Miracle-dz/UniFAR-FTUs huggingface dataset:
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning

Without knowledge of specific pockets, generating ligands based on the global structure of a protein target plays a crucial role in drug discovery as it helps reduce the search space for potential drug-like candidates in the pipeline. However, contemporary methods require optimizing tailored networks for each protein, which is arduous and costly. To address this issue, we introduce TargetVAE, a target-aware variational auto-encoder that generates ligands with high binding affinities to arbitrary protein targets, guided by a novel multimodal deep neural network built based on graph Transformers as the prior for the generative model. This is the first effort to unify different representations of proteins (e.g., sequence of amino-acids, 3D structure) into a single model that we name as Protein Multimodal Network (PMN). Our multimodal architecture learns from the entire protein structures and is able to capture their sequential, topological and geometrical information. We showcase the superiority of our approach by conducting extensive experiments and evaluations, including the assessment of generative model quality, ligand generation for unseen targets, docking score computation, and binding affinity prediction. Empirical results demonstrate the promising performance of our proposed approach. Our software package is publicly available at https://github.com/HySonLab/Ligand_Generation.
Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings

Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-speciﬁc recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modiﬁed student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using a vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort.
Germline mitotic quiescence and programmed cell death are induced in C. elegans by exposure to pathogenic P. aeruginosa

The impact of exposure to microbial pathogens on animal reproductive capacity and germline physiology is not well understood. The nematode Caenorhabditis elegans is a bacterivore that encounters pathogenic microbes in its natural environment. How pathogenic bacteria affect host reproductive capacity of C. elegans is not well understood. Here, we show that exposure of C. elegans hermaphrodites to the Gram-negative pathogen Pseudomonas aeruginosa causes a marked reduction in brood size with concomitant reduction in the number of nuclei in the germline and gonad size. We define two processes that are induced that contribute to the decrease in the number of germ cell nuclei. First, we observe that infection with P. aeruginosa leads to the induction of programmed germ cell death. Second, we observe that this exposure induces mitotic quiescence in the proliferative zone of the C. elegans gonad. Importantly, these processes appear to be reversible; when animals are removed from the presence of P. aeruginosa, germ cell death is abated, germ cell nuclei numbers increase, and brood sizes recover. The reversible germline dynamics during exposure to P. aeruginosa may represent an adaptive response to improve survival of progeny and may serve to facilitate resource allocation that promotes survival during pathogen infection.
NUCLEOPORIN1 mediates proteasome-based degradation of ABI5 to regulate Arabidopsis seed germination

NUCLEOPORIN1 (NUP1), a member of the Nuclear Pore Complex (NPC), is located on the inner side of the nuclear membrane. It is highly expressed in seeds; however, its role in seeds including germination has not been explored yet. Here, we identified an abscisic acid (ABA) hypersensitive phenotype of nup1 during germination. ABA treatment drastically changes the expression pattern of thousands of genes in nup1, including the major transcription factors (TFs) involved in germination, ABI3, ABI4, and ABI5. Double mutant analysis of NUP1 and these ABA-related genes showed that mutations in ABI5 can rescue the phenotype of nup1, suggesting that NUP1 acts upstream of ABI5 to regulate seed germination. ABI5, a key negative regulator of germination, is abundant in dry seeds and rapidly degrades during germination. However, its spatiotemporal regulation and interaction with other molecular players during degradation remained to be fully elucidated. We found that NUP1 is physically associated with ABI5 and the 26S proteasome. Mutation in NUP1 delayed ABI5 degradation through its post-translational retention in nucleolus under abiotic stress. Taken together, our findings suggest that NUP1 anchors the proteasome to NPC and modulates seed germination through proteasome-mediated degradation of ABI5 in the vicinity of NPC in the nucleoplasm.
Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis

Targeted therapy interventions using tyrosine kinase inhibitors (TKIs) provide encouraging treatment responses in ALK-rearranged lung adenocarcinomas, yet resistances occur almost inevitably. Apart from tumor cell-intrinsic resistance mechanisms, accumulating evidence supports a role of cancer-associated fibroblasts (CAFs) in affecting the therapeutic vulnerability of lung cancer cells. Here, we aimed to investigate underlying molecular networks shaping the therapeutic susceptibility of ALK-driven lung adenocarcinoma cells via tumor microenvironmental cues using three-dimensional (3D) spheroid co-culture settings. We show that CAFs promote therapy resistance of lung tumor cells against ALK inhibition by reducing apoptotic cell death and increasing cell proliferation. Using single-cell RNA-sequencing analysis, we show that genes involved in lipogenesis constitute the major transcriptional difference between TKI-treated homo- and heterotypic lung tumor spheroids. CAF-conditioned medium and CAF-secreted factors HGF and NRG1 were both able to promote resistance of 3D-cultured ALK-rearranged lung tumor cells via AKT signaling, which was accompanied by enhanced de novo lipogenesis and supression of lipid peroxidation. Notably, simultaneous targeting of ALK and SREBP-1 was able to overcome the established CAF-driven lipid metabolic-supportive niche of TKI-resistant lung tumor spheroids. Our findings highlight a crucial role of CAFs in mediating ALK-TKI resistance via lipid metabolic reprogramming and suggest new ways to overcome resistance towards molecular directed drugs by targeting vulnerabilities downstream of oncogenic signaling.
Supplement Materials for Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly

Method Details Please see Algorithms 1, 2 and 7 for pseudo-code of Phase I (Part Optimization), Phase II (Part Shift) and Phase III (Part Borrowing), respectively. For Phase II, we provide additional pseudo-code for helper functions Swap() (Algo. 3), NNSegment() (Algo. 4), Filter() (Algo. 5) and FathestConnectedComponent() (Algo. 6). Intuitively, these helper functions perform the following operations: Firstly, target to optimized parts distance matrix Q is computed, with rows(axis 0) represent points in target, and columns(axis 1) represent parts. Then in Swap(), the minimum distance matrix Qmin is computed by taking the minimum value of each row of Q. The region in the target T that is least covered by any of the parts is extracted as T−. Then for each part Di ∈ D, we compute the distance matrix Q without the effect of Di, and set the values of region T− in Q to zero. The new minimum distance matrix after swap is computed as Qmin. The part Ds that leads to the minimum value of avg(Qmin) will be replaced by T−. Then in NNSegment(), the target point cloud T is segmented into a set of segments where Si consists of all points from T which are closer to Di than to any other decoded part. Then in Filter(), segment Si is updated with discarding a small amount of points that are already well covered. Finally in FarthestConnectedComponent(), the self connectivity matrix Wadj is computed by using point to point connection when their distance is below a threshold. This connectivity matrix Wadj is used to construct the graph g, followed by the connected components C are extracted from the graph g and the connected component Ci which is farthest from any of the other segments is selected. Algorithm 1 Phase I Part Optimization 1: Input 2: Target shape T 3: Latent codes ei, i ∈ k 4: Translation vectors ti, i ∈ k 5: Rotation angles ri, i ∈ k 6: Pre-trained part decoder Decode 7: Output 8: Updated latent codes ei, i ∈ k 9: Updated translation vectors ti, i ∈ k 10: Updated rotation angles ri, i ∈ k 11: procedure Optimize 12: Di ← Decode(ei) 13: Di ← Pose(Di, ti, ri) 14: L ← Lrecon(D,T) + Loverlap(D) 15: ei ← ei +∇L(ei) 16: ti ← ti +∇L(ti) 17: ri ← ri +∇L(ri) 18: return ei, ti, ri 19: end procedure
Explorer Development and validation of a bovine macrophage specific cDNA microarray

Background: The response of macrophages to danger signals is an important early stage in the immune response. Our understanding of this complex event has been furthered by microarray analysis, which allows the simultaneous investigation of the expression of large numbers of genes. However, the microarray resources available to study these events in livestock animals are limited. Results: Here we report the development of a bovine macrophage specific (BoMP) cDNA microarray. The BoMP microarray contains 5026 sequence elements (printed in duplicate) and numerous controls. The majority of the clones incorporated on the microarray were derived from the BoMP cDNA library generated from bovine myeloid cells subjected to various stimuli, including over 900 sequences unique to the library. Additional clones representing immunologically important genes have been included on the BoMP microarray. The microarray was validated by investigating the response of bovine monocytes to stimulation with interferon-γ and lipopolysaccharide using amplified RNA. At 2 and 16 hours post stimulation 695 genes exhibited statistically significant differential expression, including; 26 sequences unique to the BoMP library, interleukin 6, prion protein and toll-like receptor 4. Conclusion: A 5 K cDNA microarray has been successfully developed to investigate gene expression in bovine myeloid cells. The BoMP microarray is available from the ARK-Genomics Centre for Functional Genomics in Farm Animals, UK. Background Macrophages (mφ) play a key role in the immune system, acting as a bridge between the innate and adaptive immune responses. Mφ are sentinel cells that instigate the removal of invading pathogens, either by phagocytosis or by signalling to other components of the immune system. These signals include the release of various cytokines and the ability of mφ to act as antigen-presenting cells. Mφ become activated when they interact with conserved, pathogen-specific stimuli, e.g. pathogen associated molecular patterns (PAMPs) such as lipopolysaccharide (LPS) and double-stranded RNA, characteristic of gram-negative Published: 01 September 2006 BMC Genomics 2006, 7:224 doi:10.1186/1471-2164-7-224 Received: 26 May 2006 Accepted: 01 September 2006 This article is available from: http://www.biomedcentral. com/1471-2164/7/224 © 2006 Jensen et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BMC Genomics 2006, 7:224 http://www.biomedcentral. com/1471-2164/7/224
Linking upstream mining to downstream water quality: Mountaintop mining in West Virginia

Mountaintop mining valley fill (MTM/VF) coal mining is currently the dominant form of land use change in the central Appalachians. MTM/VF activities level mountains, remove forests and forest soils, bury headwater streams and generate substantial amounts of acid and alkaline mine drainage. Numerous case studies have documented elevated concentrations of sulfate and trace metal and metalloids with known toxicity in surface waters downstream from MTM/VF activity, yet no comprehensive effort has been made to link landscape scale mining activity and water quality. Here, I used newly obtained remote sensing data of surface mining activity delineated from 1976 to 2005 to estimate the extent of MTM/VF impact on downstream surface water quality in the Coal and Guyandotte river basins of WV. Hydrologic connectivity between mining and water quality was estimated using an inverse distance weighting technique in GIS (ESRI, Inc.). The findings show significant biogeochemical alterations, including streamwater conductivity and sulfate concentrations, even when small amounts of surface mining (<5%) are observed. Results provide the first comprehensive analysis of the cumulative impact of mining activity in these watersheds on water quality and demonstrate the need for further investigation involving strategic water quality sampling with the ultimate goal of developing an empirical basis on which to form regulations governing MTM/VF throughout the central Appalachians.
AC 2011-1727: SELF-DIRECTED LEARNING CONTENTION: FACULTY AND STUDENT VIEWS

Self-directed learning (SDL) is a pedagogical technique that is commonly practiced within the framework of project-based learning (PjBL) SDL has been found to be useful in the development of skills necessary for engineering careers, including open-ended problem-solving, life-long learning, and critical thinking. Implemented in a variety of ways, SDL is primarily characterized by developing student autonomy. According to Stefanou et al. ’s framework, student autonomy can be promoted at three different levels: organizational, procedural, and cognitive. These three levels include varying degree of student choice: organizational autonomy takes into account the environment (e.g., due dates), procedural autonomy incorporates form (e.g., deliverable form), and cognitive autonomy involves content (e.g., designing projects). This range of possible SDL experiences allows for a wide interpretation of the role and value of SDL and student autonomy by both students and faculty. Using methods of grounded theory, three research questions were addressed: (a) How do the pedagogical practices in the first-year mathematics, physics, and engineering classes fit into Stefanou et al. ’s autonomy framework? (b) How does the level of student autonomy impact student’s participation, interest, and perception of performance in these classrooms? and (c) How do student and faculty perspectives on student autonomy affect the classroom environment? Our results indicate that students and faculty have mixed feelings regarding SDL, which drive frustration and discomfort with open-ended learning in the classroom. In general, students often do not feel well-supported in SDL environments and exhibit a lowered sense of competency and expectancy. On the other hand, faculty present blindness towards structural supports necessary for effective SDL classroom environment and specifically their own roles in scaffolding students’ SDL experiences.
Complementary Networks Meet Complementary Currencies: Guifi.net Meets Sardex.net

Reading in parallel the description of community networks and community currencies reveals many similarities and differences between these two models of self-organisation around networking infrastructures and monetary systems, respectively. This chapter brings together experts from both domains in an effort to share knowledge and experience, using as case studies two emblematic projects, Catalonia’s success story on community networks, Guifi.net, and Sardinia’s success story on community currencies, Sardex.net. The long-term objective is to build a better common understanding of the individual models but most importantly the stimulation of synergies and collaborations of researchers and activists from both sides.
CSMTP: An RL-based Adversarial Examples Generation Method for Chinese Social Media Texts Classification Models

Natural language processing models based on deep learning have been widely used to analyze Chinese social media texts, such as Sina Weibo sentiment analysis, rumor detection, and news topic classification. However, while these analysis models have made breakthroughs in performance, there are also potential adversarial attack risks. To test the robustness of these models, an adversarial examples generation method CSMTP is proposed in this paper. The algorithm includes two main parts: a key to-kens searcher and an adversarial noise selector. The key tokens searcher is used to locate the key tokens that significantly affect the classification results when the internal details of the model are unknown, and the visual perception weight is introduced to measure human attention to these keywords. The adversarial noise selector provides a variety of adversarial noise generation strategies. It uses the policy network based on deep reinforcement learning training to select the appropriate type of noise according to the features of key tokens and target sentences. Experiments based on four public datasets show that CSMTP can launch effective adversarial attacks on Chinese social media text classification models, which has specific en-lightening significance for the follow-up research on the reliability of these models.
PENERAPAN MODEL PEMBELAJARAN PICTURE AND PICTURE BERBANTUAN SPESIMEN PADA MATERI INVERTEBRATA

The aim of research determine difference between learning outcomes and student activity using learning model Picture and Picture assisted specimens on material Invertebrates. Research of design use one shoot case study. The sample was class X-1 and X-2 determined Total Sampling technique. The independent variable was learning model used Picture and Picture helped Invertebrates specimens, while dependent of variable was learning outcomes (LKS score and final exam score) and student activities. The activity student learning in classical style in class X-1 was 88% and X-2 was 86%. The cognitive learning outcomes students used classical style in class X-1 was 93,78% and class X-2 was 88,8%. The outcomes data teacher performance in class X-1 and X-2 was very good, both classes indicated average grade of 15. The students response in class X-1 was 85.1% while in class X-2 was 93,8%. Teaching response toward method showed interest and agree using LKS models Picture and Picture helped Invertebrates specimens appicated this study.The conclusions this study is application learning models Picture and Picture helped Invertebrates specimens method can increase activity students in classical style was 87% and student learning outcomes in classical style was 86.33% apply in senoir high school of Teuku Umar Semarang. Penelitian ini bertujuan untuk mengetahui perbedaan hasil belajar dan aktivitas siswa dengan menggunakan model pembelajaran picture and picture berbantuan spesimen pada materi invertebrata. Desain penelitian ini one shoot case study. Sampel penelitian ini yaitu siswa kelas X-1 dan X-2 yang ditentukan dengan teknik total sampling. Variabel bebas berupa model pembelajaran Picture and Picture berbantuan spesimen Invertebrata, sedangkan variabel terikat berupa hasil belajar (nilai LKS dan nilai akhir) dan aktivitas siswa. Hasil belajar kognitif siswa secara klasikal kelas X-1 sebesar 93,78% dan kelas X-2 sebesar 88,88. Aktivitas siswa secara klasikal pada kelas X-1 sebesar 88% dan X-2 sebesar 86%. Kinerja guru pada kelas X-1 dan X-2 sangat baik. Tanggapan siswa kelas X-1 sebesar 85,1% dan kelas X-2 sebesar 93,8%. Tanggapan guru menunjukkan bahwa guru tertarik dan setuju menggunakan media LKS dengan model Picture and Picture berbantuan spesimen Invertebrata yang diterapkan dalam proses pembelajaran. Simpulan penelitian ini adalah penerapan model pembelajaran Picture and Picture berbantuan spesimen pada materi Invertebrata dapat meningkatkan aktivitas siswa secara klasikal sebesar 87% dan hasil belajar siswa secara klasikal sebesar 86,33% di SMA Teuku Umar Semarang.
INTERACTIVE ALIGNMENT: IMPLICATIONS FOR THE TEACHING AND LEARNING OF SECOND LANGUAGE PRONUNCIATION

Interactive alignment refers to a phenomenon characterized by interlocutors adopting and re-using each other’s language patterns in the course of authentic interaction. According to the interactive alignment model, originally proposed by Pickering and Garrod (2004), this linguistic coordination in dialogue occurs at the level of the lexicon, grammar, and pronunciation and represents one way in which interlocutors achieve understanding in dialogue. The goal of this paper is to extend this model to second language (L2) pronunciation and to discuss its possible implications for L2 pronunciation teaching. Previous research suggests that repetition of language patterns at different linguistic levels is indeed a commonplace feature of communication among native speakers and can be observed and elicited in L2 learners. Therefore, pronunciation researchers and teachers might find interactive alignment as a useful framework to explain some of the complexities of L2 pronunciation development both from cognitive and sociocultural perspectives, as well as to develop or refine pedagogical activities for use in L2 pronunciation classrooms.
Relationship between attributional style and personality traits and role of gender differences

A number of studies in the field of psychology found that negative and positive attributional styles leads different cognitive differences. Also, personality related studies found neurotic and extrovert types have found similar cognitive differences as in attributional styles. Although, the related literature shows similarities in terms of empirical findings, the studies about the relationship between these concepts are sparse. Importantly, as we observe gender differences may lead distinct influences in emotion, cognition as well as in daily life, it is important to include role of gender differences in the potential relationship between attributional style and personality types. Therefore, in the present study we include gender differences as well to investigate relationships between these concepts. Overall, we aim at investigating relationship between attributional style and personality by putting into account gender differences. To do that, 110 (50 Women, 60 Men) were employed to fill attributional style questionnaire and Eysenck personality questionnaire. As a result, negative attributional style was positively correlated with neurotic personality whereas positive attributional style was positively correlated with extravert personality. These correlations were stronger in men than in women. However, no significant correlations were found between attributional styles and psychotic personality. We concluded that there is a potential relationship between attributional styles and neurotic and extravert personality types and this relationship may be moderated by gender differences as evident by strong correlation between this concepts in men.