Miracle-dz/UniFAR-FTUs huggingface dataset:
ISOLATION OF MITOCHONDRIAL CYTOCHROME B GENE AND DEVELOPMENT OF A REAL-TIME QUANTITATIVE PCR ASSAY FOR DETECTING NEOPARAMOEBA AESTUARINA

Abstract Some Neoparamoeba strains are pathogens of fish and invertebrates and were implicated in lobster mortality in Long Island Sound in recent years. To better understand the dynamics of these pathogens and their potential linkages to the mortality of marine animals, the capability to specifically detect and accurately quantify these parasites in the environment and in affected organisms is essential. Molecular markers such as mitochondrial cytochrome b (cob) can be powerful tools. In this study, we isolated cob from N. aestuarina and developed species-specific primers. Sequence analysis showed that this gene was A/T rich. Variable regions were identified and used to develop a species-specific primer set for real-time quantitative PCR (RTQ-PCR). This primer set was demonstrated to be specific (with no cross-reaction to N. pemaquidensis and other protists) and sensitive (with a detection limit of 1 cell/30 mL). Further, the quantitative capability of the RTQ-PCR system was verified by a strong correlation between the threshold cycle number and the logarithmic-transformed cob copy number used in the amplification. Using the established RTQ-PCR assay, we estimated that each N. aestuarina cell contained approximately 707 ± 63 copies of cob. This assay was applied to Lugol-fixed water samples collected from Long Island Sound during August 2002 to September 2003, which showed no detectable N. aestuarina. The cob primer set developed will be useful for future environmental detection of N. aestuarina and provides a basis from which cob gene probes for other Neoparamoeba and Paramoeba species can be developed.
Eastern Box Turtle (Terrapene Carolina Carolina) Movements in a Fragmented Landscape

Abstract In the mid-Atlantic region, agriculture and development have resulted in habitat fragmentation; however, the effect on Eastern Box Turtle (Terrapene carolina carolina) movement remains undetermined. Understanding how box turtles move in fragmented landscapes can be used to elucidate the impacts of forest fragmentation on box turtle ecology. We chose four study areas that differed in degree of fragmentation ranging from isolated forest fragments to an interior forest to investigate the effect of sex, season, and study area on turtle movements. We randomly selected five radio-tagged turtles per week per study area and attached thread trailers to measure their daily movements. Sex×season×study area and season×study area did not interact to affect turtle movements. However, sex×study area and sex×season interacted to affect turtle movements. Box Turtles in isolated areas moved less than those in more continuous habitat, and turtle movements varied among seasons. Differences in movement may be related to sex-specific life history traits such as mate searching in males and nesting followed by energy conservation in females.
Resources and Readiness: the graduate capital perspective as a new approach to graduate employability

This article presents a new perspective on graduate employability based on a graduate capital model which emphasises the significant role that various forms of capital can play in the development of their employability. We define these forms of capital here as key resources that potentially empower graduates and equip them for managing the transition from HE to the labour market. The article explores the various forms of capital which make up the model and illustrates why and how these are applicable for graduates. We finally consider the practical potential of this approach and how practitioners might be able to work with it in order to help students and graduates during and beyond higher education.
Centrifuge Modelling of Tunnelling beneath Axially Loaded Displacement and Non-Displacement Piles in Sand

Tunnelling under piled structures is becoming more common in urban areas. However, there is limited guidance available for the prediction of settlements and the loss of bearing capacity of existing piles due to tunnel excavation. This paper aims to provide an improved understanding of the response to tunnelling of axially loaded displacement and non-displacement piles. Data are provided from a series of geotechnical centrifuge tests of tunnel excavation beneath single piles in dry silica sand. The tests evaluate induced settlements of the piles at varying levels of initial safety factor (i.e. the ratio between initial bearing capacity and applied load). Furthermore, a previously published analytical approach, based on cavity expansion theory, is used to investigate the variation of the residual safety factor at constant pile load with tunnel volume loss. The outcomes of both centrifuge and analytical investigations illustrate the importance of pile installation method and initial safety factor and improve understanding of tunnel-pile interaction mechanisms.
Transposable Elements in Neural Progenitor Cells

More than 90% of DNA does not code for proteins and for a long time these sequences were referred to as “junk DNA” due to their unknown purpose. With the advent of new technologies it is now known, that the non-coding part of the genome is of great importance for regulating gene expression and is therefore indispensable. Transposable elements comprise about 50% of the genome and co-exist as symbionts regulated by epigenetic mechanisms - a highly defined machinery that controls gene expression and is mandatory for a proper development and maintenance of an organism. Although transposable elements are associated with diseases, their role in fine-tuning the host gene expression becomes more and more evident, which seems to justify the positive selection during evolution. A transposable element called Line-1 was found to be active in neural progenitor cells and in the brain. Several studies report Line-1 transcription and frequent retrotransposition during normal brain development, with further evidence that Line-1 induced retrotransposition can influence neuronal gene expression. Today, there is few published data focusing on epigenetic regulation of transposable elements in neural progenitor cells. In this thesis, I identify TRIM28 as key regulator of certain groups of transposable elements in mouse and human neural progenitor cells. This feature is unique compared to other somatic tissues, where DNA-methylation is prevalent. Here I demonstrate, that transposable elements MMERVK10C and IAP1 in mouse neural progenitor cells are repressed by the establishment of H3K9me3-associated heterochromatin. De-repressed MMERVK10C and IAP1 furthermore activate nearby genes and generate long non-coding RNAs. Homozygous TRIM28 knockout is lethal, while mice with mono-allelic TRIM28 expression are characterised with a distinct behavioural phenotype. Moreover we are also able to show that TRIM28 is regulating a fraction of young Alu-elements in human neural progenitor cells, which is not the case in human embryonic stem cells. Furthermore, we report that transcribed Alu-elements influence gene expression of close-by genes. Studying pluripotent cells revealed that TRIM28 modulates transposable elements in mouse embryonic stem cells. Activation of transposable elements upon TRIM28 depletion induces changes in gene expression of close-by genes and causes alteration of the repressive chromatin mark H3K9me3 at transposable element loci. Upregulated genes were shown to have bivalent promoters, characterised by H3K4me3 and H3K27me3 and lay close to H3K9me3 regulated transposable elements. These findings in mouse embryonic stem cells are highly relevant for the interpretation of my studies in neural progenitor cells. Taken together, this thesis demonstrates that the regulation of transposable elements in mouse and human neural progenitor cells is distinct compared to previous reports regarding somatic tissues. These results provide novel insights into why the brain has developed into such a complex organ with so many different cell types.
Coarse detritus in oligotrophic lake littoral zones : utilization by intervertebrates and contribution to carbon flow

Syftet med denna avhandling ar att oka forstaelsen av hur grovt organiskt material, sk grovdetritus, tex lov och makrofytrester, bryts ner i sjoars strandzoner. Motivet bakom intresset for detta ar .
Resource Aware Multifidelity Active Learning for Efficient Optimization

Traditional methods for black box optimization require a considerable number of evaluations which can be time consuming, unpractical, and often unfeasible for many engineering applications that rely on accurate representations and expensive models to evaluate. Bayesian Optimization (BO) methods search for the global optimum by progressively (actively) learning a surrogate model of the objective function along the search path. Bayesian optimization can be accelerated through multifidelity approaches which leverage multiple black-box approximations of the objective functions that can be computationally cheaper to evaluate, but still provide relevant information to the search task. Further computational benefits are offered by the availability of parallel and distributed computing architectures whose optimal usage is an open opportunity within the context of active learning. This paper introduces the Resource Aware Active Learning (RAAL) strategy, a multifidelity Bayesian scheme to accelerate the optimization of black box functions. At each optimization step, the RAAL procedure computes the set of best sample locations and the associated fidelity sources that maximize the information gain to acquire during the parallel/distributed evaluation of the objective function, while accounting for the limited computational budget. The scheme is demonstrated for a variety of benchmark problems and results are discussed for both single fidelity and multifidelity settings. In particular we observe that the RAAL strategy optimally seeds multiple points at each iteration allowing for a major speed up of the optimization task.
NOMA-Enabled Multi-Beam Satellite Systems: Joint Optimization to Overcome Offered-Requested Data Mismatches

Non-orthogonal multiple access (NOMA) has potentials to improve the performance of multi-beam satellite systems. The performance optimization in satellite-NOMA systems could be different from that in terrestrial-NOMA systems, e.g., considering distinctive channel models, performance metrics, power constraints, and limited flexibility in resource management. In this paper, we adopt a metric, offered capacity to requested traffic ratio (OCTR), to measure the requested-offered data rate mismatch in multi-beam satellite systems. In the considered system, NOMA is applied to mitigate intra-beam interference while precoding is implemented to reduce inter-beam interference. We jointly optimize power, decoding orders, and terminal-timeslot assignment to improve the max-min fairness of OCTR. The problem is inherently difficult due to the presence of combinatorial and non-convex aspects. We first fix the terminal-timeslot assignment, and develop an optimal fast-convergence algorithmic framework based on Perron-Frobenius theory (PF) for the remaining joint power-allocation and decoding-order optimization problem. Under this framework, we propose a heuristic algorithm for the original problem, which iteratively updates the terminal-timeslot assignment and improves the overall OCTR performance. Numerical results show that the proposed algorithm improves the max-min OCTR by 40.2% over orthogonal multiple access (OMA) in average.
Commonly Intercepted Thrips at U.S. Ports-of-Entry from Africa, Europe, and the Mediterranean. III. The Genus Thrips Linnaeus, 1758 (Thysanoptera: Thripidae)

Abstract A total of 130 species of thrips occurring in Africa, Europe, and the Mediterranean region were intercepted by U.S. agricultural quarantine officers in shipments of plants and cut flowers at the various ports-of-entry in the United States from 1983 to 1999. Of the 24 most intercepted species of thrips encountered by port identifiers during this period, 10 of them were species of the genus Thrips Linnaeus, 1758 (T. tabaci Lindeman, T. fuscipennis (Haliday), T. major (Uzel), T. vulgatissimus (Haliday), T. meridionalis (Priesner), T. flavus Schrank, T. atratus (Haliday), T. simplex (Morison), T. nigropilosus Haliday, and T. australis (Bagnall)). This is Part III of a guide to the identification of thrips intercepted from those regions. Included are keys, line drawings, and scanning electron micrographs to help identify the 18 commonly intercepted species of the genus Thrips, characterized by the presence of ctenidia located posteriad of abdominal spiracle VIII.
ISOLASI DAN IDENTIFIKASI VIRUS AVIAN INFLUENZA SUBTIPE H5N1 DI PETERNAKAN TRADISIONAL KECAMATAN GUNUNGPATI SEMARANG

Avian Influenza (AI) atau yang lebih dikenal dengan flu burung disebabkan oleh virus influenza yang bermutasi menjadi patogen. Penelitian tentang isolasi dan identifikasi virus AI subtipe H5N1 perlu dilakukan untuk mengetahui keberadaan virus tersebut khususnya di kecamatan Gunungpati. Desain penelitian adalah eks ploratif dengan pengumpulan sampel usap kloaka secara acak di lima kelurahan di kecamatan Gunungpati. Sampel usap kloaka ditumbuhkan pada telur ayam berembrio SPF, kemudian diisolasi RNA-nya dilanjutkan dengan identifikasi subtipe virus AI menggunakan Reverse Transcriptase-Polymerase Chain Reaction (RT–PCR) dengan primer pendeteksi gen H5 dan N1. Hasil positif apabila visualisasi hasil elektroforesis dari produk PCR menunjukkan pita-pita spesifik panjang 219 bp untuk H5 dan 131 bp untuk gen N1-nya. Limapuluh sampel usap kloaka yang diisolasi dari lima kelurahan di Gunungpati, delapan isolat positif VAI dan enam diantaranya positif H5N1 dengan angka prevalensi 12%. Isolat positif berasal dari 2 spesies itik (16,67%), 2 dari entok (11,76%) dan 2 dari angsa (18,18%). Dari lima kelurahan yang diambil sampelnya, tiga kelurahan ditemukan positif virus H5N1 masing-masing kelurahan Sekaran (6,67%), Kalisegoro (16,67%) dan Pakintelan (15,78%). Unggas-unggas air di peternakan unggas tradisional berpotensi sebagai penularan virus AI, khususnya subtipe H5N1. Avian Influenza (AI) or better known as bird flu is caused by influenza viruses that mutate into a pathogen. Research on the isolation and the identification of H5N1 subtype needed to be carried out to determine the presence of the virus, particularly in the subdistrict of Gunungpati. The study design was explorative by collecting cloacal swab samples randomly from five villages in Gunungpati. The cloacal swab samples were cultured in embryonated SPF chicken eggs, then the RNA was isolated and followed by the identification of AI virus subtype using Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) with H5 and N1 gene detecting primers. A positive result is obtained if the visualization of the electrophoresis of PCR products showed bands with specific length of 219 bp for H5 gene and 131 bp for N1 gene. Fifty cloacal swab samples were isolated from five villages in Gunungpati, and eight of them were positive isolates and six of them were H5N1 positive with the prevalence rate of 12%. The positive isolates were derived from two species of duck (16,67%), 2 from wild duck (11,76%) and 2 from geese (18,18%). Of the five sampled villages, three villages were found to be H5N1 positive, i.e. Sekaran village (6,67%), Kalisegoro village (16,67%) and Pakintelan village (15,78%). Water birds in traditional poultry farming were considered potential as the transmitters of AI virus, particularly of H5N1 subtype.
Learning Differential Equations that are Easy to Solve

Differential equations parameterized by neural networks become expensive to solve numerically as training progresses. We propose a remedy that encourages learned dynamics to be easier to solve. Specifically, we introduce a differentiable surrogate for the time cost of standard numerical solvers, using higher-order derivatives of solution trajectories. These derivatives are efficient to compute with Taylor-mode automatic differentiation. Optimizing this additional objective trades model performance against the time cost of solving the learned dynamics. We demonstrate our approach by training substantially faster, while nearly as accurate, models in supervised classification, density estimation, and time-series modelling tasks.
An Efficient Updation Approach for Enumerating Maximal $(\Delta, \gamma)$\mbox{-}Cliques of a Temporal Network

Given a temporal network $\mathcal{G}(\mathcal{V}, \mathcal{E}, \mathcal{T})$, $(\mathcal{X},[t_a,t_b])$ (where $\mathcal{X} \subseteq \mathcal{V}(\mathcal{G})$ and $[t_a,t_b] \subseteq \mathcal{T}$) is said to be a $(\Delta, \gamma)$\mbox{-}clique of $\mathcal{G}$, if for every pair of vertices in $\mathcal{X}$, there must exist at least $\gamma$ links in each $\Delta$ duration within the time interval $[t_a,t_b]$. Enumerating such maximal cliques is an important problem in temporal network analysis, as it reveals contact pattern among the nodes of $\mathcal{G}$. In this paper, we study the maximal $(\Delta, \gamma)$\mbox{-}clique enumeration problem in online setting; i.e.; the entire link set of the network is not known in advance, and the links are coming as a batch in an iterative manner. Suppose, the link set till time stamp $T_{1}$ (i.e., $\mathcal{E}^{T_{1}}$), and its corresponding $(\Delta, \gamma)$-clique set are known. In the next batch (till time $T_{2}$), a new set of links (denoted as $\mathcal{E}^{(T_1,T_2]}$) is arrived.
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

While deep learning methods continue to improve in predictive accuracy on a wide range of application domains, significant issues remain with other aspects of their performance including their ability to quantify uncertainty and their robustness. Recent advances in approximate Bayesian inference hold significant promise for addressing these concerns, but the computational scalability of these methods can be problematic when applied to large-scale models. In this paper, we describe initial work on the development ofURSABench(the Uncertainty, Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of bench-marking tools for comprehensive assessment of approximate Bayesian inference methods with a focus on deep learning-based classification tasks
DeepSinger: Singing Voice Synthesis with Data Mined From the Web

In this paper, we develop DeepSinger, a multi-lingual multi-singer singing voice synthesis (SVS) system, which is built from scratch using singing training data mined from music websites. The pipeline of DeepSinger consists of several steps, including data crawling, singing and accompaniment separation, lyrics-to-singing alignment, data filtration, and singing modeling. Specifically, we design a lyrics-to-singing alignment model to automatically extract the duration of each phoneme in lyrics starting from coarse-grained sentence level to fine-grained phoneme level, and further design a multi-lingual multi-singer singing model based on a feed-forward Transformer to directly generate linear-spectrograms from lyrics, and synthesize voices using Griffn-Lim. DeepSinger has several advantages over previous SVS systems: 1) to the best of our knowledge, it is the first SVS system that directly mines training data from music websites, 2) the lyrics-to-singing alignment model further avoids any human efforts for alignment labeling and greatly reduces labeling cost, 3) the singing model based on a feed-forward Transformer is simple and efficient, by removing the complicated acoustic feature modeling in parametric synthesis and leveraging a reference encoder to capture the timbre of a singer from noisy singing data, and 4) it can synthesize singing voices in multiple languages and multiple singers. We evaluate DeepSinger on our mined singing dataset that consists of about 92 hours data from 89 singers on three languages (Chinese, Cantonese and English). The results demonstrate that with the singing data purely mined from the Web, DeepSinger can synthesize high-quality singing voices in terms of both pitch accuracy and voice naturalness. Our audio samples are shown in https://speechresearch.github. io/deepsinger/.
IALE: Imitating Active Learner Ensembles

Active learning (AL) prioritizes the labeling of the most informative data samples. As the performance of well-known AL heuristics highly depends on the underlying model and data, recent heuristic-independent approaches that are based on reinforcement learning directly learn a policy that makes use of the labeling history to select the next sample. However, those methods typically need a huge number of samples to sufficiently explore the relevant state space. Imitation learning approaches aim to help out but again rely on a given heuristic. This paper proposes an improved imitation learning scheme that learns a policy for batch-mode pool-based AL. This is similar to previously presented multi-armed bandit approaches but in contrast to them we train a policy that imitates the selection of the best expert heuristic at each stage of the AL cycle directly. We use DAGGER to train the policy on a dataset and later apply it to similar datasets. With multiple AL heuristics as experts, the policy is able to reflect the choices of the best AL heuristics given the current state of the active learning process. We evaluate our method on well-known image datasets and show that we outperform state of the art imitation learners and heuristics.
Low Complexity Optimization of the Asymptotic Spectral Efficiency in Massive MIMO NOMA

Massive multiple-input multiple-output (MIMO) technology facilitates huge increases in the capacity of wireless channels, while non-orthogonal multiple access (NOMA) addresses the problem of limited resources in traditional orthogonal multiple access (OMA) techniques, promising enhanced spectral efficiency. This letter uses asymptotic capacity computation results to reduce the complexity of a power allocation algorithm for small-scale MIMO-NOMA, so that it may be applied for systems with massive MIMO arrays. The proposed method maximizes the sum-capacity of the considered system, subject to power and performance constraints, and demonstrates greater accuracy than alternative approaches despite remaining low-complexity for arbitrarily large antenna arrays.
A Critical Evaluation of Open-World Machine Learning

Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs. Previous works on open-world ML systems usually fail to test their reliability under diverse, and possibly adversarial conditions. Therefore, in this paper, we seek to understand how resilient are state-of-the-art open-world ML systems to changes in system components? With our evaluation across 6 OOD detectors, we find that the choice of in-distribution data, model architecture and OOD data have a strong impact on OOD detection performance, inducing false positive rates in excess of $70\%$. We further show that OOD inputs with 22 unintentional corruptions or adversarial perturbations render open-world ML systems unusable with false positive rates of up to $100\%$. To increase the resilience of open-world ML, we combine robust classifiers with OOD detection techniques and uncover a new trade-off between OOD detection and robustness.