Bias Properties Research Articles

Reweighted Nadaraya–Watson estimation of conditional density function in the right-censored model

For a conditional density function (CDF) in the right-censored model, the local linear (LL) estimator has superior bias properties compared with the Nadaraya–Watson (NW) one, but it may have negative values and thus give rise to unsuitable inference. In order to alleviate the possible negativity of the LL estimator, we define a reweighted NW (RNW) estimator of the CDF in the right-censored model by employing the empirical likelihood (EL) method. The RNW estimator is constructed by modifying the NW estimator slightly, so it naturally inherits the nonnegativity of the NW one. It is assumed that the censoring time is independent of the survival time with the associated covariate. Under stationary α−mixing observations, the weak consistency and asymptotic normality of the RNW estimator are developed. The asymptotic normality shows that the RNW estimator possesses the bias and variance of the LL estimator. Finally, we conduct simulations to evaluate the finite sample performance of the estimator.

A label-free impedance assay in endothelial cells differentiates the activation and desensitization properties of clinical S1P1 agonists.

Sphingosine-1 phosphate receptor-1 (S1P1 ) activation maintains endothelial barrier integrity, whereas S1P1 desensitization induces peripheral blood lymphopenia. The latter is exploited in the approval and/or late-stage development of receptor-desensitizing agents targeting the S1P1 receptor in multiple sclerosis, such as siponimod, ozanimod, and ponesimod. SAR247799 is a recently described G protein-biased S1P1 agonist that activates S1P1 without desensitization and thus has endothelial-protective properties in patients without reducing lymphocytes. As SAR247799 demonstrated endothelial-protective effects at sub-lymphocyte-reducing doses, the possibility exists that other S1P1 modulators could also exhibit endothelial-protective properties at lower doses. To explore this possibility, we sought to quantitatively compare the biased properties of SAR247799 with the most advanced clinical molecules targeting S1P1 . In this study, we define the β-arrestin pathway component of the impedance profile following S1P1 activation in a human umbilical vein endothelial cell line (HUVEC) and report quantitative indices of the S1P1 activation-to-desensitization ratio of various clinical molecules. In a label-free impedance assay assessing endothelial barrier integrity and disruption, the mean estimates (95% confidence interval) of the activation-to-desensitization ratios of SAR247799, ponesimod, ozanimod, and siponimod were 114 (91.1-143), 7.66 (3.41-17.2), 6.35 (3.21-12.5), and 0.170 (0.0523-0.555), respectively. Thus, we show that SAR247799 is the most G protein-biased S1P1 agonist currently characterized. This rank order of bias among the most clinically advanced S1P1 modulators provides a new perspective on the relative potential of these clinical molecules for improving endothelial function in patients in relation to their lymphocyte-reducing (desensitization) properties.

Characterization of parameters with a mixed bias property

Summary We study a class of parameters with the so-called mixed bias property. For parameters with this property, the bias of the semiparametric efficient one-step estimator is equal to the mean of the product of the estimation errors of two nuisance functions. In nonparametric models, parameters with the mixed bias property admit so-called rate doubly robust estimators, i.e., estimators that are consistent and asymptotically normal when one succeeds in estimating both nuisance functions at sufficiently fast rates, with the possibility of trading off slower rates of convergence for the estimator of one of the nuisance functions against faster rates for the estimator of the other nuisance function. We show that the class of parameters with the mixed bias property strictly includes two recently studied classes of parameters which, in turn, include many parameters of interest in causal inference. We characterize the form of parameters with the mixed bias property and of their influence functions. Furthermore, we derive two functional loss functions, each being minimized at one of the two nuisance functions. These loss functions can be used to derive loss-based penalized estimators of the nuisance functions.

The intrinsic exchange bias effect in the LaMnO3 and LaFeO3 compounds

This work investigates the magnetic and the intrinsic exchange bias (EB) properties of the sol-gel synthesised LaMnO3 and LaFeO3 perovskite compounds. The x-ray diffraction (XRD) has proved the high homogeneity of both compounds, which are crystallised in the orthorhombic Pnma structure (as proved by Rietveld refinement). The field cooling-zero field cooling magnetisation dependent temperature (M(T)) indicates the antiferromagnetic (AFM) nature of these compounds. Nevertheless, an anomalous ferromagnetic (FM) behaviour is observed, which is more likely, arises from the spin canting effect in the Mn3+ and Fe3+ ions. The thermal variation of the magnetisation reciprocal (M−1) has confirmed the presence of this FM component below 110K in LaMnO3 and the Curie-Weiss behaviour above 160K. Also, the magnetic hysteresis loops below 110K are corresponding to the FM-like behaviour. The spin of the FM component couples with the AFM phase spins, leading to the EB effect in both compounds that show maximum values of −1124Oe and 2343 Oe for the LaMnO3 and LaFeO3 compounds, respectively. It is observed that the EB effect is suppressed with the partial substitution of La3+ by Ba2+ due to the dominance of the FM phase and the absence of the FM/AFM coupling. Also, the influence of the FM-AFM phases co-existence on the magnetocaloric(MCE) properties of the LaMnO3 compound were studied. Where the LaMnO3 compound shows a magnetic entropy change (ΔS) of 0.42 J/kg.K with an adiabatic temperature change (ΔTad) of 0.1k that is improved in the La0·8Ba0·2MnO3 compound to 1.3 J/kg.K and 0.4K, respectively.

Evolution and impact of bias in human and machine learning algorithm interaction.

Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is that algorithms are subject to bias that is born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, people and algorithms are increasingly engaged in interactive processes wherein neither the human nor the algorithms receive unbiased data. Algorithms can also make biased predictions, leading to what is now known as algorithmic bias. On the other hand, human’s reaction to the output of machine learning methods with algorithmic bias worsen the situations by making decision based on biased information, which will probably be consumed by algorithms later. Some recent research has focused on the ethical and moral implication of machine learning algorithmic bias on society. However, most research has so far treated algorithmic bias as a static factor, which fails to capture the dynamic and iterative properties of bias. We argue that algorithmic bias interacts with humans in an iterative manner, which has a long-term effect on algorithms’ performance. For this purpose, we present an iterated-learning framework that is inspired from human language evolution to study the interaction between machine learning algorithms and humans. Our goal is to study two sources of bias that interact: the process by which people select information to label (human action); and the process by which an algorithm selects the subset of information to present to people (iterated algorithmic bias mode). We investigate three forms of iterated algorithmic bias (personalization filter, active learning, and random) and how they affect the performance of machine learning algorithms by formulating research questions about the impact of each type of bias. Based on statistical analyses of the results of several controlled experiments, we found that the three different iterated bias modes, as well as initial training data class imbalance and human action, do affect the models learned by machine learning algorithms. We also found that iterated filter bias, which is prominent in personalized user interfaces, can lead to more inequality in estimated relevance and to a limited human ability to discover relevant data. Our findings indicate that the relevance blind spot (items from the testing set whose predicted relevance probability is less than 0.5 and who thus risk being hidden from humans) amounted to 4% of all relevant items when using a content-based filter that predicts relevant items. A similar simulation using a real-life rating data set found that the same filter resulted in a blind spot size of 75% of the relevant testing set.

Exchange Bias in a La0.67Sr0.33MnO3/NiO Heterointerface Integrated on a Flexible Mica Substrate.

Flexible electronics integrating spintronics are of great potential in the areas of lightweight and flexible personal electronics. The integration of ferromagnetic and other functional oxides on flexible mica substrates is crucial for the proposed computer technology. In this work, we demonstrate the successful integration of a ferromagnetic-antiferromagnetic nanocomposite of La0.67Sr0.33MnO3 (LSMO)/NiO with unique perpendicular exchange bias properties on a flexible mica substrate. Utilization of multiple sets of buffer layers has been attempted to overcome the large mismatch between the film and the substrate and to achieve high-quality nanocomposite growth on mica. Exchange bias of ∼200 and ∼140 Oe for the applied magnetic field perpendicular and parallel to the film surface, respectively, has been achieved and attributed to the strongly coupled vertical ferromagnetic/antiferromagnetic interfaces. Such nanocomposite thin films exhibit excellent structural robustness and reliability under a cyclic bending test. This work demonstrates the enormous potential of integrating complex two-phase multifunctional oxides on mica for future flexible wearable personal devices.

Correlation function: biasing and fractal properties of the cosmic web

Aims. Our goal is to determine how the spatial correlation function of galaxies describes biasing and fractal properties of the cosmic web. Methods. We calculated spatial correlation functions of galaxies, ξ(r), structure functions, g(r) = 1 + ξ(r), gradient functions, γ(r) = d log g(r)/d log r, and fractal dimension functions, D(r) = 3 + γ(r), using dark matter particles of the biased Λ cold dark matter (CDM) simulation, observed galaxies of the Sloan Digital Sky Survey (SDSS), and simulated galaxies of the Millennium and EAGLE simulations. We analysed how these functions describe fractal and biasing properties of the cosmic web. Results. The correlation functions of the biased ΛCDM model samples at small distances (particle and galaxy separations), r ≤ 2.25 h−1 Mpc, describe the distribution of matter inside dark matter halos. In real and simulated galaxy samples, only the brightest galaxies in clusters are visible, and the transition from clusters to filaments occurs at a distance r ≈ 0.8−1.5 h−1 Mpc. At larger separations, the correlation functions describe the distribution of matter and galaxies in the whole cosmic web. The effective fractal dimension of the cosmic web is a continuous function of the distance (separation). Real and simulated galaxies of low luminosity, Mr ≥ −19, have almost identical correlation lengths and amplitudes, indicating that dwarf galaxies are satellites of brighter galaxies, and do not form a smooth population in voids. Conclusions. The combination of several physical processes (e.g. the formation of halos along the caustics of particle trajectories and the phase synchronisation of density perturbations on various scales) transforms the initial random density field to the current highly non-random density field. Galaxy formation is suppressed in voids, which increases the amplitudes of correlation functions and power spectra of galaxies, and increases the large-scale bias parameter. The combined evidence leads to the large-scale bias parameter of L⋆ galaxies the value b⋆ = 1.85 ± 0.15. We find r0(L⋆) = 7.20 ± 0.19 for the correlation length of L⋆ galaxies.

Dielectric, piezoelectric and dc bias characteristics of Bi-doped PZT multilayer ceramic actuator

In this study, the microstructure, dielectric, piezoelectric and dc bias properties of the Bi-doped PZT (PZTB) multilayer piezoelectric actuators, have been discussed. The thickness of the dielectric monolayer was about 70 μm, and a relatively uniform distribution of the grain size was developed. The application of a large dc bias field resulted in a decrease in the dielectric constant and dielectric loss of the multilayer piezoelectric ceramics. The shift of the Curie temperature (Tc) towards a higher value and stabilized temperature-dependent dielectric properties were observed under the dc bias field. The increase in parameter γ suggested the enhancement of the degree of diffuseness of the phase transition. The large displacement reached 0.11% of the thickness of the multilayer piezoelectric ceramic under a low driving voltage of 150 V, indicating the potential of commercial applications of the studied multilayer piezoelectric actuators. This study provides specific experimental data to evaluate the practical application of the Bi-doped PZT multilayer piezoelectric actuators.

Influence of grain boundaries, interface roughness and non-collinear magnetic configurations in the AF layer on the exchange bias properties of F/AF nanodots: A numerical investigation

Motivated by the insufficient understanding of the drastic effects of the disordered interfacial phases at the interface in F/AF bilayers on the exchange bias properties, we consider a realistic model for F/AF nanodots which accounts for roughness at the interface, grain boundaries (GB) and magnetic frustration in the AF layer. Then, we investigate the effect of the magnetic disorder at the F/AF interface on the exchange field by means of Monte Carlo simulations. First, using a simplified model based on a perfect crystal structure (without GB) and with an ideal interface, our results indicate that non-collinear ordered magnetic configuration at the F/AF interface (due to frustration) causes a small decrease of the exchange field (about few dozen percent). Including GB in the model, we show that this kind of structural disorder produces disordered magnetic configurations with domains at the F/AF interface. This magnetic disorder does not lead to a decrease of the exchange field compared to the perfect crystal but makes the AF layer more stable. Our study also reveals that adding roughness in the form of a percentage of mixed F and AF moments in the interfacial layer results in a more significant decrease of the exchange field (two orders of magnitude) leading to values which are comparable to experimental values.

PET imaging of neurotransmission using direct parametric reconstruction

Receptor ligand-based dynamic Positron Emission Tomography (PET) permits the measurement of neurotransmitter release in the human brain. For single-scan paradigms, the conventional method of estimating changes in neurotransmitter levels relies on fitting a pharmacokinetic model to activity concentration histories extracted after PET image reconstruction. However, due to the statistical fluctuations of activity concentration data at the voxel scale, parametric images computed using this approach often exhibit low signal-to-noise ratio, impeding characterization of neurotransmitter release. Numerous studies have shown that direct parametric reconstruction (DPR) approaches, which combine image reconstruction and kinetic analysis in a unified framework, can improve the signal-to-noise ratio of parametric mapping. However, there is little experience with DPR in imaging of neurotransmission and the performance of the approach in this application has not been evaluated before in humans. In this report, we present and evaluate a DPR methodology that computes 3-D distributions of ligand transport, binding potential (BPND) and neurotransmitter release magnitude (γ) from a dynamic sequence of PET sinograms. The technique employs the linear simplified reference region model (LSRRM) of Alpert et al. (2003), which represents an extension of the simplified reference region model that incorporates time-varying binding parameters due to radioligand displacement by release of neurotransmitter. Estimation of parametric images is performed by gradient-based optimization of a Poisson log-likelihood function incorporating LSRRM kinetics and accounting for the effects of head movement, attenuation, detector sensitivity, random and scattered coincidences. A 11C-raclopride simulation study showed that the proposed approach substantially reduces the bias and variance of voxel-wise γ estimates as compared to standard methods. Moreover, simulations showed that detection of release could be made more reliable and/or conducted using a smaller sample size using the proposed DPR estimator. Likewise, images of BPND computed using DPR had substantially improved bias and variance properties. Application of the method in human subjects was demonstrated using 11C-raclopride dynamic scans and a reward task, confirming the improved quality of the estimated parametric images using the proposed approach.

Modeling OFF-state harmonics in MOS transistors used as RF switches

In this paper we identify the dominant contribution to the OFF-state distortion of an MOS transistor coming from the overlap regions applying of device-level harmonic balance simulations in a calibrated TCAD setup. Both overlap (Cgs,Cgd) and direct capacitance (Cds) are described with novel physics-based models that are, in conjunction with an analytical Volterra series analysis, capable of describing the harmonics. This includes the dependency on process and device design parameters such as gate oxide thickness, DC gate bias and doping profile properties in the gate overlap region. The new model is compared to the overlap models of BSIM and EKV showing superior accuracy in modeling the harmonics qualitatively and quantitatively (within <3 dBm for the second and <5 dBm for the third harmonic, respectively).

In-situ study of exchange-bias in interlayer coupled Co/CoO/Co trilayer structure

Exchange bias (EB) properties of Co/CoO/Co trilayer structure with increasing top Co layer thickness has been studied in-situ using magneto-optic Kerr effect (MOKE) and reflection high energy electron diffraction (RHEED). Intermediate native oxide layer (CoO) was prepared by thermal oxidation of the bottom Co layer (Cobot), whereas the trilayer structure was formed by subsequent deposition of another Co layer (Cotop) on top. Two step magnetization reversal of trilayer at room temperature (RT) is observed due to the different coercive field (Hc) contribution of Cobot and Cotop layers. Individual contribution of magnetization reversal of both Co layers at RT as well as field cooled (FC) conditions is separated out by fitting the magnetic hysteresis loops using an adequate mathematical function. Increasing thickness of Cotop on Cobot/CoO bilayer is found to affect EB and Hc of the Cobot layer significantly. With an addition of 4 nm thick Cotop layer, EB corresponding to a Cobot layer is reduced by about 20% when FC to 153 K. Whereas, a further increase in Cotop layer thickness is found to be responsible for the relatively slow reduction in EB. Unusual asymmetric magnetization reversal with single step coherent rotation in descending branch and two step reversal in ascending branch of the loop after field cooling below Néel temperature (TN) was understood in terms of the combined effect of interface induced EB and interlayer coupling (IC) between Cotop and Cobot layers. The present study, where all the measurements are performed in-situ just after each step of deposition on the same film, provided genuine information about the EB in interlayer coupled spin valve like Co, CoO based trilayer structure.

Nonreciprocity in acoustic and elastic materials

The law of reciprocity in acoustics and elastodynamics codifies a relation of symmetry between action and reaction in fluids and solids. In its simplest form, it states that the frequency-response functions between any two material points remain the same after swapping source and receiver, regardless of the presence of inhomogeneities and losses. As such, reciprocity has enabled numerous applications that make use of acoustic and elastic wave propagation. A recent change in paradigm has prompted us to see reciprocity under a new light: as an obstruction to the realization of wave-bearing media in which the source and receiver are not interchangeable. Such materials may enable the creation of devices such as acoustic one-way mirrors, isolators and topological insulators. Here, we review how reciprocity breaks down in materials with momentum bias, structured space-dependent and time-dependent constitutive properties, and constitutive nonlinearity, and report on recent advances in the modelling and fabrication of these materials, as well as on experiments demonstrating nonreciprocal acoustic and elastic wave propagation therein. The success of these efforts holds promise to enable robust, unidirectional acoustic and elastic wave-steering capabilities that exceed what is currently possible in conventional materials, metamaterials or phononic crystals. Nonreciprocal acoustic and elastic wave propagation may enable the creation of devices such as acoustic one-way mirrors, isolators and topological insulators. This Review presents advances in the creation of materials that break reciprocity and realize robust, unidirectional acoustic and elastic wave steering.

Bias Correction With Jackknife, Bootstrap, and Taylor Series

We analyze bias correction methods using jackknife, bootstrap, and Taylor series. We focus on the binomial model, and consider the problem of bias correction for estimating $f(p)$ , where $f \in C[{0,1}]$ is arbitrary. We characterize the supremum norm of the bias of general jackknife and bootstrap estimators for any continuous functions, and demonstrate the in delete- $d$ jackknife, different values of $d$ may lead to drastically different behaviors in jackknife. We show that in the binomial model, iterating the bootstrap bias correction infinitely many times may lead to divergence of bias and variance, and demonstrate that the bias properties of the bootstrap bias corrected estimator after $r-1$ rounds are of the same order as that of the $r$ -jackknife estimator if a bounded coefficients condition is satisfied.

Characterization of agonist-dependent somatostatin receptor subtype 2 trafficking in neuroendocrine cells.

Somatostatin (SOM) receptor subtype 2 (SSTR2) is the major receptor subtype mediating SOM effects throughout the neuraxis. We previously demonstrated that the non-selective agonist [D-Trp8]-SOM induces intracellular sequestration of SSTR2, whereas this receptor is maintained at the cell surface after treatment with the SSTR2-selective agonist L-779,976 in cells co-expressing SSTR2 and SSTR5. In this study, we knocked-out SSTR5 in AtT20 cells endogenously expressing both SSTR2 and SSTR5 and used immuno-labeling and confocal microscopy to investigate the effect of SSTR5 on regulation of SSTR2 trafficking. Our results indicate that unlike [D-Trp8]-SOM-induced intracellular sequestration, L-779,976 stimulation results in the maintenance of SSTR2 at the cell surface regardless of whether SSTR5 is present or not. We then examined the trafficking pathways of SSTR2 upon stimulation by either agonist. We found that both [D-Trp8]-SOM and L-779,976 induce SSTR2 internalization via transferrin-positive vesicles. However, SSTR2 internalized upon L-779,976 treatment undergoes rapid recycling to the plasma membrane, whereas receptors internalized by [D-Trp8]-SOM recycle slowly after washout of the agonist. Furthermore, [D-Trp8]-SOM stimulation induces degradation of a fraction of internalized SSTR2 whereas L-779,976-dependent, rapid SSTR2 recycling appears to protect internalized SSTR2 from degradation. In addition, Octreotide which has preferential SSTR2 affinity, induced differential effects on both SSTR2 trafficking and degradation. Our results indicate that the biased agonistic property of L-779,976 protects against SSTR2 surface depletion by rapidly initiating SSTR2 recycling while SSTR5 does not regulate L-779-976-dependent SSTR2 trafficking.

Variable Sampling Interval Control Charts for Monitoring the Ratio of Two Normal Variables

Abstract It is well documented that the distribution of the ratio between two random normal variables is asymmetric. As a consequence, the two-sided Shewhart control chart monitoring this ratio (denoted as Shewhart-RZ chart) has an average run length–biased property. In order to overcome this drawback, we propose in this article a design for two separated one-sided control charts. Furthermore, we improve the efficiency of the Shewhart-RZ chart by combining the Shewhart chart with a variable sampling interval strategy. The chart performance is then evaluated by using an average time to signal. The numerical results show that our proposed charts outperform the two-sided Shewhart-RZ control chart in detecting process shifts.

Ferrimagnetism, exchange bias and spin-glass property of disordered La2CrNiO6

A B-site disordered double perovskite La2CrNiO6 is synthesized by citrate-gel combustion method. The material is showing an orthorhombic Pbnm crystal structure which doesn’t change upon cooling to 15 K and with an applied field of 50 kOe. Upon cooling from room-temperature to 15 K, a nearly linear dependence in the volume of the crystal structure with temperature is observed until 100 K, which is around the ferrimagnetic transition temperature of the material. The disordered Cr and Mn introduced inhomogeneous magnetic interactions, leading to Griffiths-like phase (GP) and spin glass state in the material. The material shows an exchange bias effect, owing to the GP and spin glass state. Mott’s variable range hopping is the conduction mechanism of the insulating disordered double perovskite La2CrNiO6.

Corticotropin-Releasing Factor Family: A Stress Hormone-Receptor System's Emerging Role in Mediating Sex-Specific Signaling.

No organ in the body is impervious to the effects of stress, and a coordinated response from all organs is essential to deal with stressors. A dysregulated stress response that fails to bring systems back to homeostasis leads to compromised function and ultimately a diseased state. The components of the corticotropin-releasing factor (CRF) family, an ancient and evolutionarily conserved stress hormone-receptor system, helps both initiate stress responses and bring systems back to homeostasis once the stressors are removed. The mammalian CRF family comprises of four known agonists, CRF and urocortins (UCN1–3), and two known G protein-coupled receptors (GPCRs), CRF1 and CRF2. Evolutionarily, precursors of CRF- and urocortin-like peptides and their receptors were involved in osmoregulation/diuretic functions, in addition to nutrient sensing. Both CRF and UCN1 peptide hormones as well as their receptors appeared after a duplication event nearly 400 million years ago. All four agonists and both CRF receptors show sex-specific changes in expression and/or function, and single nucleotide polymorphisms are associated with a plethora of human diseases. CRF receptors harbor N-terminal cleavable peptide sequences, conferring biased ligand properties. CRF receptors have the ability to heteromerize with each other as well as with other GPCRs. Taken together, CRF receptors and their agonists due to their versatile functional adaptability mediate nuanced responses and are uniquely positioned to orchestrate sex-specific signaling and function in several tissues.

Growth index of matter perturbations in the light of Dark Energy Survey

We study how the cosmological constraints from growth data are improved by including the measurements of bias from Dark Energy Survey (DES). In particular, we utilize the biasing properties of the DES Luminous Red Galaxies (LRGs) and the growth data provided by the various galaxy surveys in order to constrain the growth index (gamma ) of the linear matter perturbations. Considering a constant growth index we can put tight constraints, up to sim 10% accuracy, on gamma . Specifically, using the priors of the Dark Energy Survey and implementing a joint likelihood procedure between theoretical expectations and data we find that the best fit value is in between gamma =0.64pm 0.075 and 0.65pm 0.063. On the other hand utilizing the Planck priors we obtain gamma =0.680pm 0.089 and 0.690pm 0.071. This shows a small but non-zero deviation from General Relativity (gamma _{mathrm{GR}}approx 6/11), nevertheless the confidence level is in the range sim 1.3-2sigma . Moreover, we find that the estimated mass of the dark-matter halo in which LRGs survive lies in the interval sim 6.2 times 10^{12} h^{-1} M_{odot } and 1.2 times 10^{13} h^{-1} M_{odot }, for the different bias models. Finally, allowing gamma to evolve with redshift [Taylor expansion: gamma (z)=gamma _{0}+gamma _{1}z/(1+z)] we find that the (gamma _{0},gamma _{1}) parameter solution space accommodates the GR prediction at sim 1.7-2.9sigma levels.

The endomorphin-1/2 and dynorphin-B peptides display biased agonism at the mu opioid receptor.

Opioid agonist activation at the mu opioid receptor (MOR) can lead to a wide variety of physiological responses. Many opioid agonists share the ability to selectively and preferentially activate specific signaling pathways, a term called biased agonism. Biased opioid ligands can theoretically induce specific physiological responses and might enable the generation of drugs with improved side effect profiles. Dynorphins, enkephalins, and endomorphins are endogenous opioid agonist peptides that may possess distinct bias profiles; biased agonism of endogenous peptides could explain the selective roles of these ligands in vivo. Our purpose in the present study was to investigate biased signaling and potential underlying molecular mechanisms of bias using 35S-GTPγS and cAMP assays, specifically focusing on the role of adenylyl cyclases (ACs) and regulators of G-protein signaling proteins (RGSs) in CHO, N2a, and SH-SY5Y cell lines, all expressing the human MOR. We found that endomorphin-1/2 preferentially activated cAMP signaling, while dynorphin-B preferentially activated 35S-GTPγS signaling in most cell lines. Experiments carried out in the presence of an isoform selective RGS-4 inhibitor, and siRNA knockdown of AC6 in N2a cells did not significantly affect the bias properties of endomorphins, suggesting that these proteins may not play a role in endomorphin bias. We found that endomorphin-1/2 and dynorphin-B displayed contrasting bias profiles at the MOR, and ruled out potential AC6 and RGS4 mechanisms in this bias. This identified signaling bias could be involved in specifying endogenous peptide roles in vivo, where these peptides have low selectivity between opioid receptor family members.