Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of an analog channel for broadband high-dynamic acquisition system

The complex electromagnetic environment today places a greater demand on the bandwidth and dynamic range of electronic measuring devices. In the case where the dynamic range of a high-speed ADC is limited, the same signal is amplified with different gains through multiple channels, and the signal is sampled synchronously by multiple ADCs, and then, the undistorted data frames with the largest magnification factor are selected by frame from multiple sampled data for restoration and merging (reconstruction) to improve the dynamic range of the signal acquisition and the real-time performance of sampling. In this paper, the working principle and key technologies of an analog channel for a broadband high-dynamic acquisition system are described in detail, and the relevant design indicators are tested and verified. The results show that the analog channel finally achieves the technical indicators including 300MHz bandwidth and 90dB input dynamic range.
Digital cataract photography

This study compares the cost effectiveness of two methods of grading cataract changes in the Australian population. The conventional film-based method has comparatively low set-up costs, but is more subjective in its assessment and more reliant on manpower, while the digital method has high set-up costs, but the results are more objective and available more quickly using fewer people. By a careful assessment of all the costs involved it is possible to estimate how many participants need to be recruited to a study in order to make the digital method cost effective.
Ontogenetic diet shifts and diet overlap among three closely related neotropical armoured catfishes.

Analysis of stomach contents of larvae, juveniles and adults of three closely related armoured catfishes in coastal plain swamps of Suriname, South America, revealed major ontogenetic diet shifts. Larvae of Hoplosternum littorale, H. thoracatum and Callichthys callichthys showed a restricted diet of rotifers and microcrustacea. Diets of juveniles and adults were more diverse, but large volumes of microcrustacea, aquatic insect larvae and detritus were always present. In all three species diet overlap was especially low between larvae and adults. The proportion of detritus in the stomach was positively related to fish size, which probably reflected the inability of larger size classes to separate effectively benthic invertebrate prey from bottom substrata, and not a specialization towards detritivory. Adults showed only minor seasonality in diet composition, and no effect of season on diet breadth. Interspecific diet overlap among larvae, juveniles and adults did not differ significantly from 1 (complete overlap). Lack of food partitioning was probably related to a similar morphology of the three catfishes. Intraspecific between-size class diet overlap was considerably lower than interspecific within-size class overlap. It is suggested that large interspecific diet overlap occurred because food was not limiting the populations of these catfish. However, the nest habitats of the three species differed.
Induction of γ‐aminobutyric acid plays a positive role to Arabidopsis resistance against Pseudomonas syringae

Abstract Gamma‐aminobutyric acid (GABA) is an important metabolite which functions in plant growth, development, and stress responses. However, its role in plant defense and how it is regulated are largely unknown. Here, we report a detailed analysis of GABA induction during the resistance response to Pseudomonas syringae in Arabidopsis thaliana. While searching for the mechanism underlying the pathogen‐responsive mitogen‐activated protein kinase (MPK)3/MPK6 signaling cascade in plant immunity, we found that activation of MPK3/MPK6 greatly induced GABA biosynthesis, which is dependent on the glutamate decarboxylase genes GAD1 and GAD4. Inoculation with Pseudomonas syringae pv tomato DC3000 (Pst) and Pst‐avrRpt2 expressing the avrRpt2 effector gene induced GAD1 and GAD4 gene expression and increased the levels of GABA. Genetic evidence revealed that GAD1, GAD2, and GAD4 play important roles in both GABA biosynthesis and plant resistance in response to Pst‐avrRpt2 infection. The gad1/2/4 triple and gad1/2/4/5 quadruple mutants, in which the GABA levels were extremely low, were more susceptible to both Pst and Pst‐avrRpt2. Functional loss of MPK3/MPK6, or their upstream MKK4/MKK5, or their downstream substrate WRKY33 suppressed the induction of GAD1 and GAD4 expression after Pst‐avrRpt2 treatment. Our findings shed light on both the regulation and role of GABA in the plant immunity to a bacterial pathogen.
The plural of ‘anecdote’ can be ‘data’: statistical analysis of viewing distances in reports of unidentified large marine animals 1758–2000

Analysis of the nearest reported distance in accounts of seemingly unknown, large, marine animals (sea monsters) by boat- or water-based eyewitnesses, revealed that, contrary to expectation, the majority of sightings were at close distance (<200 m). This suggests that misidentification of inanimate objects or known animal species due to great distance was unlikely. Assuming a uniform distribution of objects around the boat, the reported sightings were far closer than expected, implying a bias in the sighting or reporting process. The distribution of reported distances from boat- or water-based eyewitnesses was almost identical to that of shore-based witnesses. Unidentified large marine animals were more likely to be reported to be closer if the report was anonymous or secondhand. The gap of time between recollection/reporting and the actual sighting did not influence reported distance. There was no relation between reported distance and reported length. There was some equivocal evidence that the absence of a stated distance in a report might be an indicator of a hoax.
Characterization of Atypical Protein Tyrosine Kinase (PTK) Genes and Their Role in Abiotic Stress Response in Rice

Tyrosine phosphorylation constitutes up to 5% of the total phophoproteome. However, only limited studies are available on protein tyrosine kinases (PTKs) that catalyze protein tyrosine phosphorylation in plants. In this study, domain analysis of the 27 annotated PTK genes in rice genome led to the identification of 18 PTKs with tyrosine kinase domain. The kinase domain of rice PTKs shared high homology with that of dual specificity kinase BRASSINOSTEROID-INSENSITIVE 1 (BRI1) of Arabidopsis. In phylogenetic analysis, rice PTKs clustered with receptor-like cytoplasmic kinases-VII (RLCKs-VII) of Arabidopsis. mRNAseq analysis using Genevestigator revealed that rice PTKs except PTK9 and PTK16 express at moderate to high level in most tissues. PTK16 expression was highly abundant in panicle at flowering stage. mRNAseq data analysis led to the identification of drought, heat, salt, and submergence stress regulated PTK genes in rice. PTK14 was upregulated under all stresses. qRT-PCR analysis also showed that all PTKs except PTK10 were significantly upregulated in root under osmotic stress. Tissue specificity and abiotic stress mediated differential regulation of PTKs suggest their potential role in development and stress response of rice. The candidate dual specificity PTKs identified in this study paves way for molecular analysis of tyrosine phosphorylation in rice.
Secretion in dissociated human pulmonary mast cells. Evidence for solubilization of granule contents before discharge

Mast cells were enzymatically dissociated from human lung fragments that had been sensitized with serum from human allergic to ragweed and were enriched by isopyknic and velocity gradient sedimentation. Electron microscope examination showed that the mast cells were well preserved at the end of the dissociation and isolation and that the majority of their secretory granules contained crystalline structures. These structures exhibited three patterns--scrolls, gratings, and lattices--which all could be found in the same granule. The period of crystalline structures was found to be bimodal, with maxima at 150 and 75 A. Both periods were observed in gratings that had been tilted and in scrolls that had been cut obliquely, indicating that the various gross patterns are composed of the same basic substructure. After the mast cells were stimulated by rabbit anti-human IgE to release histamine, the contents of the granule were transformed from a crystalline to an amorphous state, and only granules with amorphous contents were seen discharging from the cell. Clusters of intermediate filaments were present around the granules with amorphous contents, both deep in the cytoplasm and discharging at the cell surface. Discharge occurred both by fusion of granule membranes with the plasma membrane and by fusion of granule membranes with other granule membranes that ultimately were continuous with the plasma membrane. After discharge, the granule residue was fibrillar. Cells challenged with anti-human IgE in calcium-free medium neither released histamine nor demonstrated morphologic changes in their granules. We conclude that the crystalline state represents a storage form for materials that are solubilized before fusion of the granule membrane with the plasma membrane and discharge.
Efficient Attribute-Constrained Co-Located Community Search

Networked data, notably social network data, often comes with a rich set of annotations, or attributes, such as documents (e.g., tweets) and locations (e.g., check-ins). Community search in such attributed networks has been studied intensively due to its many applications in friends recommendation, event organization, advertising, etc. We study the problem of attribute-constrained co-located community (ACOC) search, which returns a community that satisfies three properties: i) structural cohesiveness: the members in the community are densely connected; ii) spatial co-location: the members are close to each other; and iii) attribute constraint: a set of attributes are covered by the attributes associated with the members. The ACOC problem is shown to be NP-hard. We develop four efficient approximation algorithms with guaranteed error bounds in addition to an exact solution that works on relatively small graphs. Extensive experiments conducted with both real and synthetic data offer insight into the efficiency and effectiveness of the proposed methods, showing that they outperform three adapted state-of-the-art algorithms by an order of magnitude. We also find that the approximation algorithms are much faster than the exact solution and yet offer high accuracy.
Evaluation of Hierarchical Structures for Time Series Data

Clustering is an effective unsupervised machine learning method that can be used as a stand-alone heuristic or as a part of a data mining process. The goal of clustering analysis is to partition data into groups with high intra-cluster association, and low inter-cluster association. Hierarchical clustering requires minimal parameters, has flexibility with similarity measure, and has strong visualization power, all of which makes it ideal for exploratory analysis. Hierarchical clustering is a particular branch of clustering algorithms where the results are not given as partitions, but rather a nested structure, which can represent the ordering among elements within a dataset. The study of the performance of hierarchical structure on time series data is limited. In this paper, we examine the hierarchical structure of time series datasets. The most popular hierarchical structured clustering heuristics include the Hierarchical Agglomerative Clustering, which is distance based; and Ordering Points To Identify the Clustering Structure, which is density based. Both share many similar characteristics and are suitable for time series data processing. We examine the performance of different hierarchical clustering algorithms with time series both internally as well as externally.
Gearbox fault diagnosis method based on deep convolutional neural network vibration signal image recognition

Nowadays, the internal structure of the gearbox tends to be complicated and the working environment is subject to more interference factors, so that the collected vibration signal is rich in more interference items, which makes the fault diagnosis of the gearbox more difficult. In order to find a new method to improve the efficiency and accuracy of fault diagnosis of various components in the gearbox, this paper proposes to combine the powerful image recognition capability of convolutional neural network with short-time Fourier transform to apply to gearbox diagnosis. The method transforms the one-dimensional vibration signal into a two-dimensional spectrogram by short-time Fourier transform, and performs normalization preprocessing on the image, and inputs it into the convolutional neural network through Shuffle operation to perform feature extraction to train the model. Using the operations such as Dropout makes the model training faster, and finally uses the trained model to diagnose the fault. The experimental results show that this method can effectively complete a variety of gearbox fault diagnosis and provide a possibility of a diagnostic method connected with “big data”. Compared with the traditional neural network method, the method has a higher efficiency and accuracy of about 5 percent.
Towards Locally Differentially Private Generic Graph Metric Estimation

Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics — the adjacency bit vector and node degree — from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step.
Anti-angiogenesis and Immunotherapy: Novel Paradigms to Envision Tailored Approaches in Renal Cell-Carcinoma

Although decision making strategy based on clinico-histopathological criteria is well established, renal cell carcinoma (RCC) represents a spectrum of biological ecosystems characterized by distinct genetic and molecular alterations, diverse clinical courses and potential specific therapeutic vulnerabilities. Given the plethora of drugs available, the subtype-tailored treatment to RCC subtype holds the potential to improve patient outcome, shrinking treatment-related morbidity and cost. The emerging knowledge of the molecular taxonomy of RCC is evolving, whilst the antiangiogenic and immunotherapy landscape maintains and reinforces their potential. Although several prognostic factors of survival in patients with RCC have been described, no reliable predictive biomarkers of treatment individual sensitivity or resistance have been identified. In this review, we summarize the available evidence able to prompt more precise and individualized patient selection in well-designed clinical trials, covering the unmet need of medical choices in the era of next-generation anti-angiogenesis and immunotherapy.
Aberrant Expression of miR-103, miR-184, miR-378, miR-497 and miR-506 in Tumor Tissue from Patients with Oral Squamous Cell Carcinoma Regulates the Clinical Picture of the Patients

Background: This study aimed to evaluate the expression patterns of miR-103, miR-184, miR-378, miR497 and in squamous cell carcinoma (SCC) of the tongue and to be compared with normal peripheral tissues. Methods: Tumor and marginal tissues were obtained from 50 patients with OSCC. After RNA extraction, expression level of miR-103, miR-184, miR-378, miR497, and miR506 was estimated using SYBR green master mix and real-time quantitative PCR. Results: It was observed that, there was no detectable difference in expression level of miR-103 between tumoral and marginal tissues. However, expression level of miR-184, and miR-378 showed significant increase in tumor tissue samples compared to marginal tissue samples. MiR-497 and miR-506 demonstrated considerable decrease in tumoral cells in comparison with peripheral tissues. Moreover, the expression level of miRNAs was associated with clinicopathological features of the patients. Conclusions: Our data indicated that miR-184, miR-378, miR-497, and miR-506 can be used as a potential diagnostic and prognostic biomarker in OSCC. Nevertheless, more studies are needed to confirm this claim.
Pricing Model on Consumer Based Cognition and Affection in Health Club Services

The research aims to provide a formal methodology in identifying significant emotional needs of customers in the services of a health club. From this a pricing model, a design will be developed that would result from the significant customer cognitive and affective needs. Research aims (a) to identify significant components of services attributes in the physical servicescape, social servicescape, product quality and service performance in customer perspective in health club services, (b) determine service attributes that significantly contribute to emotional experiences and service quality perception of health club customers, (c) investigate the effect of customer emotional experience and service quality perception to consumer satisfaction and value perception, and (d) to establish a pricing model from the consumer responses. Based on this previous literatures and research frameworks the major steps taken to confirm the hypothesized associations of this study consisted of (4) major stages namely: 1) Selecting the health club domains and service domains, 2) Identifying and classifying measurement variables, 3) Service assessment, and 4) Pricing model formulation. In conclusion this exploratory study took the beginning steps towards understanding how customers perceive the overall service system and how each service dimension could contribute toward behavioral intentions though perceptions and emotions. Implications for health club managers are likewise can be fully assisted by said concluded attributes in this research.
Optimistically Compressed Hash Tables & Strings in theUSSR

Modern query engines rely heavily on hash tables for query processing. Overall query performance and memory footprint is often determined by how hash tables and the tuples within them are represented. In this work, we propose three complementary techniques to improve this representation: Domain-Guided Prefix Suppression bit-packs keys and values tightly to reduce hash table record width. Optimistic Splitting decomposes values (and operations on them) into (operations on) frequently- and infrequently-accessed value slices. By removing the infrequently-accessed value slices from the hash table record, it improves cache locality. The Unique Strings Self-aligned Region (USSR) accelerates handling frequently occurring strings, which are widespread in real-world data sets, by creating an on-the-fly dictionary of the most frequent strings. This allows executing many string operations with integer logic and reduces memory pressure. We integrated these techniques into Vectorwise. On the TPC-H benchmark, our approach reduces peak memory consumption by 2-4× and improves performance by up to 1.5×. On a real-world BI workload, we measured a 2× improvement in performance and in micro-benchmarks we observed speedups of up to 25×.
Antifungal Activity of Gentamicin B1 against Systemic Plant Mycoses

Background: Gentamicin is a broad-spectrum aminoglycoside antibiotic produced by Micromonospora purpurea bacteria, effective against Gram-negative bacterial infections. Major fractions of the gentamicin complex (C1, C1a, C2, C2a) possess weak antifungal activity and one of the minor components (A, A1–A4, B, B1, X), gentamicin B1 was found to be a strong antifungal agent. Methods: This work uses in vitro and in vivo dilution methods to compare the antifusarial, antiaspergillic and anticryptococcal effects of gentamicin derivatives and structurally-related congeners. Results: The in vitro antifusarial activity of gentamicin B1 (minimum inhibitory concentration (MIC) 0.4 μg/mL) and structurally-related compounds (MIC 0.8–12.5 μg/mL) suggests that the purpuroseamine ring substituents are responsible for the specific antimycotic effect. The functional groups of the garoseamine and 2-deoxystreptamine rings of gentamicin derivatives are identical in gentamicin compounds and are unlikely to exert a significant antifungal effect. Among soil dermatophytes, Microsporum gypseum was more susceptible to gentamicin B1 (MIC 3.1 µg/mL) than Trichophyton gypseum (MIC 25 µg/mL). The in vitro antifungal effect of gentamicin B1 against plant pathogenic fungi was comparable to primary antifungal agents. Conclusion: Gentamicin is already in medical use. In vitro and preclinical in vivo synergisms of gentamicin B1 with amphotericin B suggest immediate clinical trials starting with subtoxic doses.
Statistical Estimation of Diffusion Network Topologies

Reconstructing the topology of a diffusion network based on observed diffusion results is an open challenge in data mining. Existing approaches mostly assume that the observed diffusion results are available and consist of not only the final infection statuses of nodes, but also the exact timestamps that pinpoint when infections occur. Nonetheless, the exact infection timestamps are often unavailable in practice, due to a high cost and uncertainties in the monitoring of node infections. In this work, we investigate the problem of how to infer the topology of a diffusion network from only the final infection statuses of nodes. To this end, we propose a new scoring criterion for diffusion network reconstruction, which is able to estimate the likelihood of potential topologies of the objective diffusion network based on infection status results with a relatively low statistical error. As the proposed scoring criterion is decomposable, our problem is transformed into finding for each node in the network a set of most probable parent nodes that maximizes the value of a local score. Furthermore, to eliminate redundant computations during the search of most probable parent nodes, we identify insignificant candidate parent nodes by checking whether their infections have negative or extremely low positive correlations with the infections of a corresponding child node, and exclude them from the search space. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach.
Remote sensing semantic segmentation with convolution neural network using attention mechanism

Semantic image segmentation is an essential part of remote sensing image processing because accurate understanding of the ground information is the first step in obtaining useful knowledge of surface coverage. The popular semantic segmentation convolutional neural network model (DeepLab v3+) cannot effectively use attention information, resulting in coarse segmentation boundaries. In this work, a new type of bottleneck using attention information which can extract semantic information and more abundant features from images is proposed. Compared with original network, the model using new bottleneck finely segments the target regions, solves the problem of segmentation boundary roughness better, leading to higher mIoU and accuracy. Experimental results based on the dataset in the ISPRS benchmark on urban object classification show bringing attention model into semantic segmentation neural network improves performance.