Miracle-dz/UniFAR-FTUs huggingface dataset:
Probability Sampling by Connecting Space with Households Using GIS/GPS Technologies

Abstract Sampling methods for survey studies are challenged by the replacement of landline telephones with mobile phones, the lack of timely census data, and the growing need for studies to address new health challenges. GIS/GPS-assisted methods provide a promising alternative, but these methods need further improvement. We established a stratified 3-stage GIS/GPS-assisted sampling method in which residential areas of a target population are divided into mutually exclusive cells – geographic units (geounits) as the primary sampling frame (PSF). Geounits with residential households were randomly selected from the PSF with a semi-automatic algorithm implemented in R. Novel methods were used to sample households and participants. Simulations and application studies indicated adequate feasibility, efficiency and validity of the method in sampling rural-to-urban migrants from a large city with complex residential arrangements. With this method, researchers can determine sample size and number of geounits, households and participants to be sampled; optimally allocate geounits; determine area size of sampled geounits and estimate sample weights; and complete sampling for field data collection in a short period. Our method adds an integrative approach for GIS/GPS-assisted random sampling with a de facto population assumption. Additional evaluation studies are needed to assess the utility of this method in different settings.
Magnetic Properties of Deep‐Sea Sediments From the North Pacific: A Proxy of Glacial Deep‐Water Ventilation

The magnetic properties of deep‐sea sediments in the North Pacific vary in sync with glacial‐interglacial climatic changes and have thus been considered as a paleoclimate indicator. However, the causes of these variations in magnetic properties, including the concentration, grain size, and mineralogy of magnetic minerals, remain poorly understood. Here we investigate the magnetic properties of deep‐sea sediments recovered from the Shatsky Rise in the North Pacific to investigate the cause of the variability associated with glacial‐interglacial cycles. Compared to the potential sediment provenances, the interglacial‐stage and more than half of the glacial‐stage sediments reveal magnetic mineralogical properties (S ratio and L ratio) of the Gobi and Taklimakan deserts, probably indicating dust from the both sources. During glacials, an abrupt decrease in the concentration of magnetic minerals is associated with increases in the average grain size and in the proportion of hematite relative to magnetite. The changes during glacials are mostly a result of dissolutions of magnetite by exposure to ambient ocean environments after deposition. Both magnetic and geochemical proxies validate that poor ventilation of the glacial deep waters in the North Pacific possibly induced iron‐reducing conditions. With comprehensive compilation with the S‐ratio data in the North Pacific sediments, our result indicates that the poorly ventilated deep waters reached up to water depths of ~2,500 m during glacials.
N-gram Language Modeling using Recurrent Neural Network Estimation

We investigate the effective memory depth of RNN models by using them for $n$-gram language model (LM) smoothing. Experiments on a small corpus (UPenn Treebank, one million words of training data and 10k vocabulary) have found the LSTM cell with dropout to be the best model for encoding the $n$-gram state when compared with feed-forward and vanilla RNN models. When preserving the sentence independence assumption the LSTM $n$-gram matches the LSTM LM performance for $n=9$ and slightly outperforms it for $n=13$. When allowing dependencies across sentence boundaries, the LSTM $13$-gram almost matches the perplexity of the unlimited history LSTM LM. LSTM $n$-gram smoothing also has the desirable property of improving with increasing $n$-gram order, unlike the Katz or Kneser-Ney back-off estimators. Using multinomial distributions as targets in training instead of the usual one-hot target is only slightly beneficial for low $n$-gram orders. Experiments on the One Billion Words benchmark show that the results hold at larger scale: while LSTM smoothing for short $n$-gram contexts does not provide significant advantages over classic N-gram models, it becomes effective with long contexts ($n > 5$); depending on the task and amount of data it can match fully recurrent LSTM models at about $n=13$. This may have implications when modeling short-format text, e.g. voice search/query LMs. Building LSTM $n$-gram LMs may be appealing for some practical situations: the state in a $n$-gram LM can be succinctly represented with $(n-1)*4$ bytes storing the identity of the words in the context and batches of $n$-gram contexts can be processed in parallel. On the downside, the $n$-gram context encoding computed by the LSTM is discarded, making the model more expensive than a regular recurrent LSTM LM.
Do community-level predictors of pneumococcal carriage continue to play a role in the conjugate vaccine era?

SUMMARY This paper examined whether previously identified community-level factors (high proportion of crowded households and/or persons below the poverty level) remained associated with childhood pneumococcal carriage in the heptavalent pneumococcal conjugate vaccine (PCV7) era. Using logistic regression, individual factors were used to develop base models to which community-level factors were added to evaluate impact on pneumococcal carriage within two paediatric study cohorts from Massachusetts (urban Boston, outside Boston). Six years after introduction of universal childhood PCV7 vaccination, we found no consistent evidence that census tract characteristics (e.g. population size and density, age and race distribution, percent participating in group childcare, parental education, percent lacking in-unit plumbing, poverty, and community stability) affected odds of pneumococcal carriage when added to individual predictors (e.g. younger age, current respiratory tract infections, and attendance in group childcare). How community-level factors influence pneumococcal carriage continues to change in the era of increasing immunization coverage.
A versatile computational pipeline for bacterial genome annotation improvement and comparative analysis, with Brucella as a use case

We present a bacterial genome computational analysis pipeline, called GenVar. The pipeline, based on the program GeneWise, is designed to analyze an annotated genome and automatically identify missed gene calls and sequence variants such as genes with disrupted reading frames (split genes) and those with insertions and deletions (indels). For a given genome to be analyzed, GenVar relies on a database containing closely related genomes (such as other species or strains) as well as a few additional reference genomes. GenVar also helps identify gene disruptions probably caused by sequencing errors. We exemplify GenVar's capabilities by presenting results from the analysis of four Brucella genomes. Brucella is an important human pathogen and zoonotic agent. The analysis revealed hundreds of missed gene calls, new split genes and indels, several of which are species specific and hence provide valuable clues to the understanding of the genome basis of Brucella pathogenicity and host specificity.
Energy-Aware Virtual Network Embedding

Virtual network embedding, which means mapping virtual networks requested by users to a shared substrate network maintained by an Internet service provider, is a key function that network virtualization needs to provide. Prior work on virtual network embedding has primarily focused on maximizing the revenue of the Internet service provider and did not consider the energy cost in accommodating such requests. As energy cost is more than half of the operating cost of the substrate networks, while trying to accommodate more virtual network requests, minimizing energy cost is critical for infrastructure providers. In this paper, we make the first effort toward energy-aware virtual network embedding. We first propose an energy cost model and formulate the energy-aware virtual network embedding problem as an integer linear programming problem. We then propose two efficient energy-aware virtual network embedding algorithms: a heuristic-based algorithm and a particle-swarm-optimization-technique-based algorithm. We implemented our algorithms in C++ and performed side-by-side comparison with prior algorithms. The simulation results show that our algorithms significantly reduce the energy cost by up to 50% over the existing algorithm for accommodating the same sequence of virtual network requests.
Palmar-Plantar Erythrodysesthesia Associated with Chemotherapy and Its Treatment

Palmar-plantar erythrodysesthesia (PPE), also called hand-foot syndrome, is a relatively common dermatologic toxic reaction associated with cytotoxic chemotherapy that can limit the use of such drugs. Definitive prevention and treatment strategies for PPE have not yet been established. We present a patient with recurrent ovarian cancer developing severe hand-foot syndrome after treatment with pegylated liposomal doxorubicin. A review of the relevant literature concerning pathophysiology, preventive measures and management of PPE is given. Electronic search was conducted using the Medline database for English-language records. The search terms used were ‘palmar-plantar erythrodysesthesia’, ‘hand-foot syndrome’, ‘pegylated liposomal doxorubicin’ and ‘acral erythema’.
Testing atomicity of composed concurrent operations

We address the problem of testing atomicity of composed concurrent operations. Concurrent libraries help programmers exploit parallel hardware by providing scalable concurrent operations with the illusion that each operation is executed atomically. However, client code often needs to compose atomic operations in such a way that the resulting composite operation is also atomic while preserving scalability. We present a novel technique for testing the atomicity of client code composing scalable concurrent operations. The challenge in testing this kind of client code is that a bug may occur very rarely and only on a particular interleaving with a specific thread configuration. Our technique is based on modular testing of client code in the presence of an adversarial environment; we use commutativity specifications to drastically reduce the number of executions explored to detect a bug. We implemented our approach in a tool called COLT, and evaluated its effectiveness on a range of 51 real-world concurrent Java programs. Using COLT, we found 56 atomicity violations in Apache Tomcat, Cassandra, MyFaces Trinidad, and other applications.
Speech recognition using soft decision trees

This paper presents recent developments at our site toward speech recognition using decision tree based acoustic models. Previously, robust decision trees have been shown to achieve better performance compared to standard Gaussian mixture model (GMM) acoustic models. This was achieved by converting hard questions (decisions) of a standard tree into soft questions using sigmoid function. In this paper, we report our work where soft-decision trees are trained from scratch. These softdecision trees are shown to yield better speech recognition accuracy compared to standard GMM acoustic models on Aurora digit recognition task.
- 1-Rainfall Amount and Intensity measured by the Thies Laser Precipitation Monitor

The Laser Precipitation Monitor (LPM) manufactured by Thies is a Present Weather sensor based on an optical disdrometer. Thus rainfall amounts can be derived from the disdrometric information. The factory calibration process for the volume measurement is described and the resulting measurement uncertainty is given. In this paper rainfall amount measured by the LPM is compared with measurements of reference rainfall gauges such as a pit gauge. Rainfall intensities are evaluated in comparison with postprocessed high resolution data of a Pluvio (OTT) weighing rain gauge.
Schulze et al Cardiac Lipotoxicity 1737 Evidence of Lipotoxicity in the Human Heart

The heart uses large amounts of fatty acids (FAs) as energyproviding substrates. More than 70% of all substrates used for ATP generation are derived from FAs, with the remaining sources being glucose, lactate, ketone bodies, and amino acids. The relatively tight coupling between lipid uptake and oxidation prevents accumulation of excess lipids in the cardiomyocyte. Several processes that affect heart function, including ischemia, sepsis, and heart failure, are associated with a reduction in FA oxidation (FAO) with a relative increase in anaerobic glycolysis and, in some cases, accumulation of nonoxidized FA derivatives in the form of lipids. In addition, excess circulating FA levels in type 2 diabetes mellitus and its precursor, the metabolic syndrome, also cause cardiac lipid accumulation. There is increasing evidence that many of these lipids worsen heart function and lead to structural myocardial damage, including cardiac fibrosis, myocyte apoptosis, and reduced contractility, which is often thought to be caused by mitochondrial dysfunction. However, the reversibility of this dysfunction along with several key steps of oxidative and glycolytic metabolism after the correction of cardiac stress and failure suggest that mitochondrial dysfunction is a reversible adaptation and is secondary to the altered metabolic pathways. Several altered metabolic pathways lead to lipid accumulation. Experimental and clinical data provide evidence that lipid accumulation causes or exacerbates heart dysfunction, a process termed cardiac lipotoxicity. In support of this hypothesis, several recent studies have shown that reduction of toxic lipids is associated with improved metabolism and function of the heart. Obesity, Diabetes, and Cardiovascular Diseases Compendium
Associations of C-Reactive Protein to Indices of Vascular Health and the Influence of Serum 25(OH)D Status in Healthy Adults

Elevated serum high-sensitivity C-reactive protein (hs-CRP) and low serum 25-hydroxyvitamin D [25(OH)D] are associated with increased cardiovascular disease (CVD) risk. Ethnic differences in serum hs-CRP and 25(OH)D concentrations and CVD are known. Objectives: to investigate the ethnic differences in hs-CRP concentrations, to assess the influence of 25(OH)D on these ethnic differences and to examine the influence of 25(OH)D on association between hs-CRP and cardiovascular health indices. Subjects: 62 healthy adults [26 African Americans (AA), 26 European Americans (EA), and 10 Hispanic Americans (HA)], ages 18–55 years. Serum hs-CRP and 25(OH)D as well as pulse wave velocity (PWV), augmentation index (AIx), and flow-mediated dilatation (FMD) were measured. hs-CRP was inversely associated with 25(OH)D (r = −0.25, P = 0.049), and hs-CRP was positively associated with PWV (r = 0.29, P = 0.04). The association of hs-CRP with PWV attenuated after adjustment for 25(OH)D (P = 0.15). hs-CRP was higher in AA compared to EA (P = 0.05); this differences was reduced by 32% after adjusting for serum 25(OH)D. Conclusion: eventhough the inverse association between serum 25(OH)D and CRP does not infer causality, lower serum 25(OH)D may increase risk for inflammation and endothelial dysfunction. The lower 25(OH)D in AA may predispose to greater inflammation and associated vascular dysfunction.
Solution of the Rayleigh Eigenproblem in Viscoelastic Media

We present a technique for the solution of the complex-valued eigenproblem associated with the propagation of surface waves in general linear viscoelastic media. The new technique permits simultaneous determination of the Rayleigh dispersion and attenuation curves and the displacement and stress eigenfunctions for vertically heterogeneous, linear viscoelastic media with arbitrary values of material damping ratio. The technique is based on the Cauchy residue theorem of complex analysis that takes full advantage of the holomorphic properties of the Rayleigh secular function, which is viewed as an analytic mapping of the complex-valued Rayleigh phase velocity. Because the eigenvalue problem is solved directly in the complex domain with no simplifying assumptions, the algorithm implicitly accounts for the inherent coupling between phase velocity and attenuation of seismic waves as a result of material dispersion. The technique overcomes the limitations of previous algorithms that often break up the complex structure of the problem and/or require a priori information about the number of eigenvalues and their approximate value. The algorithm is validated via comparisons with closed-form solutions for a uniform half-space. Examples are also used to compare solutions obtained with the proposed technique and one based on the assumption of weak dissipation in strongly and weakly dissipative layered media.
Opportunistic scheduling and relaying in a cooperative cognitive network

This paper considers network-layer cooperation in cognitive radio networks whereby secondary users can relay primary user's packets, in return for a more favorable spectrum access rules. Under this cooperative scheme, the paper investigates whether, and under what conditions, the primary and secondary networks can be stabilized without explicit knowledge of the packet arrival-rates. We consider a deterministic and periodic primary packet arrival process and develop a relaying and scheduling algorithm using Lyapunov drift techniques that does not require knowledge of primary and secondary packet arrival rates. The algorithm is then shown to stabilize the transmission queues in the network for all secondary packet arrival rates that lie in the interior of a certain region. The region includes all secondary arrival-rate vectors that can be supported when the secondary nodes do not cooperate. Furthermore, when the primary data arrival-rate is greater than what could have been supported without relays but less than what can be maximally supported with relays, the algorithm stabilizes the network for a non-empty set of secondary arrival-rate vectors. The significance of these results is that they show that properly designed cooperation may result in a win-win scenario for both primary and secondary users (and not just for one type of users). Finally we extend our analysis to the case of a deterministic but aperiodic primary packet arrival process.
Climate Change Impact on Water Resources in the Awash Basin, Ethiopia

Rapid growth of agriculture, industries and urbanization within the Awash basin, Ethiopia, as well as population growth is placing increasing demands on the basin’s water resources. In a basin known for high climate variability involving droughts and floods, climate change will likely intensify the existing challenges. To quantify the potential impact of climate change on water availability of the Awash basin in different seasons we have used three climate models from Coupled Models Inter-comparison Project phase 5 (CMIP5) and for three future periods (2006–2030, 2031–2055, and 2056–2080). The models were selected based on their performance in capturing historical precipitation characteristics. The baseline period used for comparison is 1981–2005. The future water availability was estimated as the difference between precipitation and potential evapotranspiration projections using the representative concentration pathway (RCP8.5) emission scenarios after the climate change signals from the climate models are transferred to the observed data. The projections for the future three periods show an increase in water deficiency in all seasons and for parts of the basin, due to a projected increase in temperature and decrease in precipitation. This decrease in water availability will increase water stress in the basin, further threatening water security for different sectors, which are currently increasing their investments in the basin such as irrigation. This calls for an enhanced water management strategy that is inclusive of all sectors that considers the equity for different users.
Expression of RAGE and HMGB1 in Thymic Epithelial Tumors, Thymic Hyperplasia and Regular Thymic Morphology

Recently, a role of the receptor for advanced glycation endproducts (RAGE) in myasthenia gravis was described. RAGE and its ligand high mobility group box 1 (HMGB1) play key roles in autoimmunity and cancer. To test whether these molecules are involved in patients with thymic abnormalities we applied immunohistochemical analysis in 33 cases of thymic epithelial tumors, comprising 27 thymomas and 6 thymic carcinomas, and 21 nonneoplastic thymuses. Both molecules were detected in neoplastic epithelial cells: RAGE staining was most intense in WHO type B2 thymomas and thymic carcinomas (p<0.001). HMGB1 nuclear staining was strongest in A and AB, and gradually less in B1 = B2>B3>thymic carcinoma (p<0.001). Conversely, HMGB1 cytoplasmic staining intensities were as follows: A and AB (none), B1 (strong), B2 (moderate), B3 and thymic carcinoma (weak); (p<0.001). Fetal thymic tissue showed a distinct expression of RAGE and HMGB1 in subcapsular cortical epithelial cells which was found in 50% of myasthenic patients. Furthermore RAGE and HMGB1 were expressed in thymocytes, macrophages, Hassall's corpuscles, thymic medulla, and germinal center cells in myasthenic patients. Immunohistochemistry results were complemented by systemic measurements (immunosorbent assay): serum levels of soluble RAGE were significantly reduced in patients with epithelial tumors (p = 0.008); and in invasive tumors (p = 0.008). Whereas RAGE was equally reduced in thymic hyperplasia and epithelial tumors (p = 0.003), HMGB1 was only elevated in malignancies (p = 0.036). Results were most pronounced in thymic carcinomas. Thus, RAGE and HMGB1 are involved in the (patho-)physiology of thymus, as evidenced by differentiated thymic and systemic expression patterns that may act as diagnostic or therapeutic targets in autoimmune disease and cancer.
Partial elimination of Epstein-Barr virus plasmids from Burkitt's lymphoma cells by transfecting the BZLF1 gene

Epstein-Barr virus (EBV) nonproducer Raji cells stably maintain approximately 45 copies of the EBV genome per cell, depending on the presence of the EBV-determined nuclear antigen 1 (EBNA-1) protein. We found that transfection of the EBV BZLF1 gene causes the disappearance of EBNA proteins on Western blots (immunoblots). On the basis of these results, we attempted to eliminate EBV plasmids in Raji cells by transfecting a BZLF1 plasmid. Among 33 clones that were cotransfected with a BZLF1 plasmid and a hygromycin B resistance plasmid and selected resistant for hygromycin B, 24 clones had decreased numbers of EBV plasmids, as revealed by the decrease in the intensity of the EBV band on Southern blots compared with that of nontransfected Raji cells.
Partisan choices in a direct-democratic campaign

Ever since Lazarsfeld and his colleagues’ (1944) seminal study, it has become common wisdom that election campaigns, if anything, serve the activation of voters’ fundamental predispositions. However, disagreement emerges on the role of partisan orientations. Although some authors consider them as fundamental predispositions, which are activated during the campaign and subsequently act as filters for incoming information, others argue that party attachments are simple running tallies of political assessments, which are constantly updated in response to campaign events, or decision shortcuts for voters innocent of substantial information. In this study, we scrutinize the role of partisan orientations in a direct-democratic campaign using data from a panel survey fielded during the run-up to the 2006 Swiss asylum law referendum. We find that, as voters accumulate knowledge in the course of the campaign, vote intentions dramatically converge on pre-campaign partisan orientations. Moreover, voters, whose earlier issuespecific and partisan orientations collide, tend to resolve their ambivalence in favour of their partisan leanings. These results corroborate the view of partisanship as a fundamental predisposition.