Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitragynine Attenuates Morphine Withdrawal Effects in Rats—A Comparison With Methadone and Buprenorphine

Background Opiate addiction is a major health problem in many countries. A crucial component of the medical treatment is the management of highly aversive opiate withdrawal signs, which may otherwise lead to resumption of drug taking. In a medication-assisted treatment (MAT), methadone and buprenorphine have been implemented as substitution drugs. Despite MAT effectiveness, there are still limitations and side effects of using methadone and buprenorphine. Thus, other alternative therapies with less side effects, overdosing, and co-morbidities are desired. One of the potential pharmacotherapies may involve kratom's major indole alkaloid, mitragynine, since kratom (Mitragyna speciosa Korth.) preparations have been reported to alleviate opiate withdrawal signs in self-treatment in Malaysian opiate addicts. Methods Based on the morphine withdrawal model, rats were morphine treated with increasing doses from 10 to 50 mg/kg twice daily over a period of 6 days. The treatment was discontinued on day 7 in order to induce a spontaneous morphine abstinence. The withdrawal signs were measured daily after 24 h of the last morphine administration over a period of 28 abstinence days. In rats that developed withdrawal signs, a drug replacement treatment was given using mitragynine, methadone, or buprenorphine and the global withdrawal score was evaluated. Results The morphine withdrawal model induced profound withdrawal signs for 16 days. Mitragynine (5–30 mg/kg; i.p.) was able to attenuate acute withdrawal signs in morphine dependent rats. On the other hand, smaller doses of methadone (0.5–2 mg/kg; i.p.) and buprenorphine (0.4–1.6 mg/kg; i.p.) were necessary to mitigate these effects. Conclusions These data suggest that mitragynine may be a potential drug candidate for opiate withdrawal treatment.
Differential Expression of Plasma Exo-miRNA in Neurodegenerative Diseases by Next-Generation Sequencing

Neurodegenerative diseases encompass a wide variety of pathological conditions caused by a loss of neurons in the central nervous system (CNS) and are severely debilitating. Exosome contains bio-signatures of great diagnostic and therapeutic value. There is proof that exosomal proteins can be biomarkers for Alzheimer’s disease (AD) and Parkinson’s disease (PD). MicroRNAs in exosome has potential to be an important source of biomarkers for neurodegenerative diseases. Here, we report exosomal microRNA performance of human plasma in neurodegenerative diseases by small RNA sequencing. A wide range of altered exo-miRNA expression levels were detected in both AD and PD patients. Down-regulated miRNAs in AD samples were enriched in ECM-receptor interaction pathway and both up-/down-regulated miRNAs in PD samples were enriched in fatty acid biosynthesis pathway. Compared to the control, 8 miRNAs were found to be significantly elevated/declined in AD and PD samples, of which 4 miRNAs were newly identified. Additionally, two exosome isolating methods were compared and the reproducibility of plasma exo-miRNA expression was confirmed, suggesting the feasibility of large-scale clinical application of this method. This study revealed exo-miRNA expression levels in neurodegenerative diseases, proposed new biomarkers and their potential functional pathway for AD and PD, confirmed the reproductivity of exo-miRNA profiles by using a different exosome isolating method, and compared the results with plasma miRNA expression. Therefore, this study also provides a precedent for identifying exosomal biomarkers of neurodegenerative diseases in plasma by high-throughput sequencing and it could extend the therapeutic repertoire of exosomal biomarkers.
Hybrid Molecular Dynamics for Elucidating Cooperativity Between Halogen Bond and Water Molecules During the Interaction of p53-Y220C and the PhiKan5196 Complex

The cooperativity between hydrogen and halogen bonds plays an important role in rational drug design. However, mimicking the dynamic cooperation between these bonds is a challenging issue, which has impeded the development of the halogen bond force field. In this study, the Y220C–PhiKan5196 complex of p53 protein was adopted as a model, and the functions of three water molecules that formed hydrogen bonds with halogen atoms were analyzed by the simulation method governed by the hybrid quantum mechanical/molecular mechanical molecular dynamics. A comparison with the water-free model revealed that the strength of the halogen bond in the complex was consistently stronger. This confirmed that the water molecules formed weak hydrogen bonds with the halogen atom and cooperated with the halogen atom to enhance the halogen bond. Further, it was discovered that the roles of the three water molecules were not the same. Therefore, the results obtained herein can facilitate a rational drug design. Further, this work emphasizes on the fact that, in addition to protein pockets and ligands, the role of voids should also be considered with regard to the water molecules surrounding them.
Cryobanking of Fish and Shellfish Egg, Embryos and Larvae: An Overview

Number of attempts have been made to cryopreserve fish and shellfish gametes. Success has been achieved to establish only sperm banks in case of some commercially important fish. In shellfish, also particularly in shrimps, though the sperm cryopreservation was successful, no attempts were made to develop sperm banks. As far as cryopreservation of egg and embryos of fish and shellfish is concerned, less research efforts were made with limited success. Number of reasons have been given for the failure of egg/embryo cryopreservation and the main barriers speculated are low membrane permeability in the eggs, the large yolk mass of the oocyte, and the presence of compartments in early developing embryos. These factors result in ice crystal formation during the freezing process. In addition, the oocytes and embryos are prone to chilling injuries unrelated to ice crystal damage. There are number of other problems reported by several researchers in the egg/embryo cryobanking protocols which are elaborately discussed in the present paper. There is an urgent need to develop a viable cryobanking technology for fish egg/embryos to enhance fish production in captive condition. Attempts to cryopreserve larvae of aquatic animals is another challenge occurring in the recent past. The aim of the present review is to collect comprehensive information on the efforts so far made on fish and shellfish egg and embryo cryobanking; and to assess the challenges in the development of viable technology and plan for future research for making this technology viable and cost effective.
The optimal stability `window': a mechanism underlying decadal fluctuations in North Pacific salmon stocks?

While recent evidence suggests that North Pacific salmon stocks are influenced by decadal variability in atmospheric forcing of the ocean, the actual combination of physical and biological processes that determines this linkage has not been identified. This paper describes a possible scenario in which water column stability is the primary factor by which the physical environment influences phytoplankton production, the basis for production at higher trophic levels. Variation in the strength of the wintertime Aleutian Low pressure area affects water column stabilities, hence primary production, along the entire eastern boundary of the North Pacific. The `optimal stability window' explains the qualitative relationship between fish stocks and the strength of the winter Aleutian Low, as well as the observed out-of-phase variation between northern and southern salmon stocks.
Pollen morphology of some species of Muscari Miller (Liliaceae-Hyacinthaceae) from Turkey

Summary Pollen grains of 14 Muscari species, five of which are endemic to the Turkey, belonging to the subgenera Muscari , Leopoldia and Botryanthus , were investigated under LM (light microscope), SEM (scanning electron microscope), and pollen of two species under TEM (transmission electron microscope). Pollen grains are shed as monads. They are monosulcate, isopolar and of bilateral symmetry. M. coeleste Fomin sometimes sheds the pollen as dyads. The characteristic structures of pollen ornamentation, observed in SEM micrographs, are reticulate and suprareticulate. The exine is semitectate and the tectum perforate.
Why measure bacterial production? A reply to the comment by Jahnke and Craven

dissolved organic carbon of different molecular weights from piration rates of marine plankton populations, p. 289-357. humic and clear waters. Appl. Environ. Microbial. 56: 1672In J. E. Hobbie and P. J. 1eB. Williams, [eds.], Heterotro1677. phic activity in the sea. Plenum. TURLEY, C. M. , AND K. LOCHTE. 1990. Microbial response to ZWEIEL, U.L. ,B. NORRMAN,AND A. HAGSTROM. 1993. Conthe input of fresh detritus to the deep-sea bed. Palaeogeogr. sumption of dissolved organic carbon by marine bacteria Palaeoclimatol. Palaeoecol. 89: 2-23. and demand for inorganic nutrients. Mar. Ecol. Prog. Ser. WILLIAMS, P. J. LEB. 1984. A review of measurements of res101: 23-32.
Índice de manejo de carbono e atributos químicos de Latossolo Vermelho sob diferentes sistemas de manejo

The objective of this work was to evaluate changes in the soil chemical attributes, carbon stock and in the C management index of a clayed Oxisol under different management systems. The soil under management systems with different historical usage was evaluated under the following covers: Zea mays, Urochloa decumbens, Panicum maximum cv. Tanzania. A cerrado area was used as check. Four composite samples were collected at 0-5, 5-10 and 10-20 cm soil depths. The phosphorus contents decreased with depth, and the higher values were observed in the area with P. maximum. In the area with Z. mays, higher values for K+, Ca++, Mg++, CEC, and base sum and saturation were observed. The total organic C content (TOC) was higher under cerrado vegetation cover, and smaller under P. maximum at 5-10 cm soil depth. The higher stocks of TOC, and of carbon in particulate fractions (C-POM) and in fractions associate to the soil mineral phase occurred in the area with U. decumbens, which promoted higher values for lability and C management indexes, similar to those for cerrado vegetation. In all coverings, the organic soil matter lability decreased with depth. The C-POM fraction had higher sensibility to variation in management practices, and is related to TOC.
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain.
Machine Learning Assisted PUF Calibration for Trustworthy Proof of Sensor Data in IoT

Remote integrity verification plays a paramount role in resource-constraint devices owing to emerging applications such as Internet-of-Things (IoT), smart homes, e-health, and so on. The concept of Virtual Proof of Reality (VPoR) proposed by Rührmair et al. in 2015 has come up with a Sense-Prove-Validate framework for integrity checking of abundant data generated from billions of connected sensors. It leverages the unreliability factor of Physically Unclonable Functions (PUFs) with respect to ambient parameter variations such as temperature, supply voltages, and so on, and claims to prove the authenticity of the sensor data without using any explicit keys. The state-of-the-art authenticated sensing protocols majorly lack in limited authentications and huge storage overhead. These protocols also assume that the behaviour of the PUF instances varies unpredictably for different levels of ambient factors, which in turn makes them hard to go beyond the theoretical concept. We address these issues in this work1 and propose a Machine Learning (ML) assisted PUF calibration scheme to predict the Challenge-Response Pair (CRP) behaviour of a PUF instance in a specific environment, given the CRP behaviour in a pivot environment. Here, we present a new class of authenticated sensing protocols where we leverage the beneficence of ML techniques to validate the authenticity and integrity of sensor data over ambient factor variations. The scheme also reduces the storage complexity of the verifier from O(p * K * l * (c + r)) to O(p * l *(c + r)), where p is the number of PUF instances deployed in the framework, l is the number of challenge-response pairs used for authentication, c is the bit lengths of the challenge, r is the response bits of the PUF, and K is the number of levels of ambient factor variations. The scheme alleviates the issue of limited authentication as well, whereby every CRP is used only once for authentication and then deleted from the database. To validate the proposed protocol through actual experiments on FPGA, we propose 5-4 Double Arbiter PUF, which is an extension of Double Arbiter PUFs (DAPUFs) as this design is more suited for FPGA, and implement it on Xilinx Artix-7 FPGAs. We characterise the proposed PUF instance from −20°C to 80°C and use Random Forest--based ML technique to generate a soft model of the PUF instance. This model is further used by the verifier to authenticate the actual PUF circuit. According to the FPGA-based validation, the proposed protocol with DAPUF can be effectively used to authenticate sensor devices across wide variations of temperature values.
Network Embedding for Community Detection in Attributed Networks

Community detection aims to partition network nodes into a set of clusters, such that nodes are more densely connected to each other within the same cluster than other clusters. For attributed networks, apart from the denseness requirement of topology structure, the attributes of nodes in the same community should also be homogeneous. Network embedding has been proved extremely useful in a variety of tasks, such as node classification, link prediction, and graph visualization, but few works dedicated to unsupervised embedding of node features specified for clustering task, which is vital for community detection and graph clustering. By post-processing with clustering algorithms like k-means, most existing network embedding methods can be applied to clustering tasks. However, the learned embeddings are not designed for clustering task, they only learn topological and attributed information of networks, and no clustering-oriented information is explored. In this article, we propose an algorithm named Network Embedding for node Clustering (NEC) to learn network embedding for node clustering in attributed graphs. Specifically, the presented work introduces a framework that simultaneously learns graph structure-based representations and clustering-oriented representations together. The framework consists of the following three modules: graph convolutional autoencoder module, soft modularity maximization module, and self-clustering module. Graph convolutional autoencoder module learns node embeddings based on topological structure and node attributes. We introduce soft modularity, which can be easily optimized using gradient descent algorithms, to exploit the community structure of networks. By integrating clustering loss and embedding loss, NEC can jointly optimize node cluster labels assignment and learn representations that keep local structure of network. This model can be effectively optimized using stochastic gradient algorithm. Empirical experiments on real-world networks and synthetic networks validate the feasibility and effectiveness of our algorithm on community detection task compared with network embedding based methods and traditional community detection methods.
The importance of the timing of quarantine measures before symptom onset to prevent COVID-19 outbreaks - illustrated by Hong Kong's intervention model

Background: The rapid expansion of the current COVID-19 outbreak has caused a global pandemic but how quarantine-based measures can prevent or suppress an outbreak without other more intrusive interventions has not yet been determined. Hong Kong had a massive influx of travellers from mainland China, where the outbreak began, during the early expansion period coinciding with the Lunar New Year festival; however, the spread of the virus has been relatively limited even without imposing severe control measures, such as a full city lockdown. Understanding how quarantine measures in Hong Kong were effective in limiting community spread can provide us with valuable insights into how to suppress an outbreak. However, challenges exist in evaluating the effects of quarantine on COVID-19 transmission dynamics in Hong Kong due to the fact that the effects of border control have to be also taken into account. Methods: We have developed a two-layered susceptible-exposed-infectious-quarantined-recovered (SEIQR) meta-population model which can estimate the effects of quarantine on virus transmissibility after stratifying infections into imported and subsequent community infections, in a region closely connected to the outbreak's source. We fitted the model to both imported and local confirmed case data with symptom onset from 18 January to 29 February 2020 in Hong Kong, together with daily transportation data and the transmission dynamics of COVID-19 from Wuhan and mainland China. After model fitting, epidemiological parameters and the timing of the start of quarantine for infected cases were estimated. Results: The model estimated that the reproduction number of COVID-19 in Hong Kong was 0.76 (95% CI, 0.66 to 0.86), achieved through quarantining infected cases -0.57 days (95% CI, -4.21 - 3.88) relative to symptom onset, with an estimated incubation time of 5.43 days (95% CI, 1.30 - 9.47). However, if delaying the quarantine start by more than 1.43 days, the reproduction number would be greater than one, making community spread more likely. The model also determined the timing of the start of quarantine necessary in order to suppress an outbreak in the presence of population immunity. Conclusion: The results suggest that the early quarantine for infected cases before symptom onset is a key factor to prevent COVID-19 outbreak.
A Conversational Robot for Older Adults with Alzheimer’s Disease

Amid the rising cost of Alzheimer’s disease (AD), assistive health technologies can reduce care-giving burden by aiding in assessment, monitoring, and therapy. This article presents a pilot study testing the feasibility and effect of a conversational robot in a cognitive assessment task with older adults with AD. We examine the robot interactions through dialogue and miscommunication analysis, linguistic feature analysis, and the use of a qualitative analysis, in which we report key themes that were prevalent throughout the study. While conversations were typically better with human conversation partners (being longer, with greater engagement and less misunderstanding), we found that the robot was generally well liked by participants and that it was able to capture their interest in dialogue. Miscommunication due to issues of understanding and intelligibility did not seem to deter participants from their experience. Furthermore, in automatically extracting linguistic features, we examine how non-acoustic aspects of language change across participants with varying degrees of cognitive impairment, highlighting the robot’s potential as a monitoring tool. This pilot study is an exploration of how conversational robots can be used to support individuals with AD.
Cortical Thymocytes Along With Their Selecting Ligands Are Required for the Further Thymic Maturation of NKT Cells in Mice

Following positive selection, NKT cell precursors enter an “NK-like” program and progress from an NK– to an NK+ maturational stage to give rise to NKT1 cells. Maturation takes place in the thymus or after emigration of NK– NKT cells to the periphery. In this study, we followed the fate of injected NKT cells at the NK– stage of their development in the thymus of a series of mice with differential CD1d expression. Our results indicate that CD1d-expressing cortical thymocytes, and not epithelial cells, macrophages, or dendritic cells, are necessary and sufficient to promote the maturation of thymic NKT1 cells. Migration out of the thymus of NK– NKT cells occurred in the absence of CD1d expression, however, CD1d expression is required for maturation in peripheral organs. We also found that the natural ligand Isoglobotriosylceramide (iGb3), and the cysteine protease Cathepsin L, both localizing with CD1d in the endosomal compartment and crucial for NKT cell positive selection, are also required for NK– to NK+ NKT cell transition. Overall, our study indicates that the maturational transition of NKT cells require continuous TCR/CD1d interactions and suggest that these interactions occur in the thymic cortex where DP cortical thymocytes are located. We thus concluded that key components necessary for positive selection of NKT cells are also required for subsequent maturation.
The Information Resilience Framework

The quality of information is crucial to the success of asset delivery, management, and performance in the Digitised Architecture, Engineering, Construction, and Operations (DAECO) sector. The exposure and sensitivity of information to threats during its lifecycle leaves it vulnerable, affecting the intrinsic, relational, and security dimensions of information quality. A resilient information lifecycle perspective that identifies capabilities and requirements is therefore needed to assure information quality amid threats. This research develops and presents an information resilience (IR) framework by drawing on the theories of resilience, information quality, and vulnerability. In developing the framework, the critical incident technique was employed in interviewing 30 professionals (average of 40 minutes) in addition to reviewing seven project-documents across three digitally-driven infrastructure projects (making up 324 pages of data). The validated capabilities and requirements identified from this study have been collated into the framework and this highlights the need for cognitive-driven capabilities and process-driven requirements in DAECO.