Office of the Chief Scientist

Tax-efficient investing can be a crucial, yet often overlooked, strategy for maximizing long-term wealth accumulation. Traditional investment analysis and strategy focus on pre-tax returns and typically ignore the negative impact that taxes may have on overall returns and long-term wealth accumulation. Failing to consider after-tax performance can significantly erode investment gains over time. In an industry dominated by large investment managers catering to non-taxable institutions (e.g., pensions, endowments, and foundations), many family offices and ultra-high-net-worth investors lack access to tax-sensitive solutions, particularly in private markets. This article introduces a comprehensive framework for tax-efficient investing in private markets, emphasizing the importance of risk-adjusted after-tax returns, standardized tax-impact metrics, and the concept of Tax Alpha (<xref>Arany et al. 2024</xref>). While public markets may provide substantial opportunities for tax efficiency (e.g., tax-loss harvesting and direct indexing), private market investments may offer more structured, controlled, and targeted opportunities for tax efficiency in other parts of a portfolio. Building on this foundation, this article takes a deeper, analytical approach to defining various return metrics in tax-efficient investing and provides concrete examples of how investment returns may be calculated through a tax-efficient lens using Tax Benefit Harvesting™. The objective is to provide a “common yardstick” for measuring returns for investments with varying degrees of tax efficiency and comparing them to non-tax-efficient investments. The methods outlined herein may be used across the investment spectrum, from public stocks and bonds to private markets (e.g., private equity, private credit, real estate, infrastructure). By integrating tax-efficient portfolio strategy, tax-efficient investment structuring, and deliberate investment selection, investors may improve after-tax returns without taking on incremental risk, ultimately building a more effective approach to wealth preservation and accumulation.

For patients with complex health needs, the periods between clinical encounters are times of significant vulnerability, during which unobserved risks can escalate into acute events. The Waymark community-based care management program was designed to reduce this vulnerability for the patients it serves who are enrolled in Medicaid programs, but the organization faced the challenge of processing thousands of unstructured daily encounter notes from its field-based teams. To meet this challenge in a scalable way, the authors developed and implemented an artificial intelligence (AI) oracle, a system that continuously analyzes these notes. The oracle has three functions: (1) to identify urgent safety red flags; (2) to surface opportunities for preventive care; and (3) to generate monthly, strengths-based feedback to foster a learning health system. Following a phased implementation that began with a silent validation period, the system now delivers real-time, actionable alerts directly into the care teams’ messaging system. Iterative refinement of the AI prompts, driven by clinical leadership review, improved the actionable true-positive rate of safety alerts from 68% of 147 to over 95% of 203 over 12 weeks. The system’s value lies in its ability to synthesize disparate data streams — such as community health worker observations; pharmacy data; rising risk scores; and hospital admission, discharge, and transfer feeds — to reveal complex risks that could be missed by manual, siloed review. The AI oracle has become an integrated, “over-the-shoulder” digital coach, transforming a reactive care model into a proactive, learning health system and offering a scalable solution to improve outpatient safety for vulnerable populations.

Abstract This article explores the evolving role of faith-based organizations (FBOs) in federal prison rehabilitation programs, particularly within the framework of the First Step Act (FSA). It addresses constitutional and legal tensions surrounding the integration of religious initiatives in correctional settings and evaluates the empirical evidence for their effectiveness. Through a discussion of correctional science principles such as the risk-needs-responsivity (RNR) model and cognitive behavioral therapy (CBT), the article considers how faith-based programs align with or diverge from established rehabilitative practices. Emphasis is placed on the need for inclusive, evidence-informed approaches that respect both religious freedom and constitutional boundaries while supporting successful reentry and reduced recidivism.

Cosmetic wipes are made for multiple functions, baby care, hand washing, feminine and personal cleansing, removing makeup, and applying products such as deodorants and sunless tanners among other uses. Despite the presence of preservatives, cosmetic wipes can become contaminated during processing steps and usage, which may lead to skin infections and other health issues for consumers. No validated method exists for the microbiological testing of cosmetic wipes. The goal of this study was to develop and validate a specific sample preparation method for the quantitative detection of microorganisms in cosmetic wipes for inclusion in the FDA Biological Analytical Manual (BAM). Ten wipe types differing in their composition and preservative combinations were inoculated with Bacillus cereus spore suspensions at three concentration levels and aged for 14 days. Three extraction methods were compared: mBAM1g (reference method using 1 g samples), mBAMww (whole wipe method based on BAM Chapter 23), and ISOww (whole wipe method based on ISO method without Tween 80). For commercial wipes, mBAMww and ISOww, using whole wipes, performed similarly (p ≥ 0.05) or significantly better (p &lt; 0.05) than mBAM1g. For laboratory-made wipes, 1 g samples showed higher recovery rates than whole wipes, likely due to cell loss during aging. Inoculation method and preservatives affect microbial distribution, survival, and recovery rates. T80 may have a positive effect on the recovery of B. cereus from wipes. This study recommends mBAMww for the microbiological analysis of cosmetic wipes.

ABSTRACT The demand for fats, oils, and greases (FOG) as biofuel feedstocks has increased in recent years. Further expansions will have uncertain, but potentially significant, effects on food prices, greenhouse gas emissions, and the U.S. farm economy. To address the market‐mediated effects of expanding FOG feedstock consumption, U.S. transportation fuel program regulations are being changed in contradictory and precedent‐setting ways. On the one hand, the largest U.S. state (California) is curtailing crediting for vegetable oil feedstocks. On the other hand, federal programs are restricting incentives for imported waste feedstocks like used cooking oil and tallow. These policy changes have created uncertainty about the “FOG” that will be used in biofuels production, since assessing the net effect of these changes is challenging. In this paper, I calculate the effect that these revised and proposed policies have on FOG feedstock incentives. First, I describe how federal and state transportation fuel programs incentivize FOG feedstock consumption. Second, I calculate how program adjustments change the incentives for various FOG feedstocks. I find that the changes have little net effect on U.S. soybean oil and distiller's corn oil (DCO) biomass‐based diesel (BBD) incentives in California. Elsewhere in the U.S., soybean oil BBD incentives increase. In contrast, incentives for BBD produced with imported feedstocks are considerably reduced. In summary, the revisions imply that FOG feedstock incentives will be higher for DCO than for other feedstocks.

Background: Seborrheic dermatitis is a chronic, reoccurring inflammatory dermatological condition commonly encountered in Homoeopathic Outpatient department. Literature cites case reports, which are based on experiences of individual practitioners making successful prescription for treatment of seborrheic dermatitis. The present study was designed as a double dummy double blind randomized control trial in comparison with conventional treatment. Predefined homoeopathic medicines (Sulphur, Psorinum, Mezerum, Graphites, Arsenic album, Natrum muriaticum, Calcarea carbonicum, Kali sulphuricum, Sepia officionalis, Hepar sulphuris calcareum, Silicea, Phosphorus, Thuja occidentalis) would be evaluated on the clinical outcome in seborrheic dermatitis and compared with the conventional group. Material &amp; Methods: The study would be conducted in dermatology department of Guru Gobind Singh Hospital, New Delhi. Patient requiring predefined Homoeopathic medicine for seborrheic dermatitis would be randomized using computer generated randomization chart. The active drug group would be prescribed predefined homoeopathic medicine in LM potency with dosage and subsequent prescription based on homoeopathic principles and active control group would be given conventional medicine (Ketoconazole 200mg) one tablet/OD daily for 20days. Assessment of change in NRS for pruritus and SDASI score before and after treatment in both the group would be done. Discussion: The study intends to conduct a randomized control trial to see the efficacy of homoeopathic treatment in seborrheic dermatitis and compare it with conventional group. The study would also help in identifying frequently used homoeopathic medicines in treatment of seborrheic dermatitis. Keywords: Double dummy double blind, Homoeopathy, NRS, RCT, seborrheic dermatitis, SDASI

This chapter provides an overview of classification, a key method in data analysis. It explains different techniques for data classification using large language models (LLMs), including open-access models and detailed prompts for zero-shot, one-shot, and few-shot classifications. The chapter illustrates how LLMs can enhance qualitative analysis by leveraging their classification capabilities. Additionally, the chapter discusses the importance of prompt design and the cautious use of LLMs. This suggests techniques for improving reliability and the use of balanced approaches when selecting methods for specific research objectives.