Project description:This study explores the impact of formally assigned leaders (team captains) and informal leaders (all-stars) on their teammates' productivity in basketball. It uses in-game injuries as random shocks to examine how the unexpected absence of leaders affects team and individual performance. The research employs a staggered difference-in-differences estimation, to study peer effects in high-stakes team environments. The key finding is that only players who are both formal and informal leaders have spillover effects on their teammates' performance. The findings could extend to team management practices across various industries, providing insights into effective team composition and leader selection.
Project description:The dynamics of proteins are important for understanding their functions. In recent years, the simple coarse-grained Gaussian Network Model (GNM) has been fairly successful in interpreting crystallographic B-factors. However, the model clearly ignores the contribution of the rigid body motions and the effect of crystal packing. The model cannot explain the fact that the same protein may have significantly different B-factors under different crystal packing conditions. In this work, we propose a new GNM, called vGNM, which takes into account both the contribution of the rigid body motions and the effect of crystal packing, by allowing the amplitude of the internal modes to be variables. It hypothesizes that the effect of crystal packing should cause some modes to be amplified and others to become less important. In doing so, vGNM is able to resolve the apparent discrepancy in experimental B-factors among structures of the same protein but with different crystal packing conditions, which GNM cannot explain. With a small number of parameters, vGNM is able to reproduce experimental B-factors for a large set of proteins with significantly better correlations (having a mean value of 0.81 as compared to 0.59 by GNM). The results of applying vGNM also show that the rigid body motions account for nearly 60% of the total fluctuations, in good agreement with previous findings.
Project description:FATCAT 2.0 server (http://fatcat.godziklab.org/), provides access to a flexible protein structure alignment algorithm developed in our group. In such an alignment, rotations and translations between elements in the structure are allowed to minimize the overall root mean square deviation (RMSD) between the compared structures. This allows to effectively compare protein structures even if they underwent structural rearrangements in different functional forms, different crystallization conditions or as a result of mutations. The major update for the server introduces a new graphical interface, much faster database searches and several new options for visualization of the structural differences between proteins.
Project description:People often struggle to do what they ideally want because of a conflict between their actual and ideal preferences. By focusing on maximizing engagement, recommendation algorithms appear to be exacerbating this struggle. However, this need not be the case. Here we show that tailoring recommendation algorithms to ideal (vs. actual) preferences would provide meaningful benefits to both users and companies. To examine this, we built algorithmic recommendation systems that generated real-time, personalized recommendations tailored to either a person's actual or ideal preferences. Then, in a high-powered, pre-registered experiment (n = 6488), we measured the effects of these recommendation algorithms. We found that targeting ideal rather than actual preferences resulted in somewhat fewer clicks, but it also increased the extent to which people felt better off and that their time was well spent. Moreover, of note to companies, targeting ideal preferences increased users' willingness to pay for the service, the extent to which they felt the company had their best interest at heart, and their likelihood of using the service again. Our results suggest that users and companies would be better off if recommendation algorithms learned what each person was striving for and nudged individuals toward their own unique ideals.
Project description:The first year of university is one of the most difficult times in a student's life due to numerous changes that occur. This cross-sectional study explores the concept of parental and peer attachment, which has been researched for its ability to predict students' success in higher education. Yet, less research has investigated the mechanisms underpinning the relationship between attachment and university adjustment among first-year students. Hence, the aim of this study was to examine the impact of parent and peer attachment on first-year university students, and understand how these attachments can facilitate university adjustment through identity exploration. This investigation is underpinned by Bowlby and Ainsworth's attachment theory and Arnett's emerging adulthood theory. Data were collected from 568 first-year students at a public university in Sabah, Malaysia, via adapted questionnaires. Structural equation modelling was employed using SmartPLS Software 3.0 to analyse the data. The study found that identity exploration mediates the relationship between parental trust, peer communication, and university adjustment. The findings of this study provide valuable insights for professionals working with emerging adult clients, especially those in higher education institutions, aiming to enhance the adjustment level among first-year students.
Project description:Sequences of fast-folding model proteins (48 residues long on a cubic lattice) were generated by an evolution-like selection toward fast folding. We find that fast-folding proteins exhibit a specific folding mechanism in which all transition state conformations share a smaller subset of common contacts (folding nucleus). Acceleration of folding was accompanied by dramatic strengthening of interactions in the folding nucleus whereas average energy of nonnucleus interactions remained largely unchanged. Furthermore, the residues involved in the nucleus are the most conserved ones within families of evolved sequences. Our results imply that for each protein structure there is a small number of conserved positions that are key determinants of fast folding into that structure. This conjecture was tested on two protein superfamilies: the first having the classical monophosphate binding fold (CMBF; 98 families) and the second having type-III repeat fold (47 families). For each superfamily, we discovered a few positions that exhibit very strong and statistically significant "conservatism of conservatism"-amino acids in those positions are conserved within every family whereas the actual types of amino acids varied from family to family. Those amino acids are in spatial contact with each other. The experimental data of Serrano and coworkers [Lopez-Hernandez, E. & Serrano, L. (1996) Fold. Des. (London) 1, 43-55]. for one of the proteins of the CMBF superfamily (CheY) show that residues identified this way indeed belong to the folding nucleus. Further analysis revealed deep connections between nucleation in CMBF proteins and their function.
Project description:Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation. In practice, manual labeling for dense estimation tasks is very difficult or even impossible, and the scenes of the dataset are often restricted to a small range, which dramatically limits the development of the community. To overcome this deficiency, we propose a synthetic dataset generation method to obtain the expandable dataset without burdensome manual workforce. By this method, we construct a dataset called MineNavi containing video footages from first-perspective-view of the aircraft matched with accurate ground truth for depth estimation in aircraft navigation application. We also provide quantitative experiments to prove that pre-training via our MineNavi dataset can improve the performance of depth estimation model and speed up the convergence of the model on real scene data. Since the synthetic dataset has a similar effect to the real-world dataset in the training process of deep model, we finally conduct the experiments on MineNavi with unsupervised monocular depth estimation (UMDE) deep learning models to demonstrate the impact of various factors in our dataset such as lighting conditions and motion mode, aiming to explore what makes this kind of models training better.
Project description:For gaining additional insights into the composition of the testicular proteome of the domestic pig (Sus scrofa domestica), we conducted 2DE-MS. Two-dimensional SDS PAGE was run on testicular lysates of three boars, with three gels per boar. Upon matching across gels, we arbitrarily selected protein spots for mass spectrometry analysis. Excised slices were vacuum dried and soaked with digestion buffer containing trypsin (0.01 μg/μl), followed by overnight incubation at 37°C in the same buffer without trypsin. Subsequently, peptides were extracted in solvents of increasing acetonitrile content, by sonication. Upon vacuum-centrifugation, peptides were reconstituted in 0.1% formic acid (FA). Following this, peptides were fractionated by reversed phase liquid chromatography (C18; buffer A: 0.1% FA dissolved in HPLC-H2O; buffer B: 0.1% FA, dissolved in CAN; flow-rate: 0.4 µL/min; gradient: 2-30% in 30 minutes). Eluted peptides were injected via an electrospray ionization interface into a Q-TOF mass spectrometer (one boar, Q TOF Ultima, Micromass/Waters, Manchester, UK) and an ion-trap mass spectrometer (two other boars, XCT ion-trap, Agilent Technologies, Waldbronn, Germany). We used ProteomeDiscoverer 2.4 (Thermo Fisher Scientific, San Jose, USA) for peptide and protein identification. Using Sequest HT, we searched peak lists (*.mgf) against the Sus scrofa reference proteome database (UniProt Proteome ID: UP000008227, 49,793 proteins).