Miracle-dz/UniFAR-FTUs huggingface dataset:
Change Making: How we adopt new attitudes, beliefs and practices

The 44 people, involved in the ‘We Can’ Campaign to end violence against women, whose experiences of change are the basis of this document, are from a volatile part of the world – South Asia – whose countries, Bangladesh, India, Pakistan and Sri Lanka are all undergoing rapid, complex, uneven and often conflict-ridden social and political change. They are people who one way or another are ‘makers of change’, embracing change in their own lives, and promoting change in the institutions of which they are a part – in the family, household, community, voluntary and private sectors, and the state. They have chosen not to be bystanders. For all of them, just as it is impossible to step back into the same water in the stream, their lives cannot be the same again. This study examines the processes of change through the voices of these Change Makers and Campaign Alliance members, who were selected for their active involvement in the ‘We Can’ Campaign, and interviewed at the end of 2010. In it we try to understand the how and why of the change process these individuals describe, and how and why they think others changed as a result of work they carried out.
Optimal feature selection for automated classification of FDG-PET in patients with suspected dementia

FDG-PET is increasingly used for the evaluation of dementia patients, as major neurodegenerative disorders, such as Alzheimer's disease (AD), Lewy body dementia (LBD), and Frontotemporal dementia (FTD), have been shown to induce specific patterns of regional hypo-metabolism. However, the interpretation of FDG-PET images of patients with suspected dementia is not straightforward, since patients are imaged at different stages of progression of neurodegenerative disease, and the indications of reduced metabolism due to neurodegenerative disease appear slowly over time. Furthermore, different diseases can cause rather similar patterns of hypo-metabolism. Therefore, classification of FDG-PET images of patients with suspected dementia may lead to misdiagnosis. This work aims to find an optimal subset of features for automated classification, in order to improve classification accuracy of FDG-PET images in patients with suspected dementia. A novel feature selection method is proposed, and performance is compared to existing methods. The proposed approach adopts a combination of balanced class distributions and feature selection methods. This is demonstrated to provide high classification accuracy for classification of FDG-PET brain images of normal controls and dementia patients, comparable with alternative approaches, and provides a compact set of features selected.
Adaptive Nonrigid Inpainting of Three-Dimensional Point Cloud Geometry

In this letter, we introduce several algorithms for geometry inpainting of three-dimensional (3-D) point clouds with large holes. The algorithms are exemplar based. Hole filling is performed iteratively using templates near the hole boundary to find the best matching regions elsewhere in the cloud, from where existing points are transferred to the hole. We propose two improvements over the previous work on exemplar-based hole filling. The first one is adaptive template size selection in each iteration, which simultaneously leads to higher accuracy and lower execution time. The second improvement is a nonrigid transformation to better align the candidate set of points with the template before the point transfer, which leads to even higher accuracy. We demonstrate the algorithm's ability to fill holes that are difficult or impossible to fill by existing methods.
Improvement of oncolytic adenovirus vectors through genetic capsid modifications

Recombinant viral vectors hold great promise in the field of cancer gene therapy. While a plethora of viruses is being evaluated as oncolytic agents, human adenoviruses of serotype 5 (HAdV-5) are among the most popular of viruses to be developed. Although clinical studies have demonstrated safety of cancer gene therapy with HAdV-5-derived vectors, the efficacy still needs further enhancement. Several factors have been identified that limit the anti-tumor efficacy. One major bottleneck is the inadequate penetration and spread of the virus within the tumor. This is attributable, at least in part, to the low or heterogeneous expression of the coxsackie and adenovirus receptor (CAR) on the tumor cells. This thesis describes the development and preclinical evaluation of novel tumor-targeted HAdV-5 vectors, through implementing the genetic fusion of capsid proteins (protein IX and fiber) with a variety of tumor-targetin g polypeptides.
Enhancing Förster nonradiative energy transfer via plasmon interaction

Plasmon-enhanced nonradiative energy transfer is demonstrated in two inorganic semiconductor systems. The first is comprised of colloidal nanocrystal CdTe donor and acceptor quantum dots, while the second is a hybrid InGaN quantum well-CdSe/ZnS quantum dot donor-acceptor system. Both structures are in a planar geometry. In the first case a monolayer of Au nanospheres is sandwiched between donor and acceptor quantum dot monolayers. The largest energy transfer efficiency is seen when the donor is ~3 nm from the Au nanopshere. A plasmon-enhanced energy transfer efficiency of ~ 40% has been achieved for a separation of 3 nm between the Au nanopshere monolayer and the acceptor monolayer. Despite the increased energy transfer efficiency these conditions result in strong quenching of the acceptor QD emission. By tuning the Au nanosphere concentration and Au nanosphere-acceptor QD separation the acceptor QD emission can be increased by a factor of ~2.8. The plasmon-enhanced nonradiative energy transfer is observed to extend over larger distances than conventional Forster resonance energy transfer. Under the experimental conditions reported herein, it can be described by the same d-4 dependence but with a larger characteristic distance. Using a Ag nanobox array plasmonic component plasmon-enhanced nonradiative energy transfer has also demonstrated from an InGaN quantum well to a ~80 nm thick layer of CdSe/ZnS colloidal quantum dots. An efficiency of ~27% is achieved, with an overall increase in the QD emission by ~70%.
Oral health status in historic population: Macroscopic and metagenomic evidence

Recent developments in High-Throughput DNA sequencing (HTS) technologies and ancient DNA (aDNA) research have opened access to the characterization of the microbial communities within past populations. Most studies have, however, relied on the analysis of dental calculus as one particular material type particularly prone to the molecular preservation of ancient microbial biofilms and potential of entire teeth for microbial characterization, both of healthy communities and pathogens in ancient individuals, remains overlooked. In this study, we used shotgun sequencing to characterize the bacterial composition from historical subjects showing macroscopic evidence of oral pathologies. We first carried out a macroscopic analysis aimed at identifying carious or periodontal diseases in subjects belonging to a French rural population of the 18th century AD. We next examined radiographically six subjects showing specific, characteristic dental pathologies and applied HTS shotgun sequencing to characterize the microbial communities present in and on the dental material. The presence of Streptococcus mutans and also Rothia dentocariosa, Actinomyces viscosus, Porphyromonas gingivalis, Tannerella forsythia, Pseudoramibacter alactolyticus, Olsenella uli and Parvimonas micra was confirmed through the presence of typical signatures of post-mortem DNA damage at an average depth-of-coverage ranging from 0.5 to 7X, with a minimum of 35% (from 35 to 93%) of the positions in the genome covered at least once. Each sampled tooth showed a specific bacterial signature associated with carious or periodontal pathologies. This work demonstrates that from a healthy independent tooth, without visible macroscopic pathology, we can identify a signature of specific pathogens and deduce the oral health status of an individual.
Vigilance, Flock Size, and Flock Geometry: Information Gathering by Western Evening Grosbeaks

Vigilance (scanning) and other behavior patterns were studied in free-ranging Evening Grosbeaks (Coccothraustes vespertinus) at feeders to assess how flock size and flock geometry influenced the behavior of individual birds. The present results indicate that the way in which individual grosbeaks are positioned with respect to one another effects many aspects of their behavior, especially when a flock contains four or more birds. Birds in a linear array who have difficulty seeing one another, when compared to individuals organized in a circle who can easily see one another, are (1) more vigilant, (2) change their head and body positions more often, (3) react to changes in group size more slowly, (4) show less coordination in head movements, and (5) show more variability in all measures. These differences in behavior can be explained from a cognitive ethological perspective that favors intentional or representational explanations. Specifically, the data suggest that individual grosbeaks, when scanning and moving about, are visually monitoring the flock in which they are feeding and gathering information about a number of variables including flock size, what others are doing, where others are, which individuals are present, phenotypic features of flock members, food resources, or the location of potential predators. Individuals likely use visual records of the behavior and perhaps the phenotypic features of others, and this information influences various aspects of their behavior.
Study of Development of Pomo-Lime Nectar Fortified with Sweet Lime Peel Powder

Nectar is ready to drink type of beverage, which is prepared from mango, litchi, guava, papaya, citrus fruits and pineapple. As per FSSA specification nectar should contain TSS not less than 15 brix, not less than 20% fruit content, and acidity not less than 1.5%. Citrus limettais popular indigenous citrus fruit relished for its cooling and therapeutic effect. It contain high amount of Vit.C which increases the resistance of individuals to several diseases. Citrus limetta peels are considered to be one of the potential sources for the screening of anticancer, antimicrobial, antioxidant, and free radical scavenging agents. Pomegranate juice is a rich source of Ellagic acid, polyphenols, tannins, anthocyanins, including vitamin C, vitamin E, coenzyme Q10, and lipoic acid. Pomegranate fruit juice, seeds, and seed oil act in prostate, breast, skin, colon, lung, oral and leukaemia cancers, through antioxidantantiproliferation, antiangiogenasis and anti-inflammatory mechanism of action. Keyword: Nectar, Anticancer, Antioxidant, Vit C, Citrus limetta peels
Evaluation of Energy Expenditure and Oxidation of Energy Substrates in Adult Males after Intake of Meals with Varying Fat and Carbohydrate Content

Obesity is a result of positive energy balance. The aim of this study was to measure (in crossover trials) the energy expenditure and oxidation of glucose and lipids, both at the fasting state and after an intake of meals with a varying macronutrient content, in normal-weight and overweight/obese people. In the study, 46 healthy adult males (23 with normal body weight and 23 overweight/obese), aged 21–58, were examined. During two consecutive visits, subjects received isocaloric standardized meals (450 kcal) with different content of basic nutrients. Resting metabolic rate and carbohydrate and fat utilization were evaluated during the fasting state and postprandially, using an indirect calorimetry method. Energy expenditure was higher in people with normal body weight and slightly higher after the high-carbohydrate meal. In overweight/obese people, increased expenditure was noted after normo-carbohydrate meal intake. The high-fat meal induced lower postprandial thermal response compared to a high-carbohydrate meal, both in people with normal body weight and in overweight/obese men. Glucose utilization was higher after the high-carbohydrate meal, and it was higher in the normal body weight group than in overweight/obese people. In addition, overweight/obese people showed a lower level of fatty acid oxidation under fasting conditions which, together with limited ability to oxidize energy substrates, depending on their availability, indicates that these people are characterized by lower metabolic flexibility.
Plant, fungal, bacterial, and nitrogen interactions in the litter layer of a native Patagonian forest

Plant–microbial interactions in the litter layer represent one of the most relevant interactions for biogeochemical cycling as litter decomposition is a key first step in carbon and nitrogen turnover. However, our understanding of these interactions in the litter layer remains elusive. In an old-growth mixed Nothofagus forest in Patagonia, we studied the effects of single tree species identity and the mixture of three tree species on the fungal and bacterial composition in the litter layer. We also evaluated the effects of nitrogen (N) addition on these plant–microbial interactions. In addition, we compared the magnitude of stimulation of litter decomposition due to home field advantage (HFA, decomposition occurs more rapidly when litter is placed beneath the plant species from which it had been derived than beneath a different plant species) and N addition that we previously demonstrated in this same forest, and used microbial information to interpret these results. Tree species identity had a strong and significant effect on the composition of fungal communities but not on the bacterial community of the litter layer. The microbial composition of the litter layer under the tree species mixture show an averaged contribution of each single tree species. N addition did not erase the plant species footprint on the fungal community, and neither altered the bacterial community. N addition stimulated litter decomposition as much as HFA for certain tree species, but the mechanisms behind N and HFA stimulation may have differed. Our results suggest that stimulation of decomposition from N addition might have occurred due to increased microbial activity without large changes in microbial community composition, while HFA may have resulted principally from plant species’ effects on the litter fungal community. Together, our results suggest that plant–microbial interactions can be an unconsidered driver of litter decomposition in temperate forests.
Univariate Model-Based Deadband Alarm Design for Nonlinear Processes

Alarm design is an essential industrial problem with significant implications for safety and performance. Standard alarm design algorithms are based on the assumption that the data are uncorrelated and stationary. In this Article, we relax these assumptions and develop a novel approach to design alarms for processes modeled as a stochastic nonlinear time-series model. In particular, we develop an algorithm to design deadband alarms by minimizing the false and missed alarm rates. The resulting algorithm is illustrated through extensive simulations on a reactor system.
Management of Preterm Labor

Preterm birth is defined as birth between 20 0/7 weeks of gestation and 36 6/7 weeks of gestation. The diagnosis of preterm labor generally is based on clinical criteria of regular uterine contractions accompanied by a change in cervical dilation, effacement, or both, or initial presentation with regular contractions and cervical dilation of at least 2 cm. Less than 10% of women with the clinical diagnosis of preterm labor actually give birth within 7 days of presentation (11). It is important to recognize that preterm labor with intact membranes is not the only cause of preterm birth; numerous preterm births are preceded by either rupture of membranes or other medical problems necessitating delivery (2, 6, 12). Historically, nonpharmacologic treatments to prevent preterm births in women with preterm labor have included bed rest, abstention from intercourse and orgasm, and hydration. Evidence for the effectiveness of these interventions is lacking, and adverse effects have been reported (13–16). Proposed pharmacologic interventions to prolong pregnancy have included the use of tocolytic drugs to inhibit uterine contractions as well as antibiotics to treat intrauterine bacterial infection. The therapeutic agents currently thought to be clearly associated with improved neonatal outcomes include antenatal corticosteroids for maturation of fetal lungs and other developing organ systems, and the targeted use of magnesium sulfate for fetal neuroprotection.
Title Evaluation of common genetic variants in 82 candidate genes as risk factors for neural tube defects

Background: Neural tube defects (NTDs) are common birth defects (~1 in 1000 pregnancies in the US and Europe) that have complex origins, including environmental and genetic factors. A low level of maternal folate is one well-established risk factor, with maternal periconceptional folic acid supplementation reducing the occurrence of NTD pregnancies by 50-70%. Gene variants in the folate metabolic pathway (e.g., MTHFR rs1801133 (677 C > T) and MTHFD1 rs2236225 (R653Q)) have been found to increase NTD risk. We hypothesized that variants in additional folate/B12 pathway genes contribute to NTD risk. Methods: A tagSNP approach was used to screen common variation in 82 candidate genes selected from the folate/B12 pathway and NTD mouse models. We initially genotyped polymorphisms in 320 Irish triads (NTD cases and their parents), including 301 cases and 341 Irish controls to perform case–control and family based association tests. Significantly associated polymorphisms were genotyped in a secondary set of 250 families that included 229 cases and 658 controls. The combined results for 1441 SNPs were used in a joint analysis to test for case and maternal effects. Results: Nearly 70 SNPs in 30 genes were found to be associated with NTDs at the p < 0.01 level. The ten strongest association signals (p-value range: 0.0003–0.0023) were found in nine genes (MFTC, CDKN2A, ADA, PEMT, CUBN, GART, DNMT3A, MTHFD1 and T (Brachyury)) and included the known NTD risk factor MTHFD1 R653Q (rs2236225). The single strongest signal was observed in a new candidate, MFTC rs17803441 (OR = 1.61 [1.23-2.08], p = 0.0003 for the minor allele). Though nominally significant, these associations did not remain significant after correction for multiple hypothesis testing. Conclusions: To our knowledge, with respect to sample size and scope of evaluation of candidate polymorphisms, this is the largest NTD genetic association study reported to date. The scale of the study and the stringency of correction are likely to have contributed to real associations failing to survive correction. We have produced a ranked list of variants with the strongest association signals. Variants in the highest rank of associations are likely to include true associations and should be high priority candidates for further study of NTD risk.
Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System

Objectives Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. Methods A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. Results The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). Conclusions The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.
GeCoTagger: Annotation of German Verb Complements with Conditional Random Fields

Complement phrases are essential for constructing well-formed sentences in German. Identifying verb complements and categorizing complement classes is challenging even for linguists who are specialized in the ﬁeld of verb valency. Against this background, we introduce an ML-based algorithm which is able to identify and classify complement phrases of any German verb in any written sentence context. We use a large training set consisting of example sentences from a valency dictionary, enriched with POS tagging, and the ML-based technique of Conditional Random Fields (CRF) to generate the classiﬁcation models.
Efficient and deterministic record & replay for actor languages

With the ubiquity of parallel commodity hardware, developers turn to high-level concurrency models such as the actor model to lower the complexity of concurrent software. However, debugging concurrent software is hard, especially for concurrency models with a limited set of supporting tools. Such tools often deal only with the underlying threads and locks, which obscures the view on e.g. actors and messages and thereby introduces additional complexity. To improve on this situation, we present a low-overhead record & replay approach for actor languages. It allows one to debug concurrency issues deterministically based on a previously recorded trace. Our evaluation shows that the average run-time overhead for tracing on benchmarks from the Savina suite is 10% (min. 0%, max. 20%). For Acme-Air, a modern web application, we see a maximum increase of 1% in latency for HTTP requests and about 1.4 MB/s of trace data. These results are a first step towards deterministic replay debugging of actor systems in production.
Yif1 associates with Yip1 on Golgi and regulates dendrite pruning in sensory neurons during Drosophila metamorphosis

ABSTRACT Pruning that selectively removes unnecessary neurites without causing neuronal death is essential for sculpting the mature nervous system during development. In Drosophila, ddaC sensory neurons specifically prune their larval dendrites with intact axons during metamorphosis. However, the important role of endoplasmic reticulum (ER)-to-Golgi transport in dendrite pruning remains unknown. Here, in a clonal screen, we have identified Yif1, an uncharacterized Drosophila homolog of Yif1p that is known to be a regulator of ER-to-Golgi transport in yeast. We show that Yif1 is required for dendrite pruning of ddaC neurons but not for apoptosis of ddaF neurons. We further identify that the Yif1-binding partner Yip1 is also crucial for dendrite pruning. Yif1 forms a protein complex with Yip1 in S2 cells and ddaC neurons. Yip1 and Yif1 colocalize on ER/Golgi and are required for the integrity of Golgi apparatus and outposts. Moreover, we show that two GTPases, Rab1 and Sar1, which are known to regulate ER-to-Golgi transport, are essential for dendrite pruning of ddaC neurons. Finally, our data reveal that ER-to-Golgi transport promotes endocytosis and downregulation of the cell-adhesion molecule Neuroglian and thereby dendrite pruning. Summary: Two novel Drosophila proteins, Yif1 and Yip1, associate with each other and regulate Golgi integrity to act as novel players of dendrite pruning in sensory neurons during early metamorphosis.