bluuebunny/crossref_metadata_2025 huggingface dataset:
Bacillus subtilis-Based Probiotic Improves Skeletal Health and Immunity in Broiler Chickens Exposed to Heat Stress
Authors: Jiang Sha , Yan Fei-Fei , Hu Jia-Ying , Mohammed Ahmed , Cheng Heng-Wei
Published: May 2021

The elevation of ambient temperature beyond the thermoneutral zone leads to heat stress, which is a growing health and welfare issue for homeothermic animals aiming to maintain relatively constant reproducibility and survivability. Particularly, global warming over the past decades has resulted in more hot days with more intense, frequent, and long-lasting heat waves, resulting in a global surge in animals suffering from heat stress. Heat stress causes pathophysiological changes in animals, increasing stress sensitivity and immunosuppression, consequently leading to increased intestinal permeability (leaky gut) and related neuroinflammation. Probiotics, as well as prebiotics and synbiotics, have been used to prevent or reduce stress-induced negative effects on physiological and behavioral homeostasis in humans and various animals. The current data indicate dietary supplementation with a Bacillus subtilis-based probiotic has similar functions in poultry. This review highlights the recent findings on the effects of the probiotic Bacillus subtilis on skeletal health of broiler chickens exposed to heat stress. It provides insights to aid in the development of practical strategies for improving health and performance in poultry.
Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes
Authors: Machado Romulos da S. , Pires Fabiano dos S. , Caldeira Giovanni R. , Giuntini Felipe T. , Santos Flávia de S. , Fonseca Paulo R.
Published: May 2021

Data centers are widely recognized for demanding many energy resources. The greater the computational demand, the greater the use of resources operating together. Consequently, the greater the heat, the greater the need for cooling power, and the greater the energy consumption. In this context, this article aims to report an industrial experience of achieving energy efficiency in a data center through a new layout proposal, reuse of previously existing resources, and air conditioning. We used the primary resource to adopt a cold corridor confinement, the increase of the raised floor’s height, and a better direction of the cold airflow for the aspiration at the servers’ entrance. We reused the three legacy refrigeration machines from the old data center, and no new ones were purchased. In addition to 346 existing devices, 80 new pieces of equipment were added (between servers and network assets) as a load to be cooled. Even with the increase in the amount of equipment, the implementations contributed to energy efficiency compared to the old data center, still reducing approximately 41% of the temperature and, consequently, energy-saving.
Automatic Mobile Warning System against People with Elevated Body Temperature
Authors: Marzec Mariusz , Wilczyński Sławomir
Published: May 2021

The paper proposes a system that allows for the automatic detection of people with elevated body temperature and estimates distance from them using a smartphone-type device and a single mobile thermal camera. The algorithm automatically finds and selects humans with the highest temperature, and tracks changes in their position in an image sequence. On the basis of the change in the position of the human head in the image, in subsequent frames, the algorithm estimates the distance between camera and human. Owing to the use of fast machine-learning methods, the proposed system can immediately alert the user about the presence of a people with an elevated temperature at a distance of 1–3 m as soon as it appears in the field of view of the camera. The effectiveness of the algorithm was assessed as the ratio of correct distance classifications in the test image set to the total number of test images. Values ranging from 73% to 100% were obtained for over 4000 images of humans at different distances. The proposed method allows for the quick and completely automatic warning aboutt people with elevated temperature, and can be used in popular Android mobile devices.
Toll-Like Receptors 1/2/4/6 and Nucleotide-Binding Oligomerization Domain-Like Receptor 2 Are Key Damage-Associated Molecular Patterns Sensors on Periodontal Resident Cells
Authors: Chen Yu , Wang Xiao Xiao , Ng Corrie H. C. , Tsao Sai Wah , Leung Wai Keung
Published: May 2021

Background: Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) are innate, damage-associated molecular patterns (DAMP) sensors. Their expressions in human periodontal resident cells and reactions toward irritations, such as hypoxia and lipopolysaccharide (LPS), remain not well characterized. This cross-sectional study aimed to investigate and characterize TLRs, NOD1/2 and NLRP1/2 expressions at the dento-gingival junction. Methods: Immunohistochemistry screening was carried out on periodontal tissue biopsies sections, while selected DAMP sensors signal and protein expression under Escherichia coli LPS (2 µg/mL) and/or hypoxia (1% O2), 24 h, by human gingival keratinocytes (HGK) or fibroblasts (HGF) were investigated. Results: Positive TLR1/2/4/5/6, NOD1/2 and NLRP1/2 immunostaining were observed in healthy and periodontitis biopsies with apparently more pocket epithelial cells positive for TLR2, TLR4 and NOD1/2. TLR1-6, NOD1/2 and NLRP1/2 messengers were detected in gingival/periodontal biopsies as well as healthy HGK and HGF explants. LPS and/or hypoxia induced signals and protein upregulation of NOD2 in HGKs or TLR1/6 and NOD2 in HGFs. Conclusion: Transcripts and proteins of TLR1/2/4/5/6, NOD1/2 and NLRP1/2 were expressed in human periodontal tissue in health and disease. Putting all observations together, NOD2, perhaps with TLR1/2/4/6, might be considered key, damage-associated molecular pattern sensors on periodontal resident cells.
Spontaneous Bone Regeneration after Enucleation of Mandibular Cysts: Retrospective Analysis of the Volumetric Increase with a Full-3D Measurement Protocol
Authors: Vitale Andrea , Battaglia Salvatore , Crimi Salvatore , Ricceri Chiara , Cervino Gabriele , Cicciù Marco , De Ponte Francesco Saverio , Leonardi Rosalia Maria , Bianchi Alberto
Published: May 2021

The goal of surgical treatment of mandibular cysts is their eradication. This result can be achieved by ensuring minimum risk of morbidity, while preserving the integrity of the bone structure and the functionality of the inferior alveolar nerve. The generation of bone defects after enucleation has always posed the dilemma of using filler materials or not. The aim of this study is to evaluate the degree of spontaneous bone regeneration, in terms of volume, in patients undergoing enucleation of mandibular cysts, measuring the real 3D volume of the cystic residual cavities. We included in our study 15 patients with mandibular cysts, treated from January 2018 to June 2020 at the Policlinico-Vittorio Emanuele and Policlinico-San Marco hospital of Catania and evaluated preoperatively (T0), 6 months after surgery (T1), and 12 months after surgery (T2). The results showed that after 6 months, independent of the initial volume of the cyst, around 90% of the cavity was filled with new generated bone. In conclusion, we believe that the full-3D measurement protocol can help us to understand the timing and modality of bone restoration after mandibular cyst enucleation.
Minimally Invasive Dentistry for Pre-Eruptive Enamel Lesions—A Case Series
Authors: Manaia Mariana , Rocha Larissa , Saraiva José , Coelho Ana , Amaro Inês , Marto Carlos Miguel , Vale Francisco , Ferreira Manuel Marques , Paula Anabela , Carrilho Eunice
Published: May 2021

Pre-eruptive enamel lesions occur during tooth formation and include fluorosis, traumatic hypomineralization, and molar incisor hypomineralization. Minimally invasive treatment approaches, such as microabrasion, should be considered for these cases. This article presents a case series of three patients with pre-eruptive enamel defects in esthetically compromised tooth regions which were treated with the microabrasion technique: two fluorosis cases, moderate and advanced, and one hypomineralization case of traumatic etiology. In Cases 1 and 3, there was a significant improvement in esthetics with a total resolution of the enamel defects. However, a slight yellowish coloration may be detected at close observation. In Case 2 (advanced fluorosis), although there was no full resolution of the white spots, there was a clear improvement in esthetics. Microabrasion is a safe and effective, minimally invasive treatment for pre-eruptive enamel lesions. It does not require local anesthesia, it is less destructive than restorative interventions, and allows good esthetic outcomes with no significant postoperative sensitivity. Its efficacy is directly related to the lesions’ severity and depth. Although there are some limitations, further improvement can be achieved with dental bleaching. More invasive treatments might be considered if results are still unsatisfactory.
Multi-Structural Optimization of Bearingless Permanent Magnet Slice Motor Based on Virtual Prototype in Ansoft Maxwell
Authors: Shen Huimin , Bian Feng , Yue Yang , Li Bingchu , Yu Ping , Yang Lihong , Chen Guangsheng , Hu Liang
Published: May 2021

As a preliminary study for bearingless permanent magnet slice motor (BPMSM) development, an effective means for BPMSM mechanical structure optimization is proposed here by developing a virtual prototype based on Ansoft Maxwell to realize overall performance improvements. First, the sensitivity evaluation index of the candidate mechanical structural parameters for individual BPMSM performance is constructed for selection. Orthogonal tests are performed to determine the dominant mechanical structural parameters to be optimized by utilizing monitored data based on Ansoft Maxwell. A linear regression model of the mechanical structural parameters for specific performances is obtained by utilizing the gradient descent method. Then, a multi-structural optimization regression model of the selected dominant mechanical structural parameters for overall performance is established using an analytic hierarchy process and solved using a genetic algorithm. The simulation results show that the performance of the optimized BPMSM has been comprehensively improved. Specifically, the passive axial stiffness, passive tilting stiffness, force-current coefficient, and motor efficiency increased by 56.4%, 71.3%, 19.6%, and 8.7%, respectively.
Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
Authors: Otala Jacqueline , Minard Alden , Madraki Golshan , Mousavian Seyedamirabbas
Published: May 2021

Graphs are powerful tools to model manufacturing systems and scheduling problems. The complexity of these systems and their scheduling problems has been substantially increased by the ongoing technological development. Thus, it is essential to generate sustainable graph-based modeling approaches to deal with these excessive complexities. Graphs employ nodes and edges to represent the relationships between jobs, machines, operations, etc. Despite the significant volume of publications applying graphs to shop scheduling problems, the literature lacks a comprehensive survey study. We proposed the first comprehensive review paper which (1) systematically studies the overview and the perspective of this field, (2) highlights the gaps and potential hotspots of the literature, and (3) suggests future research directions towards sustainable graphs modeling the new intelligent/complex systems. We carefully examined 143 peer-reviewed journal papers published from 2015 to 2020. About 70% of our dataset were published in top-ranked journals which confirms the validity of our data and can imply the importance of this field. After discussing our generic data collection methodology, we proposed categorizations over the properties of the scheduling problems and their solutions. Then, we discussed our novel categorization over the variety of graphs modeling scheduling problems. Finally, as the most important contribution, we generated a creative graph-based model from scratch to represent the gaps and hotspots of the literature accompanied with statistical analysis on our dataset. Our analysis showed a significant attention towards job shop systems (56%) and Un/Directed Graphs (52%) where edges can be either directed, or undirected, or both, Whereas 14% of our dataset applied only Undirected Graphs and 11% targeted hybrid systems, e.g., mixed shop, flexible, and cellular manufacturing systems, which shows potential future research directions.
Kinematic-Model-Free Redundancy Resolution Using Multi-Point Tracking and Control for Robot Manipulation
Authors: AlAttar Ahmad , Cursi Francesco , Kormushev Petar
Published: May 2021

Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots’ kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic structure or dynamic properties. On the other hand, model-learning controllers can overcome such challenges. Our recently proposed model-learning orientation controller has shown promising ability to simultaneously control a three-degrees-of-freedom robot manipulator’s end-effector pose. However, this controller does not perform optimally with robots of higher degrees-of-freedom nor does it resolve redundancies. The research presented in this paper extends the state-of-the-art kinematic-model-free controller to perform pose control of hyper-redundant robot manipulators and resolve redundancies by tracking and controlling multiple points along the robot’s serial chain. The results show that with more control points, the controller is able to reach desired poses in fewer steps, yielding an improvement of up to 66%, and capable of achieving complex configurations. The algorithm was validated by running the simulation 100 times, and it was found that, in 82% of the times, the robot successfully reached the desired target pose within 150 steps.
Potential for Farmers’ Cooperatives to Convert Coffee Husks into Biochar and Promote the Bioeconomy in the North Ecuadorian Amazon
Authors: Heredia Salgado Mario A. , Säumel Ina , Cianferoni Andrea , Tarelho Luís A. C.
Published: May 2021

Improving the livelihoods of communities living in fragile ecosystems, such as tropical forests, is among the main strategies to promote their conservation and preserve wildlife. In the Ecuadorian Amazon, farmers’ cooperatives are recognized as an important mechanism to improve the socioeconomic conditions of local communities. This study analyzes the integration of pyrolysis processes to convert agricultural waste into biochar as a way to implement the bioeconomy in these organizations. We found that post-harvesting processes in the studied farmers’ cooperatives are similar, and coffee husks are a potential feedstock to produce biochar. Although the environmental policies in Ecuador consider the valorization of agricultural waste, we did not find any specific standard to regulate the operation of pyrolysis facilities. Nonetheless, conversion of agricultural waste into biochar can contribute to (i) replacement of subsidized fossil fuels used in drying processes, (ii) prevention of environmental pollution caused by accumulation of waste, (iii) emergence of new income sources linked with the provision of carbon sequestration services, and (iv) the long-term maintenance of soil fertility. Currently, demonstration projects are needed to stimulate collaboration among farmers’ cooperatives, academia, the government, international cooperation agencies, and existing forest conservation initiatives.
Fraud Audit Based on Visual Analysis: A Process Mining Approach
Authors: Rodríguez-Quintero Jorge-Félix , Sánchez-Díaz Alexander , Iriarte-Navarro Leonel , Maté Alejandro , Marco-Such Manuel , Trujillo Juan
Published: May 2021

Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (dotted chart, trace alignment, fuzzy miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sectors. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.
A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems
Authors: Licciardo Gian Domenico , Russo Alessandro , Naddeo Alessandro , Cappetti Nicola , Di Benedetto Luigi , Rubino Alfredo , Liguori Rosalba
Published: May 2021

A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this paper to implement an embeddable Human Posture Recognition (HPR) system capable of very high accuracy both for laying and sitting posture recognition. The FCN exploits a new base-2 quantization scheme for weight and binarized activations to meet the optimal trade-off between low power dissipation, a very reduced set of instantiated physical resources and state-of-the-art accuracy to classify human postures. By using a limited number of pressure sensors only, the optimized HW implementation allows keeping the computation close to the data sources according to the edge computing paradigm and enables the design of embedded HP systems. The FCN can be simply reconfigured to be used for laying and sitting posture recognition. Tested on a public dataset for in-bed posture classification, the proposed FCN obtains a mean accuracy value of 96.77% to recognize 17 different postures, while a small custom dataset has been used for training and testing for sitting posture recognition, where the FCN achieves 98.88% accuracy to recognize eight positions. The FCN has been prototyped on a Xilinx Artix 7 FPGA where it exhibits a dynamic power dissipation lower than 11 mW and 7 mW for laying and sitting posture recognition, respectively, and a maximum operation frequency of 47.64 MHz and 26.6 MHz, corresponding to an Output Data Rate (ODR) of the sensors of 16.50 kHz and 9.13 kHz, respectively. Furthermore, synthesis results with a CMOS 130 nm technology have been reported, to give an estimation about the possibility of an in-sensor circuital implementation.
Special Issue Editorial: Air Pollution Estimation
Authors: Golobokova Liudmila P.
Published: May 2021

The key objectives at the current stage of humankind’s development are the processes of atmospheric air protection [...]
Analysis of Rainfall Erosivity Trends 1980–2018 in a Complex Terrain Region (Abruzzo, Central Italy) from Rain Gauges and Gridded Datasets
Authors: Di Lena Bruno , Curci Gabriele , Vergni Lorenzo
Published: May 2021

The erosive capacity of precipitation depends on its intensity, volume, and duration. The rainfall erosivity factor (R) of the Universal Soil Loss Equation (USLE) requires high frequency (subhourly) data. When these are not available, R can be estimated from simplified indices such as the Modified Fournier Index (MFI), the Precipitation Concentration Index (PCI), and the Seasonality Index (SI), which are computed from monthly precipitation. We calculated these indices for 34 stations in the complex terrain Abruzzo region (central Italy) during 1980–2018, based on both gauge (point) and grid datasets. Using 30-min rainfall data of 14 stations, we verified that MFI and PCI are reliable predictors of R (R2 = 0.91, RMSE = 163.6 MJ mm ha−1 h−1 year−1). For MFI, grid data do not capture the peaks in high-altitude stations and the low values in some inland areas, detected by the point dataset. Grid data show significant MFI positive trends in 74% of the stations, while the point data display significant positive trends in only 26% of stations and significant negative trends in four stations in the inland areas. The grid data complex orography requires preliminary validation work.
Survey on the Application of Deep Learning in Extreme Weather Prediction
Authors: Fang Wei , Xue Qiongying , Shen Liang , Sheng Victor S.
Published: May 2021

Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. Deep learning can automatically learn and train from a large number of sample data to obtain excellent feature expression, which effectively improves the performance of various machine learning tasks and is widely used in computer vision, natural language processing, and other fields. Based on the introduction of deep learning, this article makes a preliminary summary of the existing extreme weather prediction methods. These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather. They can automatically extract image features of extreme weather phenomena and predict the possibility of extreme weather somewhere by using a deep learning framework.
Conditional Intuitionistic Fuzzy Mean Value
Authors: Čunderlíková Katarína
Published: May 2021

The conditional mean value has applications in regression analysis and in financial mathematics, because they are used in it. We can find papers from recent years that use the conditional mean value in fuzzy cases. As the intuitionstic fuzzy sets are an extension of fuzzy sets, we will try to define a conditional mean value for the intuitionistic fuzzy case. The conditional mean value in crisp intuitionistic fuzzy events was first studied by V. Valenčáková in 2009. She used Gödel connectives. Her approach can only be used for special cases of intuitionistic fuzzy events, therefore, we want to define a conditional mean value for all elements of a family of intuitionistic fuzzy events. In this paper, we define the conditional mean value for intuitionistic fuzzy events using Lukasiewicz connectives. We use a Kolmogorov approach and the notions from a classical probability theory for construction. B. Riečan formulated a conditional intuitionistic fuzzy probability for intuitionistic fuzzy events using an intuitionistic fuzzy state in 2012. In classical cases, there exists a connection between the conditional probability and the conditional mean value, therefore we show a connection between the conditional intuitionistic fuzzy probability induced by the intuitionistic fuzzy state and the conditional intuitionistic fuzzy mean value.
Regulatory Action of Plasma from Patients with Obesity and Diabetes towards Muscle Cells Differentiation and Bioenergetics Revealed by the C2C12 Cell Model and MicroRNA Analysis
Authors: Khromova Natalya V. , Fedorov Anton V. , Ma Yi , Kondratov Kirill A. , Prikhodko Stanislava S. , Ignatieva Elena V. , Artemyeva Marina S. , Anopova Anna D. , Neimark Aleksandr E. , Kostareva Anna A. , Babenko Alina Yu. , Dmitrieva Renata I.
Published: May 2021

Obesity and type 2 diabetes mellitus (T2DM) are often combined and pathologically affect many tissues due to changes in circulating bioactive molecules. In this work, we evaluated the effect of blood plasma from obese (OB) patients or from obese patients comorbid with diabetes (OBD) on skeletal muscle function and metabolic state. We employed the mouse myoblasts C2C12 differentiation model to test the regulatory effect of plasma exposure at several levels: (1) cell morphology; (2) functional activity of mitochondria; (3) expression levels of several mitochondria regulators, i.e., Atgl, Pgc1b, and miR-378a-3p. Existing databases were used to computationally predict and analyze mir-378a-3p potential targets. We show that short-term exposure to OB or OBD patients’ plasma is sufficient to affect C2C12 properties. In fact, the expression of genes that regulate skeletal muscle differentiation and growth was downregulated in both OB- and OBD-treated cells, maximal mitochondrial respiration rate was downregulated in the OBD group, while in the OB group, a metabolic switch to glycolysis was detected. These alterations correlated with a decrease in ATGL and Pgc1b expression in the OB group and with an increase of miR-378a-3p levels in the OBD group.
Pepscan Approach for the Identification of Protein–Protein Interfaces: Lessons from Experiment
Authors: Rebollo Angelita , Savier Eric , Tuffery Pierre
Published: May 2021

PEPscan is an old approach that has recently gained renewed interest for the identification of interfering peptides (IPs), i.e., peptides able to interfere with protein–protein interactions (PPIs). Its principle is to slice a protein sequence as a series of short overlapping peptides that are synthesized on a peptide array and tested for their ability to bind a partner, with positive spots corresponding to candidate IPs. PEPscan has been applied with a rather large success in various contexts, but the structural determinants underlying this success remain obscure. Here, we analyze the results of 14 PEPscan experiments, and confront the in vitro results with the available structural information. PEPscan identifies candidate IPs in limited numbers that in all cases correspond to solvent-accessible regions of the structures, their location at the protein–protein interface remaining to be further demonstrated. A strong point of PEPscan seems to be its ability to identify specific IPs. IPs identified from the same protein differ depending on the target PPI, and correspond to patches not frequently involved in the interactions seen in the 3D structures available. Overall, PEPscan seems to provide a cheap and rapid manner to identify candidate IPs, that also comes with room for improvement.
The Effect of the New Lupeol Derivatives on Human Skin Cells as Potential Agents in the Treatment of Wound Healing
Authors: Malinowska Magdalena Anna , Sikora Elżbieta , Stalińska Joanna , Ogonowski Jan , Drukała Justyna
Published: May 2021

Skin barrier damage can be the result of various external factors including heat, radiation, chemicals and many others. Any interruption of the skin barrier integrity causes the exposure of the organism to harmful environmental factors. Therefore, there is an urgent need to develop novel therapeutics characterized by high bioavailability and effectiveness in skin damage recovery. Birch bark is known as a clinically proven, traditional medicinal remedy to accelerate wound healing. Lupeol, one of the main birch bark ingredients, shows a wide range of biological activity beneficial to the skin. The purpose of the research was to determine the influence of new lupeol derivatives on keratinocyte and fibroblast migration and proliferation, as well as to investigate various mechanisms of their antioxidant activity. The chemical modification of lupeol structure was intended to obtain more effective therapeutics characterized by higher bioavailability, permeability and safety of use. The novel triterpenes presented in this study were evaluated as the potential active ingredients preventing skin tissue degradation. Lupeol esters influence skin cells’ motility and proliferation. Importantly, they are able to reduce reactive oxygen species and act indirectly by protecting the skin protein structure from being oxidized by free radicals.
A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal
Authors: Saminu Sani , Xu Guizhi , Shuai Zhang , Abd El Kader Isselmou , Jabire Adamu Halilu , Ahmed Yusuf Kola , Karaye Ibrahim Abdullahi , Ahmad Isah Salim
Published: May 2021

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.
Cognitive Exercise Self-Efficacy of Community-Dwelling Older Adults: Measurement and Associations with Other Self-Reported Cognitive Exercise Factors
Authors: O’Neil-Pirozzi Therese M.
Published: May 2021

Exercise self-efficacy, the confidence a person has in their ability to develop and meet exercise goals, is key to exercise motivation. The primary objective of this pilot study was to explore associations among cognitive exercise self-efficacy, cognitive exercise frequency, challenge, and enjoyment in older adults. A prospective, cross-sectional, observational study design was used with 133 community-dwelling individuals aged 55 years and older. Respondents completed a cognitive exercise self-efficacy scale and responded to cognitive exercise queries. Individuals who engaged in cognitive exercise demonstrated greater cognitive exercise self-efficacy. Cognitive exercise self-efficacy ratings were significantly different across challenge and enjoyment conditions (Pearson’s χ2 test, df = 9, N = 133, χ2 = 123.49, p < 0.01), such that the greater the perception of each, the greater the cognitive exercise self-efficacy (p < 0.01). The comparative impact of perceived enjoyment on cognitive exercise self-efficacy was greater than the impact of perceived challenge. Study findings support positive associations among cognitive exercise self-efficacy, cognitive exercise frequency, challenge, and enjoyment. Consideration of these findings may inform design and sustained implementation of motivating cognitive exercise programs to maximize health and quality of life outcomes of healthy and neurologic older adult populations.
Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype
Authors: Li Hai-Tao , Yuan Shao-Xun , Wu Jian-Sheng , Gu Yu , Sun Xiao
Published: May 2021

Alzheimer’s disease (AD) is a neurodegenerative brain disease in the elderly. Identifying patients with mild cognitive impairment (MCI) who are more likely to progress to AD is a key step in AD prevention. Recent studies have shown that AD is a heterogeneous disease. In this study, we propose a subtyping-based prediction strategy to predict the conversion from MCI to AD in three years according to MCI patient subtypes. Structural magnetic resonance imaging (sMRI) data and multi-omics data, including genotype data and gene expression profiling derived from peripheral blood samples, from 125 MCI patients were used in the Alzheimer’s Disease Neuroimaging Initiative (ADNI)-1 dataset and from 98 MCI patients in the ADNI-GO/2 dataset. A variational Bayes approximation model based on the multiple kernel learning method was constructed to predict whether an MCI patient will progress to AD within three years. In internal fivefold cross-validation within ADNI-1, we achieved an overall AUC of 0.83 (79.20% accuracy, 81.25% sensitivity, 77.92% specificity) compared to the model without subtyping, which achieved an AUC of 0.78 (76.00% accuracy, 77.08% sensitivity, 75.32% specificity). In external validation using ADNI-1 as a training set and ADNI-GO/2 as an independent test set, we attained an AUC of 0.78 (74.49% accuracy, 74.19% sensitivity, 74.63% specificity). Identifying MCI patient subtypes with omics data would improve the accuracy of predicting the conversion from MCI to AD. In addition to evaluating statistics, obtaining the significant sMRI, single nucleotide polymorphism (SNP) and mRNA expression data from peripheral blood of MCI patients is noninvasive and cost-effective for predicting conversion from MCI to AD.
Cripto-1 as a Potential Target of Cancer Stem Cells for Immunotherapy
Authors: Ishii Hiroko , Afify Said M. , Hassan Ghmkin , Salomon David S. , Seno Masaharu
Published: May 2021

The immune system has been found to be suppressed in cancer patients. Cancer cells are extremely resistant to chemotherapeutic drugs, conventional immunotherapy, or cancer antigen vaccine therapy. Cancer immunotherapy, which is mainly based on immune checkpoint inhibitors, such as those for PD-1, PD-L1, and CTLA4, is an effective treatment method. However, no immunotherapeutic target has been found that retains validity in the face of tumor diversity. The transforming growth factor (TGF)-β cytokine family possesses broad biological activity and is involved in the induction and/or transdifferentiation of helper T cells, which are important in immunotherapy. Nodal is a member of the TGF-β family playing important roles in tissue stem cells and cancer stem cells (CSCs), interacting with the co-receptor Cripto-1, as well as with Activin type IB (Alk4) and Activin typeIIreceptors, and maintaining stemness and Notch and Wnt/β-catenin signaling in CSCs. In recent years, it has been reported that Cripto-1 could be a potential therapeutic target in CSCs. Here, we review the accumulated literature on the molecular mechanisms by which Cripto-1 functions in CSCs and discuss the potential of Cripto-1 as an immunotherapeutic target in CSCs.
Cyclin-Dependent Kinase Inhibitors in Hematological Malignancies—Current Understanding, (Pre-)Clinical Application and Promising Approaches
Authors: Richter Anna , Schoenwaelder Nina , Sender Sina , Junghanss Christian , Maletzki Claudia
Published: May 2021

Genetically altered stem or progenitor cells feature gross chromosomal abnormalities, inducing modified ability of self-renewal and abnormal hematopoiesis. Cyclin-dependent kinases (CDK) regulate cell cycle progression, transcription, DNA repair and are aberrantly expressed in hematopoietic malignancies. Incorporation of CDK inhibitors (CDKIs) into the existing therapeutic regimens therefore constitutes a promising strategy. However, the complex molecular heterogeneity and different clinical presentation is challenging for selecting the right target and defining the ideal combination to mediate long-term disease control. Preclinical and early clinical data suggest that specific CDKIs have activity in selected patients, dependent on the existing rearrangements and mutations, potentially acting as biomarkers. Indeed, CDK6, expressed in hematopoietic cells, is a direct target of MLL fusion proteins often observed in acute leukemia and thus contributes to leukemogenesis. The high frequency of aberrancies in the retinoblastoma pathway additionally warrants application of CDKIs in hematopoietic neoplasms. In this review, we describe the preclinical and clinical advances recently made in the use of CDKIs. These include the FDA-approved CDK4/6 inhibitors, traditional and novel pan-CDKIs, as well as dual kinase inhibitors. We additionally provide an overview on molecular mechanisms of response vs. resistance and discuss open questions.
Expression of Cancer Testis Antigens in Tumor-Adjacent Normal Liver Is Associated with Post-Resection Recurrence of Hepatocellular Carcinoma
Authors: Noordam Lisanne , Ge Zhouhong , Özturk Hadiye , Doukas Michail , Mancham Shanta , Boor Patrick P. C. , Campos Carrascosa Lucia , Zhou Guoying , van den Bosch Thierry P. P. , Pan Qiuwei , IJzermans Jan N. M. , Bruno Marco J. , Sprengers Dave , Kwekkeboom Jaap
Published: May 2021

High recurrence rates after resection of hepatocellular carcinoma (HCC) with curative intent impair clinical outcomes of HCC. Cancer/testis antigens (CTAs) are suitable targets for cancer immunotherapy if selectively expressed in tumor cells. The aims were to identify CTAs that are frequently and selectively expressed in HCC-tumors, and to investigate whether CTAs could serve as biomarkers for occult metastasis. Tumor and paired tumor-free liver (TFL) tissues of HCC-patients and healthy tissues were assessed for mRNA expression of 49 CTAs by RT-qPCR and protein expression of five CTAs by immunohistochemistry. Twelve CTA-mRNAs were expressed in ≥10% of HCC-tumors and not in healthy tissues except testis. In tumors, mRNA and protein of ≥ 1 CTA was expressed in 78% and 71% of HCC-patients, respectively. In TFL, CTA mRNA and protein was found in 45% and 30% of HCC-patients, respectively. Interestingly, CTA-expression in TFL was an independent negative prognostic factor for post-resection HCC-recurrence and survival. We established a panel of 12 testis-restricted CTAs expressed in tumors of most HCC-patients. The increased risk of HCC-recurrence in patients with CTA expression in TFL, suggests that CTA-expressing (pre-)malignant cells may be a source of HCC-recurrence, reflecting the relevance of targeting these to prevent HCC-recurrence.
Cell-Penetrating CEBPB and CEBPD Leucine Zipper Decoys as Broadly Acting Anti-Cancer Agents
Authors: Zhou Qing , Sun Xiotian , Pasquier Nicolas , Jefferson Parvaneh , Nguyen Trang T. T. , Siegelin Markus D. , Angelastro James M. , Greene Lloyd A.
Published: May 2021

Transcription factors are key players underlying cancer formation, growth, survival, metastasis and treatment resistance, yet few drugs exist to directly target them. Here, we characterized the in vitro and in vivo anti-cancer efficacy of novel synthetic cell-penetrating peptides (Bpep and Dpep) designed to interfere with the formation of active leucine-zipper-based dimers by CEBPB and CEBPD, transcription factors implicated in multiple malignancies. Both peptides similarly promoted apoptosis of multiple tumor lines of varying origins, without such effects on non-transformed cells. Combined with other treatments (radiation, Taxol, chloroquine, doxorubicin), the peptides acted additively to synergistically and were fully active on Taxol-resistant cells. The peptides suppressed expression of known direct CEBPB/CEBPD targets IL6, IL8 and asparagine synthetase (ASNS), supporting their inhibition of transcriptional activation. Mechanisms by which the peptides trigger apoptosis included depletion of pro-survival survivin and a required elevation of pro-apoptotic BMF. Bpep and Dpep significantly slowed tumor growth in mouse models without evident side effects. Dpep significantly prolonged survival in xenograft models. These findings indicate the efficacy and potential of Bpep and Dpep as novel agents to treat a variety of cancers as mono- or combination therapies.
Estrogens and Progestogens in Triple Negative Breast Cancer: Do They Harm?
Authors: van Barele Mark , Heemskerk-Gerritsen Bernadette A. M. , Louwers Yvonne V. , Vastbinder Mijntje B. , Martens John W. M. , Hooning Maartje J. , Jager Agnes
Published: May 2021

Triple-negative breast cancers (TNBC) occur more frequently in younger women and do not express estrogen receptor (ER) nor progesterone receptor (PR), and are therefore often considered hormone-insensitive. Treatment of premenopausal TNBC patients almost always includes chemotherapy, which may lead to premature ovarian insufficiency (POI) and can severely impact quality of life. Hormone replacement therapy (HRT) is contraindicated for patients with a history of hormone-sensitive breast cancer, but the data on safety for TNBC patients is inconclusive, with a few randomized trials showing increased risk-ratios with wide confidence intervals for recurrence after HRT. Here, we review the literature on alternative pathways from the classical ER/PR. We find that for both estrogens and progestogens, potential alternatives exist for exerting their effects on TNBC, ranging from receptor conversion, to alternative receptors capable of binding estrogens, as well as paracrine pathways, such as RANK/RANKL, which can cause progestogens to indirectly stimulate growth and metastasis of TNBC. Finally, HRT may also influence other hormones, such as androgens, and their effects on TNBCs expressing androgen receptors (AR). Concluding, the assumption that TNBC is completely hormone-insensitive is incorrect. However, the direction of the effects of the alternative pathways is not always clear, and will need to be investigated further.
Does the Duration of Primary and First Revision Surgery Influence the Probability of First and Subsequent Implant Failures after Extremity Sarcoma Resection and Megaprosthetic Reconstruction?
Authors: Theil Christoph , Schneider Kristian Nikolaus , Gosheger Georg , Dieckmann Ralf , Deventer Niklas , Hardes Jendrik , Schmidt-Braekling Tom , Andreou Dimosthenis
Published: May 2021

Complications in megaprosthetic reconstruction following sarcoma resection are quite common. While several risk factors for failure have been explored, there is a scarcity of studies investigating the effect of the duration of surgery. We performed a retrospective study of 568 sarcoma patients that underwent megaprosthetic reconstruction between 1993 and 2015. Differences in the length of surgery and implant survival were assessed with the Kaplan–Meier method, the log-rank test and multivariate Cox regressions using an optimal cut-off value determined by receiver operating curves analysis using Youden’s index. 230 patients developed a first and 112 patients a subsequent prosthetic failure. The median duration of initial surgery was 210 min. Patients who developed a first failure had a longer duration of the initial surgery (225 vs. 205 min, p = 0.0001). There were no differences in the probability of infection between patients with longer and shorter duration of initial surgery (12% vs. 13% at 5 years, p = 0.492); however, the probability of mechanical failure was higher in patients with longer initial surgery (38% vs. 23% at 5 years, p = 0.006). The median length of revision surgery for the first megaprosthetic failure was 101 min. Patients who underwent first revision for infection and did not develop a second failure had a longer median duration of the first revision surgery (150 min vs. 120 min, p = 0.016). A shorter length of the initial surgery appears beneficial, however, the notion that longer operating time increases the risk of deep infection could not be reproduced in our study. In revision surgery for infection, a longer operating time, possibly indicating a more thorough debridement, appears to be associated with a lower risk for subsequent revision.
Oligometastatic Cancer: Key Concepts and Research Opportunities for 2021 and Beyond
Authors: Szturz Petr , Vermorken Jan B.
Published: May 2021

Traditionally, clinicians distinguished three forms of cancer outgrowth [...]
Gene Expression Profiling of Olfactory Neuroblastoma Helps Identify Prognostic Pathways and Define Potentially Therapeutic Targets
Authors: Romani Chiara , Bignotti Eliana , Mattavelli Davide , Bozzola Anna , Lorini Luigi , Tomasoni Michele , Ardighieri Laura , Rampinelli Vittorio , Paderno Alberto , Battocchio Simonetta , Gurizzan Cristina , Castelnuovo Paolo , Turri-Zanoni Mario , Facco Carla , Sessa Fausto , Schreiber Alberto , Ferrari Marco , Ravaggi Antonella , Deganello Alberto , Nicolai Piero , Buglione Michela , Tomasini Davide , Maroldi Roberto , Piazza Cesare , Calza Stefano , Bossi Paolo
Published: May 2021

Olfactory neuroblastoma (ONB) is a rare sinonasal neoplasm with a peculiar behavior, for which limited prognostic factors are available. Herein, we investigate the transcriptional pathways altered in ONB and correlate them with pathological features and clinical outcomes. We analyze 32 ONB patients treated with curative intent at two independent institutions from 2001 to 2019 for whom there is available pathologic and clinical data. We perform gene expression profiling on primary ONB samples and carry out functional enrichment analysis to investigate the key pathways associated with disease-free survival (DFS). The median age is 53.5 years; all patients undergo surgery and a pure endoscopic approach is adopted in the majority of cases (81.2%). Most patients have advanced disease (stages III–IV, 81.2%) and 84.4% undergo adjuvant (chemo)radiotherapy. The median follow-up is 35 months; 11 (26.8%) patients relapse. Clinical characteristics (gender, stage and Hyams’ grade) are not associated with the outcomes. In contrast, TGF-beta binding, EMT, IFN-alpha response, angiogenesis, IL2-STAT5 and IL6-JAK-STAT3 signaling pathways are enriched in patients experiencing recurrence, and significantly associated with shorter DFS. Clustering of transcriptional profiles according to pathological features indicates two distinct molecular groups, defined by either cytokeratin-positive or -negative immunostaining. Definition of the characterizing ONB transcriptomic pathways may pave the way towards tailored treatment approaches.
Socio-Economic Deprivation and Symptom Burden in UK Hospice Patients with Advanced Cancer—Findings from a Longitudinal Study
Authors: Lloyd-Williams Mari , Shiels Christopher , Dowrick Christopher , Kissane David
Published: May 2021

Socio-economic deprivation is known to impact on cancer diagnosis, treatment and access to services, but little is known of the impact of socio-economic deprivation on symptom burden in patients with advanced cancer. Patients with advanced cancer attending hospice day services were recruited into a 24 week longitudinal study. An area-based index of social deprivation was collected along with depression and symptom burden at baseline, 8, 16 and 24 weeks. Of the 595 patients included, with an age range of 33–89 years and a mean age of 68 years, 67% were female, and 37% were diagnosed with cancer in the last 12 months. Twenty nine percent lived in one of the most deprived 20% of neighbourhoods. Patients living in the most socio-economically deprived areas were significantly likely to report receiving insufficient information regarding their cancer at diagnosis (p = 0.007), greater pain (p = 0.02), moderate to severe depression (p = 0.04) and higher global symptom burden (p = 0.04). This study is the first to report that patients with advanced cancer attending hospice services, living in the most deprived neighbourhoods experience significantly greater symptom burden, notably depression and pain. We recommend using patient outcome measures in order to provide targeted support and thereby reduce the increased symptom burden that socio-economically disadvantaged patients experience at the end of life.