Miracle-dz/UniFAR-FTUs huggingface dataset:
Light stalks increase the precision and accuracy of non-breeding locations calculated from geolocator tags: a field test from a long-distance migrant

ABSTRACT Capsule: There is a substantial gain in precision and accuracy of geolocator locations when using a light stalk. Aims: Light stalks or tubes increase the accuracy of geolocators when tracking migrant birds because they reduce potential shading of the light sensor by feathers but may increase detrimental tag effects. We aimed to determine how adding light stalks to geolocator tags increased accuracy and precision of locations. Methods: We quantified how precision and accuracy of geolocator locations was affected by comparing variation of sunrise and sunset times from tags with variable length light stalks (6 of 0 mm, 8 of 5 mm and 21 of 10 mm). Tags were fitted to Whinchats Saxicola rubetra in central Nigeria (the known location to compare accuracy), and variance in latitude and longitude of geolocator estimated locations were also compared across light stalk lengths during spring migration stationary locations, and at breeding sites in Eastern Europe, for both Geolight and FlightR methods. Results: Without a light stalk, the standard deviation of sunset and sunrise times increased by 50% and 100%, respectively (i.e. less precise): confidence intervals for latitude were larger by about 4.3 degrees at non-breeding low latitudes and 1.8 degrees at stop-over latitudes, or confidence intervals for longitude were larger by 2.3 degrees, dependent on analysis method. Estimated sun elevation angles were significantly less accurate and so calculated non-breeding locations were significantly less accurate by about 8 degrees of latitude. Precision in sunrise, sunset times, latitude and longitude, was similar when using a 5 mm or 10 mm stalk. Conclusions: The results show a substantial gain in precision and accuracy of low latitude geolocator locations when using a light stalk that brings the sensor above covering feathers. There is no advantage from longer light stalk lengths than those necessary to just expose the light sensor above the feathers, at least for small passerines.
Detection of virulence genes and investigation of the immunogenicity of outer membrane proteins of Salmonella enterica

Salmonella enterica is one of the most important food-borne pathogens, causing a variety of diseases in humans and animals. This study aimed to detect the virulence genes in 33 S. enterica strains isolated from patients and to investigate the immunogenicity of the outer membrane proteins (OMPs) of S. enterica serovar Typhimurium. The aggregative fimbriae (agfA) gene was detected in all S. enterica isolates except one strain, S. Paratyphi C strain SA7. In addition, 81.8% of the isolates harboured the sefC gene (fimbrial protein). However, all of the tested S. enterica isolates possessed the fimA, hilA, invA, stn, and misL virulence genes, regardless of serovar. The predominant OMPs of S. enterica Typhimurium SA3 identified by 12% SDS-PAGE were used as eliciting antigens in experimental mice. The results of the protection studies indicated that the selected OMPs conferred varying degrees of protection. However, the highest protection was observed using the 38-kDa OMP, which provided 100% protection to mice challenged with 50× LD50 of S. Typhimurium SA3 and 75% protection to mice subjected to an even higher bacterial challenge of 100× LD50. The humoural response in mice caused by the 38-kDa OMP was confirmed using an immunodiffusion assay. This 38-kDa OMP is a promising candidate for vaccine development against S. enterica Typhimurium. Further research on the protein structure was recommended.
Gastrointestinal cancers in Iran: Iranian scientists approach to gastrointestinal cancers researches in international databases

Introduction : Gastrointestinal (GI) cancer is one of the most common and fatal type of cancers and it is responsible for about half of mortalities from cancer (44.4%) in Iran. The purpose of the study was to evaluate scientific output of Iran in the matter of gastrointestinal cancer in selected international databases (Scopus, Medline). Regarding the considerable prevalence of GI cancers in Iran and the world and its impact on patients and society the necessity toward scientific investigations seems to be highlighted. Materials and Methods : A cross - sectional descriptive survey was conducted using Scientometrics criteria. Data were collected from two Medline and Scopus databases as search ground. To extract data from the advanced search link in Scopus database, the researcher made searching strategy designed to include all of “GI cancer-related” keywords was used. Keywords included 17 terms combined with 9 keywords about malignancy, tumor and cancer. Ultimately, we formed 153 different keywords. The search strategy in Medline database was designed based on Medical Subject headings (MeSH) via a subject specialist (medical specialist) help. Results : analyzed data showed that 1826 documents in terms of GI cancer written by Iranian researchers were indexed in Scopus and 468 in Medline databases. Regarding the subject matter, Iran’s ranking was 30 in the Scopus, 31 in Medline, 10 within Asian countries, and 3 in Middle-east. Tehran University of Medical Sciences with production of 35.17 percent of documents in Scopus database and 27.35 percent of documents in the Medline database was identified as most active centers in this field . In both of these databases, “Asian Pacific Journal of Cancer Prevention” includes most Iranian documents in this field. Conclusion : The study suggested a linear increase of scientific production in the field of GI cancers during the studied period in both databases. However, there is still a significant gap between Iran's scientific output indexed in the field and the growing number of GI cancers in the community. Therefore, the policymakers should pay more attention to improving the research activities in the field of GI.
Aquaculture potential of Bohai Red and its hybrid with Agropecten irradians concentricus in southern China

Argopecten irradians concentricus (Say), one of four geographic subspecies of the bay scallop, has become the major cultured species in southern China since its introduction in 1995. However, its population has been significantly reduced due to high mortality.Also, poor growth rate has been observed following decades of culture.Therefore, the introduction or creation of new varieties is an urgent need. This study describes the first introduction of the new strain, Bohai Red, from the north to southern China. Hybridization trials were conducted between the recently introduced new strain and the local specie, A. irradians concentricus (Say). The success of hybridization was confirmed by SSR maker.The adult and juvenile Bohai Red cannot tolerate high temperatures compared to A. irradians concentricus (Say), whether in natural waters or under laboratory conditions. Fertilization rate for A. irradians concentricus (Say) × Bohai Red exceeded 80%, and hatching rate was 70%. The hybrids exhibit heterosis in survival rate, growth rate, and high-temperature tolerance, demonstrating substantial potential to replace A. irradians concentricus (Say) in southern China.
Dynamic Objectives Learning for Facial Expression Recognition

Facial expression recognition has been widely used to solve the problems such as lie detection and human-machine interaction. However, due to the difficulties to control the application environments, current methods have the lower recognition accuracy in practice. This paper proposes a new method for facial expression recognition by considering several aspects. First, human beings are easy to recognize some expressions, while difficult to recognize others. Inspired by this intuition, a new loss function is proposed to enlarge the distances between samples from easily confused categories. Second, human learning is divided into many stages, and the learning objective of each stage is different. Thus, dynamic objectives learning is proposed, where each objective at different stage is defined by the corresponding loss function. In order to better realize the above ideas, a new deep neural network for facial expression recognition is proposed, which integrates the covariance pooling layer and residual network units into the deep convolution neural network so as to better perform dynamic objectives learning. The experimental results on the standard databases verify the effectiveness and the superior performance of our methods.
Bone Marrow Transplantation Followed by Growth Factors Injection in a Patient with Femoral Neck Nonunion: A Case Report

Background: Most fractures of femoral neck typically heal, but a significant proportion of fractures fail to heal, resulting in aseptic bone necrosis, delayed union or persistent nonunion. Specially, nonunion of fractured neck of femur in young adults is a serious problem. Some preclinical evidences show the therapeutic potential of autologous bone marrow for bone fracture healing; however, clinical outcome following of transplantation of bone marrow cells in patients with femoral neck nonunion has never been reported. Case description: We describe a 57-year-old male who had a femoral neck nonunion and received autologous bone marrow transplantation, followed by growth factors injection. The primary endpoint of this study is to achieve radiological fracture healing (union) by evaluating anteroposterior images taken after and on 6th month, following bone marrow therapy and no serious adverse event occurred. Literature review: To our knowledge, this is the first case in the literature of nonunion of femoral neck fracture treated with percutaneous autologous bone marrow transplantation. Clinical relevance: This case suggests that autologous bone marrow transplantation, followed by plasma rich in growth factors (PRGF) could be considered as an exciting option to treat nonunion in femoral neck fracture.
Motivation to teach surgery at non-university affiliated hospitals in Saudi Arabia: A qualitative study

Background: Most Saudi Arabian medical colleges rely on non-university affiliated hospitals for clinical training of their students. We explored what motivates or demotivates the surgeons who work in these hospitals to teach. Methods: Eighteen surgeons from five non-university affiliated hospitals in the Al-Qassim region of Saudi Arabia who were directly involved in teaching surgery participated in this qualitative study. The surgeons’ in-depth interviews with a semi-structured questionnaire were audio-taped, transcribed, and analyzed to identify responses related to motivating and demotivating factors for teaching surgery. Results: Surgeons identified three unique factors related to teaching: (1) to accumulate spiritual credit for good deeds (motivating), (2) refusal by patients to be examined by trainees (demotivating), and (3) refusal by female patients to be examined by male trainees (demotivating). Additional motivating factors were self-satisfaction, earning a good reputation, updating knowledge, and witnessing the success of trainees. The demotivating factors were unenthused trainees, inadequate teaching settings at the hospitals, an absence of medico-legal protection for the trainees, a heavy workload, and no incentives for teaching activities. Conclusion: Providing student-trainees legal protection, offering incentives for teaching, and marketing the hospitals as training facilities will likely improve the teaching environment in non-university affiliated hospitals in Saudi Arabia.
Toward a User Acceptance Model of Autonomous Driving

Autonomous driving is becoming the next big digital disruption in the automotive industry. However, the possibility of integrating autonomous driving vehicles into current transportation systems not only involves technological issues but also requires the acceptance and adoption of users. Therefore, this paper develops a conceptual model for user acceptance of autonomous driving vehicles. The corresponding model is tested through a standardized survey of 470 respondents in Germany. Finally, the findings are discussed in relation to the current developments in the automotive industry, and recommendations for further research are given.
Rational Design and Facile Synthesis of Novel Fluorescent Scaffolds for Biological Studies in Live Cells

Over the last several decades, there has been a remarkable growth in the number of fluorescence imaging techniques for studying biological systems. Fluorescent small molecules, in particular, have significant advantages in optical imaging and analytical sensing due to their small size, high sensitivity, and fast response time. Functional fluorescent probes, such as photoactivatable fluorophores, can also provide dynamic information concerning the localization and quantity of biomolecules in living cells. Although numerous fluorescent probes are known, within the context of biological systems, “ideal” fluorescent probes are still in high demand, as properties such as cell permeability or cytotoxicity are often difficult to predict on the basis of rational design and theoretical calculations. Hence, there is still a need for simple fluorogenic scaffolds, which allow the rational design of molecules with predictable photophysical properties and are amenable to concise synthesis for high-throughput combinatorial screening. My work in the Chenoweth and Petersson Lab has focused on developing functional fluorescent probes based on modular scaffolds that exhibit advances over classic and contemporary dyes. In undertaking this work, three functional fluorescent scaffolds have been explored: (1) a quinoline-based highly tunable dye, (2) a diazaxanthilidene-based photoconvertible probe, and (3) an acridone-based fluorescent unnatural amino acid (UAA). Each fluorescent scaffold is rationally designed, efficiently synthesized, and utilized in live cell imaging experiments to demonstrate utility. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Chemistry First Advisor David M. Chenoweth Second Advisor Ernest J. Petersson
Unspanned Risks, Negative Local Time Risk Premiums, and Empirical Consistency of Models of Interest-Rate Claims

We formalize the notion of local time risk premiums in the context of a theory in which the pricing kernel is a general diffusion process with spanned and unspanned components. We derive results pertaining to expected excess returns of options on bond futures. These results are organized around our finding that average returns of out-of-the-money puts and calls on bond futures are both negative, the consistency of which is examined through unconditional and conditional exercises. Our theoretical reconciliation warrants negative local time risk premiums, and our treatment considers models with market incompleteness, volatility uncertainty, and time-varying sign of futures risk premium.
Using Proteomics to Understand Alzheimer’s Disease Pathogenesis

ABSTRACT Our current understanding of the molecular changes that drive Alzheimer’s disease (AD) pathogenesis is incomplete. Unbiased, mass spectrometry-based proteomic studies provide an efficient and comprehensive way to quantitatively examine thousands of proteins at once using microscopic amounts of human brain tissue. Recently, the number of proteomic studies that examine protein changes in AD brain tissue has been increasing. This chapter reviews the different proteomic approaches currently being used to identify pathological protein changes in AD brain tissue including bulk tissue studies that examine protein changes throughout the progression of AD, studies of the insoluble proteome in AD, studies using proteomics to examine selective vulnerability in AD, studies of the amyloid plaque and neurofibrillary tangle proteome, studies of the synaptic proteome, and studies of the interactome of beta amyloid and tau. Combined, these complementary proteomic approaches provide increased understanding about the protein changes that occur in the AD brain. Results from these proteomic studies provide an excellent resource for future hypothesis-driven targeted studies and will help identify new biomarkers of disease and new drug targets for AD.
Autopercepção de saúde e aspectos clínico-funcionais dos idosos atendidos em uma unidade básica de saúde no norte do Brasil

Objetivo : O objetivo principal deste trabalho foi conhecer os aspectos clinicos e funcionais e a autopercepcao de idosos atendidos em uma unidade basica de saude localizada em Ananindeua-PA. Materiais e Metodos : Trata-se de um estudo transversal. Os dados foram coletados por meio de entrevista no domicilio dos usuarios ou na sala de espera da unidade de saude, com auxilio de um protocolo de pesquisa que continha questionamentos da Caderneta de Saude da Pessoa Idosa. A populacao amostral foi de 134 idosos. As hipoteses foram avaliadas pelo teste de Qui-quadrado com correcao de Yates e Teste G. Para a avaliacao da forca de associacao, foi realizado o calculo do Odds Ratio. Resultados : A maior parte da populacao era do genero feminino, com idade entre 60 e 79 anos, raca parda e escolaridade de 4 a 7 anos de estudo. A doenca mais frequente foi hipertensao arterial sistemica. A maioria dos idosos possuia autopercepcao de saude positiva. Houve associacao estatistica da autopercepcao de saude com a presenca de quedas no ultimo ano (p= 0,01), dificuldades para caminhar (p= 0,008) e incapacidade para exercer atividades instrumentais de vida diaria (p= 0,02; p=0,02 e p=0,001). Conclusao : Concluiu-se que os idosos avaliados apresentam autopercepcao de saude positiva. Em geral, os dados clinicos e epidemiologicos constatados replicam achados de estudos nacionais. A caracterizacao desta amostra favorece a formulacao de indicadores de saude e implantacao de acoes preventivas.
A Qualitative Study of the Benefits and Costs of Logging From Developers’ Perspectives

Software developers insert logging statements in their source code to collect important runtime information of software systems. In practice, logging appropriately is a challenge for developers. Prior studies aimed to improve logging by proactively inserting logging statements in certain code snippets or by learning where to log from existing logging code. However, there exists no work that systematically studies developers’ logging considerations, i.e., the benefits and costs of logging from developers’ perspectives. Without understanding developers’ logging considerations, automated approaches for logging decisions are based primarily on researchers’ intuition which may not be convincing to developers. In order to fill the gap between developers’ logging considerations and researchers’ intuition, we performed a qualitative study that combines a survey of 66 developers and a case study of 223 logging-related issue reports. The findings of our qualitative study draw a comprehensive picture of the benefits and costs of logging from developers’ perspectives. We observe that developers consider a wide range of logging benefits and costs, while most of the uncovered benefits and costs have never been observed nor discussed in prior work. We also observe that developers use ad hoc strategies to balance the benefits and costs of logging. Developers need to be fully aware of the benefits and costs of logging, in order to better benefit from logging (e.g., leveraging logging to enable users to solve problems by themselves) and avoid unnecessary negative impact (e.g., exposing users’ sensitive information). Future research needs to consider such a wide range of logging benefits and costs when developing automated logging strategies. Our findings also inspire opportunities for researchers and logging library providers to help developers balance the benefits and costs of logging, for example, to support different log levels for different parts of a logging statement, or to help developers estimate and reduce the negative impact of logging statements.
A Novel High-Voltage DC Transformer Based on Diode-Clamped Modular Multilevel Converters With Voltage Self-Balancing Capability

DC transformers are essential for dc grids to interconnect different dc networks. This article proposed a high-voltage dc transformer, which consists of two diode-clamped modular multilevel converters (DCM2Cs) front-to-front connected with a medium-frequency transformer. The DCM2C is prominent for its simplified voltage balancing control using clamping circuits. However, a voltage controller and corresponding measuring circuits are still needed for each converter arm. With the existing quasi two-level modulation technology, this article proposed a voltage self-balancing method of the DCM2C when applied to the dc transformer. This approach is beneficial for reducing the overall cost of the system with no voltage sensor requirements. Meanwhile, the power loss of the clamping circuit is kept low with the clamping inductor. Both mathematical analysis and experimental results are conducted to verify the effectiveness of the proposed converter.
The complete chloroplast genome sequence of the threatened Cypripedium calceolus (Orchidaceae)

Abstract The complete chloroplast genome of Cypripedium calceolus, a rare species in the family Orchidaceae was reported in this study. The genome size is 175,122 bp in length, and contains four sub-regions: 97,486 bp of large single copy (LSC) and 22,260 bp of small copy (SSC) regions, separated by 27,688 bp of inverted repeat (IR) regions. A total of 133 genes were annotated, including 87 protein-coding genes, 38 tRNA genes and 8 rRNA genes. The GC content of this cp genome is 34.36%. Phylogenetic analysis revealed a close relationship between C. calceolus with C. japonicum and C. formosanum. This is the first complete cp genome for C. calceolus that would be useful for conservation and phylogenetic studies of this species.
Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data.