Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pilot Study on Biochemical Profile of Follicular Fluid in Breast Cancer Patients

Breast cancer (BC) is the most common type of cancer among women in almost all countries worldwide and is one of the oncological pathologies for which is indicated fertility preservation, a type of procedure used to help keep a person’s ability to have children. Follicular fluid (FF) is a major component of oocyte microenvironment, which is involved in oocyte growth, follicular maturation, and in communication between germ and somatic cells; furthermore, it accumulates all metabolites during oocytes growth. To obtain information about changes on fertility due to cancer, we aimed at investigating potential biomarkers to discriminate between FF samples obtained from 16 BC patients and 10 healthy women undergoing in vitro fertilization treatments. An NMR-based metabolomics approach was performed to investigate the FF metabolic profiles; ELISA and western blotting assays were used to investigate protein markers of oxidative and inflammatory stress, which are processes closely related to cancer. Our results seem to suggest that FFs of BC women display some significant metabolic alterations in comparison to healthy controls, and these variations are also related with tumor staging.
Identifiability of blind deconvolution of non-white signals

Considers the problem where an unobservable, non-Gaussian and non-white stochastic information bearing signal is transmitted through two different linear time invariant systems (LTI), and there are only noisy observations of the system outputs. The goal is to identify and deconvolve the signal within a scalar and a time delay. Apparently this type of problem is ill-posed. Identifiability of the information bearing signal is defined with respect to a set of conditions that specify some of the properties of the signal and the LTI systems. Such sets of conditions are defined, and deconvolution schemes based on these sets are studied. <<ETX>>
Technical architecture of integrated big data platform

The integrated big data platform aims to provide one-stop development and operation capabilities for massive heterogeneous data. Various components are organically combined and managed in a unified way. At the same time, the underlying technical details are shielded from users, so as to finally achieve the purpose of convenient data development, reducing operation and maintenance costs, and standardizing management processes. This article introduces the functional modules that the platform should have one by one, covering data storage, data integration, data management and governance, data development, data processing and analysis, high availability, operation and maintenance management, and data security. Finally, the typical characteristics and development direction of the integrated big data platform are pointed out.
A compact multi-channel fluorescence sensor with ambient light suppression

A multi-channel fluorescence sensor has been developed for process monitoring and fluorescence diagnostics. It comprises a fiber-optic set-up with an immersion probe and an intensity-modulated high power ultraviolet light-emitting diode as a light source for fluorescence excitation. By applying an electronic lock-in procedure, fluorescence signals are selectively detectable at ambient light levels of 1000 000 times higher intensity. The sensor was designed to be compact, low cost and easily adaptable to a wide field of application. The set-up was used to simultaneously monitor three important metabolic fluorophores: NAD(P)H, flavins and porphyrins during the cultivation of a baker's yeast. Moreover, the accumulation and degradation kinetics of protoporphyrin IX induced by 5-aminolevulinic acid on the skin could be recorded by the sensor. The detection limit for protoporphyrin IX was determined to be 4 × 10−11 mol L−1. The linear signal amplification of the sensor and time courses of fluorescence signals monitored during yeast fermentations were validated using a commercial CCD spectrometer. The robust and flexible set-up of the fiber-optic measurement system promises easy implementation of this non-invasive analytical tool to fluorescence monitoring and diagnostics in R&D and production.
A Parallelized Algorithm for Channel Estimation in mmWave Massive MIMO Communications

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technology is considered as a key feature for 5G and B5G systems because of its extensive spectrum resources. Accurately and timely estimating the channel state information (CSI) is critical for guaranteeing the effective signal transmission. In this paper, we propose an algorithm called Sparse Accelerated projection consensus (SAPC) to estimate the mmWave massive MIMO channel in a parallel computing way, which should be suitable for FPGA and ASIC implementations. Also, SAPC takes into account the sparsity of the channel to reduce the complexity.
Offline RL oriented Functions Design for Dynamic Power Management on CNN Workloads

Concerning Deep Learning (esp. CNNs) workloads on integrated-GPU based heterogeneous programming models and mobile/embedded platforms, we propose a data-driven offline (batch) RL-oriented dynamic power management (DPM) agent/governor design that uses an offline RL method to learn a simple, effective and robust policy from previously-collected static datasets without further interaction with the environment, concerning intrinsically expensive training costs of generic offpolicy online RL or/and imitation learning (IL). Our preliminary results show improvements up to 20.3% in terms of energy-delay product (EDP) and a reduced inference time of 2.2% on Tiny- YOLOv3/v4, compared to the default separate CPU and GPU governors.
A Functional FMECA Approach for the Assessment of Critical Infrastructure Resilience

The damage or destruction of Critical Infrastructures (CIs) affect societies’ sustainable functioning. Therefore, it is crucial to have effective methods to assess the risk and resilience of CIs. Failure Mode and Effects Analysis (FMEA) and Failure Mode Effects and Criticality Analysis (FMECA) are two approaches to risk assessment and criticality analysis. However, these approaches are complex to apply to intricate CIs and associated Cyber-Physical Systems (CPS). We provide a top-down strategy, starting from a high abstraction level of the system and progressing to cover the functional elements of the infrastructures. This approach develops from FMECA but estimates risks and focuses on assessing resilience. We applied the proposed technique to a real-world CI, predicting how possible improvement scenarios may influence the overall system resilience. The results show the effectiveness of our approach in benchmarking the CI resilience, providing a cost-effective way to evaluate plausible alternatives concerning the improvement of preventive measures.
Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

Background Electroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI. Methods Related publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses. Results Between 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”. Conclusion The application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.
Lacticaseibacillus rhamnosus ATCC 53103 and Limosilactobacillus reuteri ATCC 53608 Synergistically Boost Butyrate Levels upon Tributyrin Administration Ex Vivo

Modulation of the gut microbiota is a trending strategy to improve health. While butyrate has been identified as a key health-related microbial metabolite, managing its supply to the host remains challenging. Therefore, this study investigated the potential to manage butyrate supply via tributyrin oil supplementation (TB; glycerol with three butyrate molecules) using the ex vivo SIFR® (Systemic Intestinal Fermentation Research) technology, a highly reproducible, in vivo predictive gut model that accurately preserves in vivo-derived microbiota and enables addressing interpersonal differences. Dosing 1 g TB/L significantly increased butyrate with 4.1 (±0.3) mM, corresponding with 83 ± 6% of the theoretical butyrate content of TB. Interestingly, co-administration of Limosilactobacillus reuteri ATCC 53608 (REU) and Lacticaseibacillus rhamnosus ATCC 53103 (LGG) markedly enhanced butyrate to levels that exceeded the theoretical butyrate content of TB (138 ± 11% for REU; 126 ± 8% for LGG). Both TB + REU and TB + LGG stimulated Coprococcus catus, a lactate-utilizing, butyrate-producing species. The stimulation of C. catus with TB + REU was remarkably consistent across the six human adults tested. It is hypothesized that LGG and REU ferment the glycerol backbone of TB to produce lactate, a precursor of butyrate. TB + REU also significantly stimulated the butyrate-producing Eubacterium rectale and Gemmiger formicilis and promoted microbial diversity. The more potent effects of REU could be due to its ability to convert glycerol to reuterin, an antimicrobial compound. Overall, both the direct butyrate release from TB and the additional butyrate production via REU/LGG-mediated cross-feeding were highly consistent. This contrasts with the large interpersonal differences in butyrate production that are often observed upon prebiotic treatment. Combining TB with LGG and especially REU is thus a promising strategy to consistently supply butyrate to the host, potentially resulting in more predictable health benefits.
On samples, data, and their mobility in biobanking: How imagined travels help to relate samples and data

Biobanking involves the assembling, curating, and distributing of samples and data. While relations between samples and data are often taken as defining properties of biobanking, several studies have pointed to the challenges in relating them in practice. This article investigates how samples and data are curated, connected, and made mobile in practice. Building on an analysis of data collected at five hospital-based biobanks in Austria, the article describes and compares biobanking in three types of biobank collections: ‘departmental collections’, ‘project-specific collections’ and ‘hospital-wide collections’. It draws attention to the invisible work going into this infrastructure and highlights the central role of visions to make samples and data travel to a different location and thus support biomedical research. It shows that while visions of future travels are often epistemologically uncertain, they are informed by social ties and relationships between the collectives involved in the curation of samples and data on the one hand and the imagined users on the other. Finally, we point to the importance that policy actors in this domain consider the aspects we identified—and, in particular, reflect the temporalities inherent in such a research infrastructure.
Dynamic Preisach Model And Energy Dissipation In Soft Magnetic Materials

In this paper, the energy dissipation in soft magnetic materials using the dynamic Preisach model is discussed. The main goal is the establishment of the f/sup 1.5/ law for the excess losses from this hysteresis model. By building up the energy balance of a lamination model combined with the dynamic Preisach theory, the connection with the statistical loss theory is identified.
Differentiating persuasive factors for tourism booking websites: Towards matrix factorization

The main challenges to increasing sales, usually lie with persuasion techniques and recommender systems. Persuasion Systems models (PSDs) analyse persuasion context, identifies the key issues and subsequently, enables mapping of persuasion techniques to functional requirements. This paper investigates persuasive factors for tourism booking websites, with the aim of utilizing derived significant factors at different stages, as the bases for weighted criteria analyses, and matrix factorization, integral in fuzzy logic computations. Two parallel case studies are carried out. The first tourism booking website is designed, based on Cialdini's persuasion techniques for the Malaysian hotel/tourism context, and tested with customers and travel agencies. The second study investigates social CRM and associations between gender, age and income on a group of different sample respondents. User evaluation indicates different weighted factors at the search, review and booking stages, as well as different perceptions, with regards to cross-selling. Comparative findings with successful cross-selling companies, such as Lazada and Shoppee further highlight three core influencing factors. These have enabled us to add specificity to some constructs in Oinas-Kukkonen and Harjumaa's PSD dialogue support model, for the sampled Malaysian contexts. This triangulated approach can enable more holistic derivation of requirements and prediction and derivation of initial rules for future decision support systems.
Assessing Bias on Entity Retrieval Models through Conjunctive Fallacies

Information retrieval methods, machine learning models, and humans can suffer from a failure in judging information representativeness. We refer to this problem as information bias. In this work, we propose a method to evaluate information bias through conjunctive fallacies. An experimental evaluation of different state-of-the-art entity retrieval models and human-curated benchmarks shows that both methods perform poorly on judging query-entity representativeness while statistically based methods perform considerably better than humans.
The Use of the Box-Cox Transformation in Limited Dependent Variable Models

Abstract The general limited dependent variable model discussed in this article permits skewness in a pretruncated variable by transforming it within the class of Box-Cox transformations. As a by-product this general model also provides a convenient nesting framework for statistically distinguishing between numerous limited dependent variable models. An application to a model of the supply of bilateral foreign aid illustrates the ability of the general model to empirically distinguish between competing specifications.
A Planar Dual-Band 4-Element MIMO Configuration for WLAN and Sub-6 GHz 5G Applications

A novel dual-band 4-port MIMO configuration is introduced in this research work for the WLAN (2.4 GHz band) and 5G (3.5 GHz band of Sub-6 GHz) wireless applications. It provides good -10 dB return loss bandwidth of 8.3% (2.3 - 2.5 GHz) and 9.92% (3.35 - 3.7 GHz) in the $\mathbf{1}^{\mathbf{st}}$ and $\mathbf{2}^{\mathbf{nd}}$ operating bands, respectively. It is implemented on an inexpensive FR-4 substrate of dimensions $\mathbf{60} \times \mathbf{60}\ \mathbf{mm}^{\mathbf{2}}$. The dual-band planar monopole MIMO antenna element comprises i) a Curve-shaped monopole and ii) an open-ended inverted L-shaped slot. The curvature of the monopole antenna element miniaturizes the design footprint by 33.33% compared to the conventional rectangular monopole antenna. It is realized with a form factor of 0.15 $\boldsymbol{\lambda}_{\mathbf{0}}\ \times \mathbf{0}. \mathbf{23}\boldsymbol{\lambda}_{\mathbf{0}}$. The elements are oriented perpendicular to each other to implement the 4-port MIMO configuration. The orthogonal arrangement provides polarization diversity that suppresses the mutual coupling between the elements below 17.5 dB. The MIMO design has omnidirectional radiation characteristics with reasonably good peak gains (2.14 and 2.48 dBi) and radiation efficiencies (84 and 91%) in the WLAN and 5G operating bands. The computed ECC is below 0.045, and TARC is less than -10 dB across both bands. The excellent diversity characteristics of the presented MIMO configuration make it well-suitable for the targeted MIMO applications.
Bridging the Discursive and Material Dimensions of Europeanity and Europeanisation

The article aims to capture the significatory diversity of the concepts of Europeanity and Europeanisation, through the development of a semantic map in order to visualize the different concepts that define Europeanity and Europeanisation, and their interconnections. Embedded in the field of Communication and Media Studies, this semantic map combines 19 different approaches, structured through one main dimension, the discursive versus the material, which allows bridging the major rift in the conceptual reflections about Europeanity and Europeanisation. Moreover, the semantic map uses two support dimensions, with the discursive dimension intersecting with the essentialist versus relationist dimension, and the material dimension intersecting with the socio-spatial versus politico-spatial dimension. In order to construct this semantic map, phases of both general and targeted literature reviews were combined with a participatory theory-building method, which was grounded in collaborative knowledge building and collaborative theory construction approaches.
Gas cylinder character recognition based on lightweight ResNet model

Characters pressed on the surface of the liquefied gas cylinder cap are used to mark each cylinder’s identity and record the information about the gas in the cylinder. An automatic character recognition based on a lightweight ResNet model is proposed for the online quality and safety management of cylinder-contained gas production. Firstly, images of pressed characters on the cylinder’s cap are collected under low angle lighting to improve the image contrast of character regions against the background. The connected component method is used to determine the position of each character and then segment the single characters. The pressed-character data set of gas cylinder is constructed by expanding data through image blurring, lightening, darkening and noise adding. According to the data scenario and scale, ResNet18 is selected as the backbone network. To implement the lightweight network model, by removing the last two residual modules in the backbone network, the number of the network layers is reduced to ten. In addition, the number of feature channels is trimmed to further reduce the redundancy of the convolution kernel and improve the calculation efficiency of the network model. Finally, the Max pooling layer is used for all channels to aggregate information and obtain character features. The experimental results show that the recognition accuracy of the proposed network is 94.7% on the constructed gas-cylinder character dataset. Compared with the comparison network, the proposed network not only has higher accuracy and robustness, but also reduces the capacity and computational complexity.
Prototype Data System of Highly Reliable Ship Message Queue based on AMQP

With the rapid development of artificial intelligence and information fusion technology in the shipbuilding industry, the difficulty and complexity of ship information system (SIS) integration have significantly increased, making it easy to form an information island between shipborne equipment and information platforms. Therefore, the SIS puts forward higher requirements for the real-time, reliability, availability, and expansibility of data communication. By summarizing and analyzing the advantages and disadvantages of SIS communication, as well as the needs of ship information fusion technology. Based on AMQP, this paper provides a unified message model of the queue and flow for data communication between the shipboard information platform and shipboard equipment. The model solves the problems of real-time transmission of ship key hotspot data and stateful data. In addition, this paper designs the storage and message delivery process of the prototype data system and adopts solutions such as distributed redundant storage, double response, and clustering to ensure the reliability and availability of services. The experimental results show that it is very effective to combine the message queue with the SIS to solve the data communication problem between the systems under the ship's heterogeneous platform.
Eucommia Polysaccharides Ameliorate Aging-Associated Gut Dysbiosis: A Potential Mechanism for Life Extension in Drosophila

The gut microbiota is increasingly considered to play a key role in human immunity and health. The aging process alters the microbiota composition, which is associated with inflammation, reactive oxygen species (ROS), decreased tissue function, and increased susceptibility to age-related diseases. It has been demonstrated that plant polysaccharides have beneficial effects on the gut microbiota, particularly in reducing pathogenic bacteria abundance and increasing beneficial bacteria populations. However, there is limited evidence of the effect of plant polysaccharides on age-related gut microbiota dysbiosis and ROS accumulation during the aging process. To explore the effect of Eucommiae polysaccharides (EPs) on age-related gut microbiota dysbiosis and ROS accumulation during the aging process of Drosophila, a series of behavioral and life span assays of Drosophila with the same genetic background in standard medium and a medium supplemented with EPs were performed. Next, the gut microbiota composition and protein composition of Drosophila in standard medium and the medium supplemented with EPs were detected using 16S rRNA gene sequencing analysis and quantitative proteomic analysis. Here, we show that supplementation of Eucommiae polysaccharides (EPs) during development leads to the life span extension of Drosophila. Furthermore, EPs decreased age-related ROS accumulation and suppressed Gluconobacter, Providencia, and Enterobacteriaceae in aged Drosophila. Increased Gluconobacter, Providencia, and Enterobacteriaceae in the indigenous microbiota might induce age-related gut dysfunction in Drosophila and shortens their life span. Our study demonstrates that EPs can be used as prebiotic agents to prevent aging-associated gut dysbiosis and reactive oxidative stress.