Absence of gemin5 from SMN complexes in nuclear Cajal bodies

<p>Abstract</p> <p>Background</p> <p>Spinal muscular atrophy is caused by reduced levels of the survival of motor neurons (SMN) protein. SMN is found in large complexes with Sm proteins and at least eight other proteins, including seven "gemins". These complexes are involved in the assembly of snRNPs in the cytoplasm and their transport into the nucleus, but the precise roles of the individual protein components are largely unknown.</p> <p>Results</p> <p>We have investigated the subcellular distribution of gemins using novel antibodies against gemins 3–7, and existing mAbs against SMN, gemin2, unrip, fibrillarin and profilin II. Most gemins were equally distributed between nuclear and cytoplasmic fractions of HeLa cells, but gemin5 and unrip were more abundant in the cytoplasm. In a cytoplasmic extract obtained by mild disruption of HeLa cells, nearly all the SMN and gemins 2–4 were in large complexes, but most of the gemin5 sedimented separately with a lower S value. Most of the unrip sedimented with gemins 6 and 7 near the top of the sucrose density gradients, separate from both SMN and gemin5. Anti-SMN mAbs pulled down gemin5 from cytoplasmic extracts, but not from nuclear extracts, and gemin5 did not co-sediment with large SMN complexes in nuclear extracts. These data suggest that gemin5 is easily detached from SMN-gemin complexes in the nucleus. By immuno-histochemistry, gemin5 was rarely detectable in nuclear gems/Cajal bodies, although it was accessible to antibody and easily detectable when present. This suggests that gemin5 is normally absent from SMN complexes in these nuclear storage sites.</p> <p>Conclusion</p> <p>We conclude that SMN complexes usually exist without gemin5 in nuclear gems/Cajal bodies. Gemin5 is believed to be involved in capturing snRNA into SMN complexes in the cytoplasm for transport into the nucleus. We hypothesize that gemin5, though present in the nucleus, is no longer needed for SMN complex function during the time these complexes are stored in gems/Cajal bodies.</p

LEGION: Harnessing Pre-trained Language Models for GitHub Topic Recommendations with Distribution-Balance Loss

Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance the discoverability of the repository networks, i.e., groups of similar repositories, GitHub introduced repository topics in 2017 that enable users to more easily explore relevant projects by type, technology, and more. It is thus crucial to accurately assign topics for each GitHub repository. Current methods for automatic topic recommendation rely heavily on TF-IDF for encoding textual data, presenting challenges in understanding semantic nuances. This paper addresses the limitations of existing techniques by proposing Legion, a novel approach that leverages Pre-trained Language Models (PTMs) for recommending topics for GitHub repositories. The key novelty of Legion is three-fold. First, Legion leverages the extensive capabilities of PTMs in language understanding to capture contextual information and semantic meaning in GitHub repositories. Second, Legion overcomes the challenge of long-tailed distribution, which results in a bias toward popular topics in PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the PTMs. Third, Legion employs a filter to eliminate vague recommendations, thereby improving the precision of PTMs. Our empirical evaluation on a benchmark dataset of real-world GitHub repositories shows that Legion can improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also can suggest GitHub topics more precisely and effectively than the state-of-the-art baseline with an average improvement of 20% and 5% in terms of Precision and F1-score, respectively.Comment: Accepted to EASE'2

Perception of having children through surrogacy in individuals with MRKH in Vietnam: a qualitative study

IntroductionMayer-Rokitansky-Küster-Hauser syndrome (MRKH) is rare condition that has a negative impact on quality of life because affected women lack a uterus and vagina, and are therefore unable to engage in sexual intercourse and experience natural pregnancy. This study evaluated perceptions of surrogacy in Vietnamese women with MRKH who have started families.MethodWomen with MRKH who had undergone successful vaginal reconstruction, were married, and had started families participated in a semi-structured, in-depth, one-on-one online video interview with an experienced female psychologist. Open-ended questions were used to encourage participants to express their perceptions of surrogacy; prominent themes were discussed, compared, and combined.ResultsTwenty women (mean age 31 years) agreed to participate. Key themes identified from interviews were the importance of having genetic offspring, consideration of surrogacy as a preferred solution to infertility, the barriers to surrogacy in Vietnam, lack of reproductive information and counselling, individuals concealing their health condition, the impact of religion on the possibility of surrogacy, the economic cost of surrogacy, and the difficulty in finding a surrogate under the restrictions imposed by Vietnamese law.DiscussionBased on the perceptions of women from MRKH from Vietnam, there is an opportunity to improve how infertility is managed in these people, including information about surrogacy. These data show that individuals with MRKH should be provided with information about the possibility of surrogacy, encouraged to be open and seek support, and be managed by a multidisciplinary team that includes psychological support; the provision of economic support for fertility treatments in women with MRKH should also be considered