Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and anti-viral activities of butanolide skeleton from Cinnamomum kotoense and Cinnamomum subavenium by inhibiting H5N1 neuraminidase

The human influenza virus and avian influenza virus represents a serious threat to human health. Although zanamivir and oseltamivir against influenza virus neuraminidase and thus the viral replication is not available. The oseltamivir-resistant H5N1 virus has been isolated from patient. Moreover, several cases have been reported where patients developed adverse symptoms due to a long-term use of oseltamivir. Effective antiviral drugs are essential for early control of an influenza pandemic. Natural products have been used as important resources for medicine and healthy food to improve the quality of life. Cinnamon bark is widely used as a spice and herb medicine. In this study, we have isolated and identified three butanolide derivatives, kotomolide A, isolinderanolide B and linderanolide B from Cinnamomum kotoense and Cinnamomum subavenium. Further, enzyme-based screening and anti-viral activity were also examined. Our study provides a starting point for developing new therapeutic or preventive agents from natural products for avian influenza virus.
Accurate Subsampling Intervals of Principal Components Factors

In the context of Dynamic Factor Models (DFMs), one of the most popular procedures for factor extraction is Principal Components (PC). Measuring the uncertainty associated to PC factor estimates should be part of interpreting them. However, the asymptotic distribution of PC factors could not be an appropriate approximation to the finite sample distribution for the sample sizes and cross-sectional dimensions usually encountered in practice. The main problem is that parameter uncertainty is not taken into account. We show that several bootstrap procedures proposed in the context of DFM with goals related to inference are not appropriate to measure the uncertainty of PC factor estimates. In this paper, we propose an asymptotically valid subsampling procedure designed with this purpose. The finite sample properties of the proposed procedure are analyzed and compared with those of the asymptotic and alternative extant bootstrap procedures. The results are empirically illustrated obtaining confidence intervals of the underlying factor in a system of Spanish macroeconomic variables.
Testing the Relative Comprehensiveness of Schwartz’s Ten Value Types with Help from Rokeach

: The universality of Schwartz’s Theory of Basic Human Values relies on the comprehensiveness of his value types, i.e., they are exhaustive of all human values, no matter where they are found. Schwartz built on Rokeach's (1973) theory of human values, however, by 1994 Schwartz had developed a theory of values that superseded Rokeach's 36 values with ten value types. Drawing on Rokeach, we tested the comprehensiveness of Schwartz's theory by statistically assessing the extent to which Schwartz's model of ten-value types and their underlying structural dimensions incorporate all of Rokeach's 36 values. We performed factor analyses on data collected from Austria, Nigeria, and South Africa with Rokeach's Values Survey Instrument and Schwartz's 21- item Portrait Values Questionnaire (PVQ-21). Our results show that while Schwartz’s theoretical model, measured with the PVQ-21, approaches comprehensiveness, it falls short because it does not statistically accommodate six of Rokeach’s values. However, longer questionnaires based on the same core theory, especially the 56-item Schwartz Value Survey (SVS), contain these six Rokeach values. Were we to repeat our experiment using the SVS, or one of the other longer versions of the PVQ, we would likely find that all of Rokeach’s values are accommodated in Schwartz’s theory.
Understanding the Radiation Soaking Effect in Neutron Survey Instruments

Abstract The drift in response of neutron survey meters that employ moderated 3He and BF3 proportional counters has been reported and investigated by instrument calibration laboratories for over 20 y. When within a constant radiation rate field of high intensity, dose rate readings of an instrument may steadily increase with a rate proportional to the field intensity before reaching a relatively stable plateau. This phenomenon, commonly referred to as the radiation soaking effect, has been attributed to the accumulation of charge on insulating surfaces inside the detector. This paper provides results of a study specifically conducted to better understand the effect and, hence, to possibly identify mitigation techniques. The magnitude of the effect in different instruments tested under the same conditions has been assessed, and a reason for the detector response variance has been clearly identified. In addition to measures used in calibration practice to compensate for the radiation soaking effect, a potential method to return an instrument sensitivity to its baseline level is also discussed.
A Flyback-Assisted Single-Sourced Photovoltaic Power Conditioning System Using an Asymmetric Cascaded Multilevel Inverter

This paper proposes a power conditioning system (PCS) for distributed photovoltaic (PV) applications using an asymmetric cascaded multilevel inverter with a single PV source. One of the main disadvantages of the cascaded multilevel inverters in PV systems is the requirement of multiple isolated DC sources. Using multiple PV strings leads to a compromise in either the voltage balance of individual H-bridge cells or the maximum power point tracking (MPPT) operation due to localized variations in atmospheric conditions. The proposed PCS uses a single PV source with a flyback DC-DC converter to facilitate a reduction of the required DC sources and to maintain the voltage balance during MPPT operation. The flyback converter is used to provide input for low-voltage H-bridge cells which processes only 20% of the total power. This helps to minimize the losses occurring in the proposed PCS. Furthermore, transient analyses and controller design for the proposed PCS in both the stand-alone mode and the grid-connection mode are presented. The feasibility of the proposed PCS and its control scheme have been tested using a 1㎾ hardware prototype and the obtained results are presented.
TAHAPAN COMMUNITY RELATIONS DALAM KEGIATAN CSR KONSERVASI LINGKUNGAN (Studi Kasus Terhadap Kegiatan CSR PT Djarum Pada Konservasi Lereng Gunung Muria)

A company is required not only provide jobs and pay taxes, but also must be able to be a good neighbor with a reciprocal relationship with the community, as well as play a role in improving the welfare of the community through various community development programs. Relationship between the company and the community then opened the activity of corporate social responsibility. Sustainability of the company relationship can be influenced by how the company manages its social responsibility to the communities where it operates and build a good communication to them. So that, the community relations activities are part of the public relations activities. This research which entitled “Community Relations Stage in CSR activity of Environmental Conservation” (Descriptive Qualitative Study of CSR Activity of PT Djarum in environment conservation on MountMuria, Kudus) discuss about the Defining the Problem Stage, Planning and Programming Stage, Taking Action and Communication Stage, and Evaluating The Program Stage of Konservasi Lereng Muria activity. This research also discuss how the PT Djarum efforts in building relationships and communication with the Kudus society, students, and local community that living in the area of Mount Muria, Kudus, Central Java. Konservasi Lereng Muria is PT Djarum’s activity in Corporate Social Responsibility that directed to answer the problem of local community based on environment damage in Mount Muria. In this environment conservation program, PT Djarum involving students and the local community of Mount Muria to plant productive tree in some critical areas in Mount Muria, Kudus, Central Java.
Practical level-of-detail aggregation of fur appearance

Fur appearance rendering is crucial for the realism of computer generated imagery, but is also a challenge in computer graphics for many years. Much effort has been made to accurately simulate the multiple-scattered light transport among fur fibers, but the computation cost is still very high, since the number of fur fibers is usually extremely large. In this paper, we aim at reducing the number of fur fibers while preserving realistic fur appearance. We present an aggregated fur appearance model, using one thick cylinder to accurately describe the aggregated optical behavior of a bunch of fur fibers, including the multiple scattering of light among them. Then, to acquire the parameters of our aggregated model, we use a lightweight neural network to map individual fur fiber's optical properties to those in our aggregated model. Finally, we come up with a practical heuristic that guides the simplification process of fur dynamically at different bounces of the light, leading to a practical level-of-detail rendering scheme. Our method achieves nearly the same results as the ground truth, but performs 3.8×-13.5× faster.
Document Recommendation on HKLII Final Report

Legal professionals around the world often reference and use online document libraries such as WorldLII to conduct legal research. In particular, under the legal system in Hong Kong, relevant judicial precedents are taken into consideration in deciding the outcome of the case, highlighting the importance of retrieving historical decisions during legal research. This project studies, implements and evaluates various algorithms and techniques to introduce semantic querying and document summarization to the Hong Kong Legal Information Institute (HKLII), with an aim to facilitating the searching and reading of documents on the system. With results conducted in previous researches, this project focused on using traditional document retrieval methods, topic modelling and transformers techniques to perform retrieval and summarization of legal documents. By introducing both features on HKLII, it is hoped that the system can better understand the intention of users’ queries and retrieve more relevant documents, while also reducing the time needed to recognize key information of the judgement, allowing users to more efficiently identify the relevance of the judgement to their needs. The system is hoped to be eventually rolled into production on the HKLII website, benefiting users of the website and optimizing the performance of the queries. The prototype of the two above-mentioned features were implemented and integrated to the modern HKLII interface, and preliminary evaluations and results are elaborated in the latter sections of the report.
Poster: Comparing Neural Network Solutions in Cryptographic API Completion

—With the strong interest in neural network based software engineering approaches and a plethora of proposed solutions, we point out the need for measurement studies in this space. Focusing on a speciﬁc application scenario, Java cryptographic API code completion, we outline several potential measurement problems, ranging from embedding design and evaluation, to methodology development of models
Identification and functional analysis of molecular mechanisms involved in the latency of ER positive breast cancer

Breast cancer is the most frequently diagnosed cancer and remains the second leading cause of death among women in Europe and United States. In this malignancy, metastasis remains to be an incurable condition, and therefore the major cause of death. Metastatic lesions can appear within a wide time ranging from months to years or decades after primary tumor resection. In particular, in the estrogen receptor (ER) positive breast cancer subgroup metastatic latency continues to be a major challenge for the researchers, clinicians and patients. This thesis reports the identification and functional analysis of molecular mechanisms involved in the latency of ER positive breast cancer. For that purpose we based our research on a comprehensive approach that relies on genetically engineered human breast cancer cells, experimental mouse models, unbiased whole-genome screen and clinical data. The first part of the thesis describes a novel mouse model of breast cancer dormancy. We showed that metastatic cells home the bone and enter the latency phase as micrometastatic lesions where tumor growth is restricted mainly due to the equilibrated ratios of cell proliferation and cell death. This experimental mouse model was used to identify genes relevant for long-latent relapse. To this end, we performed in vivo loss-of-function shRNA screening. In the screening we challenged a whole-genome library of shRNA to uncover genes whose depletion negatively regulates dormancy. Among the candidate genes revealed by the screen we focused on MSK1 as a long-latent metastasis regulator. The in vivo and in vitro validation results indicate that MSK1 plays a role in homing and differentiation of metastatic cells. We showed that MSK1 promotes the expression luminal transcription factors - FOXA1 and GATA-3. Therefore, MSK1 depletion is beneficial for metastatic cells leading to a partial phenotype shift towards a more aggressive and poorly differentiated basal population. Furthermore, our data suggest that MSK1 may be involved in metastatic cell plasticity by remodeling the chromatin. Importantly, low MSK1 gene expression levels associate with early metastasis in ER positive breast cancer.
TEE-based Remote Platform Attestation Scientific Students’ Association Report

The Internet of Things (IoT) consists of network-connected embedded devices that enable a multitude of new applications in various domains, such as industrial automation, transportation, building automation, healthcare, and agriculture - just to mention a few of them. However, as usual, new technologies also create new risks. In particular, embedded IoT devices can be infected by malware. Operators of IoT systems not only need malware detection tools, but also scalable methods to reliably and remotely verify malware-free-state of their IoT devices. In this document, we address this problem by proposing a remote attestation protocol for IoT devices that takes advantage of the security guarantees provided by a Trusted Execution Environment (TEE) running on the device. In this document, we provide the necessary information required to understand the attestation process, and various approaches to implementing remote attestation. We perform threat assessment in order to identify the possible weaknesses of remote attestation protocols. We present the overview of the TEE-based remote attestation scheme we designed. We define the two main participants, the Verifier and the Prover, in our protocol. We discuss how a secure channel is established between the two parties, and the messages used in order to carry out the attestation. We briefly describe the checks we use during remote attestation to verify the malware-free-state of the device. We provide a prototype implementation of the proposed protocol, and evaluate its features and performance. We conclude that our TEE-based remote attestation is capable of verifying the malware-free-state of IoT devices.
Real-Trading-Oriented Price Prediction with Explainable Multi-objective Optimization in Quantitative Trading

Price prediction using machine learning is a heated topic and an effective task in quantitative trading. However, the objectives that existing researches focus on are typically naive and can hardly express the diverse properties in real trading, by using which the results of price prediction cannot precisely reflect the returns in real trading. To alleviate this problem, we first formulate the characteristics of transactions and then propose a multi-objective method under a real-trading-oriented perspective, where a tree model inducing explainable trading characteristics is proposed. With lots of considerations of real trading characteristics, the results of price prediction can better obtain the properties in trading, and especially perform quite well in real trading. Multiple experiments on the Chinese Index Future Market display the effectiveness of the proposed model, where the performance of real trading has reached the industry-leading level. In particular, the difference between model prediction and returns in real trading can be diminished.
Feasibility study on adapting a machine learning based multi-sensor data fusion approach for honeycomb detection in concrete

We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
Statistical research methods for genetics

The study of gene helps people to better understand the patterns of human body. Nowadays Many diseases have been damaging people like Attention deficit/hyperactivity disorder or child psychiatry. Scientists have been trying to defect them by looking and the gene structure and organization of them. People tried to collecting data and do statistical research on it. Geno Statistics is a subject that focus on the mathematical way to study gene structure. It is widely used in the study research. It helps scientists to find a new way to analysis the mechanisms of the disease. Since this is a recent method more study is needed. Supervised Machine Learning is a very essential implement for the research of the genetics. We will explain the function of Supervised Machine Learning and then display some examples of the use of Supervised Machine Learning. We will also discuss the content of Precision Psychiatry in detail and show how it will help for the diagnose for the mental illness. In this article we will look at some practical application in these fields. Imagine genetics, child psychiatry, ADHD, Supervised Machine Learning and Precision Psychiatry.
Nintedanib-Containing Dual Conjugates Targeting αVβ6 Integrin and Tyrosine Kinase Receptors as Potential Antifibrotic Agents

αVβ6 Integrin plays a fundamental role in the activation of transforming growth factor-β (TGF-β), the major profibrotic mediator; for this reason, αVβ6 ligands have recently been forwarded to clinical phases for the therapy of fibrotic diseases. Herein, we report the synthesis and in vitro biological evaluation as antifibrotic agents of three new covalent conjugates, constituted by c(AmpLRGDL), an αVβ6 integrin-recognizing small cyclopeptide, and nintedanib, a tyrosine kinase inhibitor approved for idiopathic pulmonary fibrosis (IPF) treatment. One of these conjugates recapitulates optimal in vitro antifibrotic properties of the two active units. The integrin ligand portion within the conjugate plays a role in inhibiting profibrotic stimuli, potentiating the nintedanib effect and favoring the selective uptake of the conjugate in cells overexpressing αVβ6 integrin. These results may open a new perspective on the development of dual conjugates in the targeted therapy of IPF.
Multi-incident holography profilometry for low- and high gradient object

Digital holographic microscopy (DHM) is a non-contact, profilometric tool that allows obtaining microscopic object topography from captured holograms. However, the use of DHM is limited when the object under observation has a high gradient or is discontinuous. Multi-angle digital holographic profilometry (MIDHP) is an alternative solution for overcoming this limitation for measuring the topography with discontinuities. This method combines digital holography and multi-angle interferometry. The method requires a certain number of holograms that are processed into longitudinal scanning function (LSF). The topography of the object is recovered by finding the maxima of the LSF. MIDHP enables to enlarge the measurement range and provides a high axial resolution. This paper investigates MIDHP to measure surfaces with various (low and high) surface gradients. The calculations of LSF requires many Fourier Transforms (FT) and the computations are slow. In this paper, we improve LSF calculations by introducing two algorithms. The first algorithm reduces number of FT needed by applying summation in frequency domain. Second approach applies the method of 3D filtering, which improves the quality of the reconstructed shape. The introduced approaches are verified both numerically and experimentally.
Prediction of Eco-Environmental Vulnerability
in Mainland China under the Future Scenarios

Large-scale eco-environmental vulnerability assessment is conducive to a comprehensive understanding of the quality of ecological environment, which can help accurately capture the ecologically vulnerable areas. Under climate change scenarios, this paper predicted the eco-environmental vulnerability for future mainland China at the county level, and analyzed its trends under the four scenarios. Results shows that areas with higher levels of vulnerability are mainly located in the western China and at the junction of south provinces, while the eco-environmental vulnerability of the eastern and southern coastal areas is relatively low. In terms of temporal trends, the NECR and MGR have the most stable eco-environmental vulnerability, while the eco-environmental vulnerability in the LPR, SWCR and GXR shows an obviously increasing trend under the scenario with high radiative forcing level. The most obvious increase in eco-environmental vulnerability is identified in the North China Plain under RCP 8.5. By comparison, the increment in vulnerability grades during 2020s-2090s is significantly higher than that of the period 2020s-2050s, especially under RCP 8.5 scenario. The trends of eco-environmental vulnerability show upward tendency overall, and the northwestern and southeastern regions have the biggest changes in eco-environmental vulnerability, especially under the high emission scenario.
Location of the cross‐β structure in prion fibrils: A search by seeding and electron spin resonance spectroscopy

Prion diseases are transmissible fatal neurodegenerative disorders spreading between humans and other mammals. The pathogenic agent, prion, is a protease‐resistant, β‐sheet‐rich protein aggregate, converted from a membrane protein called PrPC. PrPSc is the misfolded form of PrPC and undergoes self‐propagation to form the infectious amyloids. Since the key hallmark of prion disease is amyloid formation, identifying and studying which segments are involved in the amyloid core can provide molecular details about prion diseases. It has been known that the prion protein could also form non‐infectious fibrils in the presence of denaturants. In this study, we employed a combination of site‐directed nitroxide spin‐labeling, fibril seeding, and electron spin resonance (ESR) spectroscopy to identify the structure of the in vitro‐prepared full‐length mouse prion fibrils. It is shown that in the in vitro amyloidogenesis, the formation of the amyloid core is linked to an α‐to‐β structural transformation involving the segment 160‐224, which contains strand 2, helix 2, and helix 3. This method is particularly suitable for examining the hetero‐seeded amyloid fibril structure, as the unlabeled seeds are invisible by ESR spectroscopy. It can be applied to study the structures of different strains of infectious prions or other amyloid fibrils in the future.