ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Influencing factors of wide pulse pressure in an elderly Chinese population: A cross-sectional study

Blood pressure and pulse pressure (PP) had their own characteristics in the elderly population. This cross-sectional study including 5030 elderly participants was conducted to describe the distribution of blood pressure and wide PP in the elderly population and find influencing factors of wide PP. Wide PP was defined as PP equal to or more than 65 mmHg, and was classified three types as low systolic blood pressure (SBP) and low diastolic blood pressure (DBP) (LSLD), high SBP and low DBP (HSLD), and high SBP and high DBP (HSHD). Using multivariate logistic regression models to analyze the associations of demographic factors, health-related factors and lifestyle factors with different wide PP types. The associations of lifestyles with wide PP by gender were estimated by subgroup analyses. Among 5030 elderly participants, 2727 (54.2%) participants had wide PP. Logistic regression models showed older age (OR = 2.48, 95%CI: 2.14-2.88), female (OR = 1.31, 95%CI: 1.07-1.60), not married (OR = 1.26, 95%CI: 1.07-1.49), having chronic diseases (OR = 1.28, 95%CI: 1.09-1.50), current alcohol drinker (OR = 1.29, 95%CI: 1.11-1.50) were positively associated, and higher body height (OR = .78, 95%CI: .62-.99), higher education level (OR = .60, 95%CI: .43-.82), current smoker (OR = .79, 95%CI: .64-.97) were negatively associated with wide PP. Among three different types of wide PP including LSLD, HSLD, HSHD, these factors had different effects. Subgroup analyses found that only among male, current smoker was negatively associated and current alcohol drinker was positively associated with wide PP.
Intravenous fluid therapy in patients with severe acute pancreatitis admitted to the intensive care unit: a narrative review

Patients with acute pancreatitis (AP) often require ICU admission, especially when signs of multiorgan failure are present, a condition that defines AP as severe. This disease is characterized by a massive pancreatic release of pro-inflammatory cytokines that causes a systemic inflammatory response syndrome and a profound intravascular fluid loss. This leads to a mixed hypovolemic and distributive shock and ultimately to multiorgan failure. Aggressive fluid resuscitation is traditionally considered the mainstay treatment of AP. In fact, all available guidelines underline the importance of fluid therapy, particularly in the first 24-48 h after disease onset. However, there is currently no consensus neither about the type, nor about the optimal fluid rate, total volume, or goal of fluid administration. In general, a starting fluid rate of 5-10 ml/kg/h of Ringer's lactate solution for the first 24 h has been recommended. Fluid administration should be aggressive in the first hours, and continued only for the appropriate time frame, being usually discontinued, or significantly reduced after the first 24-48 h after admission. Close clinical and hemodynamic monitoring along with the definition of clear resuscitation goals are fundamental. Generally accepted targets are urinary output, reversal of tachycardia and hypotension, and improvement of laboratory markers. However, the usefulness of different endpoints to guide fluid therapy is highly debated. The importance of close monitoring of fluid infusion and balance is acknowledged by most available guidelines to avoid the deleterious effect of fluid overload. Fluid therapy should be carefully tailored in patients with severe AP, as for other conditions frequently managed in the ICU requiring large fluid amounts, such as septic shock and burn injury. A combination of both noninvasive clinical and invasive hemodynamic parameters, and laboratory markers should guide clinicians in the early phase of severe AP to meet organ perfusion requirements with the proper administration of fluids while avoiding fluid overload. In this narrative review the most recent evidence about fluid therapy in severe AP is discussed and an operative algorithm for fluid administration based on an individualized approach is proposed.
Projection based weight normalization: Efficient method for optimization on oblique manifold in DNNs

Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling based weight space symmetry (SBWSS) in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over the Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art neural networks. The experimental results show that our method is able to improve the performance of different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method achievers the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively. (C) 2020 Elsevier Ltd. All rights reserved.
Using computer vision to recognize construction material: A Trustworthy Dataset Perspective

Data quality is vital in machine learning models, and data scientists spend a substantial amount of time on data quality certificates before model training. With the objective of building trustworthy construction material datasets in this study, we first built construction material datasets, namely those for concrete, brick, metal, wood, and stone data, by collecting images of different construction materials. In particular, these datasets include the main construction material (i.e., concrete, brick, metal, wood, and stone), background, and uncertainty cate-gories. Subsequently, we propose a novel cross-review framework that applies the entire corresponding datasets as the input (e.g., brick datasets) and returns clean datasets. Subsequently, several state-of-the-art deep con-volutional neural networks such as VGG16, GoogleNet, and ResNet were selected to verify the quality and effectiveness of the construction material datasets under two pipelines: friendly mobile devices and unfriendly architectures. The experimental results show that the proposed construction waste material datasets have satisfactory quality and can be effectively recognized by these state-of-the-art deep neural network models. Additionally, the results indicate the necessity to clean the data before training a deep-learning model. Further research should be combining a novel deep construction material recognition model with the cross-review framework to further improve the recognition performance.
Comprehensive data of 4502 patients newly diagnosed with colorectal liver metastasis between 2015 and 2017, and prognostic data of 2427 patients newly diagnosed with colorectal liver metastasis in 2013 and 2014: Third report of a nationwide survey in Japan

To improve treatment outcomes in patients with colorectal liver metastasis (CRLM), the Joint Committee for Nationwide Survey on CRLM was established by the Japanese Society for Cancer of the Colon and Rectum and the Japanese Society of Hepato-Biliary-Pancreatic Surgery. The aim of the study was to evaluate transition in the characteristics and treatment strategy in CRLM patients and analyze prognostic factors using large-scale data. The present study summarizes the data of patients newly diagnosed between 2015 and 2017 and presents prognostic data of patients newly diagnosed in 2013 and 2014. Survival curves were generated by the Kaplan-Meier method and compared by log-rank test. Multivariate analyses were carried out using Cox proportional hazard modeling. The data of 4502 patients newly diagnosed with CRLM between 2015 and 2017 and the prognostic data of 2427 patients diagnosed in 2013 and 2014 are included. Regarding the 2013 and 2014 prognostic data, the 5-year overall survival (OS) rates of patients who underwent hepatectomy alone was 59.8%. Multivariate analyses identified age at diagnosis of CRLM ≥70 years, concomitant extrahepatic metastasis at diagnosis of CRLM, tumor depth of primary lesion ≥subserosa/pericolic or perirectal tissue, mutant KRAS status, number of CRLM ≥5, maximum diameter of CRLM >5 cm, and surgical curability R1/R2 as independent predictors of OS. Analysis of the latest nationwide database of patients diagnosed with CRLM revealed changes in patients and oncological characteristics, a transition in treatment strategy, and different independent prognosticators to those reported previously.
Systems analysis, design, and optimization of geothermal energy systems for power production and polygeneration: State-of-the-art and future challenges

Geothermal energy has significant potential to reduce fossil fuel consumptions and environmental impacts. To improve energy conversion efficiency of geothermal energy systems, numerous systems designs have been proposed and their optimization sought. At this point, it is worth reviewing current developed geothermal energy systems because understanding configurations and principles of basic and state-of-the-art technologies is important for developing advanced energy systems. A comprehensive review of the geothermal energy systems is carried out from the perspective of systems analysis, design, and optimization. Results illustrate that limited sets of parameters have been considered in most studies on design and optimization, though these studies provide great insight into specific designs. However, all influential factors have to be fully considered for practical applications. This study identifies and organizes influential factors for geothermal energy systems. In addition, critical analyses of studies on systems design and optimization are performed to determine limitations of current studies. As polygeneration systems produce various energy products (electricity, heat, and/or cooling), it might play key roles to maximize utilization of geothermal energy. Especially, polygeneration systems with binary technology, which can effectively produce electricity from moderate temperature geothermal resources, have significant potentials to enhance the overall performance. In this regard, the energy production strategy and technology selection are of significant importance to meet electric, heating, and cooling loads efficiently. To fill the knowledge gaps and to maximize geothermal energy utilization, this review proposes state-of-the-art multi-scale modeling and optimization framework.
State of the Art Sub-Terahertz Switching Solutions

In this paper the state of the art in RF switches for mm-wave frequency range is summarized and evaluated. Several leading technologies is presented from typical semiconductor devices based on transistors and diodes on Si, SiGe or III-V semiconductor substrates to more unconventional solutions such as microelectromechanical or phase-change material switches. The most important parameters and characteristics for those technologies are gathered and compiled for comparison. Besides different technologies, also various switch topologies of mm-wave switches are presented, assessed and compared. Furthermore, new emerging technological solutions approaching mm-wave range involving two-dimensional materials are also presented. Their evaluation is focused on proposed designs and current results for experimentally evaluated prototypes. Although the performance of these devices are currently not competitive with more traditional approaches, some reported results near the mm-wave range makes them a promising solution for future mm-wave switches and an interesting topic for further research and development.
Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input-output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.
Quantifying cell adhesion through forces generated by acoustic streaming

The strength of cell adhesion is important in understanding the cell's health and in culturing them. Quantitative measurement of cell adhesion strength is a significant challenge in bioengineering research. For this, the present study describes a system that can measure cell adhesion strength using acoustic streaming induced by Lamb waves. Cells are cultured on an ultrasound transducer using a range of preculture and incubation times with phosphate-buffered saline (PBS) just before the measurement. Acoustic streaming is then induced using several Lamb wave intensities, exposing the cells to shear flows and eventually detaching them. By relying upon a median detachment rate of 50 %, the corresponding detachment force, or force of cell adhesion, was determined to be on the order of several nN, consistent with previous reports. The stronger the induced shear flow, the more cells were detached. Further, we employed a preculture time of 8 to 24 h and a PBS incubation time of 0 to 60 min, producing cell adhesion forces that varied from 1.2 to 13 nN. Hence, the developed system can quantify cell adhesion strength over a wide range, possibly offering a fundamental tool for cell-based bioengineering.
Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach

This study examined heterogeneity in the association between disaster-related home loss and functional limitations of older adults, and identified characteristics of vulnerable subpopulations. Data were from a prospective cohort study of Japanese older survivors of the 2011 Japan Earthquake. Complete home loss was objectively assessed. Outcomes in 2013 (n = 3,350) and 2016 (n = 2,664) included certified physical disability levels, self-reported activities of daily living, and instrumental activities of daily living. We estimated population average associations between home loss and functional limitations via targeted maximum likelihood estimation with SuperLearning and its heterogeneity via the generalized random forest algorithm. We adjusted for 55 characteristics of survivors from the baseline survey conducted 7 months before the disaster. While home loss was consistently associated with increased functional limitations on average, there was evidence of effect heterogeneity for all outcomes. Comparing the most and least vulnerable groups, the most vulnerable group tended to be older, not married, living alone, and not working, with preexisting health problems before the disaster. Individuals who were less educated but had higher income also appeared vulnerable for some outcomes. Our inductive approach for effect heterogeneity using machine learning algorithm uncovered large and complex heterogeneity in postdisaster functional limitations among Japanese older survivors.
Deep convolutional features for image retrieval

Nowadays, the use of Convolutional Neural Networks (CNNs) has led to tremendous achievements in several computer vision challenges. CNN-based image retrieval methods vary in complexity, growing capacity, and execution time. This work presents a state-of-the-art review in Deep Convolutional Features for image retrieval, pointing out their scope, advantages, and limitations. Moreover, the paper presents a procedure that adopts the latest architectures of pre-trained CNNs that have been initially proposed for image classification to shape image retrieval features. It investigates their suitability on several image retrieval tasks, without any optimization procedure, exhaustive preparatory work, and tuning. Each network's performance is evaluated in two different setups: one employing global and one using local representations. Extensive experiments on several well-known benchmark datasets demonstrate that a simple normalization on the pre-trained networks yields results comparable to state-of-the-art approaches. The global descriptor shapes a plug-and-play approach, which can be adopted for description and retrieval without any prior initialization or training. Moreover, the descriptor's localized version outperforms significantly much more sophisticated and complex methods of the recent literature.
Integration of Wearable Sensors Measurements for Indoor Pedestrian Tracking

Wearable sensors have great potential to ensure the safety and security of humans in hazardous and unknown environments. Real-time tracking and mapping are key aspects of safety and security. Integrating body-mounted wearable sensors measurements for human tracking is challenging due to the high degree of freedom of human motion. Particularly, time synchronization and spatial alignment between different sensors make the integration of measurements challenging. Also, weight, power, and computational resources impose additional constraints. This article addresses these challenges by demonstrating the design and implementation of a multisensor pedestrian tracking system that integrates, spatially aligns, and time synchronizes state-of-art wearable tracking and positioning sensors/technologies. The integrated measurements are packaged into labelled datasets that will be publicly available and easily accessible for researchers. The dataset includes raw and processed data of multiple wearable inertial sensors, a stereo camera, a handheld LiDAR, and an ultrawide-band (UWB) receiver. The dataset is supported by a high-accuracy ground truth reference generated by a post-processed 3D LiDAR SLAM engine. Assessment of state-of-art tracking technologies/sensors is covered, and details of tracking algorithms and their performance and challenges are demonstrated.
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography

Low-dose computed tomography (LDCT) is desirable for both diagnostic imaging and image-guided interventions. Denoisers are widely used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are becoming mainstream methods. However, there are two challenges to using DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs, which are sometimes needed for different clinical tasks; and 2) the model's generalizability might be an issue when the noise level in the testing images differs from that in the training dataset. To address these two challenges, in this work, we introduce a lightweight optimization process that can run on top of any existing DL-based denoiser during the testing phase to generate multiple image candidates with different noise-resolution tradeoffs suitable for different clinical tasks in real time. Consequently, our method allows users to interact with the denoiser to efficiently review various image candidates and quickly pick the desired one; thus, we termed this method deep interactive denoiser (DID). Experimental results demonstrated that DID can deliver multiple image candidates with different noise-resolution tradeoffs and shows great generalizability across various network architectures, as well as training and testing datasets with various noise levels.
Investigations on the Role of Iron (III) and Silica-Iron (III) for DNA Protection Against Highly Intense UV Radiation: Tracking the Connection of Prebiotic Chemistry to Biology

The mineral reaction pathways that yield organic compounds of increasing complexity would have required a means of protective screening against strong ultraviolet radiation for macromolecular assembly on early Earth. In this study, a bacterial chromosomal plasmid DNA was used as a model biomolecule that represents a complex polymeric nucleic acid containing genetic information. The plasmid DNA was exposed to UV radiation through a medium containing air, water, iron (Fe3+), or silica-iron rich aqueous solutions. Our results demonstrate that the plasmid DNA underwent covalent breakage in an aqueous solution when exposed to UV radiation but was shielded against damage due to the presence of iron and silica. It is demonstrated that a suspension of ca. 40 nm colloidal particles of silica gel embedded with Fe3+ ions adsorbed on silanol groups that formed nanoclusters of noncrystalline iron hydroxide is an extremely efficient shelter against intense UV radiation. The implications for our understanding of primitive Earth and Earth-like planets, moons, and asteroids are discussed. The stability of a chromosomal DNA molecule against UV radiation in the presence of iron and silica may provide support on how macromolecules endured early Earth environments and brought forth important implications on early molecular survival against UV radiation.
14-3-3τ drives estrogen receptor loss via ERα36 induction and GATA3 inhibition in breast cancer

About one-fourth of recurrent estrogen receptor-positive (ER+) breast cancers lose ER expression, leading to endocrine therapy failure. However, the mechanisms underlying ER loss remain to be fully explored. We now show that 14-3-3τ, up-regulated in ∼60% of breast cancer, drives the conversion of ER+ to ER- and epithelial-to-mesenchymal transition (EMT). We identify ERα36, an isoform of ERα66, as a downstream effector of 14-3-3τ. Overexpression of 14-3-3τ induces ERα36 in xenografts and tumor spheroids. The regulation is further supported by a positive correlation between ERα36 and 14-3-3τ expression in human breast cancers. ERα36 can antagonize ERα66 and inhibit ERα66 expression. Isoform-specific depletion of ERα36 blocks the ER conversion and EMT induced by 14-3-3τ overexpression in tumor spheroids, thus establishing ERα36 as a key mediator in 14-3-3τ-driven ER loss and EMT. ERα36 promoter is repressed by GATA3, which can be phosphorylated by AKT at consensus binding sites for 14-3-3. Upon AKT activation, 14-3-3τ binds phosphorylated GATA3 and facilitates the degradation of GATA3 causing GATA3 to lose transcriptional control over its target genes ERα66 and ERα36. We also demonstrate a role for the collaboration between 14-3-3τ and AKT in ERα36 induction and endocrine therapy resistance by three-dimensional spheroid and tamoxifen treatment models in MCF7 and T47D ER+ breast cancer cells. Thus, the 14-3-3τ-ERα36 regulation provides a previously unrecognized mechanism for ER loss and endocrine therapy failure.