Miracle-dz/UniFAR-FTUs huggingface dataset:
Delayed versus early pushing in women with epidural analgesia: a systematic review and meta‐analysis

Epidural analgesia is highly effective in relieving the pain of labour and childbirth, but it also interferes with the normal mechanism of labour. Trials of delayed pushing have occurred in response to concerns about the association between epidural analgesia and unwanted and potentially harmful outcomes, particularly instrumental delivery. Instrumental deliveries, especially forceps, have been associated with increased risk of urinary and faecal incontinence, sexual problems and organ prolapse. The mechanism for the association between epidural analgesia and increased instrumental deliveries is likely to be multifactorial but may include a weakened desire to push due to diminution of the bearing down reflex, reduced uterine activity and altered clinical practice. Contemporary obstetric practice has women begin pushing as soon as the cervix is fully dilated. Delaying the onset of pushing has been proposed as an alternative that may allow spontaneous descent and rotation of the fetal head, thereby reducing the instrumental delivery rate. On the other hand, delayed pushing prolongs the second stage of labour and this too has been implicated in pelvic floor trauma and subsequent maternal morbidity. Further, second stage is considered to be a time of particular risk to the fetus. A systematic review and meta-analysis of three small studies published in 1992 found both a tendency towards decreased perineal trauma and a decrease in rotational forceps with a policy of delayed pushing, however, there were insufficient data on infant outcomes and no data on pelvic floor morbidity. Since then several more trials of delayed pushing have been published and we undertook to update the systematic review and metaanalysis with the aim of assessing the effectiveness of delayed pushing among women with epidural analgesia in reducing instrumental deliveries and on other measures of maternal and infant morbidity. We aimed to compare the potential benefits and harms of a policy of delayed pushing among women with uncomplicated pregnancies and effective epidural analgesia established in the first stage of labour.
Life-Cycle Assessment and Policy Implications of Energy Efficient Lighting Technologies

Compact fluorescent lamps (CFLs) and light emitting diodes (LEDs) produce illumination four times more efficiently than standard incandescent light bulbs during their use- phase. When the large amounts of energy needed to produce these newer technologies are included in the comparison, however, their life-cycle energy efficiencies decrease by 10-30%. The current policy framework ignores this energy used for production, so any subsidies employed to foster the adoption of energy efficient lighting will over-count the resulting CO2 emissions reductions by about 5%. Lighting constitutes 22% of US electricity demand. Therefore, a small error in this calculation could easily negate the effects of very expensive emission reduction efforts in sectors such as transportation or land use. Two previous life-cycle assessments have compared CFLs to incandescents, but this is the first study to include LEDs, estimate environmental impacts of the CFL recycling industry, and put the results within the context of global warming policy. To simulate global conditions, four locations with unique electricity mixes were modeled: China, the US average, California, and a hypothetical grid with 33% renewable energy. In all locations, the manufacturing of incandescents represented less than 1% of life-cycle carbon emissions. However, depending on the electricity mix used to power CFLs and LEDs, 10-30% of life-cycle emissions were associated with manufacturing. For all bulbs, less than 1% of energy use and CO2 emissions were due to recycling or disposal. Finally, though currently very similar in performance, LEDs are likely to significantly improve in the future, while CFLs have already reached their theoretical limit.
A novel approach to determine building occupancy for cooling energy consumption prediction

Building cooling load prediction is one of the key elements in the energy conservation achievements. Most of the mathematical models using in the industry nowadays include forward and inverse modeling approaches. However, these models consume much computer resources and require a longer computational time. Multi-layer perceptron (MLP) model of artificial neural network (ANN) is adopted in this thesis. The model is widely used in engineering approaches that render good performance in adaptability, nonlinearity and mapping. It also has good ability in predicting the cooling energy consumption of buildings. It is reported that the occupants’ activities inside the buildings can have significant impact on the accuracy of the model. The existing input parameters used for the ANN models could not represent the complexity of the activities inside the buildings well. Most of the traditional ANN models adopted fixed profile or historic load data to represent building occupancy in simulating building cooling energy consumption. However, building occupancy is never still. The dynamic changes occurred in the occupancy of the buildings therefore make the forecasting of building cooling load difficult and less accurate. This thesis aims at (i) introducing a novel model to represent occupants’ presence and activities; and (ii) investigating the effect of using the novel model on improving the predictive accuracy of building cooling energy consumption. The simulation results demonstrate that building occupancy data play a significant role in building cooling energy consumption prediction and the use of the novel approach significantly improves the predictive accuracy of building cooling energy consumption model.
Short-term effectiveness and tolerability of carbon dioxide laser for anal high-grade squamous intraepithelial lesions in individuals living with HIV

Background: Ablative treatment of intra-anal high-grade squamous intraepithelial lesions (HSIL) reduces the risk of progression to anal squamous cell carcinoma. Our objective was to assess the short-term effectiveness and tolerability of the carbon dioxide laser for treating intra-anal HSIL in patients at high risk of anal cancer. Methods: This is an exploratory, pilot, single-arm, clinical trial of treatment response for anal HSIL in people living with HIV diagnosed with ≤3 not previously treated HSILs. Individuals were treated with one carbon dioxide laser treatment session. Clinical assessment by high resolution anoscopy and systematic recording of adverse events was performed. Results: Fifty-two patients with 72 HSILs were included. Response to treatment was assessed in 48 (92.3%) patients; in the per-protocol population analysis, complete, partial, and no response was seen in 50% (n = 24), 20.8% (n = 10) and 29.1% (n = 14), respectively. Being older than 40 years and having a CD4 T-cell count lower than 200 cells/μL at diagnosis of HSIL were significantly associated with a poor response to treatment. Data on adverse events was recorded for 49 patients and 69.4% (n=34) reported no symptoms after the procedure. Conclusions: Carbon dioxide laser ablation is a promising and well tolerated treatment for intra-anal HSIL.
Incorporating Estimates of Rare Clustered Events into Forest Inventories

U sually foresters are expected to answer a diverse set of questions about the forest on a very limited budget. The answers to such questions are considered important even though they often deal with conditions that occur only rarely in the forest. If a forester had the financial resources and knew where such conditions existed, the corresponding questions could be answered by designing and conducting an inventory in the vicinity of the rare occurrence. In most cases neither the money nor specific enough knowledge of the condition are available to use this approach. An alternative approach is to modify an existing inventory to address a question by adapting the field procedure only when the existence of a rare condition is noted. Naive attempts at this sort of strategy will often lead to biased estimates of the condition, so the inventory and analysis must be carefully designed to avoid potential bias. Roesch (1993) showed how to do this for forest inventories using adaptive cluster sampling. Adaptive cluster sampling is very efficient if the rare condition occurs on clustered trees, and it has two advantages over other methods of estimating rare conditions. The first advantage is that the decision to use an adaptive scheme can be made on a condition-by-condition basis, so adapting the sample for one condition does not affect the cost of estimating other conditions. The second advantage is that only the presence of the condition triggers additional cost. Most other solutions to the rare event problem, such as increasing the size or number of plots, do not have these two desirable properties. This article discusses in further detail the necessary considerations when contemplating an adaptive design and gives estimators for a population-based mean and its variance.
Hubs and Authorities in a Hyperlinked Environment 1 Searching the World Wide Web

Because diverse users each modify the link structure of the WWW within a relatively small scope (by creating web-pages on their own sites that link to other web-pages), the global stucture of the WWW is unpredictable and must be analyzed a posteriori. To make effective use of the wealth of information that the WWW provides, we need a way of extracting meaningful information with respect to a global search query that we make. We focus on broad-topic queries, i.e., “Tell me about automobile manufacturers. ” A naive search strategy might be to return any page containing the phrase “automobile manufacturers”. Unfortunately, there are literally hundreds of thousands of webpages meeting this criteria, far too many for a person to look through alone. Furthermore, the websites of major automobile manufacturers are unlikely to even contain the phrase “automobile manufacturers”. So simply returning the pages with the most occurrences of the query string will obviously not suffice (we call these pages the “text-based results”). Moreover, we maintain that any real measure of the quality of a search result is inherently subjective, as it and has much to do with the context and desires of the individual making the query. We want to identify the authoritative, informative web pages for a particular query. In the above example, the Toyota home page and important reviews might be considered authorities, while the personal home page of someone who makes his own personal cars might not. But without a formal definition of what makes a web-page an authority on a subject, we can’t codify an algorithm to identify them. Suppose you are provided the text-based search results, and have the ability to determine which pages link to which others. So what is a good heuristic for making this identification? A moment’s reflection will reveal that it is not at all obvious how to proceed. It has been suggested that authoritative pages may be identified by their popularity, as measured by the number of pages that link to it. That would imply that any very popular page was authoritative for any string it contains, e.g., Yahoo would be an authoritative page on Jessica Simpson. Clearly a more sophisticated approach is needed. In section 3 we describe the seminal work of J.M. Kleinberg [1] on using spectral analysis to find “hubs” and “authorities” for a broad-topic query over the WWW, an approach that has stood up well in practice. In section 4 we will discuss some generalizations of this technique and its relation to others, such as Google’s PageRank.
Identification of Host Biomarkers of EBV Latency IIb and Latency III

Deciphering the molecular pathogenesis of virally induced cancers is challenging due, in part, to the heterogeneity of both viral and host gene expression. Epstein-Barr Virus (EBV) is a ubiquitous herpesvirus prevalent in B-cell lymphomas of the immune suppressed. EBV infection of primary human B cells leads to their immortalization into lymphoblastoid cell lines (LCLs) serving as a model of these lymphomas. In previous studies, our lab has described a temporal model for immortalization with an initial phase characterized by expression of the Epstein-Barr Nuclear Antigens (EBNAs), high c-Myc activity, and hyper-proliferation in the absence of the Latent Membrane Proteins (LMPs), called latency IIb. This is followed by the long-term outgrowth of LCLs expressing the EBNAs along with the LMPs, particularly the NFκB-activating LMP1, defining latency III. LCLs, however, express a broad distribution of LMP1 such that a subset of these cells expresses LMP1 at levels seen in latency IIb, making it difficult to distinguish these two latency states. In this study, we performed mRNA-Seq on early EBV-infected latency IIb cells and latency III LCLs sorted by NFκB activity. We found that latency IIb transcriptomes clustered independently from latency III independent of NFκB. We identified and validated mRNAs defining these latency states. Indeed, we were able to distinguish latency IIb cells from LCLs expressing low levels of LMP1 using multiplex RNA-FISH targeting EBV EBNA2, LMP1, and human CCR7. This study defines latency IIb as a bona fide latency state independent from latency III and identifies biomarkers for understanding EBV-associated tumor heterogeneity. IMPORTANCE EBV is a ubiquitous pathogen with >95% of adults harboring a life-long latent infection in memory B cells. In immunocompromised individuals, latent EBV infection can result in lymphoma. The established expression profile of these lymphomas is latency III, which includes expression of all latency genes. However, single cell analysis of EBV latent gene expression in these lymphomas suggests heterogeneity where most cells express the transcription factor, EBNA2, and only a fraction express the membrane protein LMP1. Our work describes an early phase after infection where the EBNAs are expressed without LMP1, called latency IIb. However, LMP1 levels within latency III vary widely making these states hard to discriminate. This may have important implications for therapeutic responses. It is crucial to distinguish these states to understand the molecular pathogenesis of these lymphomas. Ultimately, better tools to understand the heterogeneity of these cancers will support more efficacious therapies in the future.
Improving activity recognition with context information

Activity recognition driven by sensor data has been heavily focused on in recent years, especially as wearable sensors become more common and popular personal equipments. As known, recognizing individual activities is one of the typical machine learning applications, which includes several basic phases, such as data collection, feature extraction, model training and performance evaluation. Originally, after data being collected and preprocessed, features are manually extracted for model training. As this may lead to a time-consuming and boring job, feature learning approaches were investigated and received a great success, especially when Deep Neural Networks, a powerful model for feature representation, were employed into this task. That is, classifier could be established with raw data. Those features, no matter manually selected or learned from raw data, depend on separate frames that were split from previous collected long data. Since human activity represented by sensor data is actually a time series signal, context information plays an important role, how to take advantage of knowledge implied in the inter-frames becomes significant. Unlike previous attempt that only enlarges the length of each frame, in this research, a new method that models context information with neighboring frames is investigated for activity recognition tasks. Based on the Daily and Sports Activities, a publicly available dataset, experiments are performed with three typical classifiers for activity recognition. The results show that the proposed method could significantly improve the recognition performance and the more the context information are considered, the higher the recognition accuracy will be.
Calibrage itératif de caméras à partir de scènes planes

Nous presentons une methode permettant le calibrage de cameras video a partir de sequences d'images d'un objet plan texture, mais inconnu. Les methodes permettant de repondre a ce probleme procedent generalement par optimisation d'un systeme non-lineaire global (utilisant toutes les images simultanement) alors que notre methode est par nature sequentielle, puisque basee sur un filtre de Kalman. Le cout de traitement est tres faible, ce qui permet de calibrer des cameras video en temps reel, en utilisant directement le flux des images produites par la camera. Le fait de pouvoir utiliser le flux video efficacement est profitable, puisqu'un auto-calibrage stable requiere que les images soient prises avec des angles de vue differents. Les methodes globales sont limitees par le nombre d'images qu'elles peuvent utiliser et il faut donc soit choisir des vues cles, soit utiliser des sequences de vues fortement espacees ce qui rend la mise en correspondance difficile. Notre methode ne souffre pas de ces limites, et peut donc traiter des sequences de plusieurs milliers d'images. Bien que non-optimale (au sens de l'ajustement de faisceaux), notre methode permet de traiter l'ensemble des informations d'une sequence. Des resultats experimentaux montrent que, aussi bien en terme de precision que de convergence, notre methode donne des resultats equivalents a un ajustement de faisceaux, et permet de plus l'auto-calibrage en temps reel.
Quinolones Modulate Ghrelin Receptor Signaling: Potential for a Novel Small Molecule Scaffold in the Treatment of Cachexia

Cachexia is a metabolic wasting disorder characterized by progressive weight loss, muscle atrophy, fatigue, weakness, and appetite loss. Cachexia is associated with almost all major chronic illnesses including cancer, heart failure, obstructive pulmonary disease, and kidney disease and significantly impedes treatment outcome and therapy tolerance, reducing physical function and increasing mortality. Current cachexia treatments are limited and new pharmacological strategies are needed. Agonists for the growth hormone secretagogue (GHS-R1a), or ghrelin receptor, prospectively regulate the central regulation of appetite and growth hormone secretion, and therefore have tremendous potential as cachexia therapeutics. Non-peptide GHS-R1a agonists are of particular interest, especially given the high gastrointestinal degradation of peptide-based structures, including that of the endogenous ligand, ghrelin, which has a half-life of only 30 min. However, few compounds have been reported in the literature as non-peptide GHS-R1a agonists. In this paper, we investigate the in vitro potential of quinolone compounds to modulate the GHS-R1a in both transfected human cells and mouse hypothalamic cells. These chemically synthesized compounds demonstrate a promising potential as GHS-R1a agonists, shown by an increased intracellular calcium influx. Further studies are now warranted to substantiate and exploit the potential of these novel quinolone-based compounds as orexigenic therapeutics in conditions of cachexia and other metabolic and eating disorders.
Nonstationary noise cancellation in infrared wireless receivers

Infrared is attracting much attention for indoor wireless access due to its enormous bandwidth, inherent privacy and low cost. Intensity modulated, directly detected infrared schemes do not experience multipath fading. However, ambient noise due to artificial lighting has been the major concern in infrared wireless systems in indoors. Conventionally, static or low frequency noise due to conventional light sources is removed using optical high pass filters. Nonetheless, interference from fluorescent lights equipped with electronic ballasts has periodic interference components up to 1 MHz and, cannot be filtered easily. In this paper, soft DSP filters are proposed to cancel the harmonics, ambient noise, and uncorrelated signal structures. Nonstationary noise is cancelled with an adaptive denoising filter, and a comb filter cancels interference from the electronic ballasts. Adaptive soft filters have the advantage that they can be easily updated and track the variations in noise characteristics. Simulation results show promising improvement in noise cancellation even under very low and varied SNR and noise source conditions.
MULTI-TEMPORAL DATA AUGMENTATION FOR HIGH FREQUENCY SATELLITE IMAGERY: A CASE STUDY IN SENTINEL-1 AND SENTINEL-2 BUILDING AND ROAD SEGMENTATION

Abstract. Semantic segmentation of remote sensing images has many practical applications such as urban planning or disaster assessment. Deep learning-based approaches have shown their usefulness in automatically segmenting large remote sensing images, helping to automatize these tasks. However, deep learning models require large amounts of labeled data to generalize well to unseen scenarios. The generation of global-scale remote sensing datasets with high intraclass variability presents a major challenge. For this reason, data augmentation techniques have been widely applied to artificially increase the size of the datasets. Among them, photometric data augmentation techniques such as random brightness, contrast, saturation, and hue have been traditionally applied aiming at improving the generalization against color spectrum variations, but they can have a negative effect on the model due to their synthetic nature. To solve this issue, sensors with high revisit times such as Sentinel-1 and Sentinel-2 can be exploited to realistically augment the dataset. Accordingly, this paper sets out a novel realistic multi-temporal color data augmentation technique. The proposed methodology has been evaluated in the building and road semantic segmentation tasks, considering a dataset composed of 38 Spanish cities. As a result, the experimental study shows the usefulness of the proposed multi-temporal data augmentation technique, which can be further improved with traditional photometric transformations.
Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues. Firstly, several image transformations which have been shown to be crucial for effective contrastive learning do not translate from the natural image to the medical image domain. Secondly, the assumption made by conventional methods, that any two images are dissimilar, is systematically misleading in medical datasets depicting the same anatomy and disease. This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time. In this paper we tackle these issues by extending conventional contrastive frameworks with a novel metadata-enhanced strategy. Our approach employs widely available patient metadata to approximate the true set of inter-image contrastive relationships. To this end we employ records for patient identity, eye position (i.e. left or right) and time series information. In experiments using two large longitudinal datasets containing 170,427 retinal OCT images of 7,912 patients with age-related macular degeneration (AMD), we evaluate the utility of using metadata to incorporate the temporal dynamics of disease progression into pretraining. Our metadata-enhanced approach outperforms both standard contrastive methods and a retinal image foundation model in five out of six image-level downstream tasks related to AMD. Due to its modularity, our method can be quickly and cost-effectively tested to establish the potential benefits of including available metadata in contrastive pretraining.