Direct Output Research Articles

A 4-Channel Optogenetic Stimulation, 16-Channel Recording Neuromodulation System with Real-Time Micro-LED Detection Function

Neuromodulation techniques are essential for exploring brain science and supporting treatments for neurological disorders. Compared to electrical neuromodulation, optogenetic neuromodulation offers advantages in cell type specificity and spatial precision. However, existing optogenetic neuromodulation systems have limited functionality (unable to simultaneously possess functions including optogenetic stimulation, recording, and micro-LED (micro-Light-Emitting Diode) status monitoring) and will restrict normal biological activities due to their large size. To this end, this paper presents an optogenetic neuromodulation system, including a specified neuromodulation IC (Integrated Circuit) and a customized optrode. The ASIC (Application Specific Integrated Circuit) includes a 16-channel neural signal recording module, a 4-channel optogenetic neurostimulator module, and a 4-channel micro-LED detection module. The micro-LED detection module monitors the micro-LED’s long-term status in real time and provides the direct output of its working status for convenient user access. The neuromodulation ASIC was fabricated in the TSMC 65 nm process, and an in situ normal saline experiment was conducted to test the neuromodulation system’s function.

Self-Powered Potentiometric Sensor Based on a Passive Signal Amplifier with Electronic Paper Display.

Self-powered potentiometric sensors are attractive because of their simple operation, low cost, fast response, and ability to be integrated with electronic components. Self-powered potentiometric sensors that give a direct colorimetric output are especially interesting, because no power supply is needed, which dramatically reduces waste. Recently reported work from our group using an electronic paper display, however, exhibits limitations, because the visualization of small pH changes is difficult. A self-powered ion-selective potentiometric sensor is introduced here that may amplify the e-paper pixel sensitivity by improving the self-powered circuit. The voltage is amplified by changing the circuit from incorporating parallel to incorporating serial capacitors. With three such capacitors, a greatly improved sensitivity is observed, amplifying the absorbance 3-fold. A portable device is realized that changes the position of the capacitors from parallel to serial through a simple mechanical sliding action. As a result, the pH information on the sample is more easily visualized with a pH uncertainty of about 0.1 when comparing the e-paper output to a color card.

Scientometric Analysis and Mapping of Scientific Articles on Multiple Sclerosis-Related Neurogenic Lower Urinary Tract Dysfunction.

Patients suffering from multiple sclerosis (MS) frequently experience lower urinary tract (LUT) dysfunction, which significantly impacts their quality of life. This study's objective was to conduct a scientometric analysis of the literature on MS-induced neurogenic LUT dysfunction. Using bibliometric methods, we examined the literature on neurogenic lower urinary tract dysfunction (NLUTD) in MS patients without restricting it to prevalence studies or specific management methods. We considered contributions from authors, organizations, nations/regions, as well as the evolution of theoretical frameworks, research subtopics, and influential papers. In January 2023, we searched the complete Scopus database, without imposing any language or date constraints, identifying relevant documents related to urology clinical investigations of MS-induced NLUTD. The original articles were categorized into 4 groups: narrative reviews, systematic reviews and meta-analyses, research of levels 1-4, and case reports/series. On January 1, 2023, our search yielded 72 sources published between 1977 and 2022, including journals and books. The average time before publication was 11.2 years. Each document received an average of 18.1 citations, totaling 1.299 citations per year. The author's analysis explored relationships, productivity, and coauthorship networks among authors and institutions based on bibliographic records. Chartier-Kastler E, Karmonik C, and Khavari R ranked highest with 8 publications each. The University of Catania claimed the top position, followed by Houston Methodist Hospital and Paris University, recognized as the leading institutions in this field. An analysis of diagnosis, therapy, and rehabilitation of MS-related NLUTD may be helpful for future bibliometric research in the field to better direct output.

Basal conditions of Denman Glacier from glacier hydrology and ice dynamics modeling

Abstract. Basal sliding in Antarctic glaciers is often modeled using a friction law that relates basal shear stresses to the effective pressure. As few ice sheet models are dynamically coupled to subglacial hydrology models, variability in subglacial hydrology associated with the effective pressure is often implicitly captured in the basal friction coefficient – an unknown parameter in the basal friction law. We investigate the impact of using effective pressures calculated from the Glacier Drainage System (GlaDS) model on basal friction coefficients calculated using inverse methods in the Ice-sheet and Sea-level System Model (ISSM) at Denman Glacier, East Antarctica, for the Schoof and Budd friction laws. For the Schoof friction law, a positive correlation emerges between the GlaDS effective pressure and basal friction coefficient in regions of fast ice flow. Using GlaDS effective pressures generally leads to smoother basal friction coefficients and basal shear stresses, and larger differences between the simulated and observed ice surface velocities compared with using an effective pressure equal to the ice overburden pressure plus the gravitational potential energy of the water. Compared with the Budd friction law, the Schoof friction law offers improved capabilities in capturing the spatial variations associated with known physics of the subglacial hydrology. Our results indicate that ice sheet model representation of basal sliding is more realistic when using direct outputs from a subglacial hydrology model, demonstrating the importance of coupling between ice sheet and subglacial hydrological systems. However, using our outputs we have also developed an empirical parameterization of effective pressure that improves the application of the Schoof friction law without requiring explicit hydrological modeling.

Abstract 14195: Right Ventricular Outflow Tract Diameter Increase With the Low-Moderate Workload: A Prospective Simultaneous Exercise Echocardiography and Invasive Cardiopulmonary Exercise Testing Study

Background: While cardiac output at rest can be estimated non-invasively with echocardiogram, change in left ventricular or right ventricular outflow tract diameter (RVOTd) with exercise makes the cardiac output estimation inaccurate. Hypothesis: RVOTd increases with exercise proportionally with an incremental workload and is determined by stroke volume augmentation. Aims: 1. Define a workload-based change in RVOTd in subjects undergoing invasive cardiopulmonary exercise test (iCPET). 2. Correlate RVOTd at exercise stages with iCPET metrics. Methods: 20 subjects undergoing simultaneous echocardiogram and iCPET were enrolled in a prospective study. Exercise datapoints were acquired at 25 W (n=20), 50W (n=16), 75W (n=14), 100W (n=6). Three hemodynamic groups included: HFpEF (n=12), PAH (n=5), controls (n=3). Utilizing iCPET-based direct Fick cardiac output and stroke volume, RVOTd was reverse engineered with RVOT velocity time integral, per ASE guidelines. Pearson correlation was used to assess correlation of RVOTd with hemodynamic metrics. p<0.05* Results: In overall cohort, age was 64±12 years, BMI 31.2±4.9 Kg/m 2 , female 60%, peak VO 2 13.7±5.7 mL/Kg.min -1 and V E /VCO 2 37±8. Increase in RVOTd with exercise is represented in the Figure. In comparison to rest, RVOTd increased significantly even at low-25W (+4.1±13.6%*) and moderate-50W (+7.6±17.2%*) workloads. Overall, RVOTd increased by 4-5% every 25W. At high workload (100W), the diameter increased by 24% (n=6). There were no differences in RVOT diameter between subgroups. RVOTd at four exercise stages (25W, 50W, 75W, 100W) correlated significantly with stroke volume index (r=0.68*, 0.80*, 0.23, 0.64) and cardiac output (r=0.50*, 0.80*, 0.08, 0.68), but non-significantly with heart rate (r=-0.15, 0.19, -0.10, 0.68). Conclusion: RVOTd increases by 4-5% every 25W on an incremental workload protocol. This behavior is predominantly associated with an increase in stroke volume index with exercise.

CO2 emissions abatement costs and drivers for Indian thermal power industry

Indian power industry contributes nearly half of its overall CO2 emissions. In this study, we answer three related questions. First, what is the abatement cost of CO2 emissions for the Indian thermal power industry under three different policy scenarios - business as usual, emissions reduction only, and simultaneous emissions reduction and energy efficiency enhancement. Second, what are the determinants of these abatement costs of CO2 emissions. Third, which abatement policy scenario is more beneficial. For this, we employ a plant level dataset covering 93% of installed capacity and use a parametric quadratic directional output distance function to estimate marginal abatement costs by applying deterministic linear programming methods. Our findings are: First, the abatement costs range between US$ 46.43–71.11 per ton CO2, depending on the policy scenario chosen. Second, plant age, location, size, ownership, and CO2 intensity are all significant drivers of abatement costs. Third, while greater emissions reductions are possible under the emissions reduction only scenario vis-à-vis simultaneous emissions reduction and energy efficiency enhancement scenario, both scenarios are compatible with meeting the Paris agreement goals. This paper helps decision makers to allocate CO2 abatement targets to plants in line with their abatement costs and potential.

Predicting precipitation on the decadal timescale: A prototype climate service for the hydropower sector

Decadal predictions present an emerging opportunity for various socio-economic sectors affected by climate variability. However, the development of associated climate services is still in an incipient stage. This study focuses on developing a prototype climate service for an end-user in the hydropower sector. The service aimed at predicting precipitation in three drainage basins (Guadalquivir, Ebro and Po) for the next ten years, and was developed in close collaboration with the user. In this paper we do not provide the real-time forecasts, but we focus on describing and evaluating the methods and the models used. Using a European multi-model ensemble, the predictive skill for precipitation is found to vary with the calendar season, the forecast range and the drainage basin considered, though it is generally low for the purposes of supporting such a climate service. To overcome this deficiency, a hybrid approach was developed making combined use of the good skill in predicting the North Atlantic Oscillation (NAO) and the observed dominant influence of the latter on the decadal variability of precipitation in the areas of interest. Implementing this hybrid approach, which combines predictive information from the dynamical models with statistical information from observations, brings significant skill improvements in all basins during the extended cold season (November-March) for the first 10 forecast years. The hybrid model outperforms the direct multi-model ensemble output, exhibiting statistically significant skill for all basins. Our results suggest that utilising large-scale predictors can significantly improve regional predictions, and provide usable information for the hydropower sector.

Can direct CMIP6 model simulations reproduce mean annual historical streamflow change?

Direct streamflow outputs from CMIP6 models are important for studying future water supply under climate change. However, the hydrological community generally ignores such data as its ability to predict streamflow change is largely unquantified and presumably considered biased. Here, we examined the ability of the 15 CMIP6 models to directly reproduce the observed mean annual streamflow change (ΔQ) between 1999–2014 and 1982–1998 for 115 large basins across the globe. The ΔQ and error of ΔQ in CMIP6 models (ΔQ_error) were then attributed to nine independent climate and vegetation variables using a stepwise regression model. Compared to the observations, almost all of the CMIP6 models overestimate ΔQ. In variable attribution, the observed ΔQ in the 115 basins can be well attributed (R2 = 0.59) to changes in precipitation (66 %), temperature (15 %), and leaf area index (-18 %). However, these variables could not explain ΔQ obtained from CMIP6 models (R2 = 0.13). In error attribution, errors in precipitation change contributes the most (69 ± 12 %) to ΔQ_error in CMIP6 models, followed by location-related variables, changes in vegetation-related variables, and changes in temperature-related variables. Although the current performance of CMIP6 model outputs varies in simulating observed streamflow change, they may become more reliable with improved model structures for better representations of climate and land surface processes.

A Global Study of Novel Agents in Paediatric and Adolescent Relapsed and Refractory B-Cell Non-Hodgkin Lymphoma (Glo-BNHL)

Overview Glo-BNHL is an adaptive prospective international academic-led multicentre platform clinical trial designed to evaluate the efficacy and safety of the most promising novel agents as monotherapy or in combination with existing therapies for the treatment of children, adolescents, and young adults with relapsed or refractory (r/r) B-cell Non-Hodgkin Lymphoma (B-NHL). A direct output of the second ACCELERATE multi-stakeholder Paediatric Strategy Forum (Pearson, 2019), Glo-BNHL was designed in collaboration with patient advocates, is supported by the Regulatory Authorities, and the intent is for trial data to support filing for marketing authorisation. Rationale Outcomes for r/r paediatric B-NHL are extremely poor with long term cure rates lower than 30%. Glo-BNHL fulfils the critical need for a single collaborative global approach to find new treatments for these rare patients for whom current therapy is inadequate. It allows for a rational approach to investigating the array of potentially promising agents currently in development for adults. The classes of agents currently prioritised for inclusion in Glo-BNHL are: Treatment Arm I: bispecific antibodies (BsAbs)Treatment Arm II: antibody-drug conjugates (ADCs) combined with standard chemotherapyTreatment Arm III: chimeric antigen receptor (CAR) T-cell products A robust systematic assessment of each asset is undertaken before a decision to include in the platform is made (published separately). Population Glo-BNHL will enrol participants in ≥45 sites in 18 countries spanning Europe, Asia, North America and Australasia. Eligible patients are aged ≤25 years with radiologically or histologically proven relapsed (≥ first) or refractory B-NHL with evaluable disease, adequate organ function, negative pregnancy test, agreement to use effective contraception and written informed consent. Key exclusion criteria include recent stem cell transplant, craniospinal radiation or investigational treatment, on-going moderate or severe acute toxicities, uncontrolled infection or primary immunodeficiency. Patients may be enrolled into any of the available treatment arms for which they are eligible. Statistical Design In this rare population we anticipate global enrolment of 30 patients per year. The Bayesian design enables initial cohorts of 15 patients to be meaningfully evaluated. A transition analysis will estimate with ≥80% certainty whether the true efficacy of the treatment exceeds the pre-defined target response rate. Agents demonstrating sufficient promise will be further evaluated in an expansion stage, following discussion with regulators to clarify data requirements to enable subsequent approval of the agent. Confirmatory analyses of all evaluable patients will estimate with ≥95% certainty whether the true efficacy exceeds the target response rate (“GO”). During both initial and expansion stages patients will be closely monitored, with regular interim futility analyses employed, allowing a treatment arm to stop early if there is very little probability of efficacy (“NoGO”). Outcome measures Primary outcomes measures are occurrence of an objective response after 12 weeks of treatment (Treatment Arm I), occurrence of complete response within a maximum of three cycles of treatment (Treatment Arm II) and occurrence of an objective response following CAR T-cell infusion (Treatment Arm III). Secondary outcome measures include event-free survival, progression-free survival, overall survival time, best overall response, duration of response, adverse events, pharmacokinetic profile and pharmacodynamics markers. Treatment Treatment Arm I: Odronextamab (Regeneron) - a human CD20xCD3 bispecific antibody - monotherapy will be given as an intravenous infusion weekly for 12 weeks, then at a decreasing frequency until progression or up to two years for responding patients. Treatment Arm II: Loncastuximab tesirine (ADC Therapeutics) - a humanised CD19-targeting monoclonal antibody with PBD dimer cytotoxin - will be given as an intravenous infusion with each cycle of modified R-ICE chemotherapy (rituximab, ifosfamide, carboplatin, etoposide and dexamethasone) for up to three cycles. Treatment Arm III: product negotiations are on-going Conclusion Glo-BNHL addresses an urgent unmet clinical need through an innovative trial design serving as a paradigm for evaluation of novel agents in very rare diseases.

Direct democracy and equality: context is the key

AbstractDirect democratic instruments are increasingly applied in many European countries. They are subject to an ongoing public—and often highly controversial—debate. The question of how direct democracy relates to equality, i.e. if direct democratic instruments have the potential to foster equality or if they lead to more inequality, is crucial in this debate. Research has struggled to come up with a general answer to this question with scholars assuming equality as well as inequality-promoting effects of direct democratic outputs. In this paper, we try to shed new light on this controversial debate and argue that the impact of direct democracy on equality essentially depends on the social and political context in which the decisions take place. In order to examine the impact of context factors on direct democratic outputs, we first analyze the influence of country-specific variables such as the level of equality and the age of democracy—applying large-N multilevel logistic regressions. Yet, these statistical analyses offer no clear results: the effects of the country-specific variables are rather blurry and hard to make meaning of. The result suggests that other factors than the ones we included in the regressions are decisive for the effect of direct democratic instruments on equality. Therefore, in a second step, we demonstrate how country-and case-specific variables such as social and political characteristics impact the outcomes of direct democracy, using the same-sex marriage referendums in Ireland and Slovenia in 2015 as examples. We conclude that context factors are too complex to be grasped in a large-N, statistical analysis. This means that to understand how context influences the effects of direct democratic instruments on equality, for now, one has to go beyond the lens of statistical analysis and look at the respective cases in great depth and detail.

Active Microvibration Control of Tool Platforms Installed on the Floors Subjected to Moving Vehicles in Industrial Factories

The vibration responses of tall flexible tool (or equipment) platforms subjected to floor excitations at the platform base are considered more crucial than those of the short ones. This study examined the microvibration control performance of the proposed active piezoelectric mass damper (APMD) or driver for tall platforms subjected to internal automated guided vehicle- (AGV-) induced floor vibrations with larger intensity and broader bandwidth in liquid-crystal-display (LCD) fabrication factories (fabs). The APMD did not require auxiliary spring and damping elements to tune the natural frequency and reduce the stroke of the mass block as required by typical active tuned mass dampers (ATMDs). The motion equation of the proposed analytical model including a continuous three-span beam (or floor) system and an active-controlled tool platform under the action of the AGV moving forces was derived. The APMD, consisting of piezoelectric stacks and a mass block, was installed on the platform subjected to the base rotation excitation, which could be attributed to the uneven vertical floor vibrations induced by AGVs. Moreover, the direct output feedback control algorithm was adopted to determine the optimal feedback gain matrix for calculating the active control force. Time history analyses of the continuous beam-platform model under different AGV weights moving at the same speed were performed, and the corresponding velocity vibration spectra of the floor and platform were further obtained through one-third octave band spectrum analysis. Numerical simulation results revealed that the microvibrations of the platform without APMD generally exceed the VC-A level regardless of the AGV weight. Significant reductions of over 90% on the platform microvibrations could be achieved after the platform was implemented with the APMD, and vibrations met the desired vibration limit (VC-B). Moreover, the APMD exhibits comparable microvibration control performance to the ATMD and requires less mass of the mass block, stroke, and applied voltage under the same active control force. In real-life high-tech production fabs, AGV-induced platform microvibrations occur all the time; therefore, the proposed APMD with less power consumption could be an economical and feasible approach for persistent microvibration control of tall platforms in LCD fabs.

DenseNet-random forest model based galaxy classification

Finding an efficient and accurate adaptive method that can automatically classify galaxies has become an industry consensus. However, most of the current studies on galaxy classification use a single model for direct output, without considering the combination with other models to output more satisfactory prediction results. Through convolutional neural network and classifier, this study studied the possibility of applying the deep learning model to the Galaxy 10 DECals dataset classification, and proposed DenseNet-Random Forest model through comparative analysis. By adjusting and training DenseNet-121 with appropriate hyperparameters, the input tensor is transferred to the basic model through the creation of a shape input layer, where GlobalAveragePooling2D is added to perform an average pooling operation on each feature map, reducing the spatial dimension of each feature map to 1. During the process, a complete connection layer with 64 neurons was added using the ReLU activation function, and a Dropout layer was added to randomly discard 20% of the neurons during training to prevent overfitting. In addition, ReLU Activation function with 32 full connection layers of neurons and softmax Activation function with 10 output layers of neurons are added. By acquiring the feature vector of the training model and the real label of the verification set, assign x and y values respectively, and import them into the Random forest classifier model. The experimental results demonstrated the model ultimately achieved a prediction accuracy of 68% when processing the Galaxy 10 DECals dataset, and achieved nearly 30% improvement in Precision, Recall, and F1 scores.

Microarchitectured strontium doped silver niobate embedded ecoflex composite films for highly efficient box-type mechanical energy harvesters

Understanding the increasing energy demands and probable utilization of mechanical energy harvesters, current research is highly focused on enhancing their performance/applicability in everyday life. Herein, we proposed a box-type hybrid nanogenerator (BT-HNG) consisting of acetone-dissolved microarchitectured strontium (Sr) doped silver niobate (ASNb)/ecoflex composite film as a highly efficient mechanical energy harvester. The simple and unique microarchitectured (MA) master mold was fabricated by immersing a plastic petri dish inside the acetone which was further utilized to obtain MA-composite films. The ASNb microparticles (MPs) with varying the Sr doping concentration were synthesized and further loaded inside the ecoflex film to enhance its dielectric properties. The MA-ASNb/ecoflex composite films were utilized as a negative triboelectric material and copper as a positive triboelectric material for the fabrication of HNG operating in a contact-separation mode. The ASNb filler concentration inside the ecoflex film was optimized in the aspect of Sr doping and filler concentrations to obtain the highest electricity from respective HNG. The highly efficient and stable electrical output produced by the HNG was further utilized to power various small-scale electronics. The BT-HNG producing unique direct current electrical output was developed by integrating five similar HNGs in it. The enhancement in the electrical output with successive addition of each HNG was thoroughly investigated. The proposed BT-HNG was successfully demonstrated as a real-time sensor that can be utilized to turn on the lamps situated at stairs, corridors, etc. in human presence. The enclosed BT-HNG along with a heavy weighing block was demonstrated to harvest vibrational/linear movements into electricity.

Adaptation of metrology-grade ac current source in velocity mode of Planck-Balance 2: direct referencing induced voltages with ac quantum voltage standard

The adaptation of developed metrology-grade ac current source (MCS) to the velocity mode of measurements of the Planck-Balance 2 as a means for generating ac mechanical oscillations is presented. The universality in operating with the MCS unit especially practical for the Planck-Balance setup for frequencies of 0.1 Hz–20 Hz (including but not limited to the negligence of a broader range of 0.01 Hz up to several hundred Hz) and for amplitudes of up to 10 mA with 16 (offset with 14)-bit effective resolution is demonstrated. MCS allows generating complex ac waveform signals as waveform synthesizers by adding to the original signal an extra five independent harmonic components, each of which with an adjustable resolution of 10 ns for phase and 16-bit for amplitude. Additionally, the MCS is supported by an external clock at 10 MHz frequency which serves also as a common reference time base for the comparison between the direct output signal of MCS, or of the induced voltage in the coil of the Planck-Balance resulting due to the applied current by MCS, with the ac quantum voltage standard at the required accuracy levels.

Homogeneous detection of viral nucleic acid via selective recognition proximity ligation and signal amplification with T7 transcription and CRISPR/Cas12a system

The synthetic biology has employed the synthetic gene networks through engineering to construct various functions in biological systems. However, the use of gene circuits to create sensors for detecting low-abundance targets has been limited due to the lack of signal amplification strategies beyond direct output of detection signals. To address this issue, we introduce a novel method utilizing Selective Recognition Proximity Ligation and signal amplification with T7 Transcription and CRISPR/Cas12a system (SRPL-TraCs), which permits the incorporation of cell-free gene circuits with signal amplification and enables the construction of high-order cascade signal amplification strategy to detect biomarkers in homogeneous systems. Specifically, the SRPL-TraCs utilizes selective recognition proximity ligation with high-fidelity T4 DNA ligase and generates a unique crRNA via T7 transcription, along with target-activated Cas12a/crRNA system to achieve excellent specificity for HIV-1 DNA. With this straightforward synthetic biology-based method, the proposed SRPL-TraCs has the potential to detect numerous other interesting targets beyond the nucleic acids.

Reinforcement Learning-Based Approach to Robot Path Tracking in Nonlinear Dynamic Environments

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system’s environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance.

Formation and highly directional output of long-lived resonances in photonic comblike structures

Formation and highly directional output of long-lived resonances in photonic comblike structures

Advancements in total knee arthroplasty kinematics: 3D implant computer aided design model creation through X-ray or fluoroscopic images

Background3D-to-2D fluoroscopic registration is a popular and important step for analyzing total-knee-arthroplasty weight-bearing kinematics. Unfortunately, in vivo analyses using these techniques cannot be completed if the associated computer-aided design implant models are not available. This study introduces a novel method that enables the accessible computation of knee replacement patients' kinematics from fluoroscopy, achieved through the reconstruction of 3-dimensional knee component models using a limited set of 2-dimensional X-ray or fluoroscopic images. MethodsThe proposed non-rigid morphing algorithm, based on the coherent point drift algorithm, scales and transforms the shape of the template model to fit with the silhouette of the corresponding fluoroscopic images without changing the structure of the knee implant. While a greater number of fluoroscopic images can lead to higher accuracy, our study utilizes only 4 images. FindingsThe morphed models show excellent results in comparison with known models with a 0.52 mm average root-mean-square error and a 2.82 mm largest source error for 17 tested knee models of various implant types. The proposed algorithm also enables direct output of patient kinematics using fluoroscopy, with an average error of only 0.54 ± 0.42 mm for femorotibial contact and 0.86 ± 0.34 degrees for axial rotation. InterpretationA novel methodology was introduced to overcome common 3-dimentional to 2-dimensional registration limitations by recreating entire families of 3 dimensional models from a limited number of fluoroscopic images for both cruciate-retaining and posterior-stabilized knee replacement implants. Our algorithm has demonstrated high levels of accuracy with multiple potential extended applications.

Chronic alcohol exposure differentially alters calcium activity of striatal cell populations during actions.

Alcohol Use Disorder (AUD) can induce long lasting alterations to executive function. This includes altered action control, which can manifest as dysfunctional goal-directed control. Cortical and striatal circuits mediate goal-directed control over behavior, and prior research has found chronic alcohol disrupts these circuits. In particular, prior in vivo and ex vivo work have identified alterations to function and activity of dorsal medial striatum (DMS), which is necessary for goal-directed control. However, unknown is whether these alterations manifest as altered activity of select DMS populations during behavior. Here we examine effects of prior chronic alcohol exposure on calcium activity modulation during action-related behaviors via fiber photometry of genetically-identified DMS populations including the direct and indirect output pathways, and fast-spiking interneurons. We find that prior chronic alcohol exposure leads to increased calcium modulation of the direct pathway during action related behavior. In contrast, prior chronic alcohol exposure led to decreased calcium activity modulation of the indirect pathway and the fast-spiking interneuron population around action-related events. Together, our findings suggest an imbalance in striatal activity during action control. This disruption may contribute to the altered goal-directed control previously reported.

FORECAST: A flexible software to forward model cosmological hydrodynamical simulations mimicking real observations

Context. Comparing theoretical predictions to real data is crucial to properly formulate galaxy formation theories. However, this is usually done naively considering the direct output of simulations and quantities inferred from observations, which can lead to severe inconsistencies. Aims. We present FORECAST, a new flexible and adaptable software package that performs forward modeling of the output of any cosmological hydrodynamical simulations to create a wide range of realistic synthetic astronomical images, and thus providing a robust foundation for accurate comparison with observational data. With customizable options for filters, field-of-view size, and survey parameters, it allows users to tailor the synthetic images to their specific requirements. Methods. FORECAST constructs a light cone centered on the observer’s position exploiting the output snapshots of a simulation and computes the observed flux of each simulated stellar element, modeled as a single stellar population, in any chosen set of passband filters, including k correction, intergalactic medium absorption, and dust attenuation. These fluxes are then used to create an image on a grid of pixels, to which observational features such as background noise and PSF blurring can be added. This allows simulated galaxies to be obtained with realistic morphologies and star formation histories. Results. As a first application, we present a set of images obtained exploiting the ILLUSTRISTNG simulation, emulating the GOODS-South field as observed for the CANDELS survey. We produced images of ~200 sq. arcmin, in 13 bands (eight Hubble Space Telescope optical and near-infrared bands from ACS B435 to WFC3 H160, the VLT HAWK-I Ks band, and the four IRAC filters from Spitzer), with depths consistent with the real data. We analyzed the images with the same processing pipeline adopted for real data in CANDELS and ASTRODEEP publications, and we compared the results against both the input data used to create the images and the real data, generally finding good agreement with both, with some interesting exceptions which we discuss. As part of this work, we have released the FORECAST code and two datasets. The first is the CANDELS dataset analyzed in this study, and the second dataset emulates the JWST CEERS survey images in ten filters (eight NIRCam and two MIRI) in a field of view of 200 sq. arcmin between z = 0–20. Conclusions. FORECAST is a flexible tool: it creates images that can then be processed and analyzed using standard photometric algorithms, allowing for a consistent comparison among observations and models, and for a direct estimation of the biases introduced by such techniques.