Miracle-dz/UniFAR-FTUs huggingface dataset:
Traces of Breachtian-Theatre Theories in Caryl Churchill’s Mad Forest: A Play from Romania

Britanya’da Ikinci Dalga Feminizm ekolunden cikmis olan Caryl Churchill modern tiyatronun onemli sosyalist ve feminist yazarlarindan biridir. Churchill, ozellikle gunumuzde dahi populer olan naturalist tiyatro gibi baskin temsil bicimlerine meydan okuyabilmesi icin oyunlarinda demokratik ve deneysel bir tiyatro pratigi yaratmasini saglayan, cesitli islevsel yontemler gelistirmistir. Churchill, Deli Orman: Romanya’dan Bir Oyun (1990) adli oyununda Brecht tiyatrosu ve teknikleri ile guclu baglari olan Epik tiyatro ve yabancilastirma etkisini kullanmistir. Bu makale, epizodik sahneler, montaj prensibi, jest (gestus), her bir sahnenin isimlendirilmesi, oyun icinde ara oyun ve sarkilarin kullanilmasi gibi ozellikler tasiyan Brecht Tiyatrosunun uygulanisini ve islevselligini, Brecht’in haleflerinden biri olan, Caryl Churchill’in Deli Orman: Romanya’dan Bir Oyun adli eserinde incelemeyi amaclamaktadir. Buna ek olarak, bu calisma hem Bertolt Brecht Tiyatrosu literaturune hem de Caryl Churchill’in oyunlari uzerine yazilmis elestirel calismalara katkida bulunmayi amaclamaktadir.
Effects of Different Paclobutrazol-Cultar Concentrations on the Micropropagation of Sugarcane (Saccharum Officinarum) Variety CPCL99-4455

Sugarcane (Saccharum officinarum), being an important cash crop in Belize, accounts for 60% of agricultural exports providing employment for thousands of Belizeans. This research was carried out to determine the effects of different Paclobutrazol (PBZ-Cultar) concentrations used in the culture media on in vitro multiplication of sugarcane variety CPCL99-4455. Three PBZ-Cultar concentrations were tested and compared with the control. The parameters evaluated to determine the effects of PBZ-Cultar were average height, number of dead leaves and multiplication coefficient. The plants from this experiment were planted in the acclimatization phase to determine if the use of PBZ-Cultar had any effect of the growth and development of the plants ex vitro. The results revealed that the culture medium supplemented with 0.08% PBZ-Cultar concentration had the best results both in the multiplication phase as well as the acclimatization phase.
Sex-Gender Differences in Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the main causes of visual loss in individuals aged 20–64 years old. The aim of this study was to investigate, in a multicenter retrospective cross-sectional study, sex-gender difference in DR in a large sample of type 2 diabetic patients (T2DM). 20,611 T2DM regularly attending the units for the last three years were classified as having: (a) No DR (NDR), (b) nonproliferative DR (NPDR), or (c) preproliferative/proliferative DR (PPDR). DR of all grades was present in 4294 T2DM (20.8%), with a significant higher prevalence in men as compared to women (22.0% vs. 19.3% p < 0.0001). Among DR patients, both NPDR and PPDR were significantly more prevalent in men vs. women (p = 0.001 and p = 0.0016, respectively). Women had similar age and BMI, but longer diabetes duration, worse glycemic metabolic control, and more prevalence of hypertension and chronic renal failure (CRF) of any grade vs. men. No significant differences between sexes were evident in term of drug therapy for diabetes and associate pathologies. Conclusions: In this large sample of T2DM, men show higher prevalence of DR vs. women, in spite of less represented risk factors, suggesting that male sex per se might be a risk factor for DR development.
QUALITY CHARACTERISTICS OF TARHANA PRODUCED WITH DIFFERENT RATIOS OF WHOLE WHEAT AND BUCKWHEAT FLOUR

Bu calismada; karabugday ununun degisik oranlarda tarhanaya ilavesi ile elde edilen urunun fiziksel, kimyasal ve duyusal ozelliklerinin incelenmesi amaclanmistir. Karabugday un orani arttikca parlaklik (L*) degerinin yas tarhana orneklerinde arttigi, ancak kuru tarhana orneklerinde azaldigi (P <0.05), kirmizilik (a*) degerlerinin yas orneklerde azaldigi, kuru orneklerde arttigi, sarilik (b*) degerlerin ise; yas orneklerde daha yuksek iken kurutmayla birlikte azaldigi belirlenmistir (P <0.05). Karabugday ilavesi ile viskozite degerlerinin azalmasina karsin protein ve kul miktarinin onemli derecede arttigi kaydedilmistir (P <0.05). En yuksek viskozite degerine 935 cP ile kurutulmus kontrol ornegi sahipken, en yuksek protein, kul miktari, DPPH serbest radikal giderme aktivitesi ve toplam fenolik madde miktarlarina sirasiyla %15.83, %3.56, 161.82 (mg Teq/g) ve 14.00 (mg GAE/g) ile %100 karabugday unu kullanilmis orneklerde oldugu belirlenmistir. Ornekler arasinda en yuksek genel begeni puanina 5.00 ve 7.55 ile sirasiyla %25 tam bugday ve %75 karabugday unu ilavesi ile uretilen orneklerin aldigi belirlenmistir (P <0.05).
Organic Selenium Reaches the Central Nervous System and Downmodulates Local Inflammation: A Complementary Therapy for Multiple Sclerosis?

Multiple sclerosis (MS) is an inflammatory and demyelinating disease of the central nervous system (CNS). The persistent inflammation is being mainly attributed to local oxidative stress and inflammasome activation implicated in the ensuing demyelination and axonal damage. Since new control measures remain necessary, we evaluated the preventive and therapeutic potential of a beta-selenium-lactic acid derivative (LAD-βSe), which is a source of organic selenium under development, to control experimental autoimmune encephalomyelitis (EAE) that is an animal model for MS. Two EAE murine models: C57BL/6 and SJL/J immunized with myelin oligodendrocyte glycoprotein and proteolipid protein, respectively, and a model of neurodegeneration induced by LPS in male C57BL/6 mice were used. The preventive potential of LAD-βSe was initially tested in C57BL/6 mice, the chronic MS model, by three different protocols that were started 14 days before or 1 or 7 days after EAE induction and were extended until the acute disease phase. These three procedures were denominated preventive therapy −14 days, 1 day, and 7 days, respectively. LAD-βSe administration significantly controlled clinical EAE development without triggering overt hepatic and renal dysfunction. In addition of a tolerogenic profile in dendritic cells from the mesenteric lymph nodes, LAD-βSe also downregulated cell amount, activation status of macrophages and microglia, NLRP3 (NOD-like receptors) inflammasome activation and other pro-inflammatory parameters in the CNS. The high Se levels found in the CNS suggested that the product crossed the blood-brain barrier having a possible local effect. The hypothesis that LAD-βSe was acting locally was then confirmed by using the LPS-induced neurodegeneration model that also displayed Se accumulation and downmodulation of pro-inflammatory parameters in the CNS. Remarkably, therapy with LAD-βSe soon after the first remitting episode in SJL/J mice, also significantly downmodulated local inflammation and clinical disease severity. This study indicates that LAD-βSe, and possibly other derivatives containing Se, are able to reach the CNS and have the potential to be used as preventive and therapeutic measures in distinct clinical forms of MS.
Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

Objective To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC). Methods We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared. Results By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis. Conclusion LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer.
Why do Pheidole oxyops (Forel, 1908) ants place feathers around their nests?

1. The ant species Pheidole oxyops (Hymenoptera: Formicidae) has the particular behaviour of placing feathers along the edge of entrances to its nests. Here two hypotheses concerning this behaviour were tested: (i) P. oxyops, due to its essentially carnivore diet and particular nest structure (which acts as pitfall traps), uses feathers to enhance the capture of arthropods; and (ii) P. oxyops uses feathers as a strategy for obtaining water, as feathers could act as retainers of night moisture.
Household’s demand for Food Commodities in Pakistan: Issues and Empirical Evidence

The householdâ€™s demand analysis is very crucial in the sense that undernourishment negatively affects productivity. Food demand is crucial to gauge the householdâ€™s ability or willingness to purchase/consume different food commodities. The study attempts to analyze the householdâ€™s food demand in Pakistan by estimating the Quadratic Almost Ideal Demand System (QUAIDS) on Household Integrated Income and Consumption Survey (HIICS) 2015-16 data. It has been found that the householdâ€™s locality, dependency ratio, living in own house, age and level of education of household head have significant impacts on demand for different food commodities. It has been further found that hoteling, fruits & dry fruits, soft drinks & bottled water, bakery products, beef & mutton, chicken &seafood are luxury goods. Eggs, sugar & sweets, pulses, tea & coffee, vegetables, rice, and edible oil & ghee are necessity goods. The spices, milk & yogurt and wheat are close to sticky food items. The price elasticities suggest that the demand for beef & mutton, seafood, eggs, and soft drinks is relatively elastic in comparison to other food commodity groups. Moreover, the demand for tea & coffee is least elastic revealing that price of tea has a very marginal impact on its demand.
Bond behaviour of FRP strengthening applied on curved masonry substrates: numerical study

Aim of the present paper is to numerically investigate the bond behaviour of FRP strengthening systems externally applied on curved masonry specimens. In particular, considering the simple spring-model approach proposed by the authors in previous research, a new constitutive law derived from the work of Thorenfeld et al. (1987) is here proposed by also considering the coupled behaviour between shear and normal forces at the reinforcement/masonry interface. Numerical analyses are developed with reference to case studies deduced from the literature and consisting of shear lap bond tests of curved masonry specimens characterised by different values of the geometric curvature and different strengthening configurations. The obtained results show the ability of the proposed modelling approach in capturing some effects, such as the beneficial friction effect when compression normal stresses develop at the interface level.
Genetic diversity and molecular fingerprinting of Prunus cerasus var. austera from central Italy

Abstract In central Italy, Prunus cerasus var. austera is cultivated as small stands or scattered trees in marginal areas for the production of jam and wine. Thanks to the healthy attributes of its products and its ability to grow in different environmental conditions, this variety has gained new interest in the development of marginal areas. We assessed the level of the genetic variability of P. cerasus var. austera germplasm from central Italy and identified a ‘core collection’ representative of the present genetic diversity. A total of 161 trees, morphologically identified as var. austera, and one tree, identified as var. caproniana were collected and genotyped by 14 SSRs. Two individuals provided by a commercial plant nursery, one of P. cerasus var. caproniana and one of P. cerasus var. austera, were used as control. Thirteen SSRs presented private alleles in austera. Seven individuals morphologically identified as austera revealed private alleles specific to caproniana. The PCoA and Bayesian clustering analysis showed a main genetic group including var. austera, while a second group included all the caproniana-like genotypes. A core collection of 31 trees (46% of austera genotypes) was selected. This study can be considered as a starting point for future investigations on this variety.
Isotherms and Isosteric Heat Desorption of Hymenaea stigonocarpa Mart. Seeds

The Brazilian Cerrado is one of the most important ecosystems and due to the extractivism it is necessary to recover the degraded areas. The tree of Hymenaea stigonocarpa Mart. is used in this reforestation system, so the study of post-harvest management of the seed is necessary for the propagation. The objective of this work was to determine the isotherms and the isosteric desorption heat of Hymenaea stigonocarpa Mart. Seeds, and to test the methodology of the Akaike’s information criterion (AIC) and Schwarz’s bayesian information criterion (BIC) for the choice of the best mathematical model. Different mathematical models were fitted to the experimental data and the model that best represents the phenomenon was selected, from the statistical parameters. To obtain the equilibrium moisture content was used static method using desiccants in incubators cameras with control of relative humidity by salt solution. The Oswin Modified model obtained better results according to analyzed parameters, being this model the one selected for prediction of the hygroscopic balance of the Hymenaea stigonocarpa Mart. seeds. It was found that the higher the temperature for the same equilibrium moisture content, the higher the water activity values. The AIC and BIC methodology contributed to the choice of the best mathematical model to predict the hygroscopicity phenomenon. The isosteric heat increased with the decrease in the equilibrium moisture content requiring a greater amount of energy to remove water from the seeds.
Investigating Non-Usually Employed Features in the Identification of Architectural Smells: A Machine Learning-Based Approach

Architectural smells (ASs) negatively affect the maintenance and evolution of software at the architectural level. Most of the current approaches for ASs identification rely on the same small and well-known set of usually employed metrics (UE-Ms) with fixed thresholds. Machine learning (ML) is a promising technique for smell identification as algorithms can learn from a rich set of metrics/features, covering several characteristics of the software and incorporating a certain degree of subjectivity. This has been explored by building datasets with a robust and rich set of features, including not only the UE-Ms but also other non-usually employed metrics (NUE-Ms). However, usually the UE-Ms determine the output of the algorithms, obfuscating other metrics that have the potential to improve the classification. This also leads to inflated and difficult to maintain datasets. In this paper, we investigate the accuracy of some ML algorithms employing only NUE-Ms. We scoped our study in the classification of two smells: God Component and Unstable Dependency. This investigation revealed a set of NUE-Ms that can be also used to identify these smells and the contribution of each one for the classification. Based on this information, software engineers can then build a final dataset just with the potential features. We also briefly present our tool, called InSet, that was used by academics and practitioners to identify smells in their systems. The feedback of them was used as the oracle to compare our tool to other approaches and good results were reached.
Neural Speech Completion

During a conversation, humans often predict the end of a sentence even when the other person has not ﬁnished it. In contrast, most current automatic speech recognition systems remain limited to passively recognizing what is being said. But applications like voice search, simultaneous speech translation, and spoken language communication may require a system that not only recognizes what has been said but also predicts what will be said. This paper proposes a speech completion system based on deep learning and discusses the construction in a text-to-text, speech-to-text, and speech-to-speech framework. We evaluate our system on domain-speciﬁc sentences with synthesized speech utterances that are only 25%, 50%, or 75% complete. Our proposed systems provide more natural suggestions than the Bidirectional Encoder Representations from Transformers (BERT) language representation model.
Phoneme-to-Grapheme Conversion Based Large-Scale Pre-Training for End-to-End Automatic Speech Recognition

This paper describes a simple and efﬁcient pre-training method using a large number of external texts to enhance end-to-end automatic speech recognition (ASR). Generally, it is essential to prepare speech-to-text paired data to construct end-to-end ASR models, but it is difﬁcult to collect a large amount of such data in practice. One issue caused by data scarcity is that the performance of ASR on out-of-domain tasks different from those using the speech-to-text paired data is poor, since the mapping from the speech information to textual information is not well learned. To address this problem, we leverage a large number of phoneme-to-grapheme (P2G) paired data, which can be easily created from external texts and a rich pronunciation dictionary. The P2G conversion and end-to-end ASR are regarded as similar transformation tasks where the input phonetic information is converted into textual information. Our method utilizes the P2G conversion task for pre-training of a decoder network in Trans-former encoder-decoder based end-to-end ASR. Experiments using 4 billion tokens of Web text demonstrates that the performance of ASR on out-of-domain tasks can be signiﬁcantly improved by our pre-training.
Secondary Phonetic Cues in the Production of the Nasal Short-a System in California English

A production study explored the acoustic characteristics of /æ/ in CVC and CVN words spoken by California speakers who raise /æ/ in pre-nasal contexts. Results reveal that the phonetic realization of the /æ/-/ɛ/ contrast in these contexts is multidimensional. Raised pre-nasal /æ/ is close in formant space to /ɛ/, particularly over the second half of the vowel. Yet, systematic differences in the realization of the secondary acoustic features of duration, formant movement, and degree of coarticulatory vowel nasalization keep these vowels phonetically distinct. These findings have implications for systems of vowel contrast and the use of secondary phonetic properties to maintain lexical distinctions.
Single-Channel Blind Direct-to-Reverberation Ratio Estimation Using Masking

Acoustic parameters, like the direct-to-reverberation ratio (DRR), can be used in audio processing algorithms to perform, e.g., dereverberation or in audio augmented reality. Often, the DRR is not available and has to be estimated blindly from recorded audio signals. State-of-the-art DRR estimation is achieved by deep neural networks (DNNs), which directly map a feature representation of the acquired signals to the DRR. Motivated by the equality of the signal-to-reverberation ratio and the (channel-based) DRR under certain conditions, we formulate single-channel DRR estimation as an extraction task of two signal components from the recorded audio. The DRR can be obtained by inserting the estimated signals in the deﬁnition of the DRR. The extraction is performed using time-frequency masks. The masks are estimated by a DNN trained end-to-end to minimize the mean-squared error between the estimated and the oracle DRR. We conduct experiments with different pre-processing and mask estimation schemes. The proposed method outperforms state-of-the-art single-and multi-channel methods on the ACE challenge data corpus.
Effect of Adding Positional Information on Convolutional Neural Networks for End-to-End Speech Recognition

Attention-based models with convolutional encoders enable faster training and inference than recurrent neural network-based ones. However, convolutional models often require a very large receptive ﬁeld to achieve high recognition accuracy, which not only increases the parameter size but also the computational cost and run-time memory footprint. A convolutional encoder with a short receptive ﬁeld length can suffer from looping or skipping problems when the input utterance contains the same words as nearby sentences. We believe that this is due to the insufﬁcient receptive ﬁeld length, and try to remedy this problem by adding positional information to the convolution-based encoder. It is shown that the word error rate (WER) of a convolutional encoder with a short receptive ﬁeld size can be reduced signiﬁcantly by augmenting it with positional information. Visualization results are presented to demonstrate the effectiveness of adding positional information. The proposed method improves the accuracy of attention models with a convolutional encoder and achieves a WER of 10.60% on TED-LIUMv2 for an end-to-end speech recognition task.
An Alternative to MFCCs for ASR

The Mel scale is the most commonly used frequency warping function to extract features for automatic speech recognition (ASR) and is known to be quite effective. However, it is not speciﬁcally designed for ASR acoustic models based on deep neural networks (DNN). In this study, we introduce a frequency warping function which is a modiﬁed version of Mel scale. This warping function is parameterized using 2 parameters and we use it to propose a new set of features called mod-iﬁed Mel-frequency cepstral coefﬁcients (MFCC), which use cosine-shaped ﬁlters. The bandwidths are computed using a new function. By evaluating the proposed features on a variety of ASR data sets, we see consistent improvements over regular MFCCs and (log) Mel ﬁlter bank energies.
Staged Knowledge Distillation for End-to-End Dysarthric Speech Recognition and Speech Attribute Transcription

This study proposes a staged knowledge distillation method to build End-to-End (E2E) automatic speech recognition (ASR) and automatic speech attribute transcription (ASAT) systems for patients with dysarthria caused by either cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS). Compared with traditional methods, our proposed method can use limited dysarthric speech more effectively. And the dysarthric E2E-ASR and ASAT systems enhanced by the proposed method can achieve 38.28% relative phone error rate (PER%) reduction and 48.33% relative attribute detection error rate (DER%) reduction over their baselines respectively on the TORGO dataset. The experiments show that our system offers potential as a rehabilitation tool and medical diagnostic aid.