Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Evaluation of the Incidence of Postoperative Pulmonary Complications After Minimally Invasive Valve Surgery vs. Full Sternotomy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials and Propensity Score-Matched Studies

Background: Postoperative pulmonary complications remain a leading cause of increased morbidity, mortality, longer hospital stays, and increased costs after cardiac surgery; therefore, our study aims to analyze whether minimally invasive valve surgery (MIVS) for both aortic and mitral valves can improve pulmonary function and reduce the incidence of postoperative pulmonary complications when compared with the full median sternotomy (FS) approach. Methods: A comprehensive systematic literature research was performed for studies comparing MIVS and FS up to February 2021. Randomized controlled trials (RCTs) and propensity score-matching (PSM) studies comparing early respiratory function and pulmonary complications after MIVS and FS were extracted and analyzed. Secondary outcomes included intra- and postoperative outcomes. Results: A total of 10,194 patients from 30 studies (6 RCTs and 24 PSM studies) were analyzed. Early mortality differed significantly between the groups (MIVS 1.2 vs. FS 1.9%; p = 0.005). Compared with FS, MIVS significantly lowered the incidence of postoperative pulmonary complications (odds ratio 0.79, 95% confidence interval [0.67, 0.93]; p = 0.004) and improved early postoperative respiratory function status (mean difference −24.83 [−29.90, −19.76]; p < 0.00001). Blood transfusion amount was significantly lower after MIVS (p < 0.02), whereas cardiopulmonary bypass time and aortic cross-clamp time were significantly longer after MIVS (p < 0.00001). Conclusions: Our study showed that minimally invasive valve surgery decreases the incidence of postoperative pulmonary complications and improves postoperative respiratory function status.
MicroRNA-221-3p Suppresses the Microglia Activation and Seizures by Inhibiting of HIF-1α in Valproic Acid-Resistant Epilepsy

One-third of patients with epilepsy suffer from drug-resistant epilepsy (DRE). Valproic acid (VPA) is a classic anticonvulsant drug, and its resistance is a crucial predictor of DRE, but the pathogenesis remain unknown. Most patients with VPA-resistant epilepsy appear distinct inflammatory response and local hypoxia. Hypoxia-inducible factor (HIF)-1α is an essential effector molecule of hypoxia and inflammation, and may exert therefore a significant effect on the development of VPA-resistant epilepsy. We systematically assess the significance of HIF-1α on children and mice with VPA-resistant epilepsy, and investigated the micro (mi) RNAs that regulate HIF-1α expression. We established models of VPA-sensitive epilepsy and VPA-resistant epilepsy in mice, and confirmed that they had significant differences in epileptic behavior and electroencephalography data. Through proteomics analysis, we identified that HIF-1α was overexpressed in mice with VPA-resistant epilepsy, and regulated the expression of interleukin-1β and tumor necrosis factor-α. Increased expression of HIF-1α led to the increase of microglia and induced their polarization from the M2 phenotype to M1 phenotype, which triggered the release of proinflammatory mediators. Bioinformatics analysis of public databases demonstrated that miR-221-3p was reduced in VPA-resistant epilepsy, and negatively regulated HIF-1α expression. Intervention using miR-221-3p mimics reduced HIF-1α expression markedly and suppressed the activation of microglia and the release of inflammatory mediators, which relieved epileptic seizures of VPA-resistant epilepsy. These observations reveal miR-221-3p/HIF-1α as essential component in pathogenesis of VPA-resistant epilepsy which represent therapeutic antiseizure targets.
Awareness, Attitude, and Current Practices Toward Influenza Vaccination Among Physicians in India: A Multicenter, Cross-Sectional Study

Background: Although annual influenza vaccination is recommended for healthcare providers (HCPs), vaccination rate among HCPs in India is generally low. This cross-sectional survey was conducted to evaluate physicians' awareness, attitude, and current practices toward influenza vaccination in high-risk groups in India. Methods: The survey was performed in June–July 2020, wherein consulting physicians, pulmonologists, diabetologists, obstetricians/gynecologists, or cardiologists across 14 cities completed a 39-item questionnaire consisting of 3 sections, one each on awareness, attitude, and practice patterns. Descriptive statistics were used to summarize the study results. Statistical analysis was performed for comparison of subgroups by physician specialty, city of practice (metro/non-metro), and zone of practice (north/south/east/west). Level of statistical significance was set at p < 0.05. Results: In all, 780 physicians completed the survey. Of these, 3.97, 53.08, and 42.95% had high, medium, and low level of awareness about influenza/influenza vaccination, respectively. Statistically significant (p < 0.05) between-group differences were found by physician specialty and zone of practice. In terms of attitude toward vaccination of high-risk group subjects, only 0.9% physicians were “extremely concerned,” while the majority (92.56%) were “quite concerned” and 6.54% were a “little concerned,” with no reported significant differences between different subgroups. With regard to practice patterns, 82.82% of physicians offered influenza vaccines to their patients, 32.69% vaccinated 10–25% of patients per month, and 38.85% required and offered the vaccine to their office staff. Physicians' reasons for not prescribing influenza vaccines to patients included fear of side effects (16.54%), cost (15.64%), lack of awareness about availability (15.38%), absence of belief that it is beneficial (14.36%), history of side effects (13.46%), and patients' fear of needles (11.28%). Conclusion: These findings suggest the need to implement educational strategies among physicians to enhance their awareness about influenza vaccination and improve their attitudes and current practices toward influenza vaccination especially in high-risk groups in India.
Interventional effect of Codonopsis pilosula oligosaccharides against d-galactose-induced aging in SD rats via suppression of oxidative stress, inflammation, and apoptosis

Abstract Oligosaccharides are one of the major bioactive components in Codonopsis pilosula. The present study was to investigate the protective effect of C. pilosula oligosaccharides (CPO) on d-galactose (D-Gal)-induced aging and to illuminate the underlying mechanisms. The results indicated that CPO effectively increased the growth rate as indicated by body weight, attenuated the decline of thymus and liver indexes, and mitigated the pathological liver injury compared with model group rats. CPO could elevate the activities of anti-oxidative enzymes, including CAT, GSH-Px, and SOD, and reduce MDA level in serum, improve SOD and GSH-Px activities in liver, down-regulate the phosphorylation of MAPK cascade signal transduction components – ERK, JNK, and p38, and inhibit the activation of downstream NF-κB signaling. In addition, CPO treatment noticeably prevented the overexpression of inflammatory factors such as TNF-α, IL-6, and IL-1β in serum, inhibited the expression of apoptosis-related proteins caspase-3, caspase-9, and the ratio of Bax/Bcl-2 in liver caused by the inflammatory cascade, reduced cell apoptosis, and improved liver damage. The above studies suggested that CPO could effectively mitigate the D-Gal-induced aging, and the underlying mechanism might be closely related to ROS-induced oxidative stress, inflammation, and apoptosis.
An Inhibitory Medial Preoptic Circuit Mediates Innate Exploration

Animals have an innate motivation to explore objects and environments with unknown values. To this end, they need to activate neural pathways that enable exploration. Here, we reveal that photostimulation of a subset of medial preoptic area (MPA) neurons expressing the vesicular-GABA transporter gene (vgat+) and sending axonal projections to the ventrolateral periaqueductal gray (vPAG) increases exploration in a chamber but causes no place preference when tested there without photostimulation. Photoinhibition of MPAvgat–vPAG projections leads to no emotional changes as measured by normal activity in an open field assay. Electrophysiological recordings revealed that most GABAergic vPAG neurons are inhibited by MPAvgat neurons. In contrast to a previous report that suggested that MPAvgat–vPAG neurons may impart positive valence to induce place preference, our results suggest that these neurons can increase innate exploration.
Intensive Dialectical Behavior Treatment for Individuals With Borderline Personality Disorder With and Without Substance Use Disorders

Treatment of borderline personality disorder (BPD) with comorbid substance use disorder can be challenging due to symptom overlap and limited assessment methods. Preliminary evidence has shown promising effectiveness of dialectical behavioral therapy (DBT) for BPD with comorbid substance use disorders. The current study compared the benefits of a 28-day transitional DBT treatment program for individuals with BPD with and without substance use disorders through evaluating the changes in coping skills, generalized anxiety, and depression symptom scales at admission and discharge. A total of 76 patients were split into two groups: Group 1 consisted of individuals with BPD without substance use disorders (n = 41), and Group 2 involved individuals with BPD and a substance use disorder (SUD) (n = 35). A univariate general linear model showed significant differences between the two groups in improvement of coping skills and depressive symptoms. After a 28-day transitional DBT treatment program there were significant decreases from severe to moderate depression scores in both groups. Our findings support the effectiveness of DBT treatment in patients with comorbid BPD and SUD.
A Structured Modular Approach: The Answer to Training in Laparoscopic Colorectal Surgery

Bakground: Laparoscopic techniques are now an integral part of the operative management of colorectal diseases. However, the specialist training that is required for this is not uniformly available. There is, therefore, a need for a structured competency-based training method so that trainees can navigate the learning curve safely. Aim. To develop a modular structured training programme for laparoscopic colorectal surgery (LCS) with the capability of ensuring competency-based progression from a novice level to independent operator. Methodology. Over the past decade, we have developed a structured approach, starting with junior surgical trainees and progressing through to consultant level, with 7 clearly defined levels of progression attending courses to achieving a trainer status. This approach allows trainees to maintain objective records of their progression and trainers to provide targeted learning opportunities. It also allows for several trainees of varying experience to be trained during the same procedure. Conclusion. Our structured training module for junior surgeons has successfully produced several competent laparoscopic colorectal surgeons in the United Kingdom and around the world. This approach may also be adaptable to training in other laparoscopic procedures as the levels of progression are generic and not procedure-specific.
Model sharing in the human medial temporal lobe

Effective planning involves knowing where different actions take us. However, natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution is to filter out features of the environment irrelevant to the task at hand. This enables a shared model of transition dynamics to be used for planning over a range of different input features. Here, we asked human participants (13 male, 16 female) to perform a sequential decision-making task, designed so that knowledge should be integrated independently of the input features (visual cues) present in one case but not in another. Participants efficiently switched between using a low-dimensional (cue independent) and a high-dimensional (cue specific) representation of state transitions. fMRI data identified the medial temporal lobe as a locus for learning state transitions. Within this region, multivariate patterns of BOLD responses were less correlated between trials with differing input features but similar state associations in the high dimensional than in the low dimensional case, suggesting that these patterns switched between separable (specific to input features) and shared (invariant to input features) transition models. Finally, we show that transition models are updated more strongly following the receipt of positive compared with negative outcomes, a finding that challenges conventional theories of planning. Together, these findings propose a computational and neural account of how information relevant for planning can be shared and segmented in response to the vast array of contextual features we encounter in our world. SIGNIFICANCE STATEMENT Effective planning involves maintaining an accurate model of which actions take us to which locations. But in a world awash with information, mapping actions to states with the right level of complexity is critical. Using a new decision-making “heist task” in conjunction with computational modeling and fMRI, we show that patterns of BOLD responses in the medial temporal lobe—a brain region key for prospective planning—become less sensitive to the presence of visual features when these are irrelevant to the task at hand. By flexibly adapting the complexity of task-state representations in this way, state-action mappings learned under one set of features can be used to plan in the presence of others.
Physics use of mathematics

Orthodox physics makes extensive use of number relation mathematics such as mapping, probability, and infinite series. This mathematics is devoid of causative relations. Other scientific disciplines such as medicine and chemistry use causative models. Using causative models would advance physics. Causation should be overtly stated in the mathematics. Causation is linked with emergence philosophy and not reductionism.
Inference for Dependent Data with Cluster Learning

: This paper proposes a cluster-based inferential procedure. Observations are grouped into clusters which are learned using a unsupervised learning algorithm given a dissimilarity measure. We consider a set of cluster-based inference procedures on the learned clusters. We give conditions under which our procedure asymptotically attains correct size. We illustrate the ﬁnite sample validity and apply our procedure to an empirical example.
GLOS: a global and local features oriented link prediction technique in social network

The link prediction has attracted majority of researchers from various domains since the beginning of behavioral science. For instance, online social networks such as Twitter, LinkedIn and Facebook change rapidly as new users appear in the graph. For all these networks, the more challenging task is to ﬁnd and recommend friends to the users. In case of social graph, the foremost objective of link prediction is to predict which new links are likely to be appearing from the actual state of the graph. Varieties of methods have been developed such as probabilistic, maximum likelihood and similarity-based techniques where similarity-based techniques are considered as the best prediction methods. Similarity-based methods uses a strategy, where each pair of nodes assigned a similarity score such that more similar nodes have more chances to connect in a future. Similarity estimation works on the global and local features i.e. path, random walk and neighbors. Local features are those features of node that consider at node level i.e. adjacent neighbors nodes. On the other hand, global features are those type of features that considers at graph level i.e. path between two nodes. Our hypothesis is that the combination of both local and global features is more powerful predictor for link formation. Here in this study, we have evaluated global, local and hybrid similarity measures. Moreover, we also proposed a hybrid approach GLOS. We performed experiments on ﬁve di ﬀ erent dataset (Astor, CondMat, GrQc, HepPh and HepTh). After the result evaluation, it is found that, hybrid approach GLOS obtained the highest accuracy by 1 on all the dataset, while, global approaches could not produced lowest accuracy on all dataset. On the other hand, HP from local similarity outperformed than rest of the local and global approaches.
Bottom-up Discovery of Reusable Sensorimotor Skills from Unstructured Demonstrations

: We present a bottom-up approach to discovering reusable sensorimo-1 tor skills from unstructured demonstrations. Our approach uses unsupervised ag-2 glomerative clustering to construct a hierarchical task structure from each demon-3 stration based on multi-sensory cues. A library of skills can thus be identiﬁed 4 from the recurring patterns across multi-task demonstrations and trained with hi-5 erarchical behavioral cloning. With these learned skills, we use a meta controller 6 to compose them to tackle complex manipulation tasks. The entire model can 7 be trained on a small set of human demonstrations collected within 30min without 8 further annotations, making it amendable to real-world deployment. We systemat-9 ically evaluate our method in simulation environments and on a real robot. Results 10 show that our method is capable of solving multi-stage tasks with closed-loop sen-11 sorimotor policies, and learning skills on multiple tasks improves task success by 12 8% on average compared to learning skills for individual tasks. 13
On the Convergence of Convolutional Approximate Message-Passing for Gaussian Signaling

SUMMARY Convolutional approximate message-passing (CAMP) is an eﬃcient algorithm to solve linear inverse problems. CAMP aims to realize advantages of both approximate message-passing (AMP) and orthogonal/vector AMP. CAMP uses the same low-complexity matched-ﬁlter as AMP. To realize the asymptotic Gaussianity of estimation errors for all right-orthogonally invariant matrices, as guaranteed in orthogonal/vector AMP, the Onsager correction in AMP is replaced with a convolution of all precedingmessages. CAMPwasprovedtobeasymptoticallyBayes-optimal if a state-evolution (SE) recursion converges to a ﬁxed-point (FP) and if the FP is unique. However, no proofs for the convergence were provided. This paper presents a theoretical analysis for the convergence of the SE recursion. Gaussian signaling is assumed to linearize the SE recursion. A condition for the convergence is derived via a necessary and suﬃcient condition for which the linearized SE recursion has a unique stationary solution. The SE recursion is numerically veriﬁed to converge toward the Bayes-optimal solution if and only if the condition is satisﬁed. CAMPiscomparedtoconjugategradient(CG)forGaussiansignaling intermsoftheconvergenceproperties. CAMPisinferiortoCGformatrices with a large condition number while they are comparable to each other for a small condition number. These results imply that CAMP has room for improvement in terms of the convergence properties.
GeDi: Generative Discriminator Guided Decoding for Faster Controllable Sequence Generation

While large-scale language models (LMs) are 001 able to imitate the distribution of natural lan-002 guage well enough to generate realistic text, 003 it is difﬁcult to control which regions of the 004 distribution they generate. This is especially 005 problematic because datasets used for train-006 ing large LMs usually contain signiﬁcant toxi-007 city, hate, bias, and negativity. One promising 008 approach to address this is to use discrimina-009 tors to guide decoding from LMs, but exist-010 ing methods for this are too slow to be useful 011 in practice for many applications. We present 012 GeDi as a signiﬁcantly faster discriminator-013 based approach for guiding decoding. GeDi 014 guides generation at each step by computing 015 classiﬁcation probabilities for all possible next 016 tokens via Bayes rule by normalizing over 017 two class-conditional distributions; one condi-018 tioned on the desired attribute, or control code , 019 and another conditioned on the undesired at-020 tribute, or anti control code . We ﬁnd that GeDi 021 gives controllability on par with or better than 022 the state of the art discriminator-based method 023 in a variety of settings, while also achieving 024 generation speeds more than 30 times faster. 025 We also show that GeDi can make GPT-2 and 026 GPT-3 signiﬁcantly less toxic while maintain-027 ing linguistic ﬂuency, without sacriﬁcing sig-028 niﬁcantly on generation speed. Lastly, we ﬁnd 029 training GeDi on only three topics allows us 030 to controllably generate new topics zero-shot 031 from just a keyword. 032
AKR1B10 expression in benign prostatic hyperplasia and its related mechanism

The aim of the present study was to investigate the expression of aldo-keto reductase family 1 member B10 (AKR1B10) in benign prostatic hyperplasia (BPH) and its related mechanism. In total, 142 BPH patients admitted from March 2017 to March 2019 at the First Hospital of Hunan University of Chinese Medicine and 140 healthy people undergoing physical examination were selected as the research subjects. The clinical value of AKR1B10 in BPH was analyzed. Twenty clean SD rats were selected, and 10 were selected to establish the prostate hyperplasia model, while the remaining 10 were set as the control group. Ten days after the model was established, AKR1B10 and NF-κB expression in prostate tissues of rats in both groups was detected by PCR and immunohistochemistry. The primary cells in prostate hyperplasia were cultured, and then they were transfected with AKR1B10 to observe the changes of cell biological behavior. AKR1B10 and NF-κB mRNA significantly increased in peripheral blood of BPH patients and prostate tissue of BPH model rats (P<0.001), and AKR1B10 had good diagnostic value for BPH (P<0.001). In addition, it was positively correlated with PSA, EGF, IL-6 and TNF-α (P<0.001). After transfection with AKR1B10-inhibitor, it was revealed that the proliferation of prostate hyperplasia cells decreased, while the apoptosis of prostate hyperplasia cells increased and the NF-κB protein expression decreased (P<0.001). Collectively, high expression of AKR1B10 in BPH promoted the proliferation of prostate cells and reduced their apoptosis, and the mechanism may be through regulation of NF-κB.
Just Label What You Need: Fine-Grained Active Selection for P&P through Partially Labeled Scenes

: Self-driving vehicles must perceive and predict the future positions of nearby actors to avoid collisions and drive safely. A deep learning module is often responsible for this task, requiring large-scale, high-quality training datasets. Due to high labeling costs, active learning approaches are an appealing solution to maximizing model performance for a given labeling budget. However, despite its appeal, there has been little scientiﬁc analysis of active learning approaches for the perception and prediction (P&P) problem. In this work, we study active learning techniques for P&P and ﬁnd that the traditional active learning formulation is ill-suited. We thus introduce generalizations that ensure that our approach is both cost-aware and allows for ﬁne-grained selection of examples through partially labeled scenes. Extensive experiments on a real-world dataset suggest signiﬁcant improvements across perception, prediction, and downstream planning tasks.