Acute osteomyelitis in the setting of chronic steroid therapy for the treatment of 17-alpha-hydroxylase deficient congenital adrenal hyperplasia

Background 
Lactation support in transgender women is increasingly recognized as clinically important yet has limited clinical precedent. While previous case reports describe successful lactation induction their protocols involve domperidone, a galactagogue unavailable for this purpose in the US and recommended against by the US Food and Drug Administration (FDA). We present a case of successful lactation induction in a transgender woman using a protocol not involving galactagogues. 
Case Presentation 
Our patient was a 36-year-old transgender woman, a United States military veteran 
receiving care through the Veterans Health Administration (VHA), who was planning on having a child with her partner and wanted to breastfeed. Her partner, a transgender man, carried and delivered the child and wished to return to testosterone use shortly after delivery. Her primary goals included sufficient milk production to allow bonding, even if not the sole source of infant nutrition. Our multidisciplinary team included a clinical pharmacist with gender-affirming hormone therapy expertise and a lactation specialist with gender-affirming therapy expertise. Our patient was status post orchiectomy with an initial testosterone level of 27 ng/dL, and on a dose of 4mg PO 17-beta estradiol. We began the patient’s lactation induction 98 days prior to her partner’s pregnancy and 377 days prior to her 
partner’s eventual due date. Our protocol involved 1) increased estrogen and 
progesterone to mimic physiologic levels during pregnancy; 2) physical nipple stimulation using a breast pump; and 3) reduced estrogen and progesterone shortly before delivery to mimic physiologic levels after birth. We opted against a galactagogue due to FDA recommendations against domperidone as well as patient concerns of side effects with other galactagogues. Interestingly, our patient’s estradiol levels remained under 200 pg/mL throughout the lactation induction process, even at higher levels of 17-beta estradiol dosing. This is in consonance with other published case-reports on lactation-induction in transwomen. 
Discussion 
Working with a multidisciplinary team including pharmacy and lactation specialists and using a protocol involving (1) initial increase of estradiol and progesterone, (2) nipple stimulation, and (3) eventual reduction of estradiol and progesterone, our patient was able to lactate without the use of galactagogues, allowing her to meet her goal of bonding with her baby. This is the first reported case of successful lactation induction without galactagogues for a transgender woman to our knowledge, and provides an example protocol modeled on physiologic lactation stimuli that other clinicians may adapt with their own patients in similar circumstances.

Inducing lactation in a transgender woman without galactagogues: A case report

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Unsupervised image segmentation aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. We also make a comparison between UISS and image-wise representation learning. Based on the analysis, we argue that the existing MI-based methods in UISS suffer from representation collapse. By this, we proposed a robust network called Semantic Attention Network(SAN), in which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise and semantic features dynamically. Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods. 

Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation

We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely point cloud sequence based  Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive spatiotemporal representations. Specifically, dense point cloud segments are first input into an encoder to extract embeddings. All but the last ones are then aggregated by a context-aware autoregressor to make predictions for the last target segment. Towards the goal of modeling multi-granularity structures, local and global contrastive learning are performed between predictions and targets. To further improve the generalization of representations, the predictions are also utilized to reconstruct raw point cloud sequences by a decoder, where point cloud colorization is employed to discriminate against different frames. By combining classic contrast and reconstruction paradigms, it makes the learned representations with both global discrimination and local perception. We conduct experiments on four point cloud sequence benchmarks, and report the results on action recognition and gesture recognition under multiple experimental settings. The performances are comparable with supervised methods and show powerful transferability.

Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-supervised Learning

Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain-specific features. However, these approaches depends heavily on the richness of the texture bank and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve the generalization. In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. Further, we design a hierarchical guidance generalization network equipped with structure-guided enhancement modules, which purpose to learn the domain-invariant generalized knowledge. Extensive experiments together with ablation studies on widely-used datasets are conducted to verify the effectiveness of the proposed model, and reveal its superiority over other state-of-the-art alternatives. 

Adaptive Texture Filtering for Single-domain Generalized Segmentation

Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation. However, all existing works rely on additional generative models to ensure the quality of results, because CLIP alone cannot provide enough guidance information for fine-scale pixel-level changes. In this paper, we introduce CLIPVG, a text-guided image manipulation framework using differentiable vector graphics,  which is also the first CLIP-based general image manipulation framework that does not require any additional generative models. We demonstrate that CLIPVG can not only achieve state-of-art performance in both semantic correctness and synthesis quality, but also is flexible enough to support various applications far beyond the capability of all existing methods.

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