Letters of Recommendation (LORs) are widely utilized for admission to both undergraduate and graduate programs, and are becoming even more important with the decreasing role that standardized tests play in the admissions process. However, LORs are highly subjective and thus can inject recommender bias into the process, leading to an inequitable evaluation of the candidates’ competitiveness and competence. Our study utilizes natural language processing methods and manually determined ratings to investigate gender and cultural differences and biases in LORs written for STEM Master’s program applicants. We generate features to measure important characteristics of the LORs and then compare these characteristics across groups based on recommender gender, applicant gender, and applicant country of origin. One set of features, which measure the underlying sentiment, tone, and emotions associated with each LOR, is automatically generated using IBM Watson’s Natural Language Understanding (NLU) service. The second set of features is measured manually by our research team and quantifies the relevance, specificity, and positivity of each LOR. We identify and discuss features that exhibit statistically significant differences across gender and culture study groups. Our analysis is based on approximately 4000 applications for the MS in Data Science and MS in Computer Science programs at Fordham University. To our knowledge, no similar study has been performed on these graduate programs. The impact of these biases on education is of great concern because education is one of the most effective ways of reducing these societal inequities or, put more colloquially, of “leveling the playing field”. For this reason, there has been increasing interest in ensuring that educational institutions do not perpetuate gender and cultural biases. Our study focuses on identifying bias in the university admissions process, with specific attention to Letters of Recommendation (LORs). These letters play an essential role in educational institutions’ holistic assessment of each candidate. However, LORs are highly subjective and hence can easily be influenced by the gender and cultural biases of the recommendation writer. The excerpted portions from two letters of recommendation from our study data, for the Recommender 1: “Recommender 2: “At a personal level, she is very bold and has never hesitated to voice her opinion without any fear. She has always taken a stand for herself and her friends in every situation and never steps back from her word.”8. Another LOR in our study from an Asian country described the applicant as being “obedient,” a term unlikely to be found in a recommendation originating in the United States. Research also supports cultural differences in LOR9. These examples serve as one motivation for our work since they can exacerbate existing inequalities in higher education. The ability to identify such biases is a key step in addressing them.These statements provide vastly different impressions, which may be influenced by the first recommender being male and the second one being female. Research shows that the content and specific linguistic terms in LORs differ based on applicant gender and/or race and ethnicity, and that this impacts the impression of the applicant10, which indicate that women earned only 33.4% of STEM MS degrees in 2021, and by our study that shows that only 37.7% of Fordham University MS Computer Science and Data Science applicants are female.This research focuses on gender and cultural biases in LORs for Computer Science and Data Science graduate programs. These disciplines are important because of rapid and continuing job growth, high pay, and the lack of trained workers; this study is particularly important because of the well-known gender disparities in these fields. This gender disparity is reflected in the United States Department of Education statisticsIt covers bias in LORs of two STEM disciplines that have not been studied previously, thereby extending the research beyond medical education, which has been the main focus of prior research.More LORs (nearly 4000) are analyzed than in most prior studies.Cultural bias is studied along with gender bias, whereas most prior research focuses exclusively on gender bias.It uses the IBM Watson Natural Language Understanding (NLU) service to extract a dozen tones and emotions from the LOR text and analyses the differences based on gender and culture; prior work uses a smaller set of automatically generated features.The specificity, relevance, and positivity of each LOR is manually rated using well-defined guidelines and analyzed for differences in the gender and culture groupings. This is unique to our study.The remainder of this paper is organized as follows. We first provide relevant background and related work. Next, we describe our data and preprocessing procedures. Then in the Methods section we introduce the features employed by this study and the procedures for extracting their values from the LORs. We then present the results and follow this with a discussion and a conclusion.While there has been some research into bias in LORs, as described in the related work section, this study extends existing research and makes the following contributions:This section describes work that is relevant to our study, including theoretical foundations, linguistic categories that have been used in the past to analyze bias, and prior LOR bias studies for graduate medical and science programs.12, which states that beliefs about gender come from the social roles of men and women. People develop different beliefs about what men and women can do based on general observations of how they behave or are expected to behave. Society perceives men to be agentic  while women are perceived to be communal . Because achievement is deemed to be more closely associated with agency, this gender bias tends to favor men over women. Agency and communalism are well established in the psychological literature and provide a conceptual framework for understanding personality and behavior13. These terms are also relevant when discussing race, as racial inequality and the associated power differential lead to the same perceptions as are observed for gender14.Gender bias in letters of recommendation can be explained by Eagly’s social role theoryGrindstone words have to do with being hardworking or diligent , while standout words have to do with being outstanding, excellent, or superb. Grindstone words, while seemingly positive, are often viewed as a “backhanded compliment”15 and are associated with weak recommendations8. Studies of bias in LORs often measure the prevalence of grindstone and standout words, and the underrepresented population that is presumably subject to bias is typically described using more grindstone and fewer standout words. Some studies also measure doubt raisers, which are negative phrases, hedges, or faint praise, such as “8 Numerous LOR bias studies16 use the Linguistic Inquiry and Word Count (LIWC)17 tool for automatically categorizing the linguistic content of text.The biases just described are often reflected in linguistic differences. 18 Another study confirms the role of cultural bias by stating that interpreting LORs is always difficult, “but especially if the cultural background of the referee and that of the reader are not the same”.9 That study compared LORs from the US and Asia and found differences in how the three main parts  were written9; for example, Asian recommenders were more likely to include a recommendation in the introduction. Another difference was that American recommenders were more likely than their Asian counterparts to provide indirect recommendations, such as “I hope that you will give favorable consideration to his application,” which are more likely to be misunderstood by non-native English speakers19.Cultural bias also impacts LORs. The schema theory of reading comprehension proposes that “the structures embodying background knowledge provide the ideational scaffolding for understanding” and “readers who share the cultural background of the writer come equipped with the appropriate schemata.”3. This last observation may confound gender norms, but many of the agentic terms had to do with competence, and other research has shown that in such cases women may be viewed favorably20.Research on bias in letters of recommendation is heavily focused on graduate medical and professional programs, which are often viewed as biased toward White and Asian men. A study of 735 Duke University radiology residency applicants showed statistically significant differences in agentic and communal terms in LORs, with Whites and Asians showing higher agency than other racial groups, and women showing more agency than men2, and a study of 332 surgical residency applicants shows that standout and agentic words most often applied to male applicants, while communal words, grindstone words, and doubt raisers are most often applied to female applicants7. Another study of 339 general surgery residents reinforced the racial differences identified in the Duke University study3 since it found that Black and Hispanic/Latinx applicants are more likely to be described by communal terms than agentic terms5.Two studies, however, support traditional gender biases. A study of 460 urology residency applicant LORs shows that letters written for male applicants contain significantly more references to personal drive, work, and power than those written for female applicants1. Their analysis showed that female applicants were only half as likely to receive “Excellent” letters as their male counterparts, with no statistically significant interaction with recommender gender. Neither recommender gender nor applicant gender had a statistically significant impact on LOR length. The study showed, however, that the recommender’s region did make a significant impact on LOR length, with LORs written in America being shorter than those from other regions1.To our knowledge, there are no LOR bias studies for graduate Computer Science or Data Science programs, so we focus on the most similar population that has been studied, namely graduate  science programs. A 2016 study of 1224 international geoscience postdoctoral fellowship LORs, which claimed at the time to be the largest study of bias in STEM LORs, classified the overall content of the letter as “Excellent”, “Good”, or “Doubtful”, and also tracked the length of the LORs6, results that generally agree with the study of geoscience postdocs described earlier. A study of LORs for job applicants in the area of experimental particle physics, an area dominated by men, demonstrated a somewhat different pattern, in that LORs written by women were more favorable toward women than toward men15.A study of 886 chemistry and biochemistry faculty LORs showed that the LORs for male applicants included significantly more standout and ability words and fewer grindstone words than for the female applicants21.The data set for our study was constructed from 3,868 LORs extracted from six years of application data from Fordham University’s Master’s in Computer Science and Master’s in Data Science programs. Both Master’s programs are operated by the Computer and Information Sciences Department. For federal reporting purposes, each applicant is required to specify their gender using the binary “male” and “female” values. Using these binary values, 63% specify “male” and “37% specify “female,” which is consistent with the gender imbalance in these disciplines. Applicants also may optionally specify their gender identity using the following six options: “cisgender woman”, “cisgender man”, “transgender woman”, “transgender man”, “non-binary”, “prefer to self-identify”, and “prefer not to disclose”. Our study assigns the gender of the applicant based on the binary gender field because: the gender identity field is optional and hence sometimes missing, when it is specified it is set to the cisgender options in well over 99% of the cases, and some of the values like “non-binary” require separate analysis that is not feasible given the size of our data set. We recognize this is a limitation of our study as gender is analyzed only as binary and because transgender applicants may specify “gender” based on birth sex or gender identity . The gender identity distribution is roughly consistent with US Census Bureau survey data from 2021 that reports Our study includes the gender of the LOR writer for some of our analyses. Unlike an applicant’s gender, this information is not always available within the application. Thus, we implemented a multi-step approach to infer the missing gender information of recommenders. We first considered the recommender’s honorific  provided in the recommender information section and assigned the gender if it was gender-specific . This approach worked for some academic recommendations and most work-related recommendations. Alternatively, when the honorifics did not provide a clear indication , we assigned the gender based on the predominant association of the LOR writer’s first name and cross-referenced the assignment with the recommender’s website, if available. In cases where both of these methods failed to provide a confident gender assignment, we resorted to analyzing the pronouns used on the relevant web pages of the recommenders. If none of the above steps enabled us to determine the LOR writer’s gender, we excluded those LORs from pertinent analyses. Using this procedure, we could not infer the recommender gender for 493 LORs, which accounts for approximately 13% of the total. As we made gender assignments only when we were confident in their accuracy, we believe the process has an overall high level of accuracy.1, in which the authors reported Our applicant and recommender gender data yield some interesting observations. First, only 29.5% of the LORs are written by women, which is lower than the percentage of female applicants (37.3%). This may be explained by the fact that many of the LORs are from academia, which has an even lower percentage of women than industry. Secondly, 35.1% of LORs for female applicants are written by women, while only 25.8% of LORs for male applicants are written by women, representing a 30.5% relative difference in the selection of female recommenders. This observation that applicants preferentially seek out recommenders of the same gender is an important result in itself, which suggests either a conscious or unconscious bias among applicants that they will receive the best recommendation from a recommender of the same gender. This preference for recommenders of the same gender was also noted in a study by Dutt et al.methods over other methods, and the results we have obtained at this stage merit sharing with a broader audience.This study was approved by Fordham’s Institutional Review Board. All procedures were carried out following relevant guidelines and regulations. To comply with the Family Educational Rights and Privacy Act (FERPA), we anonymized student and recommender identities by automatically redacting their names and removing affiliations. Nonetheless, even with these safeguards, the LORs are still far too sensitive to share publicly; recommendation writers expect that the letters will remain confidential and no automated redaction system can ensure that the details described in the LOR will be insufficient to identify a recommender or applicant. Because this study is based on proprietary applications to Fordham University and there are no available public LOR data sets, researchers interested in this work can only confirm the findings indirectly, such as by applying the same methods to their application data. However, we consider direct reproducibility a lesser issue for this work because we do not make any claims about the superiority of our specific 23, identify gender bias in LORs for general surgery residency candidates24, and evaluate teachers’ written feedback to students25. While it can be argued that most LORs are typically written in a positive manner, our research and a prior study24 consistently reveal statistically significant differences in linguistic characteristics, including “joy” and “sadness,” among LORs for different gender groups. Recognizing the significance of these subjective expressions and their potential impact on readers, we employed the IBM NLU package and tone analyzer to analyze all emotion and tone categories. Table In this study We investigated differences in LORs across different gender and culture groups, which may indicate bias. The variables employed in this study include three manually rated features, as well as emotions, tones, and sentiment scores automatically extracted using NLP  models provided by the IBM Watson NLU service. Previous studies have demonstrated the effectiveness of NLP software in capturing the nuances of emotion and tones expressed in various contexts. For instance, it has been used to analyze online reviews of orthopedic surgeonsThis study was approved by Fordham’s Institutional Review Board (IRB@fordham.edu), and informed consent was waived. All procedures were carried out following relevant guidelines and regulations.relevance, specificity, and positivity. They are each assessed independently and assigned specific values based on a set of carefully constructed guidelines, which include sample LORs that outline the expected ratings for each feature, along with explanations for the assigned values. Table “Our data set includes three manually assigned scores that collectively summarize the overall quality and strength of the recommendation. These features, which measure three different dimensions of each LOR, are While all 16 variables in Table 26, OpenAI27, Microsoft28, and IBM22. In this study, we subscribed to the Natural Language Understanding (NLU) service provided by IBM Watson, which uses deep learning to extract meaning and metadata from unstructured text, including categories, classification, entities, keywords, sentiment, emotion, relations, and syntax. We leveraged this service to infer the tones and emotions in each LOR. The tones are described by seven sub-scores , each of which ranges from 0 to 1. The definition of each tone is provided in the tone analytics section of the IBM Watson NLU documentation22 as follows:Excited—showing personal enthusiasm and interestFrustrated—feeling annoyed and irritableImpolite—being disrespectful and rudePolite—displaying rational, goal-oriented behaviorSad—an unpleasant passive emotionSatisfied—an affective response to perceived service qualitySympathetic–an affective mode of understanding that involves emotional resonanceEmotions are described using five categories  with values that range from 0 to 1. Note that according to NLU’s scheme, sadness is an emotion while sad is a tone.There has been a revolution in the application of NLP with the advent of toolkits and pre-trained models provided by leading companies such as Googlebased on my experience with numerous high-tech experts, I can fully say that his intelligent thinking, teamwork attitude and analytical skills are exceptional asset to my teamfrankly, I have little memory of him or his work since nothing was submitted in computer readable format in those daysChris’ grade of A- put him in the top 20% of that class The last variable used in our study is the sentiment score, which quantifies the LOR writer’s overall attitude toward the applicant. It is a continuous value that ranges from most negative (The Emotion and Sentiment features were extracted using the default NLU model (version 2022-04-07), while the seven tone scores were obtained using the English tone analytics model (tone-classifications-en-v1). Since the tone model only analyzes the first 2000 characters of text, we split more than half of the LORs into smaller components so the full text could be analyzed. To this end, NLTK’s sentence tokenizer was employed to ensure that all splits occurred at sentence boundaries. The final score for each letter is the average of all component scores weighted by the length of each component.In this section we analyze the LOR features across our study groups. We first compare the LORs by applicant gender and identify statistically significant features among the variables outlined in Table Table Without considering recommender gender, the results for “All Recommenders” show six features with statistically significant differences between the male and female applicant groups. Female applicants receive longer letters (4%) with higher positivity (11%) and specificity (7%), and with less anger , more positive (6%), more specific (6%), and the emotional content has less anger  than male recommenders for the male applicants; however, this pattern is not observed for the female applicant group.This section analyzes the differences in LOR characteristics across applicants from the United States (US), China (CH), and India (IN), which collectively cover most of our applicants. No other countries contribute a significant number of applicants.32 to each variable, which is an extension of the Student t-test for more than two study groups. Specifically, ANOVA compares the means among the groups and determines whether any of those means are statistically significantly different from each other. Formally, for our application, it tests the null hypothesis:33 to ensure the homogeneity of variances assumption in the ANOVA analysis. Since a statistically significant result from an ANOVA analysis only indicates at least one group differs from the others, we next performed the Tukey HSD (“Honestly Significant Difference”) post-hoc test34 to identify where the significance lies in our culture groups.We first identify variables whose group means are statistically different across the country-specific groups. To this end, we applied a one-way Analysis of Variance (ANOVA) testp-values for each variable are displayed in columns 2–4 and 5–9, respectively. The last three columns present the pairwise p-values generated by the Tukey HSD test, from which we can infer the country group with a distinctive mean. For example, the pairwise p-values for specificity for “All Applicants” are 0.001, 0.001, and 0.8487 for the US vs. CH, US vs. IN, and CH vs. IN, respectively. Consequently, this feature is significant for US vs. CH and US vs. IN, but not statistically significant for CH vs. IN, suggesting that specificity is a distinctive feature for the US group. Figure Table The LORs for the US group are distinguished by low specificity and positivity.The Chinese group stands out for its low impolite score for the underlying tone.The India group is marked by more positive sentiment, less sad and disgust emotions, and a more excited tone.Three variables  are statistically different for all country groups.The US group has the shortest LORs (2163), followed by China (2456), and India (2715). Not only are all differences statistically significant, but they are substantial: US vs. CH (12.7%), US vs. IN (22.6%), and CH vs. IN (10.0%).Our findings for LOR length are consistent with another study of a graduate STEM population, which showed that postdoctoral geoscience fellowship LORs originating from US recommenders were statistically significantly shorter than LORs from all other regions1. Interestingly, the pattern we found for LOR length is replicated for positivity, with India having the highest values, followed by China, and then followed by the US with the lowest LOR length and positivity values.An analysis of the results for the “All Applicants” block in Table 35. Overall, the US group exhibits the highest “impolite” score with the India group a close second. One might expect a reverse pattern for politeness, but this is not observed as the US and China groups both have relatively high scores while India has a significantly lower politeness score. This discrepancy can be explained by the NLU definition of “polite,” listed in the Methods section as “displaying rational, goal-oriented behavior”; in this case “polite” and “impolite” are not antonyms.There is a general lack of prior cross-cultural results for the other LOR characteristics, but the low impoliteness score for China conforms to the more general observation of Chinese linguistic politeness, which is a result of Chinese tradition and the teachings of Confucianism that emphasizes propriety and humilityWe next delve deeper into the variations within each gender group  across three countries. Our results indicate that most observations from the previous analysis hold for both gender groups, although there are some exceptions. Additionally, in certain instances, these observations are more pronounced for one gender group. For example, the US group has the lowest specificity for both gender groups, but the difference between the US group and the China and India groups is more pronounced for the male applicants than the female applicants. This pattern continues for positivity, with statistically significant results observed only for male applicants. Furthermore, The low impolite score previously found for the China group is only statistically significant for the male applicants. The high positive sentiment previously observed for the India group holds for both applicant genders and the short LORs found for the US group also holds for both genders.longer LORs, with an average of 2928 characters compared to 2419 characters for female applicants. A similar pattern is observed for specificity, where the male applicants for the US and China groups have lower LOR positivity values than those of their female counterparts. Conversely, in the India group, the male applicants receive higher specificity scores (1.8539 vs. 1.7031). Thus, for both features, the US and China groups have the more positive values assigned to the female applicants, while for the India group the more positive values are assigned to the male applicants. These findings highlight the varying impact of gender across different countries, which is consistent with the results from the Women’s Workplace Equity Index36. This index measures gender inequities on equality under the law in seven categories. Under the “finding a job” category, which is most relevant to this study, the US and China have similar scores, 57.1 and 53.3, respectively, while India has a much lower score of 37.536. These gender differences across countries is worthy of additional study.By comparing the gender-based differences across countries, we observe that the impact of gender is not always consistent. One notable difference is in the length of LOR, which we believe is one of the most important LOR features. Specifically, in the US and China groups, male applicants tend to receive slightly shorter LORs compared to their female counterparts. In contrast, for the India group, male applicants tend to receive significantly 37. Our study focuses on different aspects using content-based criteria  and linguistic components . They distinguish our work from other word statistics-based studies but also make many results incomparable. For instance, we discovered unique gender and culture differences in the manually rated features, which provides a new perspective that is arguably more relevant to the recommendation quality and has not been explored in prior LOR bias studies.Our analyses in the Results section contend that overall , female applicants to our MS in Computer Science and MS in Data Science programs received stronger letters of recommendation than their male counterparts due to higher positivity . Exploring additional cultures and countries may yield different findings, enhancing our understanding of how cultural factors influence the content and characteristics of LORs.


Self-supervised learning models, such as BERT, have improved the performance of various tasks in natural language processing. Although the effect is reduced in the out-of-domain field and not the the trained domain thus representing a limitation, it is difficult to train a new language model for a specific domain since it is both time-consuming and requires large amounts of data. We propose a method to quickly and effectively apply the pre-trained language models trained in the general domain to a specific domain’s vocabulary without re-training. An extended vocabulary list is obtained by extracting a meaningful wordpiece from the training data of the downstream task. We introduce curriculum learning, training the models with two successive updates, to adapt the embedding value of the new vocabulary. It is convenient to apply because all training of the models for downstream tasks are performed in one run. To confirm the effectiveness of the proposed method, we conducted experiments on AIDA-SC, AIDA-FC, and KLUE-TC, which are Korean classification tasks, and subsequently achieved stable performance improvement. After training to understand the characteristics of the language for large-scale corpora, the pre-trained language models are applied to downstream tasks in various natural language processing fields, showing excellent performance. However, since most of these models are trained in a general domain, the characteristics of the pre-trained language models are biased to general vocabulary. If there is an abundance of domain-specific vocabulary in the downstream domain data, it is even more difficult to the fine-tuned language models. Since the existing pre-trained language models do not have sufficient information about a specific domain, we should show the meaning of domain-specific words through the context embedding of the sub-word. Recent works have shown that using an optimized vocabulary for a specific downstream domain is effective, and thus shows the advantage of having a fixed embedding. This led to a study on adding vocabulary with a domain-specific word in the pre-trained language models ,2,3.The previous main approach was to learn the pre-trained model from the beginning with specific-domain data or to conduct an additional second pre-trained training using the existing pre-trained model using domain training data ,6,7,8,9.We propose a learning method that provides a new embedding value that is improved without changing the system structure of the pre-trained language models. We first conduct the initial learning with data from the downstream task. In this case, we do not add a new token. In other words, the pre-trained model is adapted to the downstream task. We call this step the warm-up stage. The next step is to add a new token and re-tuning. This step makes tokens, which did not learn, that will be learned according to the downstream task. We suggest adding new tokens for domains without learning the pre-trained model.We conduct experiments with various data to show the performance of the newly built model.The contributions of the proposed method are summarized as follows:BERT  is a popThe most intuitive way to solve the problems caused by domain disagreements is to build separate language models for the domain.Scibert  creates Another approach is to continue pre-training using target domain data based on the existing pre-trained language models. BioBERT  initialiIt has been proven helpful to improve performance by including domain-specific vocabularies in language models used to perform tasks for specific domains. In recent studies, the domain-specific vocabulary is added to the existing pre-trained language models, and the extended embedding is undergoing continual improvement so that the weight of the existing language models can properly handle it. exBERT  uses a mAvocado  extractsSachidananda et al.  extracteFrom previous studies, we confirmed that adding a domain-specific vocabulary and initializing them to the average of subword embedding helped improve performance. We want to expand the vocabulary and improve the method of initializing the average of subword embedding.The pre-trained language models have a fixed vocabulary and weight the models. The vocabulary that the pre-trained language models can hold is bound to have a limited number based on general-purpose data for the model’s generality. However, unlike general written and spoken language, many vocabularies are used only in medicine and law. It is difficult to express specialized domains properly using a vocabulary limited to the general domain. We suggest an effective way to add vocabulary important in the downstream task domain but not in the pre-trained language models.The domain-specific expansion vocabulary is a vocabulary that cannot be expressed sufficiently due to the limitations of the vocabulary of the pre-trained language models. In other words, these words are more frequent in documents in certain domains than in general domain documents, and the frequency should be larger enough to impact training significantly. In addition, the expansion vocabulary is not duplicated with the vocabulary of the pre-trained language models.We utilize training data from downstream tasks to automatically extract the most suitable extended vocabulary for the task based on a fixed number. Initially, the vocabulary list is populated with all characters in the dataset. Next, We identify character pairs with the highest probability ), has a label that indicates the intention of the sentence in the Korean science paper. In the sentence, nine labels indicate the intention of the sentence, including ‘hypothesis setting’, ‘technology definition’, ‘target data’, ‘data processing’, ‘problem definition’, ‘performance/effect’, and ‘theory/models’.AIDA-SC: The Korean thesis meaning tagging dataset, which was released in AIDA ) topic classification tasks unveiled in KLUE aim to classify appropriate news categories for the news title given. Since the evaluation data are not disclosed, we used the verification data to evaluate the performance of the models, and 10% of the training data were used as the verification data.KLUE-TC: The KLUE (https://aida.kisti.re.kr (accessed on 30 December 2022)) as pre-trained language models to add extension vocabulary modules. We deliver the output of the final layer for the first input token to the linear layer to predict. We choose KLUE-BERT-base , KoElectWe compare the vocabulary of the pre-trained models and the extension vocabulary that adds the domain-specific vocabulary. We also compare the curriculum learning method with a simple average initialization method for setting the initial value of the extension vocabulary. As can be seen in If you add an expansion vocabulary, the word that was tokenized to a small length can be token to be longer. Long tokens can eventually transform many letters into one token, so the number of tokens in the sentence can be reduced. Since there is a limit to the input length of the language models, reducing the number of tokens in one sentence means that the input can deliver more words of the model. The new tokenizer splits the words in the downstream task into more meaningful sub-words than the tokenizer using the vocabulary of the pre-trained language models. To analyze the effects of the expansion vocabulary, we show the sample words of each domain token with the expansion. In order to analyze the effects of the expansion vocabulary, we show the sample word of the domain in Since AIDA-FC is targeted at the paper’s title, there is a lot of expansion vocabulary for words with nouns, noun transformative endings, and noun derivative suffixes. AIDA-SC had many extended vocabularies on analytical methods that were not well represented in general domains, such as ANOVA and SPSS. In addition, unlike AIDA-FC, it has a full paper, so there is a lot of frequency of expansion vocabulary that uses nouns and particles as a vocabulary together. Since KLUE-TC targets the title of the news article, there is a lot of expansion vocabulary about the company name and person’s name, which is clearly distinguished from the AIDA datasets of the science and technology domain.For example, one of Korea’s credit card companies, ‘우리카드 ,’ is decomposed into ‘우리 ’ and ‘카드 ’ in the existing pre-trained language models. When we searched for a vocabulary category in large Korean news dataset (2021 version of everyone’s corpora), the word ‘우리 (us)’ appeared in the society field, and ‘카드 ’ appeared in the economy field. ‘우리카드 ’ has appeared a lot in the sports field. In the downstream task, ‘WooriCard’ is a lot in the ‘sports’ field. When we looked at the occurrence ratio of the vocabulary, when ‘우리 ’ and ‘카드 ’ appeared in the general corpus, the vocabulary corresponding to ‘우리카드’ was 53.97%, but occurrence rate in the downstream task data is 95.96%. The vocabulary of ‘우리카드’ is likely to have a higher importance in the downstream task domain.This change in ratio is larger in the extended vocabulary of AIDA-FC, whose domain is significantly different from that of the general corpus. For example, ‘페이스트 ’, a word that represents puree and ketchup, is decomposed into ‘페이스 (/Peis/)’ and ‘트 (/Teu/)’ in the existing pre-trained language models. In a general domain, ‘페이스 (/Peiseu/)’ is used in the sense of face and speed, and many appear in articles in society field. In the case of ‘트 (/Teu/)’, it is a letter that does not have a special meaning individually, and it appears in articles in society field and life areas. Relatively, in downstream tasks, ‘페이스 (/Peiseu/)’ is often seen at a similar rate in cultural arts and information and communication field, and ‘트 (/Teu/)’ is the most common in the ’information and communication’ field. In the case of ‘페이스트 (/Peiseut/)’, ‘페이스트 (/Peiseut/)’ is often found in the ’material’ field. When ‘페이스 (/Peiseu/)’ and ‘트 (/Teu/)’ appear simultaneously, ‘페이스 (/Peis/)’ appear only 2.34% in the general domain corpus, but appears 50% in the title of the science and technology filed corpus.If we add expansion vocabulary, the frequency of UNK tokens in the tokenized data decreases. In the case of UNK tokens, it does not reflect the meaning of individual tokens and causes clear meaning loss because several vocabularies are expressed only as one vector. It can be separated using the added extended vocabulary, which can help improve performance. AIDA-SC and AIDA-FC are the same science and technology domain but show that the types of vocabulary used in the text of the paper are distinguished. In particular, many symbols are used in vocabulary or formulas that represent measurement units, such as μg and μm, which are not used in the extension’s title added to the AIDA-SC.We experimented with Korean classification data based on curriculum-learning-based training methods to add domain vocabulary by minimizing code modification and additional operations to the existing pre-trained language models. First, we learn the models with tokenized inputs to the pre-trained tokenizer. We then change to an expanded tokenizer to conduct continuous learning. Through this, the embedding of the extended vocabulary, which is initialized as the mean of subword embedding, can attain a value closer to the meaning of the domain. Aside from an update of the tokenizer and embedding matrix in the middle of training, there is no modification of the model structure and is easily applied to the existing method because it only learns the optimization of the sentence that reflects the updated vocabulary. The proposed method shows that the performance improvement is greater than the model initialized as an embedding of the existing pre-trained language models. In particular, the performance is greatly improved when there exists a large domain difference between the pre-trained language models and the downstream task. As a result, the proposed method can effectively expand the vocabulary of the pre-trained language models through small modifications. In the future, we plan to increase the efficiency of the extended vocabulary added by adjusting the number of tokens added.


This study aimed to improve animal activity recognition (AAR) using wearable sensor data, which often faces challenges due to sensor variability and individual variability across species. To address this problem, we adopted unsupervised domain adaptation (UDA) techniques to improve the AAR performance. The main objective of UDA is to enable AAR models to perform well on the newly enrolled unlabeled sensor datasets, even when they have different distributions than the pre-trained labeled datasets. The experiments performed with the dog and horse movement sensor datasets demonstrated that UDA significantly improved the classification performance by mitigating sensor variability and individual characteristics such as size, gender, and species. These findings highlight that UDA has practical applications in real-world scenarios where labeled data is scarce in certain measurement environments.Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior. However, two major challenges hinder the development of robust AAR models: domain variability and the difficulty of obtaining labeled datasets. To address this issue, this study intensively investigates the impact of unsupervised domain adaptation (UDA) for AAR. We compared three distinct types of UDA techniques: minimizing divergence-based, adversarial-based, and reconstruction-based approaches. By leveraging UDA, AAR classifiers enable the model to learn domain-invariant features, allowing classifiers trained on the source domain to perform well on the target domain without labels. We evaluated the effectiveness of UDA techniques using dog movement sensor data and additional data from horses. The application of UDA across sensor positions (neck and back), sizes (middle-sized and large-sized), and gender  within the dog data, as well as across species (dog and horses), exhibits significant improvements in the classification performance and reduced the domain discrepancy. The results highlight the potential of UDA to mitigate the domain shift and enhance AAR in various settings and for different animal species, providing valuable insights for practical applications in real-world scenarios where labeled data is scarce. Behavior recognition of animals has emerged as a promising method for estimating their health status and monitoring abnormal behaviors. The widely-used types of sensors in identifying animal behavior include cameras ,2,3 and Despite the advantages of wearable sensors, animal activity recognition (AAR) using inertial measurement units (IMUs), consisting of accelerometers and gyroscopes, encounters several obstacles such as different sensor positions, species variability, and annotation scarcity. One of the challenges in Animal Activity Recognition (AAR) is the variability introduced by different sensor positions. The back sensor is optimized for capturing larger movements, whereas the neck sensor is designed to detect intricate movements ,14. ThisThe diversity in animal behavior, influenced by factors such as species, breeds, and sizes, presents a challenge in developing global models that can effectively generalize across different individuals or species ,20,21,22The scarcity of annotation is an additional constraint in AAR. In most studies and public datasets, animal activities are annotated via the visual inspection of animal behaviors in video recordings, often requiring substantial human resources and potentially raising privacy concerns ,17,21,22Unsupervised domain adaptation (UDA), a form of transfer learning, is a technique that reduces the variability between the labeled source data and the unlabeled target data in an unsupervised manner . In humaUDA techniques demonstrate the potential to enhance the AAR performance by mitigating data heterogeneity arising from variations in individual behaviors and sensor locations.UDA techniques exhibit versatility by being applicable to different mammal species, demonstrating their adaptability and applicability in the field of AAR.We present empirical evidence that the influence of minimizing divergence-based, adversarial-based, and reconstruction-based UDA varies depending on the specific domain under consideration.In this study, we employ unsupervised domain adaptation (UDA) techniques to address the challenges of domain variability in AAR without necessitating additional labeled data for new datasets. We employed three distinct types of UDA techniques, which have been widely adopted in the previous HAR studies. We utilized two public AAR datasets to explore the impact of UDA on domain variability between sensor positions, individual characteristics, and species. For the sensor position, we conducted domain adaptation between the neck and back sensors to reduce the sensor sensitivity depending on changes in the sensor position. We hypothesized that the activity patterns of dogs vary depending on the size (middle-sized and large-sized) and gender. Thus, to investigate the impact of these attributes, we categorized dogs into two groups, and these were used as the source and target domain, respectively. In the context of size, we segregated dogs into middle-sized and large-sized groups based on their weight. For gender exploration, we divided the dogs into male and female groups. Subsequently, we compared the classification results of three UDA techniques with those obtained when no domain adaptation technique was applied. In addition, we also evaluated inter-species variability by adapting the horse activity domain to the dog activity domain. In summary, our contribution can be described as follows:The public dataset of movement sensor data collected from 45 middle- or large-sized dogs was adopted for this study . Their aBased on the experimental paradigm and the canine demographic of this dataset, we devised three scenarios to evaluate the UDA: (1) between the neck and back sensors, (2) between middle-sized and large-sized dogs, and (3) between male and female dogs. For the division of middle-sized and large-sized dogs by weight, we employed weight as the criterion, where dogs with a weight < 25 kg were categorized as middle-sized, and dogs with a weight ≥ 25 kg were considered large-sized ,28.To evaluate the cross-species adaptability of our proposed approach, we further utilized another animal movement database collected from horses . This daTo mitigate the heterogeneity between different domains in AAR, UDA adopts a cooperative training approach that leverages both the labeled data from the source domain and the unlabeled data from the target domain. For instance, if a dataset with labels is given, it plays a role as the source domain, while a newly acquired dataset without labels becomes the target domain. As depicted in Domain adaptation aims to improve the machine learning performance by alleviating the domain shift problem between the source and target domains, which exhibit significantly different data distributions . This apIn activity recognition using wearable devices, domain specificity is highly influenced due to sensor variability ,35. FurtDomain-Invariant Feature Learning (DIFL) is a widely employed domain adaptation methodology that has been applied across various fields. The primary objective of DIFL is to extract features that are both predictive and invariant to domain differences, utilizing methodologies such as minimizing divergence, adversarial learning, and reconstruction . In thisnth layer for the classifiers, Methods based on minimizing divergence aim to reduce the variance between the source and target domains. The DAN  employs f and g denote the class classifier and the domain classifier, while Next, the DANN  is an adh is the decoder for reconstructing the target domain data and Last, reconstruction-based methods aim to achieve domain adaptation by learning representations that can accurately classify the source domain and effectively reconstruct the source or target domain. The DRCN  accompliFor a comparative performance evaluation of the three DA techniques, we standardized the hyperparameters. The hidden states of the fully connected layers for DAN, DANN, and DRCN were consistently set to 128, 64, and the number of classes, respectively. The reconstruction pipeline of DRCN was symmetrically constructed using transposed convolution layers, which is consistent with the original paper .The results of a previous study  have shoIn the feature extractor, we implemented a residual block which set up the kernel size {, , }, and applied batch normalization and Leaky ReLU as the activation functions. We stacked three residual blocks with the number of kernels {64, 128, 128}. The output of the last residual block is applied to the global average pooling to reduce the dimension of the hidden state. In the label classifier, we used two fully connected layers on the hidden state {128, 64} with batch normalization, leaky ReLU, and a dropout layer. The hidden state of the final fully connected layer is the number of classes.® Core™ i5-12400F processor and an NVIDIA GeForce RTX 3070 Ti. To implement our proposed model, PyTorch was utilized with Python 3.9, and for reproducibility, random states were fixed. We employed the Adam optimizer with a learning rate of 0.00001 and a weight decay of 0.0001 while setting the batch size to 256, dropout to 0.2, and All experiments were conducted in the local environment, employing a 12th Gen IntelFor training and evaluating each UDA model, the 2 s segments were partitioned into training and test sets with an 8:2 ratio for each UDA model. Additionally, the training set was further divided into training and validation sets with a ratio of 3:1. To assess the entire dataset, the five-fold cross-validation procedure was performed by repeating this procedure five times. In the within-setting and without-UDA setting, we selected the best model based on its accuracy in the source validation set and evaluated its performance on the target test set. In the domain adaptation setting, we reported the best performance achieved on the target test set during 100 epochs.TP, TN, FP, and FN are the number of true positives, true negatives, false positives, and false negatives, respectively.To compare the classification performance of each UDA method, AAR was evaluated using accuracy, which measures the overall performance, and the F1 score, which considers both precision and recall, as defined with the following equations:Using the dog movement dataset , we evalNext, we experimented to verify the feasibility of domain adaptation between the sizes and genders. The overall classification performance using the back sensor was higher than when using the neck sensor, regardless of applying the domain adaptation. We found that the application of domain adaptation led to improvements in the classification performance in all cases. A greater improvement was observed when the domain adaptation was performed concerning gender compared to size. In particular, when using the back sensor, applying DANN for domain adaptation between males and females resulted in an approximate 7.65% increase (81.22%→88.87%) in the F1 score for the M→F scenario and a 10.23% increase (75.47%→85.70%) in the F1 score for the F→M scenario. Similarly, when using the neck sensor, DANN demonstrated the most improved classification performance, with a 9.71% increase (62.19%→71.90%) in the F1 score in the F→M scenario. In the M→F scenario, however, DAN achieved the highest classification performance, but the F1 score only exhibited slight improvements, surpassing source-only and DANN by 2.04% and 1.85%, respectively. Meanwhile, for both the back and neck sensors, there was no consistent result with regard to the domain adaptation techniques. In the MS→LS scenario, DRCN exhibited the best classification performance, resulting in a 5.75% increase and a 3.06% increase in the F1 score when utilizing the back and neck sensors, respectively. However, in the LS→MS scenario, most UDA techniques failed to achieve significant improvements in the classification performance. Specifically, when utilizing the neck sensors, the highest accuracy was observed when domain adaptation was not applied (source only).To investigate the potential of domain adaptation in mitigating domain shifts between species, we incorporated the movement sensor data collected from horses in addition to the dog dataset. Since the animal activity classes differed between the horse and dog datasets, we focused on the three common classes: standing, walking, and trotting. Additionally, as horse activities were only measured on the neck, we performed the comparison using the neck sensor data. In both scenarios involving the use of horse data as the target domain (D→H) and the source domain (H→D), DANN consistently resulted in the most substantial improvements in the classification performance. In the D→H scenario, the application of DANN resulted in an F1 score increase of 15.45% (50.33%→65.78%). Similarly, when employing DANN for H→D, it led to an enhanced performance, showing an F1 score increase of 9.45% (76.13%→85.58%). Notably, despite the application of domain adaptation techniques, the classification performance for the walking activity was lower in the D→H scenario compared to that in the H→D scenario . These rThe influence of domain adaptation on the classification performance was further examined qualitatively using t-distributed stochastic neighbor embedding (t-SNE), a widely adopted nonlinear dimensionality reduction technique for visualizing high-dimensional data in a low-dimensional space . It consDomain adaptation with respect to the size did not result in a significant improvement of the classification performance since non-static activities including walking, trotting, and sniffing were well-classified even before the implementation of domain adaptation techniques. As shown in In the scenario of AAR classification across species, domain-shift problems became more conspicuous. In the left inset of This study aims to explore the influence of addressing domain variability, commonly encountered in AAR, by leveraging three types of UDA techniques, without the need for additional labeled data in novel datasets. As a result, the domain adaptation techniques demonstrated its potential to enhance the AAR performance by mitigating data heterogeneity arising from variations in individual behaviors and sensor locations. When dealing with newly enrolled individuals or species for efficient AAR, the importance of global models becomes evident. For instance, canine behaviors may differ based on factors such as weight, gender, and age ,19,20. UAs hypothesized, variations in canine characteristics can influence activity classification, attributed to the differences in sensor locations, velocities, behavioral patterns, and anatomical structures ,18,19,20UDA had a significant impact on reducing sensor variability, particularly when employing DAN, which is a minimizing divergence-based UDA technique. In the field of human activity recognition, domain adaptation techniques have been employed to apply pre-trained classifiers from one wearable device  to predict activities using other wearable devices  ,25,26. SOur results imply that UDA can be leveraged in AAR when novel wearable devices are introduced in different environments. This versatility is crucial in real-world scenarios, as AAR has garnered increased attention due to concerns for animal welfare and the need to enhance livestock productivity, while lots of commercial wearable devices have been developed. Moreover, these sensors can be attached to various body parts depending on the purpose of the measurement, environmental conditions, and animal characteristics. Neck sensors are widely used in AAR due to their ease of attachment to animals ,15,16,17In the domain adaptation concerning gender and size, we observed a more pronounced influence of domain adaptation in cases involving gender compared to those involving size. Previous studies have reported behavioral differences between male and female dogs linked to factors such as aggressiveness, sociability, and anatomical structure . HoweverOn the other hand, the canine size was not notably affected by the use of UDA. In general, dog breeds can be categorized into small-sized, medium-sized, and large-sized based on their weights, although there are controversies regarding the global standard. This categorization of dogs remains relevant due to its impact on behavior recognition. For example, some studies have indicated that large-sized dogs exhibit distinct behavior patterns attributed to factors such as aggressiveness and sociability ,19. AlthThe domain adaptation between the horse activity dataset and the dog activity dataset demonstrated that UDA can alleviate the domain-shift issue in inter-species AAR. Specifically, DANN exhibited the most significant enhancement in the classification performance, increasing the F1 score by 15.45% in the dog→horse scenario and 9.45% in the horse→dog scenario, respectively. Interestingly, similar to the results in gender adaptation, DANN, an adversarial-based UDA, outperformed DAN, a minimizing divergence-based one that showed the most substantial impact on sensor adaptation. In the HAR study conducted by Chang et al. (2020), adversarial-based UDA outperformed other UDA techniques in specific experimental settings, such as UDA between the foot and wrist wearable sensors, while a minimizing divergence-based UDA exhibited the most pronounced impact in the majority of sensor adaptation scenarios . The autAlthough the domain shift issues involving sensor positions, canine characteristics, and interspecies variability were mitigated with the adoption of domain adaptation techniques, there are some challenges in the application of UDA in AAR: the low classification performance of static movements. As shown in It is important to note that the success of the domain adaptation technique heavily relies on the performance of the feature extractor in the source domain. Since the main idea behind this technique involves adversarial learning to confuse the models between the source and target domain in an unsupervised manner, it does not inherently extract novel information from the novel datasets. This limitation has been observed in previous studies that exhibited considerable performance differences between using AlexNet and ResNet as feature extractors in CDAN . This hiData scarcity remains a significant factor limiting the use of deep learning methods in AAR. In the domain adaptation regarding canine size and species, a lower classification performance was observed when the number of samples in the source domain was limited. Specifically, the large-sized dog group, which exhibited no influence of any UDA techniques, had a substantially smaller number of samples for the static movements  compared to those in the middle-sized dog group . SimilarWith the advancement of digital technology and the growth of the pet market, wearable devices have emerged to enhance productivity and animal welfare. Therefore, it is anticipated that more public datasets will become available, encompassing various species and sensors in diverse environments. In this context, UDA would prove more beneficial in constructing a generalized AAR model and facilitating a better understanding of behavioral patterns to enhance animal welfare and productivity. In future studies, it is essential to conduct empirical studies that assess the practical applicability of UDA in online prediction for diverse livestock using a source model constructed from labeled offline datasets.This study explored the effectiveness of unsupervised domain adaptation in mitigating the domain shift problem in animal activity recognition using wearable sensor data. Through extensive experiments with movement sensor datasets of dogs and horses, we demonstrated that UDA aligns domain-invariant features of the target domain with those of the source domain, leading to an improved classification performance. The results showed that applying UDA not only enhanced the classification performance within the same species but also across different species. Notably, DAN exhibited the most pronounced effect in mitigating sensor variability, whereas DANN had the most significant impact in interspecies domain adaptation. In conclusion, UDA proves to be effective in effectively addressing sensor variability and individual characteristics, making it a promising solution for the real-world application of AAR in diverse settings with the limited availability of labeled data.


While our insights align with existing experimental data and are probably accurate, they need further validation through experiments.AlphaFold2 and related computational tools have greatly aided studies of structural biology through their ability to accurately predict protein structures. In the present work, we explored AF2 structural models of the 17 canonical members of the human PARP protein family and supplemented this analysis with new experiments and an overview of recent published data. PARP proteins are typically involved in the modification of proteins and nucleic acids through mono or poly(ADP-ribosyl)ation, but this function can be modulated by the presence of various auxiliary protein domains. Our analysis provides a comprehensive view of the structured domains and long intrinsically disordered regions within human PARPs, offering a revised basis for understanding the function of these proteins. Among other functional insights, the study provides a model of PARP1 domain dynamics in the DNA-free and DNA-bound states and enhances the connection between ADP-ribosylation and RNA biology and between ADP-ribosylation and ubiquitin-like modifications by predicting putative RNA-binding domains and E2-related RWD domains in certain PARPs. In line with the bioinformatic analysis, we demonstrate for the first time PARP14’s RNA-binding capability and RNA ADP-ribosylation activity   The human PARP family plays a critical role in ADP-ribosylation signalling, regulating various cellular functions in humans and other higher eukaryotes. In recent years, the ADP-ribosylation field has broadened its focus beyond a few well-studied members to explore the properties and functions of numerous PARP proteins with a different domain composition. Therefore, it is now timely to revisit the domain annotation of the PARP family to establish a better foundation for further development in this field. Recent developments in computational tools, such as AlphaFold2 (AF2), have facilitated this task, as we will discuss below.Eukaryotic proteins typically comprise multiple structured domains, i.e. distinct structural units with a relatively rigid 3D form . During +, the donor of the ADP-ribosyl moiety. The ART domain of PARP1 is distantly related to that of bacterial toxins that modify proteins with single ADP-ribose units (mono(ADP-ribosyl)ation or MARylation)  domain closely related to that of the founding member, PARP1 (for poly(ADP-ribose) polymerase 1). PARP1, a highly abundant nuclear protein found in animals, was first identified and characterised biochemically . This reylation) . FollowiRP1 gene ,9, otherRP1 gene . This diRP1 gene , which nRP1 gene ,13. WhilRP1 gene , and somRP1 gene . PARPs aRP1 gene ,12,16,17Traditionally, protein domains (considered primarily as signature sequence motifs) have been identified based on sequence homology to known instances in other proteins . This apTraditionally, tertiary protein structure has been conclusively resolved only with experimental approaches, applied on a one-by-one basis to individual domains or larger protein fragments. In that respect, the most studied members of the PARP family are PARP1, its close homologue PARP2 and tankyrases (TNKS1/PARP5A and TNKS2/PARP5B). All structured domains of PARP1 have been characterised using X-ray crystallography or nuclear magnetic resonance (NMR) , whereasRecently, experimental approaches to studying protein structure have been supplemented with highly efficient artificial intelligence (AI) prediction systems including AlphaFold—especially its AF2 release —and its https://www.alphafold.ebi.ac.uk), an on-line database of precalculated AF2 models of most Swiss-Prot- and UniProt-deposited proteins, generally excluding only very long sequences of >2700 amino-acid residues. Another online platform, ColabFold (https://www.colabfold.com), allows predicting structure from sequence using an optimised AF2-based approach. Prediction of protein complexes through ColabFold is possible by inputting multiple sequences separated by a colon. A further example is provided by FoldSeek (https://search.foldseek.com/search). The 3Di/AA  search algorithm used by FoldSeek focuses on local rather than global structural homology, which is advantageous if the relative orientation of more distantly separated elements is inaccurately predicted or has considerably diverged during evolution. Of note, FoldSeek scans only the proteins for which precalculated models are available in the AF Protein Structure Database, thus excluding some extremely long proteins. As an alternative to FoldSeek, combining an older fold recognition tool, DALI , with AF2 models has been proposed  regions in proteins, and of the fold of structured parts . The defHere, we used AF2 models, as well as some recent experimental studies, to update the domain composition of the human PARP family members relative to the annotation contained in the Pfam  and InteStandard sequences of isoforms 1 of 17 human PARPs were retrieved from UniProtKB reviewed entries with the following names: PARP1_HUMAN, PARP2_HUMAN, PARP3_HUMAN, PARP4_HUMAN, TNKS1_HUMAN, TNKS2_HUMAN, PARPT_HUMAN (for PARP7/TIPARP), PARP8_HUMAN, PARP9_HUMAN, PAR10_HUMAN, PAR11_HUMAN, PAR12_HUMAN, ZCCHV_HUMAN (for PARP13/ZAP), PAR14_HUMAN, PAR15_HUMAN, PAR16_HUMAN.https://www.alphafold.ebi.ac.uk, version 2022-11-01, created with the AlphaFold Monomer v2.0 pipeline). Structural models and corresponding matrices were analysed using the database online interface, and, in the case of the models, additionally in PyMol, which was used for structural figure preparation. Where structures are coloured according to confidence, colours have been set as defined by Konstantin Korotkov. Structural alignments with available experimental structures were performed in PyMol using the ‘super’ command with selected protein fragments. RMSDs were calculated over Calpha atoms only (specified using ‘name ca’ in PyMol).AF2 structural models were retrieved from the AF Protein Structure Database ,46 (httphttps://search.foldseek.com/search) analysis was performed using PDBs of isolated domains as input and standard settings .FoldSeek  (https:/https://toolkit.tuebingen.mpg.de/tools/hhpred) analysis was performed using the HHPRED section of the Max Planck Institute (MPI) Bioinformatic Toolkit  analysis was performed using the PDB of the isolated MVPID domain (residues 1570–1724) from the AF2 model of human PARP4 with standard settings.ConSurf  (https:/PARP1 gene (Qiagen), and subcloned into a pET28a vector using XbaI and XhoI restriction sites to carry an N-terminal His6 tag (MKHHHHHHKMQ). Full expression was performed in the presence of 10 mM benzamide and 35 μg/ml kanamycin. The plasmid was transformed into Escherichia coli BL21 (DE3) cells (Stratagene); resulting colonies were resuspended in LB medium containing 35 μg/ml kanamycin and the cells grown at 37°C, 200 rpm until a cell density of ∼2 A600 was obtained. The culture was diluted 1:40 in M9 minimal medium containing 35 μg/ml kanamycin and 10mM benzamide, and supplemented with 15NH4Cl (1 g/L) (Sigma Aldrich Isotec) as the sole nitrogen source. The cells were grown at 37°C, 200 rpm until a cell density of 0.8–1.2 A600 was achieved before arresting cell growth by incubating at 2–8°C for 1 hour. Protein expression was induced by adding 0.5 mM IPTG, supplementing with 0.1 mM ZnSO4 and incubating at 25°C, 200 rpm for 18 h. Cells were harvested by centrifugation, resuspended in 25 mM HEPES-Na, pH 8.0, 0.5 M NaCl, 0.5 mM TCEP, 1 mM PMSF and protease inhibitor mix , lysed by sonication and clarified by centrifugation. The clarified harvest was filtered , and purified on a 5 ml HisTrap FF (Cytiva) eluting with a linear imidazole gradient in 50 mM HEPES, pH 7.5, 0.5M NaCl, 0.5 mM TCEP. The eluted protein was diluted from 500 to 375 mM NaCl using 50 mM Tris, 0.5 mM TCEP, pH 7.0, and further purified using 5 ml HiTrap heparin FF (Cytiva), eluting with a linear NaCl gradient in 50 mM Tris, 0.5 mM TCEP, pH 7.0. The purified protein was then buffer exchanged into 10 mM sodium phosphate, 5% 2H2O, 222 mM KCl, 2 mM [2H10]-DTT, pH 7.4 using a 50 KDa MWCO Vivaspin 20 (Sigma-Aldrich).DNA coding for human PARP1 (residues 2–1014) containing the V762A point mutation was amplified by PCR from a codon-optimised human PARP1 BRCT domain (residues 383–525) was subcloned into the pET28a-lip vector; the resulting plasmid contains the sequence for N-terminally His6-tagged Geobacillus stearothermophilus di-hydrolipoamide acetyltransferase (UniProt P11961) lipoyl-binding domain, followed by a TEV cleavage site, followed by the sequence for PARP1(383–525). Protein was expressed and purified essentially as described previously for the uniformly  labelled ZnF1–ZnF2–ZnF3 and WGR fragments of PARP1 .of PARP1 , except The 45 nucleotide DNA dumbbell ligand (sequence 5′ P GCTGGCTTCGTAAGAAGCCAGCTCGCGGTCAGCTTGCTGACCGCG 3′) was obtained by chemical synthesis (Integrated DNA Technologies Inc.) and purified as described previously .1H,13C,15N]-cryogenic probes. NMR samples of full-length PARP1 and its complex with the DNA dumbbell were prepared and measured in 10 mM sodium phosphate, 222 mM sodium chloride, 2.7 mM potassium chloride and 2 mM [2H10]-DTT in 95:5 H2O/2H2O at pH 7.4; extensive testing revealed that under these conditions both the full-length free protein and its complex with the dumbbell DNA remained soluble at least overnight at the concentrations employed for the NMR measurements. Protein concentration for both the free and DNA-bound samples was 35.5 μM. Both were made up separately from the same freshly prepared protein solution stock; the complex was formed by careful addition of protein into a more concentrated DNA solution to reach a final slight excess of DNA . TROSY spectra were obtained at 800 MHz and 25°C using shaped sample tubes (Bruker BioSpin GmbH) designed to maximise sensitivity for lossy samples; intensities in Figure 2H11] Tris.HCl, 200 mM NaCl, 100 μM ZnSO4, 4 mM [2H10]-DTT, 0.02% NaN3 and 0.2 × Roche EDTA-free Complete protease inhibitors in 95:5 H2O/2H2O at pH 7.0. All experiments were conducted at 25°C, and 1H chemical shifts were calibrated using sodium 3,3,3-trimethylsilylpropionate (TSP) as an external 1H reference; 15N and 13C chemical shifts were indirectly referenced to the 1H shifts using the ratio of gyromagnetic ratios NH, HBHANH and HBHA(CO)NH. Partial amide group assignments for full-length PARP1 in both the free and DNA-bound states were made by careful comparisons with fully assigned spectra of separate domains or fragments of PARP1 recorded during previously published projects, specifically ZnF1–ZnF2 (F1F2) and Zn1–Zn2–Zn3 (F1F2F3)  , BRCT (t(F1F2F3) , and CAT(F1F2F3) . NMR dat(F1F2F3)  or Spark(F1F2F3) .The Cyanine3 (Cy3)-labelled RNA and DNA oligonucleotides were acquired from Merck and are listed in Table 2 and 1 mM DTT). The reactions contained 1 μM Cy3-labelled RNA or DNA oligonucleotide, 3 μM PARPs, PARP10 ART (residues 868–1025), or PARP14 WWE-ART (residues 1459–1801), and 500 μM NAD+. The reactions were incubated for 1 h at 37°C and stopped by adding 50 ng/μl Proteinase K and 0.15% SDS followed by incubating at 50°C for 30 min. Finally, the reactions were mixed with 2× TBE urea sample buffer , 20 μM Tris–HCl (pH 7.5), and bromophenol blue) and loaded on a pre-run 15% denaturing urea polyacrylamide gel electrophoresis (PAGE) gel. The gels were run at 7 W/gel and imaged using the Molecular Imager PharosFX system (BioRad) with laser excitation for Cy3 at 532 nm.ADP-ribosylation of Cy3-labelled DNA and RNA oligonucleotides was performed as described earlier ,130. ProPARP14 gene were cloned into a pET-28a vector using BamHI and XhoI restriction sites. These plasmids were transformed into Rosetta (DE3) competent cells and grown in 2× YT media supplemented with kanamycin and chloramophenicol. Induction was carried out at 0.6–0.8 OD600 using 0.5 mM IPTG and cells were allowed to grow overnight at 18°C. Bacterial pellets were lysed in lysis buffer  supplemented with BugBuster (Merck Millipore), cOmplete EDTA-free protease inhibitor cocktail (Roche), benzonase, and lysozyme. Cleared lysate was applied to a pre-washed Ni-NTA agarose resin followed by washes with lysis buffer. Proteins were eluted using elution buffer  with an incremental gradient of 10–500 mM imidazole. Proteins purity was assessed by sodium dodecyl sulphate (SDS)-PAGE gel and further dialysed overnight against 20 mM HEPES pH 7.5, 150 mM NaCl and 0.5 mM TCEP.PARP14 WWE-ART was prepared as described previously , whereas2, 1 mM DTT, and 20% v/v glycerol). The reactions contained 0.5 μM E21 RNA and 1 μM, 3 μM, or 5 μM of the target proteins PARP14 KH1–KH2 (residues 316–468), PARP14 KH8-ART (residues 1453–1801) and PARP14 WWE-ART (residues 1459–1801). The reactions were incubated for 1 h at room temperature and then loaded on a pre-run 6% native PAGE gel and run at 10 V/cm for 1 h. The gels were imaged using the Molecular Imager PharosFX system (BioRad) with laser excitation for Cy3 at 532 nm.Binding reactions were prepared in EMSA buffer (20 mM HEPES–KOH (pH 7.6), 5 mM MgClWe scrutinised AF2 structural models and corresponding matrices, deposited in the AF Protein Structure Database, of the human proteins that comprise the PARP protein family. In the core part of our study we focused on 17 canonical human PARPs as defined by the recent community consensus . AdditioThe results of our analysis of canonical PARPs are summarised in an updated overview of the domain architecture of the PARP family Figure , in whicTwo domains in our scheme—namely the helical domain at the very C terminus of PARP4 (residues 1570–1724) and the C4-type ZnF in PARP6 (residues 284–330) and PARP8 (residues 503–552)—do not bear any homology to previously characterised domains in any other human protein. Since the C-terminal segment of PARP4 corresponds to the previously reported interaction site for the major vault protein (MVP), we propose, partially following previous practice , to use In cases where multiple subdomains come together to form a larger structural arrangement—as for example in the case of the inter-alpha-trypsin heavy chain (ITIH)-like (ITIHL) region of PARP4—we label the whole region above the boxes, while also keeping labels of individual boxes corresponding to more distinct subdomains. Subdomains that are integral to the larger arrangement are not necessarily labelled with a distinct name and are shown as white boxes.Among previously unannotated domains, we have identified instances of ‘split’ domains , which are contiguous in structure but, at the sequence level, comprise two parts separated by a long insertion. Such split domains are indicated in Figure When producing the updated scheme, we tried to prevent confusion that was generated previously by the use of the same names for domains that are only superficially similar. Thus, we keep the name HD  only for the characteristic regulatory helical subdomain identified in PARP1 and also conserved in PARP2, PARP3 and PARP4. In these proteins, the HD and the ART subdomains together constitute the CAT  domain. In contrast, we propose the name HE  for a structurally different all-helical appendage to the ART domain observed in PARP6, PARP8 and PARP16. Similarly, we differentiate between different types of zinc-fingers (ZnFs) in PARPs, referring to them according to the residues that coordinate zinc , which in all these cases goes hand in hand with a different overall structure of these motifs. Since there are two structurally different C4 ZnFs in PARPs, we refer to the one in PARP6 and PARP8 as C4*, to distinguish it from the third ZnF of PARP1.Importantly, AF2 models appear to represent a protein without ligands such as DNA, RNA, or small molecules, but in reality they might reflect conformations that are only sampled in the presence of ligands. This is because the evolutionary relationships between different protein regions that AF2 detects based on a multiple sequence alignment and uses for prediction have been shaped in the biological milieu where various potential ligands important for function are present. Moreover, AF2 results might be partially influenced by previous experimental structures solved in the presence of ligands. We illustrate this problem by comparing the AF2 matrix for PARP1—which indicates possible rigidifying interactions between different PARP1 domains—with an NMR analysis of PARP1 domain flexibility in the presence and absence of DNA, which suggests that different PARP1 domains (except for the BRCT domain) become immobile relative to each other only upon binding to a DNA break.Below, we use root-mean-square deviation (RMSD) between models and experimental structures of related domains as an estimate of potential evolutionary relatedness or divergence. However, while the generally high accuracy of AF2 models suggests that such an interpretation is likely often to be justified, it should be born in mind that, if there are cases in which an AF2 model is inaccurate, these would also lead to observed deviations. We therefore caution the reader that the RMSD-value comparisons are contingent on the compared models being accurate to a similar extent.Below, we offer a detailed description of individual PARPs clustered together into small groups according to structural and evolutionary similarity. Our clustering overlaps with division of the family into clades ,58, exce+ and its analogues), allostery, conformational diversity and dynamics, all of which appear key to understanding how PARPs function. Having said that, future detailed comparison of AF2 models with specific conformations of these PARPs (e.g. autoinhibited vs. active) might illuminate the extent to which AF2 models could assist in the study of dynamic systems.DNA repair-associated PARPs —PARP1, PARP2 and PARP3—have been extensively studied with experimental methods and AF2 models do not bring considerable new knowledge about their domain composition. Moreover, the case of these PARPs demonstrates the importance of experimental approaches, which are so far indispensable for studying phenomena such as interaction with non-protein ligands  in response to DNA damage binding leads to activation of the ADP-ribosylation activity , leading to partial displacement and unfolding of the HD subdomain . Since, activity . The sam15N,1H]-TROSY spectra of 15N-labelled full-length PARP1 in the presence or absence of a DNA dumbbell ligand that mimics a single-stranded DNA break  domain—flanked by flexible linkers—which has been implicated in interactions with proteins, intact DNA, or PAR ,62. Of iNA break . Subsequy Figure . In the y Figure . Due to y Figure .Notably, the AF2 matrix, and indeed the AF2 structural models of PARP1, appear to be more consistent with the DNA-bound state, presumably reflecting the evolutionary importance of the DNA-dependent inter-domain interactions, as well as the fact that the available crystal structures of multi-domain forms of PARP1 are all of DNA-bound states.The sole human member of clade 5, PARP4 , was first identified as an associated component of vaults, enigmatic ribonucleoprotein structures present in eukaryotic cells . The assalpha atoms for the WGR domain and ∼2 Å over ∼100 Calpha atoms for the HD . Overall, similar architecture of this part suggests that PARP4—like PARP1, PARP2 and PARP3—could recognise DNA breaks (or other types of nucleic acid ligands) and be activated in an allosteric manner that involves HD. However, previous failure to detect WGR and HD homology reflects divergence at the sequence level that could indicate altered function. Detailed analysis of the conservation of specific residues might shed more light on this question.According to the AF2 model, the N-terminal region (residues 1–575) of PARP4 is very close to PARP1 in domain composition, complete with a BRCT domain, a WGR domain, an HD and the ART Figure . The WGRalpha atoms)—has been determined by X-ray crystallography, revealing similarity to integrin , and the VMA5A does not contain a residue equivalent to Arg1689, possibly reflecting functional divergence. VMA5A also contains an ITIHL region similar to that in PARP4 .Finally, we looked closely at the C-terminal segment which has been shown to mediate interaction with vaults and named MVP-interacting domain. We propose to abbreviate it to MVPID and use this as a temporary name until the function of this domain is further clarified. The AF2 model predicts that MVPID corresponds to a structured, all-helical domain of around 150 amino acids Figure . We usedPARP4 has been shown—alongside PARP9, PARP13, PARP14 and PARP15—to undergo rapid evolution that could suggest a role in host-virus rivalry ,67. The As tankyrases (clade 4) have been relatively well characterised experimentally, AF2 models do not bring much new insight into their domain organisation, but structure prediction could nonetheless aid in studying some aspects of these proteins. Tankyrases are PARylating enzymes involved in signalling. They are unique among PARPs in containing ankyrin repeat cluster (ARC) domains, which serve as substrate recruitment platforms by recognising specific linear motifs in tankyrase substrates, as illustrated by peptide-bound crystal structures . Of noteIn addition to ARCs and the C-terminal ADP-ribosyl transferase (ART) domain, tankyrases contain a sterile alpha motif (SAM) domain that is known to mediate formation of noncovalent helical filaments (head-to-tail polymers) ,36. The Lastly, AF2 models recapitulate the previously reported integrated CHCC ZnF within the ART domain of both tankyrases (residues 1232–1246 in TNKS1 ). As thi+-binding site in the way the HD of PARP1 does, suggesting a different mode of regulation or, perhaps more likely, a structural or a protein:protein interaction function  helix, which has been previously reported to target the protein to the endoplasmic reticulum (ER) , followeIn contrast, PARP6 and PARP8 do not contain the C-terminal TM helix. Instead, they are extended on the N-terminal side where they both contain a previously unannotated putative C4-type ZnF followed by a ‘split’ RWD domain  with a long, mostly putatively disordered, insertion Figure .The C4-type ZnF found in PARP6 and PARP8—which we labelled ‘C4*’ in Figure e.g. among kinetochore proteins . In addition to the split RWD domain, PARP8 contains a further, N-terminal RWD connected via an intrinsically disordered linker to the rest of the protein, but this domain appears severely diverged from canonical RWD domains .In general, the RWD domain is evolutionarily and structurally related to the ubiquitin-conjugating core (UBC) domain of E2 enzymes involved in ubiquitylation and related PTM systems, but, unlike UBC, the RWD does not typically contain a conserved cysteine residue . RWD domproteins . While tproteins . Despitep-dioxin (TCDD)-inducible PARP), PARP11, PARP12 and PARP13  belong to clade 3. They make one subset of PARPs that contain the WWE domain (named after conserved amino-acid residues), a small domain with some similarity to the beta-grasp fold of ubiquitin . Biochemical experiments showed that only the second WWE in MZAP is functional in recognising PAR chains, again with a preference for the terminal unit , by analogy to the NZAP domain mentioned below. The experimental structure of this region from PARP13 has very recently been determined independently by two groups ,81, reveInterestingly, AF2 predicts, with high confidence, that the ART domain of PARP7 forms an inter-domain interaction with the MZAP arrangement, with the result that all structured elements of PARP7 form one compact assembly Figure , as alsoOn the other hand, PARP12 and PARP13—but not PARP7—contain an N-terminal domain that we labelled NZAP  following previous convention . This doAs mentioned in the general remarks above, among the most interesting insights provided by the AF2 models of PARPs is the apparent prevalence of previously unannotated KH domains in a subset of clade 3 PARPs comprising PARP9 , PARP10, and, where there are particularly many, PARP14 . The KH domain, named after the heterogenous nuclear ribonucleoprotein (hnRNP) K in which it was first identified , is founIn PARP9, PARP10, and PARP14, one of the KH domains is ‘split’ at the sequence level, containing a large insertion. In PARP9 and PARP14, the insertion contains previously annotated Macro domains (two in PARP9 and three in PARP14), some or all of which seem to be functional as ADP-ribose binding domains ,90. In PBetween the split KH domain and the ART, there is, in each case, one more KH domain with an elongated helix that connects to the ART either through a loop or—in PARP14—through a WWE domain. The presence of this WWE domain hints at an evolutionary link between the PARPs discussed in this section and those in the previous one, as reflected in their joint classification as clade 3, but the WWE domain of PARP14 appears diverged in sequence and devoid of the PAR-binding function . In PARPIn addition to the domains described so far, PARP10 contains a further KH domain at the C-terminal side of the split KH , preceded by a long linker and the N-terminal compact arrangement of three RRM domains (for RNA recognition motif), which likely also participate in sequence-dependent RNA binding. RRM domains, which might be distantly related to KH domains, show more plasticity in terms of the type of RNA ligand that they engage . A rigidPARP14 has a more elaborate structure than PARP9 or PARP10, with six KH domains arranged in-line upstream of the split domain , capped by two tandem RRM domains and a further, N-terminal RRM connected by a flexible linker Figure . Strikinalpha atoms when comparing KHs from PARP AF2 models individually with PDB 1J5K), but some, e.g. KH1 of PARP14, are more diverged structurally .We observed that some of the KH domains predicted here can be detected with HHPRED, but that has never been reported. Most of the predicted KH domains, including the split ones, superpose well with experimental structures of canonical type I KH domains , the diverged clade 3 member only present in humans and related species , confirmin vitro assay. Since we could not purify the full-length enzyme, we focused on its extended catalytic fragment that encompasses WWE and ART domains (residues 1459–1801). Our experiments showed that while PARP14 cannot ADP-ribosylate model nucleic acid substrates with unmodified ends, it efficiently catalyses ADP-ribosylation of single-stranded RNA and DNA molecules with a phosphate at the termini , we attempted recombinant production of fragments of PARP14 that include at least some predicted KH domains. Two such fragments, encompassing KH1-KH2 (residues 316–468) or KH8-WWE-ART (residues 1453–1602), were produced in a pure recombinant form and showed an efficient binding at micromolar concentrations to an RNA probe in an electrophoretic mobility shift assay (EMSA). A catalytic fragment of PARP14, WWE-ART, introduced above was not able to interact with RNA under the same conditions.Overall, the successful detection of nucleic acid binding and ADP-ribosylation activity for PARP14 fragments demonstrates the usefulness of AF2-driven domain annotation for inferring molecular function.The PARP family is defined as a group of proteins that contain an ART domain similar to that in the founding member PARP1. We wondered whether the question of family membership could be revisited in the light of new opportunities offered by AF2-mediated structure prediction. We therefore used FoldSeek to search all available AF2 models (the AlphaFold/Proteome v4 collection) for human proteins with regions that are structurally highly similar to PARP1’s ART domain. As a search model, we used a previously published crystal structure  , which include 17 canonical human PARPs discussed above as well as two additional proteins, LRRC9 and TASOR. Both of these proteins are known to be related to the PARP family ; however, in this case the local structural similarity with PARP1’s ART domain is lower than for all canonical PARPs. NEURL4 is known to be related to PARPs  instances of the domains annotated here for the first time could have been detected with sequence-based techniques such as HHPred, AF2—combined with visual inspection and structural search and alignment—offers a surer way of identifying low homology and defining new domain types. Interestingly, AF2, possibly owing to its use of computational attention mechanisms and transformers that detect long-range inter-residue relationships , efficieWhile the newly annotated domains and other insights should be validated experimentally, the structures of all annotated regions were predicted with high accuracy  and As its main insights, our study i) predicts several previously undetected putatively RNA- or DNA-binding KH domains in PARP9, PARP10 and PARP14, ii) predicts E2-related RWD domains in PARP6 and PARP8, and iii) suggests a high degree of structural homology between parts of PARP4 and PARP1. High incidence of probable nucleic acid-binding domains in some PARPs could suggest recognition of specific long DNA or RNA ligands in a manner akin to that proposed for other proteins rich in domains of these types . Overallin vitro properties of fragments of PARP14, demonstrating the ability of KH domain-containing fragments to bind RNA and that of a catalytic fragment to ADP-ribosylate terminally-phosphorylated DNA or RNA fragments  and association/dissociation rate constants (kon/koff) that cannot be reliably obtained through computational approaches but are necessary for evaluating the functional importance of protein:protein interactions. Finally, even though AF2 can be used to predict interactions with large numbers of proteins in an automated manner—as in the new AlphaPulldown pipeline  intrinsically disordered regions found in PARP4, TNKS1/PARP5A, PARP6, PARP7, PARP8, PARP10 and PARP13. The presence of such regions—parts of which might become ordered upon binding to interaction partners—is likely functionally important and could be related to condensate formation ,117. Dispipeline —experimepipeline ,120–123.The validated high accuracy of AF2 predictions offers an unprecedented perspective on the structure-function relationship in proteins. However, it is far from providing an exhaustive description of the analysed systems. The well-studied cases of PARP1, PARP2 and PARP3, on the one hand, and tankyrases, on the other, teach us that structural models—even if very accurate—do not fully explain function. More specifically, the case of PARP1 and its closest homologues demonstrates that protein domains are not static. Our new NMR data Figure  and C an+, inhibitors, etc.). Such interactions can be proposed based on predicting homology to other domains known to mediate them—as we have done in this study. Moreover, further insight might be gained by analysing conservation of pockets and interfaces necessary for interactions. Ultimately, however, binding to nonprotein ligands must be verified and characterised experimentally. Experiments can also detect changes in protein conformation upon ligand binding, as illustrated in our NMR analysis of PARP1 bound to a DNA break  offer a direct insight are interactions with non-protein factors such as—in the case of PARPs—nucleic acids (DNA and RNA), PAR chains, or small molecules —it is possible that in the future they could be supplemented by molecular dynamics and related computational methods that take into account protein physics.We propose the above analysis of the PARP family as a springboard for further experimental investigations and modelling efforts that aim at elucidating the biological roles of the PARP-family members. Similar comprehensive analyses of structural models of other protein families could also aid their investigation.gkad514_Supplemental_FileClick here for additional data file.