Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor Growth Function in the Presence and Absence of Diet-Induced Obesity Alters Dendritic Cell

Obesity is a mounting health concern in the United States and is associated with an increased risk for developing several cancers, including renal cell carcinoma (RCC). Despite this, little is known regarding the impact of obesity on antitumor immunity. Because dendritic cells (DC) are critical regulators of antitumor immunity, we examined the combined effects of obesity and tumor outgrowth on DC function. Using a diet-induced obesity (DIO) model, DC function was evaluated in mice bearing orthotopic RCC and in tumor-free controls. Tumor-free DIO mice had profoundly altered serum cytokine and chemokine proﬁles, with upregulation of 15 proteins, including IL-1 a , IL-17, and LIF. Tumor-free DIO mice had elevated percentages of conventional splenic DC that were impaired in their ability to stimulate naive T cell expansion, although they were phenotypically similar to normal weight (NW) controls. In DIO mice, intrarenal RCC tumor challenge in the absence of therapy led to increased local inﬁltration by T cell-suppressive DC and accelerated early tumor outgrowth. Following administration of a DC-dependent immunotherapy, established RCC tumors regressed in normal weight mice. The same immunotherapy was ineffective in DIO mice and was characterized by an accumulation of regulatory DC in tumor-bearing kidneys, decreased local inﬁltration by IFN- g –producing CD8 T cells, and progressive tumor outgrowth. Our results suggest that the presence of obesity as a comorbidity can impair the efﬁcacy of DC-dependent antitumor immunotherapies. The Journal of Immunology , 2012, 189: 1311–1321.
Performance Analysis of SCTP for Multiple Parallel Streams

SCTP is a connection oriented general purpose transport protocol and applications presented in the transport layer for concept of streams. Different streams provide multiple end to end connections that enables independent send and receiver buffer at two end points. In this paper we performed performance measurement for simultaneous parallel streams. Performance of SCTP is closely examined for two parallel streams and considerable improvement of bandwidth utilization observed as compared to single stream
Occurrence and Characterization of Wheat Streak Mosaic Virus Found in Mono- and Mixed Infection with High Plains Wheat Mosaic Virus in Winter Wheat in Ukraine

Although wheat streak mosaic virus (WSMV) is a well-known pathogen inducing significant crop losses and endangering wheat production worldwide, the recent discovery of High Plains wheat mosaic virus (HPWMoV) in Ukraine raises questions on the co-existence of these two viruses having a similar host range and the same mite vector. Here we report on the screening of winter wheat industrial plantings in several important regions of Ukraine for WSMV and HPWMoV. WSMV was identified in an extremely high number of symptomatic plants (>85%) as compared to HPWMoV detected in 40% of wheat samples. Importantly, the preferred mode of HPWMoV circulation in Ukraine was mixed infection with WSMV (>30%) as opposed to WSMV, which was typically found in monoinfection (60%). Screening wheat varieties for possible virus resistance indicated that all but one were susceptible to WSMV, whereas over 50% of the same varieties were not naturally infected with HPWMoV. Overall, phylogenetic analysis of the collected WSMV and HPWMoV isolates indicated their high identity and similarity to other known isolates of the respective viruses. Here we first characterize WSMV isolates found in winter wheat plants in mono- or mixed infection with HPWMoV, which was recently reported as a typical wheat pathogen in Ukraine.
Assessing the physiological effects of an exercise intervention in older adults: Is there a role for core-stability training?

Preliminary evidence indicates that age-related changes in trunk muscle morphology and function are associated with decreased balance and increased falls risk. However, the associations between trunk muscle morphology, strength, and functional ability, as well as the trainability of these muscles are not well established. Therefore, the aims of this thesis were to identify the relationships between trunk muscle morphology, strength, and functional ability and to determine the effects of exercise training on these outcomes in healthy older adults. We initially undertook a systematic review to determine the effect of exercise training on trunk muscle morphology. Our results identified motor control and machine-based exercises targeting the trunk muscles resulted in the largest change in the trunk muscle morphology. Using a cross-sectional design, we then explored the relationships between trunk muscle morphology, strength, and functional ability in 64 older adults. Our results showed anterior and lateral abdominal and posterior trunk muscle size and strength were positively associated with functional ability. Finally, we conducted a randomised clinical trial investigating the effectiveness of a 12-week exercise programme on trunk muscle size, strength, and functional ability. Sixty-four individuals (mean(SD) age 69.8 (7.5) years; 59.4% female) were randomised to receive a multimodal exercise program comprising walking and balance exercises with or without strength/motor control training of the trunk muscles. Participants performing the trunk strengthening exercises experienced larger increases (mean difference [95%CI]) in trunk muscle hypertrophy (1.6 [1.0, 2.2] cm) and composite trunk strength (172.6 [100.8, 244.5] N), as well as 30-Second Chair Stand Test (5.9 [3.3, 8.4] repetitions), Sitting and Rising Test (1.2 [0.22, 2.2] points), Forward Reach Test (4.2 [1.8, 6.6] cm), Backward Reach Test (2.4 [0.22, 4.5] cm), and Timed Up and Go Test (-0.74 [-1.4, -0.03] seconds) outcomes. These findings further our understanding regarding 1) the relationships between trunk muscle morphology, strength, and functional ability and 2) appropriate exercise prescription aimed at improving these outcomes in older individuals.
Critical Commentary on the Stimulus for Muscle Hypertrophy in Experienced Trainees

Researchers have expressed concern recently for standardization of resistance training protocols so that valid comparisons of different training variables such as muscular fatigue, time under tension, pre-exhaust exercise and exercise order, pyramid and drop sets, amount of resistance (load), range of repetitions, frequency and volume of exercise, interset rest intervals, etc. can be more closely studied and compared. This Critical Commentary addresses some recent review articles and training studies specifically focused on the stimulus for muscle hypertrophy in participants with several years of resistance training experience. It reveals that many of the recommended resistance training protocols have their foundation in some long-held, self-described bias. Blinding of assessors and statisticians, self-plagiarism, authorship responsibility, and conflicts of interest are briefly discussed as well. The conclusion is that most of the published peer-reviewed resistance training literature failed to provide any compelling evidence that the manipulation of any one or combination of the aforementioned variables can significantly affect the degree of muscle hypertrophy, especially in well-trained participants. Although the specific stimulus for optimal gains in muscle mass is unknown, many authors are desperately clinging to their unsupported belief that a greater volume of exercise will produce superior muscle hypertrophy.
Edinburgh Research Explorer Rapidly progressive dementia with thalamic degeneration and peculiar cortical prion protein immunoreactivity, but absence of proteinase K resistant PrP

, , , Abstract Background: Human prion diseases are a group of rare fatal neurodegenerative conditions with well-developed clinical and neuropathological diagnostic criteria. Recent observations have expanded the spectrum of prion diseases beyond the classically recognized forms. Results: In the present study we report six patients with a novel, apparently sporadic disease characterised by thalamic degeneration and rapidly progressive dementia (duration of illness 2 – 12 months; age at death: 55 – 81 years). Light and electron microscopic immunostaining for the prion protein (PrP) revealed a peculiar intraneuritic distribution in neocortical regions. Proteinase K resistant PrP (PrP res ) was undetectable by Western blotting in frontal cortex from the three cases with frozen tissue, even after enrichment for PrP res by centrifugation or by phosphotungstic acid precipitation. Conformation-dependent immunoassay analysis using a range of PK digestion conditions (and no PK digestion) produced only very limited evidence of meaningful D-N (denatured/native) values, indicative of the presence of disease-associated PrP (PrP Sc ) in these cases, when the results were compared with appropriate negative control groups. Conclusions: Our observation expands the spectrum of conditions associated with rapidly progressive dementia and may have implications for the understanding of the pathogenesis of prion diseases.
Integrating Policy Summaries with Reward Decomposition Explanations

Explaining the behavior of agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and delayed reward. In this paper, we study a new way of combining local and global explanations of sequential decision-making agents in order to help understand their behavior. Specifically, we combine reward decomposition, a local explanation method that exposes agent preferences, with HIGHLIGHTS, a global explanation method that shows a summary of the agent’s behavior in “important” states. We conducted a user study to evaluate the integration of these explanation methods and their respective benefits. Our results show that local information in the form of reward decomposition contributed to participants’ understanding of agents’ preferences, while HIGHLIGHTS summaries did not lead to an improvement compared to a baseline showing frequent agent trajectories.
Edinburgh An Exploration of Emotion Regulation in Psychosis

The emotional experience of individuals who experience psychosis has historically been neglected, possibly due to the divide between the psychoses and neuroses. This study examined emotional experience and regulation in individuals who had experienced psychosis, individuals experiencing anxiety or mood disorders and non patient controls. Participants completed validated measures of emotional experience and emotion regulation. Both clinical groups were found to experience similar levels of emotions and in comparison to the non patient controls, they experienced greater levels of negatively valenced emotions and lower levels of happiness. Both clinical groups also used similar emotion regulation strategies and in comparison to non patient controls they used significantly more dysfunctional and less functional strategies, suggesting that the emotional experience and emotion regulation strategies of people who have experienced psychosis are more similar to non-psychotic disorders than have previously been thought to be the case. The theoretical and clinical implications of these findings are discussed.
Review and Analysis of Emotion Detection from Tweets using Twitter Datasets

Communication is a necessary part of our day-to-day lives, but understanding personal communication with emotion is not that easy. With the rapid growth in the field of semantic analysis and to find the sentiments in the text is quite a challenging job for the researchers. Detecting emotions in the sentiment analysis area is one of the most important applications and also serves as an advantage in the digital medium for efficient computing. In the current scenario, sentimental analysis or opinion mining of the twitter emotion detection data-set has derived much attention since the past 10 years. In this paper, Comparative study and Analysis of Emotion Detection from Tweets using Twitter Dataset has been taken into considerations for analysis purpose.
Conditional Gradient And Bisection Algorithms For Non-convex Optimization Problem With Random Perturbation ∗

In this paper, we propose an implementation of stochastic perturbation of conditional gradient and bisection (SPCGB) method (a.k.a. Frank-Wolfe method ) for solving non-convex diﬀerentiable programming under linear constraints. The goal is to attempt to avoid getting stuck in local optimum solutions. Theoretical results guarantee the convergence of the proposed method towards a global minimizer. To demonstrate the eﬀectiveness of our method, some numerical results of small and medium scale problems are given
Climate Impact and Model Approaches of Blue-Green Infrastructure Measures for Neighborhood Planning

Nowadays, most cities deal with the problem of “Urban Heat Islands”. Especially existing city districts cannot easily be adapted. In this paper, the effects of blue-green infrastructure elements (BGI) on air and surface temperature in courtyards are examined, based on on-site measurements and simulations. Recognizable effects on the temperature were observed: BGI lower the number of hot days in the courtyard, including a faster air temperature drop at night, but water elements increase the number of tropical nights due to their heat capacity. Model simulations with PALM-4U proved to be useful to analyze the effects of BGI on the microclimate. Besides analyzing existing structures, the effects of planned measures can be quantified by simulation. However, for this application, needs of improvement were recognized to evaluate the influence of BGI on the microclimate more realistically. For decision support, standard indicators such as the number of tropical nights and hot days are not differentiated enough to quantify specific climate stress of urban residents. It is suggested to consider summer days additionally, percentiles could be used instead of fixed thresholds and the entire course of the year should play a role in the evaluation of the elements and urban design.
SYMBEXCEL: Automated Analysis and Understanding of Malicious Excel 4.0 Macros

Malicious software (malware) poses a significant threat to the security of our networks and users. In the ever-evolving malware landscape, Excel 4.0 Office macros (XL4) have recently become an important attack vector. These macros are often hidden within apparently legitimate documents and under several layers of obfuscation. As such, they are difficult to analyze using static analysis techniques. Moreover, the analysis in a dynamic analysis environment (a sandbox) is challenging because the macros execute correctly only under specific environmental conditions that are not always easy to create. This paper presents SYMBEXCEL, a novel solution that leverages symbolic execution to deobfuscate and analyze Excel 4.0 macros automatically. Our approach proceeds in three stages: (1) The malicious document is parsed and loaded in memory; (2) Our symbolic execution engine executes the XL4 formulas; and (3) Our Engine concretizes any symbolic values encountered during the symbolic exploration, therefore evaluating the execution of each macro under a broad range of (meaningful) environment configurations. SYMBEXCEL significantly outperforms existing deobfuscation tools, allowing us to reliably extract Indicators of Compromise (IoCs) and other critical forensics information. Our experiments demonstrate the effectiveness of our approach, especially in deobfuscating novel malicious documents that make heavy use of environment variables and are often not identified by commercial anti-virus software.
Deep RNN Speech Recognition with Sub-Labels

In this paper, we introduce a top-performing speech recognition system using both end-to-end deep learning and sub-labeling (i.e. beginning, medium and ending stages) of basic language units. Our system achieves higher accuracy and robustness than traditional speech recognition systems and other systems applying deep learning techniques. Moreover, this system does not require lexicon, thus enabling more ﬂexibility in learning process. The main architecture of our system is a multi-layer bidirectional recurrent neural network (BiRNN), and our major breakthrough is breaking down language labels into sub-labels, thus ﬁt-ting each sub-label into more speciﬁc context and boosting recognition performance. Our system outperforms the advanced Deep Speech by 1.5% in character error rate(CER) and 1.5% in word error rate(WER) on the famous TIMIT corpus.
PLACEBO-CONTROLLED CLINICAL TRIAL

Herda, AA, Herda, TJ, Costa, PB, Ryan, ED, Stout, JR, and Cramer, JT. Muscle performance, size, and safety responses after eight weeks of resistance training and protein supplementation: A randomized, double-blinded, placebo-controlled clinical trial. J Strength Cond Res 27(11): 3091–3100, 2013—The purpose of this study was to examine the effects of 2 different types of protein supplementation on thigh muscle cross-sectional area (CSA), blood markers, muscular strength, endurance, and body composition after 8 weeks of lowor moderate-volume resistance training in healthy, recreationally trained, college-aged men. One hundred and six men were randomized into 5 groups: low-volume resistance training with bioenhanced whey protein (BWPLV; n = 22), moderate-volume resistance training with BWP (BWPMV; n = 20), moderate-volume resistance training with standard whey protein (SWPMV; n = 22), moderate-volume resistance training with a placebo (PLA; n = 21), or moderate-volume resistance training with no supplementation (CON; n = 21). Except for CON, all groups consumed 1 shake before and after each exercise session and one each on the nontraining day. The BWPLV, BWPMV, and SWPMV groups received approximately 20 g of whey protein per shake, whereas the BWP groups received 5 g of additional polyethylene glycosylated (PEG) leucine. Resistance training sessions were performed 3 times per week for 8 weeks. There were no interactions (p . 0.05) for muscle strength and endurance variables, body composition, muscle CSA, and safety blood markers, but the main effects for training were observed (p # 0.05). However, the Albumin:Globulin ratio for SWPMV was lower (p = 0.037) than BWPLV and BWPMV. Relative protein intake (PROREL) indicated a significant interaction (p , 0.001) with no differences across groups at pre; however, BWPLV, BWPMV, and SWPMV had a greater intake than did PLA or CON at post (p , 0.001). This study indicated that 8 weeks of resistance training improved muscle performance and size similarly among groups regardless of
Symbolic protocol verification with dice: process equivalences in the presence of probabilities

Symbolic protocol verification generally abstracts probabilities away, considering computations that succeed only with negligible probability, such as guessing random numbers or breaking an encryption scheme, as impossible. This abstraction, sometimes referred to as the perfect cryptography assumption, has shown very useful as it simplifies automation of the analysis. However, probabilities may also appear in the control flow where they are generally not negligible. In this paper we consider a framework for symbolic protocol analysis with a probabilistic choice operator: the probabilistic applied pi calculus. We define and explore the relationships between several behavioral equivalences. In particular we show the need for randomized schedulers and exhibit a counter-example to a result in a previous work that relied on non-randomized ones. As in other frameworks that mix both non-deterministic and probabilistic choices, schedulers may sometimes be unrealistically powerful. We therefore consider two subclasses of processes that avoid this problem. In particular, when considering purely non-deterministic protocols, as is done in classical symbolic verification, we show that a probabilistic adversary has-maybe surprisingly-a strictly superior distinguishing power for may testing, which, when the number of sessions is bounded, we show to coincide with purely possibilistic similarity.
Anabolic Steroids A Review for the Clinician

The number of athletes self-administering ergogenic pharmacological agents to increase their competitive edge continues to be a problem. Most athletes using anabolic steroids (AS) have acquired a crude pharmacological database regarding these drugs. Their opinions regarding steroids have been derived from their subjective experiences and anecdotal information. For this reason, traditional warnings regarding the lack of efficacy and potential dangers of steroid misuse are disregarded. A common widely held opinion among bodybuilders is that the anabolic steroid experts are the athletic gurus who for years have utilised themselves as the experimental participants and then dispensed their empirical findings. This review will address the common anabolic steroid misconceptions held by many of today’s athletes by providing an evaluation of the scientific literature related to AS in athletic performance.
Physician Judgment Is A Crucial Adjunct To Pneumonia Severity Scores In Low Risk Patients.

Our study investigates the reasons for hospitalization in patients with low-risk (CURB65 score 0-1) community acquired pneumonia (CAP), with a view to identify the potential for increasing outpatient management. As a part of a prospective observational study of CAP, we evaluated reasons for hospitalization in these low-risk patients. 565 patients had low-risk CAP. 420 of these were admitted (>12 hours). 39.3% had additional markers of severity justifying admission. 29.5% of the admissions were required for further management that could not be provided rapidly in the community. 11.9% had unsafe social circumstances. 19.3% had no clinical reason justifying hospitalization. 30-day mortality markers (6.7%), significantly higher compared to 0% for patients awaiting investigations and 0%, without a clear indication for hospitalization (p=0.04). In a
Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China

Solar energy is an abundant, clean, and renewable source that can mitigate global climate change, environmental pollution, and energy shortage. However, comprehensive datasets and efficient identification models for the spatial distribution of photovoltaic (PV) plants locally and globally over time remain limited. In the present study, a model that combines original spectral features, PV extraction indexes, and terrain features for the identification of PV plants is established based on the pilot energy city Golmud in China, which covers 71,298.7 km2 and has the highest density of PV plants in the world. High-performance machine learning algorithms were integrated with PV plant extraction models, and performances of the XGBoost, random forest (RF), and support vector machine (SVM) algorithms were compared. According to results from the investigations, the XGBoost produced the highest accuracy (OA = 99.65%, F1score = 0.9631) using Landsat 8 OLI imagery. The total area occupied by PV plants in Golmud City in 2020 was 10,715.85 ha based on the optimum model. The model also revealed that the area covered by the PV plant park in the east of Golmud City increased by approximately 10% from 2018 (5344.2 ha) to 2020 (5879.34 ha). The proposed approach in this study is one of the first attempts to identify time-series large-scale PV plants based on a pixel-based machine learning algorithm with medium-resolution free images in an efficient way. The study also confirmed the effectiveness of combining original spectral features, PV extraction indexes, and terrain features for the identification of PV plants. It will shed light on larger- and longer-scale identification of PV plants around the world and the evaluation of the associated dynamics of PV plants.