Miracle-dz/UniFAR-FTUs huggingface dataset:
ESTIMATION OF ATLANTIC SKIPJACK FISHERIES' PRODUCTIVITY USING A CATCH BASED METHOD AND HYPOTHESES ON STOCK RESILIENCE

SUMMARY Fisheries are managed using biological information of fish stocks, historical catch data and complex numerical models. However, the availability of reliable and complete information of both biological characteristics and fisheries yield is often incomplete, inaccurate or nonavailable. Therefore, there is a need for simple methods that allow estimating fish stocks productivity using limited data. In this study we use a simple method to investigate the productivity and historical harvest rates applied to four stocks of Atlantic skipjack, a species exploited by several nations and a diversity of gears on the tropical waters of the Atlantic Ocean and managed by the International Commission for the Conservation of Atlantic Tunas (ICCAT). Our results suggest that historical catch is within the estimated limits of the capacities of these stocks to replace the amount of biomass harvested. However, we also estimate that the probability of the recent catch to be nearby to the estimated upper confidence boundaries of MSY is high. We discuss that these results need to be supported by deeper studies and new data due to the limitations of catch based methods. RESUME
Prediction of Myopia Eye Axial Elongation With Orthokeratology Treatment via Dense I2I Based Corneal Topography Change Analysis

While orthokeratology (OK) has shown effective to slow the progression of myopia, it remains unknown how spatially distributed structural stress/tension applying to different regions affects the change of corneal geometry, and consecutive the outcome of myopia control, at fine-grained detail. Acknowledging that the underlying working mechanism of OK lens is essentially mechanics induced refractive parameter reshaping, in this study, we develop a novel mechanics rule guided deep image-to-image learning framework, which densely predicts patient’s corneal topography change according to treatment parameters (lens geometry, wearing time, physiological parameters, etc.), and consecutively predicts the influence on eye axial length change after OK treatment. Encapsulated in a U-shaped multi-resolution map-to-map architecture, the proposed model features two major components. First, geometric and wearing parameters of OK lens are spatially encoded with convolutions to form a multi-channel input volume/tensor for latent encodings of external stress/tension applied to different regions of cornea. Second, these external latent force maps are progressively down-sampled and injected into this multi-scale architecture for predicting the change of corneal topography map. At each feature learning layer, we formally derive a mathematic framework that simulates the physical process of corneal deformation induced by lens-to-cornea interaction and corneal internal tension, which is reformulated into parameter learnable cross-attention/self-attention modules in the context of transformer architecture. A total of 1854 eyes of myopia patients are included in the study and the results show that the proposed model precisely predicts corneal topography change with a high PSNR as 28.45dB, as well as a significant accuracy gain for axial elongation prediction (i.e., 0.0276 in MSE). It is also demonstrated that our method provides interpretable associations between various OK treatment parameters and the final control effect. Our project code package is available at https://github.com/Rongdingyi/PhyIntNet.
Model‐Informed Approaches to Support Drug Development for Patients With Obesity: A Regulatory Perspective

Obesity, which is defined as having a body mass index of 30 kg/m2 or greater, has been recognized as a serious health problem that increases the risk of many comorbidities (eg, heart disease, stroke, and diabetes) and mortality. The high prevalence of individuals who are classified as obese calls for additional considerations in clinical trial design. Nevertheless, gaining a comprehensive understanding of how obesity affects the pharmacokinetics (PK), pharmacodynamics (PD), and efficacy of drugs proves challenging, primarily as obese patients are seldom selected for enrollment at the early stages of drug development. Over the past decade, model‐informed drug development (MIDD) approaches have been increasingly used in drug development programs for obesity and its related diseases as they use and integrate all available sources and knowledge to inform and facilitate clinical drug development. This review summarizes the impact of obesity on PK, PD, and the efficacy of drugs and, more importantly, provides an overview of the use of MIDD approaches in drug development and regulatory decision making for patients with obesity: estimating PK, PD, and efficacy in specific dosing scenarios, optimizing dose regimen, and providing evidence for seeking new indication(s). Recent review cases using MIDD approaches to support dose selection and provide confirmatory evidence for effectiveness for patients with obesity, including pediatric patients, are discussed. These examples demonstrate the promise of MIDD as a valuable tool in supporting clinical trial design during drug development and facilitating regulatory decision‐making processes for the benefit of patients with obesity.
Tropical mountain ice core δ18O: A Goldilocks indicator for global temperature change

Very high tropical alpine ice cores provide a distinct paleoclimate record for climate changes in the middle and upper troposphere. However, the climatic interpretation of a key proxy, the stable water oxygen isotopic ratio in ice cores (δ18Oice), remains an outstanding problem. Here, combining proxy records with climate models, modern satellite measurements, and radiative-convective equilibrium theory, we show that the tropical δ18Oice is an indicator of the temperature of the middle and upper troposphere, with a glacial cooling of −7.35° ± 1.1°C (66% CI). Moreover, it severs as a “Goldilocks-type” indicator of global mean surface temperature change, providing the first estimate of glacial stage cooling that is independent of marine proxies as −5.9° ± 1.2°C. Combined with all estimations available gives the maximum likelihood estimate of glacial cooling as −5.85° ± 0.51°C.
Bioprinting of scaled-up meniscal grafts by spatially patterning phenotypically distinct meniscus progenitor cells within melt electrowritten scaffolds

Meniscus injuries are a common problem in orthopedic medicine and are associated with a significantly increased risk of developing osteoarthritis. While developments have been made in the field of meniscus regeneration, the engineering of cell-laden constructs that mimic the complex structure, composition and biomechanics of the native tissue remains a significant challenge. This can be linked to the use of cells that are not phenotypically representative of the different zones of the meniscus, and an inability to direct the spatial organization of engineered meniscal tissues. In this study we investigated the potential of zone-specific meniscus progenitor cells (MPCs) to generate functional meniscal tissue following their deposition into melt electrowritten (MEW) scaffolds. We first confirmed that fibronectin selected MPCs from the inner and outer regions of the meniscus maintain their differentiation capacity with prolonged monolayer expansion, opening their use within advanced biofabrication strategies. By depositing MPCs within MEW scaffolds with elongated pore shapes, which functioned as physical boundaries to direct cell growth and extracellular matrix production, we were able to bioprint anisotropic fibrocartilaginous tissues with preferentially aligned collagen networks. Furthermore, by using MPCs isolated from the inner (iMPCs) and outer (oMPCs) zone of the meniscus, we were able to bioprint phenotypically distinct constructs mimicking aspects of the native tissue. An iterative MEW process was then implemented to print scaffolds with a similar wedged-shaped profile to that of the native meniscus, into which we deposited iMPCs and oMPCs in a spatially controlled manner. This process allowed us to engineer sulfated glycosaminoglycan and collagen rich constructs mimicking the geometry of the meniscus, with MPCs generating a more fibrocartilage-like tissue compared to the mesenchymal stromal/stem cells. Taken together, these results demonstrate how the convergence of emerging biofabrication platforms with tissue-specific progenitor cells can enable the engineering of complex tissues such as the meniscus.
K-Ar Ages and Geochemistry of the Late-Cenozoic volcanic Rocks in Keluo, Heilongjiang Province

Fifteen volcanic rock samples and one mantle xenolith sample which were collected from 8 volcanos and other places have been studied systematically, including petrogra-phic investigation, K-Ar dating, major and trace element and REE analysis as well as O, Sr and Nd isotope determination. The chrysolites with the inject twin are found under a microscope firstly in the world, including various crystal stock with the fourling and the six-ling. The chemical compositions show that it is a suite of magma wth a characterized SiO2-unsaturated, high aboundent of K and Mg and poor Ca as well as by much rich incompatible elements, REE (ΣREE=321-1002 ppm, (La/Lu)a= 42.5-76.4) and mantle compatible element also. The values of δ18O (5.8-8.7%0) are a little higher than that of the primary mantle while the isotope compositions of Sr and Nd fell in quadrant Ⅳ along the mantle array extension and closed to the origin of the coordinates on Sr-Nd diogram, it appeared as a primary mantle. K-Ar apparent age of volcanic rocks range from 0.03Ma to 2.98Ma, majority of them belong to Late-Pleistocene. An apparent age of the xenolith is about 70Ma while the oldest basalt is from 93.3Ma to 95.5Ma. The trend analysis suggest that there is a still posibility of eruption. The comparative experiment of dating between the conventional sample and after pick out of olivine sample implied that the dated ages of the Late-Cenozoic olivine-basalts distributed in northeast and east China may be older than the real values.
Subsurface sequence stratigraphy and reservoir characterization of the mississippian limestone (kinderhookian to meramecian), south central Kansas and north central Oklahoma

Both conventional and unconventional Mississippian reservoirs in the mid-continent are largely comprised of chert-rich carbonates of Osagean and Meramecan age. The conventional reservoir target is the Mississippian “chat,” a high porosity, chert residuum interval found immediately beneath the Mississippian-Pennsylvanian unconformity. The unconventional reservoir target occurs in the lower porosity, cherty, mud-rich intervals that occur in the lower portion of the Mississippian succession. There has been considerable debate surrounding the sequence stratigraphic interpretations, depositional models, and formation names applied to the reservoir intervals within the subsurface. Another major issue with regard to the subsurface is the stratigraphic position and origin of tripolitic chert development. Previous outcrop studies within the Mississippian outcrop belt, mud logs, and well log correlations have been utilized to facilitate the application of sequence stratigraphy to the subsurface succession. Reservoir intervals appear to be preferentially developed beneath the Osagean-Meramecian and Mississippian-Pennsylvanian boundaries. The proposed depositional model challenges previous assignments of tripolitic chert development to what has been called the Reeds Spring Formation in the subsurface.
Effects of insulin-like growth factor-1 on the mRNA expression of estradiol receptors, steroidogenic enzymes, and steroid production in bovine follicles

Insulin-like growth factor-1 (IGF-1) plays a crucial role in follicular growth and stimulates steroid hormone production in bovine follicles. Steroid hormones are synthesized through the actions of steroidogenic enzymes, specifically STAR, CYP11A1, HSD3B, and CYP19A1 in both theca cells (TCs) and granulosa cells (GCs), under the influence of gonadotropins. Particularly, estradiol 17β (E2) assumes a central role in follicular development and selection by activating estrogen receptors β (ESR2) in GCs. We assessed ESR2 mRNA expression in GCs of developing follicles and investigated the impact of IGF-1 on the mRNA expression of ESR2, CYP19A1, FSHR, and LHCGR, STAR, CYP11A1, and HSD17B in cultured GCs and TCs, respectively. Additionally, we assessed the influence of IGF-1 on androstenedione (A4), progesterone (P4), and testosterone (T) production in TCs. Small-sized follicles (< 6 mm) exhibited the highest levels of ESR2 mRNA expression, whereas medium-sized follicles (7–8 mm) displayed higher levels than large-sized follicles (≥ 9 mm) (P < 0.05). IGF-1 increased the mRNA expression of ESR2, CYP19A1, and FSHR in GCs of follicles of both sizes, except for FSHR mRNA in medium-sized follicles (P < 0.05). IGF-1 significantly elevated mRNA expression of LHCGR, STAR, CYP11A1, and CYP17B in TCs of small- and medium-sized follicles (P < 0.05). Moreover, IGF-1 augmented the production of A4 and P4 but had no impact on T production in TCs of small- and medium-sized follicles. Taken together, our findings indicate that IGF-1 upregulates steroidogenic enzymes and steroid hormone production, underscoring the crucial role of IGF-1 in follicle development and selection.
Contrasting spatial patterns of distribution of genetic diversity in two important bamboo species in the Central Western Ghats, India

Bamboo fonns an important component of the tropical forests of South and Southeast Asia and provides livelihood requirements to a large number of forest fringe communities. Large-scale destruction of habitats and over-harvesting of many economically important species of barn boo has led to irreversible loss of its genetic diversity. Thus, in recent years there has been an increasing concern over the conservation of genetic resources of bamboo. However, for most species of bamboo, critical infonnation on the extent of intra-specific genetic variability of the populations is unavailable. In this direction, we have examined the population genetic variability of Dendrocalamus strictus and Bambusa bambos, the most important species of bamboo in the Central Western Ghats, India. The study shows that the two species exhibit strikingly contrasting patterns in the distribution of genetic variability. While the diversity of D. strictus was higher in the northern latitudes, the diversity of B. bambos was higher in the southern latitudes. These findings could have important implications for conservation and management of genetic resources of bamboo in the Central Western Ghats, India.
What Do I Hear? Generating Sounds for Visuals with ChatGPT

This short paper introduces a workflow for generating realistic soundscapes for visual media. In contrast to prior work, which primarily focus on matching sounds for on-screen visuals, our approach extends to suggesting sounds that may not be immediately visible but are essential to crafting a convincing and immersive auditory environment. Our key insight is leveraging the reasoning capabilities of language models, such as ChatGPT. In this paper, we describe our workflow, which includes creating a scene context, brainstorming sounds, and generating the sounds.
A global analysis of the SMEFT under the minimal MFV assumption

We present comprehensive global fits of the SMEFT under the $\textit{minimal}$ minimal flavour violation (MFV) hypothesis, i.e. assuming that only the flavour-symmetric and CP-invariant operators are relevant at the high scale. The considered operator set is determined by theory rather than the used datasets. We establish global limits on these Wilson coefficients using leading order and next-to-leading order SMEFT predictions for electroweak precision observables, Higgs, top, flavour and dijets data as well as measurements from parity violation experiments and lepton scattering. Our investigations reveal an intriguing crosstalk among different observables, underscoring the importance of combining diverse observables from various energy scales in global SMEFT analyses.
Generalized Covariant Entropy Bound in Einstein Gravity with Quadratic Curvature Corrections

We explore the generalized covariant entropy bound in the theory where Einstein gravity is perturbed by quadratic curvature terms, which can be viewed as the first-order quantum correction to Einstein gravity. By replacing the Bekenstein-Hawking entropy with the holographic entanglement entropy of this theory and introducing two reasonable physical assumptions, we demonstrate that the corresponding Generalized Covariant Entropy Bound is satisfied under a first-order approximation of the perturbation from the quadratic curvature terms. Our findings suggest that the entropy bound and the Generalized Second Law of black holes are satisfied in the Einstein gravity under the first-order perturbation from the quadratic curvature corrections, and they also imply that the generalized covariant entropy bound may still hold even after considering the quantum correction of gravity, but in this case, we may need to use holographic entanglement entropy as the formula for gravitational entropy.
P1 Center Electron Spin Clusters Are Prevalent in Type Ib Diamonds.

Understanding the spatial distribution of the P1 centers is crucial for diamond-based sensors and quantum devices. P1 centers serve as polarization sources for dynamic nuclear polarization (DNP) quantum sensing and play a significant role in the relaxation of nitrogen vacancy (NV) centers. Additionally, the distribution of NV centers correlates with the distribution of P1 centers, as NV centers are formed through the conversion of P1 centers. We utilized DNP and pulsed electron paramagnetic resonance (EPR) techniques that revealed strong clustering of a significant population of P1 centers that exhibit exchange coupling and produce asymmetric line shapes. The 13C DNP frequency profile at a high magnetic field revealed a pattern that requires an asymmetric EPR line shape of the P1 clusters with electron-electron (e-e) coupling strengths exceeding the 13C nuclear Larmor frequency. EPR and DNP characterization at high magnetic fields was necessary to resolve energy contributions from different e-e couplings. We employed a two-frequency pump-probe pulsed electron double resonance technique to show cross-talk between the isolated and clustered P1 centers. This finding implies that the clustered P1 centers affect all of the P1 populations. Direct observation of clustered P1 centers and their asymmetric line shape offers a novel and crucial insight into understanding magnetic noise sources for quantum information applications of diamonds and for designing diamond-based polarizing agents with optimized DNP efficiency for 13C and other nuclear spins of analytes. We propose that room temperature 13C DNP at a high field, achievable through straightforward modifications to existing solution-state NMR systems, is a potent tool for evaluating and controlling diamond defects.
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis

Creating high-quality view synthesis is essential for immersive applications but continues to be problematic, particularly in indoor environments and for real-time deployment. Current techniques frequently require extensive computational time for both training and rendering, and often produce less-than-ideal 3D representations due to inadequate geometric structuring. To overcome this, we introduce VoxNeRF, a novel approach that leverages volumetric representations to enhance the quality and efficiency of indoor view synthesis. Firstly, VoxNeRF constructs a structured scene geometry and converts it into a voxel-based representation. We employ multi-resolution hash grids to adaptively capture spatial features, effectively managing occlusions and the intricate geometry of indoor scenes. Secondly, we propose a unique voxel-guided efficient sampling technique. This innovation selectively focuses computational resources on the most relevant portions of ray segments, substantially reducing optimization time. We validate our approach against three public indoor datasets and demonstrate that VoxNeRF outperforms state-of-the-art methods. Remarkably, it achieves these gains while reducing both training and rendering times, surpassing even Instant-NGP in speed and bringing the technology closer to real-time.
Composite-fermion pairing at half-filled and quarter-filled lowest Landau level

The Halperin-Lee-Read Fermi sea of composite fermions (CFs) at half-filled lowest Landau level is the realization of a fascinating non-Fermi liquid metallic phase. Remarkably, experiments have found that as the width of the quantum well is increased, this state makes a transition into a fractional quantum Hall (FQH) state, the origin of which has remained an important puzzle since its discovery more than three decades ago. We perform detailed and accurate quantitative calculations using a systematic variational framework for the pairing of CFs that closely mimics the BCS theory of superconductivity. We find: (i) as the quantum-well width is increased, the single-component CF Fermi sea occupying the lowest symmetric subband of the quantum well undergoes an instability into a single-component p-wave paired state of CFs; (ii) the theoretical phase diagram in the quantum-well width - electron density plane is in excellent agreement with experiments; (iii) a sufficient amount of asymmetry in the charge distribution of the quantum well destroys the FQH effect, as observed experimentally; and (iv) the two-component 331 state is energetically less favorable than the single component paired state. Evidence for FQH effect has been seen in wide quantum wells also at quarter-filled lowest Landau level; here our calculations indicate an f-wave paired state of CFs. We further investigate bosons in the lowest Landau level at filling factor equal to one and show that a p-wave pairing instability of CFs, which are bosons carrying a single flux quantum, in agreement with exact diagonalization studies. The general consistency of the composite-fermion BCS approach with experiments lends support to the notion of CF pairing as the mechanism of FQH effects at even-denominator filling factors. Various experimental implications are mentioned.
PID-Inspired Continuous-Time Distributed Optimization

This paper proposes two novel distributed continuous-time algorithms inspired by PID control to solve distributed optimization problems. The algorithms are referred to as first-order and second-order, respectively, depend on the intrinsic dynamics of the agents in the network. Sufficient conditions are derived so that both algorithms converge exponentially over undirected connected graphs. Finally, numerical simulations illustrate the effectiveness and efficiency of the proposed algorithms.
Core determinants of quality criteria for mhealth for hypertension: evidence from machine learning instruments

Uncontrolled hypertension is a global problem that needs to be addressed. Despite the many mHealth solutions in the market, the nonadherence relative to intended use jeopardizes treatment success. Although investigating user experience is one of the most important mechanisms for understanding mHealth discontinuance, surprisingly, the core determinants of overall user experience (i.e., positive and negative) about mHealth apps for hypertension are unknown. To address the mentioned gap in knowledge, this study adopts the computational grounded theory methodological framework and employs advanced deep learning algorithms to predict core quality criteria that affect overall user experience of hypertension apps published in the Apple App Store. This study contributes to theory and practice of designing evidence-based interventions for hypertension in the form of propositions and provide valuable managerial implications and recommendations for manufacturers.