War, Cacophony & Beyond: Reexamining and Adding Security to Environmental, Social & Corporate Governance Investing

Russia’s invasion of Ukraine has exposed the current ESG approach’s weakness in many ways, most noticeably in the form of real-world consequences of negative screening strategies against defense and energy sectors. This Note attributes this weakness to a cacophony of risk and impact—a conceptual conflation between the two concepts in the relatively nascent ESG space. However, this Note finds that, even after resolving this cacophony, ESG investing still has significant problems that challenge its efficacy: (1) insufficient fairness and reasonableness of ESG ratings; (2) its uncertain future viability as an effective investment methodology under unfavorable economic conditions; and (3) its decay into a public relations and marketing tool. Eschewing ESG investing and promoting impact investing instead is a solution worth considering, but implementation issues prevent it from becoming a large-scale alternative in the short-term. This Note argues that a better solution, albeit far from perfect, is the inclusion of security—defined as elements that support the United States and its allies’ security against adversarial forces—as a component of ESG’s ambiguous “S.” It could not only address ESG’s lack of responsiveness to geopolitical risks exposed by the Russo-Ukrainian War, but also accelerate the market and the public’s new and accurate understanding of ESG as a risk framework

Intrageneric structural variation in organelle genomes from the genus Dystaenia (Apiaceae): genome rearrangement and mitochondrion-to-plastid DNA transfer

IntroductionDuring plant evolution, intracellular DNA transfer (IDT) occurs not only from organelles to the nucleus but also between organelles. To further comprehend these events, both organelle genomes and transcriptomes are needed.MethodsIn this study, we constructed organelle genomes and transcriptomes for two Dystaenia species and described their dynamic IDTs between their nuclear and mitochondrial genomes, or plastid and mitochondrial genomes (plastome and mitogenome).Results and DiscussionWe identified the putative functional transfers of the mitochondrial genes 5′ rpl2, rps10, rps14, rps19, and sdh3 to the nucleus in both Dystaenia species and detected two transcripts for the rpl2 and sdh3 genes. Additional transcriptomes from the Apicaceae species also provided evidence for the transfers and duplications of these mitochondrial genes, showing lineage-specific patterns. Intrageneric variations of the IDT were found between the Dystaenia organelle genomes. Recurrent plastid-to-mitochondrion DNA transfer events were only identified in the D. takeshimana mitogenome, and a pair of mitochondrial DNAs of plastid origin (MIPTs) may generate minor alternative isoforms. We only found a mitochondrion-to-plastid DNA transfer event in the D. ibukiensis plastome. This event may be linked to inverted repeat boundary shifts in its plastome. We inferred that the insertion region involved an MIPT that had already acquired a plastid sequence in its mitogenome via IDT. We propose that the MIPT acts as a homologous region pairing between the donor and recipient sequences. Our results provide insight into the evolution of organelle genomes across the family Apiaceae

Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound

Plane wave imaging (PWI) in medical ultrasound is becoming an important reconstruction method with high frame rates and new clinical applications. Recently, single PWI based on deep learning (DL) has been studied to overcome lowered frame rates of traditional PWI with multiple PW transmissions. However, due to the lack of appropriate ground truth images, DL-based PWI still remains challenging for performance improvements. To address this issue, in this paper, we propose a new unsupervised learning approach, i.e., deep coherence learning (DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the DL network is trained to predict highly correlated signals with a unique loss function from a set of PW data, and the trained DL model encourages high-quality PWI from low-quality single PW data. In addition, the DL-DCL framework based on complex baseband signals enables a universal beamformer. To assess the performance of DL-DCL, simulation, phantom and in vivo studies were conducted with public datasets, and it was compared with traditional beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based methods (i.e., supervised learning approach with 1-PW and generative adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial resolution, and it outperformed all comparison methods in contrast resolution. These results demonstrated that the proposed unsupervised learning approach can address the inherent limitations of traditional PWIs based on DL, and it also showed great potential in clinical settings with minimal artifacts

Flexible fiber-based optoelectronics for neural interfaces

Neurological and psychiatric conditions pose an increasing socioeconomic burden on our aging society. Our ability to understand and treat these conditions relies on the development of reliable tools to study the dynamics of the underlying neural circuits. Despite significant progress in approaches and devices to sense and modulate neural activity, further refinement is required on the spatiotemporal resolution, cell-type selectivity, and longterm stability of neural interfaces. Guided by the principles of neural transduction and by the materials properties of the neural tissue, recent advances in neural interrogation approaches rely on flexible and multifunctional devices. Among these approaches, multimaterial fibers have emerged as integrated tools for sensing and delivering of multiple signals to and from the neural tissue. Fiber-based neural probes are produced by thermal drawing process, which is the manufacturing approach used in optical fiber fabrication. This technology allows straightforward incorporation of multiple functional components into microstructured fibers at the level of their macroscale models, preforms, with a wide range of geometries. Here we will introduce the multimaterial fiber technology, its applications in engineering fields, and its adoption for the design of multifunctional and flexible neural interfaces. We will discuss examples of fiber-based neural probes tailored to the electrophysiological recording, optical neuromodulation, and delivery of drugs and genes into the rodent brain and spinal cord, as well as their emerging use for studies of nerve growth and repair.United States. National Institute of Neurological Disorders and Stroke (Grant 5R01NS086804)United States. National Science Foundation. Division of Materials Research (Grant DMR-1419807)United States. National Science Foundation. Division of Engineering Education & Centers (Grant EEC-1028725)McGovern Institute for Brain Research at MI

NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs

Panoramic radiography (panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, its applicability is limited as compared to 3D Cone-beam computed tomography (CBCT), because PX only provides 2D flattened images of the oral structure. In this paper, we propose a new framework which estimates 3D oral structure from real-world PX images. Since there are not many matching PX and CBCT data, we used simulated PX from CBCT for training, however, we used real-world panoramic radiographs at the inference time. We propose a new ray-sampling method to make simulated panoramic radiographs inspired by the principle of panoramic radiography along with the rendering function derived from the Beer-Lambert law. Our model consists of three parts: translation module, generation module, and refinement module. The translation module changes the real-world panoramic radiograph to the simulated training image style. The generation module makes the 3D structure from the input image without any prior information such as a dental arch. Our ray-based generation approach makes it possible to reverse the process of generating PX from oral structure in order to reconstruct CBCT data. Lastly, the refinement module enhances the quality of the 3D output. Results show that our approach works better for simulated and real-world images compared to other state-of-the-art methods.Comment: 10 pages, 4 figure

3D Teeth Reconstruction from Panoramic Radiographs using Neural Implicit Functions

Panoramic radiography is a widely used imaging modality in dental practice and research. However, it only provides flattened 2D images, which limits the detailed assessment of dental structures. In this paper, we propose Occudent, a framework for 3D teeth reconstruction from panoramic radiographs using neural implicit functions, which, to the best of our knowledge, is the first work to do so. For a given point in 3D space, the implicit function estimates whether the point is occupied by a tooth, and thus implicitly determines the boundaries of 3D tooth shapes. Firstly, Occudent applies multi-label segmentation to the input panoramic radiograph. Next, tooth shape embeddings as well as tooth class embeddings are generated from the segmentation outputs, which are fed to the reconstruction network. A novel module called Conditional eXcitation (CX) is proposed in order to effectively incorporate the combined shape and class embeddings into the implicit function. The performance of Occudent is evaluated using both quantitative and qualitative measures. Importantly, Occudent is trained and validated with actual panoramic radiographs as input, distinct from recent works which used synthesized images. Experiments demonstrate the superiority of Occudent over state-of-the-art methods.Comment: 12 pages, 2 figures, accepted to International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 202

Complete Sequences of Organelle Genomes from the Medicinal Plant Rhazya Stricta (Apocynaceae) and Contrasting Patterns of Mitochondrial Genome Evolution Across Asterids

Rhazya stricta is native to arid regions in South Asia and the Middle East and is used extensively in folk medicine to treat a wide range of diseases. In addition to generating genomic resources for this medicinally important plant, analyses of the complete plastid and mitochondrial genomes and a nuclear transcriptome from Rhazya provide insights into inter-compartmental transfers between genomes and the patterns of evolution among eight asterid mitochondrial genomes. Results: The 154,841 bp plastid genome is highly conserved with gene content and order identical to the ancestral organization of angiosperms. The 548,608 bp mitochondrial genome exhibits a number of phenomena including the presence of recombinogenic repeats that generate a multipartite organization, transferred DNA from the plastid and nuclear genomes, and bidirectional DNA transfers between the mitochondrion and the nucleus. The mitochondrial genes sdh3 and rps14 have been transferred to the nucleus and have acquired targeting presequences. In the case of rps14, two copies are present in the nucleus; only one has a mitochondrial targeting presequence and may be functional. Phylogenetic analyses of both nuclear and mitochondrial copies of rps14 across angiosperms suggests Rhazya has experienced a single transfer of this gene to the nucleus, followed by a duplication event. Furthermore, the phylogenetic distribution of gene losses and the high level of sequence divergence in targeting presequences suggest multiple, independent transfers of both sdh3 and rps14 across asterids. Comparative analyses of mitochondrial genomes of eight sequenced asterids indicates a complicated evolutionary history in this large angiosperm clade with considerable diversity in genome organization and size, repeat, gene and intron content, and amount of foreign DNA from the plastid and nuclear genomes. Conclusions: Organelle genomes of Rhazya stricta provide valuable information for improving the understanding of mitochondrial genome evolution among angiosperms. The genomic data have enabled a rigorous examination of the gene transfer events. Rhazya is unique among the eight sequenced asterids in the types of events that have shaped the evolution of its mitochondrial genome. Furthermore, the organelle genomes of R. stricta provide valuable genomic resources for utilizing this important medicinal plant in biotechnology applications.King Abdulaziz UniversityIntegrative Biolog

Deformable Graph Transformer

Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention.Comment: 16 pages, 3 figure