Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed and dynamic intracellular organization of extracellular information

Significance To thrive in diverse environments, cells must represent extracellular change intracellularly despite stochastic biochemistry. Here, we introduce a quantitative framework for investigating the organization of information within a cell. Combining single-cell measurements of intracellular dynamics with a scalable methodology for estimating mutual information between time series and a discrete input, we demonstrate that extracellular information is encoded in the dynamics of the nuclear localization of transcription factors and that information is lost with alternative static statistics. Any one transcription factor is usually insufficient, but the collective dynamics of multiple transcription factors can represent complex extracellular change. We therefore show that a cell’s internal representation of its environment can be both distributed across diverse proteins and dynamically encoded. Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making.
3D structure prediction of lignolytic enzymes lignin peroxidase and manganese peroxidase based on homology modelling

Lignolytic enzymes have great biotechnological value in biopulping, biobleaching, and bioremediation. Manganese peroxidase (EC 1:11:1:13) and lignin peroxidase (EC 1:11:1:14) are extracellular and hem-containing peroxidases that catalyze H2O2-dependent oxidation of lignin. Because of their ability to catalyse oxidation of a wide range of organic compounds and even some inorganic compounds, they got tremendous industrial importance. In this study, 3D structure of lignin and manganese peroxidase has been predicted on the basis of homology modeling using Swiss PDB workspace. The physicochemical properties like molecular weight, isoelectric point, Grand average of hydropathy, instability and aliphatic index of the target enzymes were performed using Protparam. The predicted secondary structure of MnP has 18 helices and 6 strands, while LiP has 20 helices and 4 strands. Generated 3D structure was visualized in Pymol. The generated model for MnP and LiP has Z-score Qmean of 0.01 and -0.71, respectively. The predicted models were validated through Ramachandran Plot, which indicated that 96.1 and 95.5% of the residues are in most favored regions for MnP and LiP respectively. The quality of predicted models were assessed and confirmed by VERIFY 3D, PROCHECK and ERRAT. The modeled structure of MnP and LiP were submitted to the Protein Model Database.
Research note: Occurrence of mcr-encoded colistin resistance in Escherichia coli from pigs and pig farm workers in Vietnam

ABSTRACT WHO considers colistin as a highest priority critically important drug for human health, and occurrence of colistin-resistant bacteria in livestock is of health concern. The current study determined occurrence of colistin-resistant Escherichia coli in pigs and workers at pig farms in Vietnam, and investigated the genetic background for resistance. Colistin-resistant E. coli were detected from pigs in 53/116 (45.7%) farms, and from workers taking care of the pigs in 21/94 (22.3%) farms. Colistin-resistant isolates showed MIC to colistin between 4–16 mg/L, they were multidrug resistant (99%) and resistance was caused by the presence of mcr-1 genes in 97/102 (95.1%) E. coli from pigs and in 31/34 (91.1%) isolates from humans. mcr-1 is considered a plasmid-encoded gene, but this was not confirmed in the current investigation. In total, one pig isolate carried both mcr-1 and mcr-3 genes, whereas mcr-2, mcr-4 and mcr-5 genes were not detected. Shared resistance profiles between pig and human isolates on the same farm was only observed in four farms. The study showed that commensal E. coli from pigs in Vietnam constitute a reservoir for colistin-resitant E. coli, however, further studies are needed to confirm that mcr genes are associated with plasmids and their importance for human health.
Modelling the probability of microhabitat formation on trees using cross‐sectional data

Context: Tree‐related microhabitats (TreMs), such as trunk cavities, peeled bark, cracks or sporophores of lignicolous fungi, are essential to support forest biodiversity because they are used as substrate, foraging, roosting or breeding places by bryophytes, fungi, invertebrates and vertebrates. Biodiversity conservation requires the continuous presence of TreMs in a forest. However, little is known about their dynamics. Moreover, we usually have only cross‐sectional TreM data (observations of many trees at a single time), making it difficult to estimate TreM formation rates. Method: This study adapted the methods of survival and reliability analysis to model the rate of TreM formation per unit of diameter increment as a function of tree diameter at breast height (DBH). We tested three variants of this model: the TreM formation rate independent of, proportional to or increasing nonlinearly with DBH. We calculated the likelihood of the models, considering cross‐sectional observations either of TreM presence/absence or TreM number on trees of different sizes. We calibrated the models in six subnatural montane forests dominated by European beech (Fagus sylvatica) and silver fir (Abies alba) – in the French Pyrenees. Assuming an annual DBH increment value, the annual formation rate of TreMs was predicted both at the level of the tree and at the level of the forest stand. Results: This method provides a coherent framework to model the probability that a TreM forms on a tree during a unit growth step and produces realistic predictions of TreM accumulation on trees. TreM formation accelerated as trees grew for A. alba but not for F. sylvatica. The TreM formation rate was twice as fast on F. sylvatica as on A. alba. We estimated a formation of 0·82–1·28 TreMs/ha per year and 0·5–0·9 TreM‐bearing trees/ha per year in the subnatural forests studied. Synthesis and applications: This method makes rigorous modelling of the formation of TreMs possible during the growth of trees and forest stands. The quantitative evaluation of TreM fluxes will help to design forest biodiversity conservation strategies favouring the development and temporal continuity of TreMs.
Drought Stress Reduces Stem Elongation and Alters Gibberellin-related Gene Expression during Vegetative Growth of Tomato

A . Drought stress reduces stem elongation and cell expansion. Since gibberellins (GAs) play an important role in controlling cell elongation, the objective of this study was to determine if the reduction in growth under drought stress is associated with altered GA metabolism or signaling. We exposed ‘Moneymaker’ tomato ( Solanum lycopersicum ) to drought stress to observe the effects on growth. Irrigation was automated using a data logger, which maintained volumetric water contents (VWC) of 0.35 and 0.15 m 3 (cid:1) m L 3 for well-watered and drought-stressed conditions, respectively. To further investigate the effect of GAs on elongation, paclobutrazol (PAC), a GA biosynthesis inhibitor, was applied to reduce endogenous GA production. Drought stress and PAC treatment reduced plant height. Internode length, cell size, and shoot dry weight displayed an interaction between the VWC and PAC treatments. The transcript levels of SlGA20ox1 , - 2 , -3 , and -4, SlGA3ox2 , and SlGA2ox2, -4 , and -5 , corresponding to enzymes in GA metabolism, and LeEXP1 , and -2 , encoding expansin enzymes related to cell wall loosening necessary for cell expansion, were analyzed. Downregulation of transcript accumulation due to drought stress was observed for SlGA20ox4 , SlGA2ox5 , and LeEXP1 , but not for any of the other genes. PAC increased expression of SlGA20ox-3 , and SlGA3ox2, potentially through feedback
A Meta‐Analysis of Change in Applicants' Perceptions of Fairness

Using an event&#8208;triggered multi&#8208;stage framework, this random&#8208;effects meta&#8208;analysis examined the changes in applicants' perceptions of fairness between consecutive stages and throughout the entire personnel selection process. We integrated findings of studies with at least two measurement points, resulting in 45 effect sizes (overall N&#160;=&#160;3,038). Trajectories of perceptions of fairness decreased nonlinearly across the process, with a steeper decrease for people who held high levels of initial fairness expectations. Unjust treatment produced a decrease in perceptions of fairness from pretest to posttest and an increase from posttest to postdecision. Furthermore, the length of the time interval moderated the changes in fairness perceptions between the posttest and postdecision stage. Practical implications and an agenda for future research are discussed.
BUGSJS: a benchmark and taxonomy of JavaScript bugs

JavaScript is a popular programming language that is also error‐prone due to its asynchronous, dynamic, and loosely typed nature. In recent years, numerous techniques have been proposed for analyzing and testing JavaScript applications. However, our survey of the literature in this area revealed that the proposed techniques are often evaluated on different datasets of programs and bugs. The lack of a commonly used benchmark limits the ability to perform fair and unbiased comparisons for assessing the efficacy of new techniques. To fill this gap, we propose BugsJS, a benchmark of 453 real, manually validated JavaScript bugs from 10 popular JavaScript server‐side programs, comprising 444k lines of code (LOC) in total. Each bug is accompanied by its bug report, the test cases that expose it, as well as the patch that fixes it. We extended BugsJS with a rich web interface for visualizing and dissecting the bugs' information, as well as a programmable API to access the faulty and fixed versions of the programs and to execute the corresponding test cases, which facilitates conducting highly reproducible empirical studies and comparisons of JavaScript analysis and testing tools. Moreover, following a rigorous procedure, we performed a classification of the bugs according to their nature. Our internal validation shows that our taxonomy is adequate for characterizing the bugs in BugsJS. We discuss several ways in which the resulting taxonomy and the benchmark can help direct researchers interested in automated testing of JavaScript applications. © 2021 The Authors. Software Testing, Verification & Reliability published by John Wiley & Sons, Ltd.
Real-time speech enhancement using optimised empirical mode decomposition and non-local means estimation

: In this study, the authors present a novel speech enhancement method by exploring the benefits of non-local means (NLM) estimation and optimised empirical mode decomposition (OEMD) adopting cubic-spline interpolation. The optimal parameters responsible for improving the performance are estimated using the path-finder algorithm. At first, the noisy speech signal is decomposed into many scaled signals called intrinsic-mode functions (IMFs) through the use of a temporary decomposition method is called sifting process in OEMD approach. The obtained IMFs are processed by NLM estimation technique in terms of non-local similarities present in each IMF, to reduce the ill-effects caused by interfering noise. The proposed NLM-based method is effective to eliminate the noise of less-frequency. Each IMF contains essential information about the signals, on some scale or frequency band. Field programmable gate array architecture is implemented on a Xilinx ISE 14.5 and the result of the proposed method offers good performance with a high signal-to-noise ratio (SNR) and low mean-square error compared to other approaches. The performance evolution is carried out for different speech signals taken from the TIMIT database and noises taken from the NOISEX-92 database in different SNR stages of 0, 5 and 10 dB, respectively.
The Spectacle of Data: A Century of Fairs, Fiches, and Fantasies

Alongside the robots, rockets, kitchen appliances, and other technical wonders displayed at the great expositions and world’s fairs of the late-19th and early-20th centuries, visitors frequently found deceptively staid demonstrations of banal bureaucratic tools: cards, fiches, and files. Yet these technologies of information management were aestheticized and presented as integral to the generation and pursuit of the fairs’ ambitious ‘world projects’: global networks, universal intelligences, efficient cities, colonized galaxies. The small, moving parts of information functioned as critical tools for city- and world-building. In this article we begin with the 1964–5 World’s Fair, where bits and fragments of information fueled (or generated? ) space-age visions, then trace those mid-century imaginaries back to the 1939–40 World’s Fair and a constellation of expos at the turn of the century, to see how the small, moving parts of information management ‘scale up’ to generate grand fantasies, and, at the same time, how they serve to index their own particular political and cultural milieux.
Morpho-Anatomical Changes in Roots of Chickpea (Cicer arietinum L.) under Drought Stress Condition

Chickpea (Cicer arietinum L.) is a crop cultivated in semi-arid and rainfed areas of Pakistan and it experiences terminal drought stress. In this paper, the morpho-anatomical study regarding roots of chickpea was carried out to investigate the drought adaptation strategies. Twelve cultivars of chickpea were grown in pots under drought stress of 70% and 35% field capacity in addition to control (100%). Root segments of mature chickpea plants were sectioned in 2 cm from root-shoot junction and used for making transverse sections. The development of sleeve and stele tissues and their proportion were markedly influenced by moisture availability to the root system. Roots length, fresh and dry weight, and number of secondary roots were decreased under field capacity of 70% and 35% as compared to control in 12 cultivars of chickpea. Root epidermis and cortex of CH47/04 consisted of cells with thickened walls and with 2-3 cell layers of sclerenchymatous cells below the epidermis. Increased number of medullary rays and high vascular region was observed in cultivars CH120/04, CH47/04, CH587/05 and CH87/02. Anatomical studies showed that CH47/04 was highly tolerant among 12 cultivars of chickpea, while CH587/05 and CH87/02 behaved moderately under both levels of drought stress (70% and 35% field capacity).
A Learning Analytics Theoretical Framework for Virtual Reality Instructional Applications

The commercial popularity of Virtual Reality attracted educators’ interest and brought new opportunities to the educational landscape. At the same time, Learning Analytics emerged with the promise to revolutionise the traditional practices by introducing ways to systematically assess and improve the effectiveness of instruction. However, the collection of ‘big’ educational data is mostly associated with web-based platforms as they offer direct access to learners’ activities with minimal effort. On the antipode, the nature of VR limits the opportunities for such data collection. Hence, in the context of this work, we present a four-dimensional theoretical framework, that accounts the information that can be gathered from VR-supported instruction, and propose a set of structural elements which can be utilised for the development of a Learning Analytics prototype system. The outcomes of this work are expected to support practitioners to maximise the potential of their interventions and provide inspiration for new ones.
Deep Learning in Radiation Oncology

Corresponding author Jinsung Kim (jinsung@yuhs.ac) Tel: 82-2-2228-8118 Fax: 82-2-2227-7823 Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.
Microtensile bond strength of different modes of universal adhesives to radiotherapy-affected dentin

Abstract Objective Radiotherapy (RT) beams are proved to have detrimental effects on dental tissues. Aim of the present study was to evaluate the microtensile bond strengths of different modes of universal adhesives on RT-effected dentin. Methods: One hundred and twenty premolars of patients who had not diagnosed with any systemic disease and 120 premolars of patients who had undergone >50 Gy (Gray) dose of radiotherapy 6–9 months ago were extracted. Teeth were sectioned perpendicularly to expose superficial dentin. Different adhesive modes [etch-and-rinse (ER), selective-etch (SE)] of universal adhesives [Single Bond Universal (SBU), Prime&Bond Universal (PBU)] were formed the 8 groups of the study (n = 30). After composite build-ups were bonded to dentin, samples were sectioned into microspecimens. Then, these specimens were subjected to micro-tensile bond strength test. Data were analyzed by t-test at p < 0.05. Results: Except for the PBU-SE group, control group had higher bond strength than treatment group. In treatment group, SBU-ER showed the highest bond strength followed by PBU-SE and SBU-SE (p > 0.05). PBU-ER had the lowest bond strength among all groups. There is also statistical significance between all modes of two universal adhesives (p < 0.05). Adhesive fracture was the most predominant in groups. Conclusion: Design of the methodology ensures the dentin samples of treatment group to benefit from oral condition changes after RT. According to results, only Prime&Etch Universal adhesive is recommended to be used in SE mode. In order to gain a common protocol for material choices, it is necessary to plan studies with similar methodologies and various universal adhesives.
The Effect of an Online Self-Help Cognitive Behavioural Intervention for Insomnia on Negative Affect and Paranoia: A Randomised Controlled Trial

common in the general population. For example, ( Freeman et al., 2005) investigated the experience of paranoia in a sample of 1,202 members of the general population. Approximately one third of the sample experienced paranoid thoughts, many of which caused distress, ranging from milder expressions (e.g., people at work are talking about me) to more serious paranoid thoughts often seen in clinical groups (e.g., my food is being poisoned by the police). Similarly, insomnia (i.e., problems getting to sleep and/or staying asleep) is also relatively common, with around one third of people experiencing at least one symptom of insomnia (Ohayon, 2002). Furthermore, there may be an association between paranoia and sleep, as many, if not all mental health difficulties are associated with problems sleeping (Baglioni et al., 2016; Benca et al., 1992), and in recent years a number of empirical studies have reported that the experience of insomnia is closely related to paranoid thinking. For example, insomnia is associated with paranoia in both clinical and non-clinical populations (Freeman et al., 2009), an effect that is likely mediated by affective experiences (Freeman et al., 2009, 2010, 2012; Scott et al., 2017) and emotion regulation (Grezellschak et al., 2017; Rehman et al., 2018). This link between insomnia, negative affect, and the experience of paranoia raises an important question; can interventions primarily aimed at improving insomnia also reduce paranoia?
Survival Analysis of Indonesian Banking Companies

Objective – Financial distress is an undesirable condition for any company. To avoid financial distress, and improve the overall financial status of a company, an understanding of the factors affecting financial distress is necessary. This research aims to identify the determinants of banking financial distress. Methodology/Technique – In this study, 41 banks comprised the sample, selected using purposive sampling. The survival cox proportional hazard analysis method to identify the determinant factors of survival of Indonesian Banks. Findings –The results show that that macro indicators (inflation and economic growth) have a significant effect on the banks’ financial distress. This implies that the government as a regulator must maintain the level of growth and inflation that stabilizes the economy so that banks can avoid financial distress. As for the banks’ management, they have an obligation to support government policies in maintaining growth and inflation. Novelty – The study uses the cox proportional hazard model. Type of Paper: Empirical. JEL Classification: G2O, G33. Keywords: Bank; Cox Model; Financial Distress; Survival Analysis. Reference to this paper should be made as follows: Kristanti, F.T. 2020. Survival analysis of Indonesian banking companies, J. Fin. Bank. Review, 5 (2): 39 – 47 https://doi.org/10.35609/jfbr.2020. 5.2(1)