In-flow holographic tomography boosts lipid droplet quantification

In their recently published paper in Opto-Electronic Ad-vances,Pietro Ferraro and his colleagues report on a new high-throughput tomographic phase instrument that precisely quantifies intracellular lipid droplets(LDs)1.LDs are lipid storage organelles found in most cell types and play an active role in critical biological pro-cesses,including energy metabolism,membrane homeo-stasis.

A narrative overview of utilizing biomaterials to recapitulate the salient regenerative features of dental-derived mesenchymal stem cells

Tissue engineering approaches have emerged recently to circumvent many limitations associated with current clinical practices.This elegant approach utilizes a natural/synthetic biomaterial with optimized physiomechanical properties to serve as a vehicle for delivery of exogenous stem cells and bioactive factors or induce local recruitment of endogenous cells for in situ tissue regeneration.Inspired by the natural microenvironment,biomaterials could act as a biomimetic three-dimensional(3D)structure to help the cells establish their natural interactions.Such a strategy should not only employ a biocompatible biomaterial to induce new tissue formation but also benefit from an easily accessible and abundant source of stem cells with potent tissue regenerative potential.The human teeth and oral cavity harbor various populations of mesenchymal stem cells(MSCs)with self-renewing and multilineage differentiation capabilities.In the current review article,we seek to highlight recent progress and future opportunities in dental MSC-mediated therapeutic strategies for tissue regeneration using two possible approaches,cell transplantation and cell homing.Altogether,this paper develops a general picture of current innovative strategies to employ dental-derived MSCs combined with biomaterials and bioactive factors for regenerating the lost or defective tissues and offers information regarding the available scientific data and possible applications.

Evolution of superconductivity and charge order in pressurized RbV_(3)Sb_(5)

The kagome metals AV_(3)Sb_(5)(A=K,Rb,Cs)under ambient pressure exhibit an unusual charge order,from which superconductivity emerges.In this work,by applying hydrostatic pressure using a liquid pressure medium and carrying out electrical resistance measurements for RbV_(3)Sb_(5),we find that the charge order becomes suppressed under a modest pressure pc(1.4 GPa<pc<1.6 GPa),while the superconducting transition temperature Tc is maximized.Tc is then gradually weakened with further increase of pressure and reaches a minimum around 14.3 GPa,before exhibiting another{maximum}around 22.8 GPa,signifying the presence of a second superconducting dome.Distinct normal state resistance anomalies are found to be associated with the second superconducting dome,similar to KV_(3)Sb_(5).Our findings point to qualitatively similar temperature-pressure phase diagrams in KV_(3)Sb_(5) and RbV_(3)Sb_(5),{and suggest a close link}between the second superconducting dome and the high-pressure resistance anomalies.

Emerging Advances to Transform Histopathology Using Virtual Staining

In an age where digitization is widespread in clinical and preclinical workflows,pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides.Over the last decade,new high throughput digital scanning microscopes have ushered in the era of digital pathology that,along with recent advances in machine vision,have opened up new possibilities for Computer-Aided-Diagnoses.Despite these advances,the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption.Here,we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology.

Applications of circulating tumor cells for prostate cancer

One of the major challenges that clinicians face is in the difficulties of accurately monitoring disease progression.Prostate cancer is among these diseases and greatly affects the health of men globally.Circulating tumor cells(CTCs)are a rare population of cancer cells that have shed from the primary tumor and entered the peripheral circulation.Not until recently,clinical applications of CTCs have been limited to using enumeration as a prognostic tool in Oncology.However,advances in emerging CTC technologies point toward new applications that could revolutionize the field of prostate cancer.It is now possible to study CTCs as components of a liquid biopsy based on morphological phenotypes,biochemical analyses,and genomic profiling.These advances allow us to gain insight into the heterogeneity and dynamics of cancer biology and to further study the mechanisms behind the evolution of therapeutic resistance.These recent developments utilizing CTCs for clinical applications will greatly impact the future of prostate cancer research and pave the way towards personalized care for men.

Surface-induced orbital-selective band reconstruction in kagome superconductor CsV_(3)Sb_(5)

The two-dimensional(2 D)kagome superconductor Cs V_(3)Sb_(5) has attracted much recent attention due to the coexistence of superconductivity,charge orders,topology and kagome physics,which manifest themselves as distinct electronic structures in both bulk and surface states of the material.An interesting next step is to manipulate the electronic states in this system.Here,we report angle-resolved photoemission spectroscopy(ARPES)evidence for a surface-induced orbitalselective band reconstruction in Cs V_(3)Sb_(5).A significant energy shift of the electron-like band aroundΓand a moderate energy shift of the hole-like band around M are observed as a function of time.This evolution is reproduced in a much shorter time scale by in-situ annealing of the Cs V_(3)Sb_(5) sample.Orbital-resolved density functional theory(DFT)calculations reveal that the momentum-dependent band reconstruction is associated with different orbitals for the bands aroundΓand M,and the time-dependent evolution points to the change of sample surface that is likely caused by the formation of Cs vacancies on the surface.Our results indicate the possibility of orbital-selective control of the band structure via surface modification,which may open a new avenue for manipulating exotic phenomena in this material system,including superconductivity.

Realization of a novel Ag/SnO_2 electrical contact material with microscopic fiber-like structure

According to the principle that fiber-like arrangement of reinforcing particles SnO2 paralleling to the direction of current is propitious to the electrical and mechanical performance of the electrical contact materials, we proposed and reported a novel precursor route used to prepare Ag/SnO2 electrical contact material with fiberlike arrangement of reinforcing nanoparticles. The mechanism for the formation of fiber-like arrangement of reinforcing nanoparticles in Ag/SnO2 electrical contact material was also discussed. The as-prepared samples were characterized by means of scanning electron microscope (SEM),optical microscope (OM),energy-dispersive X-ray spectroscopy (EDX),MHV2000 microhardness test,and double bridge tester. The analysis showed that the as-prepared Ag/SnO2 electrical contact material with fiber-like arrangement of reinforcing nanoparticles exhibits a high elongation of 24 %,a particularly low electrical resistivity of 2.08 μΩ· cm,and low arcing energy,and thus has considerable technical,economical and environmental benefits.

Study on NDRG3 in Mesenchyma of Prostate

Objective To study expression of NDRG3 in prostatic mesenchyma and effect of exogenous NDRG3 on prostatic stromal cells. Methods Immunohistochemical analysis was used to check expression of NDRG3 in prostate mesnenchyma. The WPMY-1 prostate immortalized mesenchyma cell line was stably-transfected with a NDRG3 gene expression vector. The NDRG3-stable transfected WPMY-1 sublines were studied along with parental and empty vector transfected WPMY-1 cells as controls. RT-PCR technology was applied to identity downstream gene expression under regulation of NDRG3 expression.Results Expression of DNRG3 was observed in prostate cancer mesenchyma, over-expression of NDRG3 in WPMY-1 cell up-regulated expression of chemotatic factors-CXCL3 and CXCL5. Conclusion Expression of stromal NDRG3 in prostate cancer specimens is significantly higher than that in benign prostatic hypertrophy （BPH） sample. There is a remarkable difference between the two groups of samples, NDRG3 may be related to angiogenesis in prostatic mesenchyma.

Evading the host response:Staphylococcus“hiding”in cortical bone canalicular system causes increased bacterial burden

Extremity reconstruction surgery is increasingly performed rather than amputation for patients with large-segment pathologic bone loss.Debate persists as to the optimal void filler for this“limb salvage”surgery,whether metal or allograft bone.Clinicians focus on optimizing important functional gains for patients,and the risk of devastating implant infection has been thought to be similar regardless of implant material.Recent insights into infection pathophysiology are challenging this equipoise,however,with both basic science data suggesting a novel mechanism of infection of Staphylococcus aureus(the most common infecting agent)into the host lacunar–canaliculi network,and also clinical data revealing a higher rate of infection of allograft over metal.The current translational study was therefore developed to bridge the gap between these insights in a longitudinal murine model of infection of allograft bone and metal.Real-time Staphylococci infection characteristics were quantified in cortical bone vs metal,and both microarchitecture of host implant and presence of host immune response were assessed.An orders-of-magnitude higher bacterial burden was established in cortical allograft bone over both metal and cancellous bone.The establishment of immune-evading microabscesses was confirmed in both cortical allograft haversian canal and the submicron canaliculi network in an additional model of mouse femur bone infection.These study results reveal a mechanism by which Staphylococci evasion of host immunity is possible,contributing to elevated risks of infection in cortical bone.The presence of this local infection reservoir imparts massive clinical implications that may alter the current paradigm of osteomyelitis and bulk allograft infection treatment.

Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

The immunohistochemical(IHC)staining of the human epidermal growth factor receptor 2(HER2)biomarker is widely practiced in breast tissue analysis,preclinical studies,and diagnostic decisions,guiding cancer treatment and investigation of pathogenesis.HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist,which typically takes one day to prepare in a laboratory,increasing analysis time and associated costs.Here,we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images,matching the standard HER2 IHC staining that is chemically performed on the same tissue sections.The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis,in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images(WSIs)to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts.A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail,membrane clearness,and absence of staining artifacts with respect to their immunohistochemically stained counterparts.This virtual HER2 staining framework bypasses the costly,laborious,and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

Multifunctional nanotherapeutics for treatment of ocular disease

The eye is an important organ,it provides vision and it is an important component of our facial identity(1,2).There is an old saying“The eye is the window of the soul”,clear and bright eyes bring esthetic pleasure to people.Human eye is globular and consists of two main parts,the anterior and posterior segments(1,2).Although the posterior part of the eye is comfortably located in the orbit,they are delicate because its anterior segment including the cornea is exposed to the outside world,thus accessible to wear and tear.To protect and maintain the eye functions while reduce the disruptions from outside and inside the body,the eyes are equipped with defense mechanisms for both the anterior and posterior segments(1,2).

Observation of Fermi Arcs in Non-Centrosymmetric Weyl Semi-Metal Candidate NbP

We report the surface electronic structure of niobium phosphide NbP single crystal on （001） surface by vacuum ultraviolet angle-resolved photoemission spectroscopy. Combining with our first principle calculations, we identify the existence of the Fermi arcs originated from topological surface states. Furthermore, the surface states exhibit circular dichroism pattern, which may correlate with its non-trivial spin texture. Our results provide critical evidence for the existence of the Weyl Fermions in NbP, which lays the foundation for further research.

Bioceramic scaffolds with two-step internal/external modification of copper-containing polydopamine enhance antibacterial and alveolar bone regeneration capability

Magnesium-doped calcium silicate(CS)bioceramic scaffolds have unique advantages in mandibular defect repair;however,they lack antibacterial properties to cope with the complex oral microbiome.Herein,for the first time,the CS scaffold was functionally modified with a novel copper-containing polydopamine(PDA(Cu^(2+)))rapid deposition method,to construct internally modified(*P),externally modified(@PDA),and dually modified(*P@PDA)scaffolds.The morphology,degradation behavior,and mechanical properties of the obtained scaffolds were evaluated in vitro.The results showed that the CS*P@PDA had a unique micro-/nano-structural surface and appreciable mechanical resistance.During the prolonged immersion stage,the release of copper ions from the CS*P@PDA scaffolds was rapid in the early stage and exhibited long-term sustained release.The in vitro evaluation revealed that the release behavior of copper ions ascribed an excellent antibacterial effect to the CS*P@PDA,while the scaffolds retained good cytocompatibility with improved osteogenesis and angiogenesis effects.Finally,the PDA(Cu^(2+))-modified scaffolds showed effective early bone regeneration in a critical-size rabbit mandibular defect model.Overall,it was indicated that considerable antibacterial property along with the enhancement of alveolar bone regeneration can be imparted to the scaffold by the two-step PDA(Cu^(2+))modification,and the convenience and wide applicability of this technique make it a promising strategy to avoid bacterial infections on implants.

OAM-based diffractive all-optical classification

Object classification is an important aspect of machine intelligence.Current practices in object classification entail the digitization of object information followed by the application of digital algorithms such as deep neural networks.The execution of digital neural networks is power-consuming,and the throughput is limited.The existing von Neumann digital computing paradigm is also less suited for the implementation of highly parallel neural network architectures.^(1)

All-optical image denoising using a diffractive visual processor

Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input images.In general,digital image denoising algorithms,executed on computers,present latency due to several iterations implemented in,e.g.,graphics processing units(GPUs).While deep learning-enabled methods can operate non-iteratively,they also introduce latency and impose a significant computational burden,leading to increased power consumption.Here,we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images–implemented at the speed of light propagation within a thin diffractive visual processor that axially spans<250×λ,whereλis the wavelength of light.This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features,causing them to miss the output image Field-of-View(FoV)while retaining the object features of interest.Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of~30–40%.We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum.Owing to their speed,power-efficiency,and minimal computational overhead,all-optical diffractive denoisers can be transformative for various image display and projection systems,including,e.g.,holographic displays.

Erratum to:Cadmium nanoclusters in a protein matrix:Synthesis,characterization,and application in targeted drug delivery and cellular imaging

The Electronic Supplementary Material available online erroneously only contains the first six pages of the entire supplementary material file.You will find the entire supplementary material file online linked to this publisher’s erratum.The publisher apologizes to the authors and readers for this mistake.Electronic Supplementary Material:Supplementary material(detailed description on procedures,and further characterizations,including synthesis,optimization,and characterization of CdNCs,HA-CdNCs,and DOX-HA-CdNCs;calculating quantum yield;a Jellium model for assigning the most valid number of the atoms in the NCs;drug loading and release;cellular uptake and cytotoxicity)is available in the online version of this article at https://doi.org/10.1007/s12274-016-1201-z.

Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

Large-scale linear operations are the cornerstone for performing complex computational tasks.Using optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and scalability.Previously,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination.We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,respectively.This broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥2NwNiNo.We further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation error.Massively parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

Universal linear intensity transformations using spatially incoherent diffractive processors

Under spatially coherent light,a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view(FOVs)if the total number(N)of optimizable phase-only diffractive features is≥~2N_(i)N_(o),where Ni and No refer to the number of useful pixels at the input and the output FOVs,respectively.Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs.Under spatially incoherent monochromatic light,the spatially varying intensity point spread function(H)of a diffractive network,corresponding to a given,arbitrarily-selected linear intensity transformation,can be written as H(m,n;m′,n′)=|h(m,n;m′,n′)|^(2),where h is the spatially coherent point spread function of the same diffractive network,and(m,n)and(m′,n′)define the coordinates of the output and input FOVs,respectively.Using numerical simulations and deep learning,supervised through examples of input-output profiles,we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N≥~2N_(i)N_(o).We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths,operating simultaneously.Finally,we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination,achieving a test accuracy of>95%.Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.

Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding

Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning,our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, whereλ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.