bluuebunny/crossref_metadata_2025 huggingface dataset:
Why did Swiss citizens vote to ban tobacco advertising?
Authors: Lebon Luc , Diethelm Pascal , Ballmer Valentine , Molineaux Hugo , Zürcher Karin , Cornuz Jacques
Published: October 2023

In February 2022, Swiss citizens agreed to modify the Swiss Constitution to ban tobacco advertising reaching children and adolescents. This case study analyses the arguments used by both opponents and supporters of the constitutional amendment. Opponents argued that the proposed regulation went too far, threatened the economy, restricted personal freedom, was superfluous as the current law already protected youth and that it opened the door to marketing bans of other harmful products. Proponents focused on youth protection and invoked the burden of smoking on public health and the fact that advertising bans are an effective evidence-based measure. A comparison with previous campaigns to ban tobacco advertising that had failed suggests factors accounting for the positive vote in 2022. These include the strategic framing of youth protection, the separation of tobacco from other issues (such as alcohol advertising), the deteriorating image of the tobacco industry and the ability of the proponents to mobilise a broad coalition of health and youth organisations, with improved funding and communication. The lessons may be instructive for other campaigns seeking to regulate commercial determinants of health.
Installation and capacity performance of multi-line dynamically installed anchors
Authors: Liu Jun , Li Yunfei , Guo Xinshuai , Han Congcong
Published: October 2023

The multi-line anchor achieves the “anchor sharing” goal by allowing multiple floating devices to be moored, such that the total number of anchors in a floating device array can be reduced significantly. This study proposes a dynamically installed anchor with the potential of resisting uplift loading from varied directions. The multi-line anchor is dynamically installed with the aid of a booster. The installation performance of the hybrid anchor (i.e., multi-line anchor + booster) is first examined by field tests. Then, finite-element analyses are performed to investigate the uplift capacity of the anchor under V-H-M-T combined loading conditions. Based on the numerical results, yield envelopes are established for assessing the ultimate bearing capacity of the anchor. Additionally, model tests are conducted to quantitatively study the installation and setup effects on the uplift capacity of the anchor, indicating that the vertical and torque capacities are sensitive to the installation effect. Finally, a design framework is proposed to assess the stability of the multi-line anchor under combined loading conditions.
Improving solid waste management in remote First Nations communities through inventory-theory-based optimization
Authors: Wang Ziyu , Chen Zhikun , An Chunjiang
Published: October 2023

Solid waste management (SWM) is crucially important for sustainable development and the environmental management system in First Nations (FN) communities and reserves. This research developed an inventory-theory-based optimization model under uncertainty for SWM to support FN communities. The model can be used to pick the most appropriate facilities and the most optimal waste destination for achieving minimum system cost. The opening conditions of SWM facilities depend on the situation to satisfy the demand for solid waste treatment. The economic order quantity model is combined with the developed model to determine the optimal batch size and order cycle to minimize inventory costs. The facility selection and waste allocation in the normal and winter seasons under different risk levels can all be obtained from the developed model. These solutions can guide professionals in analyzing the trade-offs between system cost and constraint-violation risks in management and operation.
Matrix element corrections in the Pythia8 parton shower in the context of matched simulations at next-to-leading order
Authors: Frixione Stefano , Amoroso Simone , Mrenna Stephen
Published: October 2023

We discuss the role of matrix element corrections (MEC) to parton showers in the context of MC@NLO-type matchings for processes that feature unstable resonances, where MEC are liable to result in double-counting issues, and are thus generally not employed. By working with Pythia8, we show that disabling all MEC is actually unnecessary in computations based on the narrow-width approximation, and we propose alternative MEC settings which, while still avoiding double counting, allow one to include hard-recoil effects in the simulations of resonance decays. We illustrate our findings by considering $$t\bar{t}$$

t

t

¯

production at the LHC, and by comparing MadGraph5-aMC@NLO predictions with those of POWHEG-BOX and standalone Pythia8.
Soft computing through supervised neural network based on Levenberg–Marquardt back propagation for numerical treatment of steady flow of Oldroyd 4-constant fluid between two rolls
Authors: Usman Muhammad , Hou Yanren , Zahid Muhammad , Ali Fateh , Rana Muhammad Afzal
Published: October 2023

This study presents a new intelligent computing method based on the Levenberg–Marquardt back-propagation-based supervised neural network (LMB-SNN) for the flow of Oldroyd 4-constant fluid in the forward roll coating process (OFC-FRCP). The mathematical formulations for OFC-FRCP are converted into ordinary differential equations using lubrication approximation theory (LAT) and suitable dimensionless parameters. The analytic expressions for velocity distribution, pressure and pressure gradients have been obtained using Adomian’s Decomposition Method (ADM) for several situations. The fluid’s separation point and coating thickness are then calculated using integration. The reference dataset for LMB-SNN has been obtained by ADM for multiple scenarios by adjusting various physical variables. The neural network’s training, testing and validation are carried out in parallel to minimize the mean-squared error function (MSEF). The proposed LMB-SNN’s effectiveness was confirmed by analyzing the MSEF, error histograms and regression plots. The precision of the method is verified by the close agreement between numerical outputs and dataset values. The intelligent numerical results computed using ADM and LMB-SNN are presented in various graphs and tables. The method’s accuracy is demonstrated by the numerical outputs closest to the built and dataset values at levels between [Formula: see text] and [Formula: see text].
Effects of viscosity ratio and surface wettability on viscous fingering instability in rectangular channel
Authors: Singh Akhileshwar , Singh Deepak Kumar , Singh Yogesh , Pandey Krishna Murari
Published: October 2023

This paper explores the impact of viscosity ratio and surface wettability on immiscible viscous fingering instability within a rectangular channel. Numerical investigations are conducted across a range of viscosity ratios (VR) from 0.0009 to 0.5 and wall wettability ([Formula: see text]) from 15° to 150°. The volume of fluid (VOF) model is employed to track the development of finger-shaped instability at the fluid interface. Our results indicate that higher viscosity ratios lead to increased displacement efficiency. Additionally, we find the formation of necking at low VR, which diminishes at higher VR values. The finger-shaped pattern splits into two parts at a wettability of 15°; beyond this threshold, no such splitting occurs. Furthermore, a transition from hydrophilic to superhydrophobic wettability abolishes necking, resulting in enhanced displacement efficiency. Notably, as wettability shifts from hydrophilic to super hydrophobic, instability shifts toward the left side. These findings hold relevance for applications in drug delivery, clinical processes and oil recovery.
Flow analysis of radiated micropolar nanofluid on a stretching/shrinking wedge surface under chemical reaction and multiple convective conditions
Authors: Bag Raju , Kundu Prabir Kumar
Published: October 2023

This paper reports the flow features and distributions of concentration and temperature of a micropolar type nanofluid (water-based) past a stretchable and shrinkable wedge, influenced by variable magnetic force, nonlinear sort thermal radiation and chemical reaction. Along with the consideration of multiple convection, the model of Buongiorno is stated. The Brownian motion and thermophoresis have been kept in the analysis. Suitable similarity alteration is approached to renovate the foremost equations to dimensionless ordinary differential equations (ODEs). Associated conditions became nondimensional forms according to this conversion. Then the numerical solutions of the reduced governing equations with boundary conditions are obtained by adopting the RK-4 technique with shooting criteria. The language MAPLE 17 assisted in developing this solution. Significant upshots of prime parameters on the fluid transmission, mass and heat transport properties are represented with suitable tables and graphs. In tabular form, we have reckoned the physical quantities of heat, mass transfer rates and drag friction coefficients to fulfill the engineering interest. This study acquaints that the material parameter negatively influenced nanofluid’s angular velocity. The fluid’s temperature improves with thermal and mass Biot numbers, but this response goes opposite for the parameter of wedge angle. Chemical reaction and wedge angle parameters amplify mass transport. This study can be beneficial in the blowing of chilled air by AC panels, the abstraction of crude oils, the nuclear power hub, the working of warships, making flaps on the wings of aeroplanes for advanced lift, submarines, the extraction of polymers and several other sectors in advanced science and industrial developments.
A Deep Vision Learning-Based Intelligent Recognition Method for Dynamic Sports Gestures
Authors: Xu Jiao , Fan Xingfeng
Published: October 2023

Nowadays, artificial intelligence-assisted automatic technological analyses of sports matches have received a more general demand. Among them, the effective recognition of sports actions from videos or images acts as the most fundamental issue to be solved. As sports gestures are characterized by remarkable specialty and instantaneity, it is required to develop specific effective recognition algorithms for this purpose. As a result, this work takes the case of Taekwondo sports as an example, and introduces deep vision learning to develop a specific intelligent recognition method for dynamic sports gestures. After sampling the key frames from dynamic videos, vital joint points of limbs are first obtained as the basic bottom features. Then, gesture expertise of this domain is formed to construct a multiview feature extraction work flow to better analyze complex characteristics of each gesture. Finally, the extracted feature representation is input into a recurrent neural network structure to output the discriminative results. At last, experiments are conducted for assessment, whose results show that the proposal can reach an accuracy of 95.6% and can be well suitable for the investigated problem scenario.
Classroom Facial Expression Recognition Method Based on Conv3D-ConvLSTM-SEnet in Online Education Environment
Authors: Fu Rong , Tian Mijuan
Published: October 2023

A Facial Expression Recognition (FER) method based on Conv3D-ConvLSTM-SEnet in an online education environment is proposed to address the issue of low accuracy in current classroom expression recognition (FER) methods. Firstly, ConvLSTM is used to integrate the local feature extraction ability of CNN and the temporal modeling ability of LSTM, and based on this, the rich spatial features of the image are characterized. Then, by introducing Depth Separated Convolution (DSC) to change the number of output channels and separate each channel, the Feature Maps (F-M) are sequentially concatenated to obtain a multi-channel output F-M. Finally, based on ConvLSTM and SEnet modules, a Conv3D ConvLSTM-based FER model was proposed by redistributing the extracted abstract features and basic texture features, achieving high accuracy classroom FER. The proposed classroom FER method and the other five methods were compared and analyzed through simulation experiments using the CK[Formula: see text], FER2013 and JAFFE datasets, respectively. The results indicate that the proposed method achieves the highest accuracy, precision, recall, and F1 score, with improvements of at least 1.85%, 2.41%, 1.18% and 2.05%, respectively, compared to the other five methods on the FER2013 dataset. The proposed method can perform FER on students in online classroom teaching, detect their emotions in real-time, and help teachers make better course teaching adjustments based on this.
IoT-Enabled Deep Learning Algorithm for Estimation of State-of-Charge of Lithium-ion Batteries
Authors: Pushpavanam B. , Kalyani S. , Prasanna M. Arul , Sangaiah Arun Kumar
Published: October 2023

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging–discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis.
Cosmographic and phase-space analysis of dynamical Chern–Simons modified and fractal gravities
Authors: Usman Muhammad , Jawad Abdul
Published: October 2023

We examine the cosmic scenario of interacting Kaniadakis holographic dark energy in dynamical Chern–Simons modified gravity and the fractal universe. For this purpose, the Hubble, deceleration, coincidence, equation-of-state and jerk parameters have been evaluated in view of the redshift parameter. It is observed that deceleration parameter [Formula: see text] evaluates the accelerated expansion of the universe in both gravities. The coincidence parameter [Formula: see text] exhibits the transition of behavior from dark matter to dark energy era of the universe. The behavior of the equation-of-state parameter [Formula: see text] describes the quintessence and vacuum regions of the universe in both gravities for maximum choices of the interacting parameters. The jerk parameter shows the correspondence of the given model with [Formula: see text]CDM model and other standard models in both gravities. All the parameters exhibit consistent behavior with the Planck 2018 data. We also analyze the dynamical stability in both frameworks by formulating dynamical models in the form of a system of differential equations and evaluating their corresponding critical points. Critical points of both models are stable and phase plots indicate attractor behavior that implies stability of the models. Further, stability conditions of both models signify the accelerating expansion of the universe. In addition, we discuss the thermodynamics of this model with the generalized second law and find its validity in both frameworks.
Generator coordinate method for nuclear octupole excitations: Status and perspectives
Authors: Zhou E. F. , Yao J. M.
Published: October 2023

Strong octupole correlations have been observed in the low-lying states of atomic nuclei across various mass regions. In this paper, we provide an overview of Beyond Mean-Field (BMF) studies of nuclear octupole collective motions with Generator Coordinate Method (GCM) in combination with quantum-number projections that are implemented to restore the broken symmetries in nuclear mean-field states. We highlight recent developments within this framework and their applications to excitation spectra and electromagnetic transition rates in octupole-shaped nuclei and hypernuclei. We discuss the novel phenomena of nucleon clustering in light nuclei. Additionally, we explore the phase transition from octupole vibrations to rotational motions as spin increases in heavy nuclei. Lastly, we examine the status and future prospects of studies on octupole deformation effects in nuclear Schiff moments. These studies, along with the upper limits of atomic Electric Dipole Moment (EDM), impose stringent constraints on beyond-standard-model time-reversal-violating nucleon–nucleon interactions.
Jost function analysis of elastic and inelastic nucleon–nucleon scattering with a new model potential
Authors: Sahoo P. , Swain B. , Laha U.
Published: October 2023

The analysis of nucleon–nucleon elastic and inelastic scattering data is presented by considering the combined interaction of nuclear and electromagnetic potentials of equal range. The exact analytical off-shell solutions of the nonrelativistic Schrödinger equation for the effective potential with the centrifugal term are investigated using ordinary differential equation approach. Numerical results of scattering phase shifts, differential cross-sections and Transition matrices are in sensible conformity with previous works.
Comparison between effects of instrument-assisted soft tissue mobilization and manual myofascial release on pain, range of motion and function in myofascial pain syndrome of upper trapezius — A randomized controlled trial
Authors: Agarwal Shweta , Bedekar Nilima , Shyam Ashok , Sancheti Parag
Published: October 2023

Background: Myofascial pain syndrome (MPS) is a muscle pain disorder characterized by the presence of Myofascial Trigger Point (MTrP) within a taut band, local tenderness, referral of pain to a distant site, restricted range of motion, and autonomic phenomena. The upper trapezius is the muscle most often affected by MTrPs. Manual myofascial release (MFR) and Instrument-Assisted Soft Tissue Mobilization (IASTM) are techniques of soft tissue release that are used to resolve MPS. Fifty six percent of physiotherapists complain of pain in multiple areas due to the massage and manual therapy that they have to perform.

Objective: The objective of this study is to find whether IASTM is better than manual MFR in treating patients with MPS in upper trapezius.

Methods: This study was a single-blinded randomized controlled trial that included 31 participants, both males and females between the age groups of 18–50 years. Participants were randomly divided into two groups. Three sessions were given over a period of one week for both groups. Group A received IASTM along with conventional treatment and Group B received Manual MFR along with the conventional treatment. The outcome measures evaluated were pain, cervical range of motion, pain pressure threshold (PPT) of trigger points, and the neck disability index. Pre- and post-measurements were taken and the analysis was done.

Results: Both the treatment methods significantly reduced pain, improved PPT, range of motion, and function. The effects between the groups showed that IASTM was significantly better than manual MFR to reduce pain. The improvement in PPT, range of motion and function were equal in both the groups.

Conclusion: IASTM and manual MFR both are effective individually as treatment procedures for pain, PPT, range of motion, and function. Neither of the treatment options can be considered better that the other. The clinician can decide based on the availability of the instrument, training, patient’s preference, and his/her comfort whether which of the two treatment methods should be used.
Research on the Investment Strategy of Private Equity Investment Fund Targeted Increase in NEEQ — An Empirical Analysis Based on BP and Hopfield Neural Network Model
Authors: Yajuan Liu , Wenbin Xu
Published: October 2023

Private equity investment funds targeted increase in NEEQ has become a new strategy for PE investment. However, the currently adopted Logit regression and one-factor ANOVA models are not suitable for analyzing nonlinear investment activities, and the investment appraisal does not work well. In this paper, all NEEQ companies that implemented private placement in 2017 are used as the study sample. This paper also empirically analyzes the current situation of domestic private equity investment funds based on BP and Hopfield neural network models, then the results of the two models are compared. It is concluded that the accuracy of the BP neural network model can be more than 90%. So, the BP neural network can be used as the optimal model of private equity investment funds investment strategy in NEEQ.
RadarHand: A Wrist-Worn Radar for On-Skin Touch-Based Proprioceptive Gestures
Authors: Hajika Ryo , Gunasekaran Tamil Selvan , Haigh Chloe Dolma Si Ying , Pai Yun Suen , Hayashi Eiji , Lien Jaime , Lottridge Danielle , Billinghurst Mark
Published: October 2023

We introduce RadarHand, a wrist-worn wearable with millimetre wave radar that detects on-skin touch-based proprioceptive hand gestures. Radars are robust, private, small, penetrate materials, and require low computation costs. We first evaluated the proprioceptive and tactile perception nature of the back of the hand and found that tapping on the thumb is the least proprioceptive error of all the finger joints, followed by the index finger, middle finger, ring finger, and pinky finger in the eyes-free and high cognitive load situation. Next, we trained deep-learning models for gesture classification. We introduce two types of gestures based on the locations of the back of the hand: generic gestures and discrete gestures. Discrete gestures are gestures that start at specific locations and end at specific locations at the back of the hand, in contrast to generic gestures, which can start anywhere and end anywhere on the back of the hand. Out of 27 gesture group possibilities, we achieved 92% accuracy for a set of seven gestures and 93% accuracy for the set of eight discrete gestures. Finally, we evaluated RadarHand’s performance in real-time under two interaction modes: Active interaction and Reactive interaction. Active interaction is where the user initiates input to achieve the desired output, and reactive interaction is where the device initiates interaction and requires the user to react. We obtained an accuracy of 87% and 74% for active generic and discrete gestures, respectively, as well as 91% and 81.7% for reactive generic and discrete gestures, respectively. We discuss the implications of RadarHand for gesture recognition and directions for future works.
The Path to Defence: A Roadmap to Characterising Data Poisoning Attacks on Victim Models
Authors: Chaalan Tarek , Pang Shaoning , Kamruzzaman Joarder , Gondal Iqbal , Zhang Xuyun
Published: October 2023

Data Poisoning Attacks (DPA) represent a sophisticated technique aimed at distorting the training data of machine learning models, thereby manipulating their behavior. This process is not only technically intricate but also frequently dependent on the characteristics of the victim (target) model. To protect the victim model, the vast number of DPAs and their variants make defenders rely on trial and error techniques to find the ultimate defence solution which is exhausting and very time-consuming. This paper comprehensively summarises the latest research on DPAs and defences, proposes a DPA characterizing model to help investigate adversary attacks dependency on the victim model, and builds a DPA roadmap as the path navigating to defence. Having the roadmap as an applied framework that contains DPA families sharing the same features and mathematical computations will equip the defenders with a powerful tool to quickly find the ultimate defences, away from the exhausting trial and error methodology. The roadmap validated by use cases has been made available as an open access platform, enabling other researchers to add in new DPAs and update the map continuously.
KiT-RT: An Extendable Framework for Radiative Transfer and Therapy
Authors: Kusch Jonas , Schotthöfer Steffen , Stammer Pia , Wolters Jannick , Xiao Tianbai
Published: October 2023

In this article, we present Kinetic Transport Solver for Radiation Therapy (KiT-RT), an open source C++-based framework for solving kinetic equations in therapy applications available at 
https://github.com/CSMMLab/KiT-RT
. This software framework aims to provide a collection of classical deterministic solvers for unstructured meshes that allow for easy extendability. Therefore, KiT-RT is a convenient base to test new numerical methods in various applications and compare them against conventional solvers. The implementation includes spherical harmonics, minimal entropy, neural minimal entropy, and discrete ordinates methods. Solution characteristics and efficiency are presented through several test cases ranging from radiation transport to electron radiation therapy. Due to the variety of included numerical methods and easy extendability, the presented open source code is attractive for both developers, who want a basis to build their numerical solvers, and users or application engineers, who want to gain experimental insights without directly interfering with the codebase.
Symphony: Orchestrating Sparse and Dense Tensors with Hierarchical Heterogeneous Processing
Authors: Pellauer Michael , Clemons Jason , Balaji Vignesh , Crago Neal , Jaleel Aamer , Lee Donghyuk , O’Connor Mike , Parashar Angshuman , Treichler Sean , Tsai Po-An , Keckler Stephen W. , Emer Joel S.
Published: October 2023

Sparse tensor algorithms are becoming widespread, particularly in the domains of deep learning, graph and data analytics, and scientific computing. Current high-performance broad-domain architectures, such as GPUs, often suffer memory system inefficiencies by moving too much data or moving it too far through the memory hierarchy. To increase performance and efficiency, proposed domain-specific accelerators tailor their architectures to the data needs of a narrow application domain, but as a result cannot be applied to a wide range of algorithms or applications that contain a mix of sparse and dense algorithms.

This article proposes Symphony, a hybrid programmable/specialized architecture that focuses on the orchestration of data throughout the memory hierarchy to simultaneously reduce the movement of unnecessary data and data movement distances. Key elements of the Symphony architecture include (1) specialized reconfigurable units aimed not only at roofline floating-point computations but also at supporting data orchestration features, such as address generation, data filtering, and sparse metadata processing; and (2) distribution of computation resources (both programmable and specialized) throughout the on-chip memory hierarchy. We demonstrate that Symphony can match non-programmable ASIC performance on sparse tensor algebra and provide 31× improved runtime and 44× improved energy over a comparably provisioned GPU for these applications.
PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency
Authors: Ghosh Soumendu Kumar , Raha Arnab , Raghunathan Vijay , Raghunathan Anand
Published: October 2023

The last decade has seen the emergence of Deep Neural Networks (DNNs) as the de facto algorithm for various computer vision applications. In intelligent edge devices, sensor data streams acquired by the device are processed by a DNN application running on either the edge device itself or in the cloud. However, “edge-only” and “cloud-only” execution of State-of-the-Art DNNs may not meet an application’s latency requirements due to the limited compute, memory, and energy resources in edge devices, dynamically varying bandwidth of edge-cloud connectivity networks, and temporal variations in the computational load of cloud servers. This work investigates
distributed (partitioned) inference
across edge devices (mobile/end device) and cloud servers to minimize end-to-end DNN inference latency. We study the impact of temporally varying operating conditions and the underlying compute and communication architecture on the decision of whether to run the inference solely on the edge, entirely in the cloud, or by partitioning the DNN model execution among the two. Leveraging the insights gained from this study and the wide variation in the capabilities of various edge platforms that run DNN inference, we propose
PArtNNer
, a platform-agnostic adaptive DNN partitioning algorithm that finds the optimal partitioning point in DNNs to minimize inference latency.
PArtNNer
can adapt to dynamic variations in communication bandwidth and cloud server load without requiring pre-characterization of underlying platforms. Experimental results for six image classification and object detection DNNs on a set of five commercial off-the-shelf compute platforms and three communication standards indicate that
PArtNNer
results in 10.2× and 3.2× (on average) and up to 21.1× and 6.7× improvements in end-to-end inference latency compared to execution of the DNN entirely on the edge device or entirely on a cloud server, respectively. Compared to pre-characterization-based partitioning approaches,
PArtNNer
converges to the optimal partitioning point 17.6× faster.
Endothelial-dependent relaxation of α-pinene and two metabolites, myrtenol and verbenol, in isolated murine blood vessels
Authors: Jin L. , Xie Z. , Lorkiewicz P. , Srivastava S. , Bhatnagar A. , Conklin D. J.
Published: October 2023

A common plant-derived biogenic volatile organic compound, α-pinene, and two of its metabolites, myrtenol and verbenol, stimulate vasorelaxation in murine superior mesenteric artery. Both α-pinene- and its metabolites induce vasorelaxation by activation of the endothelium, nitric oxide, and guanylyl cyclase. α-Pinene also activates the transient receptor potential ankyrin-1. Positive associations between greenness exposure and human cardiovascular health may be a result of the vascular action of α-pinene and its metabolites, a novel consideration.
Realizing the price of academic freedom
Authors: Chalise Upendra , King D. Ryan
Published: October 2023

Since 2010, the number of life science doctoral graduates opting into academic postdoctoral employment has steadily declined. In recent years, this decline has made routine headlines in academic news cycles, and faculty members, universities, and funding bodies alike have begun to take notice. In November 2022, the National Institutes of Health (NIH) convened a special interest group to address the problems in postdoctoral recruitment and retention. In response, the American Physiological Society Science Policy Committee highlighted several key issues in postdoctoral training and working conditions and offered the NIH solutions to consider. There are known issues that affect postdoctoral recruitment and retention efforts: low wages relative to other employment sectors, a heavy workload, and poor job prospects to name a few. Unfortunately, these concerns are frequently dismissed as “the price of doing business in academia,” and postdoctoral scholars are promised that if they overcome the trials and tribulations of this training period, the reward at the end, a career with academic freedom to pursue your own interests, justifies the means. However, academic freedom cannot and should not be used as the band-aid in a system where most of us will never actually experience academic freedom. Instead, we should systematically embrace solutions that improve the personal and professional health of early career researchers in all levels of training and independence if the goal is to truly shore up the academic workforce.
Enhanced Dynamic Network DEA: A Novel Algorithm for Sustainable Development Efficiency Assessment in “Internet Plus Logistics” Sector
Authors: Wang Yingli , Qiu Guobin , Wang Jing , Sun Qi
Published: October 2023

This study introduces the enhanced dynamic network DEA, an innovative algorithmic extension of the foundational data envelopment analysis (DEA), to assess the sustainable development efficiency of Jiangxi Province’s “Internet Plus Logistics” sector from 2002 to 2016. This methodology leverages a comprehensive evaluation indicator system, emphasizing waterway, highway, and railway logistics outputs. The integration of custom algorithms into the CCR and BCC models has provided a deeper insight into efficiency trends and highlighted the need for a shift to an intensive economic model. Key findings pinpoint specific inefficiencies and their causes, emphasizing the value of our method in offering precise insights. Ultimately, this research not only advances the DEA methodological landscape but also offers strategic directions for sustainable development in “Internet Plus Logistics.”
Ameliorating Effects of Curcumin on Testicular Cancer
Authors: Arefnezhad Reza , Goleij Pouya , Heydari Negar , Rezaei-Tazangi Fatemeh , Motedayyen Hossein
Published: October 2023

In the young men’s population, testicular cancer (TC) is characterized as one of the main causes of morbidity and mortality around the world. This cancer is mainly divided into germ cell and nongerm cell types and manifests different signs and symptoms, like bone pain, nausea, lower back pain, and anorexia. Also, TC is associated with serious conditions, such as urogenital disorders, infertility, cardiovascular diseases, and solid tumors. Unfortunately, common remedies for TC, including chemotherapy and surgery, have not provided a promising outlook due to their adverse effects on life quality. Ergo, finding a new therapeutic candidate with high effectiveness and low detrimental impacts is indispensable. Newly, herbal remedies have attracted much interest because of their potential to improve diverse diseases, like cancer. In this context, increasing evidence indicated that curcumin, as a polyphenol compound originating from the plant Curcuma longa, has beneficial effects for treating TC. Hence, in this narrative review, we aimed to review documents showing the therapeutic potential of this herbal candidate against TC.
Refractometric Sensing of Protein in Urine by the Photonic Crystal Fiber Biosensor in THz Regime
Authors: Rahman Md. Moshiur , Mou Farhana Akter , Bhuiyan Mohammed Imamul Hassan , Islam Mohammad Rakibul
Published: October 2023

The presence of albumin in human urine is one of the confirmed early symptoms of kidney dysfunction. A precise urine protein identification process is very important to monitor the kidney’s proper functioning. To identify the presence of albumin in urine, a refractometric protein sensing approach in the photonic crystal fiber (PCF) environment has been introduced here. A PCF geometry with suspended cladding and a circular hollow core has been proposed and investigated in the terahertz (THz) spectrum for protein identification in the liquid samples. Three levels of albumin concentrations in urine (7–125 mg/dl, 250–500 mg/dl, and 1000 mg/dl) are considered to evaluate the sensing performances of the proposed PCF. The numerical investigations are performed on the COMSOL Multiphysics platform where the finite element method (FEM) figures out the numerical outcomes. The performances of the proposed PCF exhibit highly sensitive characteristics for albumin identification in the different albumin concentration levels of urine. The sensitivity shows more than 98.5% for all the tested concentration levels due to the strategic selection of geometrical parameters and proper optimization. Alongside, negligible confinement loss of 10−16 cm−1 is attained at the same operating point of 4.3 THz. Furthermore, dispersion profiles and practical implementation strategies are also investigated and discussed in detail.
Research on the Quantitative Causal Transmission of Stock Price Fluctuations of Listed Companies in the Rare Earth Industry Chain
Authors: Dong Zhiliang , Jia Yanjing , Wang Ziyang , Yang Qiaoran
Published: October 2023

Rare earth is an important strategic mineral resource, and the study of the causes of stock price fluctuations of rare earth listed companies and their transmission can enable all parties in the market to have a clearer understanding of current market relationships. In this study, the quantitative causality analysis model and complex network method are used to analyze each link within the rare earth industry chain and the relationship between regions from the quantitative causality of stock price fluctuations among listed companies in the rare earth industry. The main transmission paths of fluctuations in each link between companies are also given. It is found that the quantitative causal relationship between the price fluctuations of rare earth industry stocks has positive causation, negative causation, and unclear causation, and the dominant causal relationship is mostly positive causation. The positive causal relationship between enterprises in the same region is significantly greater than that between enterprises in different regions. The negative causal relationship and unclear causal relationship between enterprises in different regions are significantly greater than the negative causal relationship and unclear causal relationship between enterprises in the same region.
SNP of Aromatase Predict Long-term Survival and Aromatase Inhibitor Toxicity in Patients with Early Breast Cancer: A Biomarker Analysis of the GIM4 and GIM5 Trials
Authors: Conte Benedetta , Boni Luca , Bisagni Giancarlo , Durando Antonio , Sanna Giovanni , Gori Stefania , Garrone Ornella , Tamberi Stefano , De Placido Sabino , Schettini Francesco , Pazzola Antonio , Ponzone Riccardo , Montemurro Filippo , Lunardi Gianluigi , Notaro Rosario , De Angioletti Maria , Turletti Anna , Mansutti Mauro , Puglisi Fabio , Frassoldati Antonio , Porpiglia Mauro , Fabi Alessandra , Generali Daniele , Scognamiglio Giovanni , Rossi Maura , Brasó-Maristany Fara , Prat Aleix , Cardinali Barbara , Piccioli Patrizia , Serra Martina , Lastraioli Sonia , Bighin Claudia , Poggio Francesca , Lambertini Matteo , Del Mastro Lucia
Published: October 2023

Purpose:

In estrogen receptor–positive (ER+) breast cancer, single-nucleotide polymorphisms (SNP) in the aromatase gene might affect aromatase inhibitors (AI) metabolism and efficacy. Here, we assessed the impact of SNP on prognosis and toxicity of patients receiving adjuvant letrozole.

Experimental Design:

We enrolled 886 postmenopausal patients in the study. They were treated with letrozole for 2 to 5 years after taking tamoxifen for 2 to 6 years, continuing until they completed 5 to 10 years of therapy. Germline DNA was genotyped for SNP rs4646, rs10046, rs749292, and rs727479. Log-rank test and Cox model were used for disease-free survival (DFS) and overall survival (OS). Cumulative incidence (CI) of breast cancer metastasis was assessed through competing risk analysis, with contralateral breast cancer, second malignancies and non-breast cancer death as competing events. CI of skeletal and cardiovascular events were assessed using DFS events as competing events. Subdistribution HR (sHR) with 95% confidence intervals were calculated through Fine-Gray method.

Results:

No SNP was associated with DFS. Variants rs10046 [sHR 2.03, (1.04–2.94)], rs749292 [sHR 2.11, (1.12–3.94)], and rs727479 [sHR 2.62, (1.17–5.83)] were associated with breast cancer metastasis. Three groups were identified on the basis of the number of these variants (0, 1, >1). Variant-based groups were associated with breast cancer metastasis (10-year CI 2.5%, 7.6%, 10.7%, P = 0.035) and OS (10-year estimates 96.5%, 93.0%, 89.6%, P = 0.030). Co-occurrence of rs10046 and rs749292 was negatively associated with 10-year CI of skeletal events (3.2% vs. 10%, P = 0.033). A similar association emerged between rs727479 and cardiovascular events (0.3% vs. 2.1%, P = 0.026).

Conclusions:

SNP of aromatase gene predict risk of metastasis and AI-related toxicity in ER+ early breast cancer, opening an opportunity for better treatment individualization.
Canonical and Nuclear mTOR Specify Distinct Transcriptional Programs in Androgen-Dependent Prostate Cancer Cells
Authors: Chen Yonghong , Han Lingwei , Dufour Catherine Rosa , Alfonso Anthony , Giguère Vincent
Published: October 2023

mTOR is a serine/threonine kinase that controls prostate cancer cell growth in part by regulating gene programs associated with metabolic and cell proliferation pathways. mTOR-mediated control of gene expression can be achieved via phosphorylation of transcription factors, leading to changes in their cellular localization and activities. mTOR also directly associates with chromatin in complex with transcriptional regulators, including the androgen receptor (AR). Nuclear mTOR (nmTOR) has been previously shown to act as a transcriptional integrator of the androgen signaling pathway in association with the chromatin remodeling machinery, AR, and FOXA1. However, the contribution of cytoplasmic mTOR (cmTOR) and nmTOR and the role played by FOXA1 in this process remains to be explored. Herein, we engineered cells expressing mTOR tagged with nuclear localization and export signals dictating mTOR localization. Transcriptome profiling in AR-positive prostate cancer cells revealed that nmTOR generally downregulates a subset of the androgen response pathway independently of its kinase activity, while cmTOR upregulates a cell cycle–related gene signature in a kinase-dependent manner. Biochemical and genome-wide transcriptomic analyses demonstrate that nmTOR functionally interacts with AR and FOXA1. Ablation of FOXA1 reprograms the nmTOR cistrome and transcriptome of androgen responsive prostate cancer cells. This works highlights a transcriptional regulatory pathway in which direct interactions between nmTOR, AR and FOXA1 dictate a combinatorial role for these factors in the control of specific gene programs in prostate cancer cells.

Implications:

The finding that canonical and nuclear mTOR signaling pathways control distinct gene programs opens therapeutic opportunities to modulate mTOR activity in prostate cancer cells.
Prediction of Food Sensitization in Children with Atopic Dermatitis Based on Disease Severity and Epidermal Layer Impairment
Authors: Tran Nguyen Le Huong , Ly Nhung Thi My , Trinh Hoang Kim Tu , Le Minh Kieu , Vo Niem  Van Thanh , Pham Duy Le
Published: October 2023

Introduction: Atopic dermatitis (AD) is characterized by an impaired epidermal barrier, which could be associated with sensitization to food allergens (FAs) and/or inhaled allergens and contribute to the severity of AD. However, no clinical guidance has been established for evaluations of food sensitization (FS) in AD patients. This study investigated how AD severity and epidermal barrier impairment are associated with FS and factors that can predict FS in children with AD. Methods: This cross-sectional study included 100 children (12–60 months) diagnosed with AD. AD severity was determined using the Scoring Atopic Dermatitis (SCORAD) index. FS was evaluated by measuring serum-specific IgE antibodies against 31 FAs using an immunoblotting method. Epidermal barrier impairment was assessed by measuring transepidermal water loss (TEWL) and stratum corneum hydration (SCH) levels. Results: 90% of participants were sensitized to at least one tested FA, with cow’s milk, egg white, beef, almond, egg yolk, and peanut being the most common. Children with moderate-severe AD had lower SCH levels than those with mild AD. Children with AD who were sensitized to >10 FAs had significantly higher TEWL and lower SCH levels, compared with those sensitized to 1–4 FAs and 5–10 FAs. The SCORAD score and SCH level in lesional skin provided moderately predictive value for sensitization to FAs in children with AD. Conclusion: FS is common in children with AD and closely associate with AD severity as well as epidermal barrier impairment. Evaluations of FS should be considered for children with moderate to severe AD and/or low SCH levels.
Plasma Cell-Free DNA Is a Potential Biomarker for Diagnosis of Calcific Aortic Valve Disease
Authors: Ma Wangge , Zhang Wei , Liu Huahua , Qian Benheng , Lai Rongguang , Yao Zijun , Wang Yidong , Yan Yang , Yuan Zuyi
Published: October 2023

Introduction: Calcific aortic valve disease (CAVD) is the third most common cardiovascular disease in aging populations. Despite a growing number of biomarkers having been shown to be associated with CAVD, a marker suitable for routine testing in clinical practice is still needed. Plasma cell-free DNA (cfDNA) has been suggested as a biomarker for diagnosis and prognosis in multiple diseases. In this study, we aimed to test whether cfDNA could be used as a biomarker for the diagnosis of CAVD. Methods: Serum samples were collected from 137 diagnosed CAVD patients and 180 normal controls. The amount of cfDNA was quantified by amplifying a short fragment (ALU 115) and a long fragment (ALU 247) using quantitative real-time PCR. The cfDNA integrity (cfDI) was calculated as the ratio of ALU247 to ALU115. The association between CAVD and cfDI was evaluated using regression analysis. Results: CAVD patients had increased ALU 115 fragments (median, 185.14 (416.42) versus 302.83 (665.41), p < 0.05) but a decreased value of cfDI (mean, 0.50 ± 0.25 vs. 0.41 ± 0.26, p < 0.01) in their serum when compared to controls. This difference was more dramatic in non-rheumatic CAVD patients (p < 0.001) versus rheumatic CAVD patients (no significant difference). Similarly, CAVD patients with bicuspid aortic valve (BAV) (p < 0.01) showed a greater difference than non-BAV CAVD patients (p < 0.05). Linear regression and logistic regression showed that cfDI was independently and significantly associated with the presence of CAVD (95% CI, 0.096 to 0.773, p < 0.05). The ROC assay revealed that cfDI combined with clinical characteristics had a better diagnostic value than cfDI alone (AUC = 0.6191, p < 0.001). Conclusion: cfDI may be a potential biomarker for diagnosis of CAVD.