bluuebunny/crossref_metadata_2025 huggingface dataset:
Differences between physical therapist ratings, self-ratings, and posturographic measures when assessing static balance exercise intensity
Authors: Ferris Jamie , Zwier Jonathan , Carender Wendy J. , Sienko Kathleen H.
Published: May 2023

Introduction

In order for balance therapy to be successful, the training must occur at the appropriate dosage. However, physical therapist (PT) visual evaluation, the current standard of care for intensity assessment, is not always effective during telerehabilitation. Alternative balance exercise intensity assessment methods have not previously been compared to expert PT evaluations. The aim of this study was therefore to assess the relationship between PT participant ratings of standing balance exercise intensity and balance participant self-ratings or quantitative posturographic measures.

Methods

Ten balance participants with age or vestibular disorder-related balance concerns completed a total of 450 standing balance exercises (three trials each of 150 exercises) while wearing an inertial measurement unit on their lower back. They provided per-trial and per-exercise self-ratings of balance intensity on a scale from 1 (steady) to 5 (loss of balance). Eight PT participants reviewed video recordings and provided a total of 1,935 per-trial and 645 per-exercise balance intensity expert ratings.

Results

PT ratings were of good inter-rater reliability and significantly correlated with exercise difficulty, supporting the use of this intensity scale. Per-trial and per-exercise PT ratings were significantly correlated with both self-ratings (r = 0.77–0.79) and kinematic data (r = 0.35–0.74). However, the self-ratings were significantly lower than the PT ratings (difference of 0.314–0.385). Resulting predictions from self-ratings or kinematic data agreed with PT ratings approximately 43.0–52.4% of the time, and agreement was highest for ratings of a 5.

Discussion

These preliminary findings suggested that self-ratings best indicated two intensity levels (i.e., higher/lower) and sway kinematics were most reliable at intensity extremes.
Addressing the educational challenges of urban poverty: a case for solution-based research
Authors: Cartwright Macey , O'Callaghan Erin , Stacy Sara , Hord Casey , Kloos Heidi
Published: May 2023

Introduction

Math achievement for economically disadvantaged students remains low, despite positive developments in research, pedagogy, and funding. In the current paper, we focused on the research-to-practice divide as possible culprit. Our argument is that urban-poverty schools lack the stability that is necessary to deploy the trusted methodology of hypothesis-testing. Thus, a type of efficacy methodology is needed that could accommodate instability.

Method

We explore the details of such a methodology, building on already existing emancipatory methodologies. Central to the proposed solution-based research (SBR) is a commitment to the learning of participating students. This commitment is supplemented with a strength-and-weaknesses analysis to curtail researcher bias. And it is supplemented with an analysis of idiosyncratic factors to determine generalizability. As proof of concept, we tried out SBR to test the efficacy of an afterschool math program.

Results

We found the SBR produced insights about learning opportunities and barrier that would not be known otherwise. At the same time, we found that hypothesis-testing remains superior in establishing generalizability.

Discussion

Our findings call for further work on how to establish generalizability in inherently unstable settings.
GALD: new diagnostic tip for early diagnosis - a case report and literature review
Authors: Zermano Silvia , Novak Alice , Vogrig Emanuela , Parisi Nadia , Driul Lorenza
Published: May 2023

Objective

Gestational alloimmune liver disease is a rare and serious condition caused by a maternal-fetal alloimmune disorder. There are not many studies about the antenatal treatment (IVIG infusion) of affected fetuses as the diagnosis is generally made postnatally. The possibility of an early diagnosis by means of ultrasonography and a gynecologist's assesment can provide prompt treatment of this disease.

Case report

We report the case of 38-year-old pregnant woman referred to our centre in view of severe fetal hydrops seen by ultrasound at 31 weeks + 1 day gestation. A male infant was born and subsequently died after developing liver failure. Postmortem examination revealed the presence of diffuse hepatic fibrosis in the absence of hemosiderin deposits and no extrahepatic siderosis. Immunohistochemical analysis was also performed which showed diffuse hepatocyte positivity for the terminal complement complex (C5b-C9) confirming the suspicion of GALD.

Methods

A comprehensive literature search published from 2000 to 2022 was conducted on PubMed and Scopus. Paper selection was performed following the PRISMA guidelines. Fifteen retrospective studies were identified and selected.

Results

A total of 15 manuscripts describing 26 cases were finally included in our research. Twenty-two fetuses/newborns with suspected GALD were studied, of which 11 had a confirmed histopathological diagnosis of GALD. Prenatal diagnosis of gestational alloimmune liver disease is difficult because ultrasound findings may be absent or nonspecific. Only one case report described fetal hydrops similar to our clinical case. As highlighted by the current case, in fetuses presenting with hydrops, once the most common etiologies have been excluded, hepatobiliary complications and liver failure caused by GALD should be considered

Conclusions

Global knowledge of this disorder and its wide spectrum of presentations may help to increase the number of cases that are diagnosed early and accurately. The recurrence rate of an infant being affected with GALD in another pregnancy is more that 90%. Recurrence however can be prevented by treatment with IVIG during pregnancy. This highlights the importance of having obstetricians and pediatricians familiar with gestational alloimmune liver disease.
Effects of urbanization on the structure of plant-flower visitor network at the local and landscape levels in the northern Argentinian Yungas forest
Authors: Amado De Santis Alejandro A. , Lomáscolo Silvia B. , Chacoff Natacha P.
Published: May 2023

Human population and cities are growing fast, with a concomitant modification of the land surface. Urbanization is driving biodiversity loss and biological homogenization, which impacts human wellbeing. In this study, we evaluated the influence of urbanization on flower visitor assemblage using an interaction network approach. We assessed the effect of different variables at the local and landscape scales on community parameters and network metrics along a gradient of urbanization located in a subtropical montane Yungas forest. We found that local variables affected the richness of flower visitors, which increased with greater flower coverage, high stability of floral resources, and the proportion of exotic plants. Moreover, local variables affected the diversity, nestedness (NODF), and specialization (H2) of the interaction network. Landscape variables, such as altitude and proportion of impervious surface (a proxy of urbanization), affected both the richness of flower visitors and specialization. The effect of urbanization on the richness of flower visitors differed across the altitudinal gradient, with higher impact at higher altitudes. In conclusion, our results indicate that local and landscape variables affect community parameters and the structure of plant-flower visitor networks to different extents and strengths.
Reversing stage III oral adenocarcinoma in a dog treated with anti-canine PD-1 therapeutic antibody: a case report
Authors: Xu Shuo , Xie Jingshu , Wang Shuaiyu , Tang Na , Feng Junli , Su Youhong , Li Gebin
Published: May 2023

Monoclonal antibody targeting programmed cell death-1 (PD-1) is one of the most promising treatment therapies for human cancers. Canine PD-1 antibodies used in clinical trials have also shown efficacy in treating canine cancers. An 11-year-old male intact border collie presented to us for evaluation of left cervical mass. Computed tomography (CT) examination revealed an irregular pharyngeal mass invading the surrounding soft tissue. Histological and immunohistochemical results were consistent with a diagnosis of adenocarcinoma, most likely originating from the minor salivary gland. An anti-canine PD-1 monoclonal antibody was administered. Two months after the initial treatment, the tumor reached partial remission and maintained as such for 6 months. Finally, the patient was euthanized due to reasons unrelated to cancer, with a survival time of 316 days. To our knowledge, this is the first report of response to PD-1 blockade treatment in canine adenocarcinoma.
A quadruplex real-time PCR assay combined with a conventional PCR for the differential detection of Marek’s disease virus vaccines and field strains
Authors: Wu Shaopeng , Ding Tian , Shao Hongxia , Qian Kun , Ye Jianqiang , Qin Aijian
Published: May 2023

To evaluate the effect of the vaccine and differentiate vaccine from virulent MDV, a new quadruplex real-time PCR assay based on TaqMan probes was developed to differentiate and accurately quantify HVT, CVI988 and virulent MDV-1. The results showed that the limit of detection (LOD) of the new assay was 10 copies with correlation coefficients >0.994 of CVI988, HVT and virulent MDV DNA molecules without cross-reactivity with other avian disease viruses. The intra-assay and inter-assay coefficients of variation (CVs) of Ct values for the new assay were less than 3%. Analysis of replication kinetics of CVI988 and virulent MDV of collected feathers between 7 and 60 days post-infection (dpi) showed MD5 had no significant effect on the genomic load of CVI988 (p > 0.05), while vaccination with CVI988 could significantly reduce the viral load of MD5 (p < 0.05). Combined with meq gene PCR, this method can effectively identify virulent MDV infections in immunized chickens. These results demonstrated that this assay could distinguish between the vaccine and virulent MDV strains and had the advantages of being reliable, sensitive and specific to confirm the immunization status and monitor the circulation of virulent MDV strains.
Effects of container type and size on thermal processing characteristics and B-vitamin retention of canned cat food
Authors: Dainton Amanda N. , Molnar Lydia M. , Aldrich Charles Gregory
Published: May 2023

Introduction

Rigid cans were the traditional container for canned cat foods, but semi-rigid trays/tubs and flexible pouches are popular options as well. Despite this, little is published on the effects of canned cat food container characteristics on thermal processing and retention of B-vitamins. Therefore, the objective was to evaluate the effects of container size and type on thermal processing and B-vitamin retention.

Materials and methods

Treatments were arranged in a factorial with two container sizes [small (85–99 g) and medium (156–198 g)] and three container types (flexible, semi-rigid, and rigid). A canned cat food formula was prepared, filled, and sealed into containers before retort processing to a heating cycle target lethality of 8 min. Internal retort and container temperatures were used to calculate accumulated lethality. Thiamin, riboflavin, niacin, pantothenic acid, pyridoxine, biotin, folic acid, cobalamin, and moisture contents were analyzed in pre- and post-retort samples by commercial laboratories. Thermal processing metrics were analyzed (SAS v. 9.4; SAS Institute, Cary, NC) with the fixed effects of container size, container type, and their interaction. Dry matter basis B-vitamin contents were analyzed with container size, container type, processing stage, and all two-way and three-way interactions as fixed effects. Means were separated using Fisher's LSD at a P-value < 0.05.

Results and discussion

Total accumulated lethality was greater (P < 0.05) for semi-rigid and flexible containers (average 14.99 min) than for rigid containers (12.86 min). The greater processing of semi-rigid and flexible containers was likely influenced by required retort settings. Thiamin and riboflavin contents decreased (P < 0.05) by 30.4 and 18.3%, respectively, due to retort processing. Niacin, biotin, and cobalamin were not affected (P > 0.05) by processing. Processing increased (P < 0.05) pantothenic acid (9.1%), pyridoxine (22.6%), and folic acid (22.6%). This was likely caused by sampling or analytical variation. No interaction involving processing stage was significant for any B-vitamin (P > 0.05). B-vitamin retention was not influenced by differences in thermal processing caused by the packaging treatments. Thiamin and riboflavin were the only B-vitamins meaningfully impacted by processing and retention was not improved by any container characteristic.
Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning
Authors: Orlova Yulia , Gorobtsov Alexander , Sychev Oleg , Rozaliev Vladimir , Zubkov Alexander , Donsckaia Anastasia
Published: May 2023

Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant attention during respiratory rehabilitation is paid to the type of human breathing. Remote rehabilitation requires the development of automated methods of breath analysis. Most currently developed methods for analyzing breathing do not work with different types of breathing. These methods are either designed for one type (for example, diaphragmatic) or simply analyze the lungs’ condition. Developing methods of determining the types of human breathing is necessary for conducting remote respiratory rehabilitation efficiently. We propose a method of determining the type of breathing using wireless sensors with the motion capture system. To develop that method, spectral analysis and machine learning methods were used to detect the prevailing spectrum, the marker coordinates, and the prevailing frequency for different types of breathing. An algorithm for determining the type of human breathing is described. It is based on approximating the shape of graphs of distances between markers using sinusoidal waves. Based on the features of the resulting waves, we trained machine learning models to determine the types of breathing. After the first stage of training, we found that the maximum accuracy of machine learning models was below 0.63, which was too low to be reliably used in respiratory rehabilitation. Based on the analysis of the obtained accuracy, the training and running time of the models, and the error function, we choose the strategy of achieving higher accuracy by increasing the training and running time of the model and using a two-stage method, composed of two machine learning models, trained separately. The first model determines whether the breath is of the mixed type; if it does not predict the mixed type of breathing, the second model determines whether breathing is thoracic or abdominal. The highest accuracy achieved by the composite model was 0.81, which surpasses single models and is high enough for use in respiratory rehabilitation. Therefore, using three wireless sensors placed on the patient’s body and a two-stage algorithm using machine learning models, it was possible to determine the type of human breathing with high enough precision to conduct remote respiratory rehabilitation. The developed algorithm can be used in building rehabilitation applications.
Efficient Mathematical Lower Bounds for City Logistics Distribution Network with Intra-Echelon Connection of Facilities: Bridging the Gap from Theoretical Model Formulations to Practical Solutions
Authors: Niu Zhiqiang , Wu Shengnan , Zhou Xuesong (Simon)
Published: May 2023

Focusing on the dynamic improvement of the underlying service network configuration, this paper aims to address a specific challenge of redesigning a multi-echelon city logistics distribution network. By considering the intra-echelon connection of facilities within the same layer of echelon, we propose a new distribution network design model by reformulating the classical quadratic assignment problem (QAP). To minimize the overall transportation costs, the proposed model jointly optimizes two types of decisions to enable agile distribution with dynamic “shortcuts”: (i) the allocation of warehouses to supply the corresponding distribution centers (DCs), and (ii) the demand coverage decision from distribution centers to delivery stations. Furthermore, a customized branch-and-bound algorithm is developed, where the lower bound is obtained by adopting Gilmore and Lawler lower Bound (GLB) for QAP. We conduct extensive computational experiments, highlighting the significant contribution of GLB-oriented lower bound, to obtain practical solutions; this type of efficient mathematical lower bounds offers a powerful tool for balancing theoretical research ideas with practical and industrial applicability.
A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
Authors: Zhao Qiao , Wang Zengping , Li Guomin , Liu Xuanjun , Wang Yuxuan
Published: May 2023

In this paper, a fault location method based on divide-and-conquer (DAC) is proposed to solve the inadequacy problem that arises when using the traditional fault section location method based on the optimization model of logic operation. The problem is that it is difficult to balance speed and accuracy after the scale of the distribution network is expanded. First, the causal link between fault information and the faulty device was described using the road vector, the equivalent transformation of the logical operations in the traditional model was implemented with the properties of the road vector, and the numerical computational model of the fault location was constructed. Based on this, the optimization-seeking variable “approximation gain” was introduced to prove that the proposed model conforms to the recursive structure of DAC, and the method of applying DAC to locate faults is proposed. The method applies the “Divide-Conquer-Combine” recursive mode to locate faults, and each level of recursion contains only linear-time “approximation gain” operations and constant-time decomposition and combination operations. The efficiency analysis and simulation results show that the proposed method has linear-time complexity and can accurately locate faults in milliseconds, providing a reference for solving the fault location problem in large distribution networks.
Explainable Artificial Intelligence (XAI) and Supervised Machine Learning-based Algorithms for Prediction of Surface Roughness of Additively Manufactured Polylactic Acid (PLA) Specimens
Authors: Mishra Akshansh , Jatti Vijaykumar S. , Sefene Eyob Messele , Paliwal Shivangi
Published: May 2023

Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. This research work focuses on the prediction of the surface roughness of additive-manufactured polylactic acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, explainable AI techniques are employed to enhance the interpretability of the machine learning models. The nine algorithms used in this study are Support Vector Regression, Random Forest, XGBoost, AdaBoost, CatBoost, Decision Tree, the Extra Tree Regressor, the Explainable Boosting Model (EBM), and the Gradient Boosting Regressor. This study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impacts of individual input parameters through explainable AI methods. The experimental results indicate that the XGBoost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XGBoost algorithm provides the most accurate predictions for surface roughness compared with other algorithms. This study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from explainable AI techniques.
Reverberations of Persepolis: Persianist Readings of Late Roman Wall Decoration
Authors: Hagan Stephanie A.
Published: May 2023

Animal combats (venationes) were a popular entertainment in the Roman world. Splashy panels of inlaid marble (opus sectile) commemorate these bloody contests in several buildings in and around Rome. Among the most well-known are survivals from the 4th century CE Basilica of Junius Bassus and, several decades later, the marble-revetted hall from Porta Marina at Ostia. On the face of it, the wall decoration from these sites memorializes typical Roman activities, but the panels expose the vast geography implicated in these combat spectacles. The brilliant stones used to render them came from lands as far off as the Caspian tigers and Asiatic lions they depicted. The iconography of the panels was also foreign: the animal combat, or symplegma (intertwining), is seen on works from pre-Achaemenid sculpture to Sasanian textiles, and most recognizably, at the Achaemenid palace at Persepolis, where a lion attacks a bull in relief on the Apadana stairway. Reading these panels through a Persianist lens illuminates the ways in which the Persepolitan model animated Roman themes and visual programs. Though they recalled events in the Roman arena, they also imparted political and astrological signification to the decoration by means of their Persian associations. By alluding to the Achaemenid empire, a great power of the past and a continuing rival in the form of the Sasanians, the Roman patron accrued to himself some measure of the veneration for this culture and showed himself able to communicate in an idiom legible to an international clientele.
Facile Synthesis of Nickel Phosphide @ N-Doped Carbon Nanorods with Exceptional Cycling Stability as Li-Ion and Na-Ion Battery Anode Material
Authors: Fu Fang , He Qiuchen , Zhang Xuan , Key Julian , Shen Peikang , Zhu Jinliang
Published: May 2023

Nickel phosphide (Ni2P), as an anode material for both lithium- and sodium-ion batteries, offers high theoretical specific and volumetric capacities. However, considerable challenges include its limited rate capability and low cycle stability arising from its volume change and degradation during cycling. To solve these issues, appropriate composite micro/nanoparticle designs can improve conductivity and provide confinement. Herein, we report a simple pyrolysis method to synthesize nitrogen-doped carbon-coated Ni2P nanorod arrays (Ni2P@NC) from nickel foam and an ionic resin as a source of carbon, nitrogen and phosphorus. The N-doped open-ended carbon shells provide Ni2P containment, good electrical conductivity, efficient electrolyte access and the buffering of bulk strain during cycling. Consequently, as a LIB anode material, Ni2P@NC has impressive specific capacity in long-term cycling (630 mAh g−1 for 150 cycles at 0.1 A g−1) and a high rate capability of 170 mAh g−1 for 6000 cycles at 5 A g−1. Similarly, as a SIB anode, Ni2P@NC retains a sizable 288 mAh g−1 over 300 cycles at 0.1 A g−1, and 150 mAh g−1 over 2000 cycles at 2 A g−1. Furthermore, due to a sizable portion of its capacity coinciding with adequately low voltage, the material shows promise for high volumetric energy storage in full-cell format. Lastly, the simple synthesis method has the potential to produce other carbon-coated metal phosphides for electrochemical applications.
Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition
Authors: Wen Zhengyao , Lin Wenzhong , Wang Tao , Xu Ge
Published: May 2023

This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle. Secondly, facial expressions simultaneously exhibit themselves through multiple facial regions, and for recognition, a holistic approach by encoding high-order interactions among local features is required. To address these issues, this work proposes DAN with three key components: Feature Clustering Network (FCN), Multi-head Attention Network (MAN), and Attention Fusion Network (AFN). Specifically, FCN extracts robust features by adopting a large-margin learning objective to maximize class separability. In addition, MAN instantiates a number of attention heads to simultaneously attend to multiple facial areas and build attention maps on these regions. Further, AFN distracts these attentions to multiple locations before fusing the feature maps to a comprehensive one. Extensive experiments on three public datasets (including AffectNet, RAF-DB, and SFEW 2.0) verified that the proposed method consistently achieves state-of-the-art facial expression recognition performance. The DAN code is publicly available.
Bioremediation of Organic Pollutants in Soil–Water System: A Review
Authors: Gupta Pankaj Kumar , Gandhi Manvi
Published: May 2023

Soil–water pollution is of serious concern worldwide. There is a public outcry against the continually rising problems of pollution to ensure the safest and healthiest subsurface environment for living beings. A variety of organic pollutants causes serious soil–water pollution, toxicity and, therefore, the removal of a wide range of organic pollutants from contaminated matrix through the biological process rather than physico-chemical methods is an urgent need to protect the environment and public health. Being an ecofriendly technology, bioremediation can solve the problems of soil–water pollution due to hydrocarbons as it is a low-cost and self-driven process that utilises microorganisms and plants or their enzymes to degrade and detoxify pollutants and thus, promote sustainable development. This paper describes the updates on the bioremediation and phytoremediation techniques which have been recently developed and demonstrated at the plot-scale. Further, this paper provides details of wetland-based treatment of BTEX contaminated soils and water. The knowledge acquired in our study contributes extensively towards understanding the impact of dynamic subsurface conditions on engineered bioremediation techniques.
Simulation Study on Airflow Organization and Environment in Reconstructed Fangcang Shelter Hospital Based on CFD
Authors: Yang Yongwen , Yang Haitao , Li Qifen , Zhang Liting , Dong Ziwen
Published: May 2023

With frequent outbreaks of COVID-19, the rapid and effective construction of large-space buildings into Fangcang shelter hospitals has gradually become one of the effective means to control the epidemic. Reasonable design of the ventilation system of the Fangcang shelter hospital can optimize the indoor airflow organization, so that the internal environment can meet the comfort of patients and at the same time can effectively discharge pollutants, which is particularly important for the establishment of the Fangcang shelter hospital. In this paper, through the reconstruction of a large-space gymnasium, CFD software is used to simulate the living environment and pollutant emission efficiency of the reconstructed Fangcang shelter hospital in summer under different air supply temperatures, air supply heights and exhaust air volume parameters. The results show that when the air supply parameters are set to an air supply height of 4.5 m, an air supply temperature of 18 °C, and an exhaust air volume of a single bed of 150 m3/h, the thermal comfort can reach level I, and the ventilation efficiency for pollutants can reach 69.6%. In addition, the ventilation efficiency is 70.1% and 70.3% when the exhaust air volume of a single bed is continuously increased to 200 and 250 m3/h, which can no longer effectively improve the pollutant emission and will cause an uncomfortable blowing feeling to patients.
Targeting Sphingosine 1-Phosphate Metabolism as a Therapeutic Avenue for Prostate Cancer
Authors: Mebarek Saida , Skafi Najwa , Brizuela Leyre
Published: May 2023

Prostate cancer (PC) is the second most common cancer in men worldwide. More than 65% of men diagnosed with PC are above 65. Patients with localized PC show high long-term survival, however with the disease progression into a metastatic form, it becomes incurable, even after strong radio- and/or chemotherapy. Sphingosine 1-phosphate (S1P) is a bioactive lipid that participates in all the steps of oncogenesis including tumor cell proliferation, survival, migration, invasion, and metastatic spread. The S1P-producing enzymes sphingosine kinases 1 and 2 (SK1 and SK2), and the S1P degrading enzyme S1P lyase (SPL), have been shown to be highly implicated in the onset, development, and therapy resistance of PC during the last 20 years. In this review, the most important studies demonstrating the role of S1P and S1P metabolic partners in PC are discussed. The different in vitro, ex vivo, and in vivo models of PC that were used to demonstrate the implication of S1P metabolism are especially highlighted. Furthermore, the most efficient molecules targeting S1P metabolism that are under preclinical and clinical development for curing PC are summarized. Finally, the possibility of targeting S1P metabolism alone or combined with other therapies in the foreseeable future as an alternative option for PC patients is discussed. Research Strategy: PubMed from INSB was used for article research. First, key words “prostate & sphingosine” were used and 144 articles were found. We also realized other combinations of key words as “prostate cancer bone metastasis” and “prostate cancer treatment”. We used the most recent reviews to illustrate prostate cancer topic and sphingolipid metabolism overview topic.
ICAM-1-suPAR-CD11b Axis Is a Novel Therapeutic Target for Metastatic Triple-Negative Breast Cancer
Authors: Li Dong , Hemati Hami , Park Younhee , Taftaf Rokana , Zhang Youbin , Liu Jinpeng , Cristofanilli Massimo , Liu Xia
Published: May 2023

Accumulating evidence demonstrates that circulating tumor cell (CTC) clusters have higher metastatic ability than single CTCs and negatively correlate with cancer patient outcomes. Along with homotypic CTC clusters, heterotypic CTC clusters (such as neutrophil–CTC clusters), which have been identified in both cancer mouse models and cancer patients, lead to more efficient metastasis formation and worse patient outcomes. However, the mechanism by which neutrophils bind to CTCs remains elusive. In this study, we found that intercellular adhesion molecule-1 (ICAM-1) on triple-negative breast cancer (TNBC) cells and CD11b on neutrophils mediate tumor cell–neutrophil binding. Consequently, CD11b deficiency inhibited tumor cell–neutrophil binding and TNBC metastasis. Furthermore, CD11b mediated hydrogen peroxide (H2O2) production from neutrophils. Moreover, we found that ICAM-1 in TNBC cells promotes tumor cells to secrete suPAR, which functions as a chemoattractant for neutrophils. Knockdown of uPAR in ICAM-1+ TNBC cells reduced lung-infiltrating neutrophils and lung metastasis. Bioinformatics analysis confirmed that uPAR is highly expressed in TNBCs, which positively correlates with higher neutrophil infiltration and negatively correlates with breast cancer patient survival. Collectively, our findings provide new insight into how neutrophils bind to CTC to facilitate metastasis and discover a novel potential therapeutic strategy by blocking the ICAM-1-suPAR-CD11b axis to inhibit TNBC metastasis.
Does ADT Influence the Risk of Suicidal Ideation among US Veteran Prostate Cancer Patients Pre-Exposed to PTSD?
Authors: Brahmbhatt Smit , Chao Herta H. , Verma Shiv , Gupta Sanjay
Published: May 2023

Post-traumatic stress disorder (PTSD) is defined as a mental health disease that has a high probability of developing among individuals who have experienced traumatic events [...]
Solid-Oxide Amperometric Sensor for Hydrogen Detection in Air
Authors: Kalyakin Anatoly , Volkov Alexander , Dunyushkina Liliya
Published: May 2023

An amperometric sensor based on CaZr0.95Sc0.05O3−δ (CZS) proton-conducting oxide for the measurement of hydrogen concentration in air was designed and tested. Dense CZS ceramics were fabricated through uniaxial pressing the powder synthesized by the solid-state method and sintering at 1650 °C for 2 h. The conductivity of CZS was shown to increase with increasing air humidity, which indicates the proton type of conductivity. The sensor was made from two CZS plates, one of which had a cavity was drilled to form an inner chamber, that were then pressed against each other and sealed around the perimeter to prevent gas leaking. The inner chamber of the sensor was connected with the outer atmosphere via an alumina ceramic capillary, which acted as a diffusion barrier. The sensor performance was studied in the temperature range of 600–700 °C in the mixtures of air with hydrogen. The sensor signal, or the limiting current, was found to linearly increase with the hydrogen concentration, which simplifies the sensor calibration. The sensor demonstrated a high sensitivity of ~60 μA per 1% H2 at 700 °C, a fast response, high reproducibility, good selectivity, and long-term stability.
Relationship between the Biological Clock and Inflammatory Bowel Disease
Authors: Giebfried Jonathan , Lorentz Axel
Published: May 2023

The biological clock is a molecular oscillator that generates a 24-hour rhythm in accordance with the earth’s rotation. Physiological functions and pathophysiological processes such as inflammatory bowel diseases (IBD) are closely linked to the molecular clock. This review summarizes 14 studies in humans and mice on the interactions between the biological clock and IBD. It provides evidence that IBD negatively affect core clock gene expression, metabolism and immune functions. On the other hand, disruption of the clock promotes inflammation. Overexpression of clock genes can lead to inhibition of inflammatory processes, while silencing of clock genes can lead to irreversible disease activity. In both human and mouse studies, IBD and circadian rhythms have been shown to influence each other. Further research is needed to understand the exact mechanisms and to develop potential rhythm-related therapies to improve IBD.
Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator
Authors: Sun Youbo , Zhao Tao , Liu Nian
Published: May 2023

In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.
Obesity and Abdominal Obesity in Indian Population: Findings from a Nationally Representative Study of 698,286 Participants
Authors: Gupta Rajat Das , Tamanna Nowrin , Siddika Nazeeba , Haider Shams Shabab , Apu Ehsanul Hoque , Haider Mohammad Rifat
Published: May 2023

This study aims to determine and compare the prevalence and correlates of obesity and abdominal obesity in India among participants aged 18–54 years. Data were acquired from the nationally representative National Family Health Survey 2019–21. Age and sex standardized descriptive analyses were conducted to determine the prevalence of obesity and abdominal obesity, and multivariable multilevel logistic regression was performed to identify the factors associated with these conditions. Gender-specific analyses were also conducted. The sample weight was adjusted throughout. The final sample size for this study was 698,286. The prevalence of obesity and abdominal obesity was 13.85% and 57.71%, respectively. Older age, being female, increased educational status and increased wealth index, being married at any point, and residing in an urban area all increased the odds of both obesity and abdominal obesity. Being a resident of the North zone and having a current alcohol intake increased the odds of abdominal obesity. On the other hand, being a resident of the South zone of India increased the odds of obesity. Targeting these high-risk groups can be a strategy for public health promotion programs.
Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests
Authors: Su Rina , Du Wala , Ying Hong , Shan Yu , Liu Yang
Published: May 2023

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.
NPP and Carbon Emissions under Forest Fire Disturbance in Southwest and Northeast China from 2001 to 2020
Authors: Zhang Wenyi , Yang Yanrong , Hu Cheng , Zhang Leying , Hou Bo , Wang Weifeng , Li Qianqian , Li Yansong
Published: May 2023

With climate change, frequent forest fires and prolonged fire period occur all over the world. Moreover, carbon emission from forest fire affects the carbon cycle of the forest ecosystem. However, this effect varies by region with no uniform conclusions, and fewer comparative studies exist on such differences between regions. In this paper, net primary productivity (NPP) data MOD17A3 were used as an important parameter of forest carbon absorption, along with MODIS fire spot data MCD14DL and burned area data MCD64A1. Forest carbon lost under forest fire interference in the northeast and southwest natural forest areas of China was studied to explore the role of forest fire in the carbon cycle process and its differences in the unlike regions of China. Here, by means of kernel density analysis and M-K trend test, the characteristics of forest fires in China’s southwest and northeast forests were calculated. Forest carbon emission under forest fire disturbance was quantified by reference to the forest fire emission factor list. We show that (1) the total number of forest fire spots in the southwest region from 2001 to 2020 was 1.06 × 105, 1.28 times that of Northeast China. However, the total burned area in the southwest was only 67.84% of that in the northeast. (2) The total carbon emissions from forest fires in the southwest from 2001 to 2020 was 37,559.94 Gg, 10.77% larger than the northeast forest, CH4 and CO2 were 13.52% and 11.29% larger respectively. Moreover, the carbon emissions of forest fire in the northeast showed a downward trend, R2 = 0.16 (p < 0.1), while it remained basically unchanged in the southwest. The contribution of carbon emissions from forest fires changed with forest types, it was shown as: evergreen needleleaf forest (14.98%) > evergreen broadleaf forest (10.81%) > deciduous needleleaf forest (6.52%) > deciduous broadleaf forest (5.22%). (3) From 2001 to 2020, under the premise that the NPP both manifested upward trends, the NPP of the burned areas showed a significant downward trend in the southwest forest, with R2 = 0.42 (p < 0.05), while it increased in the northeast forest, with R2 = 0.37 (p < 0.05). It showed negative correlation between NPP of burned areas and forest fire carbon emissions, and forest fire disturbance had no significant effect on forest NPP in Northeast China, while net carbon loss occurred in Southwest China. In general, under different forest fire characteristics, NPP, which represents forest carbon uptake, and carbon emissions from forest fires show differences. The impact of forest fire disturbance on forest carbon process varies with regions. The study can provide some ideas on the effects of forest fire disturbance on climate change.
Should Government Play a Strict or Lenient Role? An Evolutionary Game Analysis of Implementing the Forest Ecological Bank Policy
Authors: Nie Xin , Yang Mengshi , Chen Zhoupeng , Li Weijuan , Zang Ran , Wang Han
Published: May 2023

As one of the specific practices of natural resource index trading, the forest ecological bank policy (FEB) is essentially a market-based tool. With the deepening of ecological governance, the FEB policy has also become the main method chosen to solve the economic development problems in ecologically rich “low-lying” areas. However, in the process of implementing the FEB policy, the differences in the demands of various stakeholders were found to have led to a complex game phenomenon, resulting in deviations in policy implementation. This study constructs a multiplayer evolutionary game model between local governments and enterprises of different scales and analyzes the evolutionary stabilization strategy (ESS) in the implementation of the FEB policy. The results show that, under different conditions, there are three stabilization strategies in the evolutionary game system, these correspond to F1 (0, 0, 0), F4 (0, 1, 1), and F5 (1, 0, 0), respectively, the implications are that the strict government role with an active regulatory strategy leads to companies of different sizes refusing to participate (i.e., F5) and the lax government role with a negative regulatory strategy leads to companies of different sizes refusing to participate (i.e., F1) or choosing to participate (i.e., F4). Among them, the strict government role stimulates the companies to participate in the FEB policy through the high intensity of government regulation. In addition, as the policy continues to be implemented, the influence of the strict regulation on the “participation” behavior of the companies decreases. Conversely, the lax government role allows the companies to give full play to their autonomy and obtain higher ecological and environmental benefits.
A Review on Deep-Learning-Based Cyberbullying Detection
Authors: Hasan Md. Tarek , Hossain Md. Al Emran , Mukta Md. Saddam Hossain , Akter Arifa , Ahmed Mohiuddin , Islam Salekul
Published: May 2023

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented.
Among Bodies: Portuguese Cemeterial Exhumations Three Years after a Pandemic
Authors: Silva-Bessa Angela , Ferreira Maria Teresa , Dinis-Oliveira Ricardo Jorge
Published: May 2023

On 19 March 2020, a “state of emergency” was declared in Portugal due to the manifestation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with an exponential rate of infection and high mortality and morbidity rates. Since then, the state of emergency was declared a further fifteen times until 30 April 2021, during which a total of 16,974 deaths associated with COVID-19 were acknowledged in the country. Over the aforementioned period, guidelines were followed regarding the handling of suspected cases in autopsy rooms, mortuaries, and cemeteries. However, no procedures have been established regarding the handling of human remains during and after cemeterial exhumations. Furthermore, little is known about the virus survival and its spatial distribution in postmortem human tissues. Given that the minimum Portuguese legal period of inhumation is ending and cemeteries have been facing limited burial space and soon will start exhuming buried individuals, the authors believe it is important to reflect on the matter.
A New Class of Generalized Fractal and Fractal-Fractional Derivatives with Non-Singular Kernels
Authors: Hattaf Khalid
Published: May 2023

The present paper introduces a new class of generalized differential and integral operators. This class includes and generalizes a large number of definitions of fractal-fractional derivatives and integral operators used to model the complex dynamics of many natural and physical phenomena found in diverse fields of science and engineering. Some properties of the newly introduced class are rigorously established. As applications of this new class, two illustrative examples are presented, one for a simple problem and the other for a nonlinear problem modeling the dynamical behavior of a chaotic system.