Miracle-dz/UniFAR-FTUs huggingface dataset:
What’s hot and what’s not: Making sense of biodiversity ‘hotspots’

Conserving biogeographic regions with especially high biodiversity, known as biodiversity ‘hotspots’, is intuitive because finite resources can be focussed towards manageable units. Yet, biodiversity, environmental conditions and their relationship are more complex with multidimensional properties. Assessments which ignore this risk failing to detect change, identify its direction or gauge the scale of appropriate intervention. Conflicting concepts which assume assemblages as either sharply delineated communities or loosely collected species have also hampered progress in the way we assess and conserve biodiversity. We focus on the marine benthos where delineating manageable areas for conservation is an attractive prospect because it holds most marine species and constitutes the largest single ecosystem on earth by area. Using two large UK marine benthic faunal datasets, we present a spatially gridded data sampling design to account for survey effects which would otherwise be the principal drivers of diversity estimates. We then assess γ‐diversity (regional richness) with diversity partitioned between α (local richness) and β (dissimilarity), and their change in relation to covariates to test whether defining and conserving biodiversity hotspots is an effective conservation strategy in light of the prevailing forces structuring those assemblages. α‐, β‐ and γ‐diversity hotspots were largely inconsistent with each metric relating uniquely to the covariates, and loosely collected species generally prevailed with relatively few distinct assemblages. Hotspots could therefore be an unreliable means to direct conservation efforts if based on only a component part of diversity. When assessed alongside environmental gradients, α‐, β‐ and γ‐diversity provide a multidimensional but still intuitive perspective of biodiversity change that can direct conservation towards key drivers and the appropriate scale for intervention. Our study also highlights possible temporal declines in species richness over 30 years and thus the need for future integrated monitoring to reveal the causal drivers of biodiversity change.
Estimation of Stability Constants of Copper(II) Chelates with N-alkylated Amino Acids using Topological Indices

Protonation (pK(1) and pK(2)) and copper(II) binding stability constants (log beta(110) and log beta(120)) of glycine and five N-alkylated glycines (methyl, dimethyl, diethyl, tert-butyl and tert-butylmethyl) were determined by potentiometric titration. These constants and the constants of eight N-alkylated glycinates, measured previously, were correlated with four topological indices (Wiener index, W, and the first-, second- and third-order valence-connectivity indices, (1)chi(v), (2)chi(v) and (3)chi(v)) calculated for the ligand. The first protonation constants (ascribed to the dissociation of COOH group) do not correlate with any of the chosen topological indices (r(2) < 0.3), but significant correlation was obtained for pK2 (dissociation of amino group) with r(2) values up to 0.920. "Corrected stability constants (obtained by subtracting pK(2) value) yielded a generally better fit than the uncorrected ones. Our stability constants are better correlated with topological indices (max. r(2) = 0.993) than the constants measured previously (max. r(2) = 0.836). From the regression analysis it is possible to predict the values of pK(2), log beta(110) and log beta(120) with an error less than 0.1, 0.3 and 0.3 log units, respectively.
Fungal community structure and function shifts with atmospheric nitrogen deposition

Fungal decomposition of soil organic matter depends on soil nitrogen (N) availability. This ecosystem process is being jeopardized by changes in N inputs that have resulted from a tripling of atmospheric N deposition in the last century. Soil fungi are impacted by atmospheric N deposition due to higher N availability, as soils are acidified, or as micronutrients become increasingly limiting. Fungal communities that persist with chronic N deposition may be enriched with traits that enable them to tolerate environmental stress, which may trade‐off with traits enabling organic matter decomposition. We hypothesized that fungal communities would respond to N deposition by shifting community composition and functional gene abundances toward those that tolerate stress but are weak decomposers. We sampled soils at seven eastern US hardwood forests where ambient N deposition varied from 3.2 to 12.6 kg N ha−1 year−1, five of which also have experimental plots where atmospheric N deposition was simulated through fertilizer application treatments (25–50 kg N ha−1 year−1). Fungal community and functional responses to fertilizer varied across the ambient N deposition gradient. Fungal biomass and richness increased with simulated N deposition at sites with low ambient deposition and decreased at sites with high ambient deposition. Fungal functional genes involved in hydrolysis of organic matter increased with ambient N deposition while genes involved in oxidation of organic matter decreased. One of four genes involved in generalized abiotic stress tolerance increased with ambient N deposition. In summary, we found that the divergent response to simulated N deposition depended on ambient N deposition levels. Fungal biomass, richness, and oxidative enzyme potential were reduced by N deposition where ambient N deposition was high suggesting fungal communities were pushed beyond an environmental stress threshold. Fungal community structure and function responses to N enrichment depended on ambient N deposition at a regional scale.
Endoscopic Recognition and Management Strategies for Malignant Colorectal Polyps: Recommendations of the US Multi-Society Task Force on Colorectal Cancer.

Colorectal polyps are the precursors for most colorectal cancers (CRCs). Some colorectal polyps accumulate enough mutations to develop high-grade dysplasia and eventual invasion of dysplastic elements into the submucosa. The invasion of dysplastic elements into the submucosa constitutes the clinical definition of CRC (Figure 1). The term malignant polyp specifically refers to a colorectal lesion with cancer invading the submucosa but not extending into the muscularis propria. These lesions are classified as pT1 in the TNM classification system. A synonymous and more modern term is submucosally invasive lesion. We will use the nomenclature of submucosal invasion throughout this document interchangeably when referring to a malignant polyp. The prevalence of cancer in colorectal polyps ranges from 0.2% to 5%. Malignant polyps represent the earliest form of clinically relevant CRC in most patients because neoplastic invasion of the submucosa allows for possible lymphatic and vascular metastasis. The risk of metastasis depends on several endoscopic and histologic features. The clinical issue most often raised by malignant polyps is whether a patient with an endoscopically resected colorectal lesion with submucosal invasion requires surgical resection of the colorectal segment from which the lesion was removed. Some malignant polyps can be managed endoscopically because the risk of residual cancer in the bowel wall and/or adjacent lymph nodes is very low. Other endoscopically resected malignant polyps are best managed by surgical resection because endoscopic resection alone is accompanied by a very high risk of residual cancer and/or lymph node metasta-
Reactive oxygen species coordinate the transcriptional responses to iron availability in Arabidopsis

Abstract Reactive oxygen species play a central role in the regulation of plant responses to environmental stress. Under prolonged iron (Fe) deficiency, increased levels of hydrogen peroxide (H2O2) initiate signaling events, resulting in the attenuation of Fe acquisition through the inhibition of FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT). As this H2O2 increase occurs in a FIT-dependent manner, our aim was to understand the processes involved in maintaining H2O2 levels under prolonged Fe deficiency and the role of FIT. We identified the CAT2 gene, encoding one of the three Arabidopsis catalase isoforms, as regulated by FIT. CAT2 loss-of-function plants displayed severe susceptibility to Fe deficiency and greatly increased H2O2 levels in roots. Analysis of the Fe homeostasis transcription cascade revealed that H2O2 influences the gene expression of downstream regulators FIT, BHLH genes of group Ib, and POPEYE (PYE); however, H2O2 did not affect their upstream regulators, such as BHLH104 and ILR3. Our data shows that FIT and CAT2 participate in a regulatory loop between H2O2 and prolonged Fe deficiency.
INFLUENCE OF THE PERCEPTIVE BELIEFS OF BUSINESS EDUCATION TEACHERS ON THEIR ATTITUDES TOWARD APPLYING NEW TECHNOLOGIES IN THE CLASSROOM

The purpose of this study was to examine the influence of the perceptive beliefs of business education teachers on their attitudes toward applying new technologies in the classroom. The theoretical underpinning of the study was the technology acceptance model (TAM) of Davies 1989, which posits that perceived usefulness and perceived ease of use ha ve significant influence on an individual's attitude towards accepting technology. Survey design was used to conduct the study on a sample of 212 business education teachers from 13 tertiary colleges in Northwestern Nigeria. The instrument of data collecti on was a structured questionnaire adopted by the researcher from previously validated studies. Data was analyzed using SPSS 21 for descriptive statistics and AMOS 21 for structural equation modeling. The test of the hypotheses revealed that business educat ion teachers' perception of the usefulness of technology has significant influence on their attitudes towards teaching with new technologies. However, contrary to extant literature, the study revealed that teacher's perception of the ease of use of technol ogy does not have significant influence on their attitudes towards teaching with new technologies. The study concludes that although teachers have positive attitudes towards using new technologies and they are willing to use the technologies very well, the ir perceptions of their abilities for using the technologies is low and they think that new technologies are not easy for them to use. In this regard the study recommends that governing authorities of Nigerian tertiary institutions should reinforce efforts towards training and supporting teachers on the use of new technologies in the classroom.
Collaborative Visual SLAM with Multiple MAVs

The following work has been carried out within sFly , a European project that aims to implement swarms of autonomous Micro Aerial Vehicles (MAVs) collaboratively monitoring a disaster area. We present a framework for solving a core problem in this scenario, namely the collaborative localization and mapping with multiple MAVs in unknown environments. While solving the SLAM problem with multiple robots in parallel promises more efﬁcient and extensive exploration of the environment, it also increases the computational and inter-robot communication load. Similar to [8] and [7], a straightforward solution to the illustrated problem consists of adding a centralized node to the system, which receives combined sensorial information from all swarm members in order to perform multi-robot SLAM. Concentrating all information at a central position allows optimal coordination of the MAVs. However, it also increases the communication load and, thus, reduces the MAVs’ work space. As presented in [11], the alternative consists of performing SLAM on each MAV individually and then share and merge the maps once the helicopters are within the communication range. This reduces the communication load, however, it leads to a substantial increase of the computational demand on the robots. In this paper, we present an intermediate solution to the multi-robot SLAM problem: multiple MAVs continuously stream sparse and preprocessed data to a central ground station where the Collaborative Structure from Motion ( CSfM ) system creates a 3D map of the environment in real-time and tracks the robots’ position in it.
An Approach for Prolonging the Life Time of Wireless Sensor Network

Wireless sensor networks consists of thousands of tiny, low cost, low power and multifunctional sensor nodes where each sensor node has very low battery life. So to increase the network lifetime the routing algorithm must be energy efficient. LEACH uses distributed cluster formation & randomized rotation of the cluster head to minimize the network energy consumption. In this paper we proposed an algorithm which is modified version of LEACH and uses the remaining energy of a node while selecting the cluster head to increase the network lifetime. The resulting algorithm is then simulated on ns-2 under Linux operating system. We observed the significant improvement in the lifetime of the overall network.
Broken Stick Model for Irregular Longitudinal Data

Many longitudinal studies collect data that have irregular observation times, often requiring the application of linear mixed models with time-varying outcomes. This paper presents an alternative that splits the quantitative analysis into two steps. The first step converts irregularly observed data into a set of repeated measures through the broken stick model. The second step estimates the parameters of scientific interest from the repeated measurements at the subject level. The broken stick model approximates each subject’s trajectory by a series of connected straight lines. The breakpoints, specified by the user, divide the time axis into consecutive intervals common to all subjects. Specification of the model requires just three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: Subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The R package brokenstick v2.5. 0 offers tools to calculate, predict, impute and visualize broken stick estimates. The package supports two optimization methods, including options to constrain the variancecovariance matrix of the random effects. We demonstrate six applications of the model: Detection of critical periods, estimation of the time-to-time correlations, profile analysis, curve interpolation, multiple imputation and personalized prediction of future outcomes by curve matching.
Aging and perception of graphic representation: A case of icon design in mobile phone functionality

: Distinctive human attribute such as age, sex, and cultural background created a large number of problems in the understanding of graphic representation. This research is conducted by using pictorial icons in cell phone as a case study to reveal the age and cultural bias between age groups. Most of them are able to operate only the basic features. The study thus has an ultimate goal to discover a universal representation of iconic symbol, which would help enhance the learning ability of senior group in particular, and to improve the usage of other technological oriented equipments in general. We intend to study in three features: complexity of iconic symbols, the visual limitation among the age group, and the interpretation of iconic symbols. The first issue deals with learning new technology’s capability and the recollection. Next, it involves graphic icon visible format -simple, detailed and textual including size and color. Finally, we look at the 2D/3D format, realistic/symbolic, and with/without textual display. From the research, it is found that respondents are proved to be able to learn complicated level as equal as younger users. With reading glasses, they are able to visualize and understand simple icons as small as 5-10 mm, complicated as 30 mm and graphical icons with textual as 10-15 mm. Bright color is for simple icon, and cooler tone is for complicated ones. 2D is for familiar objects while 3D and realistic are for unfamiliar ones. Textual representation could enhance the understanding of representation as well.
Feature Removal Is a Unifying Principle for Model Explanation Methods

Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another. We examine the literature and find that many methods are based on a shared principle of explaining by removing - essentially, measuring the impact of removing sets of features from a model. These methods vary in several respects, so we develop a framework for removal-based explanations that characterizes each method along three dimensions: 1) how the method removes features, 2) what model behavior the method explains, and 3) how the method summarizes each feature's influence. Our framework unifies 25 existing methods, including several of the most widely used approaches (SHAP, LIME, Meaningful Perturbations, permutation tests). Exposing the fundamental similarities between these methods empowers users to reason about which tools to use and suggests promising directions for ongoing research in model explainability.
Myopic Traction Maculopathy: Diagnostic and Management Strategies

Abstract Pathologic myopia (PM) is an ocular disorder characterized by a spherical equivalent (SE) of more than – 6.0 diopters (D) or by an axial length (AL) of more than 26.5 millimeters (mm). PM is associated with myopic maculopathy (MM). The ATN classification describes all the aspects of MM which regroups atrophic, tractional and neovascular consequences to the sclera, choroid and retina of highly myopic eyes. The advent of OCT allowed to define the ultrastructural characteristics of the tractional changes in MM, described by the term myopic traction maculopathy (MTM). They include foveoschisis/maculoschisis/retinoschisis (FS/MS/RS), retinal/foveal detachment (RD/FD), lamellar macular holes (LMH) and full-thickness macular holes (FTMH) with or without RD (MHRD). The MTM staging system (MSS) describes all foveal and retinal changes related to MTM and their natural history interpreting them as different stages of a single progressive disorder. The management of MTM can be just observation for the earliest cases with good vision or surgery for the severe stages with vision loss. There are two possible surgical approaches: ab externo, that acts on the alteration of the scleral shape and includes posterior scleral reinforcement and macular buckle. Ab interno, that targets the alteration of the foveal profile and consists in pars plana vitrectomy with removal of all the epiretinal tractions, maneuvers on the internal limiting membrane, and the use of intravitreal tamponade and laser. As they target two different sides of the same pathology, the two techniques have to be selected on the base of the MTM stage, single or combined.
Fast Hybrid Cascade for Voxel-based 3D Object Classification

Voxel-based 3D object classification has been frequently studied in recent years. The previous methods often directly convert the classic 2D convolution into a 3D form applied to an object with binary voxel representation. In this paper, we investigate the reason why binary voxel representation is not very suitable for 3D convolution and how to simultaneously improve the performance both in accuracy and speed. We show that by giving each voxel a signed distance value, the accuracy will gain about 30% promotion compared with binary voxel representation using a two-layer fully connected network. We then propose a fast fully connected and convolution hybrid cascade network for voxel-based 3D object classification. This threestage cascade network can divide 3D models into three categories: easy, moderate and hard. Consequently, the mean inference time (0.3ms) can speedup about 5x and 2x compared with the state-of-the-art point cloud and voxel based methods respectively, while achieving the highest accuracy in the latter category of methods (92%). Experiments with ModelNet andMNIST verify the performance of the proposed hybrid cascade network.
Risk Assessment for Machine Learning Models

In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment. For that we carry over the risk definition from decision theory to machine learning. We develop and implement a method that allows to define deployment scenarios, test the machine learning model under the conditions specified in each scenario, and estimate the damage associated with the output of the machine learning model under test. Using the likelihood of each scenario together with the estimated damage we define \emph{key risk indicators} of a machine learning model. The definition of scenarios and weighting by their likelihood allows for standardized risk assessment in machine learning throughout multiple domains of application. In particular, in our framework, the robustness of a machine learning model to random input corruptions, distributional shifts caused by a changing environment, and adversarial perturbations can be assessed.
Optimal tiling of the Euclidean space using symmetric bodies

What is the least surface area of a symmetric body $B$ whose $\mathbb{Z}^n$ translations tile $\mathbb{R}^n$? Since any such body must have volume $1$, the isoperimetric inequality implies that its surface area must be at least $\Omega(\sqrt{n})$. Remarkably, Kindler et al.\ showed that for general bodies $B$ this is tight, i.e.\ that there is a tiling body of $\mathbb{R}^n$ whose surface area is $O(\sqrt{n})$. In theoretical computer science, the tiling problem is intimately to the study of parallel repetition theorems (which are an important component in PCPs), and more specifically in the question of whether a "strong version" of the parallel repetition theorem holds. Raz showed, using the odd cycle game, that strong parallel repetition fails in general, and subsequently these ideas were used in order to construct non-trivial tilings of $\mathbb{R}^n$. In this paper, motivated by the study of a symmetric parallel repetition, we consider the symmetric variant of the tiling problem in $\mathbb{R}^n$. We show that any symmetric body that tiles $\mathbb{R}^n$ must have surface area at least $\Omega(n/\sqrt{\log n})$, and that this bound is tight, i.e.\ that there is a symmetric tiling body of $\mathbb{R}^n$ with surface area $O(n/\sqrt{\log n})$. We also give matching bounds for the value of the symmetric parallel repetition of Raz's odd cycle game. Our result suggests that while strong parallel repetition fails in general, there may be important special cases where it still applies.