Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-acting injectable antiretrovirals for HIV treatment and prevention

Purpose of reviewLong-acting antiretroviral (ARV) drugs may improve adherence to therapy and extend opportunities for therapeutic or prophylactic intervention to underserved patient populations. This review focuses on recent advances in the development of small molecule long-acting injectable ARV agents. Recent findingsThe need for combination ART and physicochemical and dosing limitations of current ARV drugs impede attempts to redevelop them as long-acting injectable formulations. However, the intrinsic properties of rilpivirine, a nonnucleoside reverse transcriptase inhibitor, and GSK1265744, an HIV-1 integrase strand transfer inhibitor, have enabled crystalline nanoparticle formulations to progress to clinical trials. SummaryInvestigational long-acting injectable nanoformulations of rilpivirine and GSK1265744 are clinical-stage development candidates. Complementary pharmacologic properties of both agents – different mechanisms of action, resistance profiles, metabolic pathways, lack of drug interactions and low daily oral doses – offer the potential for combination use. Phase I studies of the pharmacokinetics and safety of each long-acting formulation alone and in combination indicate that a monthly dosing regimen is possible for HIV treatment. An ongoing phase IIb trial of oral GSK1265744 and oral rilpivirine is evaluating this two-drug regimen for maintenance of virologic suppression; results will inform future studies using the injectable formulations. Additional preclinical and clinical studies indicate a potential use of each agent for HIV pre-exposure prophylaxis.
Kink model for SOI MOSFET

In order to improve the modeling of SOI MOSFET a precise evaluation of the excess current due to impact ionization is needed. This increase in drain current in SOI MOSFET at higher drain voltages is known as Kink effect. In this paper a simple model is proposed for the excess current resulting from impact ionization. Model is based upon estimation of electric field in the saturated part of channel. The electric field in saturated part of channel is obtained by the solution of two dimensional Poisson's equation. The model is semi-analytical and uses only two fitting parameters which is desirable for circuit simulation. The attractive property of the model is its simplicity. The model has been verified using the experimental results and is found to be quite precise.
Relevance of Computational model for Detection of Optic Disc in Retinal images

Optic Disc (OD) detection is a key pre processing component in many algorithms designed for automatic extraction of retinal anatomical structures and lesions thus, reliable and efficient OD localization is significant tasks in ophthalmic image processing. We aim to investigate the relevance of computational saliency model in retinal images in the context of OD detection. The optic disc in a retinal image is characterized as high contrast region. This approximately corresponds to disjunctive targets in a visual search task. Saliency maps were computed using popular model Itti-Koch. Then mathematical morphology and Otsu’s algorithm used on the saliency map for OD detection. The resulted images satisfactorily showed OD detection. The results are promising and give new insight to the use of the saliency map for medical images.
Uncertain text entry on mobile devices

Users often struggle to enter text accurately on touchscreen keyboards. To address this, we present a flexible decoder for touchscreen text entry that combines probabilistic touch models with a language model. We investigate two different touch models. The first touch model is based on a Gaussian Process regression approach and implicitly models the inherent uncertainty of the touching process. The second touch model allows users to explicitly control the uncertainty via touch pressure. Using the first model we show that the character error rate can be reduced by up to 7% over a baseline method, and by up to 1.3% over a leading commercial keyboard. Using the second model we demonstrate that providing users with control over input certainty reduces the amount of text users have to correct manually and increases the text entry rate.
Cognitive Behavioural Intervention of Avoidant Personality Disorder - A Case Study

Avoidant personality disorder is characterized by pervasive behavioural, emotional and cognitive avoidances. There are evidences that both psychodynamic therapy and cognitive behavior therapy are effective treatments of personality disorders. In the current case study a 21 year old male with a diagnosis of avoidant personality disorder was taken for psychological intervention through cognitive behavioural approach. Total 33 sessions were held over a period of four months. The client reported significant improvement in therapy. His functioning level and social skills were improved and was looking forward to address his problems in more optimistic approach. ABStrACt
A low-power small-size transmitter with discrete-time baseband filter for LTE in 65 nm CMOS

This paper presents the design of a low-power small-size transmitter with discrete-time baseband filter for LTE application operating at 1.8-2 GHz. The transmitter achieves 4.5 dBm output power with more than -46 dBc of LO feedthrough suppression and -38 dBc image rejection ratio. At -0.3 dBm transmitted power of LTE 5 MHz channel, ACLR is measured below -42 dBc for both upper and lower channels with 1.83 and 2.47 % EVM for QPSK and 16-QAM modulation signals, respectively. The prototype chip is fabricated in a 65 nm CMOS technology and dissipates 59 mA current from 1.2 V supply and 13 mA from 2.5 V supply.
Pharmacogenetics of Antidepressants: Is There a Magic Bullet for Treating Depression?

Depression is one of the most common mental disorders nowadays. Many researchers in psychiatry have investigated the cause of depression. Unfortunately, the problem of individual differences in response to antidepressant treatment still lingers in the clinical fi eld. For setting up the stage for the main topics in the later part of this overview, I start introducing the topics of monoamine hypothesis, the role of serotonin in causing clinical depression, as well as advents of antidepressants (briefl y describing tricyclic antidepressants, the arrival of selective serotoninreuptake inhibitor, and novel antidepressants). Then, I introduce the concept of pharmacogenetics, the candidate genes. Afterwards, I consider that genetic factors are recognized to have an independent effect on drug responses. To demonstrate, I highlight fi ve gene studies-serotonin transporter (5-HTT/SLC6A4), serotonin receptor 2A (HTR2A), G-protein β3 subunit (GNB3), brain-derived neurotrophic factor (BDNF), and tryptophan hydroxylase (TPH). But some of candidate gene studies have been conducted, but results were not consistent. Since 2009, Genomewide Association Studies (GWAS) has provided further grounds for understanding the pharmacogenetic mechanisms of antidepressants and depression treatment. Further research and technological development are still needed.
The Holographic bound in anti-de Sitter space

The correspondence between string theory in Anti-de Sitter space and super Yang Mills theory is an example of the Holographic principle according to which a quantum theory with gravity must be describable by a boundary theory. However, arguments given so far are incomplete because, while the bulk theory has been related to a boundary theory, the holographic bound saying that the boundary theory has only one bit of information per Planck area has not been justified. We show here that this bound is the physical interpretation of one of the unusual aspects of the correspondence between Anti-de Sitter space and the boundary conformal field theory, which is that infrared effects in the bulk theory are reflected as ultraviolet effects in the boundary theory.
Transient Shifts in Bacterial Communities Associated with the Temperate Gorgonian Paramuricea clavata in the Northwestern Mediterranean Sea

Background Bacterial communities that are associated with tropical reef-forming corals are being increasingly recognized for their role in host physiology and health. However, little is known about the microbial diversity of the communities associated with temperate gorgonian corals, even though these communities are key structural components of the ecosystem. In the Northwestern Mediterranean Sea, gorgonians undergo recurrent mass mortalities, but the potential relationship between these events and the structure of the associated bacterial communities remains unexplored. Because microbial assemblages may contribute to the overall health and disease resistance of their host, a detailed baseline of the associated bacterial diversity is required to better understand the functioning of the gorgonian holobiont. Methodology/Principal Findings The bacterial diversity associated with the gorgonian Paramuricea clavata was determined using denaturing gradient gel electrophoresis, terminal-restriction fragment length polymorphism and the construction of clone libraries of the bacterial 16S ribosomal DNA. Three study sites were monitored for 4 years to assess the variability of communities associated with healthy colonies. Bacterial assemblages were highly dominated by one Hahellaceae-related ribotype and exhibited low diversity. While this pattern was mostly conserved through space and time, in summer 2007, a deep shift in microbiota structure toward increased bacterial diversity and the transient disappearance of Hahellaceae was observed. Conclusion/Significance This is the first spatiotemporal study to investigate the bacterial diversity associated with a temperate shallow gorgonian. Our data revealed an established relationship between P. clavata and a specific bacterial group within the Oceanospirillales. These results suggest a potential symbiotic role of Hahellaceae in the host-microbe association, as recently suggested for tropical corals. However, a transient imbalance in bacterial associations can be tolerated by the holobiont without apparent symptoms of disease. The subsequent restoration of the Hahellaceae-dominated community is indicative of the specificity and resilience of the bacteria associated with the gorgonian host.
Globalização e mobilidade: as condições de mobilidade contemporânea e as práticas turísticas Globalization and mobility: the mobility status of contemporary tourist practices

The forms for which the individuals pass for diverse experiences around the timespace dimensions have been considered, for some contemporary sociologists, as central dimensions for the understanding of contemporary modernity. The matter of contemporary mobility is also related, for some authors, to the reflectivity subjects. From these aspects, this work aims to reflect, from some theoretical perspectives,
D 2 D COMMUNICATIONS IN LTE-ADVANCED CELLULAR NETWORKS

Absract:The Long Term Evolution-Advanced (LTEAdvanced) networks are being developed to provide mobile broadband services for the fourth generation (4G) cellular wireless systems. Deviceto-device (D2D) communications is a promising technique to provide wireless peer-topeer services and enhance spectrum utilization in the LTE-Advanced networks. In D2D communications, the user equipments (UEs) are allowed to directly communicate between each other by reusing the cellular resources rather than using uplink and downlink resources in the cellular mode when communicating via the base station. However, enabling D2D communications in a cellular network poses two major challenges.First, the interference caused to the cellular users by D2D devices could critically affect the performances of the cellular devices. Second, the minimum quality-of-service (QoS) requirements of D2D communications need to be guaranteed. In this article, we introduce a novel resource allocation scheme (i.e. joint resource block scheduling and power control) for D2D communications in LTE-Advanced networks to maximize the spectrum utilization while addressing the above challenges. First, an overview of LTE-Advanced networks, and architecture and signaling support for provisioning of D2D communications in these networks are described. Furthermore, research issues and the current state-of-the-art of D2D communications are discussed. Then, a resource allocation scheme based on a column generation method is proposed for D2D communications. The objective is to maximize the spectrum utilization by finding the minimum transmission length in terms of time slots for D2D links while protecting the cellular users from harmful interference and guaranteeing the QoS of D2D links. The performance of this scheme is evaluated through simulations.
The Corporate Profit Base, Tax Sheltering Activity, and the Changing Nature of Employee Compensation

This paper examines the evolution of the corporate profit base and the relationship between book income and tax income for U.S. corporations over last two decades. The paper demonstrates that this relationship has broken down over the 1990s and has broken down in a manner that is consistent with increased sheltering activity. The paper traces the growing discrepancy between book and tax income associated with differential treatments of depreciation, the reporting of foreign source income, and, in particular, the changing nature of employee compensation. For the largest public companies, proceeds from option exercises equaled 27 percent of operating cash flow from 1996 to 2000 and these deductions appear to be fully utilized thereby creating the largest distinction between book and tax income. While the differential treatment of these items has historically accounted fully for the discrepancy between book and tax income, the paper demonstrates that book and tax income have diverged markedly for reasons not associated with these items during the late 1990s. In 1998, more than half of the difference between tax and book income - approximately $154.4 billion or 33.7 percent of tax income - cannot be accounted for by these factors. This paper proceeds to develop and test a model of costly sheltering and demonstrates that the breakdown in the relationship between tax and book income is consistent with increasing levels of sheltering during the late 1990s. These tests also explore an alternative explanation of these results - coincident increased levels of earnings management - and finds that the nature of the breakdown between book and tax income cannot be fully explained by this alternative explanation.
Ion Channels to Inactivate Neurons in Drosophila

Ion channels are the determinants of excitability; therefore, manipulation of their levels and properties provides an opportunity for the investigator to modulate neuronal and circuit function. There are a number of ways to suppress electrical activity in Drosophila neurons, for instance, over-expression of potassium channels (i.e. Shaker Kv1, Shaw Kv3, Kir2.1 and DORK) that are open at resting membrane potential. This will result in increased potassium efflux and membrane hyperpolarisation setting resting membrane potential below the threshold required to fire action potentials. Alternatively over-expression of other channels, pumps or co-transporters that result in a hyperpolarised membrane potential will also prevent firing. Lastly, neurons can be inactivated by, disrupting or reducing the level of functional voltage-gated sodium (Nav1 paralytic) or calcium (Cav2 cacophony) channels that mediate the depolarisation phase of action potentials. Similarly, strategies involving the opposite channel manipulation should allow net depolarisation and hyperexcitation in a given neuron. These changes in ion channel expression can be brought about by the versatile transgenic (i.e. Gal4/UAS based) systems available in Drosophila allowing fine temporal and spatial control of (channel) transgene expression. These systems are making it possible to electrically inactivate (or hyperexcite) any neuron or neural circuit in the fly brain, and much like an exquisite lesion experiment, potentially elucidate whatever interesting behaviour or phenotype each network mediates. These techniques are now being used in Drosophila to reprogram electrical activity of well-defined circuits and bring about robust and easily quantifiable changes in behaviour, allowing different models and hypotheses to be rapidly tested.
Self-Training Tree Substitution Grammars for Domain Adaptation

Parsing is the process of inferring the syntactic structure of a sentence, based on a model of syntax that specifies which sentences are possible or likely. The field of statistical parsing concerns itself with learning probabilistic syntactic models from corpora. Ideally, it should be possible to parse any grammatical sentence of any natural language. Because different languages have wildly different syntactic structures (not to mention lexical items), this will require multiple parsers, one for each language that we might want to parse. For this reason, it is important to develop syntactic models and learning algorithms which generalize well to new languages. Even within a language, there is considerable variation between domains; for example, children’s fiction will likely exhibit different syntactic and lexical properties than articles from the Wall Street Journal. One approach to dealing with these differences between domains would be to train a new parser for each new domain encountered. However, statistical parsers are typically trained in a supervised fashion on hand-annotated treebank data such as the Penn Treebank. It is expensive and time-consuming to produce this hand-annotated data, making it impractical to build a new treebank for every new domain. Thus, it is important to develop other methods for training parsers that perform well across domains. One way of ensuring that a parser will be able to handle new domains is to make that parser less domain-specific to begin with. For instance, if a parser trained on the Wall Street Journal performs equally well on children’s fiction, then there is no need to learn a separate parser for children’s fiction. One way of building a less domain-specific parser would be to train the parsing model on data from multiple domains; then, the parser would be more likely to identify the features that are constant across domains, instead of overfitting to a single genre of text. It is also important to design parsing models and learning techniques which are less likely to overfit in general. To summarize, a good parsing model should be generalizable; that is, it should be applicable to multiple languages, as well as multiple domains within each language. We identify two different senses in which a parser can be generalizable. The first sense is that the model should be able to learn equally well when trained on different datasets. That is, the model should not contain features that are specially tailored for a particular dataset or language. We refer to this sense as model generalizability. The second sense is that the model, after being trained on data from one domain, should be able to parse other datasets as well. This is important because it is impractical to train a new parser every time we wish to parse a new genre of text. Of course, any model will perform best on data that is similar to that which it was trained on; however, we can seek to improve a parser’s generalizability in this sense by ensuring that we do not overfit on a single domain during training. We refer to this sense as parser generalizability.
A New Sentinel Surveillance System for Severe Influenza in England Shows a Shift in Age Distribution of Hospitalised Cases in the Post-Pandemic Period

SUMMARY Uncertainties exist regarding the population risks of hospitalization due to pandemic influenza A(H1N1). Understanding these risks is important for patients, clinicians and policy makers. This study aimed to clarify these uncertainties. A national surveillance system was established for patients hospitalized with laboratory-confirmed pandemic influenza A(H1N1) in England. Information was captured on demographics, pre-existing conditions, treatment and outcomes. The relative risks of hospitalization associated with pre-existing conditions were estimated by combining the captured data with population prevalence estimates. A total of 2416 hospitalizations were reported up to 6 January 2010. Within the population, 4·7 people/100 000 were hospitalized with pandemic influenza A(H1N1). The estimated hospitalization rate of cases showed a U-shaped distribution with age. Chronic kidney disease, chronic neurological disease, chronic respiratory disease and immunosuppression were each associated with a 10- to 20-fold increased risk of hospitalization. Patients who received antiviral medication within 48 h of symptom onset were less likely to be admitted to critical care than those who received them after this time (adjusted odds ratio 0·64, 95% confidence interval 0·44–0·94, P=0·024). In England the risk of hospitalization with pandemic influenza A(H1N1) has been concentrated in the young and those with pre-existing conditions. By quantifying these risks, this study will prove useful in planning for the next winter in the northern and southern hemispheres, and for future pandemics.
Micro-Satellite Attitude Determination with Only a Single Horizon Sensor

Through using measurement from only a single horizon sensor, this paper presented a quaternion-based 3-axis attitude determination method, which can be implemented on board micro-satellites and applied over a whole orbital period. Firstly, a description of attitude representation on the quaternion is given. Secondly, a detailed modeling formulation with nadir vector and measurement equations on attitude estimation system is demonstrated. Afterwards, a correction is made to eliminate the estimation error resulted from Earth’s oblateness, and able to further improve the accuracy of the attitude determination algorithm. Finally, a six degree-of-freedom closed-loop simulation is used to validate the accuracy of the attitude determination method given in this paper.
'Almost Sure' Chaotic Properties of Machine Learning Methods

It has been demonstrated earlier that universal computation is 'almost surely' chaotic. Machine learning is a form of computational fixed point iteration, iterating over the computable function space. We showcase some properties of this iteration, and establish in general that the iteration is 'almost surely' of chaotic nature. This theory explains the observation in the counter intuitive properties of deep learning methods. This paper demonstrates that these properties are going to be universal to any learning method.
On prediction application of small enterprise supply chain demand based on Grey System theory

Demand management of Supply Chain has been hotly discussed recently but little attentions have been paid on that of small enterprises. Grey System Model GM (1, 1) is employed to predict the Supply Chain demand of small enterprises with insufficient related information. And Weakening Operator Conversion is utilized to improve the prediction accuracy. The results indicate that the applicability and robustness of grey prediction is acceptable and more approaches are to be brought in to improve prediction accuracy and test the prediction differentials to actual demand.
Optimized View Frustum Culling Algorithms for Bounding Boxes

Abstract This paper presents optimizations for faster view frustum culling (VFC) for axis-aligned bounding box (AABB) and oriented bounding box (OBB) hierarchies. We exploit frame-to-frame coherency by caching and by comparing against previous distances and rotation angles. By using an octant test, we potentially halve the number of plane tests needed, and we also evaluate masking, which is a well-known technique. The optimizations can be used for arbitrary bounding volumes, but we present only results for AABBs and OBBs. In particular, we provide solutions which are 2-11 times faster than other VFC algorithms for AABBs and OBBs, depending on the circumstances.