Pre-defined Range Research Articles

Finite-Time Dynamic Event-Triggered Full-State Constraints Consensus Control for Multiagent Systems with Switching Topologies and Mismatched Disturbances

This paper investigates finite-time dynamic event-triggered full-state constraints consensus control for nonlinear multiagent systems with switching topologies and unknown disturbances. First, all the system states will not exceed the predefined range, which has guiding significance for the constraint control of velocity and position in multiagent systems. Second, to balance the control errors and event-triggered control, a novel finite-time dynamic event-triggered scheme is developed for multiagent systems, which can achieve fast convergence rates and save communication resources. Third, to improve the system performances, a finite-time command filter is designed to remove transient errors resulting from filtering errors and explosion of complexity. All closed-loop signals are proved to be bounded, and the tracking errors quickly converge into a narrow neighborhood of zero in finite-time. To sum up, the proposed control scheme balances the effects of unknown disturbances, unknown nonlinear dynamics, and limited communications bandwidth, which can improve the control performance for the multiagent systems under time-varying switching topologies. Finally, simulation results validate the suggested approach using a mechanical platform model.

Investigation of Acetoin Biosynthesis by Bacillus subtilis ACA-DC 1176 Growing on Crude Glycerol in Flask and Bioreactor Trials

Acetoin biosynthesis by two Bacillus subtilis strains valorising crude glycerol was thoroughly explored within a pre-defined range of culture conditions and systems. B. subtilis ACA-DC 1176 stood out for its higher efficiency in acetoin production, prompting an investigation into the potential for enhanced productivity through the evaluation of diverse culture conditions and media compositions. The primary by-products of the biodiesel and corn industries, namely crude glycerol and corn steep liquor, respectively, were successfully employed as the principal carbon and nitrogen sources of the newly developed low-cost culture medium. Furthermore, the results of the various feeding strategies that were tested indicated that the conversion of 2,3-butanediol (BDO) to acetoin occurred exclusively when the concentration of glycerol was below approximately 5 g/L. This seemed to be necessary for the production of NADH, which is essential for maintaining cellular processes. Following the complete depletion of glycerol, acetic acid increased and became the predominant metabolite, while both acetoin and BDO decreased, presumably resulting in ATP generation. This is likely a mechanism employed by the cell to generate energy in the absence of a carbon source. In the fed-batch bioreactor culture, the kinetics of metabolites differed, as there was no conversion of BDO to acetoin at the final depletion of glycerol. At volumetric mass transfer coefficient (kLa) levels exceeding approximately 70 1/h, the production of acetoin was favoured over that of BDO, with the highest observed acetoin/BDO ratio reaching 4.29 g/g. Conversely, at kLa values below approximately 60 1/h, the titres of acetoin and BDO were found to be nearly equal. The final concentrations of acetoin and BDO reached 36.0 g/L and 25.5 g/L, respectively, resulting in a total yield of both (acetoin + BDO) per glycerol consumption of 0.40 g/g. To the best of our knowledge, this is the first study to focus on acetoin production from crude glycerol fermentative valorisation. The study presents new findings regarding the parameters influencing the level of BDO conversion to acetoin. However, further research is required in order to gain a comprehensive understanding of the underlying phenomena and metabolic pathways involved.

A Feasibility Open-Labeled Clinical Trial Using a Second-Generation Artificial-Intelligence-Based Therapeutic Regimen in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy.

Background/Objectives: Gaucher Disease type 1 (GD1) is a recessively inherited lysosomal storage disorder caused by a deficiency in the enzyme β-glucocerebrosidase. Enzyme replacement therapy (ERT) has become the standard of care for patients with GD. However, over 10% of patients experience an incomplete response or partial loss of response to ERT, necessitating the exploration of alternative approaches to enhance treatment outcomes. The present feasibility study aimed to determine the feasibility of using a second-generation artificial intelligence (AI) system that introduces variability into dosing regimens for ERT to improve the response to treatment and potentially overcome the partial loss of response to the enzyme. Methods: This was an open-label, prospective, single-center proof-of-concept study. Five patients with GD1 who received ERT were enrolled. The study used the Altus Care™ cellular-phone-based application, which incorporated an algorithm-based approach to offer random dosing regimens within a pre-defined range set by the physician. The app enabled personalized therapeutic regimens with variations in dosages and administration times. Results: The second-generation AI-based personalized regimen was associated with stable responses to ERT in patients with GD1. The SF-36 quality of life scores improved in one patient, and the sense of change in health improved in two; platelet levels increased in two patients, and hemoglobin remained stable. The system demonstrated a high engagement rate among patients and caregivers, showing compliance with the treatment regimen. Conclusions: This feasibility study highlights the potential of using variability-based regimens to enhance ERT effectiveness in GD and calls for further and longer trials to validate these findings.

A phase 1/2, first-in-human, open-label, dose-escalation study of TAK-280, an investigational B7-H3 x CD3ε conditional bispecific redirected activation (COBRA) T-cell engager, in adult patients with unresectable, locally advanced, or metastatic solid tumors.

TPS2684 Background: TAK-280 is a novel COBRA T-cell engager that targets the B7 homolog 3 protein (B7-H3), an antigen highly expressed in a range of solid tumors including metastatic castration-resistant prostate cancer (mCRPC) and non–small cell lung cancer. In its prodrug form, TAK-280 binds to B7-H3, but not to CD3ε. Once in the protease-rich tumor microenvironment, TAK-280 undergoes protease-mediated activation through formation of CD3ε-binding active dimers, which stimulate CD3 T-cell activation and cytotoxic antitumor response against co-engaged B7-H3–expressing cells. In preclinical studies, TAK-280 demonstrated cleavage-dependent conditionality, engagement of tumor target antigen, and induction of T-cell mediated tumor killing. TAK-280 has the potential to target solid tumors that are not amenable to treatment with conventional first- or second-generation T-cell engagers, while limiting toxicity to normal tissues. Methods: This phase 1/2 study is enrolling adult patients with unresectable, locally advanced, or metastatic solid tumors described in the literature to have enhanced B7-H3 expression, who are ineligible or intolerant to standard therapies. Other eligibility criteria include Eastern Cooperative Oncology Group performance status ≤1 and measurable disease per RECIST v1.1. Key exclusion criteria include history of autoimmune disease, major surgery ≤8 weeks before first dose of TAK-280, and history of clinically significant cardiac or gastrointestinal disorders. During phase 1 dose escalation, TAK-280 is administered as an intravenous infusion, once weekly, in 28-day cycles at a pre-defined dose level range. Patients who do not experience dose-limiting or other unacceptable toxicities may receive up to 14 treatment cycles depending on response; patients are treated until disease progression, unacceptable toxicity, or withdrawal from study. The dose-escalation phase will determine two recommended doses for expansion to be assessed during the cohort-expansion phase. The primary endpoint is incidence of dose-limiting or other toxicities during dose escalation; secondary endpoints include pharmacokinetic parameters, response assessment per RECIST v1.1, overall response rate, duration of response, progression-free survival, overall survival, disease control rate, and prostate-specific antigen response in patients with mCRPC. Cytokine release syndrome is closely managed and reported per American Society for Transplantation and Cellular Therapy consensus grading. Approximately 182 patients are planned to be enrolled (42 in dose escalation and 100–142 in cohort-expansion); as of the data cutoff date of Jan 10, 2024, 18 patients have received the study drug. Clinical trial information: NCT05220098 .

Light exposure profiles differ between myopes and non-myopes outside school hours

PurposeConsidering the putative role of light in myopia, and variations in socioeconomic, lifestyle, educational and environmental factors across ethnicities, we objectively investigated light exposure patterns in Indian school children.MethodsThe light...

Automatic Temperature Controller with Cooling System

This project focuses on the design and implementation of an automatic temperature controller integrated with a cooling system. The system is designed to maintain a predefined temperature range within a specified environment, providing efficient cooling when necessary. The automatic temperature controller utilizes sensors to continuously monitor the ambient temperature and compare it with the setpoint temperature. Based on this comparison, the controller activates the cooling system to regulate the temperature within the desired range. The cooling system employs a combination of methods such as air cooling, liquid cooling, or thermoelectric cooling, depending on the application requirements.

Deep learning for robust forecasting of hot metal silicon content in a blast furnace

AbstractThe hot metal silicon content is a key indicator of the thermal state in the blast furnace and it needs to be kept within a pre-defined range in order to ensure efficient operations. Effective monitoring of silicon content is challenging due to the harsh environment in the furnace and irregularly sampled measurements. Data-driven approaches have been proposed in the literature to predict silicon content using process data and overcome the sparsity of silicon content measurements. However, these approaches rely on the selection of hand-crafted features and ad hoc interpolation methods to deal with irregular sampling of the process variables, adding complexity to model training and optimisation, and requiring significant effort when tuning the model over time to keep it to the required level of accuracy. This paper proposes an improved framework for the prediction of silicon content using a novel deep learning approach based on Phased LSTM. The model has been trained using 3 years of data and validated over a 1-year period using a robust walk-forward validation method, therefore providing confidence in the model performance over time. The Phased LSTM model outperforms competing approaches due to its in-built ability to learn from event-based sequences and scalability for real-world deployments. This is the first time that Phased LSTM has been applied to real-world datasets and results suggest that the ability to learn from event-based data can be beneficial for the process industry where event-driven signals from multiple sensors are common.

At-Home Administration of Gantenerumab by Care Partners to People with Early Alzheimer's Disease: Feasibility, Safety and Pharmacodynamic Impact.

Monoclonal antibodies that target amyloid-beta and remove amyloid plaques can slow cognitive and functional decline in early Alzheimer's disease. Gantenerumab is a subcutaneously administered fully-human anti-amyloid-beta monoclonal antibody with highest affinity for aggregated amyloid-beta. Since the phase 3 GRADUATE trials did not meet the primary endpoint (change from baseline to Week 116 in Clinical Dementia Rating scale - Sum of Boxes), development of gantenerumab in sporadic Alzheimer's disease was stopped and all ongoing trials were terminated early due to sponsor decision. Subcutaneous administration at the clinic or at home by care partner would be an important option for other therapies in this class in order to increase flexibility and reduce overall burden. The insights obtained from the experience with gantenerumab home administration by care partner in the phase 2 GRADUATION trial will serve to guide the ongoing efforts with other anti-amyloid-beta antibodies. To evaluate the pharmacodynamic effects on brain amyloid load of once weekly subcutaneous administration of gantenerumab and the safety and feasibility of home administration by care partners. Phase 2, open-label, single arm study. Multicenter trial conducted in 33 sites in 8 countries from November 2020 to March 2023. Participants aged 50 to 90 with early symptomatic Alzheimer's disease (mild cognitive impairment/mild dementia due to Alzheimer's disease), and evidence of amyloid positron emission tomography positivity. Participants could receive up to 255 mg gantenerumab once-weekly, administered subcutaneously at site or at home by healthcare professionals or non-healthcare-professional care partners. The primary endpoint was the change from baseline to Week 52 and to Week 104 in brain amyloid load as measured by PET centiloid levels. The secondary endpoints were responses to the home administration questionnaire, plasma concentrations and safety. The overall number of participants enrolled was 192, with a mean (standard deviation) amyloid PET load at baseline of 101.80 (29.80) centiloids. At the time of early study termination by sponsor, 149 participants had valid Week 52 amyloid PET data (primary endpoint), and 12 participants had an early termination PET within the pre-defined time range of Week 104. The mean change in amyloid PET from baseline to Week 52 and Week 104 was -26.19 centiloids (range: -75.6-15.8; n=149) and -35.48 centiloids (range: -63.2--7.0; n=12), respectively. Responses to the home administration questionnaire at Week 52 (n=148) indicated that the majority of care partners (88-97%) considered administration of study drug at home easy (30.4%) or very easy (57.4%), and convenient (25.7%) or very convenient (70.9%). Care partners felt confident (31.1%) or very confident (62.2%) and satisfied (29.7%) or very satisfied (64.9%) with giving the injection at home. Responses by care partners at Week 36 (n=72), Week 76 (n=126) and Week 104 (n=29) and participant (patient) assessment of convenience and satisfaction at these time points were similar. There were no new safety findings associated with gantenerumab administered subcutaneously once weekly at 255 mg or safety issues associated with at-home injections by non-healthcare professional care partners. Once-weekly subcutaneous home administration of the anti-amyloid-beta antibody gantenerumab by non-healthcare-professional care partners to participants with early Alzheimer's disease was feasible, safe, well tolerated, and considered as a convenient option by both the care partners and participants with Alzheimer's disease. Although gantenerumab's development has been stopped due to lack of efficacy, this approach has the potential to reduce the frequency of hospital/outpatient clinic visits required for treatment with other anti-amyloid-β antibodies and can increase flexibility of drug administration for people living with Alzheimer's disease and their families.

Spring-assisted posterior vault expansion: a parametric study to improve the intracranial volume increase prediction

Spring-assisted posterior vault expansion has been adopted at the London Great Ormond Street Hospital for Children to treat raised intracranial pressure in patients affected by syndromic craniosynostosis, a congenital calvarial anomaly causing the premature fusion of skull sutures. This procedure involves elastic distractors used to dynamically reshape the skull and increase the intracranial volume (ICV). In this study, we developed and validated a patient-specific model able to predict the ICV increase and carried out a parametric study to investigate the effect of surgical parameters on that final volume. Pre- and post-operative computed tomography data relative to 18 patients were processed to extract simplified patient-specific skull shape, replicate surgical cuts, and simulate spring expansion. A parametric study was performed to quantify each parameter’s impact on the surgical outcome: for each patient, the osteotomy location was varied in a pre-defined range; local sensitivity of the predicted ICV to each parameter was analysed and compared. Results showed that the finite element model performed well in terms of post-operative ICV prediction and allowed for parametric optimization of surgical cuts. The study indicates how to optimize the ICV increase according to the type of procedure and provides indication on the most robust surgical strategy.

The Random Step Method for Measuring the Point of Subjective Equality.

Points of Subjective Equality (PSE) are commonly measured using staircase or constant stimuli methods. However, the staircase method is highly dependent on the step size, and the constant stimuli method is time-consuming. Thus, we wanted to develop an efficient and quick method to estimate both the PSE and the slope of the psychometric function. We developed a random-step algorithm in which a one-up-one-down rule is followed but with a random step size in a pre-defined range of test levels. Each stimulus would be chosen depending on the previous response of the subject. If the subject responded "up", any random level in the lower range would be picked for the next trial. And if the subject responded "down", any random level in the upper range would be picked for the next trial. This procedure would result in a bell-shaped distribution of the test levels around the estimated PSE, while a substantial amount of trials would still be dispersed at both bounds of the range. We then compared this method with traditional constant stimuli procedure on a task based on the Pulfrich phenomenon while the PSEs of participants could be varied using different neutral density filters. Our random-step method provided robust estimates of both the PSE and the slope under various noise levels with small trial counts, and we observed a significant correlation between the PSEs obtained with the two methods. The random-step method is an efficient way to measure the full psychometric function when testing time is critical, such as in clinical settings.

Transcutaneous Spinal Cord Stimulation to Stabilize Seated Systolic Blood Pressure in Persons With Chronic Spinal Cord Injury: Protocol Development.

Transcutaneous spinal cord stimulation (tSCS) is an emerging therapeutic strategy to target spinal autonomic circuitry to normalize and stabilize blood pressure (BP) in hypotensive persons living with chronic spinal cord injury (SCI). Our aim is to describe our current methodological approach to identify individual tSCS parameters that result in the maintenance of seated systolic blood pressure (SBP) within a pre-defined target range. The parent study is a prospective, randomized clinical trial in which eligible participants will undergo multiple mapping sessions to optimize tSCS parameter settings to promote stable SBP within a target range of 110-120 mm Hg for males and 100-120 mm Hg for females. Parameter mapping includes cathode electrode placement site (T7/8, T9/10, T11/12, and L1/2), stimulation frequency (30, 60 Hz), current amplitudes (0-120 mA), waveform (mono- and biphasic), pulse width (1000 μs), and use of carrier frequency (0, 10 kHz). Each participant will undergo up to 10 mapping sessions involving different electrode placement sites and parameter settings. BP will be continuously monitored throughout each mapping session. Stimulation amplitude (mA) will be increased at intervals of between 2 and 10 mA until one of the following occurs: 1) seated SBP reaches the target range; 2) tSCS intensity reaches 120 mA; or 3) the participant requests to stop. Secondary outcomes recorded include 1) symptoms related to autonomic dysreflexia and orthostatic hypotension, 2) Likert pain scale, and 3) skin appearance after removal of the tSCS electrode. Clinical Trials Registration: NCT05180227.

Efficient generation of random rotation matrices in four dimensions.

Four-dimensional (4D) rotations have applications in the fields of robotics, computer vision, and rigid-body mechanics. In the latter, they can be used to transform between equimomental systems of point masses. Here we provide an efficient algorithm to generate random 4D rotation matrices covering an arbitrary, predefined range of rotation angles. These matrices can be combined with Monte Carlo methods for the efficient sampling of the SO(4) group of 4D rotations. The matrices are unbiased and constructed such that repeated rotations result in uniform sampling over SO(4). The algorithm can be used to optimize the mass partitioning in coarse-grained simulation models of molecules involving coupled constraints for stable time integration.

Spatiotemporal denoising of low-dose cardiac CT image sequences using RecycleGAN.

Electrocardiogram (ECG)-gated multi-phase computed tomography angiography (MP-CTA) is frequently used for diagnosis of coronary artery disease. Radiation dose may become a potential concern as the scan needs to cover a wide range of cardiac phases during a heart cycle. A common method to reduce radiation is to limit the full-dose acquisition to a predefined range of phases while reducing the radiation dose for the rest. Our goal in this study is to develop a spatiotemporal deep learning method to enhance the quality of low-dose CTA images at phases acquired at reduced radiation dose. Recently, we demonstrated that a deep learning method, Cycle-Consistent generative adversarial networks (CycleGAN), could effectively denoise low-dose CT images through spatial image translation without labeled image pairs in both low-dose and full-dose image domains. As CycleGAN does not utilize the temporal information in its denoising mechanism, we propose to use RecycleGAN, which could translate a series of images ordered in time from the low-dose domain to the full-dose domain through an additional recurrent network. To evaluate RecycleGAN, we use the XCAT phantom program, a highly realistic simulation tool based on real patient data, to generate MP-CTA image sequences for 18 patients (14 for training, 2 for validation and 2 for test). Our simulation results show that RecycleGAN can achieve better denoising performance than CycleGAN based on both visual inspection and quantitative metrics. We further demonstrate the superior denoising performance of RecycleGAN using clinical MP-CTA images from 50 patients.

A randomized trial comparing LY01011, biosimilar candidate, with the reference product denosumab (Xgeva®) in healthy Chinese subjects

BackgroundComplications of bone metastases such as skeletal-related events lead to the impaired functional status and quality of life including death in patients with bone metastasis from solid tumors. Denosumab (XGEVA®) is indicated for the prevention of skeletal-related events in bone metastasis patients with solid tumors. The biosimilar product LY01011, a fully human anti-receptor activator of nuclear factor kappa-B ligand monoclonal antibody, was developed to be compared with the reference product denosumab. Material and methodsA randomized, double-blind, single-dose, parallel-controlled phase 1 study was conducted in healthy Chinese subjects. A total of 168 enrolled subjects were randomly assigned in a 1:1 ratio to receive a single 120 mg dose of LY01011 (n = 85) or denosumab (n = 83) subcutaneously. The primary pharmacokinetic (PK) parameters, including maximum plasma concentration (Cmax) and area under the concentration–time curve from time zero to last quantifiable concentration (AUC0→t), were collected and measured for evaluation. Other secondary PK parameters included AUC0- ∞, Tmax, CL/F, λz, t1/2, Vd/F, etc. Pharmacodynamics (PD), safety and immunogenicity profiles were also accounted for data analysis. ResultsThe geometric mean ratios (GMRs) of LY01011 and denosumab for the primary PK parameters such as Cmax and AUC0→t were 98.13% and 100.32%. The 90% confidence intervals (CIs) were all within the acceptance range of 80%-125%. The GMRs of the PD parameters including AUEC0→t and Emax were 98.71% and 99.80%, which fell within the pre-defined acceptance range of 80%-125%. The results also demonstrated PK similarity even if Cmax and AUC0→∞ had been used as primary endpoints. Safety profiles were tolerable and similar between groups. 4 (4.7%) and 2 (2.4%) subjects had experienced Grade 3 or above treatment-emergent adverse events (TEAEs) in LY01011 group and denosumab group. 3 subjects were reported to have serious adverse events (SAEs). None of the Grade 3 or above TEAEs and SAEs were related to the study drug, LY01011. No subject was tested anti-drug antibody (ADA) positive in both groups prior to the study drug administration. Following the study drug administration, only one subject in denosumab group was tested ADA positive, whereas no subject with ADA positive was reported in LY01011 group. No neutralizing antibody (Nab) was detected in either group throughout the study. ConclusionsThe study demonstrated PK and PD similarity of LY01011, a denosumab biosimilar, to denosumab in healthy Chinese subjects, with comparable safety and immunogenicity profiles.

Interlaboratory Co-validation of a UPLC-ToF MS MAM Method for Truncations of a Fc Fusion Protein.

Peptide-Fc fusion proteins are inherently heterogeneous and complex molecules. Protein post-translational modifications (PTMs) or truncation can arise during manufacturing or product storage. Some of these product attributes could potentially impact the efficacy or safety of the bio-molecule and are thus classified as critical quality attributes (CQAs). These CQAs should be controlled in order to ensure manufacturing and quality consistency. A subunit UPLC-ToF MS based MAM method was developed for identity test and quantitatively monitored two critical quality attributes (CQAs) resulting from two truncations of that fusion protein (fragment 1 and 2). Three independent laboratories are involved in the method validation according to ICH Q2(R1), ICH Q6B, FDA and NMPA guidance. This developed method fully meets the pre-defined analytical target profile (ATP), including specificity, accuracy, precision, quantitation limit, linearity, range and robustness. Three independent labs co-validate a UPLC-ToF MS based MAM method for protein drug QC release and stability testing. The experimental design of method validation can be a reference for LC-HRMS-based subunit MAM methods that have been widely used in the characterization of antibodies, ADCs and other protein-based biologics. This work paves the way for implementing MAM in QC with more targeted control of product quality.

820-P: The Proposed Insulin Lispro Biosimilar GN1101DP Shows Pharmacokinetic (PK) and Pharmacodynamic (PD) Bioequivalence to Humalog in Caucasian and Chinese Healthy Subjects

This randomized, double-blind, cross-over study compared PK/PD-properties of Humalog and GN1101DP, a proposed insulin lispro (INS) biosimilar with an identical primary structure and high physicochemical and bio-functional similarity to Humalog. Thirty-five Caucasian and 13 Chinese healthy subjects completed the study and received a single dose of 0.3 U/kg of GN1101DP and Humalog under euglycemic automated glucose clamp conditions (ClampArt®, plasma glucose (PG) target 81 mg/dL, clamp duration 12 hours post-dose, wash-out period 2-14 days between the two dosings). Plasma INS concentrations were determined by tandem ultra performance liquid chromatography-mass spectrometry. GN1101DP demonstrated both PK- and PD-bioequivalence to Humalog with superimposable INS concentration and glucose infusion rate (GIR) profiles, point estimates close to 100%, and 90% confidence intervals were within the pre-defined similarity range of 80-125%. Bioequivalence was observed in all and also separately in Caucasian and Chinese subjects (Figure). Clamp quality was high (mean PG-variations of ~5% and deviations from target <0.6 mg/dL). Both lispro formulations were well tolerated. No injection site reactions occurred. In conclusion, GN1101DP demonstrated PK- and PD-bioequivalence to Humalog in Caucasian and Chinese subjects. Disclosure T.Heise: Advisory Panel; Novo Nordisk, Consultant; Gan & Lee Pharmaceuticals, Research Support; Adocia, AstraZeneca, Biocon, Crinetics Pharmaceuticals, Inc., Eli Lilly and Company, Genova, Novo Nordisk, Sanofi, Zealand Pharma A/S, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk. E.Zijlstra: Other Relationship; Eli Lilly and Company, Speaker's Bureau; Gan & Lee Pharmaceuticals, Novo Nordisk A/S. G.Andersen: None. S.B.Selker: None. C.Shen: None. Y.Wang: None. Funding Genova Biotech Pvt. Ltd.

Thermal signature: A method to extract characteristics from infrared thermography data applied to the development of animal heat stress classifier models

Recent studies have investigated the use of infrared thermography (IRT) to monitor body surface temperature and correlate it with factors related to animal welfare and performance. In this context, this work proposes a new method for extracting characteristics for the temperature matrix obtained using IRT data from regions of the body surface of cows which, if associated with environmental variables through a machine learning algorithm it generates computational classifiers for heat stress condition. IRT data were collected from different parts of the body of 18 lactating cows housed in a free-stall, monitored for 40 non-consecutive days, three times a day (5:00 a.m., 1:00 p.m., and 7:00 p.m.), during summer and winter, along with physiological data (rectal temperature and respiratory rate) and meteorological data for each time. The IRT data is used to create a descriptor vector based on frequency, accounting for temperatures for a pre-defined range, referred to in the study as ‘Thermal Signature’ (TS). The generated database was used for training and assessing computational models based on Artificial Neural Network (ANN) to classify heat stress conditions. The models were built using the following predictive attributes for each instance: TS, air temperature, black globe temperature and wet bulb temperature. The goal attribute used for supervised training was the heat stress level classification generated from the rectal temperature and respiratory rate values measured. The models based on different ANN architectures were compared through metrics of the confusion matrix between predicted and measured data, obtaining better results with 8 TS ranges. The best accuracy for classification into four heat stress levels (Comfort, Alert, Danger, and Emergency) was 83.29% using the TS of the ocular region. The classifier for two heat levels of stress (Comfort and Danger) obtained accuracy of 90.10% also using the 8 TS bands of the ocular region.

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules.

Biotherapeutic masses are a means of verifying identity and structural integrity. Mass spectrometry (MS) of intact proteins or protein subunits provides an easy analytical tool for different stages of biopharmaceutical development. The protein's identity is confirmed when the experimental mass from MS is within a pre-defined mass error range of the theoretical mass. While several computational tools exist for the calculation of protein and peptide molecular weights, they either were not designed for direct application to biotherapeutic entities, have access limitations due to paid licenses, or require uploading protein sequences to host servers. We have developed a modular mass calculation routine that enables the easy determination of the average or monoisotopic masses and elemental compositions of therapeutic glycoproteins, including monoclonal antibodies (mAb), bispecific antibodies (bsAb), and antibody-drug conjugates (ADC). The modular nature of this Python-based calculation framework will allow the extension of this platform to other modalities such as vaccines, fusion proteins, and oligonucleotides in the future, and this framework could also be useful for the interrogation of top-down mass spectrometry data. By creating an open-source standalone desktop application with a graphical user interface (GUI), we hope to overcome the restrictions around use in environments where proprietary information cannot be uploaded to web-based tools. This article describes the algorithms and application of this tool, mAbScale, to different antibody-based therapeutic modalities.

Deep learning ensembles for accurate fog-related low-visibility events forecasting

In this paper we propose and discuss different Deep Learning-based ensemble algorithms for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep Learning (DL) architectures have been considered, from which multiple individual learners are generated. Hyperparameters of the models, including parameters concerning data preprocessing, models architecture and training procedure, are randomly selected for each model within a pre-defined discrete range. Also, every model is trained with slightly different data sampled randomly, assuring that every models introduce variety in the ensemble. Then, three different information fusion techniques are employed to build the ensemble models. The influence of the filtering process and the elitism level (the percentage of the individual models entering the ensemble) is also assessed. The performance of the proposed methodology have been tested in two real problems of low-visibility events prediction due to orographical and radiation fog, at the north of Spain. Comparison with different Machine Learning, alternative DL algorithms and meteorological-based methods show the good performance of the proposed deep learning ensembles in this problem.

Multiple UAVs Trajectory Optimization in Multicell Networks With Adjustable Overlapping Coverage

Trajectory optimization of Unmanned Aerial Vehicles (UAVs) operating as flying base stations (FBSs) evolved as a novel integration component in beyond 5G (B5G) networks and has recently received significant research attention. Notably, the vast majority of previous research has mainly concentrated on the case of a single terrestrial macro base station (BS) which is used as a depot for multiple FBSs. In this paper, we focus on the more general use case where multiple FBSs located at different macro-BSs used as a depot serve ground users (GUs) at cluster points (CPs). To this end, we formulate the FBSs trajectory optimization problem using a mixed integer linear programming (MILP) formulation with the aim to minimize total travel time (TTT) of the FBSs in a multi-cell network in which their cell coverage or boundary is adjustable for the FBSs deployment; creating in that sense virtual cells for the FBSs. Furthermore, heuristic algorithms are proposed to provide competitive solutions and reduce the computational time in view of the curse of dimensionality of the original problem. Numerical investigations reveal that the proposed FBSs path planning optimization solutions decrease the TTT and increase the efficiency of offloading collected data for the FBSs deployment with gains up to approximately 23% and 19% respectively compared to nominal schemes that consider the pre-defined coverage range of the cells or no cell boundaries. Aside from the above, compared to previously proposed nominal strategies, the proposed schemes achieve an almost 27% improvement in terms of fairness (Jain’s index) on the FBS traveling time.