Miracle-dz/UniFAR-FTUs huggingface dataset:
Berberine-microbiota interplay: orchestrating gut health through modulation of the gut microbiota and metabolic transformation into bioactive metabolites

Berberine is an isoquinoline alkaloid found in plants. It presents a wide range of pharmacological activities, including anti-inflammatory and antioxidant properties, despite a low oral bioavailability. Growing evidence suggests that the gut microbiota is the target of berberine, and that the microbiota metabolizes berberine to active metabolites, although little evidence exists in the specific species involved in its therapeutic effects. This study was performed to detail the bidirectional interactions of berberine with the broiler chicken gut microbiota, including the regulation of gut microbiota composition and metabolism by berberine and metabolization of berberine by the gut microbiota, and how they contribute to berberine-mediated effects on gut health. As previous evidence showed that high concentrations of berberine may induce dysbiosis, low (0.1 g/kg feed), middle (0.5 g/kg feed) and high (1 g/kg feed) doses were here investigated. Low and middle doses of in-feed berberine stimulated potent beneficial bacteria from the Lachnospiraceae family in the large intestine of chickens, while middle and high doses tended to increase villus length in the small intestine. Plasma levels of the berberine-derived metabolites berberrubine, thalifendine and demethyleneberberine were positively correlated with the villus length of chickens. Berberrubine and thalifendine were the main metabolites of berberine in the caecum, and they were produced in vitro by the caecal microbiota, confirming their microbial origin. We show that members of the genus Blautia could demethylate berberine into mainly thalifendine, and that this reaction may stimulate the production of short-chain fatty acids (SCFAs) acetate and butyrate, via acetogenesis and cross-feeding respectively. We hypothesize that acetogens such as Blautia spp. are key bacteria in the metabolization of berberine, and that berberrubine, thalifendine and SCFAs play a significant role in the biological effect of berberine.
Aula invertida y trabajo colaborativo en Química

Implantamos las metodologías activas de aula invertida y trabajo colaborativo dirigido en la asignatura básica de Química del Grado de Ingeniería Agroambiental en la unidad temática dedicada a los equilibrios de precipitación. Participaron 101 estudiantes en los cursos 2018/2019 y 2019/2020. Los resultados muestran que la combinación de estos dos métodos condujo a resultados de aprendizaje significativamente más altos que la tradicional clase magistral con resolución de ejercicios, y revela una mayor motivación del alumnado. Una adecuada combinación de metodologías activas puede ayudar a que el alumnado sea partícipe de su evolución y logros, tanto en las competencias específicas como en las transversales. Sin embargo, el uso de estos métodos no tuvo incidencia en la tasa de abandono de la asignatura, por lo que es necesario realizar mejoras en su desarrollo como la creación de recursos audiovisuales propios o el aumento de la ratio profesor-alumno.
Intelligent Seamless Handover in Next Generation Networks

—Providing high quality of service (QoS) to mobile end-users, and guaranteeing resilient connectivity for healthcare wearables and other mobile devices is a critical component of Industry 5.0. However, one of the biggest difficulties that network operators encounter is the issue of mobility handover, as it can be detrimental to end-users’ safety and experience. Although various handover mechanisms have been developed to meet high QoS, achieving optimum handover performance while maintaining sustainable network operation is still an unreached goal. In this paper, random linear codes (RLC) are used to achieve seamless handover, where handover traffic is encoded using RLC and then multicasted to handover destination(s) using a mobility prediction algorithm for destination selection. To overcome the limitations of current IP core networks, we make use of a revolutionary IP-over-Information-Centric Network architecture at the network core that supports highly flexible multicast switching. The combination of the RLC, flexible multicast, and mobility prediction, makes the communication resilient to packet loss and helps to avoid handover failures of existing solutions while reducing overall packet delivery cost, hence offering sustainable mobility support. The performance of the proposed scheme is evaluated using a realistic vehicular mobility dataset and cellular network infrastructure and compared with Fast Handover for Proxy Mobile IPv6 (PFMIPv6). The results show that our scheme efficiently supports seamless session continuity in high mobility environments, reducing the total traffic delivery cost by 44% compared to its counterpart PFMIPv6, while reducing handover delay by 26% and handover failure to less than 2% of total handovers.
Low-Noise Chopper-Stabilized Zero-Drift Amplifier With SAR-Assisted Automatic Offset Calibration Loop

This paper presents a low-noise chopper-stabilized zero-drift amplifier with successive approximation register (SAR)-assisted automatic offset calibration loop (AOCL). The amplifier is designed using multipath zero-drift architecture including a low frequency path (LFP) and a high frequency path (HFP). The multipath architecture can achieve the low offset, the low noise and the low drift while maintaining the wide bandwidth. The LFP amplifier exploits a chopper scheme to reduce the offset and the low-frequency noise components. The digitally assisted AOCLs in the LFP and the HFP using SAR and current-mode digital-to-analog converter (DAC) can reduce the offset coarsely. The mismatch and the offset of the LFP amplifier can generate the up-modulated output ripples. The residual ripples in the LFP can be reduced using a ripple reduction loop (RRL). To prevent secondary ripple caused by the offset of the RRL itself, an auto-zero (AZ) integrator is employed. The HFP amplifier is designed using the folded-cascode structure with class-AB output stage to enhance the power efficiency. The multipath amplifier is fabricated in a <inline-formula> <tex-math notation="LaTeX">$0.18 ~\mu \text{m}$ </tex-math></inline-formula> CMOS process. The current consumption, supply voltage and active area are <inline-formula> <tex-math notation="LaTeX">$130 ~\mu \text{A}$ </tex-math></inline-formula>, 1.8 V and 0.86 mm2, respectively. The amplifier has a unit gain bandwidth (UGBW) of 0.875 MHz, an input referred offset of <inline-formula> <tex-math notation="LaTeX">$4.6 ~\mu \text{V}$ </tex-math></inline-formula>, an input referred noise of 25.6 nV/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz, an offset drift of 53 nV/°C and a noise efficiency factor (NEF) of 11.2.
Probiotic and postbiotic analytical methods: a perspective of available enumeration techniques

Probiotics are the largest non-herbal/traditional dietary supplements category worldwide. To be effective, a probiotic strain must be delivered viable at an adequate dose proven to deliver a health benefit. The objective of this article is to provide an overview of the various technologies available for probiotic enumeration, including a general description of each technology, their advantages and limitations, and their potential for the future of the probiotics industry. The current “gold standard” for analytical quantification of probiotics in the probiotic industry is the Plate Count method (PC). PC measures the bacterial cell’s ability to proliferate into detectable colonies, thus PC relies on cultivability as a measure of viability. Although viability has widely been measured by cultivability, there has been agreement that the definition of viability is not limited to cultivability. For example, bacterial cells may exist in a state known as viable but not culturable (VBNC) where the cells lose cultivability but can maintain some of the characteristics of viable cells as well as probiotic properties. This led to questioning the association between viability and cultivability and the accuracy of PC in enumerating all the viable cells in probiotic products. PC has always been an estimate of the number of viable cells and not a true cell count. Additionally, newer probiotic categories such as Next Generation Probiotics (NGPs) are difficult to culture in routine laboratories as NGPs are often strict anaerobes with extreme sensitivity to atmospheric oxygen. Thus, accurate quantification using culture-based techniques will be complicated. Another emerging category of biotics is postbiotics, which are inanimate microorganisms, also often referred to as tyndallized or heat-killed bacteria. Obviously, culture dependent methods are not suitable for these products, and alternative methods are needed for their quantification. Different methodologies provide a more complete picture of a heterogeneous bacterial population versus PC focusing exclusively on the eventual multiplication of the cells. Alternative culture-independent techniques including real-time PCR, digital PCR and flow cytometry are discussed. These methods can measure viability beyond cultivability (i.e., by measuring cellular enzymatic activity, membrane integrity or membrane potential), and depending on how they are designed they can achieve strain-specific enumeration.
CausalABSC: Causal Inference for Aspect Debiasing in Aspect-Based Sentiment Classification

As the primary subtask of sentiment analysis, aspect-based sentiment classification (ABSC) aims to predict the sentiment polarity for a given aspect. While recent deep neural models for ABSC have shown good performance, their robustness is limited due to their reliance on spurious correlations between aspects and sentiment. Specifically, most existing models tend to assign the most frequent sentiment label to a certain aspect in different sentences, or assume different aspects in the same sentence to have the same sentiment polarity, which is vulnerable due to the aspect biases. In this paper, we propose a causal graph to identify and analyze the causal relationships among treatment variables (e.g., aspect, sentence), intermediate variables (e.g., aspect-aware content), and outcome variables (e.g., sentiment polarity) for ABSC. To address the issue of spurious relationships that mislead sentiment polarity prediction, we introduce a novel causal inference framework called CausalABSC. CausalABSC is model agnostic, allowing integration into existing methods. We conduct extensive experiments on five benchmark datasets, which demonstrate the state-of-the-art performance of CausalABSC and its effectiveness in debiasing.
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—In safety-critical applications, microcontrollers must meet strict quality and performance standards, including the maximum operating frequency ( F max ). Machine learning (ML) models can estimate F max using data from on-chip ring oscillators (ROs), making them suitable for performance screening. However, when new products are introduced, existing ML models may no longer be suitable and require updating. Training a new model from scratch is challenging due to limited data availability. Acquiring F max data is time-consuming and costly, resulting in a small labeled dataset. However, a large amount of data from legacy products may be available, along with existing ML models. In order to address the scarcity of labeled data, this paper proposes using deep learning feature extractors trained on specific MCU product data and fine-tuning them for new devices, in a Transfer Learning fashion. Experimental results show that these models can extract useful general features for performance prediction. As a result, they achieve better performance with significantly less labeled data compared to traditional shallow learning approaches.
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—Distributed compressed sensing is concerned with representing an ensemble of jointly sparse signals using as few linear measurements as possible. Two novel joint reconstruction algorithms for distributed compressed sensing are presented in this paper. These algorithms are based on the idea of using one of the signals as side information; this allows to exploit joint sparsity in a more effective way with respect to existing schemes. They provide gains in reconstruction quality, especially when the nodes acquire few measurements, so that the system is able to operate with fewer measurements than is required by other existing schemes. We show that the algorithms achieve better performance with respect to the state-of-the-art
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To signiﬁcantly improve the frequency references used in radio-astronomy and the precision measurements in atomic physics, we provide frequency dissemination through a 642-km coherent optical ﬁber link. On the frequency transfer, we obtained a frequency instability of 3 (cid:2) 10 (cid:3) 19 at 1,000 s in terms of Allan deviation on a 5-mHz measurement bandwidth, and an accuracy of 5 (cid:2) 10 (cid:3) 19 . The ultimate link performance has been evaluated by doubling the link to 1,284 km, demonstrating a new characterization technique based on the double round trip on a single ﬁber. This method is an alternative to previously demonstrated techniques for link characterization. In particular, the use of a single ﬁber may be bene-ﬁcial to long hauls realizations in view of a continental ﬁber network for frequency and time metrology, as it avoids the doubling of the ampliﬁers, with a subsequent reduction in costs and maintenance. A detailed analysis of the results is presented, regarding the phase noise, the cycle-slips detection and removal and the instability evaluation. The observed noise power spectrum is seldom found in the literature; hence, the expression of the Allan deviation is theoretically derived and the results conﬁrm the expectations.
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: Deep convolutional neural networks achieve state-of-the-art performance in image classiﬁcation. The computational and memory requirements of such networks are however huge, and that is an issue on embedded devices due to their constraints. Most of this complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. This paper proposes a complete approach to image classiﬁcation providing common layers used in neural networks. Namely, the proposed approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. The Compute Uniﬁed Device Architecture(CUDA)-based implementation of the proposed approach is evaluated over three different image classiﬁcation networks on a Tegra K1 CPU-GPU mobile processor. Experiments show that the presented heterogeneous scheme boasts a 50 × speedup over the CPU-only reference and outperforms a GPU-based reference by 2 × , while slashing the power consumption by nearly 30%.
Understanding the Callao Cave Depositional History

This paper presents a soil micromorphological analysis of the stratigraphic sequence of sediments recorded in the antechamber of Callao Cave, northern Luzon. The study builds on our understanding of the depositional and post-depositional history of the cave sediments and elucidates further our knowledge of human occupation at Callao. The results confirm three in situ periods of human frequentation in the antechamber. The most recent recognisable archaeological horizon dates to ca. 3600 BP and represents a Neolithic occupation layer with ceramics, flake tools, a spindle whorl, faunal remains and human burials. Below this is a layer of volcanic sediment that forms a hiatus in cultural deposition, preceded by a habitation at ca. 25,000 BP characterised by hearths, abundant charcoal and burnt sediments and associated chert flake tools. Below a further ca. 2 m of almost sterile deposit is the lowest cultural horizon, comprising breccia containing faunal bones and a human metatarsal, dated by uranium series to ca. 67,000 BP. The study demonstrates that Callao Cave was only intermittently and irregularly occupied over the last 70 millennia.
Diseño de Patrones de Estratificación de Cultivos en el Departamento del Huila Colombia, Minería de Datos para una Agricultura de Precisión

Currently in the department of Huila stratification of crops it is through the selection of soil type, height above sea level, and the characteristics of the seed. The existence of other factors related to the characteristics of the climate and the social conditions of the area is not taken into account. The use the stratification patterns in crops the department of Huila allows planning of agricultural areas and contributes to improved economic conditions in areas. Using tools like as CRISP-DM and WEKA software contribute to the design of these patterns. A second phase is in process with the use of spectral images, treated with compressive sampling techniques to help strengthen precision agriculture in this department.
Closest-Fit, Compression-Only Solutions for Freeform Shells

This paper introduces a powerful computational design and analysis method for shells, based on funicular networks. Based on Thrust Network Analysis, an optimization approach is presented, which finds the closest fit, compression-only solution to an arbitrary input surface for given network topologies,. This research offers new possibilities for the design of efficient, but exciting shells which start to visually blur the forms which have been associated to compression-only and freeform shell. Furthermore, this method provides the foundation for a fully three-dimensional equilibrium analysis method for historic masonry vaults with complex geometries. The paper gives new insight in the force dependencies in 3D funicular networks, which serve as a basis for the nonlinear solving procedure presented. Through several examples, the power of this novel research is demonstrated.
Effects of botanical acaricides on Tetranychus urticae and compatibility with the predatory mite Amblyseius swirskii

Abstract The evaluation of biorrational products to control the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae), is of primary importance in the agriculture sector. In this study, we evaluated the effects of commercial botanical acaricides (BAs) on T. urticae and its predatory mite Amblyseius swirskii Athias-Henriot (Acari: Phytoseiidae). In the laboratory, T. urticae was exposed to acaricide-immersed leaves and A. swirskii to acaricide-coated glass vials. In the greenhouse, potted eggplants infested with mites were sprayed with different BAs. In the laboratory, Zea mays oil-based and Glycine max oil-based BAs caused more than 95% and 80% mortality of adults after 48 h of application and caused also 100% mortality of eggs. Residual exposure of A. swirskii adults to the BAs caused 45–60% mortality. In the greenhouse, these BAs caused more than 50% decrease in the population of T. urticae for 21 d.
Development of an Ensemble Modeling Framework for Data Analytics in Supply Chain Management

—The application of Data Analytics (DA) in Supply Chain Management (SCM) provides a plethora of opportunities for improving the performance, efficiency, and resilience of organizations by predicting the behaviour of the supply chain network. In this regard, the use of complex DA approaches that involve the combination of multiple Machine Learning (ML) models such as the ensemble approaches have shown to be highly effective in improving prediction accuracy and model performance. Still, the application of these ensemble approaches has not yet been properly explored in SCM analytics. Thus, this paper presents an ensemble model framework specifically designed for SCM issues by exploring problem types and use cases where ensemble approaches can be applied in SCM. The developed framework enables the selection of the right ensemble models for specific SCM DA tasks. Thus, this paper contributes to the scientific body of knowledge by presenting a systematic approach to ensemble modelling in SCM analytics by outlining suitable ensemble model compositions for different SCM use cases and data analytics problems. Hence the results of this paper provide a potential tool for DA practitioners in SCM to further improve the performance of their Supply Chain (SC) networks by selecting appropriate ensemble elements for different SCM analytics problems.
Hierarchical spatial structuring of stream insect diversity through DNA barcoding

This project was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Discovery Grant and an NSERC Post-Graduate Scholarship, the Ontario Ministry of Research and Innovation through the Global Leadership Round in Genomics and Life Sciences program, a grant from the Government of Canada through Genome Canada and the Ontario Genomics Institute in support of the International Barcode of Life project, an Ontario Graduate Scholarship, the O’Brien Fellowship, the Northern Research Fund (NRF) of the Churchill Northern Studies Centre, the government of Canada through the Northern Scientific Training Program (NSTP), and by the University of Guelph through the Work-Study program and an International Collaborative Research travel grant from the College of Biological Sciences.
Hepatitis B Virus and microRNAs: A Bioinformatics Approach

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral–host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.
A Novel Form of Neuregulin 1 Type III Caused by N-Terminal Processing

Nrg1 (Neuregulin 1) type III, a susceptible gene of schizophrenia, exhibits a critical role in the central nervous system and is essential at each stage of Schwann’s cell development. Nrg1 type III comprises double-pass transmembrane domains, with the N-terminal and C-terminal localizing inside the cells. The N-terminal transmembrane helix partially overlaps with the cysteine-rich domain (CRD). In this study, Nrg1 type III constructs with different tags were transformed into cultured cells to verify whether CRD destroyed the transmembrane helix formation. We took advantage of immunofluorescent and immunoprecipitation assays on whole cells and analyzed the N-terminal distribution. Astonishingly, we found that a novel form of Nrg1 type III, about 10% of Nrg1 type III, omitted the N-terminal transmembrane helix, with the N-terminal positioning outside the membrane. The results indicated that the novel single-pass transmembrane status was a minor form of Nrg1 type III caused by N-terminal processing, while the major form was a double-pass transmembrane status.