Miracle-dz/UniFAR-FTUs huggingface dataset:
MSclassifR: an R Package for Supervised Classification of Mass Spectra with Machine Learning Methods

MSclassifR is an R package that has been specifically designed to improve the classification of mass spectra obtained from MALDI-TOF mass spectrometry. It offers a comprehensive range of functions that are focused on processing mass spectra, identifying discriminant m/z values, and making accurate predictions. The package introduces innovative algorithms for selecting discriminating m/z values and making predictions. To assess the effectiveness of these methods, extensive tests were conducted using challenging real datasets, including bacterial subspecies of the Mycobacterium abscessus complex, virulent and avirulent phenotypes of Escherichia coli, different species of Streptococci and nasal swabs from individuals infected and uninfected with SARS-CoV-2. Additionally, multiple datasets of varying sizes were created from these real datasets to evaluate the robustness of the algorithms. The results demonstrated that the Machine Learning-based pipelines in MSclassifR achieved high levels of accuracy and Kappa values. On an in-house dataset, some pipelines even achieved more than 95% mean accuracy, whereas commercial system only achieved 62% mean accuracy. Certain methods showed greater resilience to changes in dataset sizes when constructing Machine Learning-based pipelines. These simulations also helped determine the minimum sizes of training sets required to obtain reliable results. The package is freely available online, and its open-source nature encourages collaborative development, customization, and fosters innovation within the community focused on improving diagnosis based on MALDI-TOF spectra. Key points MSclassifR is a comprehensive R package enabling the construction of data analysis pipelines for the precise classification of mass spectra. Our R package contains an innovative method for variable selection from random forests, which delivered excellent results on real data. In-depth analysis of various machine learning-based pipelines using our package allowed us to make conclusions about the optimal m/z selection and prediction methods depending on the size of the training dataset. Using a publicly available dataset of mass spectra obtained from various MALDI-TOF instruments across different countries, MSclassifR is able to build robust pipelines capable of adapting to different instruments in an automatic way. When tested on an in-house dataset, MSclassifR pipelines consistently outperformed a commercial software in terms of prediction accuracy.
KatG catalase deficiency confers bedaquiline hyper-susceptibility to isoniazid resistant Mycobacterium tuberculosis

Multidrug-resistant tuberculosis (MDR-TB) is a growing source of global mortality and threatens global control of tuberculosis (TB) disease. The diarylquinoline bedaquiline (BDQ) recently emerged as a highly efficacious drug against MDR-TB, defined as resistance to the first-line drugs isoniazid (INH) and rifampin. INH resistance is primarily caused by loss-of-function mutations in the catalase KatG, but mechanisms underlying BDQ’s efficacy against MDR-TB remain unknown. Here we employ a systems biology approach to investigate BDQ hyper-susceptibility in INH-resistant Mycobacterium tuberculosis. We found hyper-susceptibility to BDQ in INH-resistant cells is due to several physiological changes induced by KatG deficiency, including increased susceptibility to reactive oxygen species and DNA damage, remodeling of transcriptional programs, and metabolic repression of folate biosynthesis. We demonstrate BDQ hyper-susceptibility is common in INH-resistant clinical isolates. Collectively, these results highlight how altered bacterial physiology can impact drug efficacy in drug-resistant bacteria.
Continuous fields of vegetation characteristics at the global scale at 1‐km resolution

The geographic distribution of vegetation over the Earth's land surface is traditionally described using classification schemes with discrete numbers of vegetation types. When such land cover data sets are used as boundary conditions in Earth system models, abrupt boundaries and unrealistic homogeneity are introduced into parameter estimates. This paper proposes an alternative approach to describe global land cover with continuous fields of vegetation characteristics. A linear mixture model is applied to 1-km advanced very high resolution radiometer data to estimate proportional cover for three important vegetation characteristics: life form (percent woody vegetation, percent herbaceous vegetation, and percent bare ground), leaf type (percent needleleaf and percent broadleaf), and leaf duration (percent evergreen and percent deciduous). Linear discriminants for input into the mixture model are derived from 30 metrics representing the annual phenological cycle. Through comparison with training data derived from a global network of Landsat multispectral scanner scenes, we conclude that the linear assumption implicit in the linear mixture model is not severely violated. The linear relationships between percent cover as determined from the training data and the linear discriminants are used to estimate end-member values, and the mixture model is applied to derive the seven layers of global continuous fields. The availability of Moderate Resolution Imaging Spectroradiometer data in the future holds promise for refining the simple technique used in this paper to derive improved global continuous fields.
Lg wave attenuation in Britain

SUMMARY The Lg wave quality factor (QLg) in Britain has been modelled using data from the UK Seismic Network, operated by the British Geological Survey. The data set consists of 631 vertical, mostly short-period recordings of Lg waves from 64 earthquakes (2.7–4.7 ML) and 93 stations. We have inverted for both regional average QLg and tomographic images of QLg, and simultaneously a source term for each event and a site term for each station for 22 frequencies in the band 0.9–10.0 Hz. The regional average model is 266 f 0.53 between 1.0 and 10.0 Hz and indicates that attenuation in Britain is slightly higher than in France, and significantly higher than in eastern North America and Scandinavia. Tomographic inversions at each frequency indicate that QLg varies spatially. Broadly speaking, southeastern England, the Lake District and parts of the East Irish Sea Basin, and a small region between the Highland Boundary Fault and the Southern Uplands Fault are characterized by higher than average attenuation. Southwestern England, eastern central England and northwestern Scotland are regions of relatively low attenuation. To some extent, these regions correlate with what is known about the tectonics and structure of the crust in the UK.
An energy-efficient protocol for wireless sensor networks

In the paper, we consider a network of energy- constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmission sensed data to a base station (via clusterhead) for further processing. A key challenge in data gathering is to maximize the system lifetime, given the energy constraints. We focus on reducing the power consumption of wireless sensor networks. The dominate component in energy consumption is almost always due to communication. Therefore, we heavily modify an exist- ing communication protocol, Low-Energy Adaptive Clustering Hierarchy (LEACH). We extend LEACH's stochastic cluster- head selection algorithm by a deterministic component to reduce energy consumption. Simulation results show that our modified scheme can extend the network life around 50% for First Node Dies (FND) and 62% for Last Nodes Dies (LND). Index Terms-Wireless sensor networks, energy efficiency, cluster- head selection, LEACH I. INTRODUCTION Wireless sensor network consists of hundreds to several thousands of small sensor nodes scattered throughout an area of interest. Sensor nodes may spontaneously create impromptu network, assemble the network themselves, dynamically adapt to device failure and degradation, manage movement of sensor nodes, and react to changes in task and network requirements. Each individual sensor contains both processing and communi- cation elements and is designed to monitor the environment for events specified by the deployer of the network. Information
The Mixed Lineage Nature of Nitrogen Transport and Assimilation in Marine Eukaryotic Phytoplankton: a Case Study of Micromonas

The prasinophyte order Mamiellales contains several widespread marine picophytoplankton (2 lm diameter) taxa, including Micromonas and Ostreococcus. Complete genome sequences are available for two Micromonas isolates, CCMP1545 and RCC299. We performed in silico analyses of nitrogen transporters and related assimilation genes in CCMP1545 and RCC299 and compared these with other green lineage organisms as well as Chromalveolata, fungi, bacteria, and archaea. Phylogenetic reconstructions of ammonium transporter (AMT) genes revealed divergent types contained within each Mamiellales genome. Some were affiliated with plant and green algal AMT1 genes and others with bacterial AMT2 genes. Land plant AMT2 genes were phylogenetically closer to archaeal transporters than to Mamiellales AMT2 genes. The Mamiellales represent the first green algal genomes to harbor AMT2 genes, which are not found in Chlorella and Chlamydomonas or the chromalveolate algae analyzed but are present in oomycetes. Fewer nitrate transporter (NRT) than AMT genes were identified in the Mamiellales. NRT1 was found in all but CCMP1545 and showed highest similarity to Mamiellales and proteobacterial NRTs. NRT2 genes formed a bootstrap-supported clade basal to other green lineage organisms. Several nitrogen-related genes were colocated, forming a nitrogen gene cluster. Overall, RCC299 showed the most divergent suite of nitrogen transporters within the various Mamiellales genomes, and we developed TaqMan quantitative polymerase chain reaction primer–probes targeting a subset of these, as well as housekeeping genes, in RCC299. All those investigated showed expression either under standard growth conditions or under nitrogen depletion. Like other recent publications, our findings show a higher degree of ''mixed lineage gene affiliations'' among eukaryotes than anticipated, and even the most phylogenetically anomalous versions appear to be functional. Nitrogen is often considered a regulating factor for phytoplankton populations. This study provides a springboard for exploring the use and functional diversification of inorganic nitrogen transporters and related genes in eukaryotic phytoplankton.
The decreasion of integrin α 5 β 1/FAK associates with increased apoptosis of aortic smooth muscle cells in acute type A aortic dissection

Background Acute type A aortic dissection (AAAD) is a devastating condition with the human aortic smooth muscle cells (HASMCs) reduced proliferation and increased apoptosis. Integrin α 5 β 1 and FAK are the important proangiogenic factors involved in regulating angiogenesis. The aim of the study was to investigate the role of integrin α 5 β 1 and FAK in AAAD patients and the potential mechanisms. Methods The aortic tissue samples were collected from 8 AAAD patients and 4 organ donors obtained from Zhongshan Hospital of Fudan University. The level of apoptosis in the aortic tissues were assessed by immunohistochemical staining (ICH) and Terminal-deoxynucleoitidyl transferase mediated nick end labeling assay (TUNEL). The expression of integrin α 5 β 1 and FAK were detected. Furtherly, integrin α 5 β 1 was di�ciently expressed in HASMCs and the interaction with FAK was detected by co-immunoprecipitation analysis (Co-IP). The levels of proliferation and apoptosis were assessed by cell-counting Kit-8 analyses (CCK-8) and �ow cytometry after integrin α 5 β 1 di�ciently.
BREAST CANCER DIAGNOSIS AND PROGNOSIS USING STACKING ENSEMBLE TECHNIQUE

Breast Cancer is the most common type of cancers in Egypt, early diagnosis can help to lower the risks. For many physicians, predicting a cancerous tumor remains a challenging task also deciding which treatment plan would help the most. The availability of new medical technologies and the massive amount of patient data had motivated the basis of emergence of new strategies in the prediction and detection of cancer. Data mining analysis and Machine Learning (ML) techniques can help to develop tools that can be used as effective mechanism for early diagnosis and prognosis of breast cancer, which will greatly enhance patients' survival rate. The main objective of this paper is to compare between the performance of supervised learning classification algorithms and the performance of combination of these algorithms using stacking ensemble learning approach in terms of the classification accuracy, precision, recall and ROC. We conducted the experiments on breast cancer dataset collected from University of California, San Francisco. The results demonstrate that the proposed stacking ensemble learning model outperforms individual algorithms.
Effective elasticity of fractured rocks: A snapshot of the work in progress

Exploration and development of naturally fractured reservoirs rely on understanding and interpretation of certain signatures associated with seismic waves propagating through cracked rocks. This understanding comes primarily from the effective media theories that predict an overall elastic behavior of a solid containing many inhomogeneities cracks, in particular whose sizes are too small to be “seen” individually by the waves. To model seismic responses of fractured formations, a geophysicist typically has a choice between the effective media schemes of Hudson and Schoenberg. While the two predictions usually deviate slightly for liquid-filled cracks, the differences are significant when the fractures are dry. Explaining the origin of these differences and selecting a more accurate scheme is the first goal of this tutorial. Our second, more challenging task, is to prove that simply adding the compliance contributions of cracks as if they were isolated and noninteracting remains sufficiently accurate even for fractures that grossly violate the basic theoretical assumption, of penny-shaped cracks. Real fractures have notoriously irregular shapes, might be partially closed, and often form interconnected networks. Yet, these details of fracture microgeometry turn out to be unimportant for the effective elasticity given a typical noise level in seismic data. No closed-form theory exists for irregular fracture shapes. However, take into account finite-element simulations on so-called digital rocks demonstrate which features of the crack geometry have to be taken into account because they influence propagation of long compared to the size of fractures seismic waves and which features can be ignored.
A COMPARATIVE ANALYSIS OF PHISHING WEBSITE DETECTION USING XGBOOST ALGORITHM

As most of human activities are being moved to cyberspace, phishers and other cybercriminals are making the cyberspace unsafe by causing serious risks to users and businesses as well as threatening global security and economy. Nowadays, phishers are constantly evolving new methods for luring user to reveal their sensitive information. To avoid falling victim to cybercriminals, a phishing detection algorithms is very necessary to be developed. Machine learning or data mining algorithms are used for phishing detection such as classification that categorized cyber users in to either malicious or safe users or regression that predicts the chance of being attacked by some cybercriminals in a given period of time. Many techniques have been proposed in the past for phishing detection but due to dynamic nature of some of the many phishing strategies employed by the cybercriminals, the quest for better solution is still on. In this paper, we propose a new phishing detection model based on Extreme Gradient Boosted Tree (XGBOOST) algorithm. Experimental results demonstrated that XGBOOST-based phishing detection model is promising by returning an accuracy of 97.27% which outperformed both probabilistic Neural Network (PNN) and Random forest (RF) that returned accuracies of 96.79% and 95.66% respectively.
Liquid Composite Moulding reproducibility in real world production of fiber reinforced polymeric composites: a review of challenges and solutions

Liquid Composite Molding (LCM) suffers from multiple factors that contribute to pronounced uncertainty of process characteristics. This results in compromised reproducibility which is associated to high scrap or the unpredictable behavior of approved parts. However, LCM is still attractive for Fiber Reinforced Polymeric Composites (FRPC) production due to its economic advantage (i.e. in relation to Autoclave), the capability of some of its variants to produce high performance parts and its potential for process optimization. This review analyzes each uncertainty with respect to its origins and its impact in part or process, based on a combination of past literature and original numerical results. The possible methods to counteract uncertainties are critically discussed, with an eye on both the scientific and feasibility (technical/ economical) aspects. The overall aim is to provide to future LCM implementations a roadmap of the most critical challenges and solutions regarding the establishment of a reproducible process.
The More the Better? Foreign Ownership and Corporate Performance in China

Using a panel of 21,582 Chinese firms over the period 2000-2005, we examine the relationship between the degree of foreign ownership and corporate performance in recipient firms. We find that foreign ownership is positively associated with firms' return on assets, return on sales, labor productivity, and total factor productivity. Yet, the relationship exhibits an inverted U-shaped pattern: corporate performance increases as foreign participation rises up to about 47%-64%, and declines thereafter. This suggests that in China, joint-ventures between domestic and foreign enterprises are the top performers.
The Economics of Roscas and Intra- Household Resource Allocation the Economics of Roscas and Intra-household Resource Allocation

This paper investigates individual motives to participate in rotating savings and credit associations (roscas). Detailed evidence from roscas in a Kenyan slum (Nairobi) suggests that most roscas are predominantly composed of women, particularly those living in a couple and earning an independent income. To explain this phenomenon, we propose an argument based on con°ictual interactions within the household. Participation in a rosca is a strategy a wife employs to protect her savings against claims by her husband for immediate consumption. The empirical implications of the model are then tested using the data collected in Kenya. ¤ Acknowledgement: We are grateful to the CRED and to the Mac Arthur Foundation for ¯nancial support, particularly for the collection of ¯eld data. We also thank and the \Inequality and Economic Performance" MacArthur research network meeting (MIT) for many stimulating conversations.
Why Stocks May Disappoint

Recently much progress has been made in developing optimal portfolio choice models accomodating time-varying opportunity sets, but unless investors are unreasonably risk averse, optimal holdings include unreasonably large equity positions. One reason is that most studies assume investors behave as expected utility maximizers with power utility. In this article, we provide a formal treatment of both static and dynamic portfolio choice using the Disappointment Aversion preferences of Gul (1991). While different from the Kahneman-Tversky (1979) loss aversion utility, these preferences imply asymmetric aversion to gains versus losses and are consistent with the tendency of some people to like lottery-type gambles but dislike stock in-vestments. By calibrating a number of data generating processes to actual US data on stock and bond returns, we find very reasonable portfolios for moderately disappointment averse investors with utility functions exhibiting low curvature. Disappointment aversion preferences affect intertemporal hedging demands and the state dependence of asset allocation in such a way as to not be replicable by standard expected utility functions with higher curvature. Furthermore, it is easy to reconcile the large equity premium observed in the data with disappointment aversion utility of low curvature and reasonable disappointment aversion.
Precursory changes in source parameters for the Koyna–Warna (India) earthquakes

SUMMARY Precursory changes of stress drop and corner frequency are found for five main earthquakes of M w 4.1 to 4.7 that occurred near the Koyna and Warna reservoirs during the period of our close monitoring from 1994 onwards. Earthquakes had started to occur in the area in 1962 soon after the impoundment of the Koyna reservoir and are still continuing. The epicentres are located in a 30-km-long, N‐S seismic zone extending southwards from the Koyna dam. An ew Warna reservoir, situated 25 km south of Koyna, was filled to 60 m in 1992. Most subsequent epicentres are close to the new Warna reservoir. Most M ≥ 4 earthquakes in this region have been associated with foreshocks for 15‐30 days and aftershocks for over a month. At the beginning of various foreshock sequences of M w 1.5 to 2.4, the maximum stress drop values ranged from 0.25 to 0.65 MPa; the corner frequency ranged from 7.0 to 10.0 Hz for all the sequences. The stress drops decreased by over 50 per cent of their maximum value during the precursory period of 4‐17 days prior to main shocks and remained at that low level for a few days after the main shocks. Following an initial increase, the corner frequency decreased by 8t o4 5per cent. The decrease in corner frequency implies a 9‐54 per cent increase in fault length and is inferred to be caused by increased pore pressure as a result of dilatancy.
CLASSIFICATION OF SECURITY ISSUES AND CYBER ATTACKS IN LAYERED INTERNET OF THINGS

Internet of Things (IoT) has emerged as a very significant research area. In IoT, billions of ‘things’ are connected which communicate with one another over a network. While communicating among ‘things’, their users face several types of application and technical challenges. IoT system infrastructure comprises several layers. Different researches have been conducted so far to detect vulnerabilities, threats, and attacks arising in the IoT environment. Modern IoT architectures consist of physical and network components apart from different kinds of services and solutions. IoT systems face several services and security challenges. Privacy and security problems in IoT systems are quite unpredictable. The main objective of this paper is to identify and classify various security challenges faced by IoT users. Several types of security and privacy issues have been addressed in the present paper. This paper also presents a classification of security and privacy issues considered in different levels of IoT architecture.
What you don’t know matters: An ignorance-focused investigation What you don’t know matters: An ignorance-focused investigation of theory of mind of theory of mind

This project examines the ways in which knowledge, or ignorance, impact healthy adults’ theory of mind (i.e. their considerations of others’ mental states). In a pilot study, and four experiments, an eﬀect is found which supports the hypothesis that knowledge states inﬂuence the execution of theory of mind. The present ﬁndings suggest that attention is directed diﬀerently when participants reason from positions of knowledge, or positions of ignorance, in regard to a task-relevant fact. This project provides a starting point for further research, investigating the rich contextual contributions to the ﬂuent functioning of the ‘theory of mind system. ’