The Rise and Fall of Dopamine: A Two-Stage Model of the Development and Entrenchment of Anorexia Nervosa

Dopamine has long been implicated as a critical neural substrate mediating anorexia nervosa (AN). Despite nearly 50 years of research, the putative direction of change in dopamine function remains unclear and no consensus on the mechanistic role of dopamine in AN has been achieved. We hypothesize two stages in AN– corresponding to initial development and entrenchment– characterized by opposite changes in dopamine. First, caloric restriction, particularly when combined with exercise, triggers an escalating spiral of increasing dopamine that facilitates the behavioral plasticity necessary to establish and reinforce weight-loss behaviors. Second, chronic self-starvation reverses this escalation to reduce or impair dopamine which, in turn, confers behavioral inflexibility and entrenchment of now established AN behaviors. This pattern of enhanced, followed by impaired dopamine might be a common path to many behavioral disorders characterized by reinforcement learning and subsequent behavioral inflexibility. If correct, our hypothesis has significant clinical and research implications for AN and other disorders, such as addiction and obesity.

Digitally Enabled Organizations- Leveraging New Age Technologies

When inadequate information appears via a long-winded channel, project leaders usually struggle to make timely decisions. There is frequently a lack of visibility, contractual and organizational fragmentation, and genuine facts being segregated and concealed due to an optimism bias. Despite the finest planning and estimation efforts, projects frequently exceed their budgets or experience delays of more than 30%. The paper outlines the importance of data and of data use to improve the performance in projects planning and delivery. The data value and hierarchy are reviewed in the context of the construction industry and the importance of a smooth digitalization process for ensuring acceptance and adoption is discussed. The concept of ‘digital construction blocks’TM and Lean thinking is introduced to address the problem of complexity which is commonly recognized as the main cause of cost overruns, time delays, and poor quality and safety for the construction industry. To capture the footprint of how the asset was built, the authors propose the Digital Twin of Execution adding dynamism to the commonly discussed Digital Twin of Asset, which is more static when the asset has already been constructed. The project is organized into digital blocks, allowing all project functions and disciplines to focus on a common path of construction, allowing for an earlier start of a constraint-free construction and, as a result, de-risking and compressing the total execution schedule. Data from existing systems and technologies is unlocked and placed in automated processes, allowing thousands of documents, activities, and fast-moving events to be collected in digital blocks of construction. The digital block is connected throughout project stages and taken across all aspects of the project, including plot plans, activity plans, drawings, 3D, materials, and so on, resolving the project's disarray caused by manual and analogue procedures. The entire planning, project setup, and execution process is aided by GIS, which provides visibility at various levels of magnification via an interactive geo spatial map superimposed with plot plans, timetables, and work packages. Artificial intelligence (AI) and machine learning (ML) can be used to forecast the probability of danger in various field operations. It's done by using IoT devices implanted in employees’ PPE and in the environment to process data collected on the system. Digital Control Tower can provide a smart dashboard that not only displays the KPIs but also helps the user prioritize his next steps. It may provide an overall view of the project's progress and KPIs, as well as get to the root of a problem in a specific installation area, raise red flags and alerts, and function as a user's assistant by predicting errors early on.

Mitigation of bias sources for atmospheric temperature and humidity in the mobile Raman Weather and Aerosol Lidar (WALI)

Lidars using vibrational and rotational Raman scattering to continuously monitor both the water vapor and temperature profiles in the low and middle troposphere offer enticing perspectives for applications in weather prediction and studies of aerosol–cloud–water vapor interactions by simultaneously deriving relative humidity and atmospheric optical properties. Several heavy systems exist in European laboratories, but only recently have they been downsized and ruggedized for deployment in the field. In this paper, we describe in detail the technical choices made during the design and calibration of the new Raman channels for the mobile Weather and Aerosol Lidar (WALI), going over the important sources of bias and uncertainty on the water vapor and temperature profiles stemming from the different optical elements of the instrument. For the first time, the impacts of interference filters and non-common-path differences between Raman channels, and their mitigation, in particular are investigated, using horizontal shots in a homogeneous atmosphere. For temperature, the magnitude of the highlighted biases can be much larger than the targeted absolute accuracy of 1 ∘C defined by the WMO (up to 6 ∘C bias below 300 m range). Measurement errors are quantified using simulations and a number of radiosoundings launched close to the laboratory. After de-biasing, the remaining mean differences are below 0.1 g kg−1 on water vapor and 1 ∘C on temperature, and rms differences are consistent with the expected error from lidar noise, calibration uncertainty, and horizontal inhomogeneities of the atmosphere between the lidar and radiosondes.

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate this approach by classifying between two types of cancer cell lines of different metastatic potential originating from the same patient. It is based on the fact that both the cancer-cell morphology and its mechanical properties, as indicated by the cell temporal and spatial fluctuations, change over the disease progression. We tested different fusion methods for inputting both the morphological optical thickness maps and the coinciding spatio-temporal fluctuation maps of the cells to the classifying network framework. We show that the proposed integrated triple-path deep-learning architecture improves over deep-learning classification that is based only on the cell morphological evaluation via its quantitative optical thickness map, demonstrating the benefit in the acquisition of the cells over time and in extracting their spatio-temporal fluctuation maps, to be used as an input to the classifying deep neural network.

Low-Cost Polyethylene Terephthalate Fluidic Sensor for Ultrahigh Accuracy Measurement of Liquid Concentration Variation

A low-cost polyethylene terephthalate fluidic sensor (PET-FS) is demonstrated for the concentration variation measurement on fluidic solutions. The PET-FS consisted of a triangular fluidic container attached with a birefringent PET thin layer. The PET-FS was injected with the test liquid solution that was placed in a common path polarization interferometer by utilizing a heterodyne scheme. The measured phase variation of probe light was used to obtain the information regarding the concentration change in the fluidic liquids. The sensor was experimentally tested using different concentrations of sodium chloride solution showing a sensitivity of 3.52 ×104 deg./refractive index unit (RIU) and a detection resolution of 6.25 × 10−6 RIU. The estimated sensitivity and detection resolutions were 5.62 × 104 (deg./RIU) and 6.94 × 10−6 RIU, respectively, for the hydrochloric acid. The relationship between the measured phase and the concentration is linear with an R-squared value reaching above 0.995.