Miracle-dz/UniFAR-FTUs huggingface dataset:
Invertebrate food supply and reproductive success of two native forest passerines along an elevational gradient

: Predation by mammals has been identified as the primary limiting factor of Aotearoa New Zealand native birds. Consequently, the ranges of many native forest bird species have contracted to cooler and higher elevation tracts of forest that support fewer introduced mammals. However, lower elevation forests are likely to be intrinsically more productive and able to sustain larger bird populations if control of mammalian pests removes predation as a primary limiting factor. We wanted to determine whether higher elevation forests provide less food for rat-sensitive, sedentary native insectivorous bird species, resulting in their reduced reproductive potential at higher elevations. In spring and summer 2020/21, we sampled invertebrate prey while simultaneously monitoring nest survival and number of fledglings produced by tītitipounamu / rifleman ( Acanthisitta chloris ) and miromiro / tomtit ( Petroica macrocephala ) across three elevational bands on Mount Pirongia, where mammal predators were suppressed. Tracking and camera indices together indicated that introduced mammals were at low relative abundance at all elevations, allowing us to investigate other habitat effects. Biomass of ground-dwelling invertebrates decreased with increasing elevation during the bird breeding season, but was similar across elevational bands later in the season. In contrast, biomass of flying or folivore invertebrates was independent of elevation and only showed clear seasonal trends. In both tītitipounamu and miromiro, nest survival rates marginally decreased with increasing available invertebrate prey, while the number of fledglings of successful nests marginally increased. There was no strong relationship between elevation and nest survival or number of fledglings. These results indicate no clear trend in the reproductive potential of tītitipounamu and miromiro with elevation but do suggest that food supply needs to be considered as an aspect of habitat quality that is related to reproductive success.
Isotropic transmission-line antenna and its toroid-pattern modification

The far-field strength of the \lambda/4 transmission-line antenna is derived as a complex vector containing amplitude, phase, and direction of the E vector. The product of this phasor-vector and its conjugate is constant and proves that the transmission-line antenna is an isotropic radiator with respect to the powerflow density of coherent electromagnetic waves. Folding of the line conductor parallel to the symmetry plane reduces the pattern to toroid shape analogous to radiation of a short dipole. While the unbalanced straight transmission-line antenna is used on rockets and vehicles, the unbalanced folded type is suitable as efficient transmit antenna at VLF, LF, or MF.
Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces

Convolutional neural networks (CNNs) have shown great potential in the field of brain–computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram (EEG) signals without artificial feature extraction. Some CNNs have achieved better classification accuracy than that of traditional methods. Raw EEG signals are usually represented as a two-dimensional (2-D) matrix composed of channels and time points, ignoring the spatial topological information of electrodes. Our goal is to make a CNN that takes raw EEG signals as inputs have the ability to learn spatial topological features and improve its classification performance while basically maintaining its original structure. We propose an EEG topographic representation module (TRM). This module consists of (1) a mapping block from raw EEG signals to a 3-D topographic map and (2) a convolution block from the topographic map to an output with the same size as the input. According to the size of the convolutional kernel used in the convolution block, we design two types of TRMs, namely TRM-(5,5) and TRM-(3,3). We embed the two TRM types into three widely used CNNs (ShallowConvNet, DeepConvNet and EEGNet) and test them on two publicly available datasets (the Emergency Braking During Simulated Driving Dataset (EBDSDD) and the High Gamma Dataset (HGD)). Results show that the classification accuracies of all three CNNs are improved on both datasets after using the TRMs. With TRM-(5,5), the average classification accuracies of DeepConvNet, EEGNet and ShallowConvNet are improved by 6.54%, 1.72% and 2.07% on the EBDSDD and by 6.05%, 3.02% and 5.14% on the HGD, respectively; with TRM-(3,3), they are improved by 7.76%, 1.71% and 2.17% on the EBDSDD and by 7.61%, 5.06% and 6.28% on the HGD, respectively. We improve the classification performance of three CNNs on both datasets through the use of TRMs, indicating that they have the capability to mine spatial topological EEG information. More importantly, since the output of a TRM has the same size as the input, CNNs with raw EEG signals as inputs can use this module without changing their original structures.
Untangling the Effects of Plant Genotype and Soil Conditions on the Assembly of Bacterial and Fungal Communities in the Rhizosphere of the Wild Andean Blueberry (Vaccinium floribundum Kunth)

Microbial communities in the rhizosphere influence nutrient acquisition and stress tolerance. How abiotic and biotic factors impact the plant microbiome in the wild has not been thoroughly addressed. We studied how plant genotype and soil affect the rhizosphere microbiome of Vaccinium floribundum, an endemic species of the Andean region that has not been domesticated or cultivated. Using high-throughput sequencing of the 16S rRNA and ITS region, we characterized 39 rhizosphere samples of V. floribundum from four plant genetic clusters in two soil regions from the Ecuadorian Highlands. Our results showed that Proteobacteria and Acidobacteria were the most abundant bacterial phyla and that fungal communities were not dominated by any specific taxa. Soil region was the main predictor for bacterial alpha diversity, phosphorous and lead being the most interesting edaphic factors explaining this diversity. The interaction of plant genotype and altitude was the most significant factor associated with fungal diversity. This study highlights how different factors govern the assembly of the rhizosphere microbiome of a wild plant. Bacterial communities depend more on the soil and its mineral content, while plant genetics influence the fungal community makeup. Our work illustrates plant–microbe associations and the drivers of their variation in a unique unexplored ecosystem from the Ecuadorian Andes.
Multiparameter bifurcation analysis of the south Brazilian power system

This paper presents a detailed bifurcation analysis of real multiparameter power systems. Equilibrium points are used to evaluate the system eigenvalues, obtain different bifurcation diagrams, and evaluate the stability for two systems (i.e., a simple theoretical system to illustrate the basic concepts and highlight the main issues) and the southern section of the Brazilian network to demonstrate how these ideas can be applied to a real power system. The paper studies the influence of soft and hard-limits and automatic voltage regulators (AVR) droop on the systems, using detailed generator models. The stability regions and the effect that various parameters and bifurcations have on them are studied in detail.
No Reference Opinion Unaware Quality Assessment of Authentically Distorted Images

The quality assessment (QA) of camera captured authentically distorted images is important on account of its ubiquitous applications and challenging due to the lack of a reference. While there exists a plethora of supervised no reference (NR) image QA (IQA) algorithms, there is a need to study unsupervised or opinion unaware algorithms on account of their superior generalization performance. We explore self-supervised learning (SSL) for the feature design on authentically distorted images to predict quality without training on human labels. While SSL on synthetic distortions has recently shown promise, there is a need to enrich the feature learning on authentic distortions. The key challenge in achieving this is in the learning of quality sensitive features with mitigated content dependence. We design a self-supervised contrastive learning approach which only requires positives and introduce a content separation loss by estimating a bound on the mutual information between the features learnt and the content information. We show on multiple authentically distorted datasets that our self-supervised features can predict image quality by comparing with a corpus of pristine images and achieve state-of-the-art performance. §
Optimization strategies for GPUs: an overview of architectural approaches

Modern Cyber Physical Systems (CPS) applications require hardware capable of optimized performance-per-watt efficency. This is usually obtained through massively parallel accelerators such as the GPU. Recent research is therefore investigating novel designs to optimize GPU energy consumption and performance for various applications in the Internet-of-things, autonomous navigation, and industrial robotics domains. This paper presents a survey of the current state-of-the-art approaches for optimizing GPU performance metrics; we present a complete and up-to-date summary of ideas, mechanisms, and potential improvements for next-generation GPU devices. GRAPHICAL ABSTRACT
Do Sleep-Related Metacognitive Strategies Shape My Sleep? The Relationships between Strategies for Controlling Sleep-Related Intrusive Thoughts and Subjective and Objective Sleep Quality in Young Adulthood and Older Age

This study examined the associations between thought control strategies and subjective and objective sleep quality, across the adult lifespan. One hundred forty-nine individuals without insomnia (age range 18–86 years; M = 45.35, SD = 20.53) completed the Thought Control Questionnaire Insomnia–Revised for assessing sleep-related thought control strategies. Self-reported sleep quality was measured with the Pittsburgh Sleep Quality Index. Then, subjective and objective sleep parameters (i.e., total sleep time, sleep onset latency, sleep efficiency) were recorded through a sleep diary and an actigraph across 7 days. Results from linear mixed-effects models showed that a worry strategy was associated with longer subjective sleep latency and shorter subjective total sleeping time. An aggressive suppression strategy was associated with longer subjective total sleeping time. No such involvement of thought control strategies was detected for subjective sleep efficiency and all of the objective sleep parameters. Other individual differences (i.e., age, sex, circadian preference, self-reported sleep quality) also explained both subjective and objective sleep parameters, though to a different extent depending on the sleep parameter considered. The assessment of sleep-related thought control strategies, along with other individual characteristics, should be considered to account for individual differences in sleep quality and implement practices/interventions to support it in adulthood and older age.
General relativistic magnetohydrodynamical simulations of the jet in M 87

(abridged) The connection between black hole, accretion disk, and radio jet can be best constrained by fitting models to observations of nearby low luminosity galactic nuclei, in particular the well studied sources Sgr~A* and M87. There has been considerable progress in modeling the central engine of active galactic nuclei by an accreting supermassive black hole coupled to a relativistic plasma jet. However, can a single model be applied to a range of black hole masses and accretion rates? Here we want to compare the latest three-dimensional numerical model, originally developed for Sgr A* in the center of the Milky Way, to radio observations of the much more powerful and more massive black hole in M87. We postprocess three-dimensional GRMHD models of a jet-producing radiatively inefficient accretion flow around a spinning black hole using relativistic radiative transfer and ray-tracing to produce model spectra and images. As a key new ingredient to these models, we allow the proton-electron coupling in these simulations depend on the magnetic properties of the plasma. We find that the radio emission in M87 is well described by a combination of a two-temperature accretion flow and a hot single-temperature jet. The model fits the basic observed characteristics of the M87 radio core. The best fit model has a mass-accretion rate of Mdot approx 9x10^{-3} MSUN/YR and a total jet power of P_j \sim 10^{43} erg/s. Emission at 1.3mm is produced by the counter jet close to the event horizon. Its characteristic crescent shape surrounding the black hole shadow could be resolved by future millimeter-wave VLBI experiments. The model was successfully derived from one for the supermassive black hole in center of the Milky Way by appropriately scaling mass and accretion rate. This suggests the possibility that this model could also apply to a larger range of low-luminosity black holes.
Rossby Wave Phase Speeds and Mixing Barriers in the Stratosphere. Part I: Observations

Abstract Lagrangian trajectories are used to calculate isentropic mixing properties for unfiltered and filtered Southern Hemisphere stratospheric winds. In wintertime significant mixing is confined to the surf zone between the Tropics and the edge of the polar vortex. The mixing barrier at the edge of the vortex is located near the core of the polar jet stream (where the maximum wind speeds occur), which is also approximately where the meridional potential vorticity gradient is largest. In summer there is significant mixing throughout the hemisphere, and no high-latitude mixing barrier exists. When the winds are filtered by zonal wavenumber to retain either the planetary-scale waves (1–3) or the smaller-scale waves (4–12), mixing in the surf zone is generally reduced but not eliminated. When the winds are filtered by phase speed, however, mixing is significantly reduced in restricted latitude zones where the phase speeds of the filtered waves are close to the speed of the local zonal-mean zonal wind. Thes.
Diversity of Mycoviruses Present in Strains of Binucleate Rhizoctonia and Multinucleate Rhizoctonia, Causal Agents for Potato Stem Canker or Black Scurf

In this study, the diversity of putative mycoviruses present in 66 strains of binucleate Rhizoctonia (BNR, including anastomosis group (AG)-A, AG-Fa, AG-K, and AG-W) and 192 strains of multinucleate Rhizoctonia (MNR, including AG-1-IA, AG-2-1, AG-3 PT, AG-4HGI, AG-4HGII, AG-4HGIII, and AG-5), which are the causal agents of potato stem canker or black scurf, was studied using metatranscriptome sequencing. The number of contigs related to mycoviruses identified from BNR and MNR was 173 and 485, respectively. On average, each strain of BNR accommodated 2.62 putative mycoviruses, while each strain of MNR accommodated 2.53 putative mycoviruses. Putative mycoviruses detected in both BNR and MNR contained positive single-stranded RNA (+ssRNA), double-stranded RNA (dsRNA), and negative single-stranded RNA (-ssRNA) genomes, with +ssRNA genome being the prevalent nucleic acid type (82.08% in BNR and 75.46% in MNR). Except for 3 unclassified, 170 putative mycoviruses found in BNR belonged to 13 families; excluding 33 unclassified, 452 putative mycoviruses found in MNR belonged to 19 families. Through genome organization, multiple alignments, and phylogenetic analyses, 4 new parititviruses, 39 novel mitoviruses, and 4 new hypoviruses with nearly whole genome were detected in the 258 strains of BNR and MNR.
An inverse problem for general minimal surfaces

In this paper we consider an inverse problem of determining a minimal surface embedded in a Riemannian manifold. We show under a topological condition that if $\Sigma$ is a $2$-dimensional embedded minimal surface, then the knowledge of the Dirichlet-to-Neumann map associated to the minimal surface equation determines $\Sigma$ up to an isometry. Without the topological condition, we show that a conformal factor of a general minimal surface $\Sigma$ can be recovered. We develop a semiclassical nonlinear calculus for complex geometric optics solutions, which allows an efficient error analysis for multiplication of the correction terms of the solutions. The calculus is independent of the application to the minimal surface equation and we expect it to have applications in various inverse problems for nonlinear equations in dimension $2$, in both $\mathbb{R}^2$ and geometric settings. Other applications of the results include generalized boundary rigidity problem and the AdS/CFT correspondence in physics.
The Effect of a Nurse-Led Family Involvement Program on Anxiety and Depression in Patients with Advanced-Stage Hepatocellular Carcinoma

Psychological distress is commonly reported in patients with advanced cancer. Family is considered a psychological supporter for patients during their cancer journey. This study aimed to examine the effect of a nurse-led family involvement program on anxiety and depression in patients with advanced hepatocellular cancer. This is a quasi-experimental study with a two-group, pre–post-test design. Forty-eight participants were recruited at a male medical ward in a university hospital in Southern Thailand, and assigned to either the experimental or the control group. The experimental group received the nurse-led family involvement program, while the control group received only conventional care. Instruments included a demographic data form, clinical data form, and the Hospital Anxiety and Depression Scale. Data analyses were performed using descriptive statistics, chi-square, Fisher’s exact test, and t-test. The results revealed that the mean scores of anxiety and depression in the experimental group at post-test were significantly lower than on the pretest and significantly lower than those of the control group. The results indicate that a nurse-led family involvement program has a short-term effect on the reduction of anxiety and depression in male patients with advanced HCC. The program can be useful for nurses to encourage family caregivers to engage in patient care during hospitalization.
Prediction of Clinical Outcomes with Explainable Artificial Intelligence in Patients with Chronic Lymphocytic Leukemia

Background: The International Prognostic Index (IPI) is applied to predict the outcome of chronic lymphocytic leukemia (CLL) with five prognostic factors, including genetic analysis. We investigated whether multiparameter flow cytometry (MPFC) data of CLL samples could predict the outcome by methods of explainable artificial intelligence (XAI). Further, XAI should explain the results based on distinctive cell populations in MPFC dot plots. Methods: We analyzed MPFC data from the peripheral blood of 157 patients with CLL. The ALPODS XAI algorithm was used to identify cell populations that were predictive of inferior outcomes (death, failure of first-line treatment). The diagnostic ability of each XAI population was evaluated with receiver operating characteristic (ROC) curves. Results: ALPODS defined 17 populations with higher ability than the CLL-IPI to classify clinical outcomes (ROC: area under curve (AUC) 0.95 vs. 0.78). The best single classifier was an XAI population consisting of CD4+ T cells (AUC 0.78; 95% CI 0.70–0.86; p < 0.0001). Patients with low CD4+ T cells had an inferior outcome. The addition of the CD4+ T-cell population enhanced the predictive ability of the CLL-IPI (AUC 0.83; 95% CI 0.77–0.90; p < 0.0001). Conclusions: The ALPODS XAI algorithm detected highly predictive cell populations in CLL that may be able to refine conventional prognostic scores such as IPI.
A Review of: “Randomized Clinical Trials: Design, Practice and Reporting, by D. Machin and P. M. Fayers”

Randomized Clinical Trials is a new text by David Machin and Peter M. Fayers that is ambitious in its intentions due to a broad scope, not only in topics but in its intended audience. There are 14 chapters that cover everything from historical references of testing therapeutic interventions to data collection to advanced statistical methods like adaptive trial designs and meta-analysis. The authors’ intended audience is virtually anyone who might have any role in planning, conducting, overseeing, and/or reporting results from a clinical trial, which covers an extremely broad range of functions and expertise. To that end, the authors provide a plethora of examples from previously conducted clinical trials that support the discussions throughout the text. Chapter 1 provides an introduction to clinical trial design that includes a number of summary examples of completed trials. The examples are helpful, though it becomes apparent throughout the text (see, for example, section 4.2.2) that the authors seem to be content to transcribe details of some of these examples without trying to better understand/explain the disease condition, endpoints, etc., which would make the examples more useful by providing the reader with a better context. The authors provide a nice overview of the levels of evidence that different types of trial data provide, pointing to the double-blind, randomized controlled trial (RCT) as the gold standard. This is done well via a historical review, with an appropriate level of emphasis on the need for randomization in order to conduct experiments that derive clinical evidence of efficacy. I found the regulatory overview lacking in that it omitted any reference to disease-specific guidance documents, or descriptions of regulatory expectations around, for example, dose selection and replication. Chapter 2 covers design features, such as trial objectives and hypotheses, patient populations and analysis populations, and treatment arms, as well as a brief summary of a linear model in a basic two-treatment parallel-group design. A nice addition in this chapter is the discussion of trial publication and the relevance of the Consolidated Standards of Reporting Trials (CONSORT) guidelines for reporting. Chapter 3 describes the clinical trial protocol, and the essential features necessary for documentation of the trial to be conducted. Here again, examples are used throughout, which is useful in particular to the untrained participant in the execution of clinical trials. One section of concern is 3.10, in which the role of the statistical team is described. In this section, the primary role of the statistician is noted to be sample size determination as well as analytical approaches. This ignores major contributions of statisticians in the clinical trial process, such as co-authoring