Miracle-dz/UniFAR-FTUs huggingface dataset:
HIV Control Is Mediated in Part by CD8+ T-Cell Targeting of Specific Epitopes

ABSTRACT We investigated the hypothesis that the correlation between the class I HLA types of an individual and whether that individual spontaneously controls HIV-1 is mediated by the targeting of specific epitopes by CD8+ T cells. By measuring gamma interferon enzyme-linked immunosorbent spot (ELISPOT) assay responses to a panel of 257 optimally defined epitopes in 341 untreated HIV-infected persons, including persons who spontaneously control viremia, we found that the correlation between HLA types and control is mediated by the targeting of specific epitopes. Moreover, we performed a graphical model-based analysis that suggested that the targeting of specific epitopes is a cause of such control—that is, some epitopes are protective rather than merely associated with control—and identified eight epitopes that are significantly protective. In addition, we use an in silico analysis to identify protein regions where mutations are likely to affect the stability of a protein, and we found that the protective epitopes identified by the ELISPOT analysis correspond almost perfectly to such regions. This in silico analysis thus suggests a possible mechanism for control and could be used to identify protective epitopes that are not often targeted in natural infection but that may be potentially useful in a vaccine. Our analyses thus argue for the inclusion (and exclusion) of specific epitopes in an HIV vaccine. IMPORTANCE Some individuals naturally control HIV replication in the absence of antiretroviral therapy, and this ability to control is strongly correlated with the HLA class I alleles that they express. Here, in a large-scale experimental study, we provide evidence that this correlation is mediated largely by the targeting of specific CD8+ T-cell epitopes, and we identify eight epitopes that are likely to cause control. In addition, we provide an in silico analysis indicating that control occurs because mutations within these epitopes change the stability of the protein structures. This in silico analysis also identified additional epitopes that are not typically targeted in natural infection but may lead to control when included in a vaccine, provided that other epitopes that would otherwise distract the immune system from targeting them are excluded from the vaccine.
No evidence of nitrate tolerance caused by nebivolol

Background: Continuous long-term treatment with nitrates may cause nitrate tolerance. Nebivolol is a highly selective beta1-adrenergic antagonist with additional nitric oxide (NO)-mediated vasodilatory effects. However, there have been no investigations into whether or not the long-term administration of nebivolol might cause nitrate tolerance. Methods: We performed a randomized, double-blind, placebo-controlled, cross-over study in 16 healthy men. Subjects received 5 mg nebivolol or placebo once daily for 8 days in random order divided by a drug-free interval of 2 weeks. Forearm blood flow (FBF) was measured by venous occlusion plethysmography 3 h after oral intake of the first and last doses of nebivolol and placebo, respectively. FBF was measured again following the intravenous administration of 4 μg nitroglycerin/kg body weight/min for 5 min. Results: Following 8 days of continuous intake of placebo, nitroglycerin increased FBF by 54% (p < 0.05), whereas nitroglycerin increased FBF by 96% (p < 0.01) following 8 days of continuous intake of nebivolol, and the increase after 8 days of nebivolol was significantly (p < 0.05) more pronounced than after 8 days of placebo. Conclusions: These findings indicate no evidence of nitrate tolerance caused by long-term administration of nebivolol. On the contrary, long-term intake of nebivolol increases rather than decreases the NO-mediated vasodilating effects.
Subspace Identification of Large-Scale Interconnected Systems

We propose a decentralized subspace algorithm for the identification of large-scale, interconnected systems that are described by sparse (multi) banded state-space matrices. First, we prove that the state of a local subsystem can be approximated by a linear combination of inputs and outputs of local subsystems that are in its neighborhood. Furthermore, we prove that for interconnected systems with well-conditioned, finite-time observability Gramians (or observability matrices), the size of this neighborhood is relatively small. On the basis of these results, we develop the subspace identification algorithm that identifies the state-space model of a local subsystem from the local input-output data. Consequently, the developed algorithm is computationally feasible for interconnected systems with a large number of local subsystems. Numerical results confirm the effectiveness of the new identification algorithm.
Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems

Recommender systems arose in view of the information overload present in numerous domains. The so-called content-based recommenders offer products similar to those the users liked in the past. However, due to the use of syntactic similarity metrics, these systems elaborate overspecialized recommendations including products very similar to those the user already knows. In this paper, we present a strategy that overcomes overspecialization by applying reasoning techniques borrowed from the semantic Web. Thanks to the reasoning, our strategy discovers a huge amount of knowledge about the user's preferences, and compares them with available products in a more flexible way, beyond the conventional syntactic metrics. Our reasoning-based strategy has been implemented in a recommender system for interactive digital television, with which we checked that the proposed technique offers accurate enhanced suggestions that would go unnoticed in the traditional approaches.
Moderne Diagnostik und Therapie opportunistischer Infektionen bei AIDS

Zusammenfassung Mit Einführung der hochaktiven antiretroviralen Therapie (HAART) ist die Inzidenz der opportunistischen Infektionen (OI) stark gesunken. Heute erkranken meist nur noch Patienten, denen ihre HIV-Infektion unbekannt ist, die sie nicht haben behandeln lassen oder die trotz HAART einen schlechten Immunstatus haben. Dadurch nimmt das allgemeine Wissen um Diagnostik und Therapie dieser Erkrankungen ab und es wird immer weniger an die Möglichkeit einer zerebralen OI gedacht. Dabei sind OI noch immer lebensbedrohliche Erkrankungen und die Mortalität in den ersten Wochen ist seit der prä-HAART-Ära unverändert geblieben. Daher sollte jeder, der neurologische Notfälle behandelt, mit den wichtigsten diagnostischen Schritten und den aktuellen Therapien der zerebralen OI vertraut bleiben. Es wird ein Überblick über den aktuellen Stand der Diagnostik und Behandlung der wichtigsten OI gegeben: zerebrale Toxoplasmose, progressive multifokale Leukenzephalopathie, Cytomegalievirus-Enzephalitis, Kryptokokken- Meningitis und primär zerebrales Lymphom als HIV-assoziiertes Malignom.
Improving Auto-Tuning Convergence Times with Dynamically Generated Predictive Performance Models

Automatic performance tuning is becoming an increasingly valuable tool for improving performance portability when targeting diverse ranges of processor architectures. Much of the existing work to develop auto-tuning techniques focuses solely on achieving the best possible performance, with little attention paid to the amount of time required to perform the tuning process itself. As developers begin to face progressively larger sets of target platforms, the amount of tuning time required to achieve performance goals for each platform will be a crucial factor in determining the success of different auto-tuning techniques. In this work, we describe a hybrid approach to auto-tuning that combines empirical sampling and a predictive performance model, with the goal of reducing the time needed to converge on the optimal (or near-optimal) configuration. Our approach is shown to provide a three-fold reduction in the amount of tuning time required to achieve performance within 10% of the global optimum.
An effective low powr design methodology based on interconnect prediction

The demand for low power digital systems has motivated significant research. However, the power estimation at the logic level is a difficult task because interconnect plays a role in determining the total chip power dissipation. As a result, the power optimization at the logic level may be inaccurate due to the lack of physical place and route information. In this paper, we will present an effective low power design methodology based on interconnect prediction at the logic level. The proposed design methodology includes the method to create wire load models and the design procedures to select the appropriate wire load model during synthesis. The main distinction of the proposed approach is that it constructs physical hierarchy during the synthesis stage. By taking advantage of wire load models, the proposed design methodology is able to develop low power digital systems while speeding up the design process. Experimental data shows that this design methodology achieved very good results.
SmartBlock: An Approach to Standardizing In Situ Workflow Components

Multi-step scientific workflows have become prominent and powerful tools of data-driven scientific discovery. Run-time analytic techniques are now commonly used to mitigate the performance effects of using parallel file systems as staging areas during workflow execution. However, workflow construction and deployment for extreme-scale computing is still largely an ad hoc process with uneven support from existing tools. In this paper, we present SMARTBLOCK, an approach to designing generic, reusable components for end-to-end construction of workflows. Specifically, we demonstrate that a small set of SMARTBLOCK generic components can be reused to build a diverse set of workflows, using examples based on actual analytic processes with three well-known scientific codes. Our evaluation shows promising scaling properties as well as negligible overheads for using a modular approach over a custom, "all-in-one" solution. As extreme-scale systems incorporate data analytics on simulation data as it is generated at rates that far outstrip available I/O bandwidth, tools such as SMARTBLOCK will become increasingly valuable for defining and deploying flexible, efficient workflows.
Managing comorbidities in COPD

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. Age and smoking are common risk factors for COPD and other illnesses, often leading COPD patients to demonstrate multiple coexisting comorbidities. COPD exacerbations and comorbidities contribute to the overall severity in individual patients. Clinical trials investigating the treatment of COPD routinely exclude patients with multiple comorbidities or advanced age. Clinical practice guidelines for a specific disease do not usually address comorbidities in their recommendations. However, the management and the medical intervention in COPD patients with comorbidities need a holistic approach that is not clearly established worldwide. This holistic approach should include the specific burden of each comorbidity in the COPD severity classification scale. Further, the pharmacological and nonpharmacological management should also include optimal interventions and risk factor modifications simultaneously for all diseases. All health care specialists in COPD management need to work together with professionals specialized in the management of the other major chronic diseases in order to provide a multidisciplinary approach to COPD patients with multiple diseases. In this review, we focus on the major comorbidities that affect COPD patients. We present an overview of the problems faced, the reasons and risk factors for the most commonly encountered comorbidities, and the burden on health care costs. We also provide a rationale for approaching the therapeutic options of the COPD patient afflicted by comorbidity.
Injury and progressive loss of peritubular capillaries in the development of chronic allograft nephropathy.

BACKGROUND Chronic allograft nephropathy (CAN) remains the most important cause of late renal graft loss. However, the mechanism for graft dysfunction and the process of the development of CAN are not well understood. This study examined the role of microvascular injury in the development of CAN. METHODS We studied renal biopsies obtained from grafts with CAN (N= 79) and pretransplant control kidneys (N= 20). Microvascular injury was examined morphologically, and was correlated with interstitial fibrosis, glomerular sclerosis, graft function, and the severity of CAN. The humoral and cellular immunity involved in CAN were examined by C4d, CD3, and TIA-1 staining. RESULTS In all the cases of CAN, microvascular injury was evident with or without CD3-positive T cells, TIA-1-positive cytotoxic cells, and/or C4d+ complement deposition. Irrespective of chronic rejection (N= 14), C4d+ chronic humoral rejection (N= 6), or other CAN, the development process of CAN was characterized by injury and progressive loss of identifiable peritubular capillaries (PTCs) accompanied with the development of interstitial fibrosis. Injured PTCs were characterized morphologically by the process of angioregression with the presence of apoptotic cells, lamination of the basement membrane, and loss of PTCs. The low number of PTCs correlated significantly with the severity of CAN (r=-0.74, P < 0.001), the development of interstitial fibrosis (r=-0.75, P < 0.001), graft dysfunction (r=-0.69, P < 0.001), and also correlated weakly with proteinuria (r=-0.45, P < 0.05). In the glomeruli, capillary loss significantly correlated with the degree of glomerular sclerosis (r=-0.66, P < 0.001) and proteinuria (r=-0.65, P < 0.001), but did not correlate with the severity of CAN (r=-0.24, P > 0.05) or graft dysfunction (r=-0.32, P > 0.05). CONCLUSION CAN was characterized by progressive injury to the renal microvasculature and the development of renal scarring. In particular, injury, angioregression and progressive loss of the PTC network strongly contributed to the development of interstitial fibrosis and graft dysfunction in CAN, and might play a crucial role in the development of CAN.
Audit on cardiovascular disease preventive care in general practice.

Introduction: Cardiovascular disease is a major cause of morbidity and mortality. Primary care doctors as general practitioners (GPs) play a central role in prevention, as they are in contact with a large number of patients in the community through provision of first contact, comprehensive and continuing care. This study aims to assess the adequacy of cardiovascular disease preventive care in general practice through a medical audit. Methods: Nine GPs in Malaysia did a retrospective audit on the records of patients, aged 45 years and above, who attended the clinics in June 2005. The adequacy of cardiovascular disease preventive care was assessed using agreed criteria and standards. Results: Standards achieved included blood pressure recording (92.4 percent), blood sugar screening (72.7 percent) and attaining the latest blood pressure of equal or less than 140/90 mmHg in hypertensive patients (71.3 percent). Achieved standards ranged from 11.1 percent to 66.7 percent in the maintenance of hypertension and diabetic registries, recording of smoking status, height and weight, screening of lipid profile and attaining target blood sugar levels in diabetics. Conclusions: In the nine general practice clinics audited, targets were achieved in three out of ten indicators of cardiovascular preventive care. There were vast differences among individual clinics.
The Schizosaccharomyces pombe Syntaxin 1 Homolog, Psy1, Is Essential in the Development of the Forespore Membrane

Syntaxin is a component of t-soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE), which is responsible for docking membrane vesicles at the target membrane and is highly conserved among eukaryotes. In the fission yeast Schizosaccharomyces pombe, the psy1 + gene encoding a syntaxin 1 homolog was originally isolated as a multicopy suppressor of the sporulation-deficient mutant, spo3, but little is known about the way Psy1 is involved in sporulation. Here we report the isolation of a sporulation-defective mutant, psy1-S1, generated by random PCR mutagenesis. psy1-S1 also exhibited temperature sensitivity in growth. In psy1-S1 cells, assembly of the forespore membrane (FSM) initiated near the spindle pole bodies during meiosis II, but subsequent expansion of the membrane was severely impaired. Overproduction of the cognate SNARE proteins, Syb1 and Sec9, suppressed both the temperature sensitivity and sporulation defects of psy1-S1. These results indicate that Psy1 plays an essential role in FSM formation coordinated by Syb1 and Sec9.
Why We Disagree About Climate Change: Index

Climate change is not 'a problem' waiting for 'a solution'. It is an environmental, cultural and political phenomenon which is re-shaping the way we think about ourselves, our societies and humanity's place on Earth. Drawing upon twenty-five years of professional work as an international climate change scientist and public commentator, Mike Hulme provides a unique insider's account of the emergence of this phenomenon and the diverse ways in which it is understood. He uses different standpoints from science, economics, faith, psychology, communication, sociology, politics and development to explain why we disagree about climate change. In this way he shows that climate change, far from being simply an 'issue' or a 'threat', can act as a catalyst to revise our perception of our place in the world. Why We Disagree About Climate Change is an important contribution to the ongoing debate over climate change and its likely impact on our lives.
Detection of Various Security Attacks on IoT Devices Using Multi-Layer Neural Network Model Over Sensor Networks

In a traditional network environment firewalls are used to filter user access, IoT network environment is not like a client server interaction environment model, firewalls are not possible to deploy into IoT devices, and attackers easily force attack on IoT device to hack valuable information. In this paper we proposed multi-layer neural network model to detect attacks on IoT devices. Neural network formed using set of neurons, each neuron is capable to receive signals from other neurons, at each neuron inputs are received and multiplied with weight values, and calculated output values are transferred to other neurons. In our proposed neural network model based IoT security model deploy neurons into input layer, hidden layers, and output layer. Neurons in this model are fully connected with each other. Performance evaluation metrics like true positive rate, false positive rate precision and accuracy are used to evaluate performance of proposed model.
Phase coupling and synchrony in the spatiotemporal dynamics of muskrat and mink populations across Canada

Population ecologists have traditionally focused on the patterns and causes of population variation in the temporal domain for which a substantial body of practical analytic techniques have been developed. More recently, numerous studies have documented how populations may fluctuate synchronously over large spatial areas; analyses of such spatially extended time-series have started to provide additional clues regarding the causes of these population fluctuations and explanations for their synchronous occurrence. Here, we report on the development of a phase-based method for identifying coupling between temporally coincident but spatially distributed cyclic time-series, which we apply to the numbers of muskrat and mink recorded at 81 locations across Canada. The analysis reveals remarkable parallel clines in the strength of coupling between proximate populations of both species—declining from west to east—together with a corresponding increase in observed synchrony between these populations the further east they are located.
Transparent on-demand co-allocation data access for grids

This paper presents a data sharing system called On-Demand data Co-Allocation (ODCA). ODCA integrates the advantages of the co-allocation concepts with the conventional on-demand data access scheme. It transfers the necessary file fragments from multiple data sources only when the application demands, thereby reducing data transmission time, wasted network bandwidth and required storage space. Moreover, it facilitates legacy applications to transparently access distributed grid data by using the native I/O system calls. The experimental results indicate that ODCA achieves superior performance in the turnaround time of data-intensive applications than the pre-staging and the on-demand access schemes.
On the concept of a normal metal hot-electron microbolometer for space applications

We present a theoretical analysis and experimental evaluation of a hot-electron microbolometer with normal metal absorber for ultrasensitive detecting infrared and submillimeter waves. The basic version of the antenna coupled microbolometer makes use of a hot-electron effect in the thin film resistive strip and Andreev reflection of hot electrons at SN interface between the strip and superconducting antenna. A value of NEP=5/spl middot/10/sup -18/ W/Hz/sup 1/2/ for the thermal fluctuation noise and the thermal time constant t=0.2 /spl mu/s at 300 mK have been estimated for one of the realized devices with thermal conductance G/spl ap/6/spl middot/10/sup -12/ W/K. At 100 mK, the thermal conductance has been decreased to G/spl ap/7/spl middot/10/sup -14/ W/K, that gives estimations for the thermal NEP=2/spl middot/10/sup -19/ W/Hz/sup 1/2/ and the time constant t=5 /spl mu/s. An advanced version of the microbolometer includes also additional SIN junctions connected to the resistive strip for electronic cooling the absorber. Such microbolometer is intended as a detector of millimeter and submillimeter wave radiation for space applications.
A low-complexity noise estimation algorithm based on smoothing of noise power estimation and estimation bias correction

This paper presents a low-complexity algorithm for tracking the noise spectral variance of speech contaminated by non-stationary noise sources. The proposed algorithm is based upon a recursive refinement process in which each step of the algorithm expectation of the instantaneous noise power is calculated based on information from the incoming signal and the current estimated distribution parameters, and estimation of the distribution parameter is refined accordingly to incorporate the expectation results. A bias estimation correction method is also introduced in the algorithm to avoid estimation errors that may occur when there is a significant mismatch between the statistics of the input signal and the current estimated distribution parameters. The proposed algorithm is compared to the Minimum Statistics method and it is found that the proposed algorithm achieves similar or better performances for various noise conditions and SNR settings.