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A fundamental challenge in neuroengineering is determining a proper artificial input to a sensory system that yields the desired perception. In neuroprosthetics, this process is known as artificial sensory encoding, and it holds a crucial role in prosthetic devices restoring sensory perception in individuals with disabilities. For example, in visual prostheses, one key aspect of artificial image encoding is to downsample images captured by a camera to a size matching the number of inputs and resolution of the prosthesis. Here, we show that downsampling an image using the inherent computation of the retinal network yields better performance compared to learning-free downsampling methods. We have validated a learning-based approach (actor-model framework) that exploits the signal transformation from photoreceptors to retinal ganglion cells measured in explanted mouse retinas. The actor-model framework generates downsampled images eliciting a neuronal response in-silico and ex-vivo with higher neuronal reliability than the one produced by a learning-free approach. During the learning process, the actor network learns to optimize contrast and the kernel’s weights. This methodological approach might guide future artificial image encoding strategies for visual prostheses. Ultimately, this framework could be applicable for encoding strategies in other sensory prostheses such as cochlear or limb. Encoding and downsampling images is key for visual prostheses. Here, the authors show that an actor-model framework using the inherent computation of the retinal network yields better performance in downsampling images compared to learning-free methods.

Protein C (PC) is a vitamin K-dependent factor that plays a crucial role in controlling anticoagulant processes and acts as a cytoprotective agent to promote cell survival. Several mutations in human PC are associated with decreased protein production or altered protein structure, resulting in PC deficiency. In this study, we conducted a comprehensive analysis of nonsynonymous single nucleotide polymorphisms in human PC to prioritize and confirm the most high-risk mutations predicted to cause disease. Of the 340 missense mutations obtained from the NCBI database, only 26 were classified as high-risk mutations using various bioinformatic tools. Among these, we identified that 12 mutations reduced the stability of protein, and thereby had the greatest potential to disturb protein structure and function. Molecular dynamics simulations revealed moderate alterations in the structural stability, flexibility, and secondary structural organization of the serine protease domain of human PC for five missense mutations (L305R, W342C, G403R, V420E, and W444C) when compared to the native structure that could maybe influence its interaction with other molecules. Protein-protein interaction analyses demonstrated that the occurrence of these five mutations can affect the regular interaction between PC and activated factor V. Therefore, our findings assume that these mutants can be used in the identification and development of therapeutics for diseases associated with PC dysfunction, although assessment the effect of these mutations need to be proofed in in-vitro.

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working in this field, which has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.

The orexin/hypocretin receptor type 1 (OX1R) plays a crucial role in regulating various physiological functions, especially feeding behavior, addiction, and reward. Genetic variations in the OX1R have been associated with several neurological disorders. In this study, we utilized a combination of sequence and structure-based computational tools to identify the most deleterious missense single nucleotide polymorphisms (SNPs) in the OX1R gene. Our findings revealed four highly conserved and structurally destabilizing missense SNPs, namely R144C, I148N, S172W, and A297D, located in the GTP-binding domain. Molecular dynamics simulations analysis demonstrated that all four most detrimental mutant proteins altered the overall structural flexibility and dynamics of OX1R protein, resulting in significant changes in the structural organization and motion of the protein. These findings provide valuable insights into the impact of missense SNPs on OX1R function loss and their potential contribution to the development of neurological disorders, thereby guiding future research in this field.

The output of physical systems, such as the scrambled pattern formed by shining the spot of a laser pointer through fog, is often easily accessible by direct measurements. However, selection of the input of such a system to obtain a desired output is difficult, because it is an ill-posed problem; that is, there are multiple inputs yielding the same output. Information transmission through scattering media is an example of this problem. Machine learning approaches for imaging have been implemented very successfully in photonics to recover the original input phase and amplitude objects of the scattering system from the distorted intensity diffraction pattern outputs. However, controlling the output of such a system, without having examples of inputs that can produce outputs in the class of the output objects the user wants to produce, is a challenging problem. Here, we propose an online learning approach for the projection of arbitrary shapes through a multimode fibre when a sample of intensity-only measurements is taken at the output. This projection system is nonlinear, because the intensity, not the complex amplitude, is detected. We show an image projection fidelity as high as ~90%, which is on par with the gold-standard methods that characterize the system fully by phase and amplitude measurements. The generality and simplicity of the proposed approach could potentially provide a new way of target-oriented control in real-world applications when only partial measurements are available. Machine learning has become popular in solving complex optical problems such as recovering the input phase and amplitude for a specific pattern or image measured through a scattering medium. In a more challenging application, Rahmani et al. consider the problem of also producing desired outputs for such a nonlinear system when only some intensity-only measurements of example outputs are available. They develop a neural network approach that can ensure the transmission of images through a highly nonlinear system—a multimode fibre—with a 90% fidelity.

Multimode fibers (MMF) were initially developed to transmit digital information encoded in the time domain. There were few attempts in the late 60s and 70s to transmit analog images through MMF. With the availability of digital spatial modulators, practical image transfer through MMFs has the potential to revolutionize medical endoscopy. Because of the fiber’s ability to transmit multiple spatial modes of light simultaneously, MMFs could, in principle, replace the millimeters-thick bundles of fibers currently used in endoscopes with a single fiber, only a few hundred microns thick. That, in turn, could potentially open up new, less invasive forms of endoscopy to perform high-resolution imaging of tissues out of reach of current conventional endoscopes. Taking endoscopy by its general meaning as , we review in this paper novel ways of imaging and transmitting images using a machine learning approach. Additionally, we review recent work on using MMF to perform machine learning tasks. The advantages and disadvantages of using machine learning instead of conventional methods is also discussed. Methods of imaging in scattering media and particularly MMFs involves measuring the phase and amplitude of the electromagnetic wave, coming out of the MMF and using these measurements to infer the relationship between the input and the output of the MMF. Most notable techniques include analog phase conjugation [A. Yariv, “On transmission and recovery of three-dimensional image information in optical waveguides,” , vol. 66, no. 4, pp. 301–306, 1976; A. Gover, C. Lee, and A. Yariv, “Direct transmission of pictorial information in multimode optical fibers,” , vol. 66, no. 4, pp. 306–311, 1976; G. J. Dunning and R. Lind, “Demonstration of image transmission through fibers by optical phase conjugation,” , vol. 7, no. 11, pp. 558–560, 1982; A. Friesem, U. Levy, and Y. Silberberg, “Parallel transmission of images through single optical fibers,” , vol. 71, no. 2, pp. 208–221, 1983], digital phase conjugation [I. N. Papadopoulos, S. Farahi, C. Moser, and D. Psaltis, “Focusing and scanning light through a multimode optical fiber using digital phase conjugation,” , vol. 20, no. 10, pp. 10583–10590, 2012; I. N. Papadopoulos, S. Farahi, C. Moser, and D. Psaltis, “High-resolution, lensless endoscope based on digital scanning through a multimode optical fiber,” , vol. 4, no. 2, pp. 260–270, 2013] or the full-wave holographic transmission matrix method. The latter technique, which is the current gold standard, measures both the amplitude and phase of the output patterns corresponding to multiple input patterns to construct a matrix of complex numbers relaying the input to the output [Y. Choi, et al., “Scanner-free and wide-field endoscopic imaging by using a single multimode optical fiber,” , vol. 109, no. 20, p. 203901, 2012; A. M. Caravaca-Aguirre, E. Niv, D. B. Conkey, and R. Piestun, “Real-time resilient focusing through a bending multimode fiber,” , vol. 21, no. 10, pp. 12881–12887; R. Y. Gu, R. N. Mahalati, and J. M. Kahn, “Design of flexible multi-mode fiber endoscope,” , vol. 23, no. 21, pp. 26905–26918, 2015; D. Loterie, S. Farahi, I. Papadopoulos, A. Goy, D. Psaltis, and C. Moser, “Digital confocal microscopy through a multimode fiber,” , vol. 23, no. 18, pp. 23845–23858, 2015]. This matrix is then used for imaging of the inputs or projection of desired patterns. Other techniques rely on iteratively optimizing the pixel value of the input image to perform a particular task (such as focusing or displaying an image) [R. Di Leonardo and S. Bianchi, “Hologram transmission through multi-mode optical fibers,” , vol. 19, no. 1, pp. 247–254, 2011; T. Čižmár and K. Dholakia, “Shaping the light transmission through a multimode optical fibre: complex transformation analysis and applications in biophotonics,” , vol. 19, no. 20, pp. 18871–18884, 2011; T. Čižmár and K. Dholakia, “Exploiting multimode waveguides for pure fibre-based imaging,” , vol. 3, no. 1, pp. 1–9, 2012; S. Bianchi and R. Di Leonardo, “A multi-mode fiber probe for holographic micromanipulation and microscopy,” , vol. 12, no. 3, pp. 635–639, 2012; E. R. Andresen, G. Bouwmans, S. Monneret, and H. Rigneault, “Toward endoscopes with no distal optics: video-rate scanning microscopy through a fiber bundle,” , vol. 38, no. 5, pp. 609–611, 2013].

Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wavefront propagates along its length is highly sensitive to wavelength changes. We use a tunable laser to implement a wavelength drift with a controlled bandwidth, aiming to estimate the DNN’s performance in different cases and identify the limitations. We find that when the DNNs are trained with a dataset which includes the noise induced by wavelength changes, successful classification of a speckle pattern can be performed even for a large wavelength bandwidth drift. A single training step is found to be sufficient for high classification accuracy, removing the need for time-consuming recalibration at each wavelength.

Background Cytochrome P450 4F2 (CYP4F2) enzyme is a member of the CYP4 family responsible for the metabolism of fatty acids, therapeutic drugs, and signaling molecules such as arachidonic acid, tocopherols, and vitamin K. Several reports have demonstrated that the missense variant CYP4F2*3 (V433M) causes decreased activity of CYP4F2 and inter-individual variations in warfarin dose in different ethnic groups. However, the molecular pathogenicity mechanism of missense V433M in CYP4F2 at the atomic level has not yet been completely elucidated. Methods and results In the current study, we evaluated the effect of the V433M substitution on CYP4F2 using 14 different bioinformatics tools. Further molecular dynamics (MD) simulations were performed to assess the impact of the V433M mutation on the CYP4F2 protein structure, stability, and dynamics. In addition, molecular docking was used to illustrate the effect of V433M on its interaction with vitamin K1. Based on our results, the CYP4F2*3 variant was a damaging amino acid substitution with a destabilizing nature. The simulation results showed that missense V433M affects the dynamics and stability of CYP4F2 by reducing its compactness and stability, which means that it tends to change the overall structural conformation and flexibility of CYP4F2. The docking results showed that the CYP4F2*3 variant decreased the binding affinity between vitamin K1 and CYP4F2, which reduced the activity of CYP4F2*3 compared to native CYP4F2. Conclusions This study determined the molecular pathogenicity mechanism of the CYP4F2*3 variant on the human CYP4F2 protein and provided new information for understanding the structure-function relationship of CYP4F2 and other CYP4 enzymes. These findings will aid in the development of effective drugs and treatment options.

Purpose: Due to the potential industrial and therapeutic applications of the yeast exopolysaccharides (EPSs), there has been an increasing demand to assess these biopolymers with improved characteristics. This study aimed to characterize the EPSs from Rhodosporidium babjevae (ATCC 90942 and IBRC-M 30088) as well as to evaluate their possible antioxidant, emulsifying and antiproliferative activities. Methods: Rhodosporidium babjevae was cultured for 5 days and following isolation of supernatant, EPSs precipitated with adding of cold absolute ethanol and freeze-dried. The EPSs chemical structure was determined by FT-IR, SEM, HPLC-SEC and GC-MS. Additionally the solubility, water holding capacity and emulsifying activity of EPSs were evaluated. In vitro, antioxidant activity was investigated against DPPH, superoxide and hydroxyl radicals. Finally the EPSs consequence on the cell proliferation of human breast adenocarcinoma (MCF-7) and Madin-Darby canine kidney (MDCK) cell lines was evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test. Results: R. babjevae excreted 1.6±0.2 g/L of the EPSs. The EPSs had three fractions with molecular weights of 1.02 ×106 , 5×105 and 2×105 Da. Mannose and glucose were found as the main monosaccharides of the EPSs (84:16 mol%, respectively). The EPSs exhibited emulsifying activity on sun flower oil. The scavenging activities were found to be dose-dependent and higher than hyaluronic acid. Significant difference among the EPSs treatments on the proliferation of MCF-7 and MDCK cell lines was not observed (P>0.05). Conclusion: These results show the interesting potential of the EPSs from R. babjevae as biocompatible compounds for using in food and pharmaceutical fields.

Hepatocellular carcinoma (HCC) is one of the leading fatal neoplasms and the most common primary liver malignancy worldwide. Peptide hormone ANGPTL8 (betatrophin) may act as an important regulator in HCC development through the Wnt/β-catenin pathway. We aimed to evaluate the effects of recombinant ANGPTL8 on Wnt/β-catenin signaling in human liver carcinoma cells (HepG2) and their viability. The expression of ANGPTL8 was conducted in the pET-21b-E. coli Bl21 (DE3) system and the produced peptide was purified. HepG2 cells were treated with different concentrations of ANGPTL8 (25, 50, 100, 150, 200, and 250 ng/ml) for 24, 48, and 72 hr. MTT assay was performed to detect the viability of treated cells, and apoptotic induction by ANGPTL8 was measured by flow cytometry assay. Finally, using qRT-PCR the mRNA levels of Wnt signaling modulators WIF-1 and β-catenin were evaluated in treated cells. MTT assay showed that ANGPTL8 inhibits proliferation of HepG2 cells moderately in a time-independent manner. The highest concentration of the ANGPTL8, 250 ng/ml, reduced cell proliferation after 24, 48, and 72 hr similarly about 30%. In the same concentration of ANGPTL8, after 24 hr of treatment flow cytometry assay revealed a mild increase in early and late apoptosis up to 7.7 and 14.3%, respectively. The qRT-PCR showed that in a concentration-dependent and time-independent fashion, the expression of WIF-1 and β-catenin genes respectively increased and decreased significantly ( <0.05). Our findings suggest that ANGPTL8 may act as a moderate suppressor against HCC cell proliferation possibly via affecting Wnt signaling modulators.