Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Detection and Genotyping of Dengue Viruses: Current Techniques and Future Prospect

The Dengue virus is a prevalent mosquito-borne disease, and its global incidence has been steadily increasing due to the favorable environmental conditions that promote mosquito breeding, primarily influenced by rising temperatures. Bangladesh has been particularly hard-hit by an intense and ongoing outbreak, resulting in a surge of cases and fatalities. Effective management of this disease necessitates the implementation of robust public health measures, including rigorous surveillance and early diagnosis. While serological tests are commonly employed in clinical diagnosis, molecular methods hold a critical role in identifying the specific Dengue virus strain, thereby contributing to a more comprehensive understanding of disease severity. This article serves as an extensive review, delving into various molecular testing techniques employed for both surveillance and clinical diagnosis. It offers valuable insights for research and clinical laboratories engaged in the detection of Dengue virus RNA in mosquitoes, environmental samples, and clinical specimens. The methods covered encompass a spectrum of approaches, including conventional PCR, isothermal amplification, real-time RT-PCR, Sanger sequencing, and whole-genome sequencing, providing a holistic overview of the available techniques. These methods play pivotal roles in clinical diagnosis, outbreak analysis, vector surveillance, and vaccine development. Furthermore, the article underscores the importance of integrating these techniques into existing healthcare systems, emphasizing their significance in ensuring precise dengue diagnostics to enhance the efficiency of disease management. These molecular methods are indispensable tools that contribute to accurate diagnosis, enable effective outbreak investigation, facilitate vector surveillance, and support vaccine preparation, thereby enhancing the overall management and control of dengue, ultimately working toward mitigating its impact on public health. Bangladesh J Microbiol, Volume 40, Number 1, June 2023, pp 41-49
Creating probabilistic databases from information extraction models

Many real-life applications depend on databases automatically curated from unstructured sources through imperfect structure extraction tools. Such databases are best treated as imprecise representations of multiple extraction possibli-ties. State-of-the-art statistical models of extraction provide a sound probability distribution over extractions but are not easy to represent and query in a relational framework. In this paper we address the challenge of approximating such distributions as imprecise data models. In particular, we investigate a model that captures both row-level and column-level uncertainty and show that this representation provides significantly better approximation compared to models that use only row or only column level uncertainty. We present efficient algorithms for finding the best approximating parameters for such a model: our algorithm exploits the structure of the model to avoid enumerating the exponential number of extraction possibilities.
Lightweight Piggybacking for Packet Loss Recovery in Internet Telephony

We consider an Internet telephony system in which the service provider operates a telephone gateway in each servicing city to serve the general public. We propose a packet loss recovery system, called lightweight piggybacking, for this system. This scheme applies two stages of erasure coding and fragmentation, such that only a small redundancy is piggybacked to each voice packet while this redundancy can be shared by multiple voice streams to a large extent for effective packet loss recovery. Compared with the conventional piggybacking scheme, the lightweight piggybacking scheme can effectively: (i) increase the probability of recovering the lost packets using the same or smaller amount of redundancy, and (ii) recover the loss of multiple and consecutive packets.
Favorite stories: a hypnotic pain-reduction technique for children in acute pain.

Abstract For young children (aged 3 to 6 years 11 months) with leukemia, a hypnotic trance consisting of a child's favorite story was found to be statistically more effective than behavioral distraction and standard medical practice in alleviating distress, pain, and anxiety during painful bone marrow aspirations. Measured by a behavioral checklist and judgement ratings by physician, parent, nurse, and observers, the favorite-story hypnotic technique had immediate therapeutic impact on these young patients, and the reduction in distress, pain, and anxiety was sustained on subsequent medical procedures. Self-report measures, however, were nonsignificant.
Information Transmission in a Neuron-Astrocyte Coupled Model

A coupled model containing two neurons and one astrocyte is constructed by integrating Hodgkin-Huxley neuronal model and Li-Rinzel calcium model. Based on this hybrid model, information transmission between neurons is studied numerically. Our results show that when the successive spikes are produced in neuron 1 (N1), the bursting-like spikes (BLSs) occur in two neurons simultaneously during the spikes being transferred to neuron 2 (N2). The existence of the astrocyte and a higher expression level of mGluRs facilitate the occurrence of BLSs, but the rate of occurrence is not sensitive to the parameters. Furthermore, time delay τ occurs during the information transmission, and τ is almost independent of the effect of the astrocyte. Additionally, we found that low coupling strength may result in the distortion of the information, and this distortion is also proven to be almost independent of the astrocyte.
The template type Ia supernova 1996X

ABSTRA C T UBVRIJ photometry and optical spectra of the type Ia SN 1996X obtained at the European Southern Observatory (ESO) during a 1-yr-long observational campaign are presented, and supplemented by late-time Hubble Space Telescope (HST) photometry. Spectroscopically, SN 1996X appears to be a ‘normal’ SN Ia. The apparent magnitude at maximum was Ba 13:24 ^ 0:02; and the colour B 2 Va 0:00 ^ 0:03: The luminosity decline rate, DmBO15Ua1:31 ^ 0:08; is close to average for a SN Ia. The best estimate of the galactic extinction is ABa 0:30 ^ 0:05; and there is evidence that reddening within the parent galaxy is negligible. Detailed comparison of the light and colour curves of various ‘normal’ SNe Ia shows that the assumption that multicolour light curves can be described simply as a one-parameter family is not perfect. Together with problems in the calibration of the templates, this may explain the discrepancies in the distance modulus derived adopting different calibrations of the absolute magnitude versus light-curve shape relations. Indeed, we found that MB ranges from 219.08 to 219.48 and m ranges from 32.02 to 32.48 depending on the method used. Computations of model light-curve and synthetic spectra for both early and late times confirm that 1996X is a normal type Ia SN and that a satisfactory fit can be obtained using a W7 progenitor structure only if we adopt the short distance. A larger distance would imply too large a Ni mass for this fainter than average SN Ia.
The appropriation of GitHub for curation

GitHub is a widely used online collaborative software development environment. In this paper, we describe curation projects as a new category of GitHub project that collects, evaluates, and preserves resources for software developers. We investigate 1) what motivates software developers to curate resources, 2) why curation has occurred on GitHub; 3) how curated resources are used by software developers; and 4) how the GitHub platform could better support these practices. We conduct in-depth interviews with 16 software developers, each of whom hosts curation projects on GitHub. Our results suggest that the motivators that inspire software developers to curate resources on GitHub are similar to those that motivate them to participate in the development of open source projects. Convenient tools (e.g., Markdown syntax and Git version control system) and the opportunity to address professional needs of a large number of peers attract developers to engage in curation projects on GitHub. Benefits of curating on GitHub include learning opportunities, support for development work, and professional interaction. However, curation is limited by GitHub's document structure, format, and a lack of key features, such as search. In light of this, we propose design possibilities to encourage and improve appropriations of GitHub for curation.
Combining independent modules in lexical multiple-choice problems

Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best for all problem instances. Many researchers are examining ensemble methods that combine the output of multiple modules to create more accurate solutions. This paper examines three merging rules for combining probability distributions: the familiar mixture rule, the logarithmic rule, and a novel product rule. These rules were applied with state-of-the-art results to two problems used to assess human mastery of lexical semantics -- synonym questions and analogy questions. All three merging rules result in ensembles that are more accurate than any of their component modules. The differences among the three rules are not statistically significant, but it is suggestive that the popular mixture rule is not the best rule for either of the two problems.
The impact of lawn care practices on suburban streams in metropolitan Atlanta, Georgia

Pesticides and nutrients are frequently detected in suburban streams, where stormwater runoff from residential lawns is routed directly to streams. Adverse effects of lawn care products on basal food resources and ecosystem processes in streams are not well understood. The impact of lawn care practices on the breakdown of tulip-poplar leaves (Liriodendron tulipifera L.) and fungal biomass (measured as ergosterol) was investigated in four suburban watersheds and two mixed-use watersheds near Atlanta, Georgia. Temperature, velocity, nutrient, metal, and fungicide concentrations were measured during the leaf pack experiment, and sediment accumulation and insect colonization in leaf packs were quantified. Leaf breakdown rates were compared among streams and related to physical, chemical, and biological parameters. These correlations were related to variations in human lawn care practices in the watersheds. Nutrient and fungicide concentrations were low (mean concentrations of NO3-N <0.2 mg l, mean concentrations of NH4-N <0.01 mg land soluble reactive phosphorus <0.01 mg l), with slight differences in nitrate-N among streams. There was significant fungal growth in four of the streams, but maximum ergosterol concentrations were low. There were significant differences in leaf breakdown rates among streams, with rates ranging from 0.0006 in a suburban stream to 0.0024 per degree day in both reference streams. Breakdown rates were significantly positively correlated with stream velocity (r=0.65, p=0.05), which differed significantly among streams. No clear relationship existed between breakdown rates and nutrient and fungicide concentrations, since these relationships were confounded by relationships between nutrient and fungicide
Early Treatment of Acute Stage 0/1 Diabetic Charcot Foot Can Avoid Major Amputations at One Year

Background: If unrecognized, Charcot neuro-osteoarthropathy (CNO) can be a devastating complication of diabetes. Methods: The aim of this retrospective study was to evaluate the outcomes in a cohort of diabetic patients diagnosed with active CNO managed in a tertiary level diabetic foot clinic (DFC). We included consecutive patients with active CNO, stage 0–1, according to the Eichenholtz–Shibata classification, who were referred from 1 January 2019 to 27 September 2022. Diagnosis of CNO was based on clinical signs and imaging (X-rays and magnetic resonance). All patients were completely offloaded by a total-contact cast (TCC) or removable knee-high device. Each patient was closely monitored monthly until CNO remission or another outcome. At 12 months of follow-up, the following outcomes were analyzed: remission, time to remission, major amputations (any above the ankle), and surgical indication. Results: Forty-three patients were included. The mean age was 57.6 ± 10.8 years; 65% were males and 88.4% had type 2 diabetes, with a mean duration of 20.6 ± 9.9 years. At baseline, 32.6% was affected by peripheral artery disease. Complete remission was recorded in 40/43 patients (93%), with a mean time to remission of 5.6 ± 1.5 months; major amputation and surgical indication occurred, respectively in 1/43 patients (2.3%) and 3/43 patients (7%). Conclusions: Early treatment of active Stage 0/1 CNO leads to high rates of remission and limb salvage.
Bidding Algorithms for Simultaneous Auctions: A Case Study

This paper describes RoxyBot, one of the top-scoring agents in the First International Trading Agent Competition, TAC-2000. A TAC agent simulates one vision of future travel agents: it represents a set of clients in simultaneous auctions, trading complementary (e.g., airline tickets and hotel reservations) and substitutable (e.g., symphony and theater tickets) goods. RoxyBot faced two key technical challenges in TAC: (i) allocation --- assigning purchased goods to clients at the end of a game instance so as to maximize total client utility, and (ii) completion --- determining the optimal quantity of each resource to buy and sell given client preferences, current holdings, and market prices. For the dimensions of TAC, an optimal solution to the allocation problem is tractable, and RoxyBot uses a search algorithm based on A* to produce optimal allocations. An optimal solution to the completion problem is also tractable, but in the interest of minimizing bidding cycle time, RoxyBot solves the completion problem using beam search with a greedy heuristic, producing approximately optimal completions. RoxyBot's completer relies on an innovative data structure called a priceline.
Complexity of combinatorial market makers

We analyze the computational complexity of market maker pricing algorithms for combinatorial prediction markets. We focus on Hanson's popular logarithmic market scoring rule market maker (LMSR). Our goal is to implicitly maintain correct LMSR prices across an exponentially large outcome space. We examine both permutation combinatorics, where outcomes are permutations of objects, and Boolean combinatorics, where outcomes are combinations of binary events. We look at three restrictive languages that limit what traders can bet on. Even with severely limited languages, we find that LMSR pricing is #P-hard, even when the same language admits polynomial-time matching without the market maker. We then propose an approximation technique for pricing permutation markets based on an algorithm for online permutation learning. The connections we draw between LMSR pricing and the literature on online learning with expert advice may be of independent interest.
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity

The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithmic advances in structure learning for Bayesian networks---arguably the most central class of graphical models---especially in what is known as the score-based setting. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the GOBNILP system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. Understanding fundamental aspects of cutting planes and the related separation problem( is important not only from a purely theoretical perspective, but also since it holds out the promise of further improving the efficiency of state-of-the-art approaches to solving BNSL exactly. In this paper, we make several theoretical contributions towards these goals: (i) we study the computational complexity of the separation problem, proving that the problem is NP-hard; (ii) we formalise and analyse the relationship between three key polytopes underlying the IP-based approach to BNSL; (iii) we study the facets of the three polytopes both from the theoretical and practical perspective, providing, via exhaustive computation, a complete enumeration of facets for low-dimensional family-variable polytopes; and, furthermore, (iv) we establish a tight connection of the BNSL problem to the acyclic subgraph problem.
ORE: an iterative approach to the design and evolution of multi-dimensional schemas

Designing a data warehouse (DW) highly depends on the information requirements of its business users. However, tailoring a DW design that satisfies all business requirements is not an easy task. In addition, complex and evolving business environments result in a continuous emergence of new or changed business needs. Furthermore, for building a correct multidimensional (MD) schema for a DW, the designer should deal with the semantics and heterogeneity of the underlying data sources. To cope with such an inevitable complexity, both at the beginning of the design process and when a potential evolution event occurs, in this paper we present a semi-automatic method, named ORE, for constructing the MD schema in an iterative fashion based on the information requirements. In our approach, we consider each requirement separately and incrementally build the unified MD schema satisfying the entire set of requirements.
Co-Occurrence Models in Music Genre Classification

Music genre classification has been investigated using many different methods, but most of them build on probabilistic models of feature vectors xr which only represent the short time segment with index r of the song. Here, three different co-occurrence models are proposed which instead consider the whole song as an integrated part of the probabilistic model. This was achieved by considering a song as a set of independent co-occurrences (s, xr ) (s is the song index) instead of just a set of independent (x r)'s. The models were tested against two baseline classification methods on a difficult 11 genre data set with a variety of modern music. The basis was a so-called AR feature representation of the music. Besides the benefit of having proper probabilistic models of the whole song, the lowest classification test errors were found using one of the proposed models
Transcription of the folC gene encoding folylpolyglutamate synthetase-dihydrofolate synthetase in Escherichia coli

The folC gene of Escherichia coli is cotranscribed with an upstream gene from two promoters located in the noncoding region 5' to the coding sequence of the upstream gene. Virtually all of the expression of the folC gene product, folylpolyglutamate synthetase-dihydrofolate synthetase, is therefore due to the upstream gene promoters. No promoter activity was found in the coding sequence of the upstream gene or in the 72-base-pair noncoding region between the two genes. It is shown that a third gene, which may overlap the coding sequence of the folC gene by 8 base pairs at the 3' end, nevertheless, has an promoter independent from that of the upstream gene-folC operon. These results contrast with those presented by Nonet et al. (M. L. Nonet, C. C. Marvel, and D. Tolan, J. Biol. Chem., 262:12209-12217, 1987), who concluded that folC was cotranscribed with the gene at its 3' end and the gene upstream to folC was cotranscribed with the gene(s) further upstream. A stable stem-loop structure resembling a rho-independent terminator is present within the noncoding region between the upstream gene and the folC gene. Folypolyglutamate synthetase expression is 6- to 15-fold lower than that of the upstream gene product, suggesting that the stem-loop terminates some of the transcription from the upstream gene promoter. We found by deletion mutagenesis and cloning sequences containing the stem-loop structure into a termination reporter plasmid that this stem-loop does not act as an effective terminator of transcription. We also found that the stem-loop does not protect the upstream gene message from degradation, since expression of the upstream gene product in maxicell experiments is the same whether the stem-loop structure is present or deleted.
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs

We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation. The network learns to predict both the structure of the octree, and the occupancy values of individual cells. This makes it a particularly valuable technique for generating 3D shapes. In contrast to standard decoders acting on regular voxel grids, the architecture does not have cubic complexity. This allows representing much higher resolution outputs with a limited memory budget. We demonstrate this in several application domains, including 3D convolutional autoencoders, generation of objects and whole scenes from high-level representations, and shape from a single image.
"What does the Customer Want to Tell US?" an Automated Classification Approach for Social Media Posts at Small and Medium-Sized Enterprises

Social media posts created by customers capture a lot of business relevant information for decisionmakers, e.g., current consumer expectations on products and services. For that purpose, the social media posts need to be analyzed thoroughly. In this respect, a topic-related classification facilitates managerial decision-making because business relevant topics, social media users discuss about, immediately become obvious and the need for action can be derived. For instance, it may get obvious that the majority of a company’s negative customer posts refers to a particular product or a specific campaign. However, such a classification of social media posts is particularly challenging for small and mediumsized enterprises (SMEs). This is because human resources for a manual examination of posts are missing and an automatic analysis is error-prone due to particularities of customer posts such as the occurrence of regional dialect or branch-specific expressions. We thus develop a tool, which enables the automatized topic-related classification of social media posts and matches the particular requirements of SMEs in southern Germany. Our solution is evaluated by using a data set stemming from three collaborating companies.