Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor Fibroblast Growth Factor Receptor 4 Level Predicts the Efficacy of Lenvatinib in Patients With Advanced Hepatocellular Carcinoma

OBJECTIVES: Biomarkers for optimizing the outcome of treatment with lenvatinib in patients with advanced hepatocellular carcinoma remain to be established despite intensive and comprehensive genomic research. Lenvatinib is characterized by its prominent inhibitory potency for fibroblast growth factor receptor (FGFR) 4 compared with earlier tyrosine kinase inhibitors. Thus, in this study, we focused on simplified quantification of FGFR4 in tumors as a potential predictive indicator. METHODS: According to The Cancer Genome Atlas data set curation, FGFR4 messenger RNA is broadly overexpressed in hepatocellular carcinoma in the absence of gene alteration. Gene set enrichment analysis revealed that the aggressiveness of the tumor was closely related to the FGFR4 level. To confirm the relationship between the benefits of lenvatinib and tumor addiction to the FGFR4 pathway, we analyzed protein levels in tumors and peripheral blood obtained from 57 prospectively registered patients treated with lenvatinib. RESULTS: Positive immunohistochemistry (>10% of tumor cells) for FGFR4 in biopsy samples before treatment was associated with a longer progression-free survival (2.5 vs 5.5 months, P = 0.01) and a favorable objective response rate (31% vs 81%, P = 0.006). By contrast, the concentration of soluble FGFR4 in peripheral blood as measured by an enzyme-linked immunosorbent assay was not associated with survival outcomes, because its fluctuations reflect hepatic fibrosis. Additional RNA sequencing analysis using archival surgical specimens (n = 90) suggested that alternative RNA splicing of FGFR4 in cancer may also explain this discrepancy. DISCUSSION: The tumor FGFR4 level was an independent predictor of response to lenvatinib.
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning

Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use—using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the “sample, simulate, update” (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem solving.
Learning Optimal Tree Models Under Beam Search

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems. Tree models, which formulate targets as leaves of a tree with trainable node-wise scorers, have attracted a lot of interests in tackling this challenge due to their logarithmic computational complexity in both training and testing. Tree-based deep models (TDMs) and probabilistic label trees (PLTs) are two representative kinds of them. Though achieving many practical successes, existing tree models suffer from the training-testing discrepancy, where the retrieval performance deterioration caused by beam search in testing is not considered in training. This leads to an intrinsic gap between the most relevant targets and those retrieved by beam search with even the optimally trained node-wise scorers. We take a first step towards understanding and analyzing this problem theoretically, and develop the concept of Bayes optimality under beam search and calibration under beam search as general analyzing tools for this purpose. Moreover, to eliminate the discrepancy, we propose a novel algorithm for learning optimal tree models under beam search. Experiments on both synthetic and real data verify the rationality of our theoretical analysis and demonstrate the superiority of our algorithm compared to state-of-the-art methods.
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids

The power consumption of households has been constantly growing over the years. To cope with this growth, intelligent management of the consumption profile of the households is necessary, such that the households can save the electricity bills, and the stress to the power grid during peak hours can be reduced. However, implementing such a method is challenging due to the existence of randomness in the electricity price and the consumption of the appliances. To address this challenge, in this article, we employ a model-free method for the households, which works with limited information about the uncertain factors. More specifically, the interactions between households and the power grid can be modeled as a noncooperative stochastic game, where the electricity price is viewed as a stochastic variable. To search for the Nash equilibrium (NE) of the game, we adopt a method based on distributed deep reinforcement learning. Also, the proposed method can preserve the privacy of the households. We then utilize real-world data from Pecan Street Inc., which contains the power consumption profile of more than 1000 households, to evaluate the performance of the proposed method. In average, the results reveal that we can achieve around $12\%$ reduction on peak-to-average ratio and $11\%$ reduction on load variance. With this approach, the operation cost of the power grid and the electricity cost of the households can be reduced.
Political polarization drives online conversations about COVID‐19 in the United States

Abstract Since the outbreak in China in late 2019, the novel coronavirus (COVID‐19) has spread around the world and has come to dominate online conversations. By linking 2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution of online discussions about COVID‐19. We show that COVID‐19 chatter in the United States is largely shaped by political polarization. Partisanship correlates with sentiment toward government measures and the tendency to share health and prevention messaging. Cross‐ideological interactions are modulated by user segregation and polarized network structure. We also observe a correlation between user engagement with topics related to public health and the varying impact of the disease outbreak in different U.S. states. These findings may help inform policies both online and offline. Decision‐makers may calibrate their use of online platforms to measure the effectiveness of public health campaigns, and to monitor the reception of national and state‐level policies, by tracking in real‐time discussions in a highly polarized social media ecosystem.
Critical Event Debriefing in a Community Hospital

Introduction Medical error is currently the third major cause of death in the United States after cardiac disease and cancer. A significant number of root cause analyses performed revealed that medical errors are mostly attributed to human errors and communication gaps. Debriefing has been identified as a major tool used in identifying medical errors, improving communication, reviewing team performance, and providing emotional support following a critical event. Despite being aware of the importance of debriefing, most healthcare providers fail to make use of this tool on a regular basis, and very few studies have been conducted in regard to the practice of debriefing. This study ascertains the frequency, current practice, and limitations of debriefing following critical events in a community hospital. Design/Methods This was a cross-sectional observational study conducted among attending physicians, physician assistants, residents, and nurses who work in high acuity areas located in the study location. Data on current debriefing practices were obtained and analyzed using descriptive statistics. Results A total of 130 respondents participated in this study. Following a critical event in their department, 65 (50%) respondents reported little (<25% of the time) or no practice of debriefing and only 20 (15.4%) respondents reported frequent practice (>75% of the time). Debriefing was done more than once a week as reported by 35 (26.9%) of the respondents and was led by attending physicians 77 (59.2%). The debrief session sometimes occurred immediately following a critical event (46.9%). Although 118 (90%) of the respondents feel that there is a need to receive some training on debriefing, only 51 (39%) of the respondents have received some form of formal training on the practice of debriefing. Among the healthcare providers who had some form of debriefing in their practice, the few debrief sessions held were to discuss medical management, identify problems with systems/processes, and provide emotional support. Increased workload was identified by 92 (70.8%) respondents as the major limitations to the practice of debriefing. Most respondents support that debriefing should be done immediately after a critical event such as death of a patient (123 [94.6%]), trauma resuscitation (108 [83.1%]), cardiopulmonary arrest (122 [93.8%]), and multiple casualty/disasters (95 [73.1%]). Conclusions In order to reduce medical errors, hospitals and its management team must create an environment that will encourage all patient care workers to have a debriefing session following every critical event. This can be achieved by organizing formal training, creating a template/format for debriefing, and encouraging all hospital units to make this an integral part of their work process.
Predictors of Outcome in Juvenile Myoclonic Epilepsy

Background Juvenile myoclonic epilepsy (JME) is the most common idiopathic generalized epilepsy syndrome, accounting for 10% of all epilepsy. However, there is limited information regarding the predictors of seizure outcome. The aim of this study was to determine the predictors of seizure outcome in JME patients. Methods A population-based retrospective study of JME patients who were treated at the Department of Neurology of affiliated Zhongshan Hospital, Xiamen University from 2008 to 2013. Results Sixty-three patients (30 women and 33 men) were enrolled in this study. The median age at seizure onset was 14 years old, and the average duration of epilepsy was 5 years. The onset of JME at age <16 years was found in 63.5% of patients. The epileptiform runs ≥3 s were observed in 35.8% patients. Febrile seizure was noted in 28.9% of patients. Among 63 patients, 40 patients (63.5%) had remission. Multivariate analysis identified the following factors as significant predictors of seizure outcome: the onset of JME at age <16 years, epileptiform runs ≥3 s runs, and febrile seizure. Conclusion The onset of JME at age <16 years, febrile seizures and epileptiform runs ≥3 s might be associated with poor long-term seizure outcome in patients with JME.
Spatial and Temporal Distribution of Soil Moisture in Drip-irrigated Vineyards

In arid and semiarid areas, wine grapes are frequently managed using regulated deficit irrigation (RDI) to control vegetative growth. To understand the distribution of soil moisture using RDI in a drip-irrigated vineyard, we collected soil samples after several irrigation events around six drip emitters in two 'Cabernet Sauvignon' and two 'Merlot' vineyards from late July through Mar. 2002 and 2005. The March sampling depicts soil moisture status before budbreak after winter precipitation. Soil samples were collected in four depth increments at 16 locations in a half-circle radius from immediately below the emitter to a depth of 60 cm. Both gravimetric and volumetric soil moisture content were determined. Soil moisture varied by depth, distance from the emitter, and sampling time. During late-season irrigation events, 50% to 75% of the sampled area contained plant-available water, which was less than expected. When calculated as plant-available soil moisture, regardless of time of sampling, soil sampled across a 0- to 45-cm depth provided the most representative indication of soil moisture status. Additionally, sampling directly under the emitter or directly under the drip line could result in skewed measurements compared with the sampled area. The data suggest that collecting soil samples within a 20- to 40-cm radius, either diagonal or perpendicular to the drip line emitter position, will best reflect the amount of plant-available soil water. Additionally, monitoring should be conducted on both sides of the row around each emitter selected and then averaged to avoid any patterns from hilling or disruption in water flow patterns.
Considerations for large building water quality after extended stagnation

The unprecedented number of building closures related to the coronavirus disease (COVID‐19) pandemic is concerning because water stagnation will occur in many buildings that do not have water management plans in place. Stagnant water can have chemical and microbiological contaminants that pose potential health risks to occupants. Health officials, building owners, utilities, and other entities are rapidly developing guidance to address this issue, but the scope, applicability, and details included in the guidance vary widely. To provide a primer of large building water system preventative and remedial strategies, peer‐reviewed, government, industry, and nonprofit literature relevant to water stagnation and decontamination practices for plumbing was synthesized. Preventative practices to help avoid the need for recommissioning (e.g., routine flushing) and specific actions, challenges, and limitations associated with recommissioning were identified and characterized. Considerations for worker and occupant safety were also indicated. The intended audience of this work includes organizations developing guidance.
Interactions between UsnRNP proteins and their cognate RNAs

tesplte the great sequence similarity between U1A and J2B", both proteins do have a difference in RNA linding specificity and In the way they bind to their ;ognate RNAs. The U1A protein Is able to bind ¡n vitro Л RNA independently of other factors. The U2B" >rotein binds specifically to U2 RNA in the presence )f the U2A' protein only. We have compared the effect >n RNA binding of multiple double point mutations at inalogous positions in the U1A and U2B" protein. The «suits obtained show that amino acids at almost all >f the analogous positions tested in U1A and U2B" lave a comparable qualitative effect on RNA binding ilthough the quantitative effect of mutations on U2B" s more severe than on U1A. Using U1A mutants with nternal duplications a distinct area of the RNP motif >f the U1A protein was identified which appears not to >e directly involved In U1 RNA binding. In addition, oles of the highly conserved RNP1 and RNP2 sequences of the N-terminal RNP motif of the U1A irotein, are Investigated by replacing them with the inalogous U1-70K sequences.
Gastric cancer diagnosis using hyperspectral imaging with principal component analysis and spectral angle mapper

Abstract. Significance: Hyperspectral imaging (HSI) is an emerging optical technique that has a double function of spectroscopy and imaging. Aim: Near-infrared hyperspectral imaging (NIR-HSI) (900 to 1700 nm) with the help of chemometrics was investigated for gastric cancer diagnosis. Approach: Mean spectra and standard deviation of normal and cancerous pixels were extracted. Principal component analysis (PCA) was used to compress the dimension of hypercube data and select the optimal wavelengths. Moreover, spectral angle mapper (SAM) was utilized as chemometrics to discriminate gastric cancer from normal. Results: Major spectral difference of cancerous and normal gastric tissue was observed around 975, 1215, and 1450 nm by comparison. A total of six wavelengths (i.e., 975, 1075, 1215, 1275, 1390, and 1450 nm) were then selected as optimal wavelengths by PCA. The accuracy using SAM is up to 90% according to hematoxylin–eosin results. Conclusions: These results suggest that NIR-HSI has the potential as a cutting-edge optical diagnostic technique for gastric cancer diagnosis with suitable chemometrics.
Outcomes of patients with Severe Acute Respiratory Infections (SARI) admitted to the intensive care unit: results from the Egyptian Surveillance Study 2010-2014

Background Few data exist about respiratory viral infections in Egyptian patients. Hereby we describe the outcomes of hospitalized Egyptian patients with severe acute respiratory infections (SARI) admitted to the ICU. Methods A prospective study in which all hospitalized patients meeting the WHO case definition for SARI and admitted to the ICU, during the period 2010–2014, were enrolled. Samples were tested using RT-PCR for influenza A, B, respiratory syncytial virus (RSV), human metapneumovirus (hMPV), parainfluenza virus, adenovirus, bocavirus, enterovirus, and rhinovirus. Data were analyzed to study the clinical features of SARI-ICU patients and which pathogens are related to severe outcomes. Associated comorbidities were evaluated using Charlson Age-Comorbidity Index (CACI). Results Out of 1,075 patients with SARI, 219 (20.3%) were admitted to the ICU. The highest rates were reported for RSV (37%). SARI-ICU patients had higher rates of hospital stay, pneumonia, respiratory failure, ARDS, and mortality. Multivariate logistic regression analysis identified associated respiratory disorders (p=0.001), radiological abnormalities (p=0.023), and longer hospital stay (p=0.005) as risk factors for severe outcomes. Conclusions This surveillance study showed that 20% of hospitalized Egyptian patients with viral SARI needed ICU admission. SARI-ICU patients had higher rates of hospital stay, pneumonia, respiratory failure, ARDS, and mortality. Higher comorbidity index scores, radiological abnormalities, and longer hospital stay are risk factors for severe outcomes in SARI-ICU patients in our locality.
A Double-Observer Method to Estimate Detection Rate During Aerial Waterfowl Surveys

Abstract We evaluated double-observer methods for aerial surveys as a means to adjust counts of waterfowl for incomplete detection. We conducted our study in eastern Canada and the northeast United States utilizing 3 aerial-survey crews flying 3 different types of fixed-wing aircraft. We reconciled counts of front- and rear-seat observers immediately following an observation by the rear-seat observer (i.e., on-the-fly reconciliation). We evaluated 6 a priori models containing a combination of several factors thought to influence detection probability including observer, seat position, aircraft type, and group size. We analyzed data for American black ducks (Anas rubripes) and mallards (A. platyrhynchos), which are among the most abundant duck species in this region. The best-supported model for both black ducks and mallards included observer effects. Sample sizes of black ducks were sufficient to estimate observer-specific detection rates for each crew. Estimated detection rates for black ducks were 0.62 (SE = 0.10), 0.63 (SE = 0.06), and 0.74 (SE = 0.07) for pilot-observers, 0.61 (SE = 0.08), 0.62 (SE = 0.06), and 0.81 (SE = 0.07) for other front-seat observers, and 0.43 (SE = 0.05), 0.58 (SE = 0.06), and 0.73 (SE = 0.04) for rear-seat observers. For mallards, sample sizes were adequate to generate stable maximum-likelihood estimates of observer-specific detection rates for only one aerial crew. Estimated observer-specific detection rates for that crew were 0.84 (SE = 0.04) for the pilot-observer, 0.74 (SE = 0.05) for the other front-seat observer, and 0.47 (SE = 0.03) for the rear-seat observer. Estimated observer detection rates were confounded by the position of the seat occupied by an observer, because observers did not switch seats, and by land-cover because vegetation and landform varied among crew areas. Double-observer methods with on-the-fly reconciliation, although not without challenges, offer one viable option to account for detection bias in aerial waterfowl surveys where birds are distributed at low density in remote areas that are inaccessible by ground crews. Double-observer methods, however, estimate only detection rate of animals that are potentially observable given the survey method applied. Auxiliary data and methods must be considered to estimate overall detection rate.
Multiple Chaotic Attractors in Coupled Lorenz Systems

Unidirectionally coupled Lorenz systems in which the drive possesses a chaotic attractor and the response admits two stable equilibria in the absence of the driving is under investigation. It is found that double chaotic attractors coexist in the dynamics. The approach is applicable for chains of coupled Lorenz systems. The existence of four chaotic attractors in three coupled Lorenz systems is also demonstrated.
Potential Impacts of Meteorological Variables on Acute Ischemic Stroke Onset

Purpose The effects of meteorological parameters on stroke occurrence remain debated. The aim of the study was to assess the association between meteorological parameters and ischemic stroke onset in cold seasons in Tianjin. Patients and Methods Patients with acute ischemic stroke (946) were identified by standard sampling from one stroke unit in the Second Hospital of Tianjin Medical University, Tianjin, China, from 10/1/2014 to 4/30/2019. Generalized linear Poisson regression models were used to explore the effect of meteorological parameters (air temperature, barometric pressure, and relative humidity) on daily ischemic stroke onset after adjusting for air pollutants, day of week, and public holiday. Results The results showed that ischemic stroke onset was positively associated with the diurnal variation of temperature (β coefficient: 0.020, 95% CI [0.001, 0.038] p<0.05). Significant positive correlation between ischemic stroke onset and barometric pressure (mean, minimum) was found (β coefficient: 0.010, 95% CI [0.001,0.019] p<0.05; 0.010, 95% CI [0.001,0.019] p<0.05). The subgroup analysis considering age and gender difference showed that the older and the female were more vulnerable to weather conditions. Conclusion Our study demonstrated that there was a measurable effect of weather parameters on daily ischemic stroke onset in colder seasons, suggesting that meteorological variables may, at least in part, play as risk factors for ischemic stroke onset, especially for the aging and female population.