Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Photodynamic Killing of Gram-Positive Bacteria by Synthetic Curcuminoids

In our previous study, we have demonstrated that curcumin can efficiently kill the anaerobic bacterium Propionibacterium acnes by irradiation with low-dose blue light. The curcuminoids present in natural plant turmeric mainly include curcumin, demethoxycurcumin, and bisdemethoxycurcumin. However, only curcumin is commercially available. Eighteen different curcumin analogs, including demethoxycurcumin and bisdemethoxycurcumin, were synthesized in this study. Their antibacterial activity against Gram-positive aerobic bacteria Staphylococcus aureus and Staphylococcus epidermidis was investigated using the photodynamic inactivation method. Among the three compounds in turmeric, curcumin activity is the weakest, and bisdemethoxycurcumin possesses the strongest activity. However, two synthetic compounds, (1E,6E)-1,7-bis(5-methylthiophen-2-yl)hepta-1,6-diene-3,5-dione and (1E,6E)-1,7-di(thiophen-2-yl)hepta-1,6-diene-3,5-dione, possess the best antibacterial activity among all compounds examined in this study. Their chemical stability is also better than that of bisdemethoxycurcumin, and thus has potential for future clinical applications.
Development of Eco-Sustainable PBAT-Based Blown Films and Performance Analysis for Food Packaging Applications

In this work, eco-sustainable blown films with improved performance, suitable for flexible packaging applications requiring high ductility, were developed and characterized. Films were made by blending two bioplastics with complementary properties—the ductile and flexible poly(butylene-adipate-co-terephthalate) (PBAT) and the rigid and brittle poly(lactic acid) (PLA)—at a 60/40 mass ratio. With the aim of improving the blends’ performance, the effects of two types of PLA, differing for viscosity and stereoregularity, and the addition of a commercial polymer chain extender (Joncryl®), were analyzed. The use of the PLA with a viscosity ratio closer to PBAT and lower stereoregularity led to a finer morphology and better interfacial adhesion between the phases, and the addition of the chain extender further reduced the size of the dispersed phase domains, with beneficial effects on the mechanical response of the produced films. The best system composition, made by the blend of PBAT, amorphous PLA, and the compatibilizer, proved to have improved mechanical properties, with a good balance between stiffness and ductility and also good transparency and sealability, which are desirable features for flexible packaging applications.
Profiling the Murine SUMO Proteome in Response to Cardiac Ischemia and Reperfusion Injury

SUMOylation is a reversible posttranslational modification pathway catalyzing the conjugation of small ubiquitin-related modifier (SUMO) proteins to lysine residues of distinct target proteins. SUMOylation modifies a wide variety of cellular regulators thereby affecting a multitude of key processes in a highly dynamic manner. The SUMOylation pathway displays a hallmark in cellular stress-adaption, such as heat or redox stress. It has been proposed that enhanced cellular SUMOylation protects the brain during ischemia, however, little is known about the specific regulation of the SUMO system and the potential target proteins during cardiac ischemia and reperfusion injury (I/R). By applying left anterior descending (LAD) coronary artery ligation and reperfusion in mice, we detect dynamic changes in the overall cellular SUMOylation pattern correlating with decreased SUMO deconjugase activity during I/R injury. Further, unbiased system-wide quantitative SUMO-proteomics identified a sub-group of SUMO targets exhibiting significant alterations in response to cardiac I/R. Notably, transcription factors that control hypoxia- and angiogenesis-related gene expression programs, exhibit altered SUMOylation during ischemic stress adaptation. Moreover, several components of the ubiquitin proteasome system undergo dynamic changes in SUMO conjugation during cardiac I/R suggesting an involvement of SUMO signaling in protein quality control and proteostasis in the ischemic heart. Altogether, our study reveals regulated candidate SUMO target proteins in the mouse heart, which might be important in coping with hypoxic/proteotoxic stress during cardiac I/R injury.
Matrix completion methods for the total electron content video reconstruction

The total electron content (TEC) maps can be used to estimate the signal delay of GPS due to the ionospheric electron content between a receiver and satellite. This delay can result in GPS positioning error. Thus it is important to monitor the TEC maps. The observed TEC maps have big patches of missingness in the ocean and scattered small areas of missingness on the land. In this paper, we propose several extensions of existing matrix completion algorithms to achieve TEC map reconstruction, accounting for spatial smoothness and temporal consistency while preserving important structures of the TEC maps. We call the proposed method Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data (VISTA). Numerical simulations that mimic patterns of real data are given. We show that our proposed method achieves better reconstructed TEC maps as compared to existing methods in literature. Our proposed computational algorithm is general and can be readily applied for other problems besides TEC map reconstruction.
Cellulose supported manganese dioxide nanosheet catalyzed aerobic oxidation of organic compounds

Cellulose supported manganese dioxide nanosheets, as a heterogeneous bio-supported and green catalyst, were synthesized by soaking porous cellulose in a potassium permanganate solution. The prepared catalyst was used effectively for the oxidation of various types of alkyl arenes, alcohols and sulfides to their corresponding carbonyl and sulfoxide compounds, respectively in high yields using air as the oxidant at ambient pressure. The catalyst can be recycled and reused in five runs without any significant loss of efficiency. The mild reaction conditions for the oxidation of alcohols and sulfides, high yields, recyclability of the catalyst, and very easy workup procedure are other advantages of this catalyst.
A combinatorial formula for the nabla operator

We present an LLT-type formula for a general power of the nabla operator applied to the Cauchy product for the modified Macdonald polynomials, and use it to deduce a new proof of the generalized shuffle theorem describing $\nabla^k e_n$, and the Elias-Hogancamp formula for $(\nabla^k p_1^n,e_n)$ as corollaries. We give a direct proof of the theorem by verifying that the LLT expansion satisfies the defining properties of $\nabla^k$, such as triangularity in the dominance order, as well as a geometric proof based on a method for counting bundles on $\mathbb{P}^1$ due to the second author. These formulas are related to an affine paving of the type A unramified affine Springer fiber studied by Goresky, Kottwitz, and MacPherson, and also to Stanley's chromatic symmetric functions.
How to Formally Model Human in Collaborative Robotics

Human-robot collaboration (HRC) is an emerging trend of robotics that promotes the co-presence and cooperation of humans and robots in common workspaces. Physical vicinity and interaction between humans and robots, combined with the uncertainty of human behaviour, could lead to undesired situations where humans are injured. Thus, safety is a priority for HRC applications. Safety analysis via formal modelling and verification techniques could considerably avoid dangerous consequences, but only if the models of HRC systems are comprehensive and realistic, which requires reasonably realistic models of human behaviour. This paper explores state-of-the-art solutions for modelling human and discusses which ones are suitable for HRC scenarios.
Fetal vascular malperfusion (FVM): diagnostic implications and clinical associations

Abstract Introduction Fetal vascular malperfusion (FVM) is diagnosed by the presence of vascular lesions in the muscularized fetal vessels in the placenta and the resultant changes in the downstream villi. The Amsterdam Placental Working Group recognizes two patterns of FVM namely segmental and global. The aim of this study was to estimate the frequency of FVM lesion in our population and to understand its neonatal associations. Methods Fifty-four placentas with FVM and 56 controls collected over 34 months. The maternal and neonatal details were collected from the case charts. The patterns and grades of FVM lesions were related to the clinical factors and significance analyzed statistically using the Chi-square test and t-test and p < . 05 was considered significant. Results The frequency of FVM was 8.7%. The FVM group showed lower mean gestational age, birth weight, and placental weight with a higher frequency of IUGR. Poor neonatal survival, non-reassuring fetal status, neurological abnormalities, neonatal sepsis, asphyxia, low Apgar, and respiratory support requirement were significantly higher in the FVM group. A similar frequency of segmental and global lesions was seen. High grade lesions (n = 35) were common than low grade (n = 19). Neonatal associations were more often seen in segmental and high-grade lesions. Discussion In the absence of antenatal diagnostic tools to identify FVM, placental examination is critical and the only definitive method to diagnose FVM, which alerts the clinician to monitor for several neonatal morbidities. Identification and typing the lesion as per the new guidelines proves significant risk associations with specific types of FVM.
ResPerfNet: Deep Residual Learning for Regressional Performance Modeling of Deep Neural Networks

The rapid advancements of computing technology facilitate the development of diverse deep learning applications. Unfortunately, the efficiency of parallel computing infrastructures varies widely with neural network models, which hinders the exploration of the design space to find high-performance neural network architectures on specific computing platforms for a given application. To address such a challenge, we propose a deep learning-based method, ResPerfNet, which trains a residual neural network with representative datasets obtained on the target platform to predict the performance for a deep neural network. Our experimental results show that ResPerfNet can accurately predict the execution time of individual neural network layers and full network models on a variety of platforms. In particular, ResPerfNet achieves 8.4% of mean absolute percentage error for LeNet, AlexNet and VGG16 on the NVIDIA GTX 1080Ti, which is substantially lower than the previously published works.
Mitochondrial misreading in skeletal muscle accelerates metabolic aging and confers lipid accumulation and increased inflammation

We have recently reported on an experimental model of mitochondrial mistranslation conferred by amino acid exchange V338Y in mitochondrial ribosomal protein MrpS5. Here we used a combination of RNA-seq and metabolic profiling of homozygous transgenic Mrps5V338Y/V338Y mice to analyze the changes associated with the V338Y mutation in postmitotic skeletal muscle. Metabolome analysis demonstrated enhanced levels of age-associated metabolites in the mutant V338Y animals accompanied by increased glycolysis, lipid desaturation and eicosanoid biosynthesis, and alterations of the pentose phosphate pathway. In addition, transcriptome signatures of aged V338Y mutant muscle pointed to elevated inflammation, likely reflecting the increased levels of bioactive lipids. Our findings indicate that mistranslation-mediated impairment of mitochondrial function affects specific bioenergetic processes in muscle in an age-dependent manner.
Quantitative Evaluation of the Thickness of the Available Manipulation Volume Inside the Knee Joint Capsule for Minimally Invasive Robotic Unicondylar Knee Arthroplasty

Objective: Developing robotic tools that introduce substantial changes in the surgical workflow is challenging because quantitative requirements are missing. Experiments on cadavers can provide valuable information to derive workspace requirements, tool size, and surgical workflow. This work aimed to quantify the volume inside the knee joint available for manipulation of minimally invasive robotic surgical tools. In particular, we aim to develop a novel procedure for minimally invasive unicompartmental knee arthroplasty (UKA) using a robotic laser-cutting tool. Methods: Contrast solution was injected into nine cadaveric knees and computed tomography scans were performed to evaluate the tool manipulation volume inside the knee joints. The volume and distribution of the contrast solution inside the knee joints were analyzed with respect to the femur, tibia, and the anatomical locations that need to be reached by a laser-cutting tool to perform bone resection for a standard UKA implant. Results: Quantitative information was determined about the tool manipulation volume inside these nine knee joints and its distribution around the cutting lines required for a standard implant. Conclusion: Based on the volume distribution, we could suggest a possible workflow for minimally invasive UKA, which provides a large manipulation volume, and deducted that for the proposed workflow, an instrument with a thickness of 5-8 mm should be feasible. Significance: We present quantitative information on the three-dimensional distribution of the maximally available volume inside the knee joint. Such quantitative information lays the basis for developing surgical tools that introduce substantial changes in the surgical workflow.
Learning to Transfer Visual Effects from Videos to Images

We study the problem of animating images by transferring spatio-temporal visual effects (such as melting) from a collection of videos. We tackle two primary challenges in visual effect transfer: 1) how to capture the effect we wish to distill; and 2) how to ensure that only the effect, rather than content or artistic style, is transferred from the source videos to the input image. To address the first challenge, we evaluate five loss functions; the most promising one encourages the generated animations to have similar optical flow and texture motions as the source videos. To address the second challenge, we only allow our model to move existing image pixels from the previous frame, rather than predicting unconstrained pixel values. This forces any visual effects to occur using the input image's pixels, preventing unwanted artistic style or content from the source video from appearing in the output. We evaluate our method in objective and subjective settings, and show interesting qualitative results which demonstrate objects undergoing atypical transformations, such as making a face melt or a deer bloom.
The Development of Maillard Reaction, and Advanced Glycation End Product (AGE)-Receptor for AGE (RAGE) Signaling Inhibitors as Novel Therapeutic Strategies for Patients with AGE-Related Diseases

Advanced glycation end products (AGEs) are generated by nonenzymatic modifications of macromolecules (proteins, lipids, and nucleic acids) by saccharides (glucose, fructose, and pentose) via Maillard reaction. The formed AGE molecules can be catabolized and cleared by glyoxalase I and II in renal proximal tubular cells. AGE-related diseases include physiological aging, neurodegenerative/neuroinflammatory diseases, diabetes mellitus (DM) and its complications, autoimmune/rheumatic inflammatory diseases, bone-degenerative diseases, and chronic renal diseases. AGEs, by binding to receptors for AGE (RAGEs), alter innate and adaptive immune responses to induce inflammation and immunosuppression via the generation of proinflammatory cytokines, reactive oxygen species (ROS), and reactive nitrogen intermediates (RNI). These pathological molecules cause vascular endothelial/smooth muscular/connective tissue-cell and renal mesangial/endothelial/podocytic-cell damage in AGE-related diseases. In the present review, we first focus on the cellular and molecular bases of AGE–RAGE axis signaling pathways in AGE-related diseases. Then, we discuss in detail the modes of action of newly discovered novel biomolecules and phytochemical compounds, such as Maillard reaction and AGE–RAGE signaling inhibitors. These molecules are expected to become the new therapeutic strategies for patients with AGE-related diseases in addition to the traditional hypoglycemic and anti-hypertensive agents. We particularly emphasize the importance of “metabolic memory”, the “French paradox”, and the pharmacokinetics and therapeutic dosing of the effective natural compounds associated with pharmacogenetics in the treatment of AGE-related diseases. Lastly, we propose prospective investigations for solving the enigmas in AGE-mediated pathological effects.
In Vitro and In Vivo Effects of Fermented Oyster-Derived Lactate on Exercise Endurance Indicators in Mice

Exogenous lactate administration has more recently been investigated for its various prophylactic effects. Lactate derived from potential functional foods, such as fermented oyster extract (FO), may emerge as a practical and effective method of consuming exogenous lactate. The current study endeavored to ascertain whether the lactate derived from FO may act on muscle cell biology, and to what extent this may translate into physical fitness improvements. We examined the effects of FO in vitro and in vivo, on mouse C2C12 cells and exercise performance indicators in mice, respectively. In vitro, biochemical analysis was carried out to determine the effects of FO on lactate content and muscle cell energy metabolism, including adenosine triphosphate (ATP) activity. Western blot analysis was also utilized to measure the protein expression of total adenosine monophosphate-activated protein kinase (AMPK), p-AMPK (Thr172), lactate dehydrogenase (LDH), succinate dehydrogenase (SDHA) and peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) in response to FO administration. Three experimental groups were formed: a positive control (PC) treated with 1% horse serum, FO10 treated with 10 μg/mL and FO50 treated with 50 μg/mL. In vivo, the effects of FO supplementation on exercise endurance were measured using the Rota-rod test, and Western blot analysis measured myosin heavy-chain 2 (MYH2) to assess skeletal muscle growth, alongside p-AMPK, total-AMPK, PGC-1α, cytochrome C and UCP3 protein expression. Biochemical analysis was also performed on muscle tissue to measure the changes in concentration of liver lactate, lactate dehydrogenase (LDH), glycogen and citrate. Five groups (n = 10/per group) consisted of a control group (CON), exercise group (Ex), positive control treated with Ex and 500 mg/kg Taurine (Ex-Tau), Ex and 100 mg/kg FO supplementation (Ex-FO100) and Ex and 200 mg/kg FO supplementation (Ex-FO200) orally administered over the 4-week experimental period.FO50 significantly increased PGC-1α expression (p < 0.001), whereas both FO10 and FO50 increased the expression of p-AMPK (p < 0.001), in C2C12 muscle cells, showing increased signaling important for mitochondrial metabolism and biogenesis. Muscle lactate levels were also significantly increased following FO10 (p < 0.05) and FO50 (p < 0.001). In vivo, muscle protein expression of p-AMPK (p < 0.05) and PGC-1α were increased, corroborating our in vitro results. Cytochrome C also significantly increased following FO200 intake. These results suggest that the effects of FO supplementation may manifest in a dose-response manner. FO administration, in vitro, and supplementation, in vivo, both demonstrate a potential for improvements in mitochondrial metabolism and biogenesis, and even for potentiating the adaptive effects of endurance exercise. Mechanistically, lactate may be an important molecule in explaining the aforementioned positive effects of FO.
Saffron: Chemical Composition and Neuroprotective Activity

Crocus sativus L. belongs to the Iridaceae family and it is commonly known as saffron. The different cultures together with the geoclimatic characteristics of the territory determine a different chemical composition that characterizes the final product. This is why a complete knowledge of this product is fundamental, from which more than 150 chemical compounds have been extracted from, but only about one third of them have been identified. The chemical composition of saffron has been studied in relation to its efficacy in coping with neurodegenerative retinal diseases. Accordingly, experimental results provide evidence of a strict correlation between chemical composition and neuroprotective capacity. We found that saffron’s ability to cope with retinal neurodegeneration is related to: (1) the presence of specific crocins and (2) the contribution of other saffron components. We summarize previous evidence and provide original data showing that results obtained both “in vivo” and “in vitro” lead to the same conclusion.
Improving CBCT Quality to CT Level using Deep-Learning with Generative Adversarial Network.

PURPOSE To improve image quality and CT number accuracy of daily cone-beam computed tomography (CBCT) through a deep-learning methodology with Generative Adversarial Network. METHODS 150 paired pelvic CT and CBCT scans were used for model training and validation. An unsupervised deep-learning method, 2.5D pixel-to-pixel generative adversarial network (GAN) model with feature mapping was proposed. A total of 12000 slice pairs of CT and CBCT were used for model training, while 10-cross validation was applied to verify model robustness. Paired CT-CBCT scans from an additional 15 pelvic patients and 10 head-and-neck (HN) patients with CBCT images collected at a different machine were used for independent testing purpose. Besides the proposed method above, other network architectures were also tested as: 2D vs. 2.5D; GAN model with vs. without feature mapping; GAN model with vs. without additional perceptual loss; and previously reported models as U-net and cycleGAN with or without identity loss. Image quality of deep-learning generated synthetic CT (sCT) images were quantitatively compared against the reference CT (rCT) image using mean absolute error (MAE) of Hounsfield units (HU) and peak signal-to-noise ratio (PSNR). The dosimetric calculation accuracy was further evaluated with both photon and proton beams. RESULTS The deep-learning generated synthetic CTs (sCT) showed improved image quality with reduced artifact distortion and improved soft tissue contrast. The proposed algorithm of 2.5 Pix2pix GAN with feature matching (FM) was shown to be the best model among all tested methods producing the highest PSNR and the lowest MAE to reference CT (rCT) . The dose distribution demonstrated a high accuracy in the scope of photon based planning, yet more work is needed for proton based treatment. Once the model was trained, it took 11-12 ms to process one slice, and could generate a 3D-volume of dCBCT (80 slices) in less than a second using a NVIDIA GeForce GTX Titan X GPU (12GB, Maxwell architecture). CONCLUSION The proposed deep-learning algorithm is promising to improve CBCT image quality in an efficient way, thus has a potential to support online CBCT-based adaptive radiotherapy.