Miracle-dz/UniFAR-FTUs huggingface dataset:
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

Autonomous surgical execution relieves tedious routines and surgeon’s fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promising performance for dexterous manipulation, which usually requires the simulation to collect data efficiently and reduce the hardware cost. The existing learning-based simulation platforms for medical robots suffer from limited scenarios and simplified physical interactions, which degrades the real-world performance of learned policies. In this work, we designed SurRoL, an RL-centered simulation platform for surgical robot learning compatible with the da Vinci Research Kit (dVRK). The designed SurRoL integrates a user-friendly RL library for algorithm development and a real-time physics engine, which is able to support more PSM/ECM scenarios and more realistic physical interactions. Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution. We evaluate SurRoL using RL algorithms in simulation, provide in-depth analysis, deploy the trained policies on the real dVRK, and show that our SurRoL achieves better transferability in the real world.
Deep Natural Language Processing for LinkedIn Search Systems

Many search systems work with large amounts of natural language data, e.g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help. In this paper, we introduce a comprehensive study of applying deep NLP techniques to five representative tasks in search engines. Through the model design and experiments of the five tasks, readers can find answers to three important questions: (1) When is deep NLP helpful/not helpful in search systems? (2) How to address latency challenges? (3) How to ensure model robustness? This work builds on existing efforts of LinkedIn search, and is tested at scale on a commercial search engine. We believe our experiences can provide useful insights for the industry and research communities.
Assessment of domain interactions in the fourteenth round of the Critical Assessment of Structure Prediction (CASP14)

The high accuracy of some CASP14 models at the domain level prompted a more detailed evaluation of structure predictions on whole targets. For the first time in critical assessment of structure prediction (CASP), we evaluated accuracy of difficult domain assembly in models submitted for multidomain targets where the community predicted individual evaluation units (EUs) with greater accuracy than full‐length targets. Ten proteins with domain interactions that did not show evidence of conformational change and were not involved in significant oligomeric contacts were chosen as targets for the domain interaction assessment. Groups were ranked using complementary interaction scores (F1, QS score, and Jaccard coefficient), and their predictions were evaluated for their ability to correctly model inter‐domain interfaces and overall protein folds. Target performance was broadly grouped into two clusters. The first consisted primarily of targets containing two EUs wherein predictors more broadly predicted domain positioning and interfacial contacts correctly. The other consisted of complex two‐EU and three‐EU targets where few predictors performed well. The highest ranked predictor, AlphaFold2, produced high‐accuracy models on eight out of 10 targets. Their interdomain scores on three of these targets were significantly higher than all other groups and were responsible for their overall outperformance in the category. We further highlight the performance of AlphaFold2 and the next best group, BAKER‐experimental on several interesting targets.
Clinical Risk Score for the Prediction of Incident Atrial Fibrillation: Derivation in 7 220 654 Taiwan Patients With 438 930 Incident Atrial Fibrillations During a 16‐Year Follow‐Up

Background Although several risk schemes have been proposed to predict new‐onset atrial fibrillation (AF), clinical prediction models specific for Asian patients were limited. In the present study, we aimed to develop a clinical risk score (Taiwan AF score) for AF prediction using the whole Taiwan population database with a long‐term follow‐up. Methods and Results Among 7 220 654 individuals aged ≥40 years without a past history of cardiac arrhythmia identified from the Taiwan Health Insurance Research Database, 438 930 incident AFs occurred after a 16‐year follow‐up. Clinical risk factors of AF were identified using Cox regression analysis and then combined into a clinical risk score (Taiwan AF score). The Taiwan AF score included age, male sex, and important comorbidities (hypertension, heart failure, coronary artery disease, end‐stage renal disease, and alcoholism) and ranged from −2 to 15. The area under the receiver operating characteristic curve of the Taiwan AF scores in the predictions of AF are 0.857 for the 1‐year follow‐up, 0.825 for the 5‐year follow‐up, 0.797 for the 10‐year follow‐up, and 0.756 for the 16‐year follow‐up. The annual risks of incident AF were 0.21%/year, 1.31%/year, and 3.37%/year for the low‐risk (score −2 to 3), intermediate‐risk (score 4 to 9), and high‐risk (score ≥10) groups, respectively. Compared with low‐risk patients, the hazard ratios of incident AF were 5.78 (95% CI, 3.76–7.75) for the intermediate‐risk group and 8.94 (95% CI, 6.47–10.80) for the high‐risk group. Conclusions We developed a clinical AF prediction model, the Taiwan AF score, among a large‐scale Asian cohort. The new score could help physicians to identify Asian patients at high risk of AF in whom more aggressive and frequent detections and screenings may be considered.
Submillimeter EPR of Co:Rb2MgF4 and anomalous g-values

We found new effect which changes g-values of pair and trimer Co2+ spins by EPR of Rb2Co0.3Mg0. 7F4 at 220, 370, 762.2 and 693.6 GHz in pulsed high magnetic fields. The anomalous g-values come from mixing of 1LS and strong exchange interaction.
Response to: Twelve tips to manage a breaking bad news process: Using S-P-w-ICE-S – A revised version of the SPIKES protocol

We read with interest the article by Meitar and KarnieliMiller (2021). As penultimate-year medical students, we have received communication skills teaching on the breaking bad news process and have learned significantly from the 12 tips outlined by the authors – many of which we will implement. Expanding on Tip 1, the importance of practice should not be overlooked when preparing to break bad news (BBN). In our ‘Clinical Communication Skills’ course at Cambridge University, we practise BBN with patient actors. These sessions are recorded, and recordings are sent individually to each student. This allows for constructive selfevaluation and improvement. For example, video-assisted reflection enables recognition of inappropriate tone of voice and non-verbal cues, which may otherwise be overlooked, yet can be easily corrected. Furthermore, this could be adapted to work remotely. Video conferencing software has been used successfully to facilitate BBN simulation sessions for medical students (Rivet et al. 2021). In this way, we suggest broadening Tip 1 to include practice with patient actors, which may be in-person or remote if necessary (for example with social distancing regulation amidst COVID-19). Although stepwise approaches (S-P-w-ICE-S) are useful, success comes with practice, and it is important to practise using these frameworks with actors, before taking the leap to breaking bad news to patients. Particularly for inexperienced practitioners, it can be challenging to remember the steps within the BBN process, which is why we appreciate the S-P-w-ICE-S acronym. Further still, other mnemonics, acronyms, and ‘soundbites’ can be used as memory aids to help practitioners to remember key steps within the framework. As another example, we suggest ‘chunking and checking’ to refer to giving small units of information with silence breaks, as outlined in Tip 7. Thirdly, we reiterate the importance of assessing patient understanding. In addition to ‘clarification questions’ (Tip 8), we suggest asking the patient to recall, in their own words, what they have been told. Active retrieval helps ensure understanding, while helping the practitioner to rectify any gaps. Finally, Tip 12 correctly discusses the importance of selfreflection for practitioners, to maintain his/her own wellbeing. However, for some, self-reflection is insufficient and other processes would better help with emotional wellbeing. We suggest that practitioners are made aware of both informal and formal discussion channels, including approachable colleagues or mental health and wellbeing hubs. Mindfulness may be effective for many, with the recent rise of meditation mobile applications including Headspace and Calm.
Comparative Analysis of SURF and MSER based Detection Technique of Cluttered Objects in Multi-objects Scene

: The completely unique methodologies for object detection in cluttered real pictures are proposed in this paper, where a single real time scenario is taken as model. The image edges are parted into contour segments & organized in an image representation that encodes their interconnections: the Contour segment Network. A computationally effective detection technique using Maximally Stable Extremal Regions (MSER) and Speeded up robust features (SURF) is developed to solve the issue of object detection, where paths are search through network & then resemble the model outlines with it. In this paper, real time cluttered images are considered and using proposed techniques such images to be detected from the complete scene. Simulated results show that both detection methods highlight accurate cluttered image. But, accuracy of matching the features among cluttered image and multiple objects in scene is highest in MSER while required computation time is lowest in SURF.
The comparative pathology of enterocolitis caused by Clostridium perfringens type C, Clostridioides difficile, Paeniclostridium sordellii, Salmonella enterica subspecies enterica serovar Typhimurium, and nonsteroidal anti-inflammatory drugs in horses

To determine if there were significant differences produced by 5 of the most prevalent causes of equine enterocolitis, we studied retrospectively the gross and microscopic pathology of 90 cases of enterocolitis submitted to the San Bernardino laboratory of the California Animal Health and Food Safety Laboratory. Included were cases caused by Clostridium perfringens type C (CP; n = 20), Clostridioides difficile (CD; n = 20), Paeniclostridium sordellii (PS; n = 15), Salmonella enterica subspecies enterica serovar Typhimurium (ST; n = 20), and NSAID intoxication (NS; n = 15). Grossly, necrotizing hemorrhagic typhlocolitis was seen most frequently in cases of CD, ST, and NS disease. Cases of CP and PS had enteritis or colitis in similar percentages. Congestion, hemorrhage, and pleocellular inflammatory infiltrates followed by mucosal and submucosal necrosis were the main lesions found in horses with enteritis or colitis produced by any of the etiologic agents investigated. Severe lesions were more frequent in cases of CD and CP than in cases associated with any of the other 3 etiologies. Pseudomembranes were observed with similar prevalence in the small intestine and colon affected by all agents studied. Thrombosis of the lamina propria and/or submucosa was observed in ~50% of the cases of enteritis and colitis by all etiologies, except for PS, in which the majority of the cases had thrombosis. Gross and microscopic lesions of enterocolitis were not sufficiently specific for any of these etiologic agents to enable these enteritides to be distinguished by gross and/or histologic examination.
Unsteady three-dimensional numerical simulations of methane combustion in dense fluidized bed

Natural gas combustion in dense fluidized beds containing inert particles involves complex physical mechanisms related to the bed hydrodynamic coupling with the gaseous combustion. In their experiments, Dounit et al. (Powder Technology, 2008) reported that for bed temperatures lower than a critical value (< 850 )C), almost all the combustion takes place above the bed surface. In the present study, detailed unsteady 3D CFD simulations of such experiments have been performed using NEPTUNE_CFD code, which is based on an Euler-Euler approach to compute both gas and solid phases. Time-averaged quantities were computed and compared with the available experimental measurements. The numerical results (gas temperature and gaseous-species molar fractions in and above the bed) are found to be very sensitive to the mesh refinement in such reactive dense gas-particle flows.
Review of Cardiac Malpractice Cases Involving a Trainee

Lawsuits involving medical trainees are underappreciated and poorly documented, including within cardiology-related fields. The purpose of this review was to characterize clinical characteristics and legal outcomes of cardiology- and cardiac surgery-related lawsuits involving trainees. Westlaw, an online legal research database containing legal records from around the United States, was retrospectively reviewed for malpractice cases involving medical students, residents, or fellows through November 2020. Cases included both cardiac and cardiac surgery cases. A total of 28 cases were identified, with 16 involving female patients (57%). In the 17 cases in which patient age was included, the median age was 51 years. A total of 22 (79%) cases resulted in death or permanent disability. The most common alleged errors included procedural issues (n = 14, 50%) and failure to diagnose (n = 7, 25%). A total of 14 cases (50%) ended in favor of the physicians, 9 (32%) ended in a settlement or verdict against the physician, and 5 cases had an unknown outcome. Of the 8 cases in which the settlement or verdict amount was reported, the median payment was $1,291,992 with a range of $220,507–$30,000,000. This review of cardiac and cardiac surgery malpractice cases involving a trainee suggests trainee involvement in procedures and diagnosis may confer relatively high liability risks for both trainees and their supervisors.
The antioxidant and antidiabetic potentials of polyphenolic-rich extracts of Cyperus rotundus (Linn.)

Abstract In this study, the rhizome of Cyperus rotundus L was investigated for its antioxidant and antidiabetic effects using in vitro and in silico experimental models. Its crude extracts (ethyl acetate, ethanol and aqueous) were screened in vitro for their antioxidant activity using ferric-reducing antioxidant power (FRAP) and 1,1-diphenyl-2-picrylhydrazyl (DPPH), as well as their inhibitory effect on α-glucosidase enzyme. Subsequently, the extracts were subjected to Gas Chromatography-Mass Spectrometry (GC-MS) analysis to elucidate their possible bioactive compounds. Furthermore, computational molecular docking of selected phenolic compounds was conducted to determine their mode of α-glucosidase inhibitory activity. The aqueous extract displayed the highest level of total phenolic content and significantly higher scavenging activity in both FRAP and DPPH assays compared to ethyl acetate and ethanol extracts. In FRAP and DPPH assays, IC50 values of aqueous extract were 448.626 µg/mL and 418.74 µg/mL, respectively. Aqueous extract further presented higher α-glucosidase inhibitory activity with an IC50 value of 383.75 µg/mL. GC-MS analysis revealed the presence of the following phenolic compounds: 4-methyl-2-(2,4,4-trimethylpentan-2-yl) phenol, Phenol,2-methyl-4-(1,1,3,3-tetramethylbutyl)- and 1-ethoxy-2-isopropylbenzene. Molecular docking study revealed 1-ethoxy-2-isopropylbenzene formed two hydrogen bonds with the interacting residues in the active site of α-glucosidase enzyme. Furthermore, 4-methyl-2-(2,4,4-trimethylpentan-2-yl) phenol had the lowest binding energy inferring the best affinity for α-glucosidase active site. These results suggest the possible antioxidant and antidiabetic potential of Cyperus rotundus. Communicated by Ramaswamy H. Sarma
H‐ferritin expression in astrocytes is necessary for proper oligodendrocyte development and myelination

How iron is delivered to the CNS for myelination is poorly understood. Astrocytes are the most abundant glial cells in the brain and are the only cells in close contact with blood vessels. Therefore, they are strategically located to obtain nutrients, such as iron, from circulating blood. To determine the importance of astrocyte iron uptake and storage in myelination and remyelination, we conditionally knocked‐out the expression of the divalent metal transporter 1 (DMT1), the transferrin receptor 1 (Tfr1), and the ferritin heavy subunit (Fth) in Glast‐1‐positive astrocytes. DMT1 or Tfr1 ablation in astrocytes throughout early brain development did not significantly affects oligodendrocyte maturation or iron homeostasis. However, blocking Fth production in astrocytes during the first postnatal week drastically delayed oligodendrocyte development and myelin synthesis. Fth knockout animals presented an important decrease in the number of myelinating oligodendrocytes and a substantial reduction in the percentage of myelinated axons. This postnatal hypomyelination was accompanied by a decline in oligodendrocyte iron uptake and with an increase in brain oxidative stress. We also tested the relevance of astrocytic Fth expression in the cuprizone model of myelin damage and repair. Fth deletion in Glast1‐positive astrocytes significantly reduced myelin production and the density of mature myelinating oligodendrocytes throughout the complete remyelination process. These results indicate that Fth iron storage in astrocytes is vital for early oligodendrocyte development as well as for the remyelination of the CNS.
Effect of MicroRNA-146a Modified Adipose-Derived Stem Cell Exosomes on Rat Back Wound Healing

Objective: To investigate the effect of MicroRNA-146a modified adipose-derived stem cell exosomes on the proliferation and migration of fibroblasts and the therapeutic effect on wound healing. Methods: Culture and identification of human adipose-derived stem cells (hASCs), miRNA-146a minic vector was constructed and transfected into hASCs, the exosomes of the empty group and overexpression group were extracted, identified, and quantitatively analyzed after 24 h of successful transfection. The exosomes were added into National Institute of Health Mouse Embryonic Fibroblasts (NIH/3T3) and cultured for 48 h, the proliferation and migration ability of NIH/3T3 fibroblasts was detected. The expression of serpin family H member 1 (SERPINH1) and phosphorylated extracellular regulated protein kinase (p-ERK) was detected by Western blot. The model of back wound was established. The exosomes were injected into 4 different sites with the shape of “cross” around the wound, and the scar diameter of the skin defect was measured at 3, 7, and 11 days, the skin of the defect was taken on the 14th day. platelet endothelial cell adhesion molecule-1 (CD31) was detected by immunofluorescence staining to evaluate angiogenesis, and Western blot was used to detect the expression of SERPINH1 and p-ERK. Results: The miR-146a mimic-exosome promoted the proliferation and migration of fibroblasts, and the expression of SERPINH1 and p-ERK2 was up-regulated. After the rats were treated with exosomes, the wound area decreased rapidly, neovascularization was promoted, and the expression of SERPINH1 and p-ERK2 was up-regulated. Conclusions: MicroRNA-146a modified adipose stem cell exosomes could regulate the expression of SERPINH1 and p-ERK, promote the migration and proliferation of fibroblasts, and neovascularization to promote the wound healing of rat back.
Feature Importance in a Deep Learning Climate Emulator

We present a study using a class of post-hoc local explanation methods i.e., feature importance methods for"understanding"a deep learning (DL) emulator of climate. Specifically, we consider a multiple-input-single-output emulator that uses a DenseNet encoder-decoder architecture and is trained to predict interannual variations of sea surface temperature (SST) at 1, 6, and 9 month lead times using the preceding 36 months of (appropriately filtered) SST data. First, feature importance methods are employed for individual predictions to spatio-temporally identify input features that are important for model prediction at chosen geographical regions and chosen prediction lead times. In a second step, we also examine the behavior of feature importance in a generalized sense by considering an aggregation of the importance heatmaps over training samples. We find that: 1) the climate emulator's prediction at any geographical location depends dominantly on a small neighborhood around it; 2) the longer the prediction lead time, the further back the"importance"extends; and 3) to leading order, the temporal decay of"importance"is independent of geographical location. An ablation experiment is adopted to verify the findings. From the perspective of climate dynamics, these findings suggest a dominant role for local processes and a negligible role for remote teleconnections at the spatial and temporal scales we consider. From the perspective of network architecture, the spatio-temporal relations between the inputs and outputs we find suggest potential model refinements. We discuss further extensions of our methods, some of which we are considering in ongoing work.
Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation

The linear Support Vector Machine (SVM) is a classic classification technique in machine learning. Motivated by applications in modern high dimensional statistics, we consider penalized SVM problems involving the minimization of a hinge-loss function with a convex sparsity-inducing regularizer such as: the L1-norm on the coefficients, its grouped generalization and the sorted L1-penalty (aka Slope). Each problem can be expressed as a Linear Program (LP) and is computationally challenging when the number of features and/or samples is large -- the current state of algorithms for these problems is rather nascent when compared to the usual L2-regularized linear SVM. To this end, we propose new computational algorithms for these LPs by bringing together techniques from (a) classical column (and constraint) generation methods and (b) first order methods for non-smooth convex optimization -- techniques that are rarely used together for solving large scale LPs. These components have their respective strengths; and while they are found to be useful as separate entities, they have not been used together in the context of solving large scale LPs such as the ones studied herein. Our approach complements the strengths of (a) and (b) -- leading to a scheme that seems to significantly outperform commercial solvers as well as specialized implementations for these problems. We present numerical results on a series of real and synthetic datasets demonstrating the surprising effectiveness of classic column/constraint generation methods in the context of challenging LP-based machine learning tasks.