HiLo microscopy with caustic illumination.

HiLo microscopy is an optical sectioning structured illumination microscopy technique based on computationally combining two images: one with uniform illumination and the other with structured illumination. The most widely used structured illumination in HiLo microscopy is random speckle patterns, due to their simplicity and resilience to tissue scattering. Here, we present a novel HiLo microscopy strategy based on random caustic patterns. Building on an off-the-shelf diffuser and a low-coherence LED source, we demonstrate that caustic HiLo can achieve 4.5 µm optical sectioning capability with a 20× 0.75 NA objective. In addition, with the distinct intensity statistical properties of caustic patterns, we show that our caustic HiLo outperforms speckle HiLo, achieving enhanced optical sectioning capability and preservation of fine features by imaging scattering fixed brain sections of 100 µm, 300 µm, and 500 µm thicknesses. We anticipate that this new structured illumination technique may find various biomedical imaging applications.

EventLFM: event camera integrated Fourier light field microscopy for ultrafast 3D imaging.

Ultrafast 3D imaging is indispensable for visualizing complex and dynamic biological processes. Conventional scanning-based techniques necessitate an inherent trade-off between acquisition speed and space-bandwidth product (SBP). Emerging single-shot 3D wide-field techniques offer a promising alternative but are bottlenecked by the synchronous readout constraints of conventional CMOS systems, thus restricting data throughput to maintain high SBP at limited frame rates. To address this, we introduce EventLFM, a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy (LFM), a state-of-the-art single-shot 3D wide-field imaging technique. The event camera operates on a novel asynchronous readout architecture, thereby bypassing the frame rate limitations inherent to conventional CMOS systems. We further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by EventLFM. Experimental results demonstrate that EventLFM can robustly reconstruct fast-moving and rapidly blinking 3D fluorescent samples at kHz frame rates. Furthermore, we highlight EventLFM's capability for imaging of blinking neuronal signals in scattering mouse brain tissues and 3D tracking of GFP-labeled neurons in freely moving C. elegans. We believe that the combined ultrafast speed and large 3D SBP offered by EventLFM may open up new possibilities across many biomedical applications.

Tailored modulation of S100A1 and RASSF8 expression by butanediamide augments healing of rotator cuff tears.

Objectives: This investigation sought to elucidate promising treatment modalities for rotator cuff tears (RCTs) by delving into the molecular machinations instigating the affliction. The focus was on differentially expressed genes (DEGs) linked to RCTs, and the exploration of their roles and operative pathways. Methods: DEGs were discerned from GEO datasets, followed by the establishment of a protein-protein interaction (PPI) network. Subsequently, the network's core genes were determined employing a Venn diagram. Enrichment analysis facilitated the unveiling of the biological roles and signal transduction pathways of these pivotal genes, thus shedding light on molecular strategies for RCT-targeted treatment. The Discovery Studio 2019 software was employed to sift through FDA-sanctioned drugs targeting these essential proteins. Moreover, the efficaciousness of these FDA-endorsed drugs vis-à-vis RCTs was corroborated by the construction of an in vivo animal model of the injury and the in vitro cultivation of tendon-derived stem cells. Results: Bioinformatics outcomes revealed a significant overexpression of S100A1 and RASSF8 in RCT patients. The FDA drug repository indicated that Butanediamide has a selective affinity for S100A1 and RASSF8. Subsequent in vivo and in vitro experimentation demonstrated that Butanediamide could suppress S100A1 expression and bolster TDSC proliferation, thereby facilitating RCT healing. Conclusions: S100A1 and RASSF8 are pivotal genes implicated in RCTs, and their roles have been elucidated. The FDA-approved compound, Butanediamide, may represent a prospective therapeutic agent for RCTs by targeting S100A1 and RASSF8, respectively.

Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy.

High-resolution microscopy of deep tissue with large field-of-view (FOV) is critical for elucidating organization of cellular structures in plant biology. Microscopy with an implanted probe offers an effective solution. However, there exists a fundamental trade-off between the FOV and probe diameter arising from aberrations inherent in conventional imaging optics (typically, FOV < 30% of diameter). Here, we demonstrate the use of microfabricated non-imaging probes (optrodes) that when combined with a trained machine-learning algorithm is able to achieve FOV of 1x to 5x the probe diameter. Further increase in FOV is achieved by using multiple optrodes in parallel. With a 1 × 2 optrode array, we demonstrate imaging of fluorescent beads (including 30 FPS video), stained plant stem sections and stained living stems. Our demonstration lays the foundation for fast, high-resolution microscopy with large FOV in deep tissue via microfabricated non-imaging probes and advanced machine learning.

The development and evaluation of an in-vitro shoulder simulator with active muscle simulation.

The purpose of the present study was to develop a novel active in-vitro shoulder simulator to emulate all forms of planar and non-planar glenohumeral motions with active muscle simulation on cadaver specimens or shoulder models and to critically evaluate its performance. A physiologic shoulder simulator, driven using simulated muscle force, was developed to dynamically realize accurate kinematic control in all three rotational degrees of freedom (DOF) under physiological kinetic boundaries. The control algorithm of the simulator was implemented using three parallel running independent control loops, which regulate the forces of individual muscles in the respect DOF and work asynchronously in disparate sequences adapted to specific motions (abduction, flexion/extension and rotation). Three cadaveric specimens were used to evaluate the kinematic and kinetic performance of the simulator during simulated motions. High kinematic accuracy (maximum mean deviation ≤ 2.35° and RMSE 1.13°) and repeatability (maximum and average SD of ≤ 1.21° and 0.67°) were observed in all three rotational DOF investigated. The reliabilities of all individual muscle forces actuated in the simulator during planar and non-planar motions were generally excellent, with the 95% CIs of ICC estimates of > 0.90 for most instances (30/36). A novel shoulder simulator with active muscle simulation was developed and evaluated. Its capability to reproduce kinematics and kinetics in a physiological range for all DOF was systematically evaluated for multiple kinetic and kinematic outcome variables. The presented simulator is a powerful tool for investigating the biomechanics of physiological and pathological shoulder joints and to evaluate various surgical interventions. Acquisition of reliable data in joint kinetics and translational kinematics during active motions is critical to assess shoulder pathologies and appropriate treatments. We provide a unique muscle activated physiologic shoulder simulator, which allows the comprehensive acquisition of joint kinematic and kinetic data during repeated realistic planar and non-planar motions.

A generative adversarial network with "zero-shot" learning for positron image denoising.

Positron imaging technology has shown good practical value in industrial non-destructive testing, but the noise and artifacts generated during the imaging process of flow field images will directly affect the accuracy of industrial fault diagnosis. Therefore, how to obtain high-quality reconstructed images of the positron flow field is a challenging problem. In the existing image denoising methods, the denoising performance of positron images of industrial flow fields in special fields still needs to be strengthened. Considering the characteristics of few sample data and strong regularity of positron flow field image,in this work, we propose a new method for image denoising of positron flow field, which is based on a generative adversarial network with zero-shot learning. This method realizes image denoising under the condition of small sample data, and constrains image generation by constructing the extraction model of image internal features. The experimental results show that the proposed method can reduce the noise while retaining the key information of the image. It has also achieved good performance in the practical application of industrial flow field positron imaging.

Lightweight Multiprincipal Element Alloys with Excellent Mechanical Properties at Room and Cryogenic Temperatures.

Lightweight multiprincipal element alloys (MPEAs) are promising candidates for potential application as engineering materials due to their high strength and low density. In this work, lightweight Ti70Al15V15 and Ti80Al10V10 MPEAs were fabricated via vacuum arc melting. The phases of the Ti70Al15V15 alloys consisted of a BCC phase and a small amount of B2 phase while the Ti80Al10V10 alloys displayed a dual-phase structure with BCC and HCP phases. The different phase compositions led to differences in their mechanical properties. When the temperature changed from 298 K to 77 K, the strength of the alloys further increased and maintained a certain plasticity. This is attributed to the increasing lattice friction stress at cryogenic temperature. TEM observation demonstrated that dislocation played a crucial role in plastic deformation for both the Ti70Al15V15 and Ti80Al10V10 alloys. In addition, Ti80Al10V10 exhibited significant work-hardening capabilities. By analyzing the strengthening mechanism of the alloys, the theoretical yield strength was calculated, and the results agreed with the experimental values. The present results provide new insight into developing lightweight MPEAs containing Ti and Al.

Generative Adversarial Network of Industrial Positron Images on Memory Module.

PET (Positron Emission Computed Tomography) imaging is a challenge due to the ill-posed nature and the low data of photo response lines. Generative adversarial networks have been widely used in computer vision and made great success recently. In our paper, we trained an adversarial model to improve the industrial positron images quality based on the attention mechanism. The innovation of the proposed method is that we build a memory module that focuses on the contribution of feature details to interested parts of images. We use an encoder to get the hidden vectors from a basic dataset as the prior knowledge and train the nets jointly. We evaluate the quality of the simulation positron images by MS-SSIM and PSNR. At the same time, the real industrial positron images also show a good visual effect.

A facile fluorescent sensor based on nitrogen-doped carbon dots derived from Listeria monocytogenes for highly selective and visual detection of iodide and pH.

Sensitive and visual analysis of iodide (I-) and pH is significant in environmental and food applications. Herein, we present a facile fluorescent sensor for highly selective and visual detection of I- and pH based on nitrogen-doped carbon dots derived from Listeria monocytogenes (NCDs-LM). The NCDs-LM-based fluorescent sensor showed a good linear relationship to I- concentrations, and the detection limit was calculated as 20 nmol L-1. The developed sensor was successfully applied to the detection of I- in drinking water and milk samples. Meanwhile, the as-synthesized NCDs-LM sensor can be used to detect pH, achieving a wide linear pH range. Furthermore, fluorescent test papers based on NCDs-LM were designed for semi-quantitative detection of I- and pH via the naked-eye colorimetric assay. The present work indicates that the NCDs-LM-based fluorescent sensor has high potential for use in environmental monitoring and food analysis.

Routine blood biomarkers for the detection of multiple myeloma using machine learning.

INTRODUCTION: Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available. METHODS: Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms. RESULTS: The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set. CONCLUSIONS: Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.

Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky microscope objective inside the brain. By creating a self-consistent deep neural network that is trained to reconstruct anthropocentric images from the raw signal transported by the cannula, we demonstrate a single-cell resolution (< 10µm), depth sectioning resolution of 40 µm, and field of view of 200 µm, all with green-fluorescent-protein labelled neurons imaged at depths as large as 1.4 mm from the brain surface. Since ground-truth images at these depths are challenging to obtain in vivo, we propose a novel ensemble method that averages the reconstructed images from disparate deep-neural-network architectures. Finally, we demonstrate dynamic imaging of moving GCaMp-labelled C. elegans worms. Our approach dramatically simplifies deep-brain microscopy.

Computational microscopy for fast widefield deep-tissue fluorescence imaging using a commercial dual-cannula probe.

A solid-glass cannula serves as a micro-endoscope that can deliver excitation light deep inside tissue while also collecting emitted fluorescence. Then, we utilize deep neural networks to reconstruct images from the collected intensity distributions. By using a commercially available dual-cannula probe, and training a separate deep neural network for each cannula, we effectively double the field of view compared to prior work. We demonstrated ex vivo imaging of fluorescent beads and brain slices and in vivo imaging from whole brains. We clearly resolved 4 µm beads, with FOV from each cannula of 0.2 mm (diameter), and produced images from a depth of ~1.2 mm in the whole brain, currently limited primarily by the labeling. Since no scanning is required, fast widefield fluorescence imaging limited primarily by the brightness of the fluorophores, collection efficiency of our system, and the frame rate of the camera becomes possible.

Fast γ Photon Imaging for Inner Surface Defects Detecting.

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments.

Needle-based deep-neural-network camera.

We experimentally demonstrate a camera whose primary optic is a cannula/needle (diameter=0.22mm and length=12.5mm) that acts as a light pipe transporting light intensity from an object plane (35 cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with a field of view of 18° and angular resolution of ∼0.4∘. We showed a large effective demagnification of 127×. Most interestingly, we showed that such a camera could achieve close to diffraction-limited performance with an effective numerical aperture of 0.045, depth of focus ∼16µm, and resolution close to the sensor pixel size (3.2 µm). When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset before and after image reconstructions. The former could be useful for imaging with enhanced privacy.

Comprehensive RNA expression profile of therapeutic adipose­derived mesenchymal stem cells co­cultured with degenerative nucleus pulposus cells.

Stem cell­based therapy is a promising alternative to conventional approaches to treating intervertebral disc degeneration (IDD). However, comprehensive understanding of stem cell­based therapy at the gene level is still lacking. In the present study, we identified the expression profiles of messenger RNAs (mRNAs) and long non­coding RNAs (lncRNAs) expressed within a co­culture system of adipose­derived mesenchymal stem cells (ASCs) and degenerative nucleus pulposus cells (NPCs) and explored the signaling pathways involved and their regulatory networks. Microarray analysis was used to compare ASCs co­cultured with degenerative NPCs to ASCs cultured alone, and the underlying regulatory pattern, including the signaling pathways and competing endogenous RNA (ceRNA) network, was analyzed with robust bioinformatics methods. The results showed that 360 lncRNAs and 1757 mRNAs were differentially expressed by ASCs, and the microarray results were confirmed by quantitative PCR. Moreover, 589 Gene Ontology terms were upregulated, whereas 661 terms were downregulated. A total of 299 signaling pathways were significantly altered. A Path­net and a Signal­net were built to show interactions among differentially expressed genes. An mRNA­lncRNA co­expression network was constructed to reveal the interplay among differentially expressed mRNAs and lncRNAs, whereas a ceRNA network was built to investigate their connections with microRNAs involved in IDD. To the best of our knowledge, this original and comprehensive exploration reveals differentially expressed lncRNAs and mRNAs of ASCs stimulated by degenerative NPCs, underscoring the regulation pattern within the co­culture system at the gene level. These data may further understanding of NPC­directed differentiation of ASCs and facilitate the application of ASCs in future treatments for IDD.

3D computational cannula fluorescence microscopy enabled by artificial neural networks.

Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom. We experimentally demonstrate transverse resolution of ∼6µm, field of view ∼200µm and axial sectioning of ∼50µm for depths down to ∼700µm, all achieved with computation time of ∼3ms/frame on a desktop computer.

Computational cannula microscopy of neurons using neural networks.

Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with a field of view of 200 µm (diameter) and a resolution of less than 10 µm using a cannula of diameter of only 220 µm. In addition, we show that this approach can also be extended to macro-photography.

Open subpectoral vs. arthroscopic proximal biceps tenodesis: A comparison study of clinical outcomes.

The purpose of the present study was to compare the results of open subpectoral biceps tenodesis and arthroscopic proximal biceps tenodesis for treating long head of biceps (LHB) lesions. From January 2015 to June 2016, a total of 259 patients underwent LHB tenodesis surgery. Among them, 117 patients (60 females and 57 males) who met the inclusion and exclusion criteria were enrolled into the present study and were randomly divided into two groups, including an open subpectoral tenodesis group (OSPBT; n=62) and an arthroscopic proximal tenodesis group (ASPBT; n=55). All patients were followed up for at least 12 months. The demographic characteristics of each patient were recorded in detail. Moreover, clinical examinations of LHB lesions, such as shoulder range of motion (ROM), Visual Analog Scale (VAS) scores (0, no pain, to 10, most severe pain), American Shoulder and Elbow Surgeons (ASES) scores, and Constant-Murley shoulder outcome scores, were investigated prior to surgery, as well as 3, 6 and 12 months after surgery. Postoperative complications were also comprehensively investigated. There were no significant differences in sex, body mass index, dominant shoulder, duration of pain, injury type and operation time between the groups. The mean length of hospital stay in the ASPBT group was significantly lower than that of the OSPBT group (5.4±1.8 days vs. 9.3±2.9 days; P<0.05). The clinical outcomes, including shoulder ROMs, VAS scores, ASES scores and Constant-Murley shoulder outcome scores, were significantly improved after either OSPBT or ASPBT treatment. Specifically, the VAS score, incidence of postoperative stiffness and bicipital groove tenderness in the OSPBT group were significantly lower than those in the ASPBT group at 3 months post-surgery (P<0.05). Additionally, there were no significant difference in the improvement of other clinical outcomes and postoperative complications between the two groups. ASPBT and OSPBT were both effective and safe techniques for treating LHB lesions. However, tenderness of the bicipital groove was more common in the early stages of recovery post-surgery in the ASPBT group, which may be related to tendinitis of the LHB in the bicipital groove.

The Laxity of the Native Knee: A Meta-Analysis of in Vitro Studies.

BACKGROUND: Although soft-tissue balancing plays an important role in knee arthroplasty, we are aware of no objective target parameters describing the soft-tissue tension of the native knee. In the present study, we aimed to meta-analyze data from studies investigating native knee laxity to create a guide for creating a naturally balanced knee joint. METHODS: PubMed and Web of Science were searched for studies with laxity data published from 1996 through 2016. Graphs were digitally segmented in cases in which numerical data were not available in text or table form. Three-level random-effects meta-analyses were conducted. RESULTS: Seventy-six studies evaluating knee laxity at various flexion angles (0° to 90°) were included. Knee laxity was significantly different between 0° and 90° of flexion (p < 0.001) in all 6 testing directions, with mean differences of 0.94 mm and -0.35 mm for anterior and posterior translation, 1.61° and 4.25° for varus and valgus rotation, and 1.62° and 6.42° for internal and external rotation, respectively. CONCLUSIONS: Knee laxity was dependent on the flexion angle of the knee joint in all degrees of freedom investigated. Furthermore, asymmetry between anterior-posterior, varus-valgus, and internal-external rotation was substantial and depended on the joint flexion angle. CLINICAL RELEVANCE: If the goal of knee arthroplasty is to restore the kinematics of the knee as well as possible, pooled laxity data of the intact soft tissue envelope could be useful as a general guide for soft-tissue balancing in total knee arthroplasty.