Bolder Biotechnology, Inc.

This study is a literature study that aims to analyze how deep learning curriculum can be used to develop the potential of multiple intelligences in elementary school students. Multiple intelligences or multiple intelligences proposed by Howard Gardner include eight types of intelligence, namely linguistic, logical-mathematical, spatial, musical, kinesthetic, interpersonal, intrapersonal, and naturalist. In the context of elementary education, it is important for teachers to not only focus on academic intelligence, but also accommodate the various forms of intelligence possessed by students. The deep learning curriculum focuses on in-depth, meaningful, and learner-centered learning. This approach supports the development of multiple intelligences because it provides space for students to actively explore their interests, talents, and learning styles. This study analyzed 20 national and international journals published in the last five years that are relevant to the topic. The results of the study show that the implementation of the deep learning curriculum through contextual, project-based, and collaborative learning can facilitate the development of various intelligences in elementary school students. This curriculum also requires the role of teachers as facilitators who are able to design different learning strategies according to student characteristics. This study recommends the need for teacher training and the preparation of deep learning-based teaching tools that are adaptive to the diversity of children's intelligence.

Responsible for delivering major healthcare projects, the University of California in San Francisco (UCSF) has devised creative ways of reducing waste and increasing value through project delivery. In a previous paper, we described UCSF Health's journey to rethink project delivery practices. The adoption of Target Value Delivery (TVD) is a core enabler of their success. The University has consistently adopted TVD to deliver complex healthcare projects within or below their allowable costs. Previous papers have provided evidence and insights into why and how such success has been achieved. However, the focus so far has been on collaboration and cost management. This paper describes the strategies utilized to focus on and manage value generation. The term human-centered innovation was chosen to emphasize stakeholder engagement and empathy building as input to idea generation. This approach shaped how TVD is implemented in these case studies. Its analysis provided insights into complementary design and decision-making strategies traditionally used in TVD. In particular, the design strategies observed in this research expand the documentation of TVD best practices to include not only solution development strategies but also participatory and empathic ways of understanding, framing, and reframing design problems.

Specific molecular recognition of substrates by enzymes, <em>e.g.</em>, proteases, is critical for maintaining the robustness of crucial life processes. The ability to predictably tailor protease specificity would enable targeted proteolytic cleavage of any chosen target protein. Current methods for predicting protease specificity rely on sequence pattern recognition in experimentally-derived cleavage data obtained for libraries of substrates. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the three-dimensional structure and energetics of molecular interactions between protease and substrates. We present a Protein Graph Convolutional Network (PGCN), which uses a physically-grounded, structure-based molecular interaction graph representation that describes molecular topology and energetics to predict specificity. PGCN was shown to accurately predict the specificity landscapes of several variants of two model proteases: the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving two non-canonical substrates and found high correspondence between PGCN prediction and experimental cleavage results for 19 designed protease variants. The described methodology should be applicable to the construction of tailor-made proteases for site-selectively cleaving chosen substrate sequences, enabling broad applications in protein design. Here is a brief description of each file: protease-gcnn-pytorch.zip PGCN codes. See the latest version here: https://github.com/Nucleus2014/protease-gcnn-pytorch train_val_test_graph_datasets.zip Datasets for the training process of PGCN. Statistics of them are shown in Table 1. tev_design_for_validation_pdbstructures.zip Final selected designs for experimental testing tev_oydv_design_candidates_pdbstructures.zip Candidate designs made by Rosetta

With the intent to move towards value generation, public organizations have been increasingly searching for alternative procurement and project delivery routes.Countries like the U.S., Finland, U.K., Norway, and Australia are pioneers in adopting alternative means to project delivery in the public sector.Past studies have documented the benefits of more collaborative arrangements in that sector.However, their impact on project performance and their ability to generate value still lack evidence and documentation.In addition, little is known about project management practices that helped organizations focus on value and achieve better project performance within this context.Thus, this paper aims to provide evidence about the impact of alternative delivery methods on generating better project outcomes in the public sector, highlighting fundamental mechanisms and lean management practices that have contributed to these results.This research follows a multi-case study approach, reporting the journey taken by The University of California San Francisco (UCSF) Health to rethink its project delivery methods in the public sector.A close collaboration between the University of California Berkeley (UC Berkeley) and UCSF allowed data to be collected throughout the years.This paper results from a reflection of collected data and new insights gained through focused group discussions.

苦境に立つ地域公共交通における切り札として注目を集める自動運転。未来のテクノロジーと捉える人も多いが、既に目に見える形で実用化が行われている事例が存在する。国内初の実用化の舞台となった茨城県境町を始めとする幾つかの国内事例を紹介するとともに、公共交通の新たな可能性を示す。

Background: Human milk contains both arachidonic acid (ARA) and docosahexaenoic acid (DHA). Supplementation of infant formula with ARA and DHA results in fatty acid (FA) profiles, neurodevelopmental outcomes, and immune responses in formula-fed infants that are more like those observed in breastfed infants. Consequently, ARA and DHA have been historically added together to infant formula. This study investigated the impact of ARA or DHA supplementation alone or in combination on tissue FA incorporation, immune responses, and neurodevelopment in the young pig.Methods: Male pigs (N = 48 total) received one of four dietary treatments from postnatal day (PND) 2–30. Treatments targeted the following ARA/DHA levels (% of total FA): CON (0.00/0.00), ARA (0.80/0.00), DHA (0.00/0.80), and ARA+DHA (0.80/0.80). Plasma, red blood cells (RBC), and prefrontal cortex (PFC) were collected for FA analysis. Blood was collected for T cell immunophenotyping and to quantify a panel of immune outcomes. Myelin thickness in the corpus callosum was measured by transmission electron microscopy and pig movement was measured by actigraphy.Results: There were no differences in formula intake or growth between dietary groups. DHA supplementation increased brain DHA, but decreased ARA, compared with all other groups. ARA supplementation increased brain ARA compared with all other groups but did not affect brain DHA. Combined supplementation increased brain DHA levels but did not affect brain ARA levels compared with the control. Pigs fed ARA or ARA+DHA exhibited more activity than those fed CON or DHA. Diet-dependent differences in activity suggested pigs fed ARA had the lowest percent time asleep, while those fed DHA had the highest. No differences were observed for immune or myelination outcomes.Conclusion: Supplementation with ARA and DHA did not differentially affect immune responses, but ARA levels in RBC and PFC were reduced when DHA was provided without ARA. Supplementation of either ARA or DHA alone induced differences in time spent asleep, and ARA inclusion increased general activity. Therefore, the current data support the combined supplementation with both ARA and DHA in infant formula and raise questions regarding the safety and nutritional suitability of ARA or DHA supplementation individually.