ABS Consulting (United States)

Analyzing gene expression data from three separate clinical trials, we find that the antiretroviral drug tenofovir, which belongs to the class of nucleotide analogue reverse transcriptase inhibitors, induces the type I/III interferon system of innate immunity in the mucosa. This effect occurs in the absence of HIV infection and manifests itself over various treatment durations and after both oral and topical drug delivery. Tenofovir and other related medications are important components of long-term antiretroviral treatment taken by people living with HIV. Therefore, this unexpected immunological effect might need to be considered as a potential contributor to comorbidities in people living with HIV, as well as an immunopharmacological co-factor when testing novel HIV cure interventions. This study is registered with ClinicalTrials.gov as NCT01768962, NCT01687218, and NCT01232803.

This article examines the temporal processes of information transmission by response times (RTs) in visual search. Information (hazard) rate functions of RTs were evaluated for a large dataset provided by Wolfe and colleagues. Set size accounted for a minor portion of the variance of these RTs, and the present analysis examined temporal processes involved in the larger variance within each stimulus and task condition. Momentary information rates were measured by (a) response probabilities sequentially dependent on the proportion of longer RTs, (b) with continuously varying visual influence, and (c) evaluated in bits/s. Results include the following: (1) Rates of information transmission from visual input to behavioral output varied systematically with the RTs in each search condition. (2) Information rate functions for “feature” and “conjunction” tasks were highly nonlinear, with distinct phases of accelerating and decelerating visual influence. Roughly constant information rates occurred in only a few conditions in the “spatial configuration” task. (3) Faster and slower observers responded to qualitatively different visual organizations of target-absent displays in all three tasks. (4) Perceptual organization and response control were synchronized parallel processes.

BackgroundThe available evidence regarding the sequential performance of gastrointestinal tract restoration and abdominal wall reconstruction in two surgical stages during a single hospitalization is limited and is based primarily on case series. In this study, we present our experience with the aim of describing the outcomes obtained in the repair of complex abdominal wall defects and the restoration of intestinal continuity using a two-stage approach within the same hospital stay.MethodsCase series of patients who underwent elective surgery for gastrointestinal tract restoration, followed by abdominal wall reconstruction in a second surgical stage during the same hospitalization. Medical records of procedures performed between 2018 and 2023 were reviewed. All interventions were carried out electively by a multidisciplinary team involving the abdominal wall surgery group and colorectal surgery.ResultsA total of 30 patients were included. Both surgical procedures were completed in 73% of cases, with a mean interval of 6.3 days between the two surgeries. In 26% of patients, it was not possible to complete both procedures; the most frequent causes were anastomotic leakage and surgical site infection, each occurring in 9% of cases. The mean length of hospital stay was 14 days. The most common complications were postoperative ileus, anastomotic leakage, intestinal perforation, and deep surgical site infection.ConclusionAlthough concomitant surgery is associated with a higher risk of complications particularly in the setting of complex hernias—in appropriately selected patients, sequential procedures performed during the same hospitalization can achieve favorable outcomes, especially in stoma reversal. The implementation of prehabilitation programs and the adoption of shared decision-making models are essential to optimize outcomes and reduce associated morbidity.

The paucity of diagnostic tests for isoniazid-resistant tuberculosis is concerning, given its status as the most common form of drug-resistant tuberculosis and a gateway to multidrug-resistant diseases. Molecular drug-susceptibility testing has improved access to timely diagnosis of rifampicin-resistant tuberculosis, but testing for isoniazid-resistant tuberculosis still remains rare. In this Review, we assessed the characteristics of molecular drug-susceptibility testing for detection of isoniazid-resistant tuberculosis, referencing the WHO target product profiles. 9243 citations were screened to select 238 studies published between 2000 and 2024. The diagnostics options have expanded rapidly since 2020, with 27 nucleic acid amplification tests, eight line probe assays, five DNA microarrays, two targeted next-generation sequencing platforms, and two whole-genome sequencing platforms. Most of the evaluated molecular drug-susceptibility tests met diagnostic performance targets but were often complex and costly. Although a few low-complexity nucleic acid amplification tests met key target product profile criteria, additional field validation and greater efforts are needed to ensure optimal feasibility and affordability for low-resource settings.

تتناول هذه الدراسة موضوع الاشتقاق في اللغة العربية من حيث مفهومه، وأقسامه، وفائدته، ساعيةً إلى بيان تعدد تقسيماته عند اللغويين قديمًا وحديثًا، وإبراز أهميته في إثراء الدرس اللغوي ومواكبة التطور الحضاري. اعتمد البحث المنهج الوصفي القائم على الاستقراء والتحليل، فاستعرض المعنى اللغوي والاصطلاحي للاشتقاق، وناقش العلاقة بينه وبين علم التصريف، مبيّنًا أوجه الاتفاق والاختلاف بينهما. كما عرض أقسام الاشتقاق كما وردت في كتب العلماء، مثل الاشتقاق الصغير، والكبير، والأكبر (الإبدال)، والكبّار (النحت)، والاشتقاق المركب، مع الإشارة إلى اختلاف العلماء في عددها وتسمياتها. وخلصت الدراسة إلى أن الاشتقاق أوسع مما قُسِّم إليه تقليديًا، وأن له دورًا محوريًا في توليد المصطلحات الحديثة وتعريب المفاهيم العلمية والتقنية، مما يجعله أحد أبرز مظاهر حيوية العربية وقدرتها على التجدد والاستمرار.

When oil reaches shorelines during a marine oil spill, it can persist in the sediment for decades, extending well beyond on-water operations and can become a significant expense to the overall response. Oil's persistence on beaches is initially driven by its ability to penetrate sediment, a process heavily influenced by a suite of in situ shoreline conditions as well as the oil's properties. One property of note is viscosity, which undergoes non-linear changes under the influence of temperature and influences oil's ability to penetrate shoreline sediments. Through 435 column experiments, this study systematically examines the penetration behaviour of diluted bitumen (dilbit) and a distillate very low-sulphur fuel oil (VLSFO) in unsaturated, coarse-grained, inorganic beach sediments under seven temperature regimes (Polar to Equatorial), five sediment types and two oil types. Results indicate that the sediment's temperature significantly influences oil penetration, while the presence of temperature gradients within the sediments exerts minimal influence on its penetration. Low temperatures limited oil penetration, concentrating oil near the sediment's surface. In contrast, higher temperatures in those same sediments resulted in deeper penetration and increased the net volume of oil-contaminated sediment. These findings confirm the temperature-viscosity feedback as a key control on oil migration in marine beach sediments and provide new empirical insights that directly inform shoreline response strategies and waste management planning across diverse climatic regions.

Abstract Casing deformation during hydraulic fracturing in unconventional reservoirs presents significant challenges, reducing well productivity, increasing costs, and restricting lateral access. While multi-finger calipers offer reliable diagnostics, they are often costly and typically used after issues arise. In contrast, the Casing Collar Locator (CCL) is routinely deployed, offering a cost-effective and scalable diagnostic alternative. This study evaluates the use of high-frequency CCL data for real-time anomaly detection, aiming to identify potential casing deformation earlier and proactively reduce the unstimulated lateral length with preventive actions. The goal is to give completion engineers timely insights that lower intervention costs and improve operational efficiency. The study proposes an end-to-end workflow including data ingestion, data processing, machine learning model deployment, and real-time visualizations. High-frequency CCL data (750Hz) is analyzed using an unsupervised anomaly detection algorithm based on isolation forests, eliminating the need for labeled datasets. The approach includes (1) validating the predictions with actual deformation events, (2) testing wireline data processing methods, and (3) applying downsampling algorithms. Techniques like frequency tuning, normalization, and downsampling (to 30–75Hz) improve efficiency without compromising resolution. A collar tagging method helps distinguish true deformation from collar-induced signals. CCL data is combined with stage and well context for accurate depth matching and real-time anomaly scoring. The developed Anomaly Detection Tool (ADT) has successfully identified casing deformations in multiple wells in a high-impact region. The model flagged deformation risks early, preventing operational disruptions. Notably, collar tagging enhances model precision by filtering collar-related signal noise. Higher resolution (0.01m sampling) sharpened anomaly resolution and improved detection. Overlaying the runs and anomalies enabled confirmation of casing degradation trends. Field testing included two phases: a shadow mode deployment where ADT findings supported contingency planning (e.g., slim plugs, longer stages) that enabled full lateral stimulation; and a live field test, where near real-time results informed operational decisions as deformation signals evolved, offering operators critical lead time for intervention planning. This study presents a scalable, real-time casing deformation detection system leveraging machine learning and ubiquitous CCL data without requiring additional tools or expensive sensors. The novelty lies in the use of an unsupervised learning approach, which performs well in the absence of labeled data and adapts across geologies and operational styles. Signal processing advancements, including collar tagging, further elevate the tool's reliability. This methodology was first developed in the Vaca Muerta formation and has since been validated across U.S. and Canadian basins, confirming its generalizability. Future work will focus on integrating supervised learning as labels mature to further improve precision and usability.

SNARKs enable compact proofs that an NP statement is true and that the prover knows a valid witness. They have become a key building block in modern smart contract applications, including rollups and privacy-focused cryptocurrencies. In the widely used Groth16 framework, however, long statements incur high costs. A common workaround is to pass the statement’s hash to the SNARK and move the statement into the witness. The smart contract then hashes the statement first, and the circuit that is proven additionally checks consistency of the hash and the statement. Unfortunately, virtually any hash function is expensive to call either in a smart contract (in terms of gas) or in the proven circuit (in terms of prover time). We demonstrate a novel solution to this dilemma, which we call hybrid compression. Our method allows us to use two different hash functions—one optimized for the proof circuit, and another optimized for on-chain verification—thereby combining the efficiency advantages of both. We define a clean and simple security property of the two hash functions to which our security reduces in the standard model, namely, joint UHF hardness. We then show the plausibility of this assumption in the random oracle model. Our benchmarks show that it achieves near-optimal performance in both gas usage and prover time. As an example, compressing an 8 KB statement with our approach results in a 10-second prover time and a smart contract spending 270K gas, whereas the existing approaches either need a much longer proof generation (290 seconds for SHA-256 hashing) or a much more expensive contract (5M gas for Poseidon hashing). Along the way, we develop a two-party protocol of independent interest in communication complexity: an efficient deterministic method for checking input equality when the two parties do not share the same hash function.

Child undernutrition remains a major public-health challenge in Ethiopia, often occurring in concurrent forms that are clinically more severe than single deficits. We develop a supervised machine-learning framework to classify children into concurrent nutritional states using World Health Organization anthropometric indicators. Using baseline data from the Young Lives Cohort Study, we model seven observed nutritional categories under substantial class imbalance. Models were evaluated using imbalance-aware metrics, including Macro-F1, Balanced Accuracy, and ROC-AUC. Random Forest achieved the strongest overall performance and provided improved discrimination for concurrent undernutrition cate-gories. Explainability analysis using SHAP highlighted the importance of house-hold and caregiver-related factors. These findings demonstrate the potential of explainable machine-learning approaches for modeling concurrent undernutrition and provide a foundation for future longitudinal and multi-label extensions.