Miracle-dz/UniFAR-FTUs huggingface dataset:
Hb estimation at point of care using cyanmethaemoglobin method

Background: Accurate estimation of haemoglobin (Hb) is an essential prerequisite for detection of anaemia and for assessing response to treatment. In India the gold standard cyanmethaemoglobin method is used for estimation of Hb in tertiary care institutions but accurate methods for Hb estimation are not being used in primary and secondary healthcare settings. Major national surveys have demonstrated the feasibility and accuracy of Hb estimation by indirect cyanmethaemoglobin method (ICM). However, ICM has not been used for Hb estimation in point of care because of reports that ICM underestimates Hb. A study was taken up to compare the accuracy of Hb estimation by direct (DCM) and indirect cyanmethaemoglobin (ICM) method.Methods: ANMs and laboratory technicians pipetted out duplicate samples of blood for both direct and indirect Hb estimation. Another trained laboratory technician undertook Hb estimation using colorimeter in all samples. Hb values between duplicates were compared.Results: There was excellent concordance between means and frequency distribution of Hb values between duplicates when Hb in both were estimated using DCM or ICM and also when Hb estimation was done by DCM in one and ICM in the other. Bland Altman Plots confirmed that across Hb levels there was excellent concordance between direct and indirect method of Hb estimation.Conclusions: Indirect cyanmethaemoglobin method is accurate. It can be used at point of care for screening for anaemia and assessing improvement in Hb after treatment especially in primary and secondary health care settings.
Factors for unsuccessful endoscopic hemostasis in patients with severe peptic ulcer bleeding

Abstract Objectives Although the first approach for peptic ulcer bleeding is endoscopic hemostasis, quick determination of a hemostatic strategy is important in patients with vitals indicating shock. However, the unsuccessful factors for endoscopic treatment have yet to be sufficiently examined. We aimed to investigate the factors for unsuccessful endoscopic hemostasis in severe peptic ulcer bleeding. Materials and methods Unsuccessful factors were retrospectively investigated in 150 eligible patients who underwent endoscopic hemostasis for shock-presenting peptic ulcer bleeding at our critical care center between April 2007 and March 2021. Results There were 123 and 27 cases of successful and unsuccessful endoscopic hemostasis, respectively. Causative diseases included gastric ulcer bleeding in 124 patients (82.7%) and duodenal ulcer bleeding in 26 patients (17.3%). Shock index (SI) (1.46 vs. 1.60) (p = .013), exposed blood vessel diameter (1.4 mm vs. 3.1 mm) (p < .001) identified on contrast-enhanced computed tomography (CE-CT), duodenal ulcer bleeding (p = .012), and Forrest classification Ia (p = .004) were extracted as independent factors for unsuccessful endoscopic hemostasis. In receiving operating curve analysis, when the cut-off value for the SI was set at 1.53, the sensitivity and specificity were 70.4% and 63.4%, respectively. When the cut-off value for the exposed blood vessel diameter was set at 1.9 mm, these were 88.9% and 83.7%, respectively. Conclusions When these factors (SI ≥ 1.53, exposed blood vessel diameter ≥1.9 mm identified on CE-CT, duodenal ulcer bleeding, and Forrest Ia) are present in patients with severe peptic ulcer bleeding, non-endoscopic hemostasis, such as interventional radiology (IVR) and surgery, should be considered.
Etiology and treatment of cervical kyphosis: state of the art review-a narrative review.

Objective To provide state of the art review regarding cervical kyphosis. Background Cervical spine kyphosis has been increasingly common due to the growing elderly population. Clinicians should comprehensively understand its symptoms, biomechanics, etiology, radiographic evaluation, classification, and treatment options and complications of each treatment. Comprehensive review will help clinicians improve the management for patients with cervical kyphosis. Methods The available literature relevant to cervical kyphosis was reviewed. PubMed, Medline, OVID, EMBASE, and Cochrane were used to review the literature. Conclusions This article summarizes current concepts regarding etiology, evaluation, surgical treatment, complications and outcomes of cervical kyphosis. Major etiologies of cervical kyphosis include degenerative, post-laminectomy, and ankylosing spondylitis. Clinical presentations include neck pain, myelopathy, radiculopathy, and problems with horizontal gaze, swallowing and breathing. Cervical lordosis, C2-7 sagittal vertical axis, chin-brow to vertical angle, and T1 slope should be evaluated from upright lateral 36-inch film. The most widely used classification system includes a deformity descriptor and 5 modifiers. A deformity descriptor provides a basic grouping of the deformity consisting of five types, cervical, cervicothoracic, thoracic, coronal cervical deformity, and cranio-vertebral junction deformity. The 5 modifiers include C2-7 sagittal vertical axis, chin-brow to vertical angle, T1 slope minus cervical lordosis, myelopathy based on modified Japanese Orthopaedic Association score, and SRS-Schwab classification for thoracolumbar deformity. Current treatment options include anterior discectomy and fusion, anterior osteotomy, Smith-Peterson osteotomy, pedicle subtraction osteotomy, or a combination of these based on careful preoperative evaluation.
Advances in micro lithium-ion batteries for on-chip and wearable applications

Miniature power sources have gained widespread attention and accelerated progress with portable, wearable, and integrated electronic technologies. Micro lithium-ion batteries (μLIBs) featured small size, lightweight, high capacity, and long cycle life, which also offer stability, safety, and compatibility with microfabrication, make them the ideal choice for energy storage. Researchers have engaged in the performance optimization and application expansion of μLIBs, especially in the on-chip μLIBs with compatible integration for microdevices and flexible μLIBs for wearable applications. This paper reviews the working principles, performance metrics, and design methodologies of μLIBs, highlighting the advantages of the architecture design. Typical materials and fabrication methods are introduced, and their effects on the device performance and system integration are analyzed. The developments of μLIBs are summarized from the perspective of 3D architecture design to the on-chip and wearable applications. At last, the challenges and the prospects that inspire further research and development of μLIBs are concluded.
Nickel Manganite-Sodium Alginate Nano-Biocomposite for Temperature Sensing

Nanocrystalline nickel manganite (NiMn2O4) powder with a pure cubic spinel phase structure was synthesized via sol-gel combustion and characterized with XRD, FT-IR, XPS and SEM. The powder was mixed with sodium alginate gel to form a nano-biocomposite gel, dried at room temperature to form a thick film and characterized with FT-IR and SEM. DC resistance and AC impedance of sensor test structures obtained by drop casting the nano-biocomposite gel onto test interdigitated PdAg electrodes on an alumina substrate were measured in the temperature range of 20–50 °C at a constant relative humidity (RH) of 50% and at room temperature (25 °C) in the RH range of 40–90%. The material constant obtained from the measured decrease in resistance with temperature was determined to be 4523 K, while the temperature sensitivity at room temperature (25 °C) was −5.09%/K. Analysis of the complex impedance plots showed a dominant influence of grains. The decrease in complex impedance with increase in temperature confirmed the negative temperature coefficient effect. The grain resistance and grain relaxation frequency were determined using an equivalent circuit. The activation energy for conduction was determined as 0.45 eV from the temperature dependence of the grain resistance according to the small polaron hopping model, while the activation energy for relaxation was 0.43 eV determined from the Arrhenius dependence of the grain relaxation frequency on temperature.
Analysis and Optimization of Process Parameters in Abrasive Waterjet Contour Cutting of AISI 304L

Abrasive waterjet machining is applied in various industries for contour cutting of heat-sensitive and difficult-to-cut materials like austenitic stainless steel 304L, with the goal of ensuring high surface integrity and efficiency. In alignment with this manufacturing aspiration, experimental analysis and optimization were carried out on abrasive waterjet machining of austenitic stainless steel 304L with the objectives of minimizing surface roughness and maximizing material removal rate. In this machining process, process parameters are critical factors influencing contour cutting performance. Accordingly, Taguchi’s S/N ratio method has been used in this study for the optimization of process parameters. Further in this work, the impacts of input parameters are investigated, including waterjet pressure, abrasive mass flow rate, traverse speed and material thickness on material removal rate and surface roughness. The study reveals that an increasing level of waterjet pressure and abrasive mass flow rate achieved better surface integrity and higher material removal values. The average S/N ratio results indicate an optimum value of waterjet pressure at 300 MPa and abrasive mass flow rate of 500 g/min achieved minimum surface roughness and maximum material removal rate. It was also found that an optimized value of a traverse speed at 90 mm/min generates the lowest surface roughness and 150 mm/min produces the highest rate of material removed. Moreover, analysis of variance in the study showed that material thickness was the most influencing parameter on surface roughness and material removal rate, with a percentage contribution ranging 90.72–97.74% and 65.55–78.17%, respectively.
A Novel Ergodic Discrete Difference Equation Model of Central Pattern Generator: Theoretical Analysis and Efficient Implementation

A novel ergodic discrete difference equation (DDE) oscillator and its theoretical analysis method are presented. Theoretical analyses reveal the oscillator is more suitable to build a central pattern generator (CPG) compared to a standard non-ergodic DDE oscillator. Further, it is shown the ergodic DDE CPG can generate rhythmic signals suitable for realizing fundamental gaits of a quadruped robot. Then a prototype of the ergodic DDE CPG is implemented by a field programmable gate array and experiments show it can realize the fundamental gaits of the quadruped robot. Further, it is shown the ergodic DDE CPG consumes much lower power and much fewer circuit elements compared to a typical conventional CPG implemented as a customized digital signal processor.
Potential of Using Medium Electric Vehicle Fleet in a Commercial Enterprise Transport in Germany on the Basis of Real-World GPS Data

The intensive electrification of the automotive sector means that the energy system must be able to adapt to the current market situation. The increase in energy demand is a major factor associated with electric vehicles. The study analyzed the operation of a grid-connected facility operating a vehicle fleet providing transport services in the region Halle/Saale, Germany. Measurement data were used in the analysis, including global positioning system data of the vehicles and technical data, including average fuel consumption on a given route section, daily load demand of the industrial facility, and energy generation from photovoltaics. This paper shows the impact of using a battery electric vehicles (BEVs) fleet in the load distribution for the industrial facility considered. The NEDC energy consumption profile for the Nissan e-NV200 were used in this study. Furthermore, the paper presented simulation results allowing one to determine the usage potential, energy demand, and consumption of EVs using real data, reliably representing the processes related to EV daily use. The measurement data were captured using available specialized equipment: Dako-Key (GPS data), PV power generation (Siemens 7KM PAC4200), and load (Janitza UMG 604-Pro) in September, 2018. On this basis, it is possible to identify the effects and variations in load on the power grid during the replacement of combustion vehicle fleets used currently by EVs for the provision of transport services. Three models were presented, making it possible to calculate changes in energy demand for each scenario. In the first model, EVs were charged exclusively from the distribution network. In the second, the energy generation from a renewable source was considered and the possibility of compensating the energy demand of the vehicles from this source was demonstrated. In the third model, the daily load profile and the period of maximum load in the electricity grid were considered. The results are presented in graphical and tabular form. Finally, the potential of using an EV fleet to increase the functionality of a modern industry object was determined and discussed. Based on data for the adopted scenarios, electrification of transport can increase demand for energy by 40.9% for individual enterprises. The electrification of the automotive sector will increase the instantaneous energy demand of businesses, forcing the integration of renewable energy sources during designing new invests.
Transpiration efficiency of corn hybrids at different growth stages

ABSTRACT Understanding transpiration efficiency (TE), especially in water-limited environments, is likely to result in increased crop yield. Two successive greenhouse experiments were conducted in 2017 to determine season-long TE in three corn (Zea mays L.) hybrids (P1151AM, 33D53AM, and P1564HR) and investigate whether the TE varies among different plant growth stages (six-leaf, flag leaf, mid-grain filling, and physiological maturity). Corn plants were grown in wooden boxes up to maturity while eliminating water loss except that lost in the form of transpiration. To prevent the belowground water loss, wooden boxes were lined with plastic sheets and sealed to the lids. The TE was determined as the ratio of aboveground biomass to cumulative water used in transpiration. The mean TE was the highest (6.27 kg m−3) when plants were harvested at the mid-grain-filling stage, followed by the flag leaf stage (5.91 kg m−3), and physiological maturity (5.81 kg m−3), and lowest at the six-leaf stage (5.25 kg m−3). Two-drought tolerant (DT) hybrids, P1151AM (6.20 kg m−3) and P1564HR (6.17 kg m−3), had higher TE than the conventional hybrid, 33D53AM (5.42 kg m−3). Grain-filling period is critical for water management in corn. This study showed that TE was also highest when plants were harvested at the mid-grain-filling stage and decreased thereafter. Therefore, producers, who have limited irrigation facilities, should develop a strategy to apply a sufficient amount of water before and during the grain-filling period for maximizing corn yield.
Social Disparities in Post-diagnosis Health Behaviors of Iranian Breast Cancer Survivors: The Mediating Role of Psychosocial Factors as Potential Buffering Agents

Background: Breast cancer (BC) is surging as a public health issue in Iran and engagement in positive health behaviors improves the odds of survival and reduces the risk of concomitant comorbidities in BC survivors. Objectives: The primary purpose of this study was to investigate the impact of socioeconomic inequalities on leisure-time physical activity (LTPA) and fruit and vegetable (F & V) consumption among Iranian BC survivors. Another objective of this study was to examine the role of psychosocial factors, such as stress, self-efficacy, and social support as mediators between socioeconomic status (SES) and these health behaviors. Few studies have investigated social disparities in the health behaviors of cancer survivors. Likewise, the mediating role of psychosocial factors in the SES-health behavior gradient has rarely been explored in the cancer context. Psychosocial factors might have positive implications for socioeconomically disadvantaged survivors. Methods: Cross-sectional data were obtained from 196 patients with BC by a telephone-administrated questionnaire. The Behavioral Risk Factor Surveillance System (BRFSS) 2013 was partially employed to measure exercise and F & V consumption. Cross-sectional analyses (correlation and logistic regression) were conducted to assess the relationship between SES, psychosocial factors, and health behaviors (P < 0.05). Results: Regarding LTPA and F & V consumption, more than half of the survivors complied with the recommended levels, consecutively. Both education and family income exhibited a positive association with LTPA and F & V consumption. Furthermore, self-efficacy and social support showed a positive relationship with LTPA and F & V consumption. Conclusions: The SES impacted health behaviors directly and via intermediary psychosocial factors. The results can inform future studies and interventions; psychosocial factors could buffer the effects of social inequalities on health behaviors. A key policy priority should, therefore, be planning and implementing psychosocial empowerment interventions to promote exercise and a healthy diet among impoverished cancer survivors.
A sparse coded composite descriptor for human activity recognition

This paper proposes a novel algorithm for computing discriminative descriptors named as a sparse coded composite descriptor (SCCD) for robust human activity recognition. The proposed method blends the state‐of‐the‐art handcrafted features and the discriminative nature of the sparse representation of visual information. The human activity is firstly modelled using any handcrafted feature, and then the sparse codes computed on a discriminative sparse dictionary of these features are embedded to provide discrimination in the feature set. Finally, a support vector machine (SVM) is trained using the proposed SCCDs to perform classification of different human activities. A new feature named as differential motion descriptor (DMD) is also proposed to extract the motion as well as spatial information from an activity video. The simulation results reveal that in comparison with the handcrafted feature, the corresponding SCCD improves the recognition accuracy significantly. The proposed method is compared with state‐of‐the‐art methods on KTH, Ballet, UCF50, and HMDB51 datasets and the proposed methodology of composite features outperforms these methods in terms of recognition accuracy.
Deep Eutectic Solvents and Pharmaceuticals

Deep eutectic solvents (DES) are eutectic mixtures that present a deviation from the ideal thermodynamic solid–liquid phase behavior, where a significant depression in the melting temperature occurs. If properly designed and chosen, DES may be liquid at room and the human body’s temperatures and display a biocompatible character, thus representing relevant options in the pharmaceutical field. Accordingly, DES have been studied as alternative solvents or in formulations of pharmaceuticals to improve their solubility and stability. Depending on the DES components, these mixtures might exhibit interesting biological activities compatible with several applications. The use of DES as functional agents or as novel liquid forms of active pharmaceutical ingredients (API-DES) with the goal of improving bioavailability, permeability and therapeutic efficacy of a given API stands as alternative strategies in the pharmaceutical field for drug delivery purposes.
Deep learning methods improve polygenic risk analysis and prediction for Alzheimer’s disease

Recent advances in genetic sequencing have enabled comprehensive genetic analyses of human diseases, resulting in the identification of numerous genetic risk factors for heritable disorders including Alzheimer’s disease (AD). Such analyses enable AD risk prediction well before disease onset, which is critical for early interventions. However, current analytical approaches have limited ability to accurately estimate the risk effects of genetic variants owing to epistatic effects, which have been overlooked in most previous studies, resulting in unsatisfactory disease risk prediction. Herein, we modeled AD polygenic risk using deep learning methods, which outperformed existing models (i.e., weighted polygenic risk score and lasso models) for classifying disease risk. Moreover, by examining the associations between the outcomes from deep learning methods and multi-omics data obtained from our in-house Chinese AD cohorts, we identified the pathways that are potentially regulated by AD polygenic risk, including immune-associated signaling pathways. Thus, our results demonstrate the utility of deep learning methods for modeling the genetic risks of human diseases, which can facilitate both disease risk classification and the study of disease mechanisms.
Finding Solutions of the Navier‐Stokes Equations through Quantum Computing—Recent Progress, a Generalization, and Next Steps Forward

Efficient simulation of a quantum system's dynamics is expected to be an important application area for quantum computers as existing classical computers cannot do this. However, quantum systems are not unique in being hard to simulate. For example, classical nonlinear continuum systems and fields are governed by nonlinear partial differential equations whose solution is also hard for classical computers. Solving such equations is essential for many economically important industries/applications such as the aerospace industry, weather‐forecasting, fiber‐optics communication, and plasma magneto‐hydrodynamics. This raises the question: can a quantum computer speed‐up solving these equations? In this Review, a new quantum algorithm is described for solving nonlinear partial differential equations for which the answer is yes. First, a new quantum algorithm is discussed for solving the Navier‐Stokes nonlinear partial differential equations which govern the flow of a viscous fluid. Its construction, verification, and computational cost are described, and it is shown that a significant quantum speed‐up is possible. Its generalization to a quantum algorithm for solving nonlinear partial differential equations is described. The Review closes with a discussion of next steps forward. These new quantum algorithms open up a large new application area for quantum computing with substantial economic impact, including the trillion‐dollar aerospace industry.
Determination of intraspecific variation in seed weight, leaf functional traits, and sapling size of Betula ermanii using a common garden experiment

ABSTRACT Habitat loss of forest tree species distributed in cold temperate and alpine zones is a serious issue in the context of global warming. Considering that intraspecific variation is essential for adaptation to changing environments, intraspecific variation in seed weight, leaf traits, and sapling size of a sub-alpine broad-leaved tree species, Betula ermanii, was examined in this study using seeds collected from 11 populations across its natural distribution. Seeds were sown in seed beds, and containerized seedlings were grown in a greenhouse with a uniform environment (a common garden experiment). The effects of four climate variables, i.e. warmth index (WI), minimum temperature of the coldest month (TMC), summer precipitation (PRS), and maximum snow depth (MSD) were evaluated on seed weight, leaf functional traits, and sapling size using a generalized linear mixed model (GLMM). Positive latitudinal clines were detected for seed weight, whereas negative clines were detected for both specific leaf area and leaf nitrogen. These clines were not consistent with the general trends observed in previous studies. The reasons might be partly explained by geographic variation in climatic variables and nutrient availability. The findings obtained from this study should be useful for conservation of mountainous forest ecosystems in the age of climate change.