy

Aim The diagnosis of cervical spondylotic myelopathy (CSM) relies on several methods, including x-rays, computed tomography, and magnetic resonance imaging (MRI). Although MRI is the most useful diagnostic tool, strategies to improve the precise and independent diagnosis of CSM using novel MRI imaging techniques are urgently needed. This study aimed to explore potential brain biomarkers to improve the precise diagnosis of CSM through the combination of voxel-based morphometry (VBM) and tensor-based morphometry (TBM) with machine learning techniques. Methods In this retrospective study, 57 patients with CSM and 57 healthy controls (HCs) were enrolled. The structural changes in the gray matter volume and white matter volume were determined by VBM. Gray and white matter deformations were measured by TBM. The support vector machine (SVM) was used for the classification of CSM patients from HCs based on the structural features of VBM and TBM. Results CSM patients exhibited characteristic structural abnormalities in the sensorimotor, visual, cognitive, and subcortical regions, as well as in the anterior corona radiata and the corpus callosum [P < 0.05, false discovery rate (FDR) corrected]. A multivariate pattern classification analysis revealed that VBM and TBM could successfully identify CSM patients and HCs [classification accuracy: 81.58%, area under the curve (AUC): 0.85; P < 0.005, Bonferroni corrected] through characteristic gray matter and white matter impairments. Conclusion CSM may cause widespread and remote impairments in brain structures. This study provided a valuable reference for developing novel diagnostic strategies to identify CSM.
Design and assessment of a reconfigurable behavioral assistive robot: a pilot study

Introduction For patients with functional motor disorders of the lower limbs due to brain damage or accidental injury, restoring the ability to stand and walk plays an important role in clinical rehabilitation. Lower limb exoskeleton robots generally require patients to convert themselves to a standing position for use, while being a wearable device with limited movement distance. Methods This paper proposes a reconfigurable behavioral assistive robot that integrates the functions of an exoskeleton robot and an assistive standing wheelchair through a novel mechanism. The new mechanism is based on a four-bar linkage, and through simple and stable conformal transformations, the robot can switch between exoskeleton state, sit-to-stand support state, and wheelchair state. This enables the robot to achieve the functions of assisted walking, assisted standing up, supported standing and wheelchair mobility, respectively, thereby meeting the daily activity needs of sit-to-stand transitions and gait training. The configuration transformation module controls seamless switching between different configurations through an industrial computer. Experimental protocols have been developed for wearable testing of robotic prototypes not only for healthy subjects but also for simulated hemiplegic patients. Results The experimental results indicate that the gait tracking effect during robot-assisted walking is satisfactory, and there are no sudden speed changes during the assisted standing up process, providing smooth support to the wearer. Meanwhile, the activation of the main force-generating muscles of the legs and the plantar pressure decreases significantly in healthy subjects and simulated hemiplegic patients wearing the robot for assisted walking and assisted standing-up compared to the situation when the robot is not worn. Discussion These experimental findings demonstrate that the reconfigurable behavioral assistive robot prototype of this study is effective, reducing the muscular burden on the wearer during walking and standing up, and provide effective support for the subject's body. The experimental results objectively and comprehensively showcase the effectiveness and potential of the reconfigurable behavioral assistive robot in the realms of behavioral assistance and rehabilitation training.
Inconsistency of surface‐based (SYNOP) and satellite‐based (MODIS) cloud amount estimations due to the interpretation of cloud detection results

Surface‐based and satellite‐based observations remain the fundamental source of cloud amount data for climatologists. However, both data sets show inconsistency related to the interpretation of instantaneous cloud detection, whether measured using the okta scale for surface‐based (SYNOP) observations or cloud mask classes (for satellite‐based observations). This study compared mean monthly SYNOP cloud amount with those reported for the moderate resolution imaging spectroradiometer (MODIS) cloud imager onboard Aqua and Terra satellites. Data were collected simultaneously (±10 min) over Poland between 2003 and 2013 (a total of 3240 MODIS passes). Results showed that in daytime conditions the average range of inconsistency for SYNOP (10.8%) is similar to MODIS (10.1%). However, for MODIS it doubles (20.9%) at nighttime. This is attributed to the change in frequency of MODIS cloud mask intermediate classes. Furthermore, in particular months' range of inconsistency for MODIS exceeded 20% in daytime and 50% at nighttime, while maximum values for SYNOP were always lower than 20% (a consequence of the okta scale). The findings also confirmed that the standard procedure used to interpret SYNOP okta values (i.e. the assumption that 1 okta equals 12.5%) is a relatively accurate (±2%) estimate of monthly mean cloud amount, although this assumes that surface‐based observers always correctly assign the correct okta value to the cloud fraction. Finally, the error in the SYNOP cloud amount estimation was correlated with the average cloud amount for a given location. Those findings will improve the reliability of cloud amount validation efforts that use SYNOP data as a reference.
New p-Terphenyl from the Fruit of Citrullus colocynthis (Cucurbitaceae)

One new substituted p-terphenyl (1) has been isolated along with the known metabolites; cucurbitacin E 2-O-β-D-glucoside (2) and α-amyrin (3) from ethyl acetate and dichloromethane fractions of fruit extract of Citrullus colocynthis. The structure of the new metabolite was elucidated by combined analysis of 1D (1H and 13C) and 2D (HSQC and HMBC correlations) NMR and MS spectral data. The known metabolites were characterized by comparison of the spectral data with those reported in literature.
Understanding tourists in Uganda : exploring motivation and characteristics of non-resident visitors to Uganda

Uganda’s tourism industry is a major source of foreign exchange income, creating much-needed employment and development opportunities. This study provided an overview of motivations and characteristics of non-resident visitors to Uganda. This exploratory research was conducted in Uganda during July and August 2011. Information obtained includes the popular activities and places visited, visitor expenditures, demographic information, motivation for visiting Uganda, and motivation for traveling and volunteering in general. This study concluded that the majority of non-resident visitors leaving Uganda were volunteers, with a majority being religious volunteers who did not represent a sustainable tourism market within Uganda. Data provided in this study can allow tourism stakeholders to draw conclusions on what is needed in Uganda to help the tourism sector to continue growing more sustainably and to remain competitive amongst other East African tourist destinations. Key Terms: volunteer tourism, sustainable tourism, motivation, Uganda, non-resident visitors, Leisure Motivation Scale, religious volunteer. Understanding Tourists in Uganda
Smart parking management system

The present invention relates to a smart parking control unit to perform, security / crime prevention function and parking control related to a smart parking control unit at the same time. Is for this purpose recognize the vehicle through a car number recording unit, an image of a license plate number for recording an in car park and connected to the vehicle smart parking control unit, the smart parking control unit for control of the ipchulip into the building or the building of the vehicle parking time and the smart parking control device located in a store of the first terminal, and the building or buildings receives the parking fee information, it characterized in that a second terminal of the payment amount of money corresponding to the purchase price from the buyer are provided.
Estimation of Dynamice Traffic Density for Official Statisitcs based on Conbined Use of GPS and Loop Detector Data

Traffic density is one of the important variables to identify traffic states, which attracts a lot of attention form both traffic management and Statistics Netherlands. In this paper, we aim to validate the usage of GPS information on estimating traffic density for traffic statistics. Our method of traffic density estimation is based on the definition of traffic density and the combination of loop detector and GPS data. Then the estimated dynamic density is scaled up to the whole road network to be used for official statistics. As such, it provides dynamic information and offers opportunities to reduce administrative burdens in statistics office.
Ultra weak photon emission—a brief review

Cells emit light at ultra-low intensities: photons which are produced as by-products of cellular metabolism, distinct from other light emission processes such as delayed luminescence, bioluminescence, and chemiluminescence. The phenomenon is known by a large range of names, including, but not limited to, biophotons, biological autoluminescence, metabolic photon emission and ultraweak photon emission (UPE), the latter of which shall be used for the purposes of this review. It is worth noting that the photons when produced are neither ‘weak’ nor specifically biological in characteristics. Research of UPE has a long yet tattered past, historically hamstrung by a lack of technology sensitive enough to detect it. Today, as technology progresses rapidly, it is becoming easier to detect and image these photons, as well as to describe their function. In this brief review we will examine the history of UPE research, their proposed mechanism, possible biological role, the detection of the phenomenon, and the potential medical applications.
Salivary Biomarkers for Parkinson’s Disease: A Systematic Review with Meta-Analysis

Parkinson’s Disease (PD) is a common neurodegenerative disease which manifests with motor features, such as bradykinesia, resting tremor, rigidity, and postural instability. Using the non-invasive technique of saliva collection, we designed a systematic review to answer the question “Are salivary biomarkers reliable for the diagnosis of Parkinson’s Disease? ”. Following inclusion and exclusion criteria, 30 studies were included in this systematic review (according to the PRISMA statement guidelines). Mostly proteins were reported as potential biomarkers in saliva. Based on meta-analysis, in PD patients, salivary levels of total alpha-synuclein were significantly decreased, and those of oligomeric alpha-synuclein were significantly increased. Also, according to pooled AUC, heme oxygenase-1 demonstrated significant predictive value for saliva-based PD diagnosis. In conclusion, some potential biomarkers, especially alpha-synuclein, can be altered in the saliva of PD patients, which could be reliably useful for early diagnosis of this neurodegenerative disease differentiating other synucleopathies.