Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Oligosaccharide Ligands Expressed on SW1116 Cells Recognized by Mannan-binding Protein

Mannan-binding protein (MBP) is a C-type serum lectin and activates complement through the lectin pathway when it binds to ligand sugars such as mannose, N-acetylglucosamine, and fucose on microbes. In addition, the vaccinia virus carrying the human MBP gene was shown to exhibit potent growth inhibitory activity toward human colorectal carcinoma, SW1116, cells in nude mice. We have proposed calling this activity MBP-dependent cell-mediated cytotoxicity (MDCC) (Ma, Y., Uemura, K., Oka, S., Kozutsumi, Y., Kawasaki, N., and Kawasaki, T. (1999) Proc. Natl. Acad. Sci. U. S. A. 96, 371–375). In this study, the MBP ligands on the surface of SW1116 cells were characterized. Initial experiments involving plant lectins and anti-Lewis antibodies as inhibitors of MBP binding to SW1116 cells indicated that fucose plays a crucial role in the interaction. Subsequently, Pronase glycopeptides were prepared from whole cell lysates, and oligosaccharides were liberated by hydrazinolysis. After being tagged by pyridylamination, MBP ligand oligosaccharides were isolated with an MBP affinity column, and then their sequences were determined by mass spectrometry and tandem mass spectrometry after permethylation, in combination with endo-β-galactosidase digestion and chemical defucosylation. The MBP ligands were shown to be large, multiantennary N-glycans carrying a highly fucosylated polylactosamine type structure. At the nonreducing termini, Leb/Lea or tandem repeats of the Lea structure prevail, a substantial proportion of which are attached via internal Lex or N-acetyllactosamine units to the trimannosyl core. The structures characterized are unique and distinct from those of other previously reported tumor-specific carbohydrate antigens. It is concluded that MBP requires clusters of tandem repeats of the Leb/Lea epitope for recognition.
Helmet use among Alaskan children involved in off-road motorized vehicle crashes

Background Off-road motorized vehicle crashes are a common source of trauma among Alaska children. Injury morbidity is worse in Alaska Native children than non-Native children, but the reasons are unclear. Objective To evaluate the differences in helmet use between the Native and the non-Native children, and to assess the impact of helmet use on injury patterns and outcomes. Design This retrospective cohort study identified patients aged 17 or younger admitted after all-terrain vehicle, snowmobile or motorbike injury between 2001 and 2011 from the Alaska Trauma Registry. Helmeted and non-helmeted patients were compared with respect to demographics, central nervous system (CNS) injury and the overall risk of death or permanent disability. Logistic regression was used to evaluate predictors of helmet use and the effects of ethnicity and helmet use on outcomes. Results Of the 921 injured children, 51% were Alaska Native and 49% were non-Native. Helmet use was lower among Native versus non-Native patients on unadjusted comparison (24% vs. 71%) and multivariable logistic regression (OR 0.17, 95% CI 0.11–0.27, p<0.0001). Prevalence of CNS injury was higher among Native children (39.7% vs. 30.4%, p=0.016). However, on logistic regression with adjustment for helmet use, Native ethnicity was not a significant predictor of CNS injury (OR 1.07, 95% CI 0.68–1.68, p=0.78), whereas helmet use was strongly protective against CNS injury (OR 0.28, 95% CI 0.18–0.44, p<0.0001) as well as death or permanent disability (OR 0.26, 95% CI 0.10–0.67, p=0.006). Conclusions Helmet use is lower among Alaska Native children involved in off-road motorized vehicle crashes. These ethnic disparities in helmet use contribute to higher rates of CNS injury among Native children. Helmet use significantly improves overall outcome. Helmet promotion efforts should be expanded, especially in Native communities.
Practical Issues for Retroperitoneal Sarcoma.

BACKGROUND Retroperitoneal sarcoma is rare. Using initial specimens on biopsy, a definitive diagnosis of histological subtypes is ideal but not always achievable. METHODS A retrospective institutional review was performed for all cases of adult retroperitoneal sarcoma from 1996 to 2015. A review of the literature was also performed related to the distribution of retroperitoneal sarcoma subtypes. A meta-analysis was performed. RESULTS Liposarcoma is the most common subtype (45%), followed by leiomyosarcoma (21%), not otherwise specified (8%), and undifferentiated pleomorphic sarcoma (6%) by literature review. Data from Moffitt Cancer Center demonstrate the same general distribution for subtypes of retroperitoneal sarcoma. A pathology-based algorithm for the diagnosis of retroperitoneal sarcoma is illustrated, and common pitfalls in the pathology of retroperitoneal sarcoma are discussed. CONCLUSIONS An informative diagnosis of retroperitoneal sarcoma via specimens on biopsy is achievable and meaningful to guide effective therapy. A practical and multidisciplinary algorithm focused on the histopathology is helpful for the management of retroperitoneal sarcoma.
Matrix metalloproteinase inhibition: a review of anti-tumour activity.

BACKGROUND Matrix metalloproteinases are a homologous family of proteolytic enzymes. Collectively, these proteinases are capable of degrading all components of the extracellular matrix, including proteolytically resistant fibrillar collagens. Extracellular matrices constitute the principal barrier to tumour growth and spread, and there is now experimental evidence that malignant tumours utilise matrix metalloproteinases to overcome this barrier. Inhibitors of matrix metalloproteinases may therefore be of therapeutic value in the treatment of metastatic disease. DESIGN This review describes the activity of matrix metalloproteinases inhibitors (MMPIs), in experimental tumour models and in phase I/II clinical studies. RESULTS Studies with MMPIs in vitro have shown that these agents are not cytotoxic but can inhibit the degradation of extracellular matrix by tumour cells. In experimental tumour models in vivo, MMPI treatment caused inhibition of tumour growth and metastatic spread in both rodent syngeneic and human xenograft models. MMPIs have also been shown to inhibit angiogenesis, a process essential for the rapid growth of most malignancies. CONCLUSIONS MMPI therapy has the potential to arrest tumour growth and spread. As a non-cytotoxic 'tumourostatic' approach it may offer an ideal complement to surgery, radiotherapy and chemotherapy in the successful long-term treatment of metastatic disease.
SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern

Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations. While such enablements have positive effects, as a negative side, now anyone can digitally attack face (biometric) recognition systems. For example, face swapping application of Snapchat can easily create “swapped” identities and circumvent face recognition system. This research presents a novel database, termed as SWAPPED — Digital Attack Video Face Database, prepared using Snap chat's application which swaps/stitches two faces and creates videos. The database contains bonafide face videos and face swapped videos of multiple subjects. Baseline face recognition experiments using commercial system shows over 90% rank-1 accuracy when attack videos are used as probe. As a second contribution, this research also presents a novel Weighted Local Magnitude Pattern feature descriptor based presentation attack detection algorithm which outperforms several existing approaches.
Tariquidar Is an Inhibitor and Not a Substrate of Human and Mouse P-glycoprotein

Since its development, tariquidar (TQR; XR9576; N-[2-[[4-[2-(6,7-Dimethoxy-3,4-dihydro-1H-isoquinolin-2-yl)ethyl]phenyl]carbamoyl]-4,5-dimethoxyphenyl]quinoline-3-carboxamide) has been widely regarded as one of the more potent inhibitors of P-glycoprotein (P-gp), an efflux transporter of the ATP-binding cassette (ABC) transporter family. A third-generation inhibitor, TQR exhibits high affinity for P-gp, although it is also a substrate of another ABC transporter, breast cancer resistance protein (BCRP). Recently, several studies have questioned the mechanism by which TQR interfaces with P-gp, suggesting that TQR is a substrate for P-gp instead of a noncompetitive inhibitor. We investigated TQR and its interaction with human and mouse P-gp to determine if TQR is a substrate of P-gp in vitro. To address these questions, we used multiple in vitro transporter assays, including cytotoxicity, flow cytometry, accumulation, ATPase, and transwell assays. A newly generated BCRP cell line was used as a positive control that demonstrates TQR-mediated transport. Based on our results, we conclude that TQR is a potent inhibitor of both human and mouse P-gp and shows no signs of being a substrate at the concentrations tested. These in vitro data further support our position that the in vivo uptake of [11C]TQR into the brain can be explained by its high-affinity binding to P-gp and by it being a substrate of BCRP, followed by amplification of the brain signal by ionic trapping in acidic lysosomes.
A Low-Power High-Speed Self-Calibrated Differential Comparator

A self-calibrated differential voltage comparator with a high speed/power ratio is presented. The comparator employs a triple differential input transconductance stage followed by a regenerative flip-flop and an output latch, and achieves a resolution of 1 mV at 40 MHz with less than 300 ¿W of total power dissipation. Fabricated in 1.2 ¿m CMOS technology, the circuit occupies 200 × 100 ¿m2 of silicon area.
Aphid Transmission of Banana Bunchy Top Virus to Bananas After Treatment With a Bananacide

ABSTRACT Field and laboratory studies were conducted to determine the impact of using a herbicide as a bananacide on aphid transmission of Banana bunchy top virus (family Nanoviridae, genus Babuvirus, BBTV) to healthy banana (Musa spp.) plants. BBTV-infected banana plants in a commercial orchard were treated with Roundup Weathermax herbicide. Using polymerase chain reaction, the time after herbicide treatment that BBTV could no longer be detected in the infected plants was determined. The impact of the herbicide treatment on Pentalonia nigronervosa Coquerel (Hemiptera: Aphididae) virus acquisition and ability to inoculate healthy banana plants with BBTV also were determined. Generally, banana plants were dead beyond 42 d after herbicide injection (DAI), and BBTV was detected in a similar high percentage of treated plants from 0 up to 21 DAI. During two field trials, 0 and 32% of P. nigronervosa acquired the virus from treated plants at 42 DAI, respectively, but none successfully inoculated a healthy banana plant beyond 35 DAI. Finally, 22% of P. nigronervosa colonies collected directly from the pseudostem of injected plants at the final sample date (42 DAI) tested positive for BBTV and infected 9.5% of the healthy banana plants. The findings indicate that banana plants may remain a potential source of virus inoculum 6 wk after injection with a bananacide. The implications of these findings with respect to BBTV management are discussed.
Adaptive Management of Migratory Birds Under Sea Level Rise

The best practice method for managing ecological systems under uncertainty is adaptive management (AM), an iterative process of reducing uncertainty while simultaneously optimizing a management objective. Existing solution methods used for AM problems assume that the system dynamics are stationary, i.e., described by one of a set of pre-defined models. In reality ecological systems are rarely stationary and evolve over time. Importantly, the effects of climate change on populations are unlikely to be captured by stationary models. Practitioners need efficient algorithms to implement AM on real-world problems. AM can be formulated as a hidden model Markov Decision Process (hmMDP), which allows the state space to be factored and shows promise for the rapid resolution of large problems. We provide an ecological dataset and performance metrics for the AM of a network of shorebird species utilizing the East Asian-Australasian flyway given uncertainty about the rate of sea level rise. The non-stationary system is modelled as a stationary POMDP containing hidden alternative models with known probabilities of transition between them. We challenge the POMDP community to exploit the simplifications allowed by structuring the AM problem as an hmMDP and improve our benchmark solutions.
Complete mitogenome of Hemiculter leucisculus (Cyprinidae: Cultrinae)

Abstract The complete mitogenome was first determined for a minnow Hemiculter leucisculus in this study. It is 16,622 bp in length and has 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes and 1 control region. Overall nucleotide base composition is 30.4% A, 27.10% C, 17.10% G and 25.40% T. All the tRNA genes can fold into the typical clover-leaf secondary structures, except the tRNA-Ser2 which loses the dihydrouridine arm and is replaced with one 11 bp loop. The origin of light-strand replication (OL) was found between the tRNA-Asn and tRNA-Cys genes while the termination associated sequence (TAS) and the conserved sequence blocks (CSB1-3) were found in the control region.
Personalized Mobile Web Service Discovery

Mobile devices with their various form factors have become the most convenient and pervasive computing platform, whether to carry out everyday business or to get online. Mobile users tend to adopt the fast food trend even in consuming online mobile services and functionalities. The Web service approach promises great flexibility in offering software functionality over the network, while maintaining interoperability between heterogeneous platforms. However, the diversity that exists in mobile devices and their platforms with variations in capabilities present unique challenges in developing services that can accommodate such diversity. Recent years have witnessed the rise of user-facing service developments that can be consumed on the go with standard interface, such as RESTful Web services. However, the discovery of such services does not match their growing popularity. In addition, existing discovery approaches lack supporting mechanisms that ensure the proper functioning of discovered services within the user context and failing to match personal preferences. This paper introduces personalized Web service discovery for mobile environments. Preliminary results show that incorporating user preferences and context significantly improves the overall precision of service discovery.
Microdevice Platform for In Vitro Nervous System and Its Disease Model

The development of precise microdevices can be applied to the reconstruction of in vitro human microenvironmental systems with biomimetic physiological conditions that have highly tunable spatial and temporal features. Organ-on-a-chip can emulate human physiological functions, particularly at the organ level, as well as its specific roles in the body. Due to the complexity of the structure of the central nervous system and its intercellular interaction, there remains an urgent need for the development of human brain or nervous system models. Thus, various microdevice models have been proposed to mimic actual human brain physiology, which can be categorized as nervous system-on-a-chip. Nervous system-on-a-chip platforms can prove to be promising technologies, through the application of their biomimetic features to the etiology of neurodegenerative diseases. This article reviews the microdevices for nervous system-on-a-chip platform incorporated with neurobiology and microtechnology, including microfluidic designs that are biomimetic to the entire nervous system. The emulation of both neurodegenerative disorders and neural stem cell behavior patterns in micro-platforms is also provided, which can be used as a basis to construct nervous system-on-a-chip.
CLASSIFICATION OF POLARIMETRIC-SAR DATA USING FUZZY MAXIMUM LIKELIHOOD ESTIMATION CLUSTERING WITH CONSIDERATION OF COMPLEMENTARY INFORMATION BASED-ON POLARIMETRIC PARAMETERS , TARGET SCATTERING CHARACTERISTICS , AND SPATIAL CONTEXT

This paper shows a study on an alternative method for unsupervised classification of polarimetric-SAR data. First, two feature extraction methods are performed in order to exploit the information of fully polarimetric-SAR data properly. One is based on derivation of features from polarimetric covariance matrix (totally nine parameters which represent each polarization power, polarimetric coherence, and polarimetric phase difference), and other is based on Cloude’s polarimetric decomposition (totally three parameters which characterize the target’s scattering mechanism). A feature reduction technique based on maximum noise fraction (MNF) transformation is then applied to these features to obtain most pertinent information and remove any redundant and other irrelevant information. Classification stage is then performed using fuzzy maximum likelihood estimation (FMLE) clustering algorithm. FMLE algorithm allows for ellipsoidal clusters of arbitrary extent and is consequently more flexible than standard fuzzy K-means clustering algorithm. However, basic FMLE algorithm makes use exclusively the spectral (or intensity) properties of the individual pixel vectors and spatial-contextual information of the image was not taken into account. Hence, poor (noisy) classification result is usually obtained from SAR data due to the presence of speckle noise. In this paper, we propose a modified FMLE which integrate basic FMLE (pixel-by-pixel basis) clustering result with spatial-contextual information by statistical analysis of local neighborhoods. The proposed method has been tested on ESAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Results obtained show classified images improving landcover discrimination performance, exhibiting homogeneous region, and preserving edge and other fine structures.
Alocação de pacientes em grupos via Boosting: uma comparação com métodos tradicionais de classificação (pp.814-819)

Érica Aparecida Pereira Tiago Martins Pereira 1 Introdução Problemas de classificação são rotineiros em nosso dia-a-dia. Desde a infância somos capazes de reconhecer dígitos e letras. Caracteres pequenos ou grandes, manuscritos ou impressos, escritos de maneira desordenada ou até mesmo parcialmente ocultos — todos são facilmente reconhecidos. Esta habilidade de reconhecer ou classificar padrões faz com que nós humanos sejamos os melhores classificadores existentes, ainda que não conheçamos totalmente o mecanismo através do qual reconhecemos padrões. Segundo Ferreira (2007), um procedimento de classificação ou simplesmente classificador é algum método formal capaz de decidir, com base em informações fornecidas, a que grupo ou população um determinado objeto pertence. De uma maneira geral, estamos interessados em construir, com base em uma amostra de dados, uma regra de classificação e utilizá-la para classificar novos objetos. A classificação de dados tem se tornado uma tarefa difícil devido à grande quantidade de informação disponível. Ferreira (2007) ainda lembra, que o mercado exige cada vez mais a precisão dos classificadores, a qual é necessária em várias áreas de estudo, por exemplo, na medicina, em diagnóstico de doenças através de raio X, em bancos, como tomada de decisões para crédito, nas área de entretenimento, como na classificação de gêneros musicais, tornando-se essencial para muitas atividades na vida cotidiana, entre outras. Os métodos de classificação (ou discriminação) mais conhecidos e mais utilizados em estatística aplicada são, provavelmente, regressão logística (HOSMER e LEMESHOW, 1989) e análise discriminante (JOHNSON e WICHERN. 2002). Esses métodos, cujos fundamentos estão estabelecidos na teoria estatística há bastante tempo, são lineares, têm baixo custo computacional, e em geral produzem resultados razoáveis, apesar de não raro serem utilizados em situações que contradizem suas suposições paramétricas distribucionais. As propriedades estatísticas destes métodos são conhecidas há muito tempo, e suas interpretações são claras.
A Dynamic Fuzzy Cluster Algorithm for Time Series

This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.
SphericalMesh: A novel and flexible network topology for 60GHz-based wireless data centers

Data center networks are becoming increasingly important for computing industry. By exploiting millimeter wavelength wireless technology (e.g., 60GHz), data centers are going to be more convenient for the interconnection of different components without complex wiring. 60GHz technique has been widely studied, and great progress has been achieved indeed. However, there are two significant challenges unsolved in 60GHz-based wireless data centers networks: link blockage and long network diameter. In this paper, we propose a new network topology, called Spherical Mesh, to improve the performance of wireless data center networks. Spherical Mesh can greatly mitigate link blockage by putting antennas at different heights, and decrease the network diameter by splitting the whole mesh into several equivalent units. Furthermore, we have implemented Spherical Mesh and evaluated its performance in different scenarios. Our evaluation results show that Spherical Mesh achieves minimal routing distance and guarantees the balance of workload.
People Tracking and Recognition using the Multi-Object Particle Filter Algorithm and Hierarchical PCA Method

This paper presents a method to detect, recognize and track people using mount cameras fixed on a building. The method consists of two independent stages. One is dedicated to detect and track any moving object within the image frame. The other one is in charge to discard any moving object that is not a human being. To perform the first task, a particle filter algorithm is used, in such way that it can perform the tracking of multiple objects. For the recognition stage a PCA (principal components analysis) method is applied to several body parts (head, arms, etc.) respecting their geometrical constraints. The performance of the system has been tested successfully. Some experimental results and conclusions are presented