Miracle-dz/UniFAR-FTUs huggingface dataset:
[Evolution of voice assessment and quality of life after endoscopic laser cordectomy].

OBJECTIVES Endoscopic laser cordectomy is a minimally invasive surgical technique for Tis, T1 and selected T2 glottic carcinoma. The purpose of the study was to evaluate prospectively pre-operatively and 3 months after surgery i) acoustic and perceptual parameters, ii) quality of life through a self evaluation (VHI) test. METHODS A prospective longitudinal evaluation of 13 male patients, shared-out onto 3 surgical procedures (cordectomy type I, grouped type II and III, and type V). Patients were recorded before and at 1 and 3 months after surgery. The parameters recorded were; acoustic parameters (F0, Jitter, Shimmer), Maximum Phonation Time (MPT), perceptual evaluation using the GRBAS scale and the self evaluation scale with the Voice Handicap Index (VHI). RESULTS No statistics could be done because of the limited number of patients in each group. Perceptual and self evaluation data showed a favorable progression between 1 and 3 months for patients in group Type I, II and III cordectomy. Evolution of acoustic parameters is more difficult to analyze. The voice of the Cordectomy type V group remains whispery at 3 months after surgery. CONCLUSION The quality of voice is a very important criterion for the patients. During the first 3 months after laser cordectomy, the perceptual vocal function and the self evaluation improves. But for the acoustic data, 3 months is probably too short to objectively quantify an improvement especially after type V cordectomy.
Antisense Phosphorothioate Oligodeoxynucleic Acid for CD10 Suppresses Liver Metastasis of Colorectal Cancer

CD10 expression is associated with metastases of colorectal cancer (CRC). In the present study, we examined association of CD10 with liver metastasis of CRC cells to clarify the therapeutic significance of CD10. CD10-positive human colon cancer cell line, HT29 cells showed inhibition of growth, invasion and colony formation by treatment with CD10 antisense S-oligodeoxynucleotide (S-ODN). In the mouse liver metastasis mode, CD10 antisense S-ODN-treated HT29 cells made less embedded cells in the liver than control HT29 cells. Number and size of metastatic foci in nude mice liver were reduced in CD10 antisense S-ODN-treated HT29 cells. Treatment with CD10 antisense S-ODN decreased phosphorylation of ERK1/2 and EGFR in HT29 cells. Intraperitoneal administration of liposome-capsulated CD10 antisense S-ODN inhibited establishment of liver metastasis and growth of established metastasis in nude mice. These findings suggest that CD10 is associated substantially with liver metastasis of CRC cells and might be a molecular target of CRC treatment.
Adaptive discrete lens antenna array for direction of arrival detection

This paper discusses a hardware-software architecture for adaptive direction-of-arrival (DOA) estimation using receive antenna arrays. Analog pre-processing, the spatial Fourier transform of the sources, is performed by a discrete lens array (DLA), and is followed by downconversion and processing at baseband using a least means square (LMS) adaptive algorithm
Sensing through the continent: Towards monitoring migratory birds using cellular sensor networks

This paper presents CraneTracker, a novel sensor platform for monitoring migratory birds. The platform is designed to monitor Whooping Cranes, an endangered species that conducts an annual migration of 4,000 km between southern Texas and north-central Canada. CraneTracker includes a rich set of sensors, a multi-modal radio, and power control circuitry for sustainable, continental-scale information delivery during migration. The need for large-scale connectivity motivates the use of cellular technology in low-cost sensor platforms augmented by a low-power transceiver for ad-hoc connectivity. This platform leads to a new class of cellular sensor networks (CSNs) for time-critical and mobile sensing applications. The CraneTracker is evaluated via field tests on Wild Turkeys, Siberian Cranes, and an on-going alpha deployment with wild Sandhill Cranes. Experimental evaluations demonstrate the potential of energy-harvesting CSNs for wildlife monitoring in large geographical areas, and reveal important insights into the movements and behaviors of migratory animals. In addition to benefiting ecological research, the developed platform is expected to extend the application domain of sensor networks and enable future research applications.
Membrane-associated RING-CH (MARCH) 8 mediates the ubiquitination and lysosomal degradation of the transferrin receptor

Summary The transferrin receptor (TfR) mediates the uptake of transferrin (Tf)-bound iron from the plasma into the cells of peripheral tissues. The TfR continuously recycles between the plasma membrane and early/recycling endosomes. TfR expression is tightly controlled by the intracellular iron concentration through the regulation of TfR mRNA stability. However, much less is known about the mechanism by which TfR is degraded in cells. Previously, we reported a correlation between TfR ubiquitination and its iron-induced lysosomal degradation. The identification and characterization of a specific ubiquitin ligase for TfR is important in understanding the mechanism of iron homeostasis. Here, we show that membrane-associated RING-CH (MARCH) 8 ubiquitinates TfR and promotes its lysosomal degradation. Similar to other RING-type ubiquitin ligases, the RING-CH domain of MARCH8, which is located in the N-terminal cytoplasmic domain, is essential for the ubiquitination and downregulation of TfR. MARCH8 specifically recognizes the transmembrane domain of TfR and mediates ubiquitination of its cytoplasmic domain. In addition, the six-amino-acid sequence located in the C-terminal domain of MARCH8, which is highly conserved among different species, is required for the downregulation of TfR. Finally, and most importantly, TfR expression was markedly increased by siRNA-mediated knockdown of endogenous MARCH8. These findings demonstrate that the endogenous level of MARCH8 regulates TfR protein turnover through the downregulation and ubiquitination of TfR.
Lightweight Modeling of Complex State Dependencies in Stream Processing Systems

Over the last few years, Real-Time Calculus has been used extensively to model and analyze embedded systems processing continuous data/event streams. Towards this, bounds on the arrival process of streams and bounds on the processing capacity of resources serve as inputs to the model, which are used to calculate end-to-end delays suffered by streams, maximum backlog, utilization of resources, etc. This "functional'' model, although amenable to computationally inexpensive analysis methods, has limited modeling capability. In particular, "state-based'' processing, e.g. blocking write -- where the processing depends on the "state'' or fill-level of the buffer -- cannot be modeled in a straightforward manner. This has led to a number of recent proposals on using automata-theoretic models for stream processing systems (e.g. Event Count Automata [RTSS 2005]). Although such models offer better modeling flexibility, they suffer from the usual state-space explosion problem. In this paper we show that a number of complex state-dependencies can be modeled in a lightweight manner, using a feedback control technique. This avoids explicit state modeling, and hence the state-space explosion problem. Our proposed modeling and analysis therefore extend the original Real-Time Calculus-based functional modeling in a very useful way, and cover much larger problem domain compared to what was previously possible without explicit state-modeling. We illustrate its utility through two case studies and also compare our analysis results with those obtained from detailed system simulations (which are significantly more time consuming).
On Detecting Camouflaging Worm

Active worms pose major security threats to the Internet. In this paper, we investigate a new class of active worms, i.e., camouflaging worm (C-Worm in short). The C-Worm has the capability to intelligently manipulate its scan traffic volume over time, thereby camouflaging its propagation from existing worm detection systems. We analyze characteristics of the C-Worm and conduct a comprehensive comparison between its traffic and non-worm traffic. We observe that these two types of traffic are barely distinguishable in the time domain, however, their distinction is clear in the frequency domain, due to the recurring manipulative nature of the C-Worm. Motivated by our observations, we design a novel spectrum-based scheme to detect the C-Worm. Our scheme uses the power spectral density (PSD) distribution of the scan traffic volume and its corresponding spectral flatness measure (SFM) to distinguish the C-Worm traffic from non-worm traffic. We conduct extensive performance evaluations on our proposed detection scheme against the C-Worm. The performance data clearly demonstrates that our proposed scheme can effectively detect the C-Worm propagation
Model Driven Performance Simulation of Cloud Provisioned Hadoop MapReduce Applications

Hadoop is a widely adopted open source implementation of MapReduce. A Hadoop cluster can be fully provisioned by a Cloud service provider to provide elasticity in computational resource allocation. Understanding the performance characteristics of a Hadoop job can help achieve an optimal balance between resource usage (cost) and job latency on a cloud-based cluster. This paper presents a method that estimates the performance of a MapReduce application in a Cloud provisioned Hadoop cluster. We develop a model-driven approach that models a cloud provided independent Hadoop MapReduce model and customizes it for a specific Cloud deployment. These models are further transformed into a simulation model that produces estimations of end-to-end job latency. We explore this method in the design space of MapReduce applications to estimate the performance for different sizes of input data. Our approach provides a model-to-simulation-to-prediction method for observing the performance behavior of MapReduce applications given a configuration of a MapReduce platform.
Phase-Field Modeling of Electrochemical Phenomena

In this article, we review the progress in the field of application of phase-field models for simulating electrochemical phenomena such as etching, electro-deposition, electromigration, intercalation etc. As we will see the present models can be considered as extensions of the already existing models for diffusion coupled phase transformations. We briefly visit the essential thermodynamics of the electrochemical interfaces and the basis of phase-field formulations existing in literature for modeling electrochemical reactions and electromigration. Thereafter, we give a brief overview of the present state of literature in this field.
Under Consideration for Publication in J. Functional Programming Meta-programming with Names and Necessity

Meta-programming is a discipline of writing programs in a certain programming language that generate, manipulate or execute programs written in another language. In a typed setting, meta-programming languages usually contain a modal type constructor to distinguish the level of object programs (which are the manipulated data) from the meta programs (which perform the computations). In functional programming, modal types of object programs generally come in two flavors: open and closed, depending on whether the expressions they classify may contain any free variables or not. Closed object programs can be executed at run-time by the meta program, but the computations over them are more rigid, and typically produce less efficient residual code. Open object programs provide better inlining and partial evaluation, but once constructed, expressions of open modal type cannot be evaluated. Recent work in this area has focused on combining the two notions into a sound type system. We present a novel calculus to achieve this, which we call ν. It is based on adding the notion of names inspired by the work on Nominal Logic and FreshML to the λ-calculus of proof terms for the necessity fragment of modal logic S4. The resulting language provides a more fine-grained control over free variables of object programs when compared to the existing languages for meta-programming.
Identifying sites of replication initiation in yeast chromosomes: Looking for origins in all the right places

DNA fragments that contain an active origin of replication generate bubble‐shaped replication intermediates with diverging forks. We describe two methods that use two‐dimensional (2‐D) agarose gel electrophoresis along with DNA sequence information to identify replication origins in natural and artificial Saccharomyces cerevisiae chromosomes. The first method uses 2‐D gels of overlapping DNA fragments to locate an active chromosomal replication origin within a region known to confer autonomous replication on a plasmid. A variant form of 2‐D gels can be used to determine the direction of fork movement, and the second method uses this technique to find restriction fragments that are replicated by diverging forks, indicating that a bidirectional replication origin is located between the two fragments. Either of these two methods can be applied to the analysis of any genomic region for which there is DNA sequence information or an adequate restriction map.
Ultrasonic tomographic flowmeter using dual-transducers for the reconstruction of an asymmetric velocity profile

Tomographic ultrasonic flow meter (TUF) is a system that is capable of reconstructing the velocity profile and estimating the average flow velocity with better accuracy than the conventional ultrasonic flow meter (USM). Unfortunately, the TUF has a lower temporal resolution due to its need of large number of measurement data from tranduscers. In this work, two pairs of transducer modules, called dual-ultrasonic transducers, are implemented to reduce the required time for data collection. Each pair of the transducers consists of a transmitter and a receiver. This technique can measure the time of flight(TOF) of ultrasonic wave from upstream to downstream, and vice versa, simultaneously. Numerical simulations and experiments are conducted to investigate the performance of the TUF for reconstructing an asymmetric velocity profile. The numerical simulation result shows that the TUF with six pairs of ultrasonic transducers successfully reconstruct a 2D velocity profile that is close to the results obtained by theoretical analysis. The path configuration of six pairs of dual-ultrasonic transducers has been implemented experimentally on a TUF prototype. The experimental results show that the processing time for data acquisition is about 40ms. The velocity profile obtained by TUF is also confirmed with the USM technique, based on the diametrical and mid radius configurations. The results show that the air velocity measured by every path of those configurations is consistent with that obtained by the TUF in the same position. The differences vary from 0.355% to 19.62% and from 0.304% to 14.51% for mid radius and diametral, respectively, even though none of their acoustic path are directly coincide.
Understanding Individual and Collaborative Problem-Solving with Patient-Generated Data

Making effective use of patient-generated data (PGD) is challenging for both patients and providers. Designing systems to support collaborative and individual use of PGD is a topic of importance in CSCW, considering the limitations of informatics tools. To inform better system design, we conducted a study including focus groups, observations and interviews with patients and providers to understand how PGD is interpreted and used. We found that while PGD is useful for identifying and solving disease-related problems, the following differences in patient-provider perceptions challenge its effective use - different perceptions about what is a problem, selecting what kinds of problems to focus on, and using different data representations. Drawing on these insights, we reflect on two specific conceptualizations of disease management behavior (sensemaking and problem-solving) as they relate to data specific activities of patients and providers and provide design suggestions for tools to support collaborative and individual use of PGD.
Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data

Research in oncology quality of care and health outcomes has been limited by the difficulty of identifying cancer stage in health care claims data. Using linked cancer registry and Medicare claims data, we develop a tool for classifying lung cancer patients receiving chemotherapy into early vs. late stage cancer by (i) deploying ensemble machine learning for prediction, (ii) establishing a set of classification rules for the predicted probabilities, and (iii) considering an augmented set of administrative claims data. We find our ensemble machine learning algorithm with a classification rule defined by the median substantially outperforms an existing clinical decision tree for this problem, yielding full sample performance of 93% sensitivity, 92% specificity, and 93% accuracy. This work has the potential for broad applicability as provider organizations, payers, and policy makers seek to measure quality and outcomes of cancer care and improve on risk adjustment methods.
Belief Revision and Progression of Knowledge Bases in the Epistemic Situation Calculus

Fundamental to reasoning about actions and beliefs is the projection problem: to decide what is believed after a sequence of actions is performed. Progression is one widely applied technique to solve this problem. In this paper we propose a novel framework for computing progression in the epistemic situation calculus. In particular, we model an agent's preferential belief structure using conditional statements and provide a technique for updating these conditional statements as actions are performed and sensing information is received. Moreover, we show, by using the concepts of natural revision and only-believing, that the progression of a conditional knowledge base can be represented by only-believing the revised set of conditional statements. These results lay the foundations for feasible belief progression due to the unique-model property of only-believing.
Timing driven gate duplication

In the past few years, gate duplication has been studied as a strategy for cutset minimization in partitioning problems. This paper addresses the problem of delay optimization by gate duplication. We present an algorithm to solve the gate duplication problem. It traverses the network from primary outputs(PO) to primary inputs(PI) in topologically sorted order evaluating tuples at the input pins of gates. The tuple's first component corresponds to the input pin required time if that gate is not duplicated. The second component corresponds to the input pin required time if that gate were duplicated. After tuple evaluation the algorithm traverses the network from PI to PO in topologically sorted order, deciding the gates to be duplicated. The last and final traversal is again from PO to PI, in which the gates are physically duplicated. Our algorithm uses the dynamic programming structure. We report delay improvements over other optimization methodologies. Gate duplication, along with other optimization strategies, can be used for meeting the stringent delay constraints in today's ultra complex designs.
Challenges in Achieving IaaS Cloud Interoperability across Multiple Cloud Management Frameworks

In academia, many institutes have deployed IaaS cloud services in isolation, based on cloud management frameworks of their choice, making the cloudscape quite heterogeneous. Now, with the effort to federate cloud resources well underway, the challenge is not to make everyone switch to a common solution, but rather to allow everyone to keep their setup but still provide uniform access to essential services. This seems to be best achieved by implementing common standards. This paper discusses the different areas such standardization effort must address, ranging from the choice of the actual standardization approach (i.e., Choosing a recognized open standard or possibly a non-standard solution with a strong community backing) through authentication and virtual machine life-cycle management to monitoring or accounting (billing) services. The discussion is based on the authors' own practical experience with implementing standard compliance in multiple cloud management frameworks, producing a real-world interoperability solution -- the rOCCI Framework - which is also introduced in the paper.
An empirical study of user preference and performance with UML diagrams

Elicitation and validation of user requirements depend, to a large extent, on the effectiveness of the tools and techniques used as a vehicle for discussion between developers and users during the requirements process. This effectiveness may, in turn, be influenced by user preference for a particular approach or requirements technique. This paper describes a study that was carried out to investigate the relationship between user preference for sequence or collaboration diagrams in UML, with their accuracy in understanding information contained in the diagrams. Results showed that user preference for one of the two diagram types before carrying out the task was not reflected in improved performance with that type of diagram compared with the other. However, after carrying out the task, user statements about which type of diagram they preferred working with were matched by improved performance with that type of diagram.
Heterogeneous Cellular Networks Using Wireless Backhaul: Fast Admission Control and Large System Analysis

We consider a heterogeneous cellular network with densely underlaid small cell access points (SAPs). Wireless backhaul provides the data connection from the core network to SAPs. To serve as many SAPs and their corresponding users as possible with guaranteed data rates, admission control of SAPs needs to be performed in wireless backhaul. Such a problem involves joint design of transmit beamformers, power control, and selection of SAPs. In order to tackle such a difficult problem, we apply ℓ1-relaxation and propose an iterative algorithm for the ℓ1-relaxed problem. The selection of SAPs is made based on the outputs of the iterative algorithm, and we prove such an algorithm converges locally. Furthermore, this algorithm is fast and enjoys low complexity for small-to-medium sized systems. However, its solution depends on the actual channel state information, and resuming the algorithm for each new channel realization may be unrealistic for large systems. Therefore, we make use of the random matrix theory and also propose an iterative algorithm for large systems. Such a large-system iterative algorithm can produce the asymptotically optimum solution for the $\ell_1$-relaxed problem, which only requires large-scale channel coefficients irrespective of the actual channel realization. Near optimum results are achieved by our proposed algorithms in simulations.
The Computational Complexity of Provability in Systems of Modal Propositional Logic

The computational complexity of the provability problem in systems of modal propositional logic is investigated. Every problem computable in polynomial space is $\log $ space reducible to the provability problem in any modal system between K and $S4$. In particular, the provability problem in K, T, and $S4$ are $\log $ space complete in polynomial space. The nonprovability problem in $S5$ is $\log $ space complete in nondeterministic polynomial time.