Biological Mechanism-based Neurology and Psychiatry: a BACE1/2 and Downstream Pathway Model

: In Oncology, comprehensive Omics and functional enrichment studies led to an extensive profiling of (epi)genetic and neurobiological alterations that can be mapped onto a single tumor’s clinical phenotype and divergent clinical phenotypes expressing common pathophysiological pathways. Consequently, molecular pathway-based therapeutic interventions for different cancer typologies, namely tumor type- and site-agnostic treatments, have been developed, encouraging real-world implementation of a paradigm shift in medicine. Given the breakthrough nature of the new-generation translational research and drug development in Oncology, there is an increasing rationale to transfertilize this blueprint to other medical fields including Psychiatry and Neurology. To illustrate the emerging paradigm shift in neuroscience, we provide a state-of-the-art review of translational studies on the β-site amyloid precursor protein cleaving enzyme (BACE) and its most studied downstream effector, neuregulin, which are molecular orchestrators of distinct biological pathways involved in several neurological and psychiatric diseases. This body of data aligns with the evidence of a shared genetic/biological architecture among Alzheimer’s disease, schizoaffective and autism spectrum disorders. We engage in a speculative intellectual exercise gravitating around the BACE-related science, here used as paradigmatic case, to facilitating a forward-looking discussion about a potential first step towards the adoption of biological pathway-based, clinical symptom agnostic, categorization models in clinical Neurology and Psychiatry for precision medicine solutions. We draw a perspective whereby pathway-based therapeutic strategies could be catalyzed by high-throughput techniques, embedded in systems-scaled biology, neuroscience, and pharmacology approaches that will help overcome the constraints of traditional descriptive clinical symptom and syndrome-focused constructs in Neurology and Psychiatry.

Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

The United Nations’ (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.

Evaluasi Produktivitas Bongkar Muat Petikemas pada PT. Berlian Jasa Terminal Indonesia (BJTI Port) di Tanjung Perak-Surabaya

Evaluation of loading and unloading productivity is an important thing that must be done because it minimizes the occurrence of obstacles in loading and unloading and helps determine the right steps to increase productivity in the following year. The purpose of this study is to determine the factors that affect the productivity of loading and unloading, calculate the percentage of productivity and determine the forecasting value of loading and unloading productivity for the next 10 years. This is expected to improve services with technological innovation and the development of related science. The method used was the time series method. The results were obtained from PT. BJTI in 2017 – 2020, the obstacles that occurred tended to decrease, so the percentage of productivity increased every year. Based on the forecasting results with the time series method, loading and unloading productivity in 2021 - 2030 showed a significant increase. In 2030 the total productivity of loading and unloading was obtained at 1,397,463 boxes. This showed that the total loading and unloading in 2030 did not affect the equipment's ability in the loading and unloading process because the equipment capacity was still sufficient to accommodate up to 2,242,560 boxes per year. Therefore, it was not necessary to add more HMC tools at Berlian Terminal. This was inversely proportional to the loading and unloading stack obstacles whose solution must be to add RTG heavy equipment so that the loading and unloading process would be faster.