ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Burn Depth Analysis Using Multidimensional Scaling Applied to Psychophysical Experiment Data

In this paper a psychophysical experiment and a multidimensional scaling (MDS) analysis are undergone to determine the physical characteristics that physicians employ to diagnose a burn depth. Subsequently, these characteristics are translated into mathematical features, correlated with these physical characteristics analysis. Finally, a study to verify the ability of these mathematical features to classify burns is performed. In this study, a space with axes correlated with the MDS axes has been developed. 74 images have been represented in this space and a k-nearest neighbor classifier has been used to classify these 74 images. A success rate of 66.2% was obtained when classifying burns into three burn depths and a success rate of 83.8% was obtained when burns were classified as those which needed grafts and those which did not. Additional studies have been performed comparing our system with a principal component analysis and a support vector machine classifier. Results validate the ability of the mathematical features extracted from the psychophysical experiment to classify burns into their depths. In addition, the method has been compared with another state-of-the-art method and the same database.
Efficient Frequency Scaling Algorithm for Short-Range 3-D Holographic Imaging Based on a Scanning MIMO Array

Millimeter-wave (MMW) holographic imaging technology is widely used in plenty of short-range applications like security and medical diagnosis. When combining with multiple-input-multiple-output (MIMO) array, such a technology can acquire precise reconstruction with wider field of view and higher dynamic range. However, to focus the higher dimensional data set obtained from MIMO architecture, the complicated iteration or interpolation employed by the previous state-of-the-art focusing techniques prevents the real-time operation of such an imaging system under a general computation power. It is more economical to increase the operational speed by improving the algorithm efficiency. Hence, a novel fast imaging algorithm that uses multistatic frequency scaling technique is proposed in this article for achieving real-time 3-D imaging on a 1-D MIMO scanning system. Only fast Fourier transform (FFT)/inverse FFT (IFFT) and multiplications are employed in the algorithm, which can be easily implemented. Compared with the previous state-of-the-art techniques, the proposed algorithm has the lower computation complexity. Practical experiments with self-developed MMW MIMO scanning radar prove the accuracy and efficiency of the algorithm. On a common laptop without any acceleration technology, the proposed algorithm requires less than one tenth of the time as required by the previous state-of-the-art techniques.
Modeling public holidays in load forecasting: a German case study

We address the issue of public or bank holidays in electricity load modeling and forecasting. Special characteristics of public holidays such as their classification into fixed-date and weekday holidays are discussed in detail. We present state-of-the-art techniques to deal with public holidays such as removing them from the data set, treating them as Sunday dummy or introducing separate holiday dummies. We analyze pros and cons of these approaches and provide a large load forecasting study for Germany that compares the techniques using standard performance and significance measures. Finally, we give general recommendations for the treatment of public holidays in energy forecasting to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated. This is especially useful, as the incorporation of holiday effects can improve the forecasting accuracy during public holidays periods by more than 80%, but even for non-holidays periods, the forecasting error can be reduced by approximately 10%.
Glutathione S-transferase P1 gene rs4147581 polymorphism predicts overall survival of patients with hepatocellular carcinoma: evidence from an enlarged study

As the most important detoxifying enzymes in liver, glutathione S-transferases (GSTs) can protect hepatocytes against carcinogens. We conducted a large cohort study to investigate the prognostic value of single nucleotide polymorphisms (SNPs) in seven encoding genes of GSTs for hepatocellular carcinoma (HCC). Twelve SNPs were genotyped and correlated with overall survival in 469 HCC patients. The median follow-up time of all patients was 21 (range 3-60) months, and the median survival time was 22 months. By the end of the study, 135 (28.8 %) patients were alive. Only rs4147581 in GSTP1 gene exhibited a significant association with survival of HCC patients (P = 0.006), with its mutant allele bearing a significantly lower risk of death (hazard ratio, 0.71; 95 % confidence interval 0.53-0.90), compared with the homozygous wide-type. A longer median survival time in patients with rs4147581 mutant allele was noticed than those homozygous wide-type (P = 0.03), and there was a marked adverse effect on survival conferred by smoking exposure in these patients. Conclusively, our findings provide supporting evidence for a contributory role of GSTP1 rs4147581 polymorphism in predicting the prognosis of HCC.
Functional Brain Network Classification Based on Deep Graph Hashing Learning

Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships in semantic space. To overcome this problem, we propose a novel brain network classification method based on deep graph hashing learning named BNC-DGHL. Specifically, we first extract the deep features of the brain network and then learn a graph hash function based on clinical phenotype labels and the similarity of diagnostic labels. Secondly, we use the learned graph hash function to convert deep features into hash codes, which can maintain the original semantic spatial relationships. Finally, we calculate the distance between hash codes to obtain the predicted category of the brain network. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that our method achieves better classification performance of brain diseases compared with some state-of-the-art methods, and the abnormal functional connectivities between brain regions identified may serve as biomarkers associated with related brain diseases.
Super Resolution Wide Aperture Automotive Radar

State-of-the-art automotive radars have an angular resolution of 1 degrees, which is insufficient for estimating the objects shape and boundaries at long distance. In this paper, we design a novel automotive radar with 1m aperture, which is an order of magnitude larger aperture than state-of-the-art automotive radars, and thus achieves super high angular resolution of 0.1 degrees. The radar is designed to overcomes the major technical challenges of a wide aperture radar, which are: non-robust phase coherency over a wide aperture, spanning a wide aperture with sparse antenna elements, simultaneous non-interfering transmissions from a relatively large number of transmit antennas with unambiguous target speed estimation, and high complexity of near-field beamforming. We demonstrate the super high resolution performance of the radar in automotive scenarios, and show that it attains comparable results to a high resolution LIDAR at short range, and outperforms the LIDAR at long ranges. The wide aperture radar has the potential to enable autonomous driving at higher speed, and also to increase the operation of autonomous driving to more regions and climates.
Extracellular production of Ulp1403-621 in leaky E. coli and its application in antimicrobial peptide production

Small ubiquitin-like modifier (SUMO) tag is widely used to promote soluble expression of exogenous proteins, which can then be cleaved by ubiquitin-like protease 1 (Ulp1) to obtain interested protein. But the application of Ulp1 in large-scale recombinant protein production is limited by complicated purification procedures and high cost. In this study, we describe an efficient and simple method of extracellular production of Ulp1403-621 using a leaky Escherichia coli BL21(DE3), engineered by deleting the peptidoglycan-associated outer membrane lipoprotein (pal) gene. Ulp1403-621 was successfully leaked into extracellular supernatant by the BL21(DE3)-Δpal strain after IPTG induction. The addition of 1% glycine increased the extracellular production of Ulp1403-621 approximately four fold. Moreover, extracellular Ulp1403-621 without purification had high activities for cleaving SUMO fusion proteins, and antimicrobial peptide pBD2 obtained after cleavage can inhibit the growth of Staphylococcus aureus. The specific activity of extracellular Ulp1403-621 containing 1 mM EDTA and 8 mM DTT reached 2.0 × 106 U/L. Another commonly used protease, human rhinovirus 3C protease, was also successfully secreted by leaky E. coli strains. In conclusion, extracellular production of tool enzymes is an attractive way for producing large-scale active recombinant proteins at a lower cost for pharmaceutical, industrial, and biotechnological applications. KEY POINTS: • First report of extracellular production of Ulp1403-621 in leaky Escherichia coli BL21(DE3) strain. • One percent glycine addition into cultivation medium increased the extracellular production of Ulp1403-621 approximately four fold. • The specific activity of extracellular Ulp1403-621 produced in this study reached 2.0 × 106 U/L.
Highly effective remediation of high arsenic-bearing wastewater using aluminum-containing waste residue

Wastewater from non-ferrous metal smelting is known as one of the most dangerous sources of arsenic (As) due to its high acidity and high arsenic content. Herein, we propose a new environmental protection process for the efficient purification and removal of arsenic from wastewater by the formation of an AlAsO4@silicate core-shell structure based on the characteristics of aluminum-containing waste residue (AWR). At room temperature, the investigation with AWR almost achieved 100% As removal efficiency from wastewater, reducing the arsenic concentration from 5500 mg/L to 52 μg/L. With Al/As molar ratio of 3.5, the structural properties of AWR provided good adsorption sites for arsenic adsorption, leading to the formation of arsenate and insoluble aluminum arsenate with As. As-containing AWR silicate shells were produced under alkaline conditions, resulting in an arsenic leaching concentration of 1.32 mg/L in the TCLP test. AWR, as an efficient As removal and fixation agent, shows great potential in the treatment of copper smelting wastewater, and is expected to achieve large-scale industrial As removal.
Occurrences, distribution and risk assessment of polar pesticides in Niger River valley and its tributary the Mekrou River (Niger Republic)

The increase in food needs due to high population growth in Niger has led to the intensification of urban agriculture and the increased use of pesticides. The objective of this study is primarily to assess the polar pesticide contamination (mainly herbicides) of the Niger River and its tributary, the Mekrou River, in Niger, using both grab sampling and POCIS (Polar Organic Chemical Integrative Samplers), and then to evaluate the risk to the aquatic environment. Two water sampling campaigns were carried out during the wet and dry seasons. The polar pesticides were analyzed by liquid chromatography coupled with tandem mass spectrometry, which allowed the identification of compounds with concentrations in the grab samples above the WHO guide values and the EU directive: diuron with 2221 ng/L (EU quality guideline: 200 ng/L), atrazine with 742 ng/L (EU quality guideline: 600 ng/L) and acetochlor with 238 ng/L (EU quality guideline: 100 ng/L). The risk assessment study indicated that diuron and atrazine present a high risk for the aquatic environment during the wet season. The main source of water contamination is the intensive use of pesticides in urban agriculture near the city of Niamey, and the intensive cotton farming in the Benin. Moreover, the surveys (30 producers interviewed) showed that 70% of the pesticides used are not approved by the Interstate Committee for Drought Control in the Sahel (CILSS) and some are prohibited in Niger. The inventory of pesticides sold in the zone showed that active ingredients used by producers are 48% insecticides, 45% herbicides, and 7% fungicides.
Cross-Scale Cost Aggregation for Stereo Matching

This paper proposes a generic framework that enables a multiscale interaction in the cost aggregation step of stereo matching algorithms. Inspired by the formulation of image filters, we first reformulate cost aggregation from a weighted least-squares (WLS) optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Our key motivation is that while the human stereo vision system processes information at both coarse and fine scales interactively for the correspondence search, state-of-the-art approaches aggregate costs at the finest scale of the input stereo images only, ignoring inter-consistency across multiple scales. This motivation leads us to introduce an interscale regularizer into the WLS optimization objective to enforce the consistency of the cost volume among the neighboring scales. The new optimization objective with the inter-scale regularization is convex, and thus, it is easily and analytically solved. Minimizing this new objective leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation approaches, including discrete and continuous parameterization methods, can be easily integrated into the proposed framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, Middlebury Third, KITTI, and New Tsukuba data sets.
Comparison of the Clinical and Genotypic Characteristics of Uropathogenic Escherichia coli Strains According to Sex in Korea

In this study, we compared the microbiological, genotypic, and antibiotic resistance characteristics of uropathogenic Escherichia coli (UPEC) strains in patients with pyelonephritis in Korea according to sex based on data corresponding to the February 2015 to June 2018 period. Based on Escherichia coli phylogenetic group analysis, gene virulence detection, and subgroup analyses by sex, we observed that the antibiotic resistance percentages and proportions corresponding to extended-spectrum beta-lactamase producing UPEC were higher in males than in females. In addition, phylogenetic group B2 showed predominance in both the male and female groups, which further showed similar adhesion molecule distributions. Toxin-associated factors, hlyA and cnf1, were more common in males. In clinical presentations, urinary predisposing factors, complicated urinary tract infections (UTIs), concomitant bacteremia, and persistent fever were also more common with males. Although females and males showed UPEC genotypic differences, there were no differences between them with respect to poor outcomes. Persistent fever was associated with community-acquired infection and bacteremic UTI and relapsed UTI within 3 months was associated with urinary tract stone. In future, it will be necessary to conduct multicenter studies, involving more cases on UPEC to validate our results.
Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

With the gradual increase in the difficulty of competitive martial arts, athletes must complete fine, stable, high-quality and difficult movements in order to achieve excellent performance. The real-time extraction of martial arts movements is a topic that many martial arts enthusiasts care about. This study mainly discusses the real-time extraction and simulation of VR image target in the difficult movement technology of martial arts routine. Considering the complex characteristics of martial arts movements, this article will analyze the preprocessing content of existing images. This includes image enhancement and image filtering, and uses median filtering methods to enhance the characteristics of the collected images. In this way, the visual effect of the original image can be improved, and the processed image will contribute to the subsequent segmentation. A new image segmentation method is proposed for the color model of the image. According to the H component of the HSV model representing the characteristics of chromaticity, the color image is transformed into the HSV model, and the H component is extracted. The histogram concept applies to H components. Based on the histogram of the H component, the segmentation threshold is determined, and the cropping target in the image is detected. Because the model space is very sensitive to color, VR technology is used to automatically determine the segmentation target. Combined with the above division methods, the automatic extraction of objects in the image is completed. The method of using VR technology for image extraction processing has high precision, and the error value is 3.92%<5%. The research results show that the method has good segmentation results, and is suitable for image segmentation under complex background and automatic image extraction under complex background.
Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters

Objective: To assess the clinical value of a radiomics model based on low-dose computed tomography (LDCT) in diagnosing benign and malignant pulmonary ground-glass nodules. Methods: A retrospective analysis was performed on 274 patients who underwent LDCT scanning with the identification of pulmonary ground-glass nodules from January 2018 to March 2021. All patients had complete clinical and pathological data. The cases were randomly divided into 191 cases in a training set and 83 cases in a validation set using the random sampling method and a 7:3 ratio. Based on the predictor sources, we established clinical, radiomics, and combined prediction models in the training set. A receiver operating characteristic (ROC) curve was generated for the training and validation sets, the predictive abilities of the different models for benign and malignant nodules were compared according to the area under the curve (AUC), and the model with the best predictive ability was selected. A calibration curve was plotted to test the good-of-fitness of the model in the validation set. Results: Of the 274 patients (84 males and 190 females), 156 had malignant, and 118 had benign nodules. The univariate analysis showed a statistically significant difference in nodule position between benign nodules and lung adenocarcinoma in both data sets (P <.001 and .021). In the training set, when the nodule diameter was >8 mm, the probability of nodule malignancy increased (P < .001). The results showed that the combined model had a higher prediction ability than the other two models. The combined model could distinguish between benign and malignant pulmonary nodules in the training set (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617; specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predict benign and malignant nodules in the validation set (AUC: 0.695; 95%CI: 0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850; NPV: 0.651). The calibration curve had a P value of 0.775, indicating that in the validation set, there was no difference between the value predicted by the combined model and the actual observed value and that the result was a good fit. Conclusion: The prediction model combining clinical information and radiomics parameters had a good ability to distinguish benign and malignant pulmonary ground-glass nodules.
Robotic Surgery: A Narrative Review

In general surgery, the use of robotic and laparoscopic methods has increased. Robotic surgery that requires the least incision has advanced over the years in a short period of time, benefitting both the patient and the surgeon. According to this, robotic platforms and tools are now being used and improved more commonly in general surgery. In a quickly growing and dynamic environment of research and development, the goal of this review is to explore the present and emerging surgical robotic technologies. Future progress in robotics will focus primarily on more durable haptic systems that would provide tactile and kinesthetic input, miniaturisation and micro-robotics, better visual feedback with higher fidelity detail and magnification, and autonomous robots. It is recommended to develop a structured training course with benchmarks for success and evidence-based training strategies. This usually includes a step-by-step progression starting with observation, case aid in programming and manipulation of surgical instruments, learning the basics of robotics in a dry and wet lab setting, attaining non-technical skills on an individual and team level, and monitored modular console training, accompanied by autonomous practice. Prior to independent practice, basic robotics skills and procedural activities must be performed safely and effectively as part of robotic surgical training. It is advised to create a systematic training programme with performance indicators and research-based instructional techniques.
Tissue-specific mitochondrial HIGD1C promotes oxygen sensitivity in carotid body chemoreceptors

Mammalian carotid body arterial chemoreceptors function as an early warning system for hypoxia, triggering acute life-saving arousal and cardiorespiratory reflexes. To serve this role, carotid body glomus cells are highly sensitive to decreases in oxygen availability. While the mitochondria and plasma membrane signaling proteins have been implicated in oxygen sensing by glomus cells, the mechanism underlying their mitochondrial sensitivity to hypoxia compared to other cells is unknown. Here, we identify HIGD1C, a novel hypoxia-inducible gene domain factor isoform, as an electron transport chain complex IV-interacting protein that is almost exclusively expressed in the carotid body and is therefore not generally necessary for mitochondrial function. Importantly, HIGD1C is required for carotid body oxygen sensing and enhances complex IV sensitivity to hypoxia. Thus, we propose that HIGD1C promotes exquisite oxygen sensing by the carotid body, illustrating how specialized mitochondria can be used as sentinels of metabolic stress to elicit essential adaptive behaviors.