Miracle-dz/UniFAR-FTUs huggingface dataset:
Linkage to HIV care and its determinants in the late HAART era: a systematic review and meta-analysis

ABSTRACT Poor engagement into HIV care limits the effectiveness of highly active antiretroviral therapies (HAART) to improve survival and reduce transmission. The design of effective interventions to enhance linkage to care is dependent on evidence about rates of entry into HIV care. This is a systematic review and meta-analysis on linkage measurement and its determinants in the late era of HAART (post-2003), in high-income countries. We searched the PubMed and Web of Science databases, restricting our sample to the late HAART era (post-2003) until February 2016, and to high-income countries. We retained only studies that produced quantified outcomes. We rejected the studies with a high risk of bias, and followed a standard meta-analytic approach. Because there was a high heterogeneity ( I 2 > 90%), the aggregated findings were based on a random-effects model. A total of 43 studies were identified, all of them following a cohort of patients newly diagnosed until referred to specialized care. For a one-month period, the meta-proportion was 71.1% (IC95%: 61.0%–81.2). For a three-month duration, the meta-proportion of linkage to care was 77.0% (IC95%: 75.0%–79.0). For a one-year period, the meta-proportion was 76.3% (IC95%: 54.2%–98.4%). The proportions were lower when lab tests were used as referral indicator, with a pooled meta-proportion of 76.7% (IC95%: 73.0%–80.4), in comparison to a value of 80.8% (IC95%: 68.7%–92.9) for consultations. Being black or male were the most commonly observed determinants of delayed entry into care. Young people, injecting drug users, people with low socioeconomic status, or at a less advanced stage of disease also experienced lower proportions of timely linkage. Timely engagement into care is below 80% and specific sub-groups are particularly at risk of late entry. These findings confirm earlier evidence that linkage to care remains low, and that efforts should focus on vulnerable populations.
Expression, purification, crystallization, and diffraction analysis of a selenomethionyl lipase Lip8 from Yarrowia lipolytica

ABSTRACT Yarrowia lipolytica is a nonconventional model micro-organism with multiple biotechnological applications. It is also considered to be an excellent producer for lipase. Genome survey shows that Y. lipolytica possesses various paralogs of genes coding for extracellular, cell-bound, and intracellular lipolytic enzymes. However, little structural information on these isoenzymes is available. With the aim to facilitate crystal structure solution of Lip8, one of the most valuable lipases from Y. lipolytica, a less conventional protein expression technique—selenomethionyl protein expression was used to produce recombinant selenomethionine (SeMet)-Lip8 in Escherichia coli. Finally, three Met residues of Lip8 were all substituted with SeMet. A total of 72 mg of SeMet-Lip8 was obtained from a liter of the SeMet medium. Using sodium acetate as a precipitant and ammonium sulfate as an additive, crystals of the SeMet-Lip8 with 1.9 Å were successfully cultured through hanging-drop vapor diffusion method. The estimated crystal dimensions were 0.11 × 0.11 × 0.14 mm2. The crystal belonged to the space group I4 with unit cell parameters a = b = 128.87 Å, c = 171.77 Å, α = β = γ = 90°. It is the second member of lipase crystal family from Y. lipolytica. This work will provide a platform for further studying lipases from a structural insight.
Interpersonal violence, early life adversity, and suicidal behavior in hypersexual men

Background and aims There are significant gaps in knowledge regarding the role of childhood adversity, interpersonal violence, and suicidal behavior in hypersexual disorder (HD). The aim of this study was to investigate interpersonal violence in hypersexual men compared with healthy volunteers and the experience of violence in relation to suicidal behavior. Methods This case–control study includes 67 male patients with HD and 40 healthy male volunteers. The Childhood Trauma Questionnaire – Short Form (CTQ-SF) and the Karolinska Interpersonal Violence Scale (KIVS) were used for assessing early life adversity and interpersonal violence in childhood and in adult life. Suicidal behavior (attempts and ideation) was assessed with the Mini-International Neuropsychiatric Interview (version 6.0) and the Montgomery–Åsberg Depression Rating Scale – Self-rating. Results Hypersexual men reported more exposure to violence in childhood and more violent behavior as adults compared with healthy volunteers. Suicide attempters (n = 8, 12%) reported higher KIVS total score, more used violence as a child, more exposure to violence as an adult as well as higher score on CTQ-SF subscale measuring sexual abuse (SA) compared with hypersexual men without suicide attempt. Discussion Hypersexuality was associated with interpersonal violence with higher total scores in patients with a history of suicide attempt. The KIVS subscale exposure to interpersonal violence as a child was validated using the CTQ-SF but can be complemented with questions focusing on SA for full assessment of early life adversity. Conclusion Childhood adversity is an important factor in HD and interpersonal violence might be related to suicidal behavior in hypersexual men.
Correlating Digital Footprints for Discovering Social Connections in Crowds

This paper presents an approach for guessing the degree of "social" connection among individuals and groups moving and evolving within real environments that uses information gleaned from omnipresent surveillance and individuals digital foot prints. This task is supported by observations of individuals' behaviour within urban spaces when they are alone, and when they are part of a group (i.e., crowd). This knowledge is used as reference to predict their possibility of forming communities and how they can establish relationships with other communities in the presence of specific events (e.g., alarm, disaster). We address the challenge of combining an individual's location with a real-time graphic vision of the urban environment she is moving within, using data produced by GPS, mobile and telephone networks, and security cameras. Discovering communities within crowds leads to social graphs. Computing and processing these graphs requires computing and memory resources we therefore on our HPC infrastructure for performing tests.
Comparing species interaction networks along environmental gradients

Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co‐variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant–hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis‐based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients.
Handwriting Recognition by Attribute Embedding and Recurrent Neural Networks

Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently, these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model.
Knowledge management - learning from knowledge engineering

From the Publisher: Knowledge Management (KM) is strongly rooted in the discipline of Knowledge Engineering (KE), which in turn grew partly out of the artificial intelligence field. Despite their close relationship, however, many KM specialists have failed to fully recognize the synergy or acknowledge the power that KE methodologies, techniques, and tools hold for enhancing the state of the art in Knowledge Management.Knowledge Management: Learning from Knowledge Engineering addresses this vacuum. It gives concise, practical information and insights drawn from the author's many years of experience in the fields of expert systems and Knowledge Management. Based upon research, analyses, and illustrative case studies, this is the first book to integrate the theory and practice of artificial intelligence and expert systems with the current organizational and strategic aspects of Knowledge Management.The time has come for Knowledge Management professionals to appreciate the synergy between their work and the work of their counterparts in Knowledge Engineering. Knowledge Management: Learning from Knowledge Engineering is the ideal starting point for those in KM to learn from and exploit advances in that field, and thereby advance their own.
A Robust and Binarization-Free Approach for Text Line Detection in Historical Documents

Text line extraction from complex handwritten documents, especially for historical collections, is still an unsolved problem. There is a strong demand for reliable and robust approaches since text line extraction is a crucial pre-processing step for modern text recognition and keyword spotting systems. We propose a binarization-free system which employs a newly developed clustering approach based on so-called 'superpixels'. Although multiple ways of generating superpixels were developed in the past, we demonstrate that even a standard method yields impressive results. Our clustering approach is applicable to various scenarios by making use of general characteristics of text lines (e.g., curvilinearity, interline spacings, local homogeneity), and without adapting its parametrization. State-of-the-art results are achieved by the same parametrization for 8 different well-established benchmarking datasets. These datasets cover historical and modern texts as well as images with diverse resolutions and fonts. The system is developed for detecting text lines in complex scenarios. It is not tuned to assign foreground pixels to detected text lines. Thus, superior performance is achieved for the historical datasets for which no pixel hit accuracy of 95% is required. Remarkably, for the dataset of the ICDAR 2015 Competition on Text Line Detection in Historical Documents, the average cost per text line was reduced from 9.77 (winning team) to 8.19.
Integrated analysis of promoter methylation and expression of telomere related genes in breast cancer

Telomeres at the ends of eukaryotic chromosomes play a critical role in tumorgenesis. Using microfluidic PCR and next-generation bisulfite sequencing technology, we investigated the promoter methylation of 29 telomere related genes in paired tumor and normal tissues from 184 breast cancer patients. The expression of significantly differentially methylated genes was quantified using qPCR method. We observed that the average methylation level of the 29 telomere related genes was significant higher in tumor than that in normal tissues (P = 4.30E-21). A total of 4 genes (RAD50, RTEL, TERC and TRF1) showed significant hyper-methylation in breast tumor tissues. RAD51D showed significant methylation difference among the four breast cancer subtypes. The methylation of TERC showed significant association with ER status of breast cancer. The expression profiles of the 4 hyper-methylated genes showed significantly reduced expression in tumor tissues. The integration analysis of methylation and expression of these 4 genes showed a good performance in breast cancer prediction (AUC = 0.947). Our results revealed the methylation pattern of telomere related genes in breast cancer and suggested a novel 4-gene panel might be a valuable biomarker for breast cancer diagnosis.
The trans-membrane domain of Bcl-2α, but not its hydrophobic cleft, is a critical determinant for efficient IP3 receptor inhibition

The anti-apoptotic Bcl-2 protein is emerging as an efficient inhibitor of IP3R function, contributing to its oncogenic properties. Yet, the underlying molecular mechanisms remain not fully understood. Using mutations or pharmacological inhibition to antagonize Bcl-2's hydrophobic cleft, we excluded this functional domain as responsible for Bcl-2-mediated IP3Rs inhibition. In contrast, the deletion of the C-terminus, containing the trans-membrane domain, which is only present in Bcl-2α, but not in Bcl-2β, led to impaired inhibition of IP3R-mediated Ca2+ release and staurosporine-induced apoptosis. Strikingly, the trans-membrane domain was sufficient for IP3R binding and inhibition. We therefore propose a novel model, in which the Bcl-2's C-terminus serves as a functional anchor, which beyond mere ER-membrane targeting, underlies efficient IP3R inhibition by (i) positioning the BH4 domain in the close proximity of its binding site on IP3R, thus facilitating their interaction; (ii) inhibiting IP3R-channel openings through a direct interaction with the C-terminal region of the channel downstream of the channel-pore. Finally, since the hydrophobic cleft of Bcl-2 was not involved in IP3R suppression, our findings indicate that ABT-199 does not interfere with IP3R regulation by Bcl-2 and its mechanism of action as a cell-death therapeutic in cancer cells likely does not involve Ca2+ signaling.
Luminosity function of clusters of galaxies

The composite galaxy luminosity function (hereafter LF) of 39 Abell clusters of galaxies is derived by computing the statistical excess of galaxy counts in the cluster direction with respect to control elds. Due to the wide eld coverage of the digitised POSS-II plates, we can measure eld counts around each cluster in a fully homogeneous way. Furthermore, the availability of virtually unlimited sky coverage allows us to directly compute the LF errors without having to rely on the estimated variance of the background. The wide eld coverage also allows us to derive the LF of the whole cluster, including galaxies located in the cluster outskirts. The global composite LF has a slope 1:1 0:2 with minor variations from blue to red lters, and M 21:7; 22:2; 22: 4m ag (H0 =5 0 km s 1 Mpc 1 )i ng;r and i lters, respectively (errors are detailed in the text). These results are in quite good agreement with several previous determinations and in particular with the LF determined for the inner region of a largely overlapping set of clusters, but derived making use of a completely dierent method for background subtraction. The similarity of the two LFs suggests the existence of minor dierences between the LF in the cluster outskirts and in the central region, or a negligible contribution of galaxies in the cluster outskirts to the global LF.
Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques

Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented—from protected areas to payments for ecosystem services (PES)—to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing ‘matching’ to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods—an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators—due to the presence of unobservable bias—that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that if time-invariant unobservables are not present, matching combined with differences in means or cross-sectional regression leads to similar estimates of program effectiveness as matching combined with fixed effects panel regression. These results highlight the importance of considering observable and unobservable forms of bias and the methodological assumptions across estimators when designing an impact evaluation of conservation programs.
Nucleic Acid Binding by Mason-Pfizer Monkey Virus CA Promotes Virus Assembly and Genome Packaging

ABSTRACT The Gag polyprotein of retroviruses drives immature virus assembly by forming hexameric protein lattices. The assembly is primarily mediated by protein-protein interactions between capsid (CA) domains and by interactions between nucleocapsid (NC) domains and RNA. Specific interactions between NC and the viral RNA are required for genome packaging. Previously reported cryoelectron microscopy analysis of immature Mason-Pfizer monkey virus (M-PMV) particles suggested that a basic region (residues RKK) in CA may serve as an additional binding site for nucleic acids. Here, we have introduced mutations into the RKK region in both bacterial and proviral M-PMV vectors and have assessed their impact on M-PMV assembly, structure, RNA binding, budding/release, nuclear trafficking, and infectivity using in vitro and in vivo systems. Our data indicate that the RKK region binds and structures nucleic acid that serves to promote virus particle assembly in the cytoplasm. Moreover, the RKK region appears to be important for recruitment of viral genomic RNA into Gag particles, and this function could be linked to changes in nuclear trafficking. Together these observations suggest that in M-PMV, direct interactions between CA and nucleic acid play important functions in the late stages of the viral life cycle. IMPORTANCE Assembly of retrovirus particles is driven by the Gag polyprotein, which can self-assemble to form virus particles and interact with RNA to recruit the viral genome into the particles. Generally, the capsid domains of Gag contribute to essential protein-protein interactions during assembly, while the nucleocapsid domain interacts with RNA. The interactions between the nucleocapsid domain and RNA are important both for identifying the genome and for self-assembly of Gag molecules. Here, we show that a region of basic residues in the capsid protein of the betaretrovirus Mason-Pfizer monkey virus (M-PMV) contributes to interaction of Gag with nucleic acid. This interaction appears to provide a critical scaffolding function that promotes assembly of virus particles in the cytoplasm. It is also crucial for packaging the viral genome and thus for infectivity. These data indicate that, surprisingly, interactions between the capsid domain and RNA play an important role in the assembly of M-PMV.
Machine-Learning Based Performance Estimation for Distributed Parallel Applications in Virtualized Heterogeneous Clusters

In a virtualized heterogeneous cluster, for a distributed parallel application which runs in multiple virtual machines (VMs) concurrently, there are a huge number of possible ways to place its VMs. This paper investigates a performance estimation technique for distributed parallel applications in virtualized heterogeneous clusters. We first analyze the effects of different VM configurations on the performance of various distributed parallel applications. We then present a machine-learning based performance model for a distributed parallel application. Using a heterogeneous cluster with two different types of nodes, we show that our machine-learning based models can estimate the runtimes of distributed parallel applications with modest error rates.
How Risks and Innovativeness Influence the Selection of Partner in NPD Alliances

Most often, if a new product is complex, a unique company doesn't have all the competences or means to achieve alone its development. In this case alliances must be formed to carry on the development project. However, alliances often meet difficulties in achieving their objectives. That is mainly due to difficulties of communication between partners, or to missing skills or means required to go further in the innovative development. To successfully overcome risks of collaboration dysfunction, an alliance must be created between partners able to communicate, presenting a complementarity in the project and being presenting a maximum of characteristics useful for the new project. This paper aims to increase our understanding of how to make alliance with the purpose of exploring the opportunities emerging from a partner innovativeness and risks management. We present an original model of alliance assessment composed by two partners intending to develop a particular innovative project. Based on this model, we propose a decision support system helping choosing firms for an alliance. A case study from the aeronautical industry is presented and results are analysed.