Pengaruh Inhibitor Aromatase (Ia) terhadap Perkembangn Oosit pada Ikan Mas Koki (Carassius Auratus)

Penelitian ini bertujuan untuk mengetahui dosis optimal Inhibitor Aromatase (IA) pada perkembangan oosit ikan mas koki. Dosis yang digunakan adalah k = kontrol (disuntik NaCl fisiologis), Pl = 2.5 mg/kg berat tubuh (b.t), P2 = 7.5 mg/kg b.t., dan P3 = 12.5 mg/kg b.t. Untuk mengetahui pengaruh IA terhadap perkembangan gonad diukur kandungan hormon estradiol-l7β dalam plasma darah dan level protein gonad mulai dari awal perlakukan kemudian setiap tujuh hari sekali, yaitu hari ke-7, ke-14, dan ke-21. Hasil penelitian menunjukkan bahwa, pada hari ke-7 hasil semua perlakuan P1, P2, P3 menunjukkan penurunan hormon estradiol- 17β diikuti penurunan level protein gonad, hasil analisis pengamatan histologi menunjukkan terjadi atresi pada sel gonad. Pada hari ke-14 kadar hormon estradiol-17β perlakuan menunjukkan terjadi peningkatan diikuti peningkatan level protein gonad. Pada hari ke-21 kadar hormon estradiol-17β perlakuan sudah sama dengan kadar hormon kontrol namun level protein gonad masih dibawah level protein gonad kontrol

Identifying Human Strategies for Generating Word-Level Adversarial Examples

Adversarial examples in NLP are receiving increasing research attention. One line of investigation is the generation of word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammaticality. Previous work found that human- and machine-generated adversarial examples are comparable in their naturalness and grammatical correctness. Most notably, humans were able to generate adversarial examples much more effortlessly than automated attacks. In this paper, we provide a detailed analysis of exactly how humans create these adversarial examples. By exploring the behavioural patterns of human workers during the generation process, we identify statistically significant tendencies based on which words humans prefer to select for adversarial replacement (e.g., word frequencies, word saliencies, sentiment) as well as where and when words are replaced in an input sequence. With our findings, we seek to inspire efforts that harness human strategies for more robust NLP models

Online influence, offline violence: Language Use on YouTube surrounding the 'Unite the Right' rally

The media frequently describes the 2017 Charlottesville ‘Unite the Right’ rally as a turning point for the alt-right and white supremacist movements. Social movement theory suggests that the media attention and public discourse concerning the rally may have engendered changes in social identity performance and visibility of the alt-right, but this has yet to be empirically tested. The presence of the movement on YouTube is of particular interest, as this platform has been referred to as a breeding ground for the alt-right. The current study investigates whether there are differences in language use between 7142 alt-right and progressive YouTube channels, in addition to measuring possible changes as a result of the rally. To do so, we create structural topic models and measure bigram proportions in video transcripts, spanning approximately 2 months before and after the rally. We observe differences in topics between the two groups, with the ‘alternative influencers’, for example, discussing topics related to race and free speech to a larger extent than progressive channels. We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally. While most changes relate to mentions of the rally itself, the alternative group also shows an increase in promotion of their YouTube channels. In light of social movement theory, we argue that language use on YouTube shows that the Charlottesville rally indeed triggered changes in social identity performance and visibility of the alt-right

Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification

Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible. In this work, we investigate this through the lens of human language ability. We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example. Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. We analyze how human-generated adversarial examples compare to the recently proposed TEXTFOOLER, GENETIC, BAE and SEMEMEPSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate. Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples, though they do so by being much more computationally efficient

Novel mutations in the voltage-gated sodium channel of pyrethroid-resistant Varroa destructor populations from the Southeastern USA

The parasitic mite Varroa destructor has a significant worldwide impact on bee colony health. In the absence of control measures, parasitized colonies invariably collapse within 3 years. The synthetic pyrethroids tau-fluvalinate and flumethrin have proven very effective at managing this mite within apiaries, but intensive control programs based mainly on one active ingredient have led to many reports of pyrethroid resistance. In Europe, a modification of leucine to valine at position 925 (L925V) of the V. destructor voltage-gated sodium channel was correlated with resistance, the mutation being found at high frequency exclusively in hives with a recent history of pyrethroid treatment. Here, we identify two novel mutations, L925M and L925I, in tau-fluvalinate resistant V. destructor collected at seven sites across Florida and Georgia in the Southeastern region of the USA. Using a multiplexed TaqMan® allelic discrimination assay, these mutations were found to be present in 98% of the mites surviving tau-fluvalinate treatment. The mutations were also found in 45% of the non-treated mites, suggesting a high potential for resistance evolution if selection pressure is applied. The results from a more extensive monitoring programme, using the Taqman® assay described here, would clearly help beekeepers with their decision making as to when to include or exclude pyrethroid control products and thereby facilitate more effective mite management programmes