Miracle-dz/UniFAR-FTUs huggingface dataset:
Infants of Depressed Mothers Living in Poverty

More than half of all infants living in poverty have a mother suffering from depression. Urban Institute Fellow Olivia Golden discusses the dangers and developmental risks these babies and toddlers face. While depression is treatable, many poor mothers do not receive care. But opportunities do exist, Golden explains, to identify these depressed mothers and connect them to help and support.
The effects of excitotoxicity and microglial activation on oligodendrocyte survival.

ii Dedication. iv Acknowledgments . v Vita . viii List of Figures . xi Chapters: Chapter 1: Introduction . 1 Chapter 2: A sublethal dose of TNFα Increases Excitotoxic Cell Death in Optic Nerve Oligodendrocytes . 18 Introduction . 18 Materials and Methods . 23 Results . 27 Discussion . 33 Chapter 3: Characterization of the Response of Cortical Oligodendrocytes to Kainic Acid Excitotoxicity . 40 Introduction . 40 Materials and Methods . 44 Results . 52 Discussion . 62 Chapter 4: The Effects of LPS-Activated Microglia on Oligodendrocyte Progenitor Cells and Mature Oligodendrocytes. 73 Introduction . 73 x Materials and Methods . 75 Results . 78 Discussion . 90 Chapter 5: Microglial Cell Death in Response to LPS-Activation . 95 Introduction . 95 Materials and Methods . 97 Results . 98 Discussion . 102 Chapter 6: Summary, Conclusions and Future Directions . 107 References . 114
Feline mast cell tumours

Mast cell tumours (MCTs) are among the most common cutaneous tumours seen in cats and most can be managed in practice. The aetiology is unknown, although a genetic predisposition has been proposed in some breeds. MCTs usually present as a solitary mass. The most common site for cutaneous tumours is on the head, although the trunk and limbs may also be affected. The spleen is the most common site for visceral MCTs. This article describes the approach to take when presented with a cat with a MCT, the histological and immunohistological prognostic indicators, and the different treatments available.
Tree-Like Context Embeddings and Advisers for Neural Language Models

Statistical language models are key to many prominent applications in natural language processing. State-of-the-art language models are built around recurrent neural networks and form the basis for important problems such as machine translation, speech recognition, and automatic summarization. The present thesis is concerned with the investigation of state-of-the-art statistical language models and their weaknesses. A new way of organizing the context embeddings of neural network based language models is explored. In particular, the treelanguage model is introduced and tested: A model which, inspired by the recent advances in hyperbolic deep learning, explicitly represents information from a given sequence of text in a prefix tree-like manner. Furthermore, an easy-to-use extension to language models is developed and applied to a state-of-the-art language model resulting in an improved perplexity on the well-known Penn Treebank benchmark.
Mechanistic dissection of pseudomonas aeruginosa anaerobic respiration : implications for treatment of cystic fibrosis airway disease.

Pseudomonas aeruginosa is an opportunistic pathogen that causes chronic lung infection in cystic fibrosis (CF) patients. During the course of infection, P. aeruginosa develops biofilms that are resistant to antibiotic treatment and undergoes a phenotypic switch to a mucoid alginate-overproducing form. Due to the hyperabsorption of airway surface liquid caused by a defective or absent cystic fibrosis transmembrane conductance regulator (CFTR), bacterial infection ensues. Recently, the CF mucus lining the airway was determined to be anaerobic. In addition, the mucus layer becomes acidic because of abnormal bicarbonate transport across the airway epithelium. During in vitro anaerobic biofilm growth, P. aeruginosa formed ~7-fold more biofilm compared to those developed aerobically, supporting the idea that CF pathogenesis is associated with bacterial biofilm formation under anaerobic conditions. We discovered that defects leading to overproduction of nitric oxide (NO) resulted in significant decrease in viability under such conditions. Major contributors that minimized NO-mediated damage were found to be (i) quorum sensing circuitry, (ii) NO reductase (a ma jor NO detoxifier in P. aeruginosa) and (iii) anti-sigma factor, MucA, that is required for full activity of NO reductase. However, in a norCB mutant where abnormally high levels of NO accumulate during anaerobic respiration, a master anaerobic regulator, ANR, senses NO through its [4Fe-4S] cluster and, thus, inhibits further production of NO. Simultaneoulsy, high levels of NO in this mutant were scavenged by two Fe-cofactored enzymes, the synthesis of which was stimulated at the result of ANR inactivation. We have also shown that nitrite (NO2) can selectively kill mucoid mucA mutant P. aeruginosa at a slightly acidic pH that mimics the CF airway. The mucA mutant harbored negligible nitrite reductase
Contribution of value co-creation support elements to repurchase intention: A theoretical approach

Objetivo: o trabalho visa estabelecer ligações entre os elementos de sustentação da cocriação de valor e da intenção de recompra, por meio de alguns antecedentes como a confiança, comprometimento e satisfação. Metodologia/abordagem: por meio de uma reflexão teórica, o modelo de gerenciamento de cocriação de valor proposto por Payne, Storbacka Frow (2008) e o modelo DART de Prahalad Ramaswamy (2004) são revisitados, enfatizando os pontos de interação entre fornecedores e clientes e os elementos de sustentação da cocriação de valor.Principais resultados: a ligação entre os elementos de sustentação da cocriação de valor e antecedentes da intenção de recompra é explicitada em umframework conceitual, evidenciando as relações entre os temas, efetivadas por construtos como confiança, compromisso e fortes relacionamentos. Contribuições teóricas: a participação positiva dos elementos de sustentação da cocriação de valor para com os antecedentes da intenção de recompra fica evidenciada por meio da contribuição do modelo DART para a aprendizagem conjunta e interações personalizadas. Relevância/originalidade: na medida em que não foram identificados estudos anteriores unindo conceitualmente estes dois temas, comumente tratados separadamente, o presente trabalho esclarece como os mecanismos de interação típicos dos processos de cocriação de valor poderiam ajudar a explicar a intenção de recompra por meio de alguns de seus antecedentes.
In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing

Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R2 = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods.
Constructing Progress Maps of Digital Technology for Diagnosing Mathematical Proficiency

This research aims to construct and validate progress maps of digital technology for diagnosing the multidimensional mathematical proficiency (MP) in Number and Algebra for Grade 7 students utilizing the Construct Modeling Approach. Researchers employed four building blocks as follows. Firstly, researchers developed the progress maps as an assessment framework of multidimensional MP. This is followed by creating the test for diagnosing MP. Next, researchers assigned scoring criteria and created the transition points of students’ MP levels. Finally, researchers validated the quality of the progress maps through empirical evidence. A total sample 1,500 Grade 7 students was used to support the validity and reliability evidence of the progress maps through the Wright Map using Multidimensional Random Coefficients Multinomial Logit Model. Results revealed that there were two dimensions of progress maps, namely mathematical procedures (MAP) and structure of learning outcome (SLO), and the researchers investigated three strands of validity evidence, namely test content, response processes, and internal structure. The reliability values in the MAP and SLO were 0.84 and 0.80 respectively. Finally, the Grade 7 students were mainly found to be at level-2 in the MAP dimension (44.95%) and the SLO dimension (61.57%). The experts’ evaluation results showed that the digital technology that was developed at the “most appropriate” quality levels in terms of usefulness, suitability, and accuracy, and at the “very appropriate” for the feasibility aspect, and hence is successfully contributing to the clarification of learning goals, to support for student-centered instruction, and that it is helpful in improving in teacher professional development.
Robust unit commitment with n − 1 security criteria

The short-term unit commitment and reserve scheduling decisions are made in the face of increasing supply-side uncertainty in power systems. This has mainly been caused by a higher penetration of renewable energy generation that is encouraged and enforced by the market and policy makers. In this paper, we propose a two-stage stochastic and distributionally robust modeling framework for the unit commitment problem with supply uncertainty. Based on the availability of the information on the distribution of the random supply, we consider two speciﬁc models: (a) a moment model where the mean values of the random supply variables are known, and (b) a mixture distribution model where the true probability distribution lies within the convex hull of a ﬁnite set of known distributions. In each case, we reformulate these models through Lagrange dualization as a semi-inﬁnite program in the former case and a one-stage stochastic program in the latter case. We solve the reformulated models using sampling method and sample average approximation, respectively. We also establish exponential rate of convergence of the optimal value when the randomization scheme is applied to discretize the semi-inﬁnite constraints. The proposed robust unit commitment models are applied to an illustrative case study, and numerical test results are reported in comparison with the two-stage non-robust stochastic programming model.
SCOAP-based Testability Analysis from Hierarchical Netlists

Circuits of VLSI complexity are designed using modules such as adders, multipliers, register files, memories, multiplexers, and busses. During the high-level design of such a circuit, it is important to be able to consider several alternative designs and compare them on counts of area, performance, and testability. While tools exist for area and delay estimation of module-level circuits, most of the testability analysis tools work on gate-level descriptions of the circuit. Thus an expensive operation of flattening the circuit becomes necessary to carry out testability analysis. In this paper, we describe a time and space-efficient technique for evaluating the well known SCOAP testability measure of a circuit from its hierarchical description with two or more levels of hierarchy. We introduce the notion of SCOAP Expression Diagrams for functional modules, which can be precomputed and stored as part of the module data base. Our hierarchical testability analysis program, HISCOAP, reads the SCOAP expression diagrams for the modules used in the circuit, and evaluates the SCOAP measure in a systematic manner. The program has been implemented on a Sun/SPARC workstation, and we present results on several benchmark circuits, both combinational and sequential. We show that our algorithm also has a straightforward parallel realization.
Preparation and Characterization of Acid Resistant Rubber Composites Reinforced with Nano Silica

The natural rubber has good resistance for some acids such as hydrochloric acid. Therefore, natural rubber composites are used in the lining of acid containers. Acid resistance of the natural rubber can be improved by types and levels of fillers. This research studies the effect of silica nanoparticles on acid resistance of natural rubber composites for (37% w/w) hydrochloric acid. Six rubber compounds were prepared with various levels of nano silica of 0, 1, 2, 3, 4, 5 (parts per hundred parts of rubber weight). Tensile properties, hardness (Shore A), compression set, tear resistance and abrasion resistance were tested. Test specimens of tensile properties were immersed in hydrochloric acid for 70 h at room temperature. After immersion, they were dried and tested for determining acid resistance. The acid resistance of rubber composites improved by the addition of nano silica and the best acid resistance of rubber composite was gotten at 3.
Improving text representations for NLP from bags of words to strings of words

Natural language as we use it daily is often ambiguous, as words and sentences can have different meanings depending on the context in which they are used. Humans derive meaning from words using contextual information and word relations, which is something we do automatically. Most computer algorithms rely on rules and an absence of ambiguity to process information. Since the contextual cues we use to communicate meaning cannot be readily captured in rules, such algorithms cannot reliably interpret natural language. Machine learning algorithms enable computers to model in some way what human-made texts mean. In this thesis, a machine learning algorithm is used to estimate whether reviews of movies and tv shows taken from imdb give a positive or negative appraisal of their respective movie or show. The reviews are represented as vectors of numbers to enable the machine learning algorithm to process the text. The method of representing these reviews that is currently standard, the ‘bag of words’ representation, represents the reviews in terms of how frequently each word in a predefined word list occurs in each review. The original arrangement of the words in the review is lost, as the representation is ordered in terms of the predefined word list. This limits the use of contextual information and word relations, which forms a barrier to interpreting what was meant. An alternative to the bag of words text representation is presented, which enables the original arrangement of the words in a text to be used. The alternative ‘string of words’ representation represents texts in terms of the original words of a text, in the original order. This differs from the bag of words representation, which represents the text in the order of the words that are known to the model. To find out if a machine learning classifier can be improved by the string of words representation, it is tested against the bag of words representation in a neural network that classifies movie and tv show reviews into two categories: positive and negative reviews. For both conditions in the comparison between word representations, the same neural network layers were used as a basis for the machine learning model. To compare the two representations, performance and time measures were taken. The performance was measured as the MCC value, a combined confusion matrix measure, of the classifier model that was yielded using that representation. The impact of representation length of the string of words representation and the lengths of the classified reviews were assessed in an exploratory analysis. FROM BAGS TO STRINGS OF WORDS 3 The string of words representation outperforms the bag of words in time measures as well as performance measures but does come with its own limitations. The string of words representation performs best with texts that deviate little in length from the training texts and offers an advantage over the bag of words representation only if the text length is shorter than the number of words known to a machine learning model.
Antiplasmodial Activity Assay of 3-Chloro-4-(4-chlorophenoxy) Aniline Combinations with Artesunate or Chloroquine In Vitro and in a Mouse Model

Malaria is the eighth highest contributor to global disease burden with 212 million cases and 429,000 deaths reported in 2015. There is an urgent need to develop multiple target drug to curb growing resistance by Plasmodia due to use of single target drugs and lack of vaccines. Based on a previous study, 3-chloro-4-(4-chlorophenoxy) aniline (ANI) inhibits Plasmodia enoyl acyl carrier protein reductase. This study aimed at evaluating the antiplasmodial activity of ANI combinations with artesunate (AS) or chloroquine (CQ) against P. falciparum in vitro based on the semiautomated microdilution assay and P. berghei in vivo based on Peters' 4-day test. Data were analysed by linear regression using version 5.5 of Statistica, 2000. From the results, on the one hand, a combination of 1.1 ng/ml AS and 3.3 μg/ml of ANI inhibited 50% growth of W2, while a combination of 0.8 ng/ml of AS and 2.6 μg/ml of ANI inhibited 50% growth of 3D7. On the other hand, a combination of 22 ng/ml CQ and 3.7 μg/ml of ANI inhibited 50% growth of W2, while a combination of 4.6 ng/ml CQ and 3.1 μg/ml of ANI inhibited 50% growth of 3D7. In in vivo assays, a combination of ED50 concentrations of AS and ANI cleared all parasites, while 1/2 and 1/4 ED50 combinations inhibited 67.0% and 35.4% parasite growth, respectively. ED50 combinations of CQ and ANI inhibited 81.0% growth of parasites, while 1/2 and 1/4 ED50 combinations inhibited 27.3% and 10.2% parasite growth. Assuming a linear relationship between percentage chemosuppression and combination ratios, only 0.88 mg/kg of AS combined with 1.68 mg/kg of ANI or 1.78 mg/kg of CQ with 3.15 mg/kg of ANI inhibited 50% parasite growth in vivo. ANI combinations with AS or CQ are thus potential antimalarial drug combinations if their clinical efficacy and safety are ascertained.
EVALUATION OF SELECTED COST AGGREGATION METHODS ON HIGH RESOLUTION SATELLITE STEREO IMAGES

Abstract. Dense stereo processing requires a critical step that called cost aggregation or cost optimization. Most of the cost aggregation methods are evaluated on close range stereo images from Middlebury or KITTI datasets. While the effect of cost aggregation on high resolution satellite stereo processing has not yet been sufficiently evaluated. In this paper, three typical cost aggregation methods together with another approach which is a combination of these methods are evaluated on high resolution satellite stereo images and then are compared with LiDAR ground truth. These methods including Semi-Global Matching (SGM), Guided Filtering (GF), iterative GF (IGF), and SGM followed by GF (SGM-GF) with Census and Zero Normalized Cross Correlation (ZNCC) cost functions. Although the Census cost function has a good performance on the border of the objects and low blurring effects, the results of both cost functions, i.e. Census and ZNCC, have same treatment on all stereo methods. Also, in order to make an impartial assessment, for all stereo methods, we do not perform any disparity map refinement. The bad-pixel criteria with an absolute difference height error greater than 2 meters for SGM, GF, IGF, and SGM-GF methods is 36.7%, 34.8%, 33.8%, and 28.6% respectively. Also, the Normalized Median Absolute Difference (NMAD) error for SGM, GF, IGF, and SGM-GF is 1.29, 1.15, 1.06, and 0.94 meters, respectively. Overall, the experimental results on WV III stereo images demonstrate that the SGM method has lower accuracy and SGM-GF method is accurate than other methods.
Internet Based ICT by Indonesian SMEs

In order to gain competitive advantages and compete in the global market, SMEs are pushed to adopt the internet based ICT in their business process. Despite of numerous benefit provided by the internet adoption, the internet adoption in developing countries’ SMEs are still low. Thus this study is aimed to examine the factors influencing internet based ICT adoption in developing countries’ SMEs. This study was conducted in Indonesia with Batik Trusmi, Cirebon, West Java as the case study. Partial least square-structural equation model (PLS-SEM) was used as the analytical tool. The findings showed six factors that significantly influencing the internet based ICT adoption in Batik Trusmi: CEO’s knowledge, firm size, competitor, pressure from buyer/supplier, perceived benefit, and cost.
Working paper PRECAUTION , INFORMATION AND TIME-INCONSISTENCY : DO WE NEED A PRECAUTIONARY PRINCIPLE ? 1 November 11 , 2019

We study the optimal action plan chosen by a decision-maker who faces the possibility of an irreversible catastrophe. That event follows a non-homogenous Poisson process with a rate that depends on the stock of past actions. Passed a tipping point, the rate of arrival of such a disaster increases. We describe optimal trajectories under various informational scenarios with uncertainty on where the tipping point lies. When the mere fact of having passed the tipping point is immediately known, the optimal action plan is time-consistent. When having passed the tipping point remains unknown, a case of deeper uncertainty, a time-inconsistency problem arises. We characterize the unique Markov-perfect equilibrium among the decision-maker’s different selves with feedback rules being conditioned only on the existing stock of past actions. We interpret the Precautionary Principle as an institutional restriction on actions which aims at solving this time-inconsistency problem. Unfortunately, such restriction is never optimal as it means refusing acting on useful information that will arrive in the future. We investigate and discuss the relevance of other potential solutions to restore commitment. keywords. Precautionary Principle, Environmental Risk, Regulation, Learning. JEL codes. D83, Q55.