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In this paper we propose a scalable framework for large-scale farm scene modeling that utilizes remote sensing data, specifically satellite images. Our approach begins by accurately extracting and categorizing the distributions of various scene elements from satellite images into four distinct layers: fields, trees, roads, and grasslands. For each layer, we introduce a set of controllable Parametric Layout Models (PLMs). These models are capable of learning layout parameters from satellite images, enabling them to generate complex, large-scale farm scenes that closely reproduce reality across multiple scales. Additionally, our framework provides intuitive control for users to adjust layout parameters to simulate different stages of crop growth and planting patterns. This adaptability makes our model an excellent tool for graphics and virtual reality applications. Experimental results demonstrate that our approach can rapidly generate a variety of realistic and highly detailed farm scenes with minimal inputs. 

Biomolecular condensates, sometimes also known as membraneless organelles (MLOs), can form through weak multivalent intermolecular interactions of proteins and nucleic acids, a process often associated with liquid-liquid phase separation. Biomolecular condensates are emerging as sites and regulatory platforms of vital cellular functions, including transcription and RNA processing. In the first part of this Review, we comprehensively discuss how alternative splicing regulates the formation and properties of condensates, and conversely the roles of biomolecular condensates in splicing regulation. In the second part, we focus on the spatial connection between splicing regulation and nuclear MLOs such as transcriptional condensates, splicing condensates and nuclear speckles. We then discuss key studies showing how splicing regulation through biomolecular condensates is implicated in human pathologies such as neurodegenerative diseases, different types of cancer, developmental disorders and cardiomyopathies, and conclude with a discussion of outstanding questions pertaining to the roles of condensates and MLOs in splicing regulation and how to experimentally study them. 

To study and monitor the adverse health consequences of using electronic cigarettes, a user’s puff topography, which are quantification parameters of the user’s vaping habits, plays a central role. In this work, we introduce a topography sensor to measure the mass of total particulate matter generated in every puff and to estimate the nicotine yield. The sensor is compact and low-cost, and is integrated into the electronic cigarette device to promptly and conveniently monitor the user’s daily puff topography. The topography sensor is comprised of a photometric sensor and a pressure sensor. The photometric sensor measures the mass concentration of the aerosol, based on scattering of near-infrared light from airborne particles, while the pressure sensor measures the flow rate. The topography sensor was tested under various conditions including a wide range of atomizer power, puff duration, and inhalation pressure. The sensor’s accuracy was validated by comparing the sensor’s readings with reference measurements, and the results matched closely with the trends reported by existing studies on electronic cigarettes. An example application for tracking a user’s puff topography was also demonstrated. Our topography sensor holds great promise in mitigating the health risks of vaping, and in promoting quality control of electronic cigarette products. 

Intrinsically disordered regions within human proteins play critical roles in cellular information processing, including signaling, transcription, stress response, DNA repair, genome organization, and RNA processing. Here, we summarize current challenges in the field and propose cutting-edge approaches to address them in normal physiology and disease, with a focus on cancer. 

This work introduces an emerging application of aerosol sensors integrated inside electronic cigarettes. Using the aerosol sensors, particle size distribution, aerosol temperature, and target chemicals of the generated aerosols are measured before the aerosol is inhaled by a user. When a hazardous aerosol is detected, the user will be warned immediately to stop vaping. With extensive sensor data collected from every puff, an electronic cigarette becomes a smart mobile device, capable of tracking and improving a user's vaping habits. Experimental results on a prototype are presented and have shown great promises towards minimizing the health risks of vaping.