Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of biogeochemical factors affecting organic matter (lipid biomarkers) degradation

To understand the effects of intracellular structural associations on degradation of algal chloropigments, we conducted a series of microcosm experiments by incubating Emiliania huxleyi cells (a marine haptophyte) in natural oxic and anoxic seawaters collected from a stratified water column in the Cariaco Basin. The incubated cell detritus were sequentially treated with two buffer solutions to separate pigment components into soluble and insoluble fractions. By using non-denaturing gel electrophoresis, several chlorophyll-complexes, free chlorophyll, and another unknown chlorophyll-containing component were further separated from the soluble fraction. The chlorophyll-complexes included those bound with high molecular weight core-proteins (CP-I and CP43+CP47) and low molecular weight polypeptides (LHC-I and LHC-II) in the cellular photosystems PS-I and PS-II. Pigment recovery from these fractions and gel bands was well equivalent to the total amount from direct acetone extraction of the cells. We followed the time-dependent concentration changes of chlorophyll-a (Chl-a), phaeophytin-a (Ppta) and phaeophorbide-a (Ppb-a) in all fractions and complexes to estimate the degradation rate constants of chloropigments in natural oxic and anoxic seawaters. Our experimental results demonstrated that the intracellular structural associations had important influences on degradation of chloropigments under different redox conditions. In general, total Chl-a (direct acetone extracted) degraded faster (~4x) in oxic seawater than in anoxic seawater. However, the rate differences between oxic and anoxic conditions varied among the fractions and complexes. Degradation rate constants of Chl-a in soluble fraction were much higher (>10x) than those in insoluble fraction under both oxic and anoxic conditions. Chl-a bound with the complexes in PS-II appeared to be more reactive (~2x) than that in PS-I under oxic conditions but the difference in degradation rate constants between two photosystems became indistinguishable
Electrophoretic Characterization and the Relationshipbetween Some Brassica Species

The objective of this study was to assess the genetic divergence available in different genotypes of oilseed Brassica based on analyses of seed storage proteins, for the identification of genetically diverse and agronomically superior genotypes of Brassica seed, which may generate putative transgressive segregates on hybridization. Diversity within species of Brassica can be analyzed at molecular level with the help of seed storage protein by using SDS-PAGE. On the basis of banding pattern zymogram (diagrammatical representation of different protein bands) were sketched, by which molecular weight of specie specific bands were calculated on the basis of Rf values of the bands on the gel. Through statistical analyses dendrogram was formed and genotypes were clustered into different groups by applying UPGMA (unweighed pair group mean analyses).
Floristics of forests across low nutrient soils in Sulawesi, Indonesia

The island of Sulawesi formed from the joining of proto‐islands roughly three million years ago. Regions of zoological endemism, corresponding to the proto‐islands, have been reported. Sulawesi's tree communities, however, remain poorly documented. In better‐studied tropical regions, soil types similar to those found in Sulawesi often have distinctive tree communities. To gather data on Sulawesi's tree communities, we established ten (0.25 ha) plots on four soil types across three regions. We documented diversity, endemism, dominance, and species composition. Linear models of species composition showed greater influence of geographic distance rather than soil, and no relationship with climate. This suggests that the legacy of Sulawesi's formation may have influenced tree communities more so than the soil types we sampled. Most of our plots were on stressful soil types making it difficult to conclude on the importance of edaphic specialization in the Sulawesi tree flora. The lack of climatic effects reflects Sulawesi's position within the wet tropics where the small climatic differences are unlikely to have large influence on tree communities.
Forget About Cleaning up Your Micrographs: Deep Learning Segmentation is Robust to Image Artifacts

Quantitative image analysis almost always requires a thorough segmentation, i.e. a pixel-wise labeling of the different phases of the imaged material. With traditional tools, that segmentation can proceed only after complicating image artifacts have been filtered away or otherwise compensated for so they do not lead to spurious labeling. Identifying the right image processing operations to mitigate artifacts and restore images can be tedious, and executing those operations may introduce side-effects in the form of image processing artifacts. We show here that Deep Learning segmentation, image processing by convolutional neural networks, produces high quality labeling without any prior image cleanup, greatly simplifying the workflow for yielding valuable quantitative analyses. We performed serial-section imaging to collect a 3D image stack of an ordinary Portland cement sample mortar sample with water/cement ratio of 0.485 sample, using an FEI Helios nanoLab PFIB-SEM dual-beam system(ThermoFisher, Inc) at the Canadian Centre for Electron Microscopy, housed at McMaster University. The full stack was 156 slices, where each slice has a horizontal field-of-view of 96.7 microns with image dimensions of 3868 x 4062, giving us a total of 2450 Mpixel of image data for segmentation. As customary in PFIB, the Xe milling introduced strong curtaining artifacts that manifest as linear grooves at varying angles across the imaged surface of the sample. For each imaged slice, we acquired both in chamber electron detector (ICE) and through-the-lens detector (TLD) signals with an e-beam landing voltage of 2 keV. Also, common in these experiments, the evolving sample geometry introduced occlusion between the imaged surface and the detector; this manifests in non-uniform
Supervised Classification of Normal and Tumorous Brain MR Images Using Machine Learning Schemes

Manual interpretation of these huge amounts of image volumes are susceptible to inter-reader variability and human error. Thus, accurate automated CAD scheme is highly desirable in clinical pathological diagnosis. In this research, plethora of machine learning paradigms (e.g. feature extraction, dimensionality reduction and supervised classification methods) were explored, evaluated, compared and analyzed to identify the optimal pathway for brain MR images (normal vs neoplastic) binary classification task. External validation dataset was used to test the generalizability of the optimal predictive models implemented. Relevant and informative features were selected to construct cross-validated decision tree and eventually simple rule set was built based on the decision tree. The experimental results show that almost all pattern recognition paradigms achieve high accuracy with careful selection of number of attributes. LDA+ELM with 55 features are the optimal pipelines which achieve perfect classification when training and test data are of same source; and achieving (accuracy=97.5%, AUC=0.989, sensitivity=95% and specificity=100%) under balanced test dataset; (accuracy=99.5%, AUC=0.988, sensitivity=95% and specificity=100%). Cross-validated decision tree model also shows comparable performance: accuracy=98.8%, AUC=99.1%, sensitivity=99.6% and specificity=98.2%. Three highly relevant and robust attributes are visualized and selected for construction of decision tree models and finally a rule sets are read directly off the decision tree. This rule sets can potentially serve as fast and accurate classification algorithm.
Design of Intelligent Robot Platform based on Multi-sensor Fusion

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.
An Artificial Neural Networks Based Fake Currency Detection System

We propose a technique for web access by infusing or embeddings ordering different nations notes. An Image is separating and preparing procedure to recognize and match the distinguished information required cash picture and the first reference picture, each money note taken a Region of Interest (ROI) on existing money note condition. A separated cash picture ROI can be utilized to different example development and acknowledgement procedures and ANN hubs recognizing systems. At once, numerous cash notes are distinguished by coordinated notes then a web seek based following framework to recognize coordinating procedure is allowed for getting to for their specified timeframe. At first, we secure required the cash note by average level picture scanner on settled dpi shading with a required size arrangement; the dpi pixels level is set to get an ordinary picture utilizing picture preparing strategy. Barely any cutting edge picture channels are connected to proposed picture remarkable estimation of required cash take note of, this relegated esteem or images are contrasted and the doled out info sign images to coordinate unique note esteem, at that point web-based getting to technique controls by the microcontroller to examine all prerequisite fields and fundamental activities. 1
Coalescent models for populations with time-varying population sizes and arbitrary offspring distributions

The coalescent has been used to infer from gene genealogies the population dynamics of biological systems, such as the prevalence of an infectious disease. The offspring distribution affects the relationship between population dynamics and the genealogy, and for infectious diseases, the offspring distribution is often highly overdispersed. Here, we provide a general formula for the coalescent rate for populations with time-varying sizes and any offspring distribution. The formula is valid in the same large population limit as Kingman’s original derivation. By relating our derivation to existing formulations of the coalescent, we show that differences in the coalescent rate derived for many population models may be explained by differences in the offspring distribution. The coalescent derivations presented here could be used to quantify the overdispersion in the offspring distribution of infectious diseases, which is useful for accurate modelling disease outbreaks.
Studies on the Evaluation of the Half-sib Progenies to Identify the Prepotency of the Mother Clones of Hevea brasiliensis in Meghalaya

Two field experiments were conducted at Ganolgre Farm in the year 2008 and 2009, respectively under the Rubber Research Institute of India, Regional Research Station, Tura, West Garo Hills, Meghalaya (India) to identify the prepotent mother clones suitable for future breeding programmes in this region. Fully matured seeds were collected directly from the trees of the individual clones of Hevea and put on the germination bed. The germinated seeds were planted in two fields 10 days after collection. Growth observations viz. plant height, plant girth, number of whorls and the number of leaves were recorded annually before the start of the test tapping. After that all the seedlings were subjected to 10 test tapping. In 2008-trial the selected plants showed mean test tap yield range from 3.3–6.06 g t -1 10 t -1 in which the maximum yield was recorded in the clone PB 5/51 (6.06 g t -1 10 t -1 ) and minimum was in RRIM 600 (3.3 g t -1 10 t -1 ) whereas in 2009-trial the selected plants showed mean test tap yield range from 2.4–5.4 g t -1 10 t -1 . The maximum yield in 2009 trial was recorded in RRII 105 (5.4 g t -1 10 t -1 ) and minimum was in GL 1 (2.4 g t -1 10 t -1 ). On the basis of the test tap yield the top yielding seedlings were selected and the selected bud-woods are maintained in the field for further breeding evaluation programme. Half-sib progenies, girth, yield, prepotency,
Body Composition Distribution in Type 2 Diabetic Patients at The Hospital De San José

Objectives: To determine the distribution of the percentage of body fat and the muscle percentage of patients diagnosed with Diabetes mellitus type 2. Method: It is a cross-sectional study. Included subjects of external consultation of Endocrinology with a diagnosis of diabetes mellitus type 2. Anthropometric data measured by tetrapolar bioimpedance, 6 meters speed test and handgrip dynamometry. Results: The prevalence body fat was 40.5% (32.2 43.0) females and 32.3% (22.4 38.60) males and muscle mass females 26.5%(22.0 29.5) and males 29.5%(27.6 35.1). Also, it was found Sarcopenia prevalence in 32% according to the European consensus of sarcopenia of 2010 and 10% with the new cut-off points of the 2018 consensus, we also highlight that only the 12% are in BMI index below 25 kg/mt2. Conclusion: The majority of the population of patients diagnosed with DM 2 are overweight and obese diagnosed not only by weight but according to their percentage of fat measured by impedance, a risk factor not taken into account or treated, which leads to higher cardiovascular risk in addition to leading to the development of sarcopenia and dynapenia, which we consider is a modifiable factor through monitored and supervised regular physical activity that deals with direct benefits on the Diabetes Mellitus management and prognostic.
Ethical Detection of Online Influence Campaigns Using Transformer Language Models

The past five years have seen the rapid escalation of online influence campaigns: coordinated attempts to covertly exploit social media platforms to undermine democratic elections and manipulate public opinion. These campaigns threaten the electoral process of democratic countries, erode confidence in integrity of online social spaces, and undermine trust in mainstream news media. The detection of online influence campaigns (OIC) is a formidable problem, with significant active development within the field of applied artificial intelligence. Models based on the “Transformer” architecture — a specific type of neural network architecture amenable to transferring the capability of large pre-trained language models to novel domains — are a promising instrument for counteracting these campaigns. The focus of this thesis is the intelligent application of such deep learning techniques under real-world conditions for the improved detection of online influence campaigns, while remaining mindful of the ethical implications of automated systems that impact public political expression. This thesis contributes new methodologies for reducing algorithmic bias in supervised detection of online influence campaigns, as well as a novel unsupervised process for improving OIC detection. In the case of supervised approaches, where labelled text from past influence campaigns is used for detecting new campaigns, we present a method for reducing algorithmic bias through careful additional preprocessing and evaluation procedures. In the case of unsupervised approaches, which operate in the absence of labelled data from prior campaigns, algorithmic bias is mitigated through the incorporation of a human analyst to provide additional oversight. The supervised detection approach presented in this thesis includes an assessment of the potential for discrimination against non-native English speakers that may result from Transformer-based classifiers when applied to OIC detection in online communities. The findings indicate that while Transformer features derived from the text of user comments can be leveraged to identify suspect activity, this approach can lead to the emergence of algorithmic bias targeting non-native English grammar and keywords over-represented in past influence campaigns. Drawing on research in native language identification (NLI), “named entity masking” (NEM) is demonstrated to create sentence features robust to this shortcoming, while maintaining comparable classification accuracy. The novel unsupervised process incorporates the creation of a user representation, created through the averaging of multiple Transformer output embeddings for user-provided
Exploring the cultural conceptualization of emotions across national language varieties

Supporting the hypothesis that emotions are culturally constructed, this article compares the cultural conceptualization of pride in European and Brazilian Portuguese (EP/BP). Individualistic/collectivistic as well as other cultural influences that determine the conceptual variation of pride in pluricentric Portuguese are examined. Adopting a sociocognitive view of language and applying a multifactorial usage-feature and profile-based methodology, this study combines a feature-based qualitative analysis of 500 occurrences of orgulho ‘pride’ and vaidade ‘vanity’ from a corpus of blogs with their subsequent multivariate statistic modeling. The multiple correspondence analysis reveals two clusters of features, namely, self-centered pride and other-directed pride. Logistic regression confirmed that EP appears to be more associated with other-directed pride, which is in line with the more collectivist and restrained Portuguese culture, whereas BP is more connected with self-centered pride. Accordingly, morally good pride is salient in EP. Brazil’s high power distance can also explain the prominence of negative and bad pride in BP.
COVID-19: Immediate Predictors of Individual Resilience

COVID-19 is a pandemic event not seen in a century. This research aims to determine important predictors of resilience towards the COVID 19/Coronavirus Pandemic. This study uses a cross-sectional design, with purposive snowball sampling, for primary survey data collected over 10 weeks starting the first week in April 2020. Participants completed a self-administered questionnaire on demographics and behavioral factors. Resilience was assessed using the 10-item Connor-Davidson Resilience Scale and perceived stress was assessed using the 10-item Perceived Stress Scale. 374 adults participated in the survey. OLS regression was performed to determine key associations among demographic variables, resilience measures, and perceived stress brought on by COVID-19. Age and education were statistically significantly positively associated with resilience, while English as a second language was significantly negatively associated. Participants who reported needing help from family and neighbors, total number of days in lockdown, and higher perceived stress were all significantly negatively associated with resilience. This study adds to immediate predictors of individual resilience to the ongoing infectious disease catastrophe created by the COVID-19 pandemic.
Chapter 17 FUNCTIONAL GENOMICS AND ENZYME EVOLUTION Homologous and Analogous Enzymes Encoded in Microbial

Computational analysis of complete genomes, followed by experimental testing of emerging hypotheses - the area of research often referred to as "functional genomics" - aims at decyphering the wealth of infonnation contained in genome sequences and at using it to improve our understanding of the mechanisms of cell function. This review centers on the recent progress in the genome analysis with special emphasis on the new insights in enzyme evolution. Standard methods of predicting functions for new proteins are listed and the common errors in their application are discussed. A new method of improving the functional predictions is introduced, based on a phylogenetic approach to functional prediction, as implemented in the recently constructed Clusters of Orthologous Groups (COG) database (available at http://www.ncbi.nlm.nih.gov/COG). This approach provides a convenient way to characterize the protein families (and metabolic pathways) that are present or absent in any given organism. Comparative analysis of microbial genomes based on this approach shows that metabolic diversity generally correlates with the genome size -parasitic bacteria code for fewer enzymes and lesser number of metabolic pathways than their free-living relatives. Comparison of different genomes reveals another evolutionary trend, the non-orthologous gene displacement of some enzymes by unrelated proteins with the same cellular function. An examination of the phylogenetic distribution of such cases provides new clues to the problems of biochemical evolution, including evolution of glycolysis and the TCA cycle. The first complete genome sequences of cellular life forms have become available just in the last three years. The number of sequenced microbial genomes has been growing exponentially ever since, and at this time we already have complete sequences of twelve bacterial, four archaeal, and one