Serum amyloid A expression in liver promotes synovial macrophage activation and chronic arthritis via NFAT5.

Nuclear factor of activated T-cells 5 (NFAT5), an osmo-sensitive transcription factor, can be activated by isotonic stimuli, such as infection. It remains unclear, however, whether NFAT5 is required for damage-associated molecular pattern-triggered (DAMP-triggered) inflammation and immunity. Here, we found that several DAMPs increased NFAT5 expression in macrophages. In particular, serum amyloid A (SAA), primarily generated by the liver, substantially upregulated NFAT5 expression and activity through TLR2/4-JNK signalling pathway. Moreover, the SAA-TLR2/4-NFAT5 axis promoted migration and chemotaxis of macrophages in an IL-6- and chemokine ligand 2-dependent (CCL2-dependent) manner in vitro. Intraarticular injection of SAA markedly accelerated macrophage infiltration and arthritis progression in mice. By contrast, genetic ablation of NFAT5 or TLR2/4 rescued the pathology induced by SAA, confirming the SAA-TLR2/4-NFAT5 axis in vivo. Myeloid-specific depletion of NFAT5 also attenuated SAA-accelerated arthritis. Of note, inflammatory arthritis in mice strikingly induced SAA overexpression in the liver. Conversely, forced overexpression of the SAA gene in the liver accelerated joint damage, indicating that the liver contributes to bolstering chronic inflammation at remote sites by secreting SAA. Collectively, this study underscores the importance of the SAA-TLR2/4-NFAT5 axis in innate immunity, suggesting that acute phase reactant SAA mediates mutual interactions between liver and joints and ultimately aggravates chronic arthritis by enhancing macrophage activation.

Topological magnetic structure generation using VAE-GAN hybrid model and discriminator-driven latent sampling.

Recently, deep generative models using machine intelligence are widely utilized to investigate scientific systems by generating scientific data. In this study, we experiment with a hybrid model of a variational autoencoder (VAE) and a generative adversarial network (GAN) to generate a variety of plausible two-dimensional magnetic topological structure data. Due to the topological properties in the system, numerous and diverse metastable magnetic structures exist, and energy and topological barriers separate them. Thus, generating a variety of plausible spin structures avoiding those barrier states is a challenging problem. The VAE-GAN hybrid model can present an effective approach to this problem because it brings the advantages of both VAE's diversity and GAN's fidelity. It allows one to perform various applications including searching a desired sample from a variety of valid samples. Additionally, we perform a discriminator-driven latent sampling (DDLS) using our hybrid model to improve the quality of generated samples. We confirm that DDLS generates various plausible data with large coverage, following the topological rules of the target system.

Super-resolution of magnetic systems using deep learning.

We construct a deep neural network to enhance the resolution of spin structure images formed by spontaneous symmetry breaking in the magnetic systems. Through the deep neural network, an image is expanded to a super-resolution image and reduced to the original image size to be fitted with the input feed image. The network does not require ground truth images in the training process. Therefore, it can be applied when low-resolution images are provided as training datasets, while high-resolution images are not obtainable due to the intrinsic limitation of microscope techniques. To show the usefulness of the network, we train the network with two types of simulated magnetic structure images; one is from self-organized maze patterns made of chiral magnetic structures, and the other is from magnetic domains separated by walls that are topological defects of the system. The network successfully generates high-resolution images highly correlated with the exact solutions in both cases. To investigate the effectiveness and the differences between datasets, we study the network's noise tolerance and compare the networks' reliabilities. The network is applied with experimental data obtained by magneto-optical Kerr effect microscopy and spin-polarized low-energy electron microscopy.

Screening through Lead Optimization of High Affinity, Allosteric Cyclin-Dependent Kinase 2 (CDK2) Inhibitors as Male Contraceptives That Reduce Sperm Counts in Mice.

Although cyclin-dependent kinase 2 (CDK2) is a validated target for both cancer and contraception, developing a CDK2 inhibitor with exquisite selectivity has been challenging due to the structural similarity of the ATP-binding site, where most kinase inhibitors bind. We previously discovered an allosteric pocket in CDK2 with the potential to bind a selective compound and then discovered and structurally confirmed an anthranilic acid scaffold that binds this pocket with high affinity. These allosteric inhibitors are selective for CDK2 over structurally similar CDK1 and show contraceptive potential. Herein, we describe the screening and optimization that led to compounds like EF-4-177 with nanomolar affinity for CDK2. EF-4-177 is metabolically stable, orally bioavailable, and significantly disrupts spermatogenesis, demonstrating this series' therapeutic potential. This work details the discovery of the highest affinity allosteric CDK inhibitors reported and shows promise for this series to yield an efficacious and selective allosteric CDK2 inhibitor.

Searching for the ground state of complex spin-ice systems using deep learning techniques.

Searching for the ground state of a given system is one of the most fundamental and classical questions in scientific research fields. However, when the system is complex and large, it often becomes an intractable problem; there is essentially no possibility of finding a global energy minimum state with reasonable computational resources. Recently, a novel method based on deep learning techniques was devised as an innovative optimization method to estimate the ground state. We apply this method to one of the most complicated spin-ice systems, aperiodic Penrose P3 patterns. From the results, we discover new configurations of topologically induced emergent frustrated spins, different from those previously known. Additionally, a candidate of the ground state for a still unexplored type of Penrose P3 spin-ice system is first proposed through this study. We anticipate that the capabilities of the deep learning techniques will not only improve our understanding on the physical properties of artificial spin-ice systems, but also bring about significant advances in a wide range of scientific research fields requiring computational approaches for optimization.

Optimization of physical quantities in the autoencoder latent space.

We propose a strategy for optimizing physical quantities based on exploring in the latent space of a variational autoencoder (VAE). We train a VAE model using various spin configurations formed on a two-dimensional chiral magnetic system. Three optimization algorithms are used to explore the latent space of the trained VAE. The first algorithm, the single-code modification algorithm, is designed for improving the local energetic stability of spin configurations to generate physically plausible spin states. The other two algorithms, the genetic algorithm and the stochastic algorithm, aim to optimize the global physical quantities, such as topological index, magnetization, energy, and directional correlation. The advantage of our method is that various optimization algorithms can be applied in the latent space containing the abstracted representation constructed by the trained VAE model. Our method based on latent space exploration is utilized for efficient physical quantity optimization.

Weight change and the risk of hip fractures in patients with type 2 diabetes: a nationwide cohort study.

Both weight gain and weight loss in type 2 diabetic population were associated with increased risk of hip fracture, while maintaining weight lowered the risk of hip fracture. Regarding the risk of hip fracture, we can propose active monitoring to maintain the weight of type 2 diabetes patients. INTRODUCTION: In type 2 diabetes, patients are often asked to control their weight in order to reduce their diabetic morbidity. The American Diabetes Association recommends that diabetic patients conduct high-intensity interventions for regulating diet, physical activity, and behavior to reduce weight, followed by long-term comprehensive weight maintenance programs. Although such weight control attempts are required in diabetic patients, there are few studies on the effect of weight change on hip fracture in this population. We aim to investigate the association between body weight change and the incidence of hip fracture in subjects with type 2 diabetes using large-scale, nationwide cohort data on the Korean population. MATERIALS AND METHODS: A total of 1,447,579 subjects (894,204 men and 553,375 women) > 40 years of age, who were diagnosed with type 2 diabetes, were enrolled in this study. Weight change within 2 years was divided into five categories: from weight loss ≥ 10% to weight gain ≥ 10%. The hazard ratios (HRs) and 95% confidence intervals for the incidence of hip fracture were analyzed, compared with the reference of the stable weight group (weight change < 5%). RESULTS: Among 5 weight change groups, more than 10% weight loss showed the highest HR (HR, 1.605; 95% CI, 1.493 to 1.725), followed by more than 10% weight gain (HR, 1.457; 95% CI, 1.318 to 1.612). The effect of weight change on hip fracture risk was greater in males than in females, and those under 65 years of age were greater than those over 65 years of age. Baseline BMI did not play a role of weight change affecting the risk of hip fracture. The HR for hip fracture of subjects with regular exercise was lower than those without regular exercise. CONCLUSIONS: In the type 2 diabetes population, both weight gain and weight loss were significantly associated with a higher risk of hip fracture, whereas maintaining body weight reduced the risk of hip fracture the most.

Effect of follow-up raloxifene therapy after denosumab discontinuation in postmenopausal women.

Follow-up raloxifene therapy after denosumab discontinuation resulted in a decrease in bone mass to the pre-denosumab levels and a rebound increase of bone turnover markers. The decrease in lumbar bone mineral density was particularly evident when the body mass index was low, there were previous vertebral fractures, or lumbar bone mineral density before denosumab administration was low. INTRODUCTION: Selective estrogen receptor modulators may be an alternative to bisphosphonates for treating rebound resorption after discontinuing denosumab. This study aimed to investigate the effects of follow-up raloxifene therapy after denosumab discontinuation in postmenopausal women. METHODS: This retrospective observational study included 61 patients who received 12-month follow-up raloxifene therapy after denosumab discontinuation. The primary endpoint was the bone mineral density change. The secondary endpoints were the changes in bone turnover markers and the incidence of new vertebral fractures. RESULTS: Raloxifene administration for 12 months after denosumab discontinuation resulted in a significantly lower bone mineral density at all sites compared to the level at 6 months after the last denosumab treatment (lumbar spine, - 5.48%; femoral neck, - 2.95%; total hip, - 3.52%; all, p < 0.001). The decrease in lumbar bone mineral density was particularly evident when the body mass index was low, there were previous vertebral fractures, or lumbar bone mineral density before denosumab administration was low. Marked increases in the bone turnover markers from baseline were noted after switching to raloxifene. However, no new vertebral fractures occurred during raloxifene treatment. CONCLUSIONS: Follow-up raloxifene therapy after denosumab discontinuation resulted in a decrease in bone mass to the pre-denosumab levels and a rebound increase of bone turnover markers. Therefore, raloxifene administered sequentially after denosumab discontinuation was not effective in preventing rebound phenomenon.

Response After Repeated Ketamine Injections in a Rat Model of Neuropathic Pain.

Ketamine, an N-methyl-D-aspartate antagonist, reduces pain by decreasing central sensitization and pain windup. However, chronic ketamine use can cause tolerance, dependency, impaired consciousness, urinary symptoms, and abdominal pain. This study aimed to investigate the effects of repeated ketamine injections and ketamine readministration after discontinuation in a rat model of neuropathic pain. To induce neuropathic pain, partial sciatic nerve ligation (PSNL) was performed in 15 male Wistar rats, and these animals were divided into three groups: PSNL (control), PSNL + ketamine 5 mg/kg (K5), and PSNL + ketamine 10 mg/kg (K10; n=5 each). Ketamine was injected intraperitoneally daily for 4 weeks, discontinued for 2 weeks, and then readministered for 1 week. Following PSNL, the mechanical withdrawal threshold was determined weekly using the Von Frey. The K10 group showed a significant increase in the mechanical withdrawal threshold, presented here as the target force (in g), at 21 and 28 days compared to the time point before ketamine injection (mean±SE, 276.0±24.0 vs. 21.6±2.7 and 300.0±0.0 vs. 21.6±2.7, respectively; P<0.01) and at 14, 21, and 28 days compared to the control group (108.2±51.2 vs. 2.7±1.3, 276.0±24.0 vs. 2.5±1.5, and 300.0±0.0 vs. 4.0±0.0, respectively; P<0.05). However, in the K10 group, the ketamine effects decreased significantly at 7 days after readministration compared to those after 28 days of repeated injections (P<0.05). In the K10 group, repeated ketamine injections showed a significant increase in antinociceptive effect for >2 weeks, but this ketamine effect decreased after drug readministration.

MSX1 Drives Tooth Morphogenesis Through Controlling Wnt Signaling Activity.

Tooth agenesis is a common structural birth defect in humans that results from failure of morphogenesis during early tooth development. The homeobox transcription factor Msx1 and the canonical Wnt signaling pathway are essential for "bud to cap" morphogenesis and are causal factors for tooth agenesis. Our recent study suggested that Msx1 regulates Wnt signaling during early tooth development by suppressing the expression of Dkk2 and Sfrp2 in the tooth bud mesenchyme, and it demonstrated partial rescue of Msx1-deficient molar teeth by a combination of DKK inhibition and genetic inactivation of SFRPs. In this study, we found that Sostdc1/Wise, another secreted Wnt antagonist, is involved in regulating the odontogenic pathway downstream of Msx1. Whereas Sostdc1 expression in the developing tooth germ was not increased in Msx1-/- embryos, genetic inactivation of Sostdc1 rescued maxillary molar, but not mandibular molar, morphogenesis in Msx1-/- mice with full penetrance. Since the Msx1-/-;Sostdc1-/- embryos exhibited ectopic Dkk2 expression in the developing dental mesenchyme, similar to Msx1-/- embryos, we generated and analyzed tooth development in Msx1-/-;Dkk2-/- double and Msx1-/-;Dkk2-/-;Sostdc1-/- triple mutant mice. The Msx1-/-;Dkk2-/- double mutants showed rescued maxillary molar morphogenesis at high penetrance, with a small percentage also exhibiting mandibular molars that transitioned to the cap stage. Furthermore, tooth development was rescued in the maxillary and mandibular molars, with full penetrance, in the Msx1-/-;Dkk2-/-;Sostdc1-/- mice. Together, these data reveal 1) that a key role of Msx1 in driving tooth development through the bud-to-cap transition is to control the expression of Dkk2 and 2) that modulation of Wnt signaling activity by Dkk2 and Sostdc1 plays a crucial role in the Msx1-dependent odontogenic pathway during early tooth morphogenesis.

Brain metastases: An update on the multi-disciplinary approach of clinical management.

IMPORTANCE: Brain metastasis (BM) is the most common malignant intracranial neoplasm in adults with over 100,000 new cases annually in the United States and outnumbering primary brain tumors 10:1. OBSERVATIONS: The incidence of BM in adult cancer patients ranges from 10-40%, and is increasing with improved surveillance, effective systemic therapy, and an aging population. The overall prognosis of cancer patients is largely dependent on the presence or absence of brain metastasis, and therefore, a timely and accurate diagnosis is crucial for improving long-term outcomes, especially in the current era of significantly improved systemic therapy for many common cancers. BM should be suspected in any cancer patient who develops new neurological deficits or behavioral abnormalities. Gadolinium enhanced MRI is the preferred imaging technique and BM must be distinguished from other pathologies. Large, symptomatic lesion(s) in patients with good functional status are best treated with surgery and stereotactic radiosurgery (SRS). Due to neurocognitive side effects and improved overall survival of cancer patients, whole brain radiotherapy (WBRT) is reserved as salvage therapy for patients with multiple lesions or as palliation. Newer approaches including multi-lesion stereotactic surgery, targeted therapy, and immunotherapy are also being investigated to improve outcomes while preserving quality of life. CONCLUSION: With the significant advancements in the systemic treatment for cancer patients, addressing BM effectively is critical for overall survival. In addition to patient's performance status, therapeutic approach should be based on the type of primary tumor and associated molecular profile as well as the size, number, and location of metastatic lesion(s).

An observational study of hydrodynamic impact on water mass transport due to tidal power generation.

The world's largest Sihwa Tidal Power Plant (TPP), located on the west coast of Korea, was built in 2011 for the purpose of improving water quality and producing renewable energy. After several years of actual operation, most of the original purpose was achieved, but unexpected coastal environmental changes such as tidal flat damage and sediment accumulation also occurred. In this study, in order to understand the causes of these environmental changes, field observations were conducted near TPP, and spatial and temporal variability of flow structure and water exchange process were investigated. Three-dimensional velocity data were collected along the closed line surrounding the outside of the TPP for 11 h during spring tide and analyzed according to two discharge phases: power generation phase (PGP) and drainage phase (DP). The results show that the depth-averaged maximum current velocity was more than three times greater at DP than at PGP. Jet-like flow during DP caused very high horizontal shear, whereas vertical shear was relatively weak, indicating that the horizontal and vertical flow structures were very different. The most notable result is that the mass transport patterns between PGP and DP are significantly different, i.e., during PGP, mass transport is dominated on the left side of the TPP, whereas during DP, it occurs at the front of the TPP. This means that there is a strong spatiotemporal asymmetry between the inflow from the downstream (outside of the TPP) during PGP and the outflow from the upstream (inside of the TPP) during DP. These asymmetric processes can have a significant impact on the material exchange and sediment transport near the TPP. Since observational studies on TPP are extremely rare, this study is expected to contribute to future TPP related research, such as numerical modeling.

Estimating the effective fields of spin configurations using a deep learning technique.

The properties of complicated magnetic domain structures induced by various spin-spin interactions in magnetic systems have been extensively investigated in recent years. To understand the statistical and dynamic properties of complex magnetic structures, it is crucial to obtain information on the effective field distribution over the structure, which is not directly provided by magnetization. In this study, we use a deep learning technique to estimate the effective fields of spin configurations. We construct a deep neural network and train it with spin configuration datasets generated by Monte Carlo simulation. We show that the trained network can successfully estimate the magnetic effective field even though we do not offer explicit Hamiltonian parameter values. The estimated effective field information is highly applicable; it is utilized to reduce noise, correct defects in the magnetization data, generate spin configurations, estimate external field responses, and interpret experimental images.

Carotid Plaque Composition Assessed by CT Predicts Subsequent Cardiovascular Events among Subjects with Carotid Stenosis.

BACKGROUND AND PURPOSE: Currently, the characteristics of carotid plaques are considered important factors for identifying subjects at high risk of stroke. This study aimed to test the hypothesis that carotid plaque composition assessed by CTA is associated with an increased risk of future major adverse cardiovascular events among asymptomatic subjects with moderate-to-severe carotid artery stenosis. MATERIALS AND METHODS: This single-center, retrospective cohort study included 194 carotid plaques from 176 asymptomatic subjects with moderate-to-severe carotid artery stenosis. The association of CTA-determined plaque composition with the risk of subsequent adverse cardiovascular events was analyzed. RESULTS: During a median follow-up of 41 months, the adverse cardiovascular event incidence among 194 carotid plaques was 19.6%. There were significant differences in plaque Hounsfield units (P < .001) and spotty calcium presence (P < .001) between carotid plaques from subjects with and without subsequent adverse cardiovascular events. Multivariable analysis revealed carotid plaque Hounsfield unit density (P < .001) and spotty calcium (P < .001) as independent predictors of subsequent adverse cardiovascular events. In association with moderate carotid artery stenosis, the plaque Hounsfield unit values were significantly lower among carotid plaques from subjects who experienced subsequent adverse cardiovascular events (P = .002), strokes (P = .01), and cardiovascular deaths (P = .04); the presence of spotty calcium was significantly associated with the occurrence of adverse cardiovascular events (P = .001), acute coronary syndrome (P = .01), and cardiovascular death (P = .04). CONCLUSIONS: Carotid plaque Hounsfield unit density and spotty calcium were independent predictors of a greater risk of adverse cardiovascular event occurrence.

Diazepam induced sleep spindle increase correlates with cognitive recovery in a child with epileptic encephalopathy.

BACKGROUND: Continuous spike and wave of sleep with encephalopathy (CSWS) is a rare and severe developmental electroclinical epileptic encephalopathy characterized by seizures, abundant sleep activated interictal epileptiform discharges, and cognitive regression or deceleration of expected cognitive growth. The cause of the cognitive symptoms is unknown, and efforts to link epileptiform activity to cognitive function have been unrevealing. Converging lines of evidence implicate thalamocortical circuits in these disorders. Sleep spindles are generated and propagated by the same thalamocortical circuits that can generate spikes and, in healthy sleep, support memory consolidation. As such, sleep spindle deficits may provide a physiologically relevant mechanistic biomarker for cognitive dysfunction in epileptic encephalopathies. CASE PRESENTATION: We describe the longitudinal course of a child with CSWS with initial cognitive regression followed by dramatic cognitive improvement after treatment. Using validated automated detection algorithms, we analyzed electroencephalograms for epileptiform discharges and sleep spindles alongside contemporaneous neuropsychological evaluations over the course of the patient's disease. We found that sleep spindles increased dramatically with high-dose diazepam treatment, corresponding with marked improvements in cognitive performance. We also found that the sleep spindle rate was anticorrelated to spike rate, consistent with a competitively shared underlying thalamocortical circuitry. CONCLUSIONS: Epileptic encephalopathies are challenging electroclinical syndromes characterized by combined seizures and a deceleration or regression in cognitive skills over childhood. This report identifies thalamocortical circuit dysfunction in a case of epileptic encephalopathy and motivates future investigations of sleep spindles as a biomarker of cognitive function and a potential therapeutic target in this challenging disease.

Comparison of fracture risk between type 1 and type 2 diabetes: a comprehensive real-world data.

Population-based cohort study of 6,548,784 Korean subjects demonstrates that the risk of fracture was higher in patients with diabetes than in nondiabetic subjects. Furthermore, patients with type 1 diabetes were associated with a higher risk of fracture than patients with type 2 diabetes for all measurement sites. INTRODUCTION: Diabetes mellitus is associated with increased fracture risk. Although the pathophysiologic effect on bone metabolism differs according to the type of diabetes, a higher risk of fracture in patients with diabetes than in nondiabetic patients has been consistently demonstrated. Considering the ever-increasing number of patients with diabetes, we aimed to provide updated information on whether this phenomenon remains valid in real-world settings by using large-scale population datasets. METHODS: We conducted a retrospective longitudinal study using data from the Korean National Health Insurance Service dataset of preventive health check-ups between January 2009 and December 2016. The hazard ratios were calculated for any fracture, vertebral fracture, and hip fracture and were analyzed according to the presence and type of diabetes. Among 10,585,818 subjects, 6,548,784 were eligible for the analysis (2418 patients with type 1 diabetes mellitus [T1DM] and 506,208 patients with type 2 diabetes mellitus [T2DM]). RESULTS: The mean follow-up duration (in years) was 7.0 ± 1.3 for subjects without diabetes, 6.4 ± 2.0 for those with T1DM, and 6.7 ± 1.7 for T2DM. Patients with T1DM had a higher incidence rate for all types of fractures per 1000 person-years. The fully adjusted hazard ratios (HRs) for any fracture, vertebral fracture, and hip fracture were higher in T1DM than in T2DM (1.37 [95% confidence interval (CI): 1.23-1.52] for any fracture, 1.33 [95% CI: 1.09-1.63] for vertebral fracture, and 1.99 [95% CI: 1.56-2.53] for hip fracture). CONCLUSIONS: In this large-scale population analysis, diabetes was associated with a higher risk of all types of fractures. Patients with T1DM had a higher risk of fracture than those with T2DM for all measurement sites, and hip fractures had the highest risk. Therefore, fracture prevention training for patients with diabetes is advisable.

Optically transparent microwave absorber based on water-based moth-eye structures.

We propose an approach to realize an optically transparent microwave absorber based on water-based moth-eye metamaterial structures. The absorber is made of a periodic array of properly shaped glass caps infiltrated with distilled water. Analytical calculations and numerical simulations show that the water-based metamaterial absorbs electromagnetic waves over a wide spectral band ranging from 4GHz to well above 120GHz, showing absorption levels close to 100% for incident radiation that ranges from normal to grazing angles, for both TE and TM polarizations. Yet, the structure is optically transparent, offering exciting opportunities in a variety of civil and military applications, such as for camouflage and shielding systems and in energy harvesting structures.

Unveiling the neuromechanical mechanisms underlying the synergistic interactions in human sensorimotor system.

Motor synergies are neural organizations of a set of redundant motor effectors that interact with one another to compensate for each other's error and ensure the stabilization of a performance variable. Recent studies have demonstrated that central nervous system synergistically coordinates its numerous motor effectors through Bayesian multi-sensory integration. Deficiency in sensory synergy weakens the synergistic interaction between the motor effectors. Here, we scrutinize the neuromechanical mechanism underlying this phenomenon through spectral analysis and modeling. We validate our model-generated results using experimental data reported in the literature collected from participants performing a finger force production task with and without tactile feedback (manipulated through injection of anesthetic in fingers). Spectral analysis reveals that the error compensation feature of synergies occurs only at low frequencies. Modeling suggests that the neurophysiological structures involving short-latency back-coupling loops similar to the well-known Renshaw cells explain the deterioration of synergy due to sensory deprivation.

Magnetic Hamiltonian parameter estimation using deep learning techniques.

Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.

Shear stress induced by fluid flow produces improvements in tissue-engineered cartilage.

Tissue engineering aims to create implantable biomaterials for the repair and regeneration of damaged tissues. In vitro tissue engineering is generally based on static culture, which limits access to nutrients and lacks mechanical signaling. Using shear stress is controversial because in some cases it can lead to cell death while in others it promotes tissue regeneration. To understand how shear stress works and how it may be used to improve neotissue function, a series of studies were performed. First, a tunable device was designed to determine optimal levels of shear stress for neotissue formation. Then, computational fluid dynamics modeling showed the device applies fluid-induced shear (FIS) stress spanning three orders of magnitude on tissue-engineered cartilage (neocartilage). A beneficial window of FIS stress was subsequently identified, resulting in up to 3.6-fold improvements in mechanical properties of neocartilage in vitro. In vivo, neocartilage matured as evidenced by the doubling of collagen content toward native values. Translation of FIS stress to human derived neocartilage was then demonstrated, yielding analogous improvements in mechanical properties, such as 168% increase in tensile modulus. To gain an understanding of the beneficial roles of FIS stress, a mechanistic study was performed revealing a mechanically gated complex on the primary cilia of chondrocytes that is activated by FIS stress. This series of studies places FIS stress into the arena as a meaningful mechanical stimulation strategy for creating robust and translatable neotissues, and demonstrates the ease of incorporating FIS stress in tissue culture.