Miracle-dz/UniFAR-FTUs huggingface dataset:
125kV, 100kA, 150ns, 5pps test facility with solid state switched distributed pulse compression?

Electra is a repetitively pulsed, electron beam pumped Krypton Fluoride (KrF) laser at the Naval Research Laboratory. [1] This program is developing technologies to meet the Inertial Fusion Energy (IFE) requirements for durability, efficiency, and cost. The Electra laser main amplifier requires a 500kV, 100kA, 150ns, 5Hz pulsed power source. At present, the main amplifier uses gas insulated spark gap switches with lifetimes of 105 pulses. Solid state switched pulsed power sources are being developed to achieve lifetimes of 108 pulses. Applied Pulsed Power has developed a 48kV compact solid state switch for this application that operates at 8kA and can withstand fault currents of 14kA and 80% current reversal. Individual modules used in the switch have been tested for ≫200 million pulses without failure, and lifetime testing of the complete switch is in progress. Using these switches, we are manufacturing and testing a quarter scale version of the Electra pulsed power source. Scaling was done by reducing the number of Marx stages and the impedance of the downstream pulse forming components and load by a factor of four, resulting in a 125kV, 100kA, 150ns, 5pps test facility. This approach ensures that each Marx stage operates at the design voltage and current, and the pulse forming components and magnetic switches operate at the same design current and timing as for the full scale system. The electric fields in the pulse forming components for the quarter scale version exceed the design values for the full scale system.
Multi-level Governance of Land Use Changes in the Brazilian Amazon: Lessons from Paragominas, State of Pará

Land use governance in the Brazilian Amazon has undergone significant changes in the last decade. At the national level, law enforcement capacity has increased and downstream industries linked to commodity chains responsible for deforestation have begun to monitor some of their suppliers’ impacts on forests. At the municipal level, local actors have launched a Green Municipality initiative, aimed at eliminating deforestation and supporting green supply chains at the territorial level. In this paper, we analyze the land use transition since 2001 in Paragominas—the first Green Municipality—and discuss the limits of the governance arrangements underpinning these changes. Our work draws on a spatially explicit analysis of biophysical variables and qualitative information collected in interviews with key private and public stakeholders of the main commodity chains operating in the region. We argue that, up to now, the emerging multi-level scheme of land governance has not succeeded in promoting large-scale land use intensification, reforestation and rehabilitation of degraded lands. Moreover, private governance mechanisms based on improved product standards, fail to benefit from potential successful partnerships between the public and private sector at the territorial level. We propose a governance approach that adopts a broader territorial focus as a way forward.
Synthetic small molecules that control stem cell fate

In an attempt to better understand and control the processes that regulate stem cell fate, we have set out to identify small molecules that induce neuronal differentiation in embryonic stem cells (ESCs). A high-throughput phenotypic cell-based screen of kinase-directed combinatorial libraries led to the discovery of TWS119, a 4,6-disubstituted pyrrolopyrimidine that can induce neurogenesis in murine ESCs. The target of TWS119 was shown to be glycogen synthase kinase-3β (GSK-3β) by both affinity-based and biochemical methods. This study provides evidence that GSK-3β is involved in the induction of mammalian neurogenesis in ESCs. This and such other molecules are likely to provide insights into the molecular mechanisms that control stem cell fate, and may ultimately be useful to in vivo stem cell biology and therapy.
LysM Effectors: Secreted Proteins Supporting Fungal Life

Fungi occupy a plethora of niches and play essential roles indiverse environments through decomposition of organic materialas saprophytes or through establishment of symbiotic relationshipswith plants and animals that range from mutually beneficial topathogenic. During colonization of their niches, fungi secreteproteins that include carbohydrate-degrading enzymes to feed oncomplex molecules and effectors that mediate the establishment ofinteractions with host organisms [1]. Although effectors aretypically thought to be species- or even lineage-specific, someeffectors are widespread among pathogens, such as the necrosis-and ethylene-inducing-like proteins (NLPs) that are widely spreadin bacteria, fungi, and oomycetes [2,3]. Several studies have shownthat NLPs contribute to pathogen virulence through phytotoxicactivity, but more recent work has revealed that some NLPs act inprocesses other than pathogenicity, such as fungal growth andsporulation [4]. A more recently identified class of conservedeffectors are LysM effectors: fungal effectors that carry norecognizable protein domains other than lysin motifs (LysMs)[5]. Intriguingly, like NLPs, LysM effectors occur in bothpathogenic and in nonpathogenic fungi.
Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments.
The future of the World Heritage Convention for marine conservation

The 1972 World Heritage Convention is founded on the premise of international cooperation. It recognizes that some places are of irreplaceable value to humankind, and signifies the commitment of all nations to support their protection. This global covenant is uniquely suited to facilitate conservation of the world’s ocean, where connectedness is a fact of life. Recognizing that World Heritage had a key role to play in ocean conservation, the World Heritage Marine Programme was established in 2005 with the goal to ensure the protection of marine places of Outstanding Universal Value.
Context-Dependent Sense Embedding

Word embedding has been widely studied and proven helpful in solving many natural language processing tasks. However, the ambiguity of natural language is always a problem on learning high quality word embeddings. A possible solution is sense embedding which trains embedding for each sense of words instead of each word. Some recent work on sense embedding uses context clustering methods to determine the senses of words, which is heuristic in nature. Other work creates a probabilistic model and performs word sense disambiguation and sense embedding iteratively. However, most of the previous work has the problems of learning sense embeddings based on imperfect word embeddings as well as ignoring the dependency between sense choices of neighboring words. In this paper, we propose a novel probabilistic model for sense embedding that is not based on problematic word embedding of polysemous words and takes into account the dependency between sense choices. Based on our model, we derive a dynamic programming inference algorithm and an Expectation-Maximization style unsupervised learning algorithm. The empirical studies show that our model outperforms the state-of-the-art model on a word sense induction task by a 13% relative gain. © 2016 Association for Computational Linguistics
A Phenomenological Synapse Model for Asynchronous Neurotransmitter Release

Neurons communicate with each other via synapses. Action potentials cause release of neurotransmitters at the axon terminal. Typically, this neurotransmitter release is tightly time-locked to the arrival of an action potential and is thus called synchronous release. However, neurotransmitter release is stochastic and the rate of release of small quanta of neurotransmitters can be considerably elevated even long after the ceasing of spiking activity, leading to asynchronous release of neurotransmitters. Such asynchronous release varies for tissue and neuron types and has been shown recently to be pronounced in fast-spiking neurons. Notably, it was found that asynchronous release is enhanced in human epileptic tissue implicating a possibly important role in generating abnormal neural activity. Current neural network models for simulating and studying neural activity virtually only consider synchronous release and ignore asynchronous transmitter release. Here, we develop a phenomenological model for asynchronous neurotransmitter release, which, on one hand, captures the fundamental features of the asynchronous release process, and, on the other hand, is simple enough to be incorporated in large-size network simulations. Our proposed model is based on the well-known equations for short-term dynamical synaptic interactions and includes an additional stochastic term for modeling asynchronous release. We use experimental data obtained from inhibitory fast-spiking synapses of human epileptic tissue to fit the model parameters, and demonstrate that our model reproduces the characteristics of realistic asynchronous transmitter release.
Ceramide kinase regulates phospholipase C and phosphatidylinositol 4, 5, bisphosphate in phototransduction

Phosphoinositide-specific phospholipase C (PLC) is a central effector for many biological responses regulated by G-protein–coupled receptors including Drosophila phototransduction where light sensitive channels are activated downstream of NORPA, a PLCβ homolog. Here we show that the sphingolipid biosynthetic enzyme, ceramide kinase, is a novel regulator of PLC signaling and photoreceptor homeostasis. A mutation in ceramide kinase specifically leads to proteolysis of NORPA, consequent loss of PLC activity, and failure in light signal transduction. The mutant photoreceptors also undergo activity-dependent degeneration. Furthermore, we show that a significant increase in ceramide, resulting from lack of ceramide kinase, perturbs the membrane microenvironment of phosphatidylinositol 4, 5, bisphosphate (PIP2), altering its distribution. Fluorescence image correlation spectroscopic studies on model membranes suggest that an increase in ceramide decreases clustering of PIP2 and its partitioning into ordered membrane domains. Thus ceramide kinase–mediated maintenance of ceramide level is important for the local regulation of PIP2 and PLC during phototransduction.
Interobserver agreement for the assessment of handicap in stroke patients.

Interobserver Agreement for the Assessment of Handicap in Stroke Patients To the Editor: Van Swieten et al deserve commendation for carefully studying interobserver reliability of a potentially useful global scale that rates disability in stroke victims. Unfortunately, however, the authors detract from the value of their publication by their choice of a title. The modified Rankin Scale seems to rate disability, that is, functional deficits in performing essential daily activities; it does not rate handicap, that is, limitations in social role. While disability and handicap often vary in parallel, they are not synonymous terms. Scientific articles in general, and journals in particular, should strive to stay with accepted terminology. Ralph F. Bloch, MD, FRCP(C), PhD Service of Rehabilitation and Physical Medicine McMaster University Hamilton, Canada
The Relationship of Hedonic Hunger, Macronutrient Balance, Nutrition Knowledge, and Body Image and Weight Control with Dietary Intake in Student Athletes and Exercisers

Dietary intake is known to impact athletic performance. The factors that influence dietary intake have been investigated widely, but their collective effect has not been examined. The primary aim of this cross-sectional study was to assess the relationship between dietary intake and nutrition knowledge, body image, weight control, macronutrient balance, and hedonic hunger. Forty-two student athletes or active individuals were recruited through contact with sporting organisations and course coordinators, advertising via twitter, and flyers posted within university buildings. Nutrition knowledge, body image, weight control, macronutrient balance, and hedonic hunger were measured using the Abridged Nutrition for Sport Knowledge Questionnaire, Body Image Disturbance Questionnaire and Contour Drawing Rating Scale, a Weight Fluctuation Measure, Australian Eating Score, and Power of Food Scale, respectively. Hierarchical regression analysis, correlation testing, and mean difference testing were applied. Carbohydrate intake, body image disturbance scores, weight fluctuation, and hedonic hunger for food tasted had a significant relationship (R2 = 64.6%, Adj R2 = 0.608%, p < 0.001) with dietary energy intake. Student athletes’ dietary intakes are influenced by multiple potentially modifiable factors. Future studies should use larger sample sizes, with interventions focusing on individual modifiable factors to determine how dietary intake can be most significantly impacted.
Maternal Depression and Trait Anger as Risk Factors for Escalated Physical Discipline

To test the hypothesized anger-mediated relation between maternal depression and escalation of physical discipline, 122 economically disadvantaged mothers were assessed for current and lifetime diagnoses of depression using the Current Depressive Episode, Past Depression, and Dysthymia sections of the Structured Clinical Interview for DSM-IV (SCID) and a measure of current depressive symptoms, the Beck Depression Inventory—Second Edition (BDI-II). Escalation of physical discipline was assessed using a video analog parenting task; maternal anger not specific to discipline was assessed using the Spielberger Trait Anger Expression Inventory. Reports of anger were associated with the diagnosis of depression and depressive symptoms. Bootstrap analyses of indirect effects indicated that the link between depression and escalated discipline was mediated by anger. Parallel analyses based on BDI-II scores identified a marginally significant indirect effect of depression on discipline. Findings suggest that anger and irritability are central to the putative link between depression and harsh discipline.
Early detection of nasopharyngeal carcinoma through machine‐learning‐driven prediction model in a population‐based healthcare record database

Abstract Objective Early diagnosis and treatment of nasopharyngeal carcinoma (NPC) are vital for a better prognosis. Still, because of obscure anatomical sites and insidious symptoms, nearly 80% of patients with NPC are diagnosed at a late stage. This study aimed to validate a machine learning (ML) model utilizing symptom‐related diagnoses and procedures in medical records to predict nasopharyngeal carcinoma (NPC) occurrence and reduce the prediagnostic period. Materials and Methods Data from a population‐based health insurance database (2001–2008) were analyzed, comparing adults with and without newly diagnosed NPC. Medical records from 90 to 360 days before diagnosis were examined. Five ML algorithms (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting [XGB], Multivariate Adaptive Regression Splines [MARS], Random Forest [RF], and Logistics Regression [LG]) were evaluated for optimal early NPC detection. We further use a real‐world data of 1 million individuals randomly selected for testing the final model. Model performance was assessed using AUROC. Shapley values identified significant contributing variables. Results LGB showed maximum predictive power using 14 features and 90 days before diagnosis. The LGB models achieved AUROC, specificity, and sensitivity were 0.83, 0.81, and 0.64 for the test dataset, respectively. The LGB‐driven NPC predictive tool effectively differentiated patients into high‐risk and low‐risk groups (hazard ratio: 5.85; 95% CI: 4.75–7.21). The model‐layering effect is valid. Conclusions ML approaches using electronic medical records accurately predicted NPC occurrence. The risk prediction model serves as a low‐cost digital screening tool, offering rapid medical decision support to shorten prediagnostic periods. Timely referral is crucial for high‐risk patients identified by the model.
Chord segmentation and recognition using EM-trained hidden markov models

Automatic extraction of content description from commercial audio recordings has a number of important applications, from indexing and retrieval through to novel musicological analyses based on very large corpora of recorded performances. Chord sequences are a description that captures much of the character of a piece in a compact form and using a modest lexicon. Chords also have the attractive property that a piece of music can (mostly) be segmented into time intervals that consist of a single chord, much as recorded speech can (mostly) be segmented into time intervals that correspond to specific words. In this work, we build a system for automatic chord transcription using speech recognition tools. For features we use “pitch class profile” vectors to emphasize the tonal content of the signal, and we show that these features far outperform cepstral coefficients for our task. Sequence recognition is accomplished with hidden Markov models (HMMs) directly analogous to subword models in a speech recognizer, and trained by the same Expectation-Maximization (EM) algorithm. Crucially, this allows us to use as input only the chord sequences for our training examples, without requiring the precise timings of the chord changes — which are determined automatically during training. Our results on a small set of 20 early Beatles songs show frame-level accuracy of around 75% on a forced-alignment task.
Spinning and non-spinning reserve allocation for stochastic security constrained unit Commitment

This paper proposes a formulation for a stochastic security constrained unit commitment, dispatch and reserve scheduling, considering N-1 security and non-spinning reserves. We employ a randomized optimization technique that is based on scenario generation of the uncertain variables, in this case the wind power, and offers probabilistic certificates regarding the robustness properties of the resulting solution. To demonstrate the efficacy of the proposed methodology, we carry out a simulation based study on the IEEE 30 bus system comparing the proposed formulation to other variants, as well as examine its behavior on networks with different congestion levels. In all case studies, we verify empirically, via Monte Carlo simulations, the probabilistic performance of our solution methodology.
Nitrogen Fixation by Symbiotic and Free-Living Spirochetes

Spirochetes from termite hindguts and freshwater sediments possessed homologs of a nitrogenase gene (nifH) and exhibited nitrogenase activity, a previously unrecognized metabolic capability in spirochetes. Fixation of 15-dinitrogen was demonstrated with termite gut Treponema ZAS-9 and free-livingSpirochaeta aurantia. Homologs of nifH were also present in human oral and bovine ruminal treponemes. Results implicate spirochetes in the nitrogen nutrition of termites, whose food is typically low in nitrogen, and in global nitrogen cycling. These results also proffer spirochetes as a likely origin of certainnifHs observed in termite guts and other environments that were not previously attributable to known microbes.
Modeling Nondilute Species Transport Using the Thermodynamically Constrained Averaging Theory

Nondilute transport in porous media results in fronts that are much sharper in space and time than the corresponding transport of a conservative, nonreactive dilute species. A thermodynamically constrained averaging theory model for such situations is developed. A novel closure scheme is formulated, which is cross‐coupled between flow and transport in its most general form. Experiments are performed to investigate the effects of density, viscosity, and chemical activity. An adaptive numerical approximation method is developed to efficiently solve the formulated model. Parameter estimation is performed, and excellent agreement between laboratory data and model simulations is obtained. Accurate prediction of experimental data not used to estimate model parameters is found. It is also shown that chemical activity effects contribute to asymmetric breakthrough curves for nondilute transport in porous medium systems.
Controlled Wi-Fi Sharing in Cities: A Decentralized Approach Relying on Indirect Reciprocity

In densely populated cities, Wi-Fi networks-private or otherwise-are ubiquitous. We focus on the provision of citywide broadband communication capability to mobile users through private Wi-Fi networks that are in range but belong to others. We form a club that relies on indirect reciprocity: Members participate in the club and provide free Wi-Fi access to other members in order to enjoy the same benefit when they are away from their own Wi-Fi network. Our club scheme does not require registration with an authority and does not rely on centrally issued club identities: Members create their own identities (public-private key pairs) and receive signed digital receipts when they provide Wi-Fi service to other members. These receipts form a distributed receipt graph, parts of which are used as input to an indirect reciprocity algorithm that classifies club members according to their contribution. We show that our algorithm can sustain cooperation within the club and is robust to attacks by free-riders. We implement and evaluate our proposed club algorithms on commodity Wi-Fi routers and dual-mode cellular/Wi-Fi phones. Because we anticipate that Wi-Fi telephony will be a popular club application, we present and evaluate a secure and decentralized architecture for citywide voice (and multimedia) communications that is compatible with our club both from an architectural as well as an incentives perspective.